1 THE IMPACT OF BACKGROUND AND DEMOGRAPHIC DIFFERENCES ON SELECTION STANDARD S AND ACADEMY PERFORMANCE By KELESHA A. NEVERS A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT O F THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2014
2 Â© 2014 Kelesha Nevers
3 ACKNOWLEDGMENTS I would like to thank my relatives, my mother and stepfather , Jean and Clarence Benaine , and my twin si ster, Kesha Nevers, for always supporting me. A great thank you to the Director and staff of the Florida police academy for quickly responding to all my emails and allow ing me to collect the data. I am thankful to t he members of my committee: Dr. Lonn La nza Kaduce, Dr. Ronald Akers, Dr. Marvin Krohn, Dr. Charles Peek, and Dr. Marilynn Swisher. Thank you to Gregory Toth for all his assistance. I would also like to thank the staff members of the O ffice of Graduate Minority Program s for their kind words an d tremendous support over the years.
4 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 3 TABLE OF CONTENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 6 LIST OF ABBREVIATIONS ................................ ................................ ............................. 8 A BSTRACT ................................ ................................ ................................ ..................... 9 CHAPTER 1 BACKGROUND ................................ ................................ ................................ ...... 11 Introduction ................................ ................................ ................................ ............. 11 The roles of law enforcement ................................ ................................ ........... 14 Requirements for entry into the academy ................................ ......................... 19 Basic recruit training program ................................ ................................ ........... 21 The importance of academy training ................................ ................................ 27 Restatement of the proble m and purpose of the study ................................ ..... 31 Theoretical Perspectives ................................ ................................ ......................... 34 Cognitive Ability ................................ ................................ ................................ 34 Masculinity ................................ ................................ ................................ ........ 36 Critical Race Theory and Intersectionality Perspective ................................ .... 38 2 LITERATURE REVIEW ................................ ................................ .......................... 41 Psychopathological and Personality Inventories and Performance ........................ 41 Intelligence, Cognitive Ability and Performance ................................ ...................... 43 Education and Performance ................................ ................................ ................... 48 Sex, Race, Age and Performance ................................ ................................ .......... 51 Biographic factors and Perfor mance ................................ ................................ ....... 54 Studies of subgroup differences in Performance ................................ .................... 55 Associations between Performance Outcomes ................................ ...................... 57 Gaps in the Literature ................................ ................................ ............................. 58 Research questions for main relationships ................................ ....................... 61 Research question s for interactions ................................ ................................ . 63 3 METHODS ................................ ................................ ................................ .............. 65 Setting and Samples ................................ ................................ ............................... 65 Measur es and Definitions ................................ ................................ ....................... 67 Dependent Variables ................................ ................................ ........................ 67 Predictor Variable ................................ ................................ ............................. 72
5 Other independent variables and interaction terms ................................ .......... 73 Control Variables ................................ ................................ .............................. 75 Analytic Strategies ................................ ................................ ................................ .. 76 Descriptive Statistics and Correlations ................................ ............................. 76 Main Effects for the Regression Analyses ................................ ........................ 76 Inter action effects for the Linear Regression Analyses ................................ .... 77 Interaction Effects for the Logistic Regression Analyses ................................ .. 78 Data Limitation s ................................ ................................ ................................ ...... 79 4 RESULTS OF ANALYSES ................................ ................................ ..................... 82 The Descriptive Results ................................ ................................ .......................... 82 Differe nces between graduates and non graduates ................................ ......... 82 CJBAT mean level differences across interactions ................................ .......... 86 Academy curriculum outcomes ................................ ................................ ........ 86 Correlation Matrix ................................ ................................ ................................ ... 87 Linear Regression and LEO, High Liability and Overall Performance ..................... 90 Interactions terms LEO Performance ................................ ................................ ...... 92 Interaction terms and High Liability Performance ................................ ................... 94 Interaction terms and Reactive Performance ................................ .......................... 96 Interaction terms and Proactive Performance ................................ ......................... 98 Interaction terms and Overall Performance ................................ .......................... 101 Interaction terms and Graduating from the Academy ................................ ........... 103 A brief summary of all results ................................ ................................ ................ 104 5 CONCLUSION AND DISCUSSION ................................ ................................ ...... 120 Implications and Future Research ................................ ................................ ........ 132 Limitations ................................ ................................ ................................ ............. 139 APPENDIX A CRIMINAL JUSTICE BASIC ABILITIES CONTENT ................................ ............. 141 B LAW ENFORCEMENT AND HIGH LIABILITY CONTENT ................................ ... 143 C CORRELATION MATRIX ................................ ................................ ..................... 145 D MULTICOLLINEARITY TABLES ................................ ................................ .......... 148 E INDIVIDUAL CHAPTER RESULTS ................................ ................................ ...... 150 LIST OF REFERENCES ................................ ................................ ............................. 161 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 172
6 LIST OF TABLES Table page 4 1 Characteristics of the full sample, graduates and non graduates ................. 106 4 2 Descriptive of the interaction variables. ................................ ............................ 107 4 3 Descriptives of academy course scores. ................................ .......................... 108 4 4 Descriptive of the academy performance outcome variables. .......................... 109 4 5 Training curriculum factor analysis with factor loadings without rotations. ........ 110 4 6 Training curriculum factor analysis with factor loadings for varimax rotation. ... 111 4 7 Training curriculum factor analysis with factor loadings for promax rotation. .... 112 4 8 OLS regression of law enforcement, high lia bility, and overall performance. ... 113 4 9 OLS regression with main and interaction effects on LEO performance. ......... 114 4 10 OLS regre ssion with main and interaction effects on high liability performance. ................................ ................................ ................................ .... 115 4 11 OLS regression with main and interaction effects on reactive performance. .... 116 4 12 OLS regression with main and interaction effects on proactive performance. .. 117 4 13 OLS regression with main and interaction effects on overall performance. ...... 118 4 14 The results from the logistic regression on graduation from the academy. ....... 119 A 1 Description of the subdivisions of the basic abilities exam. .............................. 141 B 1 The Law Enforcement curriculum content. ................................ ....................... 143 B 2 The High Liability curriculum content. ................................ ............................... 144 C 1. Correlation matrix for each course curriculum. ................................ ................. 145 C 2 Correlations for eight factor after promax rotation. ................................ ........... 146 C 3 Correlations between predictors, controls, and dependent variables. .............. 147 D 1 Multicollinearity check for LEO performance. ................................ ................... 148 D 2 Multicollinearity check for high liability performance. ................................ ........ 148
7 D 3 Multicollinearity check for reactive performance. ................................ .............. 148 D 4 Multicollinearity check for proactive performance. ................................ ............ 149 D 5 Multicollinearity check for overall performance. ................................ ................ 149 D 6 Multicollinearity check for graduation. ................................ ............................... 149 E 1 OLS Regression of each law enforcement and high liability performance chapters ................................ ................................ ................................ ............ 157
8 LIST OF ABBREVIATIONS BAT Basic Abilities Test BMI Body Mass Index CAT Cognitive Abilities Tests CJBAT Criminal Justice Basic Abilities Test CJSTC Criminal Justice Standards and Training Commission CP Cadet program CPI California Personal ity Inventory CRT Critical Race Theory FDLE Florida Department of Law Enforcement FTO Field Training Officers GCA General Cognitive Ability GED General Educational Development HM Hegemonic masculinity IP Intersectionality Perspective IPI Inwald Pe rsonality Inventory LEO Law Enforcement MMPI Minnesota Multiphasic Personality Inventory PAI Personality Assessment Inventory POP Problem Oriented Policing POST Peace Officer Standards and Training SOCE State Officer Certification Exam VIF Varianc e Inflation Factor
9 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy THE IMPACT OF BACKGROUND AND DEMOGRAPHIC DIFFERENCES ON SELECTION STANDARD S AND ACADEMY PERFORMANCE By Kelesha A. Nevers August 2014 Chair: Lonn Lanza Kaduce Major: Criminology, Law , and Society Basic academy training begins a critical step in development of police officers. To graduate from an academy, recruits must successfully complete approximately seven hundred and seventy hours of training under rigorous scrutiny. Failure to receive positive evaluations will lead to delays in employment or even dismissal; at great costs of time , finances , and reputation to the academy and individual. For these reasons, determining the potential of new academy recruits is of great importance. F actors that predict performance in the academy should also predict graduation from the academy. Unfortunately, too little is known abo ut the relationships between background/demographic variables, selection standards, and academy performance and grad uation. One variable that stand s out as predictive of academy performance is cognitive ability. This study focuses on cognitive ability as measured by a standardized cognitive abilities test for potential officers. The study contributes to the field in two ways: (1) addressing a neglected segment of research on the multidimensionality of performance during academy and; (2) examining the im portant role of the interactions of race, sex, sponsorship and education. Seeking to address deficits in policing literature regarding these matters,
10 data from two samples of recruits were collected and analyzed through multivariate regressions. For the first sample, the study analyzed predictors of in class curriculum and/or white recruits are more likely to perform better on curriculum exams , and males are less li kely to perform well. The second sample ass esses predictors of graduation . As cognitive abilities increase recruits are more likely to graduate from the academy, sponsorship by a law enforcement agency is positively associated with successfully graduatin g from the academy, and a body mass index greater than thirty reduces the likelihood of graduation from the academy. Support was found for the interaction effects sample assessing graduation from the academy. Evidence for two distinct constructs of performance and the implication of these findings in regards to selection, training, and theoret ical perspectives are discussed along with l imitations of the study and future r esearch potential .
11 CHAPTER 1 BACKGROUND Introduction Cognitive ability is one of the most important factors used to screen police applicants for admission into the training academy. More important than its function to screen recruits in order to select candidates that stand a better chance of becoming competent officers, is its role in facilitating learning during basic training and preparing recruits for the various duties of policing. Cognitive ability can be, and has been used to a li mited degree, to predict competent performance across the various police assessment standards. Given the important role policing has in society, it is important to fully understand the implications of the many dimension of officer performance. Poor perfo rmance during relations of any nature (with superiors, suspects, witnesses, etc.) or equipment/force utilization can have disastrous consequences. Training academies are the gateway into the law enforcement profession; how academies recruit and screen ap plicants, and then train those who are admitted, sets performance. In this latter regard, for policing to be effective, it must be problem oriented and meet the needs of commu nities. Achieving this necessitates a police force diverse enough to both work and identify with the members of those communities. The means for successful adoption of such paradigms must begin its development in academy, where the diversity of the served communities should be appropriately mirrored. If, however, the diversity of matriculating academy cohorts is not represented in graduating cohorts this ability is restricted. Therefore, exploration of factors that may
12 disadvantage minority groups and pos sibly bias appraisals of performance within cohorts is in order. This dissertation seeks to contribute to the understanding of what affects academy performance in two ways. First, the dissertation consciously examines different ways to measure academy pe rformance and derives more exacting measures of the dimensions of performance. The impact of cognitive ability measures and other factors thought to relate to performance are examined across a broad spectrum of performance measures. Second, it explores whether or how cognitive ability measures may interact with other factors to predict performance. The facile assumption is that cognitive ability relates to performance in a uniform way. Prior studies have relied on a single global measure of performanc e (Ford & Kraiger, 1993 and Rafilson & Sison, 1996) or have used a narrow measure of performance (Gruber, 1986). This prior research, and theories based there within, predict that males and whites may perform better than females and non whites. Most of t his prior research, however, utilizes a narrow set of performance indicators. There is great need to explore these standing expectations by using indicators of various dimensions, constituting more comprehensive examination. For example, some studies have used only overall performance outcomes (average academy scores) or a few stand alone/individual performance outcomes (traffic investigation scores, rules of evidence scores and/or report writing scores). There has been little effort to explore what makes up a comprehensive training curricula and how each performance measure may relate to each other.
13 Arguably there are some important dimensions that capture or cut across various l separates content into two curricula, high liability training and law enforcement training. The assumption being that these two areas capture different but important features of policing. While these are understood to interact with other dimensions of policing the extent of such interaction is seldom explored and the concept of interaction is seldom applied elsewhere in the field. There is theory and a policy implications from a limited amount of research, however, that suggest cognitive ability intera cts with other variables in manners that may contribute to the predictability of performance outcomes. Furthermore, some critical theories direct attention to the prospect that cognitive ability can be a major predictor of academy performance when conside ring its interaction with race and sex; especially in light of concepts such as race hegemony and hegemonic masculinity. One manner in which race hegemony is perpetuated is if an important predictive measures like cognitive abilities operated better for whites than other groups. Similarly, hegemonic masculinity suggests the prospect that male hegemony is sustained if the cognitive abilities predictor operates better for males than females. Both critical race theory and hegemonic masculinity emphasize wh ite male dominance in law enforcement. These theories suggest that the predictive utility of cognitive ability may be different for women and non white recruits due to the traditional structure and culture in law enforcement . Synergy between Critical Race Theory, Intersectionality Perspective, and Hegemonic Masculinity suggest that white males in particular will be benefitted. This
14 in the academy (i.e., a statistical interaction is worth pursuing). Moreover, while performance on the various dimensions of training may well predict who graduates and who does not graduate, there may be a disconnection between academy performance and graduation. Some institutionalized f orms of white dominance and hegemony are to seemingly open doors and provide opportunities to do well without the process indicators relating to such opportunities actually being pursuant of the ultimate goal (i.e. perhaps graduation is not predicted by pe rformance in all purported matters). Finally, there is also some preliminary research that suggests interaction between cognitive ability and other factors. For example, Morstain (1984) concludes that a cognitive ability measure (the CAT) had utility a nd validity for the assessment of minority police officers but not non minority officers. This result is unexpected (and not (1984) and critical race perspective and hege monic masculinity underscore the importance of further research (such as this dissertation) to understand how cognitive ability measures relate to performances. To this end, this research will use factor analysis to explore what dimensions load into ident ifiable factors by looking at recruit exam scores on fifteen subject matter areas. What follows in this chapter are details about the role of law enforcement, requirements for entrance into police academies, a description of the training program, and an e laboration on problems to be addressed in this study. The culmination will elucidate the purpose of the study. The r oles of law enforcement Police work yields multiple dimensions underlining performance and, as such, scholars and practitioners have no s tandard or exact measure when evaluating officers
15 (Skogan & Hartnett, 1997, Langworthy, 1999; Alpert et al., 2001; Roberg et al., 2005). However, a review of the literature demonstrates a reasonable degree of accord assessed based on an assortment of outcomes, such as academy grades; graduating or not graduating from an academy; peer and supervisor evaluations; reprimands and commendations from management; being fired or leaving the job; and complaints and litigations (Burkhart, 1980; Falkenberg et al ., 1991). As a result police performance studies have used many different standards of measurement and measurement techniques. These measurements produce various results, making it difficult to compare across studies an d draw conclusions as to the state of knowledge on performance (Roberg, et al., 2005). Police officers roles as generalist s , addressing various on the job tasks related to criminal and noncriminal activities, further increases the difficulty associated wit h evaluating performance. For example, assessing the number of arrests made by an officer or the impact of policing on crime rates are common measures. However, despite the prevalence of these methods, neither is without faults. Measuring performance ba sed on number of arrest s does not account for the fact that police officers spend most of their time addressing non criminal activities, such as nuisance calls and traffic violations (Walker & Katz, 2012). Relying on crime rate fluctuations is inadvisable (Alpert & Moore, 1993) because of the many crimes that cannot be controlled by police officers or that go unreported. In addition, certain performance measures are not quantifiable, such as , police (e.g., where one officer may choose to arrest another may not ) . Knowing when to use discretion can be an indicator of good performance but,
16 evaluation. In spite of these difficulties, assess ing police performance is an imperative and research on the subject continues to move forward. Ways in addressing the performance problems is focuses on recruitment, selection and training. Prior to 1985, the majority of police officers in the United St ates were white, high school educated males (Lonsway, et al., 2002) . Since that time, the average amount of schooling and representation of minority groups and females has increased (Lan gston, 2010; Walker & Katz, 2012 ). A significant advance in this rega rd was the enactment of Title VII of the 1964 Civil Rights Act, which made it unlawful for employers to consider color, sex, religion, or nationality as factors for denying or terminating an employee 1 . Subsequent to the Title VII Act, the 1972 Equal Emplo yment Opportunity Act 2 extended its scope by prohibiting state and local government from discriminating against groups. Through a series of lawsuits, several discriminatory police selection practices were restricted. For example, a height requirement of at least five feet ten inches for police officers, which discriminated against women, was modified (Appier, 1998) . In addition, studies were commissioned to evaluate if there are racial biases during the selection process (Aamodt, 2004) . In hopes of reco mpense for past injustices, an affirmative 1 Civil Rights Act of 19 64, Pub. L. No. 88 352, Â§Â§ 701 718, 78 Stat. 253 (current version codified at 42 U.S.C. Â§Â§ 2000e to 2000e 17 (1970 & Supp. V 1975)). 2 Equal Employment Opportunity Act of 1972, Pub. L. No. 92 261, 86 Stat. 103, amending 42 U.S.C. Â§Â§ 2000e to 20O0e 15 (197 0).
17 action plan 3 was set in motion to recruit minorities and women. Today, diversity, or increased representation in policing is a major goal of law enforcement agencies. Across the United States, police department s are committed to diversity through the process of recruiting, socializing, retaining, rewarding, and promoting minorities and women (Walker, 1985). Diversity is an important goal because police departments should reflect the communities they serve ( that is, communities are diverse and therefore, police departments should also be diverse ) . A statement supported by the fact that increased minority representation has been shown to improve police minority relations. For example, police departments that mor e accurately represent the communities served, receive fewer accusations of disproportionate use of physical and deadly force (Sun & Payne, 2004) . A multicultural department is said to increase perceptions of legitimacy in the police for minority communi ties (Stokes, 1997). An increase in the number of minorities and women police officers will potentially close the social distance gap between the police and citizens. By hiring officers that live in the neighborhoods they patrol, and more importantly min ority officers, liaisons are created between police culture and residents of these neighborhoods. Such practices promote improved understanding and communication between police forces and minority groups (Goldstein, 1977; Decker & Smith, 1998). Furtherm ore, the hiring of minorities and women increases interaction in the department between officers of various backgrounds, which creates an 3 3 C.F.R 339 (1965), as amended by Executive Order No. 11,375, 3 C.F.R. 320 (1967), 42 U.S.C. Â§ 2000e (Supp. V, 1970).
18 environment that contributes to retention and also weakens the police subculture of secrecy, solidarity, and violence (Walker, Spo h n, & DeLeone, 2000). In spite of several decades of legislative and policy efforts, including the formation of national and international associations to support the recruitment of minorities and women into policing, these groups ( more so fe male police officers ) are still underrep resented (Lonsway, et al., 2002; Reaves, 2009; Langston, 2010). According to the Bureau of Justice (2007), African Americans police officers represented 11.7 and 11.9% of the national police forces in 2003 and 2007 respectively, while Hispanics represented 9.1 and 10.3%. Noteworthy however, is the fact that in some departments, racial minorities represents the majority composition of the department (which is due to the composition of the community): in Miami, Florid a, Hispanics are 51.7%, African Americans are 19.4%, and Caucasians are 27.9% of the department; and in Atlanta, Georgia, the department comprises of 55.6% African Americans, 39.9% Caucasians , and 3.5% Hispanics. In a report by the National Center for Wo men in Policing on the overall status of sworn law enforcement personnel, women represented 12.7% in 2001 (Lonsway, et al., 2002). A later report by the Local Police Departments (2009) indicated a decrease in female representation to 11.9% in 2007. Simil ar findings on the lack of representation of women in law enforcement show that in the largest police departments, such as Detroit, Philadelphia, and District of Columbia, women represent 27%, 25% and 23%, respectively (Langston, 2010). The disproportion is even more severe as women move up the ranks in police departments. Of the women in large police agencies, 13.5% hold line o p eration positions, 9.6% are in s upervisory positions, and only 7.3% hold top
19 c ommand positions (Lonsway, et al., 2002). There fore, a majority of the women are positioned at the lower end of the command structure. These figures on women in policing are far more significant because women should have equal representation t o men but this is not the case. Aside from the focus on ta rgeting certain demographics, most importantly, police academies and departments are concerned with effective training of police officers. Requirements for entry into the academy The recruitment and selection of police officers involves meeting minimum q ualifications; going through checks focused on age, education, and background; and then going through a series of screening procedures. Training academies use these determinants to a ss ess whether or not the applicant meets fixed requirements. Applicants are required to be at least nineteen years of age and a citizen of the United States of America. Police departments also have selection requirements , and basic academy training centers take these requirements into consideration before admitting any applic ant. In most states, the minimum age requirement for joining a police department is twenty one years of age. Due to concerns for health related issues, some police departments have historically placed restrictions on the maximum age for recruits at thir ty five years (Walker & Katz, 2012). Additionally, applicant s must have no convictions for perjury or false statement, never been dishonorably or undesirably discharged from the military, and no standing or pending domestic violence charges (Klockars et a l., 2007) . The next level in the screening process comprises psychological, physical, background, driving record, credit history, and drug tests (current drug status); applicants should have a history of minimal drug and non prescribed medication usage (K lockars et al., 2007) .
20 August Vollmer, Chief of Police in Berkeley, California between 1905 and 1932, was one of the first police administrators to recommend police officers to have higher education. Vollmer subsequently founded the Berkeley Police Sch ool, which was one of the first academies created in the United States (Walker & Katz, 2004). Today, a large percentage (81%) of police departments require that a candidate have a high school degree or equivalent (Local Police Departments, 2007). And, even though a high school degree or its equivalent is the minimum requirement, police departments show a preference for hiring police officers with some college education (Walker & Katz, 2012). For example, c adet programs (CP) are police sponsorships or a pprenticeships for applicants interested in becoming police officers after graduation from college. While nationally ubiquitous, there is no overarching relationship between CP. However, the aims and techniques of such programs are often very similar. Th e main goal of any CP is to attract more qualified applicants to a given police department , often through financial incentivization (Caro, 2011) . After completing the first two years of college, the police department supports a recruit financially. This financial support comes in the form of employment, where the cadet is hired by the police department to work part time, while they complete the remaining two years of study. For example, t he Florida time employment and f ull tuition scholarship to successful applicants. Outside the classroom, a cadet will work with the coordinator on developing law enforcement skills. In doing so, cadets are exposed to the inner workings of the department while acquiring relevant knowle dge and skills. This exposure assists cadets with matriculating into the police academy and, later, the police department. Once
21 cadets complete their university studies, they register for and take the cognitive ability/basic abilities examination. Upon su ccessfully passing this exam, cadets, like other applicants, go through a police academy screening process. If the cadet passes the screening process, they are able to enroll in the next recruit class for training. After graduating from the police academ y, cadets are contractually obliged to serve at least two years with the sponsoring police department. Aside from educational requirements, academies and departments consider background factors such as criminal and drug use history, although police depar tments sometimes to a more stringent degree than local academies, and will not hire a new recruit with an adult or juvenile felony conviction record (Caro, 2011) . An important first step in the selection process occurs before entry into the basic academy , where potential recruits are required to take the Law Enforcemen t Basic Abilities Test (B AT). BAT is a cognitive abilities or aptitude test developed in 2007 by the Criminal Justice Standards and Training Commission (CJSTC) that predicts the potential s uccess of applicants. The B AT is multiple choice and written exams that includes ( Table A 1 in Appendix A ): 1. Deductive Reasoning 2. Inductive Reasoning 3. Written Comprehension 4. Written Expression 5. Problem Sensitivity 6. Information Ordering 7. Memorization 8. Spatial Orientation Basic recruit training p rogram Beginning in 1829, the founder of the Metropolitan Police of London, Sir Robert Peel, advocated for professional training in police departments. This request was slow
22 to come to fruition as traditionally, recrui ts and officers received very little training beyond the rulebook they were given on the first day of the job. Between the 1920 and 1960s, California became the first state to actively engage in the professionalization of law enforcement, with the first l aw enforcement program created in 1931 at the San Jose State College (McCampbell, 1987) . Countrywide efforts toward the professionalization of police departments began when the National Commission on Law Observation and Enforcement, also known as the Wick ersham Commission, brought to light the unethical and violent practices of some officers which jarringly pointed out the need for improvement in training (Skolnick, 2011) . Today, training takes place before entrance into any department (basic academy trai ning), immediately upon entrance into the police department (San Jose Training Program), and while on the job (in service training). Basic training academies use quasi military style training to transform civilians into law enforcement officers (Chappell & Lanza Kaduce, 2010) . Upon entrance into the academy, indoctrination commences with cadets being told that they are working towards building a law enforcement family, with a shared mission and values. Recruits are then socialized into the traditions of the organization while their longstanding practices are simultaneously unlearned (Chappell & Lanza Kaduce, 2010) . The recruits gain understandings of criminal and constitutional law and technical and interpersonal skills, which are constantly evaluated t hroughout the training process. In addition, the academy acts as an environment to establish social capital beneficial to the recruit throughout their law enforcement career.
23 The curricula used in basic academy programs varies across the United States h owever, each is informed by the philosophies or models of various policing eras. Three types of policing models that informs the curricula are traditional policing, community oriented policing, and problem solving policing (Scott, 2000) . Traditional poli calls for help, the decision to arrest or not to arrest the accused, and following up with an investigation (Shafer, 2001). The traditional model of policing is conside red reactive to criminal activity, authoritarian, rigid, and impersonal. Within this model, there is hardly any interaction with police officers and citizens (Scott, 2010) . Problem oriented policing (POP) originated largely as a function of the researc h undertaken during the traditional era of law enforcement (Walker & Katz, 2012). The main concern of POP in regards to traditional law enforcement is its reactive nature. Therefore, prominent scholar such as Goldstein (197 7 ) states that police officers should do more than respond to that initial call. Instead police officers should attempt to find a permanent resolution to the issues that lead to the initial call (Goldstein, 197 7 ). Therefore, POP has more of a preventive and proactive approach to crim e control. repeat calls for service and would be applied by police officers throughout several police agency as a part of their daily work. These problem solving strategies can be ef fective in reducing and solving problems, thus curbing the repeated calls for service. Community policing is made up of several programs or strategies, and involves the community playing a practical part in the mission of community problem solving (Trojano wica & Bucqueroux, 1990). Therefore, s everal of the key elements of
24 community policing involves engaging and interacting with the community, solving community problems, adapting internal elements of the organization to support these new strategies, commi tment to crime prevention, public scrutiny of the police, accountability of police actions to the public, customized police service, and community organi zation (Bayley & Bittner, 1984 ). Community oriented policing also have an emphasis on proactive and p reventative orientation and less of an emphasis on reactive policing, which again is dominant in the traditional policing model. Community policing seeks to build more positive interactions, trust and confidence between the police officers and the communi ty. Citizens may act on their own while working within the communities or they can collaborate with the police by volunteering with the police department. Many academy curricula today still focus on traditional era styles of training, which focus on t echnical training such a driving, firearms, and self defense; even though most of the tasks of police officers do not put to use these skills (Alpert & Dunham, 1997). The curricula focus less on topics that falls under community era policing such as commu nication and problem solving (Birzer, 1999; Bradford & Pynes, 1999). Bradford and Pynes (1999) evaluated the syllabi and curricula of several police academies and concluded that an overwhelming amount of the training time is directed towards driving, defe nsive tactics, and methods of carrying out an arrest. Consequently, less than five percent of the training time is directed towards cognitive and decision making skills, such as handling scenarios and reasoning skills. This study focuses on law enforcement curriculum . T he training process takes place over the course of nineteen weeks (day time shift) or nine months
25 (night time shift), wherein the recruit completes 770 hours of formal training on the Florida Basic Recruit Training Program: Law Enforcement (Volume 1) and High Liabilit y (Volume 2) (FDLE, 2012 ). The law e nforcement manual (Volume 1) comprises of eleven chapters and 454 hou rs of training time (Table B 1 in Appendix B) : 1. Introduction to Law Enforcement 2. Legal 3. Communication 4. Human Iss ues 5. Patrol 1 6. Patrol 2 7. Crime Scene Investigation 8. Criminal Investigation 9. Traffic Stops 10. DUI Traffic Stops 11. Traffic Crash Investigation In each chapter, there are units addressing several k ey issues in policing, such as communication and interpersonal skills, human i nteraction ( professional behavior in a diverse society), and problem solving (community policing and safety, ethics, community, u nde rstanding, response, e valuation (S ECURE)). Most of the law e nforcement training cur riculum hours are dedicated to c o mmunicati ons, l egal, and p atrol , and the high l iability curriculum mostly focuses on firearms and defensive tactics training. The h i gh liability (v olume 2) curriculum contains six chapters and consists of 316 hou rs of training time (Table B 2 in Appendix B ) (FDLE, 2012 ) : 1. CMS Law Enforcement Vehicle Operations 2. CMS First Aid for Criminal Justice Officers 3. CMS Criminal Justice Firearms 4. CMS Criminal Justice Defensive Tactics 5. Dart Firing Stun Gun 6. Criminal Justice Officer Fitness Training
26 The recruits are test ed on each chapter separately throughout this process and given final and overall examination scores. At the end of the training process, most recruits take the State Officer Certification Exam (SOCE) which must be passed in order to graduate the academy. The SOCE can be taken during or after the basic training process. The basic academy curriculum is used to create the questions on the SOCE. A comprehensive basic academy training program has obvious benefits to the department and community. A qualified and well trained police officer protects and serves the community while avoiding potential pitfalls that could cause trouble for the department, such as complaints and civil litigations. If officers are not trained effectively in skills, such as communic ating with the public and determining the proper degree of necessary force, negative perceptions of the law enforcement agency will undoubtedly ensue , nd support and cooperation with the police (Triplett et al., 2005; Car less, 2006; Gainey & Payne, 2009; Sun et al., 2004). Since the Florida curricula is made up of two separate areas (law enforcement and high liability), it is prudent to consider whether or not the same expectations apply to both dimensions. To consider if there are different impacts on the two dimensions, more information regarding presentation and complexity of the materials during the academy is warranted. The law enforcement officer material is presented first and at this stage, everything is new to the recruits (i.e. before they have learned how to adjust to academy expectations). The law enforcement curriculum is of a general nature and therefore suites this period appropriately. The high liability curriculum is complex and therefore the associa ted activities rely more intensely on cognitive abilities than those of the law enforcement curriculum.
27 The importance of academy training After completing basic training, recruits begin approximately seventeen weeks of a field instruction program most p olice department s in Florida follow the San Jose Police Department Field Training model. Prior to the San Jose Police Department Field indiscriminately to an experienc ed officer with no incentive to perform the job of mentor and, therefore, the quality of the training was poor. A more systematic approach began in the early 1960s when the California Commission on Peace Officer Standards and Training (POST) initiated a training system that included a recruit training checklist to evaluate recruits (McCampbell, 1987) . One of the strongest incentives to change training practices was an incident in San Jose, California wherein a recruit with poor driving skills caused a tr affic accident resulting in fatal injury to a citizen (McCampbell, 1987) . Subsequently, in 1971, a vital training model was developed, called the Recruit Training and Management Program and Field Training and Evaluation Program (McCampbell, 1987) . In an updated form, the San Jose Model continues to be used today throughout the state of Florida. Recruits are assigned to Field Training Officers (FTO) in order to receive additional training in the field. The FTO is primarily responsible for the training a nd evaluation of new officers; therefore, the FT O are mentors to the new recruit and play a pivotal role in their development. Field training provides a semi structured environment in which new recruits learn to apply the theoretical skills, knowledge, an d abilities acquired in the classroom to actual police work (Engelson, 1999) . Recruits are required to complete the field training before being assigned to solo patrol.
28 Therefore, academy training is the critical first step in becoming a police officer. After successfully completing the academy, the next step is when the officer enters the field training program where he/she is stringently evaluated by the FTO. The academy curriculum gives recruits the knowledge, skills and abilities to make it through evaluations on performance tasks, communication, knowledge, attitude/relationships and appearance. There are five phases of the field training program and the new officer relies upon the previously learned academy teachings through each phase of field t raining (Haberfeld, 2002) performance on a daily b asis, cataloguing results in a daily observation r eport. At the end of each phase, ning r eport. In the first phase, trainees are mainly observers while the FTO drive s the vehicles and demonstrate s how the job is performed. During the second phase, the probationary officer gets a new FTO, squad, and Sergeant and procedures similar to tho se in phase one are followed . However , during this second phase the new officers are encouraged to participate in as many field activities as they are able to , and at these initial stages new officers are more reliant on what was previously learned in the academy. During phase three, the probationary officer receives yet another FTO. At this stage scrutiny is the fourth phase, the new officer returns to the original FTO, w ho then wears plain clothes attire and becomes the observer. Here, the FTO does not participate in the activities of the new officer unless necessary. During phase five, the new officer nd corporal
29 responsible for the remainder of the training. New officers progress through each phase when they successful complete the current phase; and successfully making it to this final point is dependent on both what is learned during the academy and field training program (Haberfeld, 2002 and Engelson, 1999) . Research establishes the importance of the academy training in predicting field training performance and on the job behavior (Campa, 1993; Fyfe & Kane, 2006; Provine, 2006; Fore ro et al., 2009 ; Henson, et al., 2010; Caro, 2011; Haarr, 2001). The groundbreaking study by Fyfe & Kane (2006) of the New York City Police Department, or forced resignations or reti those who were not caught for misconduct (p. 20). This study thought it was important to evaluate police officers who misbe haved because this group may provide information on whether the processes used to select officers do predict unsatisfactory and/or satisfactory performance. The study found that those who perform well in the police academy are less likely than marginal pe rformers to be separated from the police department as unsatisfactory probationers. In addition, top performers in the academy are less likely to be dismissed for misconduct (Fyfe & Kane, 2006). Caro (2011) analyzed the relationship between state traini ng academy scores and impacts on field training scores. The outcome variables were a composite score of appearance, knowledge, performance, attitude, and relationships assigned by the field officer. The independent variables were an array of curriculum s cores: report writing, orientation to criminal justice, firearms, o leoser in chemical spray, monadnock defensive
30 tactics system, monadnock expandable baton system, legal aspects, patrol activities, traffic services, investigations, intoxilyzer 5000, standar d field sobriety testing, specialized activities, radar, l idar, Northwest ern University crash i nvestigati on, and the comprehensive POST/f inal examination. The study concludes that 10% of the variance in field training scores is accounted for by the state academy curriculum. Henson et al. (2010) assessed the influence academy performance (quiz average, spelling average, midterm exam, notebook score, final exam) on active service performance (first year evaluation, second year evaluation, third year evalua tion, 3 year evaluation average, complaints and commendations). Overall academy score is significantly related to first year evaluation scores. The study finds that there is some evidence that civil service scores are positively and significantly associ ated with evaluation scores ; however, the relationship does not remain significant when overall academy performance score and physical agility rating scores are entered into the models. In one of the only studies that uses structural equation modeling (S EM), F ore ro et al. (2009), conducted an international study with a sample of recruits at Catalan Institute for Public Safety in Barcelona, Spain. The study modeled the causal path for performance in the field, with variables from The Law Enforcement Asse ssment and Development Report, to assess personality patterns and also used observations on training attitudes, job efficacy, responsibility, practical judgment, initiative and autonomy, adaptation to norms, integration in the team, social skills, and tole rance/flexibility during rights, statutory law, traffic law, criminal law, administrative law, public safety system,
31 sociology, geography, first aid, criminal investigation, p olice intervention, report briefing, mediation in conflicts, and theory contents of self 96). The study finds that academy performance scores predicted field performance. F ore ro et al. (2009) find that academy training mediates the relationship between personality and field performance. The full model predicts 60% of the variances for aver age law enforcement academy scores. When the study entered demographic variables into the model there were no differences in predicting the power of the fit of the model ; therefore, the demographic variables were removed from the study. Therefore, assess ing performance during the police academy stage is important because academy performance predicts future performance . E ven more important , is the need to determine what factors predict successful performance during the academy. Restatement of the p robl em and purpose of the study Police performance has been a long time concern. Traditionally, police officers may receive outstanding evaluations even when their performance was below standards (Walker & Katz, 2012). In addition, performance evaluations have been an issue because the definition of performance is multifaceted, somewhat subjective, and may not capture actual performance. For example, the ability of an officer to determine whether to use or not use force during an interaction with a citize n is not captured in traditional performance evaluations. Today, this inability is less of a problem during basic academy and field training programs, because there are concrete performance appraisals which take into account various observable and unobser vable measures of
32 At the academy level today, the main objective of performance evaluations is selecting the best qualified officers. An effective training academy system should select good recruits and should identify any recruits with performance problems. Recruits are screened and if they do not meet the minimum requirements , they are dismissed. The accepted recruits are socialized into the ways of law enforcement. These training systems are able to eval uate and possibly correct any deficits experienced during the orientation process. Failure to adequately select and train recruits leads to problems during the academy and, subsequently, field training programs (White & Escobar, 2008) . An officer who per forms below expectations can potentially negatively influence the community, the department, and themselves. The community loses trust in the police, complaints and lawsuits are filed, and the time spent in, and costs of, training are lost. Traditiona lly, policing is considered reactive to criminal activity, authoritarian, rigid, and impersonal (Hanewicz, 1978) . Within this model, there was hardly any interaction between police officers and citizens. Now, basic academy and field training are reactive in that, once such issues have been identified, changes in the curricula offer the means to address these issues, adding new dimensions to performance based policing (Palmi e tto, et al., 2000). New philosophies emphasize expansion of police presence in c ommunities to provide an array of additional services to citizens. Accordingly, current curricula address communication, interpersonal skills, and human interaction. For example, training in diversity and cultural competency helps to explain and understa nd cultural differences and improves upon the performance of police officers. The philosophy of c ommunity and problem solving policing has multiple effects
33 in the community, by influencing fear of crime, crime rates, and disorder. Therefore, whether or n ot a recruit is adequately prepared for the field and what factors contribute to his/her readiness are of great importance and concern to selection centers, training academies, and police departments. In the police performance literature, there are very few studies that investigate the interaction effects of cognitive abilities, education/work experience, and demographic variables (race, sex and age) on academy training performance (law enforcement scores, high liability scores, overall scores, and comple tion). In addition, detailed review of the existing performance based literature demonstrates an (over ) reliance on association studies evaluating the main effect of single constructs of personality and psychopathological inventories and background/demog raphic predictors on academy performance. Furthermore, the literature neglects to address the importance of interaction effects of these variables in predicting academy training in producing an officer prepared for solo police work. Studies that have pred icted performance during the academy have primarily focused on performances such as graduating or not graduating from the academy and/or overall performance, and do not account for the subsets of performance. Therefore, there exists a research gap in pred icting differences in outcomes for the law enforcement and high liability curricula and completion. While such studies do exist, unfortunately the numbers are very limited in regards to more recent works. Moreover, there are even fewer studies investigat ing the impact of multiple predictors and the relation with academy training. T his study proposes that cognitive ability should be considered the logical predictor of performance in the academy; however, background
34 and demographic predictors may interact with each other and with cognitive ability and influence academy performance. This study goes beyond previous studies to account for interaction effects as factors that influences academy training performance. The outcome of this study will have a practic al impact on the selection of recruits who enter the basic training academy. In addition, the study provides an understanding of the knowledge, skills, and abilities that must be learned in the academy to demonstrate acceptable performance in the field. T heoretical Perspectives Cognitive Ability General Intelligence, Obj ectively Determined and , Spearman described all aspects of mental abilities as existing in an interactive framework. The result of this was the creation of a perpetuated paradigm in which all faculties have been considered closely correlated to o ne another. Behind this varied range of mental ability tests. Continued research revealed to Spearm an particular realms of testing in which performance is even more specifically correlated. To distinguish between these relationships Spearman developed Factor Theory of in this theory, mental abilities tests may reflect a gener factor or degree to which performance in one test could be predicted based on performance in
35 another could be more closely estimated (Spearman, 1904). Howeve r, the overriding theme was that individuals who performed well on one test tend to perform well on other tests. Similarly, individuals who perform poorly on one test tend to perform poorly on other tests. Currently, GCA describes the mental capacity nee ded to acquire, process, encode, recall, and use information. Some previous studies have operationally defined g other studies have operationalized GCA as the score summa tion of numerous items such as verbal skills, mathematical ability, problem solving, and memory. While there is no one generally accepted definition of GCA as a construct, and/or methods for measure, the literature on the predictive validity of GCA is we ll established (Hunter, 1986; Aamodt, 2004). There is undeniable agreement that GCA, measured and conceptualized in numerous forms of Cognitive Abilities Tests (CAT), is a predictor of an array of training successes and job performance outcomes. Since has become more specialized, that is, tests are now tailored towards specific occupations. Within policing selection literature, CAT can be placed into four categories (Aamodt, 2004a): (1) national publishers test not develop ed for la w enforcement agencies, (2) national developed tests developed for law en forcement agencies, (3) federal government test, and (4) locally developed civil service exams. Within each category there are various tests that tap into different aspect s of readi ng comprehension and vocabulary; abstract thinking and reasoning ability; critical thinking; vocabulary, analogies, math, and logic; reading and grammar; and writing skills.
36 aptitud e in eight specific areas: (1) deductive reasoning, (2) i nductive r easoning, (3) written comprehension, (4) written expression, (5) problem sensitivity, (6) information ordering, (7) memorization, and (8) spatial o rientation. The items for each of the eight sub tests are operationalized as a summation of scores which provide a single numeric measure of GCA. Several meta analyses and review studies have linked GCA to numerous training outcomes and job performances (Hunter, 1986; Salgodo, et al., 20 03; Aamodt, 2004a; 2004b). Cognitive ability is said to be the strongest predictor of performance when paired against other variables (Hunter, 1986; Hunter & Hunter, 1984). Aamodt (2004a) found that reading tests and exams specifically created to assess are better predictors of performance than tests not specifically developed for police officers. Masculinity Hegemonic masculinity (HM) should be considered crucial to any in depth analysis of policing. ( 1987 ) concepti on of HM, in which a subset of men dwell in positions of wealth and power and then legitimate and reproduce patriarchy through social relations, will form the foundation of discussion. Within this notion there is a hierarchy of dominance in which forms of nate to hegemons (Connell, 1987; Connell & 2005). For these hegemons, the definition of masculinity entails perceptions of physical strength, br avery and stoicism. Hegemonic masculinity has maintained a position of respect in policing due to both the (ill conceived) notion
37 that these traits are of central importance and the tradition perpetuated by early establishment of such thought going unchal lenged. Several researchers have used HM to explain the institutionalization of have found that most police departments have an extreme adherence to this single ideolog y of masculinity (Herbert, 1998; Martin, 1999; Messerschmidt, 1999; Prokos & Padavic, 2002; Haarr, 2005). This is because of the somewhat universal view that some aspects of policing require a commanding presence and the ability to punish disrespect (both ways of exhibiting HM) to gain and maintain control over situations proactively, and exhibiting aggressiveness. Punishing disrespect is thought to maintain this presence and, alth ough it is not necessarily physical in nature, is, unfortunately, frequently linked to excessive use of force. Police officers high in masculinity are thought to punish disrespect because they view it as a threat to the masculine persona they perceive the mselves projecting (Martin, 1999). Studies have also found that HM is prevalent even at the recruitment phase of academy training (Haarr, 2005; Prokos & Padavic, 2002). Recruits enrolled in law enforcement training programs take part in an explicit curr iculum that is designed to be gender neutral. However, the execution of this curriculum suffers from an undercurrent of masculine focus that treats women as outsiders (Prokos & Padavic, 2002). Women are frequently excluded from examples used in classes t o demonstrate scenarios and from bonding experiences, enhancing male solidarity and power (Prokos & Padavic, 2002). In addition, mirroring societal shortcomings, policewomen are often denigrated
38 and objectified or viewed as sexual objects. Furthermore, m any female centric issues, such as sex crime victimization, are not viewed as important as other issues. When some women manage to achieve positions of power in spite of these obstacles, policemen resist their authority. Therefore, the curricula and culture of training academies do not adequately integrate wom en and, in turn, this influences differences in outcomes for recruits. Critical Race Theory and Intersectionality Perspective During the 1970s, Critical Race Theory (CRT) emerged from works seeking to understand race relations (Delgad o, 1995), at first i n the context of, and later in contrast to, Critical Legal Studies; both derivative of basic Critical Theory in which norms are believed to be shaped through ideology. CRT focuses on studies of white privilege, white male ideology, and racism, and how the se factor into the formation of the institutional environment. In simple terms, CRT is a means of analyzing systematic oppression in group context, be it overt or an unintended (even subliminal) symptom in the formation of a legal system or hierarchy. Th is conceptualization of oppression used by CRT is informed by the Intersectionality Perspective (IP) , a framework used to examine how the meeting of race, ethnicity, gender, class, and/or nationality forms meaning and shapes behaviors (Crenshaw, 1991; Coll ins, 2000). As the norms and ideologies of organizations reproduce those of the societies in which they are contained, police subculture in America is capable of being analyzed using societal level mechanisms such as CRT and IP.
39 Using CRT to evaluate ineq ualities in policing allows us to see both the broader picture of hierarchy and the intersection of elements that help to create it , be these in recruitment, training, or field work. For example, in the context of the academy, the theory inspires us to e xamine how the intersections of being White, male, and a college graduate may positively influence completion. At the same time, such examination can are diff erent from the schema (such as b lack, female, or non college graduate), may be less likely to complete the academy. In the field, the immense amount of power and on racial p rofiling demonstrate how police work allows for the facilitation of racism. For example, when a White policeman stops and/or arrests a minority citizen based on race, this enhances subordination and oppression of the racial group. In the context of the d epartment, Bolton (2003) exam ined the experiences of b lack policemen and found that they perceive systematic barriers. These barriers limit advancement and the length of their careers in the department. In addition, b lack male officers identified lack of support, conflict, and stress as issues they perceived facing to a greater extent than other officers on a regular basis. Furthermore, academy recruits and officers noted experiencing more overt forms of racism such as name calling, slurs, and racial jok es (Bolton, 2003 and Schlosser, 2011) . Further studies find that race and sex create different experiences for different groups (Haarr, 1997; Martin, 1994; Morash et al., 2006; Texeira, 2002; Zhao, et al. , 2001 ; Hassell & Brandl , 2009 , Pogrebin, et al., 2000 ). Most of the research on the intersection of race and sex is focused on b lack police officers. Martin (1994) found that
40 b lack policemen use masculinity to align themselves with White policemen, attempting to beco me part of the dominant group (m ale) (Martin, 1994). Pike (1985) reports similar findings among w hite policewomen, in their attemp ts to distance themselves from b lack policewomen to be part of the dominant group (w hite) . To the further detriment of b lack policewomen, b lack policemen are fo und unsupportive and uninterested in understanding their experiences (Texeira, 2002; Dodge & Pogrebin, 2001). Therefore, IP is especially useful in understanding how individuals such as females of a minority group are positioned differently (i.e. marginal ized) and can be used as an analytical tool to determine if there are differences in performance for various groupings.
41 CHAPTER 2 LITERATURE REVIEW Psychopathological and Personality Inventories and Performance Personality and psychopathology inventories are widely used police screen out tool. Research on these inventories uses an array of assessments such as the Minnesota Multiphasic Personality Inventory (MMPI), California Personality Inventory (CPI), Inwald Personality Inventory (IPI), and /or the Personality Assessment Inventory (PAI) to assess personality (behavior and emotional problems) and psychopathological (abnormal behavior) structure and its influence on basic academy success and job performance (Sander, 2008; Tett et al., 1991; Bar ric k & Mount, 1991; Salgado, 1997; Aamodt, 2004a; Varela et al., 2004; Provine, 2006). Several meta analytic and comprehensive studies that involve police officers ( among other occupational groups), show the strength of personality predictors in relation to performance assessment (Tett et al., 1991; Barrick & Mount, 1991; Salgado, 1997; Aamodt, 2004; Varela et al., 2004). analyses which specifically reviews the police selection literature, highlights personal disposition, such as the CPI tolerance scale, as important for predicting job performance. H owever, in general, individual scales and constructs of psychopathology are not strong predictors of police performance outcomes. In addition, studies have concluded that the average police candidate and officer is a psychologically healthy individual (Aamodt, 2004a). Provine (2006) improves upon previous research in a study with strong internal validity that included constructs from competing perspective s , to rule out rival h ypotheses. The study accounts for a combination of predictors such as MMPI, sex, level of education, aptitude test, physical abilities, oral interview and their effects on academy and field performance. The
42 findings are that aptitude scores account for t he majority of variance in academy 2 significantly accounted for 9.7% of the variance in employment selection measures are not able to predict predicts performance but it is mediated by academy training (Fore ro, et al., 2009). Further yet, some researchers have attempted to establish a relation ship between psychological tests and job performance but found no re lationship (Wright et. al., 2011 ; Cuttler & Muchinsky, 2006). Though most of the police selection studies have assessed outcomes with the MMPI, PAI, CAI, and/or IPI, many argue in favor of the use of other indicators for selecting law enforcement personnel (Aylward, 1985; Ash et al., 1990; Dwyer et al., 1990; White, 2008) , and some have called for a moratorium on the use of personality traits (Barrick, et al., 2001). Though personality m easures are very important indicators for screening out individuals that could potentially be problematic for law enforcement officers (Varela et al., 2004), there needs to be attention placed on ways of screening in police officers (White, 2008) ( that is, a method of selecting police officers who will be successful during and after train ing ) . Furthermore, several studies on personality inventories have major limitations in that they only assess validity and predictable of personality variables without dev eloping more elaborate models. These shortcoming compels future studies to take into account the academy curriculum and background/ demographics in conjunction with cognitive abilities to ensure a more comprehensive evaluation.
43 Intelligence, Cognitive Ability and Performance Most of the early studies on police selection did not use CAT specifically developed for policing (Burberk, 1985) . Police selection agencies tested for GCA and/or intelligence through the Wechsler Adult Intelligence Scale (verbal comprehension, perceptual reasoning, working memory, and processing speed), Otis Lennon School Ability Test (abstract thinking and reasoning), the Nelson Denny Readings Test (reading comprehension and vocabulary), Watson Glaser Critical Thinking Appraisal (evaluation of arguments, interpretation, recognizing assumptions, deductions, and inferences), and the Wonderlic Personnel Test (vocabulary, math, analogies, and logic). Burkhart (1980) found that there are correlations between low IQ scores and poor per formance; however, his research also found that higher IQ scores are not c orrelated to good performance. Kleiman and Gordon (1976) stated that recruits with higher IQ scores on Otis Lennon School Ability test were more likely to perform better in the acad emy and more likely to transfer that knowledge to the community. Kenn e y and W a tson (1990) concluded that GCA measured by the Wechsler Adult Intelligence Scale was related to academy performance, oral interview score, and overall rating of applicants prior to being hired. Champion (1994) determined that there was a relationship between the Watson Glaser Critical Thinking Appraisal and academy performance. An important aspect of police work is report writing and therefore some studies have only assessed rea ding and/or writing levels. These analyses have found that increased reading (White, 2008) and writing levels (Boehm, et al., 1983) are linked to increased performance. Those with a twelfth grade reading level or higher are top performers during academy t raining (White, 2008). Rose (1995) demonstrates the
44 importance of GCA with the finding that reading level scores from the Nelson Denny Reading Test significantly accounted for 34.5% of the variance when predicting exam scores, while education accounted fo r only 2.3% of the variance. Reading ability and comprehension are significantly associated with academy grades, state officer certification scores, and field trai ning performance (Whitton, 1990; Campa, 1993). In one of the most comprehensive studies of police selection, Provine (2006) included various predictors such as sex, level of education, law enforcement aptitude test score (reading ability, reading comprehension, and vocabulary), Wonderlic Personnel Test, physical abilities, oral interview, and p sychological test results to assess academy score outcomes. Wonderlic score and education level were significantly related to academy scores. Likewise, Cuttler and Muchinsky (2006) found that aptitude scores from the Wonderlic Personnel Test were positiv ely related to successful completion of law enforcement training at both the national and local levels. There are nationally developed CAT for law enforcement, such as the Police Officer Selection Test (POST) and there are also locally developed civil se rvice CAT, such as the Law Enforcement Basic Ability Test . One of the first studies to use a CAT specifically developed for police officers was by DuBois and Watson (1950). The researchers examined a sample of males within St. Louis Police Department to determine the relationship of GCA (determined through writing samples, in house tests, an army general classification test, Bennett mechanical comprehension, and Minnesota paper form board) and vocation interest (police interest) with academy grades and ma rksmanship scores. Results from the study showed that all measures of GCA
45 predicted academy grades. In addition, the Bennett Mechanical Comprehension CAT and Minnesota Paper Form Board CAT predicted marksmanship performance. Gruber (1986) conducted a p redictive validity study on in house CAT assessing verbal communication, judgment, observation, and learning/recall. A summed and averaged cognitive ability score was created to assess influence on total academy scores and single scores for education, fir earms qualifications, human relation, traffic laws and investigation, report writing, bylaws and statutes, rules of evidence, and criminal law exams. The study finds that CAT results are significantly related to education (r =.25) and total academy scores (r =.24). In addition, GCA is significantly correlated to scores on criminal law (r =.57), rules of evidence (r =.55), bylaws and statutes (r =.60), report writing (r=.48), and traffic laws and investigation (r =.42). However, cognitive ability was not significantly correlated to human relations and firearms qualification scores. Ford and Kraiger (1993) conducted a predictive validation study using the Multijurisdictional Police Officer Examination (MPOE), which was developed to examine twelve cogniti ve abilities: (1) flexibility and closure, (2) serial recall, (3) verbal comprehension, (4) spatial scanning, (5) visualization, (6) semantic ordering, (7) problem sensitivity, (8) induction, (9) memory for relationships, (10) paired associate memory, (11) memory for ideas, and (12) spatial orientation. The subtests of GCA were summed into a single score because there was high internal consistency. The reliability scores indicate high internal consistency to suggest the test measures a single collective a
46 assessed against overall academy performance, finding MPOE to be a valid predictor of academy scores (r =.65). Rafilson and Sison (1996) summarized the findings from seven v alidity studies on the National Police Officer Selection Test (POST) which assess arithmetic skills, reading comprehension, grammar, and incident report writing skills from various academy performances such as completion, average test scores and post train ing exam scores. The study found that POST is strongly associated with training academy outcomes. Most of the early predictive validity studies focused only on selection variables such as psychological testing and cognitive abilities and did not incorpo rate other background/demographic variables. These studies, addressing multiple predictors of performance, revealed that law enforcement aptitude scores (reading ability, reading comprehension, and vocabulary) are responsible for most of the average quiz /exams and firearms scores (Provine, 2006). Similarly, Henson et al. (2010) found that there is a positive and significant relationship between civil service exams and all academy performance outcomes (quiz, spelling, midterm, notebook, final and overall scores) using a model that incorporated age, sex, education, race, foreign language skills, prior law enforcement, military experience, and physical abilities testing. General Cognitive Ability and interest in the military predicted academy performance (S paulding, 1980); GCA and assessment center scores predicted academy outcome (Dayan, et al. 2002); education and GCA were significantly related to academy performance (Rose, 1995); and education, reading, and GCA were significantly related to the final grad e in police academy (Barbas, 1992). These research findings show that cognitive abilities predict
47 multiple outcomes and remain significant when several additional predictors are included in the model (Henson et al., 2010; Kenney & W a tson, 1990; Spaulding, 1980; Hooper, 1988; Dayan, et al., 2002; Rose, 1995; Barbas, 1992). In a meta analysis, Aamodt (2004a) examined the relationship between GCA and performance while in the police academy. These studies were journal articles, theses, and dissertations tha t were published and unpublished between 1970 and 2003. The study assessed GCA measures that were developed for police officers and also abilities test not developed specifically for police officers. In addition, the meta analyses examined CAT scores tha t were measured as a single/combination of constructs or performance measure in 62 studies (N=14,474) and that the correlation for GCA and academy performance was r =.41. Aa modt (2004a) concludes that GCA (police test, r =.47; reading tests, r =.50; general IQ, r = .45; writing tests, r = .40; federal tests, r =.37; general IQ, r = .12; federal tests, r =.05; civil service exam, r =.19) is a valid predictor of academy perform ance (p.37). However, CAT that were developed for police officers are better predictors of academy performance (Aamodt, 2004a) than those that are not developed specifically for police officers. In summary, CAT, those developed for police selection and others; with predictive and criteria variables measured as a single construct or individually, have received a great amount of testing. It is well established that there is a positive relationship with CAT and an array of academy training performances (Aa modt, 2004a). Furthermore, these findings hold up in models that include other predictions of performance. Though GCA is thoroughly explored in the literature, there are areas in
48 which the research is lacking. Questions still remain as to whether cognit ive ability may be more related to one dimension of performance than another and whether GCA functions in the same way when predicting performance for differing demographics and/or backgrounds. Education and Performance Debate on the need for higher educat ion and its impact on policing continues today. It is proposed that officers with a higher level of education have better decision making skills, and therefore, make better law enforcement officers (Worden, 1990). In addition, police officers with higher education are said to be less authoritarian, more receptive towards the community, better at race relations, and less likely to be involved in liability issues and complaints (Roberg et al., 2005). Though these findings have received considerable attenti on, the empirical evidence on education and performance primarily concerns experienced police officers. This focus leaves numerous questions in regards to education and academy performance (Riksheim & Chermak, 1993; Valera, 1999; Roberg et al., 2009; White , 2008). Researchers find that education level has the greatest impact on police academy training scores and defensive tactics (Wexler & Sullivan, 1982; Copley, 1987; Campa, 1993). Recruits with higher educational levels perform better during basic acad emy training (final exams and overall scores) (Sanderson, 1977). There is a positive relationship between having a college degree and performance, in comparison to recruits with just a high school or associates degree (Aamodt, 2004a). Recruits with an as sociate degree outperform recruits with a high school degree (Aamodt, 2004a). Furthermore, studies have found that education is significantly related to successful completion of the police training academy (McGlamery, 1998; Roberg et al., 2005).
49 Those wi th only a high school degree were less likely to graduate from the academy (Wright et al., 2011). College educated white males are more likely to graduate from the training program (Lester, 1979). Education (amount of college education) is related to aca demy performance, state licensing exam scores, and firearms scores (Johnson, (Wexler and Sullivan, 1982). Studies have also looked at the academy majors of recruits and have found that criminal justice majors have similar performance as other majors (Aamodt, 2004a). Though numerous studies have found support for a positive impact of high education (level of education/degrees obtained) on performance, other studies id entified no differences in the performance of individuals with and without college degrees (Hopper, 1988); no statistically significant difference exists for different levels of making /problem solving, quality of work, and job knowledge (McDonnell, 2008). Given the discordant findings one wonders whether higher education may interaction with another factor or factors so that its effect emerges or is masked depending on what other var iables are included in the analysis. Because of the predictive importance of cognitive abilities it is prudent to investigate whether higher education interacts with cognitive abilities to affect academy performance. The best guess is that those with bac abilities to perform better. If this is the case we would expect the interaction to help explain academy performance.
50 An area that is considerably unexplored in the education literature is the cadet program (CP). There is a prospect of sponsorship for academy cadets. The sponsoring departments prescreen applicants to look for qualities that are thought to be associated with successful police officers. Recruits are interviewed and an investig ation is qualified recruit receives financial and social support. Recruits are integrated into the department they associate with sworn officers on job related tasks a nd they are exposed to police culture in ways that should make their adjustment to the academy easier. Theories suggest that to the extent that departments are masculinist and there is dominance by whites, this prescreening and selection would favor white males. On the other hand, to the extent that affirmative action programs are instituted in these departments, the sponsorship may be a vehicle for enhancing the prospects of minorities (women or non whites). In other words, there could be an interaction between sponsorship and the cognitive abilities that are predictive of academy performance. Given all of the support that is offered to recruits who are sponsored and the prescreening of those recruits, these sponsored recruits will have higher cognitive abilities and these factors will be more predictive of performance. The prescreening and especially the support will allow these recruits to engage fully in the curriculum. Support will reduce the chance of other issues interfering with success or perfor mance. One study that assessed the effects of the CP on academy outcome found that CP recruits perform better than non CP recruits on average academy scores (White, 2008). However, cadet participants are not significantly more likely to be top performer s
51 in the academy. Eterno (2008) compared high school/GED graduates, non cadet college graduate, and cadet college graduates to determine if there are differences in their performances. There were more non cadet college graduates and cadet college graduat es in the detective and sergeant positions compared with those who graduated with only a high school/GED education. Cadet participants outperformed high school graduates in regards to less sick leave, civil complaints and central personnel index; however, they did not outperform the non cadet college graduate group. Non cadet college graduates had more arrests than did cadet college graduates and high school/GED graduates. In regards to department vehicle accidents, high school/GED graduates had the most , followed by cadet college graduates and then non cadet college graduates. Overall, a majority of studies support the finding for officers with higher education levels performing better than those without higher education when academy grades and departm ental performance evaluation scores are used to measure performance (Finnegan, 1976; Lester, 1979; Copley, 1987; Sanderson, 1977; Roberg, 1978; Smith & Aamodt, 1997; McGlamery, 1998; Truxillo et al., 1998; Aamodt & Flink, 2001; Fyfe & Kane, 2006; Aamodt, 2 004a). However, s tudies on CP are limited in number and mainly include descriptive works and/or explore outcomes for cadets while they are in the field, rather than performance while in the academy. In addition, CP has not been explored as an educationa l construct that may provide incremental validity to cognitive ability as a predictor of academy performance. Sex, Race, Age and Performance Studies have found that sex is significantly related to completion of the police training academy (Charles, 1976; McGlamery, 1998; Henson, et al., 2010; &
52 That is, males are more likely to successfully complete the basic training academy than females (McGla mery, 1998; Wright, et al., 2011 ); males outperform females (White, 2008); MMPI and sex explains most of the variance in academy performance. However, other studies find that there are no differences in academic and technical scores for males and females going throug h training (Charles, 1976). Henson et al. (2010) find that during academy training, sex is not significant, with the exception of notebook scores as a measure of academy performance. In regards to the intersection of race and sex, young minority women ar e less likely to graduate from the poli ce academy (Wright, et al., 2011 ). One of the first studies assessing the performance of women was completed in 1968, when policewomen were assigned to patrol work (Bloch & Anderson, 1974). This study evaluated whether policewomen performed as well as policemen. This experimental study matched in and us ed questionnaires and in depth interviews &
53 & & able to calm agitated and violent citizens and did not need more s upport than males ( Grant, 2000 ); had on average smaller physical builds that were less women were better at handling domestic cases and had better rapport with the community (Silvestri, 2007); and women received fewer complaints than males (Henson, et al., 2010). However, females get lower evaluation score s than males (Henson et al., 20 1 0 ). Whites and males outperform blacks and females during field training (Fyfe & Kane, 2006). Beyhan (2005) found that when age and marital status are taken into account, sex did not have a significant relationship with job performance. Overall, the direct effect of sex and race on performance during academy training is well established but, more importantly, as demonstrated by Beyhan (2005), there is a gap in the literature that fails to take into account the roles of additional variables as to whether gaps exist and why these gaps exist, when predicting training outcomes. Several studies have explored the relationship between background/ demograph ic predictors and completion of the police training academy (Cohen & Chaiken, 1973; Charles, 1976; Lester, 1979; Lester, 1985; Plummer, 1979; Spielberger et al., 1979; Schwartz & Stucky, 1993; McGlamery, 1998; Cuttler & Much insky, 2006, Wright et al., 2011 ). The race of a recruit has been identified as a reoccurring indictor of performance. Race is correlated with graduation from the police training academy
54 (Lester, 1979; McGlamery, 2008); w hite s score better on exams (Henson et al., 20 10 ); w hites are mor e likely to complete training than other racial/ethnic groups (L ester, 198 5; McGlamery, 2008); w hites, Asians, a nd o thers are more likely than Hispanics and African Americans to be top performers (White, 2008). Essentially, these stu dies seem to indicate that non w hite recruits do not do as well as White recruits in the academy (Aamodt, 2004a; Aamodt, 2004 b; Wright, et al., 2011 ). What is unclear in the literature is the role of race in regards to the relationship between cognitive ability and performanc e; and if that relationship holds for various racial group s . There is very little research on the age of recruits and performance. One study finds that, in basic academy, as recruits get older, evaluation scores and overall performance decreases (White, 2008) . W hile in the field, however, older police officers receive more commendations and better performance appraisals (Aamodt, 2004b). However, Beyhan (2005) and Henson et al. (2010) conclude that there is no effect of age on performance. Therefore, age is a construct that is in dire need of exploration in regards to its effects on performance. Overall, there is a lack in the literature which have led researchers to appeal for more works on demographics and performance (Aamodt, 2004a). Biographic fa ctors and Performance Studies have also evaluated the direct impact of other background factors such as work history, prior law enforcement, military experience, drug and criminal history on academy outcomes. Prior work experience is related to successful graduation from the academy. In addition, there is a positive and significant relationship between prior law enforcement experience and quiz average, final exam scores, and overall performance (Henson et al., 2010 ). However, a la ter study by Wright et a l. (2011 ) , which attempted
55 to predict successful completion of the academy with predictors such as education, race, sex, criminal record, military and job experience, found that prior police and military experience are not related to successfully completin g the academy. Military veterans are more likely to be involuntarily dismissed from the job (Fyfe & Kane, 2006). Furthermore, studies found that work history, drug use and criminal history is negatively associated with training outcomes and job difficul ties (Cuttler & Muchinsky, 2006; Sarachione, et al., 1998). Fyfe and Kane (2006) also found that poor work history, and a history of traffic violations and /or arrests are related to termination from police departments. Kane and White (2009) examined back ground characteristics to determine the relationship to types of career ending misconduct ; however, they did not find a strong connection between those who were dismissed for job related misconduct and pre employment background factors. According to Provin e (2006), ultimately employment variables are not accurate predictors of training performance and are not as accurate as other selection standards, such as cognitive ability. Studies of subgroup differences in Performance A tremendous gap in the police selection literature exists in regards to the relationship between demographics (e.g. race, sex), CAT and constructs of performance. One area of police selection research evaluates the correlation between predictor variables and whether these improve the estimation of the regression model when combined with another variable. Based on 21 studies, Aamodt (2004a) concludes that predictors of performance, such as education and cognitive ability are not highly correlated with each other. This is evidence th at these measures do not measure the same construct. The meta analysis could not draw conclusions as to moderating (or interacting) effects because there are too few studies and much variability in correlation
56 across these studies. Aside from interaction amongst predictors, another area of police selection research focuses on difference between groups and performance. performances on CAT and outcomes (Roth et al., 2001). One of the early research by assessment battery for selecting police officers. Using the Nelson Denny Reading Test scores, comparisons were made between Caucasian males, mino rity males, and females on passage (completed the first year of the probationary period) or failure (discharged or resigned) of the police academy. Females and Caucasian males, who successfully complete the probation period, score significantly higher on the Nelson Denny Reading Test. The sample size of the failure group for minority males was too study compared outcomes within groups, rather than across race/ethnicity , and as a result, no conclusion can be drawn on the fairness of this police selection process. Morstain (1984) was the first researcher to validate the Multijurisdictional Police Officer Exam for minority and non minority police officers. The study asse sses subsets of CAT scores (verbal comprehension, spatial scanning, visualization, sematic ordering, memory for ideas, spatial orientation, problem sensitivity, induction, memory for relationships, and paired associate memory) and differences in performanc e (supervisor ratings). The assessment was administered to six law enforcement agencies and then officers had significantly lower mean scores than whites on subtest cognit ive abilities measures and total scores. The research concludes that there is utility and validity in
57 the assessment for minority police officers but not non minority officers. To date, no study was located on differential predictablity of GCA (Florida B asic Abilities Test) and performance outcomes due to race, education, sex and CP. Furthermore, studies on the relationship between GCA and performance focus on comparing outcomes related to job performance rather than academy performance. Research that f inds differences in performance by subgroups (between men and women; blacks and whites) do not fully analyze or attempt to draw inferences. To address this issue, the current research uses multiple regression on the sample as a whole, allowing for more a ppropriate statistically method (Bartlett, et al., 1978). Not splitting sub groups prior to the regression analyses allows for more accurate comparisons (Bartlett et al. 1978). Associations between Performance Outcomes Police selection research has measur ed performance is various ways, such as police academy performance, field training performance, job performance, supervisor ratings, discipline problems, commendations, activity, absenteeism, activity, peer rating, and self ratings (Aamodt, 2004). Researc h in this area find s support for the importance of academy training in predicting field training performance and on the job performance (Campa, 1993; Haarr, 2001; Provine, 2006; Fyfe & Kane, 2006; F ore ro et al., 2009; Henson, et al., 2010; Caro, 2011; Aamo dt, 2004a) This study focuses on performance during the police academy. The literature has measured police academy performance in numerous ways such as average academy scores; academy grade point average; top 50% of the class and bottom 50% of the class, top performers; quiz scores; firearms qualifications or average; human relation; traffic laws and investigation; report writing; spelling average; interim scores;
58 bylaws and statutes; rules of evidence and criminal law; driver average; defensive tactics; e ffective behavior; attitude; physical fitness; class standing (rank), commendations, class evaluation, commendations; final exam grades; graduating or not graduating; success or failure; passing or failing state exams; final training score. A majority of the studies has used a single overall measure of academy performance. Very few studies have evaluated whether outcomes vary between curricula components . In summary, though studies on police selection have explored the effect of numerous predictors, more studies are needed on the interaction effects of predictor on academy outcomes; and furthermore, these studies neglect to address other dimensions of police performance. Therefore, what we do not know about police selection is whether cognitive abili ty produces different outcomes based on the type of curricula. In addition, questions still remain as to whether there are relationships between the predictors and how these relationships influence performance. Accordingly, it is the intention of this st udy to explore these possibilities. Gaps in the Literature Most studies on police selection have used personality and psychopathological inventories to screen out potentially problematic police officers (Metchik, 1999) . Cognitive ability is one of the most important factors used to screen police applicants into the academy. Results from these studies are that there is strong support for this predictor as having a positive and significant effect on academy and post academy success (Aamodt, 2004a). How ever, though there are numerous studies on GCA, to date, no studies were found on the Florida Criminal Justice Basic Abilities Test and
59 academy performance. Furthermore, this study adds to the police selection literature by using a cognitive abilities mea sure that is specifically related to the job of policing. Studies find strong support for higher education leading to better performance, compared to those without higher education, when academy grades and departmental performance evaluation sc ores are u sed as measures (Finni gan, 1976; Lester, 1979; Sanderson, 1977; Copley, 1987; Roberg, 1978; McGlamery, 1998; Smith & Aamodt, 1997; Truxillo, et al., 1998; Aamodt & Flink, 2001; Fyfe & Kane, 2006; Aamodt, 2004a). Even more so, a combination of education an d GCA is suggested to be a better predictor of performance (Aamodt, 2004b). However, one area that is neglected in education studies is the impact of CP on academy performance. Most of the studies on cadet programs are descriptive and exploratory (Osterb urg & Trubitt, 1970; Police Foundation, 1992; Pate & Hamilton, 1991). Therefore, more studies are needed on the effect of participating in a CP on performance aside from final academy average of the top performers (White, 2008 ). A lso studies are needed on how the CP influences the relationship between GCA and performance. Studies including demographic variables find that race and sex are significantly related to performance, with results showing that whites and males are more likely to successfully c omplete academy and outperform minorities and women. However, one construct that is frequently neglected in police selection research is age and its influence on performance. Furthermore, previous research has not examined the degree to which demographi c variables interact the GCA performance relationship. These moderation or interaction variables will stipulate when the effects between the independent and dependent variables will hold in the model (Baron & Kenny,
60 1986) 4 . That is, the interaction va riable may change the direction of the relationship between the variables from positive to negative or from negative to positive (Baron & Kenny, 1986). T his variable can have an enhancing /synergistic , buffering, or antagonistic effect. For example , an en hancing effect occurs for a n interaction whereby the impact of cognitive abilities on performance would be better for males than females (i.e. that is, the prediction line or slopes for cognitive ability and performance would be steeper for males than fema les ) . A buffering effect occurs when , for example , education and performance are positively related, however, GCA and performance is negatively related and the interaction of education and cognitive ability is reduced . An antagonistic effect is a positi ve effect between sponsorship and performance; and GCA and performance, however there is a negative relationship between the interaction of sponsorship and GCA and its influence on performance. Therefore, further examination of groupings in education and GCA can give valuable insight into when these differences exist in performance. The inclusion of several variables to explain academy performance is especially appropriate because rarely is one factor the lone predictor of academy perf ormance of any give n behavior. Furthermore, exploring these options gives opportunities for greater dynamic relationships between the variables. Examining the relationships will help to contextualize the disparities that are evidenced in the literature , and , therefore, thi s will increase our knowledge of the roles of race, sex, education and CP in shaping basic academy performance. Lastly, it is important to determine if these findings are 4 Baron and Kenny (1986) use moderation very broadly. Because moderation has come to have a narrower meaning for some, this disse rtation will use the term interaction to avoid confusion with this narrower connotations that moderation may hold.
61 generalizable to all subgroups , and therefore erasing concerns of biases and unfair ness. In regards to the dependent variable, a majority of the studies have used overall academy scores as the outcome variable , failing to evaluate relationships between law enforcement curriculum and high liability curriculum measures that could potenti ally give evidence for creating two distinct performance indicators. Traditionally, the curriculum focused on training recruits in areas such as medical first responder, criminal justice defensive tactics, criminal justice weapons training, legal, communi cations, interpersonal skills, vehicle operation, patrol, traffic, and investigation. However, there has been a shift in the training curriculum to incorporate community policing and problem solving, and therefore, training now set s out to increase the po knowledge. In turn, new performance indicators are created due to the change in training. Furthermore, to date, there are no studies that have assessed if GCA influences the law enforcement and high liability curriculum differently and if there are differences based on race, sex, education and CP participation. Similarly, there are no studies on GCA and the performance indicators that demonstrate proficiency to enter the work force (graduation). Research q uestions for main r elationships Based on the literature, it is expected that al l variables ( Criminal Justice Basic Abilities Test ( CJBAT ) , race, sex, white male, education level , and cadet p rogram (here after: sponsored or unsponsored ) ) will have a significant and positive relationship w ith academy performance (law enforcement scores, high liability /high stress scores, reactive scores, proactive scores, overall scores, and graduation).
62 Main hypotheses: Hypothesis (a): (i) a s CJBAT scores increase performance (law enforcement scores, high liability/high stress scores, reactive scores, proactive scores, and overall scores) increases; and (ii) a s CJBAT score increases recruits have a greater likelihood of graduating from the academy. Hypothesis (b): (i ) r ace is significantly related to performance (law enforcement scores, high liability/high stress scores, reactive scores, proactive scores, and overall scores) , such that the magnitude of the relationship is stronger for whites than blacks, A sians, H ispanics ther s after: no n w hites); and (ii) w hites have a greater likelihood of gradua ting from the academy than non w hites. Hypothesis (c): (i) s ex is significantly related to performance (law enforcement scores, high liability/high stress scores, reactive scores, proactive scores, and overall scores), such that the magnitude of the rel ationship will be stronger for males than f emales; and (ii) m ales have a great er likelihood of gr aduating from the academy than f emales. Hypothesis (d): (i ) e ducation is positively related to academy performance (law enforcement scores, high liability/high stress scores, reactive scores, proactive scores, and overall scores), that is recruits with a b outperform recruits without a b ; and (ii) recruits with a b ach greater likelihood of graduating from the academy. Hypothesis (e): (i) s ponsored cadets are more likely t o outperform unsponsored cadets , that is the magnitude of the relationship with performance (law enforcement scores, high liab ility/high stress scores, reactive scores, proactive scores, and overall scores) is stronger for sponsored cadets than unsponsored cadets ; and (ii) s ponsored cadets have a greater likelihood of graduating from the academy. More importantly, based on the literature, it is proposed that interaction effects will be evident in this study. Therefore, the effect of CJBAT on performance will be conditioned by group context. C onsistent findings are that cognitive is significantly related to performance (Provin e, 2006; Henson et al., 2010; Kenney & Wa tson, 1990; Spaulding, 1980; Hooper, 1988; Dayan, et al., 2002; Rose, 1995; Barbas, 1992). Furthermore, GCA is said to be the strongest predictor of performance when paired against other variables (Hunter & Hunter, 1984; Hunter, 1986). However, the previously
63 discussed studies have not unwaveringly linked GCA to numerous training outcomes and job performances (Hunter, 1986; Salgodo, et al., 2003; Aamodt, 2004a; 2004b). Additionally, there is a notably lack of any empirical evaluation of CJBAT and its influence on performance in regards to the contextual effects such on race, sex, education, and sponsored . Consequently, the previous literature points to an over reliance on assumptions about the role of CJBAT. The refore, conclusions on whether or not conditional effects exist can only be made through deeper exploration. Through such exploration using the interaction effect, this work serves to alleviate that reliance by providing empirical evidence of the CJBAT pe rformance relationship with subgroups. The implication for these interaction effects are that circumstances will be identified as to the mechanisms related to positive or negative academy outcomes. Furthermore, identifying interactions is important to selection centers and academy administrators in search of reasons for successful or unsuccessful performance by police recruits. Research questions for interactions The relationship s between p sychological a ssessment (CJBAT), and academy performance (law enforcement scores, high liability/high stress scores, overall scores, and graduatio n) vary based on d emographics (race, s ex, and WM), education level, and b iograp hic (sponsored and unsponsored ). Interaction hypotheses: Hypothesis (a): t he relationship b etween CJBAT and performance is conditioned by race. T he relationship between CJBAT and performance is stronger for w hite recruits than non w hite recruits . Hypothesis (b): t he relationship between CJBAT and perfo rmance is conditioned by sex. It is ex pected that s ex will interact with CJBAT to affect performance , such that the relationship is stronger for males than females .
64 Hypothesis (c): t he relationship between CJBAT and performance is conditioned by education. It is expected that for those wi th a b greater influence on performance. Hypothesis (d): t he relationship between CJBAT and per formance is conditioned by sponsorship . It is expected that b eing sponsored will interact with CJBAT to affect performance , such that CJBAT will have a greater influence on performance for sponsored cadets . Hypothesis (e): t he relationship between r ace and performance is conditioned by s ex . It is more likely that WM will graduate from the academy.
65 CHAPTER 3 METHODS Setting and Sample s Data for this study were collected from a police academy located in Central Florida. The training academy is a part of the local community college. This training facility services municipal, county, st ate, and international law enforcement agencies. The instructors at the academy are certified criminal justice officers and/or must have three years of experience with the curriculum material. In addition, instructors must complete eighty hours of the F DLE instructor techniques course. This Florida Criminal academy and data are collected from the police recruits prior to and during the academy training program. The i nst itute requires some standard features for academy training in the state and conducts the state certification examination after recruits complete academy training. The theoretical population of the study is defined as all recruits who participated in a lo cal police academy program in a community college setting in the Florida . The accessible population in this study is all recruits who participated in a police academy in an urban are a of Florida. The sampling frame is a list of all recruits who were adm itted into this Flori da police academy program. The list is constructed by the training academy staff specifically, the administrative office has records of all the students who applied to the academy, which includes those who entered ; and completed and d id not complete the academy between J anuary 2011 and December 2013. The study contains a non probability sample of 662 police recruits who were admitted in the basic training academy.
66 The application for admission into the academy gathers information on demographics, education, technical certifications, foreign languages, military history, employment history, and several other factors. An applicant who successful makes it through this screening process then takes the CJBAT. Each exam contains 125 ques tions, but students are evaluated on 100 questions (the remaining 25 ungraded questions are used as field test questions). Once admitted into the academy, recruits go through successions of assessments in the form of multiple choice exams on each chapter of the Law Enforcement (Volume 1) and High Liability (Volume 2) curriculum. An overall Successfully passing the entire curriculum partially e nable s recruits to graduate f rom the academy. The application data, CJBAT scores, and academy test scores were collected and compiled into an excel database. training. Consequently, this research will d eal with two samples. The first sample will be all of the recruits and will analyze which variables relate to graduating or not graduation from the academy training. The sample size for this line of analysis will be 662. The second sample will focus on those who complete d the academy . A nyone who does not complete will have missing data on those measures. T he second line of analysis will be of recruits with in class performance outcomes (approximately 500 recruits) .
67 Measures and Definitions Dependent Variables The job of police officers i s diverse and always changing. Academy training is designed to prepare recruits for the various job demands. Therefore, the training may reflect multiple constructs. B ecause some job knowledge may be more highly dep endent on CJBAT than others, the inclusion of various measures of performance may improve The performance indicators are measures of achievement knowledge and skills acquired during the academy across a number of areas . Therefore, these performance indicators are distinct from the basic abilities or aptitude scores received prior to entrance into the academy , including the cognitive measure captured by the CJBAT . Academy performance is measure d through four dependent variables, which are graduation, overall performance, law enforcement performance, and high liability and/or high stress p erforman ce. On its face, police performance seems to be multidimensional, but much of the research heretofore has not delved into the possible different dimensions of performance at the academy. Researchers are confronted with the challenge of how to measure per formance (Sanders, 2003) across the various subject matter contents of training. Using all of them separately in research produces so much detail that it becomes hard to establish patterns. Therefore, it is important to combine performance
68 indicators from the respective curriculum chapters in some way. 5 The prospect of combining scores across the many units while retaining different dimensions is explored in two ways in this research. First, one can look at the content of the training. Some of the variables below are derived from that content. They will be grouped in ways that make conceptual sense and reliability analysis will be performed when performance scores ar e aggregated. The reliability results will be reported for the respective dimensions. From the content of the courses at the academy, three dependent variables are derived: law enforcement performance, high liability and/or high stress performance, and t heir combination into a measure of overall performance. (a ) Law enforcement p erformance is a measure compris ing of scores from the multiple choice examinations assessing knowledge of eleven ch apters of the curriculum: (1) i ntroduction to law enforcement , (2) legal, (3) communication ; (4) human issues , (5) patrol 1, (6) patrol 2 ; (7) crime s cene investigation ; (8) criminal investigation ; (9) traffic stops , (10) DUI traffic stops , and (11) traffic crash i nvestigation. To date, no other study was found wit h such an inclusive measure of the law enforcement curriculum. Each chapter is measured continuously (0 100) ; the respective scores were summed and averaged . T for the eleven law enforcement units was .8399. ( b ) High liability and/ or high stress p erformance is a measure from the multiple choice examinations that focus more on knowledge of traditional policing and acquaints 5 Analyses of each of the 16 chapters/units of curriculum were conducted and the results are reported in Appendix A. This was done as a chec k to be sure that the composite measures described in this methods chapter and used to produce the results for subsequent chapters of this dissertation did not miss something or distort relationships between performance measures and CJBAT and the other ind ependent or control variables.
69 recruits with actions to be taken in these four curricular chapter areas: (1) firearm use, (2) defensive tactic s, (3) vehicle o peration, (4) f irs t aid, and (5) d art firing s tun g un. To date, no other study was found that measures performance with a focus mainly on principles of traditional policing. Each of variable is a c ontinuous measure from 0 to 100; the scor es on the five chapters were summed and average. T for performance on these five curriculum units is low ( .6299), but high enough to use in this investigative venture into how cognitive ability works with other factors to affect academy performance . (c ) Overall performance is a measure of total points achieved by recruits at the end of training for the chapters across the academy curriculum and include both the law enforcement and high liability subscales . This measure improves upon methods used in previous studies by being a comprehensive performance indicator, that is, the measure accounts for all exams taken by the recruits. T he scores from the sixteen c hapters are summed and averaged. T he r A second way to approach the prospect of multidimensionality in academy performance would be throug h exploratory factor analysis. Here, the various performance indicators were analyzed statistically to determine if there are multip le dimensions to the police academy performance measures and if so, determine the best way to group together interpretable factors. In Table 4 5, an exploratory factor analysis was conducted showing the factor loadings without a rotation. The factor anal ysis with no rotation gives one eigenvalue greater than 1, which is 5.55. The first component explains most of the variance which is 94.69%. In addition, according to the scree plot,
70 most of the items load on the first component. Furthermore, based on t he scree plot, there is a great difference between the first and second component. These findings suggest that the scale is one dimensional. Given the exploratory nature of the study, both a varimax and promax rotations are conducted. In Table 4 6, for t he varimax rotation, several of the variable s load onto multiple factors and not simply one factor. Traffic crash investigation loads unto three factors (factor1, factor 2, and factor 3). Introduction to law enforcement, human issues, communication, vehi cle operations, first aid, firearm, and criminal investigation load highly onto two factors (factor 1 and factor 2). DUI loads unto factors 1 and factor 4. Legal, patrol 1, patrol 2, defensive tactics, crime scene investigation, traffic stop, and DUI tra ffic load highly to only factor 1. Whereas, defensive tactics and dart firing stun gun load separately onto factor 3 and factor 4, respectively. For the promax rotation (Table 4 7), traffic crash investigation, patrol 1, patrol 2, criminal investigati on, crime scene investigation, and traffic stop items load only on factor 1. While, introduction to law enforcement, legal, human issues, communication, vehicle operations, and first aid loads onto factor 2. DUI traffic stop load highly onto factor 4 an d Defensive on factor 5. The remaining variables, DUI traffic stop and dart firing stun gun load on factor 4 and defensive tactics loads onto factor 5. These outcomes s uggest that the sixteen items may not be measuring performance as one latent construct . Therefore, the varimax and promax rotations suggest multidimensionality. The varimax rotation has a more complex structure than the promax rotation. Consequently, a promax or oblique rotation is better suited for the data t he correlation
71 matrix in Ta ble C 2, for the eight factors, show that several of the factors a re highly correlated (Appendix C ). Furthermore, the promax rotation presents a simpler structure: there are significant loadings on two distinct factors; there are few complex variables, th at is, each variable load strongly on a single factor and not on several factors; and there are several zero loadings to other factors. Therefore, in ad dition to the originally proposed dependent variables , based on the factor analysis two additional outc ome variables are constructed to represent the two loadings: reactive and proactive performance. (d) Rea ctive performance is a measure constructed from multiple choice examinations gauging knowledge on six chapters of the curriculum: (1) introduction to law enforcement, (2) legal, (3) human issues, ( 4 ) communication, ( 5 ) vehicle operation, and (6) first aid (Table 4 4) . Each of the chapter score is conti nuous (0 100). The scores for the six chapt ers were summed and averaged . (e) Proactive performance is a measure created from multiple choice examinations assessing understanding on six chapters of the curriculum: (1) traffic crash investigation, (2) patrol 1, (3) patrol 2, (4) criminal investigat ion, (5) crime scene investigation, and (6) traffic stop proactive performance is .8110 (Table 4 4). Each chapter is measure d continuous ly (0 100). The respective scores for the six chapters were summed and averaged. The f inal dependent variable used in this study is graduation. Graduation is a measure indicating completion of all the requirements of the basic academy program. G ood performance on the various curricular components of academy training should
72 culminate in gr aduation. Recruits should successfully pass each chapter exam developed by the Florida Department of Law Enforcement (FDLE) staff and subject matter experts with a score of eighty percent or above. Recruits are allowed to take the exam on two occasions. Retaking a failed exam should result in a passing score. If the second exam receives a failing grade, the recruit will be recommended for dismissal from the academy. Aside from the classroom examinations, recruits are eligible to graduate if they succe ssfully complete all practical examinations semiautomatic handgun firing, night fire, long gun fire, physical fitness tests (jumps, sit ups, short and long distance runs and push ups), and the basic life support for healthcare provider written exam. In ad dition, recruits are eligible for graduation if they do not exceed the allotted amount of policy violations , for example, tardiness, absences and disciplinary write ups. (f) G raduation is a measure c onstruct ed dichotomously ( completed =1 and did not comp lete = 0 ) . Graduation is conceptually distinct from the other performance measures because theoretically this is an indicator of mastery of the curricula and whether or not a recruit is capable of successfully passing the SOCE, completing the field training program, and progressing to solo police work. Recruits who failed to complete the academy once or more are pla ced in the failure category. Therefore, if a recruit fails the academy and returns at a later date and graduates from the academy, the first pe rformance measures are included in the study and the second performance scores are excluded from the study. Predictor Variable The Criminal Justice Basic Abilities construct (CJBAT) is an indicator of aptitude or basic abilities and is used to determine
73 success during the basic academy training program (Federal Department of Law Enforcement). Therefore, the CJBAT measure is an indicator of latent abilities for abstract, critical, and logical reasoning capabi lities and how recruits process and solve problems. The exam is timed and contains multiple choice questions. CJBAT scores are operationalized as a continuous variables representing an average of all eight indicators: (1) deductive r easoning, (2) inducti ve reasoning, (3) information ordering, (4) memorization, (5) problem sensitivity , (6) spatial orientation, (7) written expression, (8) and written c omprehension. A passing score on the exam is 79% and therefore, the scores will range from 79% to 100%. T his study cannot conduct validity and reliable test of these measures because the data provided only has the total scores and not the individual scores for each subtest. However, similar measures of police aptitude from prior police study found that there is high internal consistency amongst each subset, therefore, suggesting that cognitive ability is one general construct (Ford and Kruger, 1993). In addition, the organization involved in creating the test of aptitude have validated the CJBAT for its cont ent and predictability of academy performance and state officer certification exam (I/O Solutions, Inc., 2013). Other independent variables and interaction terms (a) Race is a socially constructed assignment of attributes. Recruits self report on their ra ce/ethnic gr oup as white, black, Hispanic , Asian , and o ther. Race/ethnicity is treated as a dummy variable and is categ orized as w hite = 1 and non w hite = 0 . The interaction terms 6 are CJBAT*race (CJBAT*w hite ), which are created by mean 6 The interaction terms and the appropriateness of product terms as a way to construct these measures are discussed in detail in the analysis section.
74 centering CJBAT a nd creating product terms for the mean center CJBAT and dummy variable for each race. (b) Sex is the biological categorization of male and female and each recruit self report this information during the application process. A dummy variable is create d i n the study for categories 1 = males and 0 = f emales. The interaction term is CJBAT*sex (CJBAT*m ale), where CJBAT is mean centered and the dummy variable created for sex are both used to create the product terms. (c) Sponsored recruit s may have differe nt incentives to perform well in the academy, therefore, it is important to include this variable to take into account any other factors that might contribute to differences in performances. Dummy variable (no = 0) and (y es = 1) represent whether or not t he new recruit is sponsored by a police department. C J BAT*sponsored is the interaction term which involves mean centering the CJBAT scores and creating the product terms with the sponsored dummy variable. (d) Education is measured categorically as educa tion level (1 = G ED (general educational development ) ) ; 2 = high s chool diploma ; 3 = AA/AS degree ; 4 = b achelor s degree ; 5 = m aster s degree ) and they are then transformed into dummy variables ( degree = 0) . C J BAT*education (CJBAT*b achelor s degree ) is the interaction term which involves mean centering the CJBAT scores and creat ing the product terms with the b achelor s degree dummy variable. (e) A race and sex measure (WM) is constructed as a n interaction term by generating a product terms (white*male) with dummy variable with white = 1 and non white = 0 and male = 1 and f emale = 0.
75 Control Variables (a) Academy c ohort indicates term of acceptance into the training program . Five sessions are held in approximately one year. The terms range from c ohort 1101 to cohort 1305. A d ummy variable is assigned with c lassroom 1101 = 1 and the others = 0. The later cohorts (1102, 1103, 1104, 1105, 1201, 1202, 1203, 1204, 1205, 1301, 1302, 1303, 1304, and 1305) are also coded as dummy variab les. The first academy class (c lassroom 1101) is treated as the excluded group in the regression model. This construct is created to take into account effects of shared training culture that may influence recruits. (b) Age is a continuous variable self reported in years by recruits at the beginning of the academy program. (c) Military experience is a self reported variable on whether a recruit served or is currently serving any branch of the military and is mea s ured as a dummy variable (1 = yes, 0 = n o). (d) Arrested or notice to appear is a self reported variable on all prior juvenile and adult record. This variable is mea sured as a dummy variable (1 = yes, 0 = n o). (e) License revoked is a self reported me a sured coded as a dummy variable (1 = yes, 0 = n o) for resulted in a license revocation. (f) Employed is a variable representing recruits current employment status and is mea sured as a dummy variable (1 = ye s, 0 = n o). (g) Terminated from a job is measured as a dummy variable (1 = y es, 0 = n o). Recruits self report on whether they were involuntarily ever dismissed from a job, for various reasons, such as, tardiness or violation of company policy or they w ere never dismissed from a job.
76 (h) Body mass i ndex (BMI) is calculated through weigh t and height measurements . The formula is weight subtracted from height, and the result is then squared and multiplied by 703 . This continuous measure is then categor ized into four BMI groups : (1) (2) n orm al weight (BMI 18.5 24.9) , (3) o ve rweight (BMI 25 29.9), and (4) o bese (BMI ). A dummy variable is created to represent o bese (BMI ) = 1 or 0 = n o. Analytic Strategies Descriptive Statistics and Correlatio ns The study computes the means and standard deviations for CJBAT, law enforcement curriculum scores, high liability/high stress curriculum scores, reactive curriculum score, proactive curriculum scores and overall curriculum scores. Frequencies and perce ntages are calculated for the interaction and control variables by graduated and did not graduate from the academy. Difference tests (t test and chi squared) are computed for the interaction and control variables by graduated and did not graduate group. r to assess the relati onships between the variables. The correlation matrixes are computed for each of the individual curriculum chapters, the factors of the promax rotation, and for each of the outcome measures with all other variables (predictor and controls). In addition, diagnostic tests are conducted prior to and after the regression models. Main Effects for the Regression Analyses The hypotheses are tested by estimating multivariate linear (for the sample who only completed the academy) and logistic regressions (for the entire sample/graduates and non graduates) . The study first needs to determine the relationship between CJBAT and the continuous performance variables by evaluating the shape an d direction
77 of scatterplot. If the data show that as CJBAT increases performance increases, linear regression is the most appropriate technique for the data. However, if the relationship between cognitive ability and performance is curvilinear, that is, as CJBAT increases performance increase, but at a certain point, if performance decreases as CJBAT increases, this gives evidence for a curvilinear relationship. Therefore, if the scatterplot depicts a curvilinear relationship, then a polynomial regressio n fits well with this non linear relationship. A straight line fit through the data that do not have a linear relationship weakens the relationship between CJBAT and performance. The previously discussed police selection literature supports a positive a nd linear relationship between CJBAT and performance, therefore a curvilinear relationship is not expected. Ordinary least squares (OLS) regression models will be estimated to determine whether the predictors of performance is significant when the contr ol variables are entered in the model for the law enforcement, high liability/stress , overall, reactive, and proactive performance . Logistic regressions will be estimated for the relationship between the predictor (CJBAT) and graduation from the academy , with the control variabl es also entered into the analysi s. Interaction effects for the Linear Regression Analyses For the c ontinuous dependent variables (law enforcement, high liability/high stress scores, overall s cores , reactive scores, proactive score s ), multiple regression models are estimated. These models contain a quantitative/continuous predictor (CJBAT) and the other independent variables used to construct the interaction terms (sex, race, sponsorship , education) , as well as control variables . T he first step is to
78 mean center 7 the predictor variable (Jaccard & Turrisi, 2003). The second step is to incorporate the four dummy independent variable s, making one of each group a reference category 8 . The third step is to include the interaction or pr oduct term 9 : (1) CJBAT*w hite , (2) C JBAT*m ale, (3) CJBAT* sponsored , (4) CJBAT*b achelor s degree . The final step is to run linear regression s for performance with the predictors, interaction terms, and also the control variables. 10 The goal is to determ ine if interactions exist , that is, whether or not the effect of CJBAT on performance (law enforcement scores, high liability scores, and overall scores) differ based on race (white or non white), sex (male or f emale), sponsorship (sponsored or unsponsored ), a nd e ducation (received a b degree or did not receive a b five regression models are estimated for total performance (m odel 1: no interaction terms; m odel 2: CJBAT*s ex; m odel 3: CJBAT*w hite; m odel 4 : CJBAT* b egree; m odel 5 : CJBAT*s ponsored ). Interaction E ffects for the Logistic Regression Analyses Gradu ation is a categorical outcome variable and so logistic regressions are conducted for the next models. Logistic regression is the most appropriate method to use because the link function convert s the probability of the dependent variable and 7 The zero point of the scale corresponds with the mean . 8 When zero equ als female s and one equals males, the regression is interpreted for females and when zero equals males and one equals females, the regression is interpreted for males. So the reference groups change through rescoring and for each mult iply the value by the centered CJ BAT variable and rerun the analysis. 9 Product terms are better suited than separate regression models because it allows for a formal way of testing differences between the coefficients (Jaccard & Turrisi, 2003). 10 Each of t hese steps relate to all the performance variables (law enforcement scores, high liability scores, and overall score s) .
79 linearizes the model to fit the data (Long and Freese, 2001). The predictor variable (CJBAT) is quantitative/continuous and the interaction variables are categorical ( sex , race, education, sponsored , and white males ). T he first step is to mean center CJBAT , and then mu ltiply CJBAT by the dummy variables (male = 1 and female = 0; white = 1 and non w hite = 0; sponsored = 1 and unsponsored = 0) to produce the interaction ter ms: degree. In addition, an interaction term is created for white males (White *Male). L ogistic regression s are estimated with the variables (predictors, interaction terms , and co ntrol s ) for graduation to determine if the relationship is conditioned on sex, race, education, and/or sponsorship , and if WM are more likely to graduate from the academy. Therefore, six logistic re gression models are estimated: model 1: no interaction te rms; model 2: WM; m odel 3: CJBAT*sex; m odel 4: CJBAT*w hite ; m odel 5: CJBAT*b ache lor s degree; model 6: CJBAT*s ponsored. The goal is to determine if interactions exist , that is, whether or not the effect s of CJBAT on graduation diffe r significantly based on sex (m ales or females), race (whites or n on whites), sponsorship ( sponsored and unsponsored), education (b achelor s degree) and WM (white males or non white males and f emales). Therefore, for the regression models, the study will interpret the coefficien ts, the exponents of the coefficients, odds ratios, the f statistic s , t tests, the two tailed 95% confidence intervals, and p values. Data Limitations A limitation of this study is the suitability of linear and logistic regression analyses to test hypothes es with data that have recruits clustered in classrooms. A more appropriate statistical analysis would be hierarchical linear m odeling, to take into
80 account the individuals clustered within the classrooms. The study was unable to utilize the data in such a way because the level one units (individuals) is too small there are 15 classrooms with an average of 40 students enrolled during an academy session. However, the study accounted for this issue in the regression models by creating dummy variables and e xcluded categories for classrooms. The study needs to be concerned with attrition and ways to handle missing data for recruits forced to leave the academy and recruits who failed to complete th e academy curriculum . Recruits admitted to the aca demy but w ho failed to graduation , were excluded from the study assessing course curricula performance . Recruits with missing performance outcome data (LEO performance, high liability performance, overall performance, reactive performance, and proactive performance ) cannot be analyzed and therefore were excluded from those analyses. However, i t is important to determine what factors are related to poor performance and excluding this group limits our understanding of those influences . Therefore , performance of no n completers are captured in the logistic models assessing predictors of graduating and not graduating from the academy . A third concern of the study is that CJBAT scores are only available for recruits who score between a certain range (79 100) and thi s results in restriction of the range of scores. Those falling outside this range were not accepted into the academy and only those falling within the range were accepted into the academy. This narrow range leads to an underestimation of the true rela tionship between the variables. A wider range of scores is preferable because this produces more variation in the CJBAT
81 scores. Therefore, the generalization of this study is limited to a restricted samples, rather than the entire population of applicant s (unrestricted and restricted samples).
82 CHAPTER 4 RESULTS OF ANALYSES The Descriptive Results Differences between graduates and non graduates First, descriptive statistics for the explanatory, control and depend ent variables are discussed. This analysis will also determine if there are significant characteristic differences between recruits who graduated from the academy and those who did not graduate from the academy. In addition, bivariate correlation analyse s between the explanatory variables and each outcome variable are performed. Lastly, multivariate regression models are estimated. Table 4 1 shows that the total number of recruits within the sample is 662, with 484 recruits who graduated and 178 who did not graduate from the police academy . Therefore, o ver the three year period, 73.1 % of the recru its completed the academy and 26.9 % did not complete the academy . In regards to the predictor variable, basic abilities (CJBAT), the average score s for the en tire sample is 87.3 , with graduate s having a higher score ( 87.8 %) than non graduates (85.5 %) . This difference between the group s is statistically significant (t= 5.80, A few of the interaction variables show statistically significant differenc es between the graduates and non graduates. T he chi square analysis for types of d egrees completed by the recruits shows significant differences for graduates and non 2 = 28.2 ) . Within the total sample, 38.4%, 35.4%, 18 .7%, 3.9%, and 3.4% received a bachelor s degree , high school diploma , associate s degree , GED
83 or m a plurality of the recruits received their b ( 4 1.91 % , n=277 ) . Of those who received a b re i s a less er percentage of fail ures out of the academy, in comparison t o those who received a GED, high s chool diploma , and AA/AS degree. T hose with only a GE D have a lower percentage of graduate s and a higher percentage of non graduates, in comparison to t he other groups. Sponsored and un sponsored recruits have significant differences in graduation 2 ) . A higher percentage of recruits are not sponsored (72 .0% , n=477 ) by a police department or c ounty s e of sponsored recruits graduate d from the academy (90.2% ) and a larger percentage of re cruits who are not sponsored did not graduate from the academy (33.5% ). It is important to note that consistent with HM perspective males are more likely to be sponsor ed by a police department than females. The sample comprises of a larger percentage of males, that is, of those entering, 86.1% were males (n=570) and 13.9% were females (n=92) over the three year period . Over those years, a higher percentage of males graduated (72.9%) from the academy than those who did not graduate (27.0%) from the academy . Similar to males, a higher percentage of females graduate d (73.9%) from the academy , than those who failed graduate (26.0%) from the academy. Of the sample of g raduate s , a higher percentage of females graduated from the academy (73.9%, n=68) than males (72.9%, n=416). Of the sample of non graduates , a higher percentage of males failed to graduate from the academy , in comparison to the
84 sample of females 27.0% ( n=154) an d 26.0 % (n=24), respectively. Though differences exist between male and female graduates and non graduates, these differences are not statistically significant. Whites make up the highest percentage of recruits, followed by Hispanics, blacks, A sians, and o thers 58.5%, 20.9%, 15.4%, 3.0%, and 1.2% respect ively. A higher percentage of w hites have graduated from th e academy, followed by blacks, Asians, H ispanics, and o thers 75.8%, 71.6%, 70.0%, 68.1%, 62.5% respectively. The chi square analysis found no significant race/ethnicity differences between graduates and non graduates. In regards to the control variables, only one variable sh owed statistical significant differences between the graduate and non graduate group. There are significant ag e differences between graduates and non graduate s of the police academy (t=2.08, ; but the differences are not great. The average age for the sample is 27.12 years old, with non graduates having a higher average of 27.84 years, in comparison to graduate s, who are on average 26.86 years in age . Though there are differences bet ween the cohorts, these differences are not statistically significant. Cohort 1304 had an above average enrollment of 65 recruits, however, c ohort 1204 had the highest percentage of graduates (86 .0 %, n=36) and c ohort 1103 had the highest percentage of non graduates (38.7%, n=12). No significant differences exist for employed and unemployed status in regards to graduation. A majority of the recrui ts were employed when they started the academy (86.6%, n=520) and employed recruits have a higher percentage of graduates (75.0%) . Unemployed recruits make up a higher percentage of non graduates.
85 In regards to m ilitary enlistment, n o significant differences exist for graduate and non graduates. A majority of the recrui ts have never been enlisted in the military ( 72.9%), while 24.1% have enlisted and served in the military. Of those with prior experience in the military, a majority of that group graduated from the academy (71.5%). Of those with no military experience, a similar majority graduated from the acade my (73.7%). There are no significant differences between graduates and non graduates in regards to prior arrest. More recruits reported that they were never arrested (81%, n=533) and non arrested graduates make up 74% (n=395) of the sample. Of the re cruits that were arrested or received a notice to appear, a higher percentage graduated from the academy (69.6%, n=87) in comparison to those who did not graduate. For those who have never been arrested, a higher percentage graduate d (74%, n=407), in comp arison to non graduates. No significant differences exist in regards to graduation for those who have had their licenses revoked , in comparison to those who have never had their license revoked. A majority of the sample have never had their license revoke d (83%, n=553). Those who have never had a revoked license have a higher graduation rate (73.6%, n=407). Of those who did not graduate, a higher percentage were those who have had their license revoked in the past (29.8%, n=31). There is no significant differences for graduates in regards to termination from a job. A majority of the recruits have never been terminated from a position (83.7%, n=552). Of those who graduated, a majority have not been terminated (74.2%, n=410)
86 and for those who did not gra duate, a majority have been terminated from a job (32.7%, n=35). CJBAT mean level differences across interactions In T able 4 2 are the descriptive statistics for the basic abilities test and interacting variables. Females received a higher mean on the bas ic abilities (CJBAT) exam than males, 87.89% v ersu s 87.21%. However, this difference between the sexes is not statistically significant. There are though significant differences between the sexes for the other interacting v ariables. In regards to race, w hites score hi gher on the CJBAT than non w hites . The average CJBAT score for whites is 88.40% and non w hites in 85.70% (t= 7.38 , who received a b the CJBAT than recruits who did not receive a b score for those who received a b or those who did not receive a b e, the mean score is 85.92%. These differences between the groups are sta tistically significant (t= 9.28, . Sponsored recruits score (89.10%) higher on the CJBAT, than un s ponsored recruits (86.60%). This difference between the group is statistica lly significant (t= 9.28 , A cademy curriculum outcomes Over the course of the training academy, recruits were tested on sixteen chapters from the law enforcement and high liability tr aining curriculum. In Table 4 3 , on average, recruits scored h igher on the dart firing stun gun exam (mean=95.36%), followed by crime scene investigation (94.84%), DUI traffic (94.06), Patrol 1 (mean=92.39%), defensive tactics (mean=91.86%), traffic stops (mean=91.52%), firearms (mean=91.21%), crash investigation (me an=90.55%), vehicle operation (mean=90.33%), first aid (mean=90.08%), patrol 2 (mean=89.89%), criminal
87 investigation (mean=89.79%), communications (mean=89.70%), legal (87.68%), human issues (mean=87.35%), and introduction to law enforcement (mean=85.92%). In Table 4 4, o n average, recruits scored highest on the high liability performance exams (mean=92.01%) . Followed by proactive performance which has a mean of 91.55%, then the LEO performance exams (mean=90.64%), and lastly the reactive performance is 88.97%. Overall, the average score for total performance is 91.09%. Correlation Matrix Before the regression models are assess ed , correlation matrix es are performed to determine the relationships between the predictor, interactions , co ntrols, and outc ome variables and to inform the multivariate analyses . In regards to the relationship betw een the performance variables, the results are reported in Appendix C. T able C 1 shows that a majority of the variables are highly statistically significant at the (Appendix C ) . In addition, each relationship is positively correlated. Only three relationships were not statistically correlated, whic h are the relationship between defensive tactics and criminal investigation, defensive tactics and t raffic stop, and d art f iring s tun gun and defensive t actics. In T able C 3 (Appendix C ) , there is the bivariate correlation matrixes for the relationships between the predictor, interactions , control s and performance variables. In regards to the predictor variable ( CJBAT) and the dependent variable ( LEO performance ) , the relationship is positive and highly statistically significant (r=.446 , ). Therefore, as CJBAT increases , LEO performance increases. Similarly, a positive relationship exists be tween CJBAT and high l iability p erformance (r=.431 , ). A s CJBAT increase, high l iability p erformance increases. CJBAT has a similar hi gh statistically significant relationship ( ) with reactive performan ce that is also
88 positive (r=.495 ). Similarly, CJBAT is positively and significantly related to proactive performance (r=.345 , Lastly, the relationship between CJBAT and gr aduating from the police academy is positive (r= .220) and high .001). In regards to s ex, there are negative and statistically significant relationship s with LEO p erformance (r= .199 , ) , o verall p erformance (r= .164 , 001 ), reactive performance (r= .135 , 0 1 ) , and p roactive performance (r= .199 , . The relationships between sex and high l iabi lity and sex and g raduation are negative, but not statistically significant. The relationship s between w hite and all pe rformance measures are positive (LEO performance r=.236; high liability r=.329; o verall performance r=.284; r eactive performance r=.285; p roactive performance r=.191 ) and highly statistically sign ificant ( ). Therefore, being w hites is strongly corre lated to performance . T here is no statisticall y significant relationship for w hites and g raduation from the police academy. There are negative relationships between a ge and performance (LEO performance r= .003, high l iability performance r= .040 , t otal performance r= .012 , and reactive performance= .014 ). These relationships are not statistically significant. There is a positive relationship between a ge and p roactive performance (r=.0 5 4 ) but the relationship is not statistically significant . The relat ionship between age and g raduation is negative (r= .080 ) and statistically significant ( 05). For b itive (LEO performance r=.345; high l iability p erformance r=.224; overall p erformance r= .327; reactive p erformance r=.3 8 7 ; p roactive p erformance r= .260 ; graduation r=.195) and statistically significant .
89 Being s ponsored by a law enforcement agency and all performance measures are positively related (LEO p erformance r=.305; high liability r=.202; ov erall p erformance r=.288; reactive p erformance r=.308; p roactive performance r=.226; g raduation r=.241) and highly statistically significant Negative relationships exist for employment and all five performance outcomes (LEO r= .058 ; high l iabilit y r= .080 ; o verall r= .068; reactive = .0 7 1; p roactive r= .026 ) and the relationships are not statistically significant. The relationship between employment and g raduation is negative ( r= .068 ) and not statistically significant. For those in the m ilitary , there is a negative and statistically significant relationship w ith LEO p erformance (r= .131 ) , overall p erformance (r= .064; reactive p erformance (r= ) , and p roactive performance (r= .146 ; . The relationship s between military and h igh liability and military and g raduation are negative and not statistically si gnificant. The relationships between the variables licensed r evoked (r= .02 ) , t erminate d (r= .05), a rrested (r= .04) and g raduation are negative and not statistically significant. The relationship between BMI and g raduation is statistically signific ant and negative relationship (r= .130 , p=.001). Overall, the relationship between the variables are m ostly positive and statistically significant, with several moderate and weak correlations . Tables D 1 t hr o ugh D 5 show the results for the variance infl ation factor (VIF) which assess es multicollinearity issues related to the independent variables (Appendix D ) . T he results for each VIF value ranges from 1.02 to 1.3 0 and the mean VIF for LEO is 1.13, high l iabili ty is 1.13, and overall is 1.15 , p roactive is 1.15 , r eactive is 1.14 and
90 graduation is 1.14 . T herefore, demonstrating the independent variables have no multicollinearity issues. Linear Regression and LEO, High Liability and Overall Performance In T able 4 8 , the LEO p erformance model is statistical ly significant (p .001) and the variables in the OLS regression explain 35% of t he variance in LEO p erformance. The estimated regression unstandardized coefficient for CJBAT is .21 , male is 1.0 8 , w hite is .86 , b achelor is 1. 1 4 , s ponsor ed is 1.42 , and e mployed is .78 (p= .05 3 ) . Therefore, with each percentage increase in CJBAT, LEO p erformance incr eases, on average, by .21 in score ; for m ales, perform ance decreases on average by 1.08 ; for those with a b performa nce increases on average by 1.14 over the other education levels ; and for s ponsored individual s, performance increase by 1.42 over unsponsored cadets . The standardized coefficient for CJBAT i s .31 (se=.03) , sex is .11 (se=.40) , white is .13 (se= .28) , b achelor s degree is .17 (se=.29) , and sponsored is .21 (se=.30) . Therefore, CJBAT has the largest standa rdized coefficient. In T able 4 8 , the high l iability model is .001) and the R 2 in the OLS regression is .29, therefore, the variables in the OLS regression expl ain 29% of the variance in high liability p erformance. The estimated regression unstandardized coefficient for CJBAT is .20 001 ) , w hite is 1.44 , and s ponsored is 1. 1 8 . Therefore, with each percentage increase in CJBAT scores , high liability p erforma nce incre ases, on average, by .20 in score . For w hite recruits , performa nce increase on average by 1.44. For s ponsored recruits , high liability performance increase by 1. 1 8 . The standardized coefficient for CJBAT is .30 (se=.03) , w hite is .21 (se=.29) , and s ponsored is .17 (se=.32) . Therefore, CJBAT has the largest
91 standardized coe fficient and therefore, changes at a greater rate than the other significant coefficients. In T able 4 8 , the overall p erformance model is statistically significant (p .001) and the variables in the OLS regression explain 36% of the variance. The estimated regression unstandardized coefficient for CJBAT is .21 ex is , w , b achelor s degree is 1.06 , s ponsored is 1.30 ( , and e mployed is .72 (p = .05 7 ). Therefore, with each unit increase in CJBAT scores , o verall p erformance increases, on average, by .21 of a unit . performance is on av erage .85 of a unit lower than females . Recruits with a b s degree performance 1.06 units higher than recruits with out . Lastly, s ponsored recruits experience on average a 1.30 higher overall pe rformance than unsponsored cadets . The standardized coefficie nt for CJBAT is .34 (se=.02) , s ex is .09 (.37) , w hite is .16 (se=26) , b achelor s degree is .15 (se=.27) , and s ponsored is .20 (se=.28) . Therefore, CJBAT has the l argest standardized coefficient. In summary, CJBAT and race are consistent predictor s of LEO performance, high liability perform ance and overall performance . , sex, and sponsorship are positively associated to LEO performance and overall performance, but are not related to high liability performance. Sex is ne gatively related to performance . In addition, employm ent is negatively related to LEO and overall performance, but the variable is not related to high liability performance. Furthermore, within these model s several of the cohort dummy variables are statistically significant and these cohorts are less likely to do well on law enforcement (cohort 1301 and 1303), high liability (cohort 1105, cohort 1202, cohort 1203, cohort 1205, cohort 1302, and cohort 1303) , and
92 overall performance (cohort 1202, cohort 1203, cohort 1205, cohort 1301, cohort 1303, and cohort 1 304) . I n the upcoming models, the collective contribution of the cohort variables is in significant , therefore, t he cohort variables are d ropped from the analyses . Interactions terms LEO Performance Beginning with Table 4 9, the results with interaction te rms are included in the analysis are reported. The table begins by presenting the analysis with out interaction terms and then it proceeds to introduce each of the interaction terms. The first research question assesses whether CJBAT predict s LEO performa nce. Since CJBAT is a prime requirement to enter into the academy and it is expected to be strongly correlated with performance, an increase in CJBAT is expected to increase LEO performance in the academy. Con sistent with the pre diction, model 1 11 in T abl e 4 9 shows that an increase in basic abilities (CJBAT) is significantly related to LEO performance (b=.2 1 , se =.02, .001). In addition, w hite (b se =.28, , those with a b (b =1.02 se =.28, , and s pon sored recruits (b =1.27, se =.29 , are more likely to perform well on the LEO performance exams. An unexpected finding is the negative relationship between sex and LEO performance, that is, male s are less likely to perform well on that exam (b = 1.12 , = .12, se =.38 , . 01) . Overall, result from model 1 supports the hypotheses, with the exception of sex , and these results are net of other variables included in the model (F statistic =24.30, R 2 =.32 , n=428 ) . 11 The models to follow excludes the dummy variables for the cohorts. After performing a Wald test with the set of dummy variables, the resu lts showed that the values of the coefficients are not significant.
93 Mode l 2, in T able 4 9 , introduc es the hypothesis that sex interacts with basic abilities (CJBAT) to influence LEO performance. In order to estimate this relationship, the interaction term is introduced into the model. The interaction term does not improve the explained variance ( R 2 =.3 2 ). In addition, t he results show that basic abilities are not conditioned by sex (b = .13 , .19 , se =.07 , p=.073 ). Therefore, the interaction hypothesis for sex is not supported. The effect of CJBAT on LEO performance is not different for one sex than the other sex . There are similar findings when interaction term for race (CJBAT*white) is i ntroduced into m od el 3. It is hypothesized that r ace alters the relationship between basic abilities and LEO performance ; however, the results show no significance for the interaction term. Therefore, the hypothesized interaction between w hite is unsuppo rted ; and so the influence of CJBAT on LEO performance is not different for w hites from non whites . Furthermore, the introduction of the interaction term into model 3 (R 2 =.32) does not change the varian ce explained, in comparison to m odel 1 (R 2 =.32 ). In T able 4 9 is model 4, which assess es the roles played my b degree in regards to the relationship between basic abilities and LEO performance. Th e study hypothesize s that b achelor s degree interactions with CJBAT to affect LEO performance. The res ults show that the eff ect of CJBAT is conditioned by b degree (b =.16 , , se =.05 , ) . Therefore, these findings support the hypothesis and show that the influence of basic abilities on LEO performance is better for those with a b The regression slope for CJBAT and LEO performance is steeper for tho in comparison to those with a
94 (i.e. higher education enhances the prediction of CJBAT for LEO performance) . To examine the fifth research question concerning whether basic abilities and being s ponsored inc rease LEO performance, m odel 5 is estimated in T able 4 9 . But first, the results show being s ponsored exerts a strong independent effect on LEO performance, which are similar to previous studies . The results support the hypothesis and demonstrate that th e relationship between basic abilities and LEO performance is signific antly higher for those who are s ponsored by a police department or sh office (b=.12 , , se =.05 , . The result is that the slope for CJBAT and LEO performance is steeper for those who are sponsored by a law enforcement agency than the relationship is for those who are un sponsored (i.e. sponsorships enhances the prediction of CJBAT for LEO performance). Interaction terms and High Liability Performance Turning to the seco nd dependent variable, which is high l iability performance, the study examines whether there are significant main and interaction effects . In T able 4 10 2 statistics and a significant statistical test of alpha shows that all five models fit the data well (m odel 1: R 2 =.2 5 , ; m odel 2: R 2 odel 3: R 2 =.25, m odel 4: R 2 ) ; m odel 5: R 2 In model 1, testing the main effects, without the interaction t erms, the predictor variable (basic a bilities) is positive and statistical =.03; , n=430 ). As CJBA T increases, h igh l iability performance increases. Simi larly, being s ponsore d has a significant and positive relationship with high l iability performance (b=.93 , , se =.31 , to perform well on the high liability exams (b= .83 , .08 , se =.42 ,
95 An interaction is presente d in m odel 2 that focuses on CJBAT and s ex . The study finds that s ex does not interact with CJBAT to affect high liability performance (b = .09 , = .12 , se =.08). The results in m odel 3 in the table is s imilar to model 2 . W hite is not found to condition t he effects of CJBAT on high liability performance (b= .09 , = .10 , se =.08) . Therefore, the relationship between CJBAT and performance is not for significantly different for whites and non whites. Model 4 examines the interaction term between CJBAT and b a gree . The interaction between CJBAT and (b=.16 , , se =.06 , ) . Therefore, the interaction hypothesis is supported and the relationship between CJBAT and performance is greater for those with a b degree in comparison to those . The interaction effect slightly improves upon on model 4 (R 2 =. 25 ) , when compared to model 1 (R 2 =. 26 ) . T he slope for CJBAT and high liability performance is steeper for those who have a , in comparison to (i.e. higher education enhances the prediction of CJBAT for high liability performance) . The finding is in support of the interaction hypothesis. Model 5 of Table 4 10 introduces th e hypothesis that being sponsored interactions with the effects of basic abilities (CJBAT) on hig h liability performance. When m odel 1 (R 2 =.25) and 5 (R 2 =.25) are compared, t he interaction term does not improve the explained variance. T he sta tistic for t he interaction term shows that sponsorship does not interaction with basic abilities to predict high liability performance (b =.08 , =.08 , se =.06 ). Therefore, t he impact of CJBAT on high liability performance is
96 not different from cadets who are s ponsored , in comparison unsponsored cadets and consequently t he interaction hypothesis is not supported. Interaction terms and Re active P erformance Table 4 1 1 presents the OLS regression findings for the main and interaction terms reactive performance outcome variable (n=452) . Each model is stron gly statistically significant (m odel 1: R 2 =.35 odel 2: R 2 =.36 odel 3: R 2 =.3 6 odel 4: R 2 =.37 odel 5: R 2 =.36 . Accounting for most of the variance in reactive performance in m odel 4 with the interaction terms of basic abilities (CJBAT) and bachelor s degree . As expected, as basic abilities increase, the rmance significant ly increa ses (b=.29, , se=.03 , w hites (b=1.61 , , se=.02 , ( b=1.67 , , se=.35 , cadets s ponsored ( b=1.55 , , se=.36 , la w enforcement agency, are significant ly more likely th an non w hites, those without a b those who are not s ponsored to perform well on the reactive performance examinations. In addition, the study finds that re cruits who are employed ( b= .07, se=.37 , are significantly less likely to performance well on the examinations. An unexpected finding is that m ales are not significantly more likely than females to perform above average on the reactive examinations. Regarding m od el 2, testing the interaction term of basic abilities and sex, there is not a significant outcome. Therefore, the relationship between basic abilities and reactive per formance is not conditioned by s ex (b= .08 .08, se=.09 ) . A similar result is eviden t for the CJBAT and white interaction term in m odel 3 ; where r ace does not condition the relationship (b= .13, .12 , se=.07 , p=.06) . Therefore, the relationship
97 between basic abilities does not interact with race to predict reactive performance . These hypothes e s on the interaction influence of sex . Model 4 presents the results for the interaction between basic abilities and . For those with a b reactive p erformance are more strongly related (b=.21 , .16 , se=.06 , . Therefore, the research hypothesis is supported. In addition, t he interaction term in model 4 (R 2 =.3 7 ) improves upon model 1 (R 2 =.3 5 ). T he slope for CJBAT and reactive performance is ree than it is for those who do not have a (i.e. higher education enhances the prediction of CJBAT on reactive performance). The research hypothesis about an interaction between higher education and basic abilities is supported. Model 5, introduces the hypothesis that sponsor ship condition s the relationship of basic abilities (CJBAT) and reactive performance. When m odel 1 (R 2 =.35) is compared to model 5 ( the interaction term model ), there is a slight improve ment upon the model (R 2 =.36 ) . The interaction of sponsorship with basic abilities (CJBAT) is significantly related to reactive performance (b=.14 , , se=.07 ) . This means that cognitive abilities predict reactive performance better for cadets who are sponsored than for cadets who are not sponsored . T he slope for CJBAT and reactive performance is steeper for those who are sponsored than the slope is for those who are un sponsored (i.e. sponsorship enhances the prediction of CJBAT for reactive performance). Therefore, t he research hypothesis about an interaction between higher education and basic abilities is supported.
98 In summary, the interaction hypothese s for b s ponsored recruits are supported and so the impact of CJBAT on reactive performance is greater for those recruits . However, the conditioned effect s of sex and r ace are not supported. Therefore, f or males and w hites, there are no significant differences in the change of the relationship between basic abilities and reactive performance. Inte raction terms and Proactive Performance In T able 4 12 , m odel 1 12 is the OLS regression model without the interaction terms and model s 2 to 5 test the four interaction terms (n=440) . Each model is strongly predictive of proactive performance (m odel 1: R 2 =.2 3 odel 2: R 2 =.2 4 m odel 3: R 2 =.2 3 odel 4: R 2 =.2 ; m odel 5: R 2 =.2 3 s 2 and 4 have the large st coefficient s of determination , therefore, m ost of the variance in proactive performance is ac counted for by the vari ables included in those model s . Model 1 with the main effects show s that an increase in basic abilities (CJBAT) is significantly related to proactive performance (b=.16 , , se=.03 , 1). W hite recruits (b=.79 , =.12 , se =.29 , and recruit s s ponsored by an agency (b =.86 , = .1 3 , se =.31 , 1) are more likely to perform better on the proactive performance exams . Another variable found to be positively and significantly related to proactive performance is age (b=.08 , =.1 4 , se=.02 , 0 1). Therefore, as recruits increase in age, they are more likely to have increased performance on the proactive performance examinations. The study also finds that recruits who have had their licenses revoked in 12 This model includes a set of measures indicating prior deviant and criminal behavior (arrest history, license revocation, and job termination). The Wald test shows that the values of the coefficients for the set of variables is significant.
99 the past are less likely to perform well on pr oactive examinations (b= 1.10 , .1 2 , se=.39 , An unexpected finding is the negative relationship between s ex and proactive performance . M ale s are less likely to have better performance on the proactive exam s , in comparison to females (b= 1.1 4 , = .12 , se=.40 , 01 ). In addi tion, those with m ilitary background are more likely to have decreased performance on the proactive examinations (b= .81 , = .11 , se=.32 , The non degree (b=.57 , , se=.30 , p=.061 ) and proactive performance is also surprising, given that having a s degree has been a consistently significant predictor in other models. Overall, results from model 1 support the hypotheses, and these results hold , net of other variables included in the model ( with the exc eption o f s ex and ) . Turnin g to the interaction models, m o del 2 tests the influence of s ex on the relationship between basic abilities and proactive performance. In comparison to model 1, the interaction term in model 2 (R 2 =.24 ) slightl y improves upon model 1 (R 2 =.23 ) . As hypothe sized, the interaction term is significantly related to proactive performance (b = .18 , .2 5 , se=.07 , .05 ). However, the direction of the result is unexpected. T hat is, the effect of CJBAT on proactive perf ormance d uring police academy training is less pronounced for males than for females . The results show that the effect of basic abilities (CJBAT) on proactive performance is conditioned by sex and so, the re is interaction by sex but in an unexpected direc tion . The effect of basic abilities on proactive performance is less fo r males in comparison for females . T he slope for CJBAT and proactive performance is steeper for females than it is for males (i.e. being
100 female enhances the prediction of CJBAT for pr oactive performance). Th is research hypothesis about an interaction between sex and basic abilities is supported but in the direction anticipated. In Model 3, the study hypothesized that r ace interacts with basic ab ilities and influences proactive perfo rmance. The interaction term in m odel 3 (R 2 =.23) does not substantially improve the varian ce explained, in comparison to m odel 1 (R 2 =.23) . T he results show that the hypothesis is not supported and that there is no significant differences when the interac tion term is included . T he influence of CJBAT on proactive performance is not different for w hites. Next, m odel 4 examines the influence of b on the relationship between basic abilities and proactive performance. The interaction term in Model 4 (R 2 =.24) slightly improves the variance explained in Model 1 (R 2 =.23). The study hypothesize s that having a b interacts with basic abilities to affect proactive performance. This hypothesis is supported and , t he refore, the result s show that the impact of basic abilities is conditioned by b =.14 , , se=.05 , T he slope for CJBAT and proactive performance is steeper for those having a Model 5 examines the hypothesis on whether being s ponsored by an agency interacts with basic abilities (CJBAT) to predict proactive performance . The results show th at the relationship between basic abilities (CJBAT) and proactive performance is not significant. In model 5 (R 2 =.23), there is no change in the variance explained when the interaction term is included compared to model 1 without the interaction term
101 (R 2 = .23). In short, the results demonstrate that the extent to which being s ponsored influences the relationship between basic abilities and proactive performance is not different for s ponsored recruits in comparison to unsponsored recruits . In summary, for reactive p erformance, both b achelor s degree and s ponsored are significant in the interaction models . Wh ile for proactive performance, b degree is significant but sponsorship is not significant in the interaction mode ls. In addition, s ex is sig nificant for the proactive performance outcome, however , sex is not significant for the reactive performance outcome. Interaction terms and Overall Performance In Table 4 13 , the OLS regression models presenting both unstandardized and standardized coeff icients for a model without the interaction terms and four models with interaction terms (n=421) . Each model is strongly predictive of o verall performance, with Model 4 , explaining more of the variance (model 1: R 2 =.34 R 2 =.35 2 =.34 2 =.3 ; model 5: R 2 =.35 , . As hypothesized, basic abilities or CJBAT is positively and significantly related to overall performance, net of the effects of the other terms within the model (b=.21, , se=.02, A s basic abilities increase, overall performance increases during police academy training. Since the estimated regression coefficient for basic abilities is .21, with each additional score of basic abiliti es, overall pe rformance increases, on average by .21. Also as hypothesized, whites (b=1.0 6 , 1 7 , se=.26 , =.73 , 2 , se=.2 8 , by an agency (b=1. 0 4 , 6 , se=.2 8 , are more likely to outperform other groups. Contrary to the proposed hypothesis that males will outperform females, the regression coefficient show
102 that there is a negative and sign ificant effect for males (b= .77 , .0 8 , se=.36 , For males, on average , overall p erformance decrease s. Presumably, being employed has a negative effect on overall performance. Therefore, if a recruit is working during the time of the academy he/she is less likely to have a good overall performance, in comparison to those who are not employed (b= .74 , .08 , se=.37 , 5 ). was revoked is negatively relatio n to overall performance (b= .92 , .11 , se=.35 , 1 ) . The re fore, overall performance for these recruits is significantly decrease d. In Table 4 13 , the primary research hypotheses concern the interaction effects on overall performance. Model 2 introduces the hypothesis that sex interacts with basic abilities (CJBAT) to predict overall performance. In comparison to model 1 (R 2 =.35) , the interaction term slightly improves the explained variance in model 1 (R 2 =.3 4 ). However, the results show that the impact of basic abilities on overall performance is not conditioned by sex (b= .13 , .20 , se=.07 , p=.055 ). However, the p value approaches significance. Nevertheless , the interaction hypothesis for sex is not supported and so the influence of basic abilities on overall performance is not different for males from females . In model 3, it is hypothesized that race interacts with basic abilities to predict overall performance . H owever, the results show no significance when the interaction term is introduced into the model (b= .06 , .07 , se=.05). Therefore, the interaction effect for C JBAT and white is not supported ; the effect of basic abilities on overall performance is not different for whites from non whites . Moreover, the interaction term
103 in model 3 (R 2 =.34) does not substantially improves the variance explained in comparison to m odel 1 (R 2 =.34) . Model 4 evaluates the interaction effect of having CJBAT and on overall performance. The study hypothesize s interacts with basic ability to predict overall performance. The results show that the i nfluence of CJBAT is conditi , , se=.05 , That is, the interaction term is statistically significant. In model 4 (R 2 =.36), there is a change in the variance explained when compared to model 1 (R 2 =.34). Therefor e, thes e findings show that the impact of basic abilities on overall performance is improved for those with and therefore, th e research hypothesis about an interaction between higher education and basic abilities is supported. Model 5 e xamines the hypothesis concerning whether basic abilities and being sponsored interacts to affect overall performance. The results demonstrate that the relationship between basic abilities and overall performance is significantly higher for those who a re sponsored by an agency (b=.11 , , se=.05 , The interaction term in model 5 (R 2 =.35) slightly improves the variance explained in model 1 (R 2 =.34). In short, the results demonstrate that the extent to which basic abilities influence overall perf ormance is enhanced for recruits who are sponsored. Therefore, t he research hypothesis about an interaction between sponsorship and basic abilities is supported. Interaction terms and Graduating from the Academy This section of the dissertation discusses predictors of performance for graduates and non gradua tes of the police academy (n=662 ). Table 4 1 4 shows the results of the logistic regressions. In model 1, testing the main effect of CJBAT on
104 graduation , with the control variables, there is a highly s i gnificant model (p<.001 ), with a likelihood ratio test showi ng a chi squared statistic of 66 . 4 0. The odds ratio shows that there is a statistically significant main effect of CJBAT on graduation (p<.05 ). An increase in percentage of CJBAT increases the likelihood of graduating from the academy by 5 % (OR=1.05, se=.02) . There is a statistically significant relationship between s pon sored and completion (p<.001). A sponsored cadet has an increase d likelihood of graduation of 3 21 % (OR=4 .2 1 , se=1.30) . Last ly, the relationship between body mass index (BMI) greater than thirty and graduation is statistically significant (p=.01). Having a BMI greater than thirty decreases the likelihood of g raduation from the academy by 53 % (OR=.47, se=.13) . The next questi ons are whether the relationship between basic abilities and graduation from the police academy are conditioned by race, sex, education and/or spon sorship. The find ing s that emerge d from the results in m odels two through six , which include interaction ter ms, are unexpected and contrary to the hypotheses . That is, no interaction effects were found. Therefore, the relationship between basic abilities and graduation from the academy is not more likely f or whites, males, those with a egree and th ose who received sponsorship from a police agency. A brief summary of all results In summary, based on the all the models estimated for the sample who graduated from the academy , there are five variables that consistently have main effects on curriculum pe rf ormance basic abilities (CJBAT) , race, sex, b degree , and s ponsor ship . Therefore, t he hypotheses on the relationships between basic abilities (CJBAT), race, sex, performance are supported (with the exception of the relationships between s
105 degree, high liability and proactive performance; the relationship between sex and reactive performance). In addition, the significant relationship between sex and performance results in the opposite direc tion of what was initially proposed. This study also finds support for interaction ip; and sex interacts the CJBAT and proactive performance relationship. In regards to the study assessing the entire sample (gra duates and non graduates), there are three variables that are related to graduation from the academy basic abilities, sponsorship and BMI. No interaction effects were found for this segment of the study.
106 D escriptive Statistics Table 4 1. C haracteristics of the full sample, graduates and non graduates. Variables Total (n=662) G raduate(n=484) Non graduate (n=178) 2 or t test CJBAT 87.30 87.94 85.56 5.80*** Race White Hispanic Black Asian Other 393 (59.46% ) 138 (20.88%) 102 (15.43%) 20 (3.03%) 8 (1.21%) 298 (75.83%) 94 (68.12%) 73 (71.57%) 14 (70 .00 %) 5 (62.50%) 95 (24.17%) 44 (31.88%) 29 (28.43%) 6 (30 .00 %) 3 (37.50%) 3.91 Sex Male Female 570 (86.10%) 92 (13.90%) 416 (72.98%) 68 (73. 91%) 154 (27.02%) 24 (26.09%) .03 Degree GED High School AA/AS Master s 26 (3.93%) 234 (35.40%) 124 (18.76%) 254 (38.43%) 23 (3.48%) 14 (53.86%) 153 (65.38%) 86 (69.35%) 212 (83.46%) 19 (82.61%) 12 (46.15%) 81 (34. 62%) 38 (30.65%) 42 (16.54%) 4 (17.39%) 28.2 0 *** Sponsored Yes No 185 (27.95%) 477 (72.05%) 167 (90.27%) 317 (66.46%) 18 (9.73%) 160 (33.54%) 38.45*** Cohort 1101 1102 1103 1104 1105 1201 1202 1203 1204 1205 1301 1302 1303 1304 1305 Employed Yes No 30 (4.53%) 43 (6.50%) 31 (4.68%) 54 (8.16%) 47 (7.10%) 33 (4.98%) 46 (6.95%) 57 (8.61%) 42 (6.34%) 49 (7.40%) 36 (5.44%) 46 (6.95%) 41 (6.19%) 65 (9.82%) 42 ( 6.34%) 520 (86.67%) 80 (13.33%) 20 (66.67%) 32 (74.42%) 19 (61.29%) 39 (72.22%) 33 (70.21%) 22 (66.67%) 36 (78.26%) 42 (73.68%) 36 (86.05%) 41 (83.67%) 26 (72.22%) 31 (67.39%) 29 (70.73%) 51 (78.46%) 27 (64.29%) 390 (75.00%) 55 (68.75%) 10 (33.33%) 11 (25.58%) 12 (38.71%) 15 (27.78%) 14 (29.79%) 11 (33.33%) 10 (21.74%) 15 (26.32%) 6 (13.95%) 8 (16.33%) 10 (27.78%) 15 (32.61%) 12 (29.27%) 14 (21.54%) 15 (35.71%) 130 (25.00%) 25 (31.25%) 14.10 1.41 Age Military Yes No Prior Arrest Yes No License revoked Yes No 27.12 179 (27.04%) 483 (72.96%) 125 (19.00%) 533 (81.00%) 104 (17. 00%) 553 (83.00%) 26.86 128 (71.51%) 356 (73.71%) 87 (69.60%) 395 (74.11%) 73 (70.19%) 407 (73.60%) 27.84 51 (28.49%) 127 (26.29%) 38 (30.40%) 138 (25.89%) 31 (29.81%) 146 (26.40%) 2.08 .32 1.05 .51 Terminated from job Yes No 107 ( 16.24%) 552 (83.76%) 72 (67.29%) 410 (74.28%) 35 (32.71%) 142 (25.72%) 2.22
107 Table 4 2 . Descriptive of the interaction variables. Variables n Mean S.D. t Sex Male 570 87.21 4.78 1.26 Female 92 87.89 4.83 White Yes 393 88.40 4.69 7.38*** No 268 85.70 4.48 Bachelor s Ye s 277 89.23 4.56 9.28*** No 384 85.92 4.47 Sponsor ed Ye s 477 89.1 0 4.79 6.18*** N o 185 86.6 0 4.61
108 Table 4 3 . Descriptives of academy course scores. Variables n Mean S.D. Min Max Introduction to LEO 588 85.92 8.73 44 100 Legal 530 87.68 6.18 43 99 Human Issues 506 87.35 5.77 65 100 Communications 514 89.70 4.95 69 100 Vehicle Operation 513 90.33 6.09 62 100 First Aid 508 90.08 5.87 70 99 Firearms 499 91.21 5.84 66 100 Defensive Tactics 473 91.86 5.25 76 100 Patrol 1 494 92.39 3.85 78 100 Patrol 2 494 89.89 5.04 73 100 Criminal Investigations 488 89.79 4.61 74 99 Crime Scene Investigations 490 94.84 3.72 78 100 Traffic Stop 490 91.52 4.98 72 100 DUI traffic stop 473 94.06 4.07 78 100 Traffic Crash Investigation 488 90.55 4.51 77 100 Dart Firing 485 95.36 4.36 68 100
109 Table 4 4 . Descriptive of the academy performance outcome variables. Variables n Mean S.D. Min Max LE O Performance 467 90.64 3.22 81.63 98.72 .8399 Hi g h Liability Performance 469 92.01 3.29 80.60 99.20 .6299 Reactive Performance 495 88.97 4.23 74.83 98.33 .7938 Proactive Performance 483 91.55 3.19 82.5 0 99.66 .8110 Overall Performance 464 91.09 3.04 82.18 98.50 .8702
110 Table 4 5 . Training curriculum factor analysis with factor loadings without rotations. Variables Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 I .582 .042 .154 .095 .027 .059 .089 .016 L .654 .249 .021 .080 .032 .013 .097 .027 HI .628 .125 .015 .088 .103 .109 .018 .007 C .688 .188 .124 .015 .067 .022 .039 .081 VO .668 .172 .150 .024 .010 .125 .079 .007 FA .587 .001 .224 .142 .018 .046 .036 .049 F .527 .009 .073 .077 .074 .032 .095 .054 TCI .657 .056 .229 .016 .047 .027 .014 .034 P1 .596 .136 .212 .123 .116 .015 .016 .037 P2 .706 .258 . 083 .027 .032 .048 .018 .054 CI .696 .254 .090 .150 .026 .035 .038 .009 CSI .555 .136 .051 .133 .152 .088 .045 .018 TS .551 .246 .053 .058 .108 .096 .037 .012 DUI .536 .004 .106 .251 .073 .091 .024 .019 DT .313 .404 .244 .003 .009 .013 .010 .015 DF .287 .061 .015 .303 .074 .059 .029 .016 Note: I=Introduction to Law Enforcement; L=Legal; HI=Human Issues; C=Communications; VO=Vehi cle Operation; FA=First Aid; F=Firearms; TCI= Traffic Crash Investigation; P1= Patrol 1; P2=Patrol 2; CI=Criminal Investigation; CSI=Crime Scene Investigation; TS=Traffic Stops; DUI=DUI traffic stop; D T =Defensive Tactics; DF=Dart Firing stun gun
111 Table 4 6 . Training curriculum factor analysis with factor loadings for varimax rotat ion. Variables Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 I .348 .492 .050 .032 .020 .011 .029 .032 L .295 .567 .285 .056 .036 .047 .085 .068 HI .366 .489 .206 .043 .029 .142 .032 .033 C . 321 .625 .167 .098 .031 .035 .016 .061 VO .300 .612 .145 .116 .071 .065 .107 .040 FA .312 .491 .028 .267 .008 .004 .041 .094 F .335 .366 .074 .184 .052 .011 .136 .009 TCI .463 .326 .379 .151 .027 .039 .042 .012 P1 .573 .217 .234 .013 .085 .100 .005 .033 P2 .667 .282 .091 .211 .016 .006 .005 .024 CI .654 .385 .038 .0 31 .043 .009 .031 .026 CSI .507 .270 .112 .011 .018 .190 .001 .026 TS .528 .215 .033 .170 .193 .004 .006 .017 DUI .342 .262 .210 .379 .044 .005 .001 .007 DT .010 .270 .497 .034 .001 .008 .001 .013 DF .166 .136 .036 .339 .157 .011 .001 .032 Note: I=Introduction to Law Enforcement; L=Legal; HI=Human Issues; C=Communications; VO=Vehicle Operation; FA=F irst Aid; F=Firearm; TCI= Traffic Crash Investigation; P1=Patrol 1; P2=Patrol 2; CI=Criminal Investigation; CSI=Crime Scene Investigation; TS=Traffic Stops; DUI=DUI traffic stop; D T =Defensive Tactics; DF=Dart Firing stun gun
112 Table 4 7 . Training curri culum factor analysis with factor loadings for promax rotation. Variables Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 I .006 .339 .273 .102 .025 .003 .028 .036 L .062 .443 .144 .039 .208 .105 .032 .049 HI .248 .358 .052 .005 .112 .013 .038 .140 C .161 .643 .070 .016 .027 .001 .020 .035 VO .046 .560 .015 .031 .094 .166 .048 .043 FA .015 .308 . 212 .223 .091 .073 .022 .025 F .188 .165 .011 .097 .054 .208 .060 .015 TCI .327 .036 .005 .086 .369 .060 .031 .043 P1 .570 .033 .021 .061 .183 .004 .096 .073 P2 .702 .057 .085 .182 .051 .009 .025 .031 CI .673 .207 .022 .053 .164 .054 .028 .026 CSI .485 .096 .030 .040 .052 .016 .001 .212 TS .435 .048 .051 .099 .047 .023 .243 .016 DUI .193 .010 .007 .412 .141 .001 .001 .002 DT .159 .150 .032 .008 .546 .011 .034 .017 DF .001 .063 .015 .332 .018 .009 .208 .017 I=Introduction to Law Enforcement; L=Legal; HI=Human Issues; C=Communications; VO=Vehi cle Operation; FA=First Aid; F=Firearms; TCI= Traffic Crash Investigation; P1=Patrol 1; P2=Patrol 2; CI=Criminal Investigation; CSI=Crime Scene Investigation; TS=Traffic Stops; DUI=DUI traffic stop; D T =Defensive Ta ctics; DF=Dart Firing stun gun
113 Tabl e 4 8 . OLS regression of law e nforcement, high liability, and o verall p erformance. b coefficients and the next row with standard error in parentheses. *p tailed); **p tailed); ***p .001 (two tailed). Variables Law Enforcement (n=428) High Liability (n=430) Overall Performance (n=42 5) Basic Abilities (CJBAT) Sex (male=1) .21*** .31(.03) 1.08** .11(.40) .20*** .30(.03) .40 . 04(.42) .21*** .34(.02) .85* .09(.37) Race (white=1) .86** .13(.28) 1.44*** .21( .29) 1.01*** .16(.26) Age (in years) .03 .05(.02) .01 .01(.02) .02 .04(.02) Degree (b achelor s=1) 1.14*** .17(.29) .56 .08(.30) .94*** .15(.27) Sponsored 1.40*** .21(. 30) 1.18*** .17(.32) 1.30*** .20(.28) Employed Military .78 .08(.40) .36 .05(.31) .79 .08(.42) .02 .01(.33) .72 .07(.37) .27 .04(.29) Cohort Dummy 1102 Dummy 1103 Dummy 1104 Dummy 1105 Dummy 1201 Dummy 1202 Dummy 1203 Dummy 1204 Dummy 1205 Dummy 1301 Dummy 1302 Dummy 1303 Dummy 1304 Dummy 1305 1.20 .09(.81) .91 .05(.89) .59 .05(.78) .77 .06(.81) .45 .02(.88) 1.45 .10(.85) 1.13 .09(.79) 1.32 .10(.81) 1.19 .10(.79) 1.91** .13(.82) .93 .06(.84) 2.82*** .21(.85) 1.46 .13(.78) .85 . 05(.86) 1.00 .07(.85) 1.01 .06(.95) 1.54 .13(.82) 2.18** .17(.85) .95 .06(.93) 2.39** .17(.89) 3.05*** .26(.83) 1.57 .12(.86) 2.18** .19(.83) 1.51 .10(.86) 1.84* .13(.87 ) 2.54** .18(.90) 1.45 .13(.82) .86 .06(.90) 1.12 .09(.75) .87 .05(.84) .86 .08(.72) 1.18 .10(.75) .61 .04(.82) 1.72* .13(.79) 1.69* .15(.74) 1. 32 .11(.76) 1.45* .13(.74) 1.78* .13(.76) 1.09 .08(.78) 2.78*** .21(.80) 1.42* .14(.72) .76 .05(.81) Constant R 2 F statistic 72.34 (2.89) .35 9.88*** 75.17 (3.06 ) .29 7.69*** 72.70 (2.73) .36 10.23***
114 Table 4 9 . OLS regression with main and interaction effects on LEO performance . b indicates the sta ndardized coefficients with standard error in p tailed); ***p .001 (two tailed ). n=428. Variables Model 1 Model 2 Model 3 Model 4 Model 5 Basic Abilities ( CJBAT) Sex (male=1) .21*** .31(.02) 1.12** .12(.38) .33*** .49(.07) .98** .10(.39) .23*** .34(.04) 1.11** .11(.38) .13*** .19(.04) 1.10** .11(.38) .17*** .25(.03) 1.01** .10(.38) Race (white=1) Age .95*** .14(.28) .03 .06(.02) .95*** .14(.28) .03 .06(.02) .96*** .14(.28) .03 .06(.02) .98*** .14(.27) .04 .06(.02) .91*** .13(.28) .04 .07(.02) Degree (b achelor =1) Sponsored Employed Military CJBAT*m ale CJBAT*w hite CJBAT*b achelor s CJBA T*s ponsored Constant R 2 F statistic 1.02*** .16(.28) 1.27*** .19(.29) .70 .07(.39) .47 .06(.30) 71.22*** (2.75) .32 24.30*** 1.03*** .16(.28) 1.20*** .18(.29) .65 .06(.39) .49 .06(.30) .13 .19(.07) 60.74*** (6.44) .32 22.07 *** 1.02*** .15(.28) 1.28*** .19(.29) .69 .07(.39) .46 .06(.30) .03 .03(.05) 69.35*** (4.21) .32 21.60*** .90** .14(.28) 1.24*** .18(.29) .78* .08(.39) .42 .05(.30) .16** .17(.05) 78.03*** (3.59) .33 22.92*** 1.02*** .16(.28) 1 .13*** .16(.30) .68 .07(.39) .48 .06(.30) .12* .11(.05) 74.57*** (3.16) .32 22.27***
115 Table 4 10 . OLS regression with main and interaction effects on hi gh liability performance . b indicates the standardized coefficients with standard error in tailed); ***p .001 (two tailed) . n=430. Variables Model 1 Model 2 Model 3 Model 4 Model 5 Basic Abilities (CJBAT) Sex (male=1) .21*** .31(.03) .08 .01(.41) .29*** .42(.07) .00 .00(.49) .27*** .39(.05) .07 .01(.41) .13** .19(.04) .07 .01(.40) .18*** .26(.03) .01 .01(.41) Race (white=1) Age (in years) 1.49 *** .22(.29) .01 .01(.02) 1.49*** .22(.29) .01 .01(.02) 1.50*** .22(.29) .01 .01(.02) 1.52*** .22(.29) .01 .01(.02) 1.46*** .22(.29) .01 .01(.02) Degree (b achelor =1) .43 .44 .42 .30 .43 Sponsored Employed Military CJBAT* m ale CJBAT*w hite CJBAT*b a chelor CJBAT*s ponsored .06(.30) .93** .13(.31) .83* .08(.42) .18 .02(.32) .06(.30) .88** .13(.31) .79 .08(.42) .19 .02(.32) .09 .12(.08) .06(.30) .95** .14(.31) .81* .08(.42) .15 .02(.32) .09 .10(.06) .04(.30) .91** .13(.31) .92* .09(.41) .13 .01(.32) .16** .16(.06) .06(.30) .83** .12(.32) .81* .08(.42) .18 .02(.32) .08 .08(.06) Constant 72.54*** (2.93) 65.47*** (6.84) 67.53*** (4.51) 79.46*** (3.86) 75.02*** (3.41) R 2 .25 .25 .25 .26 .25 F statistic 17.44*** 15.66*** 15.78*** 16.56*** 15.77***
116 Table 4 11 . OLS regression with main and interaction effects on reactive performance. b indicates the standardized coefficients with standard erro r in p arentheses. *p .05 (two tailed) **p .01 (two tailed), ***p .001 (two tailed). n=452. Variables Model 1 Model 2 Model 3 Model 4 Model 5 Basic Abilities (CJBA T) Sex (male=1) .29 *** .33 (.03) .7 9 .06(.46 ) .36 *** .42(.08 ) .72 .0 6(.47 ) .37 *** 43(.05 ) . 75 .06 (.4 6 ) .19*** .22(.04 ) . 7 6 .06(.46 ) .24 *** .27(.04 ) .6 7 .05(.47 ) Race (white=1) Age (in years) 1.61 ** * .18 (. 3 4) .02 .03(.03 ) 1.62* ** .19(.34 ) .02 .03(.03 ) 1. 62 ** * .19 (.3 4 ) .02 .03(.03 ) 1 .63 *** .19(.34 ) .03 .03(.03 ) 1 .58 *** .18(.34) .03 .04(.03 ) Degree (b achelor s=1) 1.67 *** .20(.35 ) 1.67*** .20(.35 ) 1.68*** .20(. 3 5 ) 1.51*** . 1 8(.35 ) 1.69 *** .20(.35) Sponsored Employed Military CJBAT* male C JBAT*w hite CJBAT*b achelor s CJBAT*s ponsored 1.55 *** .17(.36 ) .96 * .07(.48) .06 .01(.37) 1.5 1*** .17(.36 ) .93* .07(.48 ) .07 .01(.37) .08 .08(.09) 1.6 8*** .20(.35 ) .92 * .07(.48) .01 .01(.37 ) .13 .12(.07) 1.54 *** .17(.36 ) 1.06* .17(.36) .01 .01(.37) .21*** .16(.06) 1.39 *** .16(.37 ) .93 * .07(.48) .06 .07(.37) .14 * .10(.07) Constant 6 1.61 *** (3.38 ) 5 5 .23 *** (7.78 ) 54.2 0*** ( 5 . 1 7) 7 0.32 *** (4.41 ) 6 5.74 *** (3.94 ) R 2 .3 5 .3 6 . 36 .37 .36 F statistic 30.47 *** 27.16 *** 27.63 *** 28.71 *** 27.73 ***
117 Table 4 12 . OLS regression with main and interaction effects on proactive performance. b indicates the standardized coefficients with standard error in parentheses. *p .05 (two tailed), **p .01 (two tailed), ***p .001 (two tailed). n=44 0 . Variables Model 1 Model 2 Model 3 Model 4 Model 5 Basic Abilities (CJBAT) Se x (male=1) .16 *** .2 5 (.03) 1.1 4 * * .12(.40 ) .3 2 *** .4 8 (.07 ) .97 * .10 (.40 ) .1 6 ** * .24(.04 ) 1.1 5 * * .12(.40 ) .10 * * . 1 5 (.04 ) 1.1 3 * * .12(.39 ) .13 *** .19 (.03 ) 1.0 7 * * .11(.40 ) Race (white=1) Age (in years) .79 ** .12 (.29 ) .08 ** * .1 4 (.02 ) . 81 ** .12 (.2 9 ) .0 8 ** * .14 (.02 ) . 7 8 ** .12(.29 ) .08 * * * .14 (.02 ) .8 0 ** .12 (.29 ) .0 9 ** * .15 (.02 ) .76 ** .11 (.29 ) .0 9 ** * .1 5 (.0 2 ) Degree (b achelor s=1) . 5 7 . 0 8 (.3 0 ) . 5 6 . 1 0 (.29 ) . 57 . 0 8 (.30 ) . 46 .0 7 (.3 0 ) . 5 7 . 0 8 (.30 ) Sponsored Employed Military Prior a rrest License revoked Job termination CJBAT* male CJBAT*w hite CJBAT*b achelor s CJBAT*s ponsored .86 ** .13 ( .31 ) .41 .04(.41 ) .81 ** .11(.32 ) .07 .01(.37) 1.10** .12(.39) .00 .00(.39) .7 7 ** .1 1 (.31 ) .35 .03(.41 ) . 8 3 ** .11(.32 ) .17 .02(.37) 1.12 ** .12(.39) .04 .01(.39) .18 * .25 (.07 ) . 86 * * .13 (.31 ) .41 .04(.41 ) .81 ** .11(.32 ) .07 .01(.37) 1.10** .12(.39) .00 .00(.39) .01 .01 (.06) .83 ** .12 (.3 1 ) .4 7 .04(.41 ) .7 6 * .10(.3 2 ) .11 .01(.37) 1.10 ** .12(.39) .03 .00(.39) .1 4 * .14 (. 05 ) .74 * .11(.32 ) .3 9 .04(.41 ) . 81 ** .11(.32 ) .10 .01(.37) 1.11** .12(.39) .01 .00(.06) .10 .0 9 (.06 ) Constant 7 5.22 *** ( 2 . 8 7 ) 6 1 .4 5 *** ( 6.59 ) 75. 7 8 *** (4.39 ) 80 . 95 *** (3.7 2 ) 78.16 *** ( 3.32 ) R 2 .2 3 .2 4 . 2 3 .2 4 .2 3 F statistic 11 .8 2 *** 1 1.39 *** 1 0.8 1 *** 1 1 . 43 *** 1 1. 1 4 ***
118 Table 4 13. OLS regression with main and interaction effects on overall performance. b tailed). n=421. Variables M odel 1 Model 2 Model 3 Model 4 Model 5 Basic Abilities (CJBAT) Sex (male=1) .21*** .33(.02) .77* .08(.36) .33*** .52(.06) .63 .07(.36) .25*** .40(.04) .76* .08(.36) .13*** .21(.03) .75* .08(.35) .17*** .27(.03) .67 .07(.36) Race (white=1) A ge (in years) 1.06*** .17(.26) .04 .08(.02) 1.07*** .17(.26) .04 .08(.02) 1.07*** .17(.26) .04 .08(.02) 1.10*** .17(.26) . 05* .08(.02) 1.03*** .16(.26) .05* .09(.02) .73** .12(.27) .73** .12(.27) .72** .11(.27) .60* .09(.27) .73** .1 2(.27) Sponsored Employed Military Prior a rrest License revoked Job t erminat ion CJBAT*m ale CJBAT*w hite CJBAT*b CJBAT*s ponsored 1.04*** .16(.28) .74* .08(.37) .39 .05(.29) .31 .04(.33) .92** .11(.35) .03 .00(.35) . 96*** .15(.28) .70 .07(.37) .41 .06(.28) .38 .04(.33) .93** .11(.35) .00 .00(.07) .13 .20(.07) 1.05*** .16(.28) .73* .07(.37) .37 .05(.29) .30 .03(.33) .94** .11(.35) .02 .00(.35) .06 .07(.05) 1.01*** .16(.27) .83* .0 9(.37) .35 .05(.28) .38 .04(.33) .91** .11(.35) .01 .00(.35) .16** .17(.05) .90*** .14(.28) .72 .07(.37) .40 .05(.28) .35 .04(.33) .93** .11(.35) .01 .00(.35) .11* .11(.05) Constant 71.17*** (2.60) 60.70*** (6.01) 67.67*** ( 3.99) 78.00*** (3.42) 74.41*** (3.01) R 2 .34 .35 .34 .36 .35 F statistic 19.52*** 18.32*** 18.02*** 19.01*** 18.41***
119 Table 4 14 . The result s from the logistic regression on graduation from the academy . Note: O dds ratio wit h standard error in parentheses. *p ***p .001. n=596 . Variables M odel 1 Model 2 Model 3 Model 4 Model 5 Model 6 Basic Abilities (CJBAT) Sex (male=1) 1.05 * (.02) 1.74(.52 ) 1.05*(.02) 1.43(.61 ) .99(.05) 1.8 1 *(.5 4 ) 1.09*(.0 4 ) 1.79 (.5 4 ) 1 .07* (.03) 1. 77(.53 ) 1 .06* (.02) 1.73(.52 ) Race (white=1) Age (in years) 1.17 (.25) .97(.01) .85(.45) .97(.01) 1.17(.25) .97(.01) 1.09(.24 ) .97(.01) 1.1 8 (.25) .97(.01) 1.1 8 (.25) .97(.01) Degree (b achelor s=1) 1.27 (.29) 1.2 7 (.29) 1.2 6 (.29) 1.28 (.29) 1.26(.29) 1.2 6 (.29) Sponsored Employed Military W*M CJBAT*male CJBAT*w hite CJBAT*b achelor CJBAT*s ponsored 4.21***(1.27 ) 1.17(.32 ) .98 (.2 2 ) . 47**(.13) 4. 2 3 ***(1.28 ) 1.18 (.3 2 ) .98(.22 ) .48**(.13) 1.4 6(.84 ) 4.37 ***(1.3 4 ) 1.16 (.3 2 ) . 99(.22 ) .48 **(.13) 1.0 7 (.06) 4 . 2 5***(1. 2 9 ) 1. 18 (.3 2 ) .99 (.2 2 ) . 49 **(.13) .94(.04) 4.24***(1. 2 8 ) 1.19(.33 ) .97(.22 ) .48 **(.13) .95(.04) 4.2 2 ***(1. 27 ) 1. 16 ( 3 .2) .98 (.2 2 ) .47**(.13) .98(.05) Intercept 4. 1 2* 3.91 .97 7.16 * 5. 5 8 * 4.35* 2 66.40 *** 66.84 *** 67 .54 *** 67.82 *** 6 7.50 *** 6 6.50 *** Psuedo R 2 . 10 .10 .10 .10 . 10 . 10
120 CHAPTER 5 CONCLUSION AND DISCUSSION This study explores the relation one of the most important factors used to select police applicants and several performance outcomes. The research contributes new evidence to the police selection literature by exploring an apt itude measure (Criminal Justice Basic Abilities Test) specifically developed for the field of policing. The existing literature regarding tests of this nature primarily focuses on predictability of overall performance as a single dimension. There is litt le research exploring whether academy performance is multidimensional. This study has a comprehensive measure of overall performance and also identifies measures of other dime nsions of academy performance. T he study also explores predictors of completion of the academy. Most importantly, the current study adds to the literature on police selection by exploring whether the relationship between basic abilities (CJBAT) and performance varies by demographic and background factors. The upcoming sections of t his study highlight the major hypotheses and findings related to the areas discussed above. The first research question examined the main effect of basic abilities (CJBAT), demographics (race and sex), and biographic (education and sponsored) on academy pe rformance (law enforcement scores, high liability/high stress scores, overall scores, and graduation). The factor analysis determined that the performance variables were multidimensional and therefore, two additional distinct performance constructs (react ive and proactive) were created. Therefore, the study also examined how the main effect variables influenced proactive and reactive performance.
121 First, the predictor variable revealed an expected relationship between CJBAT and performance. CJBAT does predict if recruits will have the ability to perform well and graduate from the academy. This finding is consistent across the board for each performance outcome and is similar to prior research that found positive effects for the relationship between CJB AT (civil service exams, read ability, reading comprehension, vocabulary, and general cognitive abilities) and performance (top performers and average outcome) (White, 2008; Provine, 2006; Henson et al., 2010). Furthermore, this finding is expected becaus aptitude during the academy. In addition to supporting the prior research findings, the results confirm that a measure of predictor s of performance (more so for curriculum performance than graduation from the academy) when compared against other variables (Hunter & Hunter, 1984; Hunter, 1986) . T herefore, it is a valuable selection criterion to use during the application process. In future research, basic abilities should receive greater merit as a predictor of performance rather than as a correlate of performance. With respect to the second hypothesis, the effect of race on performance, the results for whites are consistent with prio r research (Lester, 1995; McGlamery, 2008; White, 2008; Aamodt, 2004a, Aamodt, 2004b; Wright et al., 2011). White recruit s significantly outperform non w hites recruits. These results hold up even when other variables are included in the model. Explanati ons for these results are theoretically consistent with critical race perspective. CRT discusses the ways group contexts produce overt or inadvertent systematic oppression; and in an institutional environment dominated by white male ideology and white pri vilege, non white recruits may perceive
122 systematic barriers that white recruits may not perceive. Non white officers may find less support from staff member s and their peers and may experience conflicts to a greater extent than white recruits. These barr iers may limit advancement within the police academy for non white recruits. Therefore, whites and non whites have different experiences during the police academy that may shape their performance. Another explanation for the difference in outcomes for wh ites and non whites may be performance on the basic abilities test. Whites consistently perform better on the CJBAT; that is , they score significantly higher than non whites, indicating an aptitude for better performance in the academy. Reasons as to why whites score higher on the CJBAT need to be identified. One reason might be the intertwined relationship of race, education, and socioeconomic status. High socioeconomic status amongst whites may produce more resources for higher education and so whites may have more opportunities to develop valued skills that promote better performance on the CJBAT. The upcoming discussion on the role of race and the relationship between CJBAT and performance will further illuminate the above postulation. Regarding the effect of race on graduation from the academy, the result for this study is unexpected because it is not consistent with prior research. Contrary to Wright, et al. (2011) findings that non whites are less likely to graduate from the academy and Lester (1 979) findings that white recruits are more likely to graduate from the academy, this study found no support for whites having a greater or lesser likelihood of graduating from the academy. An explanation for this finding may be that recruits are given se veral opportunities to improve upon weak performances, improving their chances of
123 graduation. With that said, racial differences may not exist because chances of graduation are very likely for both groups. The third hypothesis is that males will outperf orm females and males will have a greater likelihood of graduating from the academy than females. The findings from this study do not support the hypotheses and are not consistent with White (2008) who found that males have a significantly higher final av erage than females. Rather the current study finds that for LEO performance, overall performance, and proactive performance males are significantly less likely to outperform females. F or reactive and high liability performance, there are no statisticall y significant results ; males and females perform similarly. One possible explanation as to why males do not outperform fe males may be the institutionalization of male dominance in police academy (however, this dominance does not work in favor of men). I t was expected that in this environment males should outperform females because l aw enforcement is defined as a masculine occupation (Herbert, 2001) . However, though males who enter into this occupation may view law enfo rcement as appropriate work for the ir sex , they may then not feel as pressured to prove themselves in the classroom . W hile f emales who decide to enter into an occupation dominated b y males may attempt to a cquire status by being unique or different in some way from male recruits . Females may make more of an effort to achieve top marks in the classroom, as a way of distinguishing themselves (Martin, 1980). In addition, female recruits may also compare thems elves to other females and are then still more inclined to work harder in the academy (Rabe Hemp, 2009).
124 In regards to sex and graduation from the police academy, the study is inconsistent with Wright et al. (2011) who found that females are less likely to complete police academy training. This study found no support for a higher likelihood of graduation for males. I t is important to point out , however, that the relationship between sex (being a male) and graduation approached significance. Neverthele ss, the result counter s the prediction . Another explanation as to why there are no significant differences between males and females may be due to self selection into the academy. That is, a majority of recruits who enter into the police academy are fami liar with the knowledge and skills needed to complete the academy and can somewhat gauge whether or not they will be able to meet those requirements. For example, a major weeding out tool used in the academy i s the physical abilities test. Differences be tween males and females in regards to completing the exercises may not exists because female recruits who decide to enroll in the academy maybe be just as likely as male recruits to complete the pull up and sit up requirements . The fourth hypothesis is tha perform better in the academy and they will have a greater likelihood of graduating. In regards to in performance outcomes, with the exception of high liability and proactive performance. The significant findings are expected since higher education provides recruits with valued test taking skills and strategies to perform well on the academy examinations . It is difficult to identify explanations for the insignificant fi degree, proactive and high liability performance; and as discussed earlier , sex and high
125 liability performance. Race and basic abilities test scores seem to be bett er predictors format and content of the exams. Recruits are tested on the LEO performance materials at the beginning of training and the high liability material towards the end of training. There may be no significant differences for the high liability performance material because as recruits progress through training and are giv en the high liability exams, differences in performance are significantly diminished, because the recruits have become accustomed to taking such exams. Therefore, no significant differences are observed for proactive and high liability outcome. In rega rds to graduation from the academy, having a did not predict graduation; which is inconsistent with Wright et al. (2011) who found that those with only a high school education are less likely to complete the academy. These findings tenta from the academy. These results on graduation from the academy are understandable since the a cademy takes into account several other factors in addition to in class performance, such as, fitness and practical skills, to determine graduation eligibility. The fifth hypothesis is that recruits sponsored by an agency are more likely to outperform unsp onsored recruits and have a greater likelihood of graduating from the academy. The results verify that those sponsored by a law enforcement agency are significantly more likely to outperform unsponsored recruits and graduate more ofthen from the academy. In fact, sponsorship seems to be as important as basic abilities
126 when predicting performance outcomes. This finding is consistent to Wright et al. (2011) who found that cadet sponsored recruits outperform non cadet sponsored recruits and White (2008) who found that recruits who participated in cadet sponsorship are significantly more likely to have a higher final average. This finding is expected because sponsored recruits are selected based on recruitment efforts to get the top recruits who have higher education and are motivated and committed to pursuing a career in law enforcement . In addition, being sponsored allows recruits to become integrated into police culture and this familiarity allows them to navigate the police academy with more ease than ot her police recruits. Furthermore, sponsored recruits may be more motivated to perform well because they are given financial incentives and are guaranteed a job after graduation . This finding is important because it adds to the literature by assessing whe ther or not sponsored recruits are more likely to complete the academy. Based on these results, the admission priority given to sponsored recruits is an effective selection standard. In addition to the above independent variables , there are several cont rol variables that influence performance. One construct that is often neglected in police selection research is age and its influence on performance. Age is only significant for one of the outcome variables proactive performance. As age increases, recru its are more likely to perform well in this area. This is contrary to White (2008) who found that age is negatively related to final average performance and Wright et al. (2011) who found that age is negatively related to successfully completing the polic e academy. The results of this study may vary from prior research because of the outcome measure. of
127 six chapter s on how to initiate and follow procedures , while the measure s used in the other studies assessed graduation and overall performance. However, i t is reasonable to expect that as age increases recruits are more invested in their occupation and are therefore more willing to become more knowledgeable on proactive task s. Another control variable included in the analysis is BMI (body mass index), which is significant and negatively related to graduation from the academy. This is an expected finding given that recruits with a BMI greater than thirty may fail to complete the physical abilities training portion of the academy, whi ch involves running a mile within a specific amount of time. And therefore, failure to complete this activity may motivate a recruit to drop out of the academy or the recruit may be recommended f or dismissal after repeated failures. Contrary to the work of Wright et al. (2011), this current study found that employment is not significantly related to graduation from the police academy. However , employment is negatively related to high liability, reactive, and overall performance. Therefore, those who are employed are less likely to perform well. The inability to replicate Wright et al. (2011) findings may be due to the fact this study measures current employment status, that is, whether recruits are employed or unemployed, while Wright et al. (2011) measures prior employment history in years. Military experience was also found to be negatively related to proactive p erformance. S imilar to Wright et al. (2011) the study found that military backgr ound is not significantly related to completion of the academy. The stud y also finds that prior arrest and termination from a job are not significantly related to proactive and overall performance in the acade my. However,
128 prior research found that work and criminal history is negatively related to training and job outcomes (Cuttler & Muchinsky, 2006 and Sarachione et al., 2006). Th e results for the current study may be insignificant because most of the prior records of the recruits are juvenile records (increasing the likelihood that the offenses are minor). Such offenses would not be expected to impede success in the academy because the recruits are now mature adults and do not have any serious unlawful behavior on record. R ecruits with problematic ba ckgrounds , which may negatively influence performance , are screened out during the application process. The study did however find that recruits who have had their licenses revoked are less likely to perform well on the proactive and o verall performance e xaminations. This finding is similar to Fyfe & Kane (2006) who found that traffic violations are related to termination from the police department. These findings are understandable a license is revoked through a series of violations ( accumulating numero us amount of points) or a serious violation (driving while intoxicated ) , which indicates a history of noncompliance and disregard. T his group of recruits may fail to take the necessary steps to adequately prepare for the examinations (such as, attending c lass regularly, being attentive, and/or taking notes). The second research question sought to examine the interaction effect o f sponsored) and demographic variables (race and sex) on the relationship between basic abili ties (CJBAT) and performance (LEO performance, high liability performance, reactive performance, proactive performance, and overall performance). Previous research has not examined the degree to which
129 background and demographic factors interacts with the basic abilities and performance relationship. The first hypothesis is that the relationship between CJBAT and performance is conditioned by race. That is, the relationship between CJBAT and performance is stronger for white recruits. This hypothesis w as not supported for eith er of the outcome variables. The results show that at increasing levels of CJBAT, whites outperform non whites, but those differences are not significant. The conclusion drawn here is similar to the assumption made in the Morstai n (1984) study that found CAT (multijurisdictional police officer exam) to be a useful and valid predictor of supervisor ratings . Nevertheless , the interaction term is not significant and therefore, CJBAT scores are not more predictive of performance for a particular racial group. This result offers support for the predictive validity of CJBAT across different racial groups for various performance outcomes. The second demographic hypothesis is that the CJBAT and performance relationship is significantl y stronger for male recruits than female recruits. The results show that for the proactive performance outcome sex interacts the relationship. However, for LEO performance, high liability performance, reactive performance and overall performance, there a re no significant interaction effects by sex . The interaction findings are somewhat counter to the expectations of the study. It was expected that males would outperform females at all levels of the CJBAT performance relationship. However, for males, a t each level of CJBAT there is a less extreme increase in proactive performance. This result gives evidence for a predictor of performance that is not gender neutral. A nother explanation may be the environment in which males have
130 to perform. In contrast to females, males may not feel a pressure to excel in the classroom. Males may be more focused on excelling in o ther areas of training, such as on the physical fitness tests, where they can strive for more masculine ideals of bravery and strength (Herber t, 2001). The third hypothesis is that the relationship between CJBAT and performance is conditioned by education , CJBAT will have a greater influence on performance. The results for each o utcome variable provide support of the interaction effects. Moreover, the main effect of a ree on proactive performance was expected to be significant, however, the n on significant outcome is an indicator for further exploration of a n intera ction effect . r predictor of performance . Furthermore, t he significant findings are expected because ase to succeed in the academy. The fourth hypothesis is that the relationship between CJBAT and performance is conditioned by sponsorship. That is, being sponsored by a law enforcement agency will interact with the relationship between CJBAT and perform ance, such that, the CJBAT performance relationship is greater for sponsored recruits in comparison to unsponsored recruits. The results find support for LEO performance, overall performance, and reactive performance. The combination of CJBAT and being s ponsored by an agency enhances performance for those three outcomes. This result is understandable, since sponsored recruits are exemplary recruits who receive more social and financial support.
131 Significant outcomes were not identified for the interacti on term (CJBAT and sponsored) and the high liability and proactive performance outcomes. It is difficult to determine why sponsorship does not interact the CJBAT to influence proactive and h igh liability performance . This is even more inexplicable due the fact that the proactive measure is a subset of LEO performance, which has a significant interaction term. An explanation for these findings is that as sponsored and unsponsored recruits progress through training, CJBAT has comparable effects on proact ive and high liability performance outcomes because the differences that existed in the beginning of training have equalized in terms of garnering social support , possibly in the form of comradery with fellow recruits . The fifth hypothesis is that white males will outperform non white males and females making it more likely that white males will succeed in the academy. It is expected that the intersection of sex and race position WM at the top of the hierarchy or as the dominant group in the police acad emy. Within such an institutional environment , with less diversity and more shared beliefs and values, more cooperation is promoted, which results in better performance amongst recruits. This hypothesis is not supported; and white males are not more like ly to graduate from the po lice academy. This result is inconsistent to Wright et al. (2011) who found that young minority women are less likely to graduate from the police academy. In interpreting this result, the policy of the academy to allow recruits to retake exam s is a likely contribut ing factor to this finding. That is, recruits are given two attempts to pass an exam and on the second attempt recruits tend to receive a passing grade, reducing the chances of observing differences in outcomes.
132 Implic ations and Future Research CRT and IP lead to the expectation that race and its intersection with sex will play a role in academy performance and graduation. The most immediate way to address that expectation is to look at the main effects of race. Consi stent with expectation, race had a direct effect on all of the measures of performance during the academy (LEO, high liability, reactive, proactive, overall); whites performed better. This advantage did not interact with the predictive utility of cognitiv e abilities (CJBAT). It is important to note that there were mean differences by race in CJBAT. At another level however, the differences in performance by race did not carry over to graduation. Graduation rate for whites and non whites did not differ a nd it is graduation rather than performance during the academy that opens the door to the profession. This raises several possibilities that warrant future research. Further research should attempt to determine what is happening over and above formal a cademy testing that is causing some recruits to fail to complete academy? Are assessment that go beyond paper and pencil tests (like disciplinary issues, physical training requirements, proficiency in firearms, defensive tactics and driving) overriding tes t performance during academy to in effect filter out weaker cadets? This research suggests that these factors may be affecting white officers more than minority officers. Otherwise, we would see an impact from race on graduation, given the weaker performa nce on academy tests for minority recruits. This would run counter to expectations of critical race theory and warrants future research delving deeper into this possibility. A related question would focus more directly on those recruits who drop out of the academy. At what point and for what reason(s) do cadets drop out and are there
133 different reasons for white cadets to drop out? Given the current results it may be that minority cadets drop out because of poor test scores during the academy more often than do white cadets. The bottom line is that performance during the academy does not appear to get in the way of diversifying policing. Concerns with underrepresentation of non white officers need to look elsewhere for explanations. For example, does recruitment and admission favor white applicants or does police work itself increase minority officer turnover. Future research needs to attend to recruitment admission decisions or early on the job experiences to see if there is a differential racial i mpact. The dissertation does not find clear evidence of institutionalized racism during the academy that would be expected by CRT. It is important to note that this dissertation only examined intersectionality when it came to analyzing graduation. For that analysis an interaction term for white males was included. Intersectionality would lead to the expectation that white males would benefit by institutional arrangements generally, including police academies. The white male interaction term did not relate to graduation. The dissertation did not find support for CRT and IP within the academy setting. While the dissertation did not find much evidence of institutionalized racism in the academy, its findings regarding sex are more complicated. As to test performance indicators during the academy, there is some evidence of a direct effect of sex but it favors females. This seems to fly in the face of expectations derived from hegemonic masculinity. Females perform better on LEO exams (covering topic s such as communication, human issues, and legal subjects, as well as patrol 1 and 2, and traffic
134 stops) and proactive exams (traffic crash investigation, patrol 1, patrol 2, criminal investigation, crime scene investigation, and traffic stops). In as muc h as LEO covers in line with stereotypical roles of women, the relationship of sex to LEO performance that shows better performance for women may not be so surprising. Wha t is surprising but therein women continue to perform better than men. In addition, the interaction term between sex and cognitive abilities (CJBAT) also predicted performance on the proactive dimension. Finally, women also perform better on the overall performance measure in all fifteen subject matters. This pattern of findings appears to be inconsi stent with the kind masculine structure and culture anticipated b y the hegemonic masculinity perspective. There is however a complexity that calls for a deeper and more nuanced considerations. Sex does not predict graduation. In fact, what appears to be a better performance overall on academy subject areas for female s changes direction when it comes to graduation. In absolute terms females are less likely to graduate than males, although the relationship only approaches statistical significances. This reversal of direction is provocative. Is this another instance w here processes appear to be fair and translate into better graduation rates? Hegemonic masculinity would direct attention to institutionalized ways in which informal and l ess standardized procedures may inhibit or discourage women. As such it remains an important sensitizing perspective to frame future research questions. Would qualitative data from observations about the
135 unmeasured features of the academy shed light on w hy and how this reversal in roles place demands on women that are external to the academy and interfere with their graduation, despite satisfactory performance during th e academy. Or it may be that measures of performance (some of which are subjective) on some stereotypical male features of academy training like firearm and driving proficiency (that were not a part of the official data accessed for this dissertation) hel p explain the reversal. At any rate future research into this matter is needed. CRT, IP, and HM would all suggest that selection issues would arise if there is institutionalized racism and/or sexism in police training. Although selection/admission int o the academy is not a primary focus of this dissertation, several key variables are included because of their relationship to selection. Educational level (measured here by he academy and are highlighted in this dissertation to see how these affect performance directly or through interactions with cognitive abilities (CJBAT). Higher education had a direct relationship to three performance indicators during the academy (LEO, reactive, and overall). More importantly, higher education interacted with cognitive ability (CJBAT) to predict all the performance measures during performance, in a synerg istic way. Although CJBAT predicted performance for Again, however the enhancement in performance during the academy did not translate
136 into improved graduation rates. gradation and its interaction with CJBAT had no effect on graduation. The impact of sponsorship was the most striking finding in these data. Being sponsored into the academy by a police department not o nly had a direct effect on all performance measures during academy but it also predict graduation. The coefficients indicated that sponsorship was the strongest predictor. In addition, sponsorship interacted with CJBAT to predict performance for LEO, rea ctive, and the overall performance measure. The consistent findings for higher education and sponsorship, both of which are important for selection into the academy, give reason for further consideration. It could be consistent with perspectives that em phasize institutionalized racism and sexism: males and whites are advantaged because they have higher education and more sponsorship. Although this question is beyond the purpose of this dissertation, these data did show that whites were more likely to be degrees and males were more likely to be sponsored. Is there a built in advantage for whites and males because of institutional arrangements? On the other hand, females difficult to know whether this accomplishment reflected a self screening into police work or not. That is, to the extent that policing has traditionally been a male occupation, it may be that the women who self select into this career are highly motivat ed and use college education as a preparation for entry. Males on the other hand have always had this career path open to them in ways where they did not have to break from stereotypical roles or take special steps to enhance their suitability for policin g. The findings from these data
137 happening when it comes to police performance, especially given the disconnect ion between predicting performances during the academy and gra duation. Significantly, only sponsorship predicts all performance outcomes, including graduation. Regardless of the theoretical implications of the predictive utility of sponsorship, that utility also holds practical implications. Certainly, sponsorshi p enhances performance during the academy and virtually guarantees graduation. In other words, the investment in sponsored recruits pays off. If this payoff can be shown to carry over through field training and into police careers, there would be reason to invest more heavily into sponsorship. For example, government programs that seek to improve policing may want to start with providing funding for sponsorship. This recommendation would have more solid footing were there more research looking at the lo ng term effects of sponsorship and academy success on police performance in the field. Some of the findings of dissertation departed from results reported by others in earlier studies. First, the findings regarding sex go in a different direction for pe rformance indicators during the academy. Other studies find that males perform better than females within the academy and are more likely to graduate (Wright et al., 2011 and White, 2008). Second, neither Wright et al. (2011) nor White (2008) found an im portant effect of higher education. Third, although White found a relationship as it is in these dissertation data (Wright et al. 2011 did not include sponsorship data). These differences are important enough to compare the methodology of this dissertation with those of the other studies to see whether that might explain the
138 different outcomes. This dissertation consisted of more comprehensive curriculum measures and foc used on graduation because it had data on those who did not complete. The data for this dissertation were collected from a community college based academy in the South that help meet the needs of both an urban area and surrounding counties. The dissertat ion data were collected for the years 2011 to 2013, a time that was affected by the economic collapse of 2008. The data for the Wright et al. (2011) and White (2008) were collected prior to the recession. Perhaps the economic downturn created a history effect so that a broader range of recruits were using the academy to enter a relatively secure career. That could explain why females and those with college degrees seem to perform better in 2011 through 2013, than they did in data that was collected in 2 003 (White, 2008) or between 1999 and 2005 (Wright et al., 2011). Future research needs to continue to examine how sex and higher education play out in selection, academy success and long term police performance. The issues are far from settled. Wrigh sponsorship variable was not nearly as strong a predictor of success as sponsorship was in the dissertation data. Perhaps sponsorship at the dissertation data site is systematical ly different from the other sites. Or perhaps the labor market conditions for new police officers changed over time, so that sponsorship became more important for entry into academies. No matter the reason, the findings of this dissertation highlight the need to research how important sponsorship is, how it may be done differently from
139 Limitations The measures of performance used in this study overlap and therefore are not all distinct pe rformance measures. The overall performance measure is a combination of the law enforcement and high liability performance measures. While the proactive performance measures is a subset of the law enforcement performance measure. The reactive performanc e measure contains components of both the law enforcement and high liability performance measures . Another limitation of this study is the incomplete curriculum data for the non graduates of the police academy. Steps need to be taken by the police academ y to prevent failure of examinations or drop out of recruits. Future studies should analyze additional performance measures, such as physical abilities, driving skills, commendations, and disciplinary write ups. However, the curriculum performance measur e s used in this study are exhaustive and unique in that they were constructed through statistically driven techniques . Therefore, i n the future, more studies should explore the dimensions of academy performance. Another limitation of this study is its l ack of a qualitative co mponent, which would have comple mented the quantitative aspects of the study. The quantitative methods in this study provide measures of direction and strength of the relationships, however qualitative research, such as the work by Prokos and Padavic (2002), allow s for more in depth analysis of the informal curriculum in police academy . For example, th is study found that the relationship between aptitude and performance is conditioned by sex; in this case, qualitative methods can b e used to frame sex from a structural and social performance perspective. In turn , this fosters critical analysis of the quantitative outcome and therefore identifies explanations and provides deeper understanding of the outcomes.
140 Though this study does not specifically test concepts of theories, it is important to point out that the findings from the study do not support several of the theoretical perspectives used to guide the discussion of the results. T he unexpected finding on graduation and the lac k of association with race , sex and the intersection of race and sex, goes against the expectations of CRT , HM, and IP . A nd so , the use of qualitative methods would complement the study by identifying themes in the institutional environment of the academy that contributed to these findings. For the sample of non graduates, qualitative methods would also be very useful in determining reasons for not completing the academy. Despite these limitations, the findings of this study substantially advance the unde rstanding of the role of aptitude and performance during police academy training and makes contributions to the literature by pointing out the importance of demographics and background factors in contextualizing the relationship between aptitude and perfor mance.
141 APPENDIX A C RIMINAL JUSTICE BASIC ABILITIES CONTENT Table A 1. Description of the subdivisions of the basic abilities exam. Subdivisions Descriptions Deductive Reasoning log ical answers. This ability involves applying general rules to specific Manual on Jail Standards, policies and procedures to specific situations . Example: under what conditi ons to make an arrest or the proper use of force, in deciding which route to take when taking into Inductive Reasoning answers to problems to form general r ules or conclusion. It involves the ability to think of possible reason why things go together, such as giving a conc Information Ordering arrange things or actions in a certain order. The rules must be given. The things or actions must be put in order and can include numbers, letters, words, pictures, procedures, sentences and transporting prisoners, conducting fire drills. It is also used in applying first aid, f ollowing a checkout procedure in operating equipment, arranging sentences in a meaningful paragraph Memorization e important information presented in the Florida Statues, legal bulletins, shift briefings, BOLOs remembering new names, faces, codes, telephone numbers, geographic
142 Table A 1. Continued. Subdivisions Descriptions Pr oblem Sensitivity individuals where you have to judge whether or not a situation is going to deteriorate or get wors e. It could involve recognizing the symptoms of a physical problem requiring first aid, the likelihood that a riot or other type of disturbance may occur. It Spatial Orientation ll where you are in relation to the location of some objects, or to tell where the object is in relation to you. It with respect to the points of a compass. The ability allows one to stay ori ented in a vehicle as it changes direction and and a call comes in that you must attend to. You must visualize where you are in relation to where you are going to be able to get Written Comprehension Written Expression rds and sentences so others will You would use this ability when it is necessary to write incident/use of force/discipline reports, memos, affidavits or ( I/O Solutions, Inc., 2013 )
143 APPENDIX B L AW ENFORCEMENT AND HIGH L I ABILITY CONTENT Table B 1. The Law Enforcement curriculum content. Course Chapter Titles Description Introduction to Law Enforcement Officer training program overview; criminal justice values and ethics; sexual harassment; criminal justice system and co mponents; chain of command Legal Introduction to Law; Legal Concepts; Substantive Criminal Law; Use of Force; Civil and Criminal Liability; Response to Civil Issues; Juvenile Law Communication Telecommunications; Communication and Interpersonal Skills; H uman Interactions; Interviewing; Report Writing Human Issues Crisis Intervention; Disability Awareness; Responding to the Elderly; Responding to Suicide; Substance Abusers Patrol 1 Problem Solving; Officer Safety and Survival; Patrolling the assigned are a; Patrol functions Patrol 2 Incident Command System; Crown Control; Criminal Street Gangs and Extremist Groups Hazmat; Bombs and Weapons of Mass Destruction Crime Scene Investigation Responding to a Crime Scene; Processing the Crime Scene Criminal Inve stigation Crimes Against Persons; Crimes Against Property; Follow up Investigations; Court Procedures Traffic Stops Traffic Law; Professional Traffic Stops and Discriminatory Profiling; Unknown Risk Traffic Stops; High Risk Traffic Stops DUI Traffic Stop s Overview of the DUI problem; Legal issues; DUI detection; Standardized field sobriety tests; drug impaired driving; report writing Traffic Crash Investigation Assessing and securing the scene; Investigating the crash; Documenting the crash; Returning th e scene to normal (FDLE, 2012)
144 Table B 2. The High Liability curriculum content. Course Chapter Titles Description CMS Law Enforcement Vehicle Operations Vehicle Inspection; Proactive Driving Skills; Lights and Sirens CMS First Aid for Criminal Justice Officers Preparing to respond to emergencies; Responding to emergencies; Trauma Related Issues; Medical Issues CMS Criminal Justice Firearms Firearms Safety; Firearms Familiarization; Ammunition; Fundamentals of Marksmanship; Drawing and Holstering a h andgun; Loading and uploading; Use of cover; Weapons malfunctions; Weapons cleaning; Survival shooting CMS Criminal Justice Defensive Tactics Use of Force; Defensive Tactics Techniques Dart Firing Stun Gun Use of the dart firing stun gun (FDLE, 2012)
145 APPENDIX C C ORRELATION MATRIX Table C 1. Correlation matrix for each course curriculum. I L HI C VO FA F TCI P1 P2 CI CSI TS DUI DT DF I 1 L .479*** 1 HI .398*** .481*** 1 C .455*** .557*** .503*** 1 VO .421*** .495*** .443*** .569*** 1 FA .406*** .463*** .401*** .447*** .508*** 1 F .352*** .336*** .357* ** .381*** .433*** .372*** 1 TCI .346*** .426*** .392*** .417*** .410*** .337*** .364*** 1 P1 .321*** .362*** .425*** .357*** .327*** .296*** .301*** .446*** 1 P2 .352*** .411*** .428*** .440*** .399*** .411*** .405*** .442*** .498*** 1 CI .419*** .380*** .445*** .472*** .435*** .422*** .385*** .428*** .444*** .552*** 1 CSI .336*** .379*** .297*** .301*** .36 8*** .261*** .283*** .382*** .330*** .455*** .451*** 1 TS .328*** .269*** .281*** .326*** .345*** .313*** .261*** .359*** .396*** .432*** .417*** .330* ** 1 DUI .246*** .317*** .318*** .340*** .291*** .366*** .286*** .388*** .261*** .414*** .340*** .273*** .299*** 1 DT .172*** .311*** .264*** .264*** .283*** .097*** .170*** .337*** .216*** .106* .067 .1 35** .068 .230*** 1 DF .137*** .158*** .158*** .194*** .198** .213*** .157*** .182*** .137*** .234*** .119** .112* * .233*** .240*** .050 1 I=Introduction to Law Enforcement; L=Legal; HI=Human Issues; C=Communications; VO=Vehi cle Op erations ; FA=First Aid; F= Firearms; TCI= Traffic Crash Investigation; P1=Patrol 1; P2=Patrol 2; CI=Criminal Investigation; CSI=Crime Scene Investigation; TS=Traffic S tops; DUI=DUI traffic sto p; D=Defensive Tactics; DF=Dart Firing stun gun *p **p ***p .001
146 Table C 2. Correlations for eight factor after promax rotation. Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 Factor 1 1 Factor 2 .634 1 Factor 3 .757 .707 1 Factor 4 .490 .525 .424 1 Factor 5 .480 .565 .304 .350 1 Facto r 6 .475 .483 .473 .561 .068 1 Factor 7 .220 .061 .019 .167 .069 .265 1 Factor 8 .025 .006 .065 .068 .058 .214 .026 1
147 Table C 3. Correlations between predictors , controls, and dependent variables. LEO High Liability Overall Reactive Pr oactive Completed CJBAT . 446*** .431*** .478** * .495 *** .345 *** .220*** Male .199*** .060 .164* ** .135 ** .199 *** .007 White .23 6*** .329*** .284*** .285 *** .191 *** .071 Age .0 0 3 .040 .0 1 2 .014 . 0 5 4 .080 * Bachelor .34 5*** .224** * .327*** .387 *** .260 *** .195*** Sponsored .3 05*** .202*** .288*** .308 *** .226 *** .241*** Employed .0 58 .080 .068 .071 .026 .048 Military .1 31** .064 .114** .086* .146 *** .022 Licenses r evoked .028 Terminated .058 Arrested .040 .130*** ***p .001
148 APPENDI X D M ULTICOLLINEARITY TABLES Table D 1. Multicollinearity check for LEO performance. Variables VIF 1/VIF CJBAT 1.21 0.824508 1.24 0.806322 Sponsored 1.21 0.826001 White 1.12 0.894229 Male 1.07 0.938895 Age 1.08 0.926698 Employed 1. 02 0.982709 Military 1.12 0.889521 Mean VIF 1.13 Table D 2. Multicollinearity check for high liability performance. Variables VIF 1/VIF CJBAT 1.22 0.820864 1.26 0.795343 Sponsored 1.21 0.825593 White 1.12 0.888995 Male 1.06 0.9 39781 Age 1.07 0.934412 Employed 1.02 0.982842 Military 1.12 0.893545 Mean VIF 1.14 Table D 3. Multicollinearity check for reactive performance. Variables VIF 1/VIF CJBAT 1.22 0.820941 1.26 0.795283 Sponsored 1.21 0.824959 Whi te 1.12 0.893518 Male 1.07 0.930320 Age 1.08 0.925530 Employed 1.02 0.980083 Military 1.12 0.889881 Mean VIF 1.14
149 Table D 4. Multicollinearity check for proactive performance. Variables VIF 1/VIF CJBAT 1.22 0.821322 1.28 0.7 78397 Sponsored 1.27 0.789291 White 1.12 0.892142 Male 1.08 0.923638 Age 1.13 0.887619 Employed 1.02 0.979701 Military Arrested License revoked Terminated from a job 1.13 1.16 1.14 1.06 0.886205 0.862198 0.877659 0.944090 Mean VIF 1.15 Table D 5 . Multicollinearity check for overall performance. Variables VIF 1/VIF CJBAT 1.24 0.806576 1.30 0.768369 Sponsored 1.25 0.801074 White 1.14 0.877135 Male 1.07 0.938019 Age 1.14 0.875420 Employed 1.02 0.982124 Military 1.13 0.88106 9 Arrested License revoked Terminated from a job Mean VIF 1.16 1.15 1.06 1.15 0.862755 0.872638 0.943970 Table D 6. Multicollinearity check for graduation. Variables VIF 1/VIF CJBAT 1.25 0.7997 1.28 0.7809 Sponsored 1.25 0.8007 Wh ite 1.15 0.8715 Male 1.09 0.9140 Age 1.07 0.9335 Employed 1.02 0.9831 Military 1.10 0.9087 1.07 0.9379 Mean VIF 1.14
150 APPEDIX E I NDIVIDUAL CHAPTER RESULTS Linear Regression and Individual Course Outcomes In Table E 1 (Appendix E) , the OLS regressions for each course shows that there is a statistically significant relationship for all law enforcement model (model I), the variables explain 33% of the variance in are statistically significant; and there are also several cohort effects. The estimated regression unstandardized coefficient fo se=.07 , , se=.68 , , se=.71 , , se= .77 , increase in CJBAT, introduction to law enforcement scores increase . In addition, cadets who are sponsored are significantly more likely to outperform other recruits (non and unsponsored cadets) . For the legal course (model L), the variables in the OLS regression explain 27% of the variance in the outcome. The CJBAT predictor variable is positive and statistically significant (b=.41 , , se=.05 , white , The estimated regression unstandardized coefficien t for w , se=.53), , , se=.59). Therefo re, with each increase in CJBAT scores performance increases . For recruits with bachelor s degrees and who are sponsored , legal performance increase s . In
151 regards to the significant varia bles discussed above, CJBAT has the largest standardized coefficient and white has the smallest standardized coefficient. In the model for communications (model C), the variables in model explain 28% male , white, bachelo r s degree and sponsored are statistically significant. All the relationships are positive, wi th the exception of sex (se=.59, (se=.04 , 2.08, white is 1.30 (se =.42 , (se=.44 , , and sponsored is 1.68 (se=.46, re, with each increase in CJBAT performance increases . O n average, males have weaker performance in comparison to females . F or white recruits, on av erage there is an increase in performance. F performance increase s. F or sponsored recruits, communication performa nce increases . The standardized coefficient for CJBAT is .24, sex is .15, white is .13, bachelor s degree is .17 and sponsored is .16. Therefore, CJBAT has the largest standardized coefficient and White has the smallest standardized coefficient. sponsored have significa nt effects on the increase likelihood of performing well, as shown by the statistically significant regression coefficients. All relationships are positive with the exception of sex. Therefore, as CJBAT increases performance increase (b=.26 , , se=.0 5 , W degree cadets sponsored by a law enforcement agency outperform non whites, recruits without a bachelor degree and unsponsored cadets. An
152 unforeseen finding is that male recruits are less likely to perform well (b= 1.90 , .11 , se=.73 , =.22). The patrol 1 model (model P1), illus trates a significant model with a R 2 of .21 The statistically significant regression coefficients are CJBAT (b=.09 , 1.44 , b=.1.08 , , important to point out that as CJBAT scores increases, performance on the Patrol 1 exam increases. Furthermore, sponsored cadets are more likely to outperform cadets who are un sponsored by a law enfor cement agency . Amongst all the significant =.17) Table E 1 shows the OLS regression for P2 and that the model is statistically significant with a R 2 of .21, which illustrates that 2 1% of the variance in the model is explained by the inclu ded variables. CJBAT (b=.22), white (b=1.04), a ge (b=.10), and highest standardized coefficient is CJBAT ( =.22). The CSI model is a statistically significant model with R 2 =.13. Therefore, 13% of the variance in the model is explained by the variables. CJBAT is the only variable with a significant regression coefficient of .14 ( =.18 , , as CJBAT increases CSI performance increases. CJBAT has the largest standardized coefficient in the model. 2 of .23. Therefore, 23% of the variance in the model is explained by the variable s. There are three statistically significant standardized coefficient in the model, which are CJBAT (b=.15 ,
153 sponsored (b=1.68 , 1.24 , increase, CI performance increase. Those who are sponsored are more likely to perform well on the CI exam. However, those who are employed are less likely to perform well on the CI exam. Sponsorship has the largest standardized coefficient ( =.17). In terms of the model for TS, the variables in model explain 22% of the variance. significant. The estimated regression unstandardized coefficient for CJBAT is .19, sex is 1.51, age is .08, and sponsored is 1.41. All the relationships are positive, with the exception of sex. Therefore, with each increase in CJBAT, performance increases ; Males have significantly lesser scores than females. A s age increase TS performance increase . For cadets who are sponsored TS performance increases . The standardized coefficient for CJBAT is .18, age is .09, and sponsored is .13. Therefore, CJBAT has the largest standardized coefficient. The DUI model, illustrates a significant model with a R 2 of .12, indicating that 12% of the variance is explained statistically significant regression coefficients are CJBAT (b=.11 , 1.33 , increases. Furthermore, male recruits are less likely to perform well on the DUI exam. Amongst all the significant variables, CJBAT has the highest standardized coefficient ( =.14) In Table E 1 , the OLS regressions for TCI shows that there is a statistically significant relationship for the mod 2 = .19. The variables in the
154 model explain 19% of the variance. Within the model, CJBAT, sex, white, and sponsored are statistically significant; and there are also several cohort effects. The estimated regression unstandardized coe 2.01 CJBAT scores TCI scores increases. Whites and cadets that are sponsored are significantly more likely to outperform non whites and unsponsored cadets on TCI exam. However, males are less likely to perform well on the TCI exam. The standardized coefficient for CJBAT is .25, male is .15, white is .14, and sponsored is .08. Therefore, CJBAT has the largest standardized coe fficient and explains most of the variance in the model. The results for the OLS regression model predicting VO show as statistically 2 =.24. The variables in the model accounts for 24% predic tor of performance and as CJBAT scores increase, performance increase by .35. VO (b=2.65). In addition, sponsored cadets outperform (b=1.90) unsponsored cadets . CJBAT has the largest unstandardized coefficient ( =.28). The findings for the defensive tactics regression model is that the model is 2 =.39. Therefore, the variables e xplain 39% of the variance in the model. CJBAT is statistically significant and so as CJBAT increase, defensive tactics performance increase (b=.18 , less likely to do well on the defensive tactics exam ( b= 1.38, ). White recruits are
155 more likely to outperform non white recruits ( b=.90, standardized coefficient is for CJBAT ( =.16). The firearms model (model F) illustrates a significant model with a R 2 of .30 g 30% of the variance in the outcome variable is explained by the independent variables in the model. CJBAT has a statistically significant regression coefficients (b=.16 , firearm exam incr eases. Race (b=1.42 , , sponsored (b=1.21 , law enforce ment agency are more likely to outperform non whites, those with a cadets . Amongst all the significant variables in the model, CJBAT ( =.13) have the highest standardized coefficient. In the model for first aid (model FA) the variables in model explain 23% of the significant. All the above significant relationships are positive. The estimated regression unsta re, with each increase in CJBAT performance increases. O n average, males have weaker performance , in comparison to females. W hite recruits are more likely t o outperform non white recruits. F and those sponsored by a law enforcement agency , first aid performance increase s. The standardized
156 sponsored is .10. Therefore, the variable CJBAT and white has the largest standardized coefficient. For the dart firing stun gun (model DF), the variables in the OLS regression expla in 19% of the variance in the outcome. The CJBAT predictor variable is positive and s (p=.019) and sponsored (p=.019) are statistically significant. The estimated regressio n Therefo re, with each increase in CJBAT performance increases . Sponsorship by a law enforcement agency facilitates an increase dart firing performance . In regards to the significant variables discussed above, CJBAT has the largest standardized coefficient and explains most of the variance in the outcome. In summary, CJBAT is the only variable that is consistently related to each of the individual performance measures. , sponsorship show a similar trend, but are insignificant for a few of the individual outcome variables.
157 Table E 1. OLS Regression of each law enforcement and high liability performance chapters Variables I (n=533) L (n=481) C (n=469) HI (n=463) P1 (n=452) P2 (n=452) CSI (n=451) CI (n=449) Basic Abilities (CJBAT) .38*** .41*** .24*** .26*** .08* .22*** .14*** .15** .21(.07) .32(.05) .24(.04) .22(.05) .11(.03) .21(.04) 18(.03) .14(.04) Sex (male=1) .01 .45 2.08*** 1.90** .72 0.93 0.51 .98 .00(.94) .02(.74) .15(.59) .11(.73) .06(.51) .06(.66) .04(.51) .07(.61) Race (white=1) 2.10** 1.11* 1.30** 1.81*** 0.23 1.04* .09 .75 .12(.68) .08(.53) .13(.42) .15(.52) .03(.36) .10(.47) .01(.36) .07(.50) Ag e (in years) .04 .01 .02 .05 .07** .10** .05 .01 .03(.06) .01(.04) .02(.03) .05(.04) .11(.37) .10(.04) .08(.03) .17(.77) 4.49*** 2.22*** 1.67*** 1.39** 1.29** 1.03* .59 .88 .25(.71) .18(.55) .17(.44) .12(.54) .17(.46) .10(.4 8) .08(.37) .07(.55) Sponsored 4.51*** 1.26* 1.68*** 1.17* 1.17** .85 .53 1.68*** .24(.77) .09(.59) .16(.46) .09(.74) .15(.39) .08(.51) .07(.39) .17(.48) Employed 1.27 1.06 .48 .25 .26 .18 .48 1.24* .05(.95) .05(.76) .03(.61) .01(.74) .02(.52) .01(.67) .04(.52) .09(.62) Military .72 .94 .01 0.71 0.75 0.50 .58 1.18 .04(.75) .06(.60) .00(.47) .05(.59) .09(.41) .04(.52) .07(.40) .09(.49) Cohort Dummy 1102 2.27 3.09* .61 2.20 .50 .47 .23 1.26 .06(1.92) .12(1.53) .03(1.23) .09(1.52) .03(1.05) .02(1.36) .01(1.05) .06(1.27) Dummy 1103 3.38 4.16** 1.15 2.28 .01 .21 .70 .78 .08(2.08) .14(1.65) .04(1.34) .08(1.64) .00(1.17) .01(1.50) .03(1.16) .03(1.40) Dummy 1104 .98 2.38 .02 .93 1.26 .41 .11 .35 .03(1.84) .10(1.46) .00(1.18) .04(1.45) .09(1.02) .02(1.30) .01(1.01) .02(1.23) Dummy 1105 3.10 2.82* 2.00 1.30 1.40 .70 .87 .50 .09(1.91) .11(1.53) .10(1.23) .05(1.53) .09(1.06) .03(1.36) .06(1.05) .02(1.2 7) Dummy 1201 .75 1.14 .85 1.94 1.57 .18 .29 .14 .01(2.07) .03(1.67) .03(1.31) .06(1.64) .08(1.14) .01(1.46) .01(1.13) .01(1.37)
158 Table E 1. Continued. Variables I (n=533) L (n=481) C (n=469) HI (n=463) P1 (n=452) P2 (n=452) CS I (n=451) CI (n=449) Dummy 1202 1.65 4.08** 1.57 2.31 .89 .86 .33 .51 .04(1.99) .16(1.57) .07(1.27) .09(1.56) .05(1.09) .04(1.40) .02(1.09) .02(1.31) Dummy 1203 2.37 1.21 2.01 1.91 .09 .80 1.06 1.13 .07(1.85) .05( 1.46) .12(1.17) .09(1.45) .00(1.01) .04(1.30) .08(1.01) .06(1.23) Dummy 1204 3.45 3.27* .96 4.25** .19 1.61 .29 2.12 .09(1.95) .13(1.52) .05(1.23) .19(1.51) .01(1.05) .08(1.36) .02(1.05) .11(1.27) Dummy 1205 3.83* .17 1.52 1.05 .54 .59 .21 3.45** .12(1.87) .01(1.49) .09(1.19) .05(1.47) .04(1.02) .03(1.32) .01(1.02) .21(1.24) Dummy 1301 6.03** .80 .64 1.82 .01 2.23 2.11* 3.27** .16(1.94) .02(1.56) .03(1.26) .07(1.55) .00(1.07) .10(1.38) .13 (1.07) .16(1.29) Dummy 1302 6.58*** .04 1.75 0.73 1.49 1.75 .25 2.72* .19(1.91) .01(1.53) .08(1.25) .03(1.53) .09(1.07) .08(1.37) .01(1.06) .14(1.28) Dummy 1303 4.51* 2.86 2.04 1.76 1.24 5.12*** 1.17 4.77*** .12(1.98) .11(1.57) .10(1.29) .07(1.58) .08(1.10) .24(1.42) .07(1.09) .24(1.32) Dummy 1304 3.93* .45 1.43 0.04 0.63 2.27 .04 2.60* .13(1.83) .02(1.45) .09(1.17) .00(1.44) .05(1.00) .13(1.29) .00(1.00) .17(1.21) Dummy 1305 1.65 .05 .19 1.28 0.13 1.15 .56 1.70 .04(2.01) .01(1.63) .01(1.28) .05(1.58) .01(1.10) .05(1.42) .03(1.10) .08(1.33) Constant 50.89 52.17 69.59 66.97 82.30 68.27 81.46 78.29 7.04 5.54 4.35 5.39 3.72 4.79 3.71 4.44 R 2 .33 .27 .27 .21 .15 .21 .13 .23 F statistic 11.63*** 7.52*** 7.63*** 5.28**** 3.42*** 5.20*** 2.79*** 5.76*** b indicates the standardiz tailed).
159 Table E 1. Continued. Variables TS (n=451) DUI (n=434) TCI (n=449) VO (n=437) DT (n=434) F (n=455) FA (n=462) DF (n=446) Basic Abilities (CJBAT) .19*** .11** .23*** .35*** .17*** .16** .25*** .12** .18(.04) .13(.04) .25(.04) .29(.05) .16(.04) .13(.05) .20(.05) .13(.04) Sex (male=1) 1.51* 1.33* 2.01*** .86 .38 .21 .99 .79 .10(.65) .11(.57) .15(.60) .05(.74) .20(.62) .01(.73) .06(.74) .06(.59) Race (white=1) .88 .51 1.33** 2.65*** .90* 1.42** 2.38*** .99* .08(.46) .06(.40) .14(.42) .22(.53) .08(.43) .12(.51) .20(.54) .11(.42) Age (in years) .08* .04 .06 .05 .00 .04 .04 .04 .09(.04) .06(.03) .07(.03) .05(.04) .00( .04) .03(.04) .03(.04) .05(.03) .86 .07 .35 .78 .05 1.57** 1.36** .15 .08(.47) .01(.41) .03(.44) .06(.54) .01(.45) .13(.52) .11(.55) .01(.43) Sponsored 1.41** .78 .83* 1.90*** .87 1.21* 1.29* 1.08* .13(.50) .09(.44) .0 8(.46) .15(.58) .08(.47) .10(.56) .10(.58) .11(.46) Employed .42 .88 .97 .69 1.38* .21 1.03 .70 .02(.66) .07(.58) .07(.61) .03(.75) .08(.62) .01(.74) .05(.77) .05(.60) Military .01 .08 .56 .35 .30 .23 .55 .42 .00(.51) .01(.45) .0 5(.48) .02(.59) .02(.49) .01(.57) .04(.60) .04(.47) Cohort Dummy 1102 3.70** .33 .78 1.24 .39 3.85** .39 .18 .18(1.35) .02(1.17) .04(1.25) .05(1.49) .01(1.26) .16(1.44) .01(1.48) .01(1.23) Dummy 1103 2.47 .39 .36 1.95 .71 8.78*** 1.12 .44 .10(1.49) .02(1.29) .01(1.38) .06(1.64) .02(1.39) .30(1.60) .03(1.66) .02(1.35) Dummy 1104 4.78*** .06 .81 .69 2.61* 4.17** .54 1.19 .26(1.31) .01(1.12) .05(1.21) .03(1.41) .14(1.22) .20(1.38) .02(1.40) .07(1.18) D ummy 1105 2.70* 1.42 .53 .57 1.42 8.04*** 1.45 1.75 .13(1.36) .09(1.17) .03(1.25) .02(1.50) .07(1.27) .35(1.45) .06(1.49) .10(1.23) Dummy 1201 2.45** .36 1.54 1.96 .84 6.07*** 1.60 0.63 .10(1.46) .01(1.27) .07(1.35) .07(1.60) .03(1.3 7) .22(1.52) .05(1.59) .02(1.32)
160 Table E 1. Continued. Variables TS (n=451) DUI (n=434) TCI (n=449) VO (n=437) DT (n=434) F (n=455) FA (n=462) DF (n=446) Dummy 1202 2.99* .96 .40 .95 .61 6.83*** .06 4.43*** .14(1.40) .05(1.22) .02( 1.29) .03(1.54) .02(1.32) .27(1.49) .00(1.53) .24(1.27) Dummy 1203 4.03** 1.31 .18 .83 1.89 9.86*** 1.01 2.03 .23(1.30) .09(1.14) .01(1.21) .04(1.43) .10(1.23) .48(1.38) .04(1.41) .13(1.18) Dummy 1204 5.21*** 1.30 2.35 .32 6.12 *** 5.20*** 4.56** 4.63*** .26(1.36) .08(1.18) .13(1.25) .01(1.48) .30(1.27) .23(1.43) .19(1.48) .26(1.23) Dummy 1205 4.86*** 1.31 1.21 .47 3.91*** 9.11*** 2.45 1.03 .28(1.31) .09(1.15) .07(1.22) .02(1.44) .21(1.24) .44(1.41) .12 (1.44) .06(1.20) Dummy 1301 4.51*** .93 .71 1.37 5.64*** 8.96*** 2.12 2.26 .20(1.38) .05(1.19) .03(1.27) .05(1.50) .25(1.28) .36(1.45) .08(1.50) .11(1.25) Dummy 1302 4.75*** .11 .64 .51 4.74*** 10.34*** 1.38 .80 .23(1.37) .01(1. 20) .03(1.27) .02(1.51) .22(1.33) .43(1.45) .05(1.50) .04(1.24) Dummy 1303 6.25*** 2.10 .06 .07 4.77*** 10.85*** 2.09 2.71* .30(1.41) .13(1.23) .00(1.30) .00(1.56) .21(1.21) .43(1.52) .08(1.55) .15(1.28) Dummy 1304 6.38*** 2.90** 2.07 .78 3.08** 5.05*** 1.20 4.31*** .39(1.29) .23(1.12) .14(1.19) .04(1.41) .17(1.22) .26(1.37) .06(1.40) .29(1.18) Dummy 1305 7.00*** 2.40* 1.80 .65 4.04** 6.65*** .80 3.26** .32(1.42) .13(1.23) .09(1.31) .02(1.56) .17(1.33) .26( 1.50) .03(1.55) .17(1.28) Constant 76.91 87.73 68.86 54.25 75.19 82.73 67.15 88.03 4.71 4.13 4.37 5.41 4.47 5.21 5.51 4.32 R 2 .22 .12 .19 .24 .39 .30 .23 .19 F statistic 5.50*** 2.49*** 4.63*** 6.34*** 11.96*** 8.58*** 5.81*** 4.42 *** b tai tailed).
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172 BIOGRAPHICAL SKETCH Kelesha Nevers was born in Montego Bay, Jamaica. She moved to F alls Church, Virginia in 1995. Kelesha Nevers receiv ed her b egree in c rimin al j ustice from Old Dominion Un iversity and her Master of Art s (M.A.) in Criminology and Criminal Justice from University of Maryland, College Park. After receiving her M.A. d egree, she worked as a Research Associate for a government contracting company in the Washington, D.C. area. Kelesha earned her Docto r of Philosophy in Criminology, Law and Society from the University of Florida in 2014 . Her areas of research interests are police performance measurement , theoretical integration, and victimization.