The Drive of Perfectionism Behind Maximization

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The Drive of Perfectionism Behind Maximization
Ye, Huan
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[Gainesville, Fla.]
University of Florida
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1 online resource (87 p.)

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Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Counseling Psychology
Committee Chair:
Rice, Kenneth G.
Committee Members:
Neimeyer, Greg J.
Cottrell, Catherine
Villegas, Jorge
Graduation Date:


Subjects / Keywords:
Cognitive psychology ( jstor )
Happiness ( jstor )
Modeling ( jstor )
Need for cognition ( jstor )
Perfectionism ( jstor )
Personality psychology ( jstor )
Psychological counseling ( jstor )
Psychological research ( jstor )
Psychology ( jstor )
Psychometrics ( jstor )
Psychology -- Dissertations, Academic -- UF
depression, maximization, need, perfectionism
City of Gainesville ( local )
Electronic Thesis or Dissertation
born-digital ( sobekcm )
Counseling Psychology thesis, Ph.D.


Past research identified maximization as a trait-like decision-making style associated with negative psychological consequences. Based on a large college student sample, Study 1 of the current research used the core conceptualization of maximization and Confirmatory Factorial Analyses to address the concerns in the previous maximization scale by Schwartz et al. (2002). The outcome was a refined unidimensional maximization measurement, the Maximization Behavior Scale (MBS). Based on the MBS, Study 2 results revealed that perfectionism dimension high personal standards (HS) and perceived discrepancy between one s standards and actual performance (DIS) both correlate positively with maximization orientation. In addition, the relationships between maximization and psychological well-being are explained partly (or completely in the case of depression) by DIS, and are suppressed by HS. Other findings related to maximizations are also reported and discussed. ( en )
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Thesis (Ph.D.)--University of Florida, 2010.
Adviser: Rice, Kenneth G.
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by Huan Ye.

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2010 Huan Jacqueline Ye 2


To m y Mom and Dad, I hope my wo rk realize their dreams which we re destroyed by the political oppression at that time; to my mentors: Drs. Kenneth Rice, Pe ter Sherrard, James Morgan, and David Suchman, each of them has taught me the dynamics and beauty of the human being, and supported my adventure to the enriching and unknown. 3


ACKNOWL EDGMENTS I would like to express my gratitude to all those who gave the possibility to complete this dissertation, and those who have been tremendous impacts on me in the past four years of my doctoral study. First, I thank my supervisor Dr. Kenneth Rice, who has helped me understand the value of high standards for a credible researcher and urged me to jump over every gap and reach for my very best. I would also like to thank my committee members, Dr. Gregory Neimeyer, Dr. Catherine Cottrell, and Dr. Jo rge Villegas, whose stimulatin g suggestions and warm support have largely contributed to the quality of th is dissertation. In addition, I thank Ms. Taylor Thomas, who helped me to collect the remarkab le amount of data and make them manageable for me; my friend Dr. Andres Mendez, who always come to my rescue at my computer troubles and a lot more. Next, I would like to thank my mentors and friends: Dr. James Morgan, who wisdom and compassion gave me a home to thousands miles away from my hometown; Dr. David Suchman, who helped me approach a much clear understanding of self in relation to the world so I can maneuver through graduate school; Dr. Peter Sherra rd, who opened my eyes to the wisdom of emotions and tremendous new possibilities at ev ery conversation; my friend Dr. Gizem Toska, whose love and ceaseless acceptance soothe d me at my most difficult moments. Last but not least, I thank my mother, who planted the idea of becoming a Ph.D. in my little brain from very early on, alt hough at that time I did not know what Ph.D. really means. I thank my father, who is always ready to support me at every step I take. I thank my sister. Although we have undertaken different life paths we love each other the same. The past four years of my doctoral study have composed so far the most challenging and enriching part of my lif e journey. I would not be who I am today had I not come across all the above individuals. 4


TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........7 LIST OF FIGURES.........................................................................................................................8 ABSTRACT.....................................................................................................................................9 CHAPTER 1 INTRODUCTION................................................................................................................. .10 2 LITERATURE REVIEW.......................................................................................................12 What Is Maximization?...........................................................................................................12 Measurement of Maximization...............................................................................................14 Maximization and Psychological Well-being.........................................................................16 Maximization and Perfectionism............................................................................................19 Maximization and Need for Cognition...................................................................................23 Demographic Factors Related to Individual Differences in Maximization............................25 Research Questions and Hypotheses......................................................................................26 3 METHODS...................................................................................................................... .......28 Study 1....................................................................................................................................28 Participants and Procedures.................................................................................................... 28 Measures..........................................................................................................................29 Development of new maximization items................................................................29 Maximization scale..................................................................................................30 Regret scale..............................................................................................................31 Study 2....................................................................................................................................31 Participants and Procedures.............................................................................................31 Measures..........................................................................................................................33 Perfectionism............................................................................................................33 Need for cognition....................................................................................................34 Depression................................................................................................................35 Subjective happiness................................................................................................36 Life satisfaction........................................................................................................36 4 RESULTS...................................................................................................................... .........38 Study 1....................................................................................................................................38 Confirmatory Factor Analyses........................................................................................38 Relations of MBS to the Demographic Variables...........................................................42 5


Study 2 ....................................................................................................................................43 Preliminary Analyses.......................................................................................................43 Cross-validation of MBS and Re lated Preliminary Analyses.........................................44 Relations of MBS to Perfectionism.................................................................................45 Quadratic Relations of MBS to Psychological Well-being.............................................47 HS as Suppressor and DIS as Confound between Maximization and Psychological Well-being....................................................................................................................48 5 DISCUSSION................................................................................................................... ......60 Summary of Results............................................................................................................. ...60 The Refined Maximization Measurement MBS..............................................................60 Perfectionism as the Drive for Maximization and Explanation for the Associated Unhappiness.................................................................................................................62 Other Features of Maximizers.........................................................................................64 Limitations and Directions for Future Research.....................................................................65 Concluding Remarks............................................................................................................. .68 APPENDIX A QUESTIONNAIRE OF STUDY 1.........................................................................................70 Maximization and Regret Scale..............................................................................................70 Newly-developed Maximization Items...................................................................................71 Demographic Questions.......................................................................................................... 72 B CONSENT FORM OF STUDY 2..........................................................................................73 C QUESTIONNAIRE OF STUDY 2.........................................................................................74 Maximization and Regret Scale..............................................................................................74 Almost Perfect Scale-Revised................................................................................................74 Subjective Happiness Scale....................................................................................................7 6 Satisfaction with Life Scale................................................................................................... .77 IS-Item Need for Cognition Scale..........................................................................................77 BECK DEPRESSION INVENTORY, SHORT FORM.........................................................79 Demographic Questions.......................................................................................................... 80 LIST OF REFERENCES...............................................................................................................81 BIOGRAPHICAL SKETCH.........................................................................................................87 6


LIST OF TABLES Table page 4-1 Four-factor models of ma ximization: Fit comparison.......................................................52 4-2 One-factor models of ma ximization: Fit comparison........................................................52 4-3 The item standardized loadings and relia bility of the 10-item Maximization Behavior Scale...................................................................................................................................53 4-4 Study 2: Means, standard deviations and score internal consistencies.............................56 4-5 Bivariate correlations..................................................................................................... ....56 4-6 Regression results for simple me diations, APS-R HS as mediator...................................58 4-7 Regression results for simple me diations, APS-R DIS as mediator..................................59 7


LIST OF FI GURES Figure page 4-1 Study 1: The individual factorial loadings of the 10-item model......................................54 4-2 Maximization (MBS) scores as related to gender and ethnicity........................................55 4-3 The quadratic model predicting depression by maximization (MBS)...............................57 4-4 Suppression/Mediation Model 1a based on Sobel test......................................................57 8


9 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy THE DRIVE OF PERFECTIONISM BEHIND MAXIMIZATION By Huan Jacqueline Ye August 2010 Chair: Kenneth G. Rice Major: Counseling Psychology Past research identified maximization as a tr ait-like decision-making style associated with negative psychological consequences. Based on a large college student sample, Study 1 of the current research used the core conceptualization of maximization and Confirmatory Factorial Analyses to address the concerns in the previ ous maximization scale by Schwartz et al. (2002). The outcome was a refined unidimensional ma ximization measurement, the Maximization Behavior Scale (MBS). Based on the MBS, St udy 2 results revealed that perfectionism dimension high personal standards (HS) and perceived discrepancy between ones standards and actual performance (DIS) both correlate positively with ma ximization orientation. In addition, the relationships between maximization and psyc hological well-being are explained partly (or completely in the case of depression) by DIS, and are suppressed by HS. Other findings related to maximizations are also reported and discussed.


CHAP TER 1 INTRODUCTION Our lives involve making numerous choices, ra nging from small choices, such as deciding which TV channel to watch and what type of food one wants for dinner, to big life style matters such as determining a career and a relationship part ner. Our characteristic choice-making style is likely to impact the choices we make and sh ape our life outcomes. One such decision-making method, maximization, has attracted growing research interest in recent years. Maximization, an individuals tendency to get the best choice out of th e situation rather th an settle for a good enough choice, is viewed as a decision-making trait that is more predominant in some individuals choice-making behavi ors than others, regardless of the decision context. Schwartz, Ward, Monterosso, Lyubomirsky, White and Lehm an (2002) found support for the existence of such a maximization trait measured by their Ma ximization Scale (MS). Individuals who scored high on this scale were labeled as maximizers an d tended to optimize their choices consistently across different life domains. In addition, they associated the maximization tendency with negative psychological outcomes such as de pression and life dissa tisfaction. Additional inferences have since been drawn by other researchers (e.g., Iyengar, Wells, & Schwartz, 2006; Bergman, Nyland, & Burns, 2007) and presented in major media (Schwartz, 2004a). In fact, Schwartz (2004a) stated in the journal Scientific American that maximization may be the recipe for unhappiness (p. 72) and that the abundance of choices in the modern society is probably responsible for the epidemic of depression in American population. Despite the significant amount of research and the strong inferences based on the Maximization Scale (Schwartz et al., 2002), the ps ychometric properties of this scale were less than satisfactory. Two recent studies, Nenkov, Mo rrin, Ward, Schwartz and Hulland (2008) and Diab, Gillespie and Highhouse (2008), have de monstrated different paths to improving the 10


11 maximization measurement and hence re-evaluat ing the connection between unhappiness and the maximization. Meanwhile, it remains unclear what the underlying mechanisms linking maximization with unhappiness are. If, as Schwar tz (2004a) argued, that maximization behaviors were responsible for psychological suffering, th en Schwartzs (2004a) suggestion on reducing maximization and limiting number of choices may provide help for unhappiness. If the alternative is true, that maximization is rather a display of other personal ity traits, and the latter determine the outcome of unhappiness, reduction in maximization is likely to fail as an intervention strategy. The current research therefore aimed to address the above issues in maximization research.


CHAP TER 2 LITERATURE REVIEW What Is Maximization? To further research on maximization and clarif y some existing confusion, it is essential to have a viable definition of maximization th at is supported by theories and captured by measurement. Drawing from the rational decision-making model by von Neumann and Morgenstern (1944), Schwartz et al. (2002) defined maximization as a decision-making approach with the goal of obtaining the best available choice in a given situation, rath er than just setting a threshold of just accepting a good enough c hoice. The other end of the continuum, when individuals aim for a choice good enough, is defined as satisficing. Individuals who maximize make efforts to expand their option pool and collect as much information as possible. They carefully weigh the costs and benefits of each option and then determine the choice most likely to result in the maximum benefits available. Similarly, Diab et al. (2008) described maximizing as a process involving spending more re sources in an effort to make incrementally better decisions (p.365); a final decision is often reached when indi viduals are certain that they are getting the best out of the situation. In sum, maximization as a decision-making process involves a range of activities in information collection and evaluation, with the goal of optimizing the final choice outcome. Past literature has supported either maximizi ng or satisficing as valuable choice-making methods. For instance, in rational choice theo ry, maximization is considered the rational decision-making method which allows the most ec onomical gains for individuals (von Neumann & Morgenstern, 1944). The limitation of this model is that it fails to take into consideration the cost of information collecting and processing du ring the choice evaluation, which can be rather high in actual applicatio n. In some cases, it may not be even plausible to find out all relevant 12


inform ation about the available options. Even if th e information accessibility is not an issue, the amount of information processing needed for ma ximization may turn out to be too demanding for human beings in some cases (Payne, 1982; Payne, Bettman, & Johnson, 1993). Recognizing the complexity of the environment and the li mitation of human cogni tive processing, Simon (1955, 1956) proposed that human beings in fact satisfice in choice -making instead of maximize. The satisficing approach may cost us the best option available, but gives us a relatively satisfactory choice and saves our energy a nd resources in seeking for the best choice. As both of these decision-making processes have arguable values and limitations in theory, it is of interest to observe their utilization in real life human behaviors and the choice outcomes. Schwartz et al. (2002) started this line of research by identifying individuals who show characteristically high levels of maximization te ndency across various decision-making contexts, i.e., maximizers. Individuals on the opposite end of the continuum are labeled as satisficers. In other words, they proposed the maximization dime nsion as a personality trait factor. Indeed, respondents to the maximization scale they constructed showed a consistent tendency to maximize in a variety of life situations tapped by different measurement items, such as surfing all TV channels before determining which one to watch and trying out several relationships before deciding which one to pursue. However, so me of their measurement items also described behaviors and attitudes of ha ving high standards and inde cisiveness. These additional components are likely deviations from the co re definition of maximization and obscure the interpretation of the high sum scores. This raised the question as whether the operation definition of maximization is better capture d by including several other pote ntially related phenomena as a multidimensional construct or by focusing on its core definition as a unidimensional construct. 13


Diab et al. (2008) supported the unidim ens ional approach, arguing that maximization attribute is in theory a single dimension construc t and the inclusion of additional factors leads to low validity of the measurement. The current rese arch takes a similar standing and proposes to address maximization from its core conceptualiza tion as a decision-making process that involves certain behavior patterns such as purposeful exploration of the op tion pool and striving to get the best out of the situation. This approach cons iders traits like having high standards and being indecisive as peripheral to th e core definition of maximization. For instance, individuals may seek to maximize their benefits in relation to a given situation as a rational decision method without there being the po ssibility of achieving the high standard s. Perhaps, a stock-trader looks for the optimal combination of investments based on the market availability rather than wait until they believe the stocks might be at their historic highs or lo ws. Indecisiveness is a phenomenon distinct from maximization, a lthough the attempt to maximize ones decision can delay the arrival of a final decision and cause decision di fficulty. Indecisiveness invo lves factors such as information deficit, valuation difficulty, and outcome uncertainty (Ras sin, 2007). Apparently, those factors may or may not be present in maximization. Maximizers may possess good knowledge and clear judgment of some options but are stuck in the expectation for better alternatives not yet present. Therefore, it may not be appropriate to incl ude these constructs in the operational defini tion of maximization. Measurement of Maximization The assumption of the unidimensionality of the maximization construct requires a unidimensional measurement. The initial Maximiza tion Scale (MS) develope d by Schwartz et al. (2002; see Appendix A, Items 1-13) contained th ree factors: Factor 1 consisted of such behavioral examples of maximization as being open to better jobs, songs on the radio, television shows, and relationships (6 items: 2, 3, 4, 5, 9, 13) Factor 2 involved other behavior examples of 14


m aximization concerning primarily shopping behaviors (4 items: 6, 7, 8, 10), and Factor 3 involved having high standards (3 items: 1, 11, 12)1. In a closer examination, only Factor 1 items were closely tied with the core conceptualizati on of maximization. Three of the Factor 2 items contained a component of decisi on difficulty or indecisiveness. For example, Item 6 was, I often find it difficult to shop for a gift for a fr iend. The other item in Factor 2 (Item 10) involved a preference for ranking li sts. Two of the Factor 3 item s, No matter what I do, I have the highest standards for myself, and I never se ttle for second best, are very similar to the high personal standards items in so me perfectionism measures such as, I set very high standards for myself, and I expect the best from my self (Slaney et al., 2001). Furthermore, the underlying assumption that people would translat e their high standards from their personal domains to their choices in various occasions se ems premature. For instance, Shea, Slaney and Rice (2006) found that having high standards towards oneself is only partially co rrelated with having high standards for relationship partners. The multiple dimensions of the MS and apparent overlap with other constructs have called into question whether the MS measures what it ha s claimed to measure. Diab et al. (2008) have challenged the empirical validity of the MS because MS scores exhibite d stronger correlations with variables such as avoidance and neurotic ism than with the majority of maximization variables such as behavior repor ts and situational dilemmas. In conclusion, Diab et al. (2008, p. 367) stated that MS is a broad band-width measur e that taps a number of attitudes and traits rather than focuses on a single construct. Nenkov et al. (2008) derived three dimensions from the 1One of the three items that loaded on having high standards (loading was .51) also cross-loaded on regret (loading was .36). The item reads, Whenever Im faced with a choice, I try to im agine what all the other possibilities are, even ones that arent present at the moment. Schwartz et al. (2002) determined that this item belonged to the having high standards dimension after 10 out 11 of their informants judged it to be about maximizing rather than regret, the latter factor existed in the initial exploratory factorial analysis as a fourth factor but was later excluded from the Maximization Scale. 15


shortened versions, nam ely Alternative Search, Decision Diffi culty, and High Standards. Among them, Alternative Search consists of the items fr om the initial identified Factor 1 maximization behaviors, Decision Difficulty consists of those from Factor 2 maximization behaviors, and High Standards consists of those from Factor 3 ha ving high standards. They reported differential patterns of correlations between the three dimens ions and other related psychological variables. High Standards in particular ofte n exhibited the opposite direction of correlations compared with the directions of effects with the other two dime nsions. This finding again called attention to the distinctions among those dimensions. Probably related to the above validity concerns, scores from the MS appear to have relatively low internal consistency reliability (Cronbachs s ranged from .60 to .73 in Schwartz et al., 2002). In addition, half of the MS items have correct ed item-total correlations below the conventional .25 level (Diab et al., 2008). The pres ence of the above psychometric concerns are probably not surprising, given the fact that dur ing measurement development, the factorial structure for the MS was tested by exploratory factorial analysis (EFA ) and has never been independently and more rigorously verified by any other approach, such as for example, a Confirmatory Factorial Analysis (CFA). CFA is commonly known as a much more parsimonious method in testing factorial structure than EFA (for a review, see Marsh, Hau, Balla, & Grayson, 1998). Given the high degree to which research ers have used the MS for maximization measurement in the past several years, it is clear that a more detailed and statistically rigorous examination of the MS should be conducted, one aimed at confirming it measurement structure and potentially improve the measurement if warranted. Maximization and Psychological Well-being Recall that the two major theoretical m odels argue the opposite in terms of the consequences of maximization. The rational choice theory camp emphasizes the external gains 16


of m aximization, which may lead to psychological benefits in some cases. In contrast, the satisficing model supporters claim that maximizati on starts with unrealis tic goals and leads to unproductive efforts and excessive cognitive overload. Past research has lent support to the satisficing model and suggested that maximizati on leads to negative outcomes in psychological well-being. For example, Iyengar et al. (2006) found that job-seek ing college graduates secured jobs with 20% higher salaries if they were high in maximizing tendencies than if they were low. However, despite their salary gain s, the maximizing graduates reported less satisfaction with their job offers than their sati sficing counterparts, and had su ffered higher levels of negative affect during the job-search process. Parker, de Bruin, and Fischhoff (2007) examined several specific life domains and concl uded maximizers were likely to run into troubles and ended up with unfavorable life outcomes, such as ruining clothes in the laundry, having a check bounce, having a mortgage or loan foreclosed, and bei ng in jail overnight. In a broader sense of psychological outcomes, Schwartz et al. (2002) reported that the maximi zation orientation was related with overwhelmingly negative psycholog ical experiences, including high depression, low dispositional happiness, low dis positional optimism, low life satisfaction, and low self-esteem. Despite the accumulated evidence on the negative psychological implications of maximization, no causal directions have been defined and the mechanisms behind such relationships are yet to be determined. Some re searchers speculated that maximization is the cause leading to unhappiness, by paths such as cognitive overload. For example, Iyengar and Lepper (2000) demonstrated that in making jam or chocolate purchasing choice, a small choice pool (6 options) tended to produce more purchasi ng decisions and higher consumer satisfaction than would a large choice pool (24 or 30 options ). Given that maximizers are apt to consider more, and potentially better, choices (Schwartz et al., 2002), it is possible that they end up 17


suffering from the increasing pool of choices, pr esum ably by the incurred information overload regarding the choices. However, one may argue that the scenario of consumer choice over trivial items with relative little distin ction (differences of flavors) as in Iyengar and Lepper (2000) is hardly generalizable to the real life c hoices we face, such as job options. In addition to cognitive overload due to a bundance of choices, Schwartz et al. (2002) identified regret as a par tial mediator for the relations hip between maximization and unhappiness. The term regret here represents a range of ps ychological experiences including the tendency to be preoccupied by imaginary choices or newly emerging choices after the choice has already been made and the like lihood to be disappointed ove r the gap between the potential options and their reality choice. They suggested that maximizers often experience regret in relation to choice-making situati ons; the regret in turn lead s to unhappiness and depression, partly by undermining individuals sense of gratification through making good choices and keeping them from engaging in dissonance reduction and other adaptations to their choices. However, this explanation does not provide mu ch additional information in understanding the function of maximization due to the significant conceptual overlap between maximization and regret. Developed initially to be pa rt of the maximization construct, regret can also be interpreted as a special case of maximization, that is, maxi mization efforts when the maximization goal is apparently inaccessible, similar to extreme high levels of maximization tendency when the gains no longer overweigh the costs. By definition, such behaviors of re gret are doomed to dissatisfaction, thus its associa tion with unhappiness. On the ot her hand, regret may not account for the function of maximization when optimization goals are possible. Overall, the above attempts in explaini ng the association between maximization and unhappiness shed some light on the function of maximization yet left an important question 18


unanswered what are the m echanisms leading to individuals continuous maximization efforts, if such efforts repeatedly lead to disappointment and misery? This is particularly curious, given the fact that maximizers are individuals who strive to get the optimal outcome in the first place. Perhaps the explanations lie in other personality factors which lead to both maximization and unhappiness. In other words, there may be some confounding factors which produce the connection between maximization and unhappiness, and maximization itself does not necessarily generate unhappiness. The current research proposed perfectionism as a potential confounding variable. Another possibility not addressed in past research is that maximization may not be completely debilitating. Rather, maximization be low a certain degree may be associated with increasing benefits and affordable costs. Only when it reaches a certain threshold might the costs begin to outweigh the benefits. This hypothesi s moves beyond a simple linear relationship between maximization and psychological well-bei ng and incorporates both the rational choice and satisficing models. The current paper proposed a quadratic model to test this possibility. Maximization and Perfectionism Although the literature on perfectionism ha s not reached a complete consensus on its definition, it is commonly conceived as a pe rsonality style charac terized by striving for flawlessness and setting of excessively high standards for performance (Stoeber & Otto, 2006, p. 295). Besides the traditionally recogni zed maladaptive/dysf unctional aspects, perfectionism also contains adaptive/functional dimensions (e.g., Frost, Heimberg, Holt, Mattia, & Neubauer, 1993, Terry-Short, Owens, Slad e, & Dewey, 1995, Slaney, Rice, & Ashby, 2002). For instance, Slaney and colleagues (2002) sugge st that perfectionism can be motivating and functional when individuals per ceive a close match between thei r actual performance and their high personal standards. On the other hand, perfectionism is psychologically detrimental when individuals perceive a large gap between their high standa rds/expectations and actual 19


perform ance. In both cases, striving to meet hi gh standards, approachi ng perfection and reducing degrees of imperfection may underlie their se arch strategies for better life choices. Schwartz et al. (2002) made an early attempt to correlate perfectionism and maximization. Their findings provided initial suppo rt for the expected connection, yet failed to give a complete picture of perfectionism and its role in maximization, primarily because they measured perfectionism as a single dimensional variable rather than as a multi-dimensional construct. Specifically, they only used the Self-oriente d Perfectionism subscale from the original Multidimensional Perfectionism Scale (MPS, Hewitt & Flett, 1990, 1991) and omitted the other two subscales capturing the interp ersonal dimensions of perfectio nism. Furthermore, this Selforiented Perfectionism scale, and by associati on, Schwartz et al. (2002) made no distinction between adaptive and maladaptive dimensions of perfectionism. The value of such a distinction has been supported by recent extensive reviews of the perfectionism literature (Stoeber & Otto, 2006; but also see Shafran, Cooper, & Fairburn, 2002 on clinical perfectionism ). It is thus difficult to connect the findings of Schwartz et al. (2002) with some other related studies on perfectionism. In fact, Bergman and colleagues (2007) provided empirical evidence that the connection between perfectionism and maximization cannot be simply captured in negative or pathological terms. They employed a measurement of perfecti onism distinguishing two types of perfectionism labeled as positive and negative perfectionism (Positive and Negative Perfectionism Scale, PNP, Terry-Short et al., 1995 ). This measure was constructed ba sed on the conceptualization that both positive and negative perfectionism involves high standards, as co nsistent with other commonly used perfectionism scales, yet uniquely defined positive perfectionism as related to fulfillment of high standards and the incurred positive reinforcement. They defined negative 20


perf ectionism as related to failure of mee ting high standards and the resulted negative reinforcement, including negative emotions. This research evidenced a significant association between perfectionistic and maximizing tenden cies, with a larger effect size between maximization and negative perfectionism than that between maximization and positive perfectionism. In other words, perfectionism contributed to the degr ee of maximization in relation to individuals long-term experiences of success or failure in meeting their high standards. Individuals who have frequent experiences of success and satisfaction from fulfilling their high expectations maximize at a moderate level but forgo maximizing before such tendency reaches an extremely high level. Perhaps, the experiences of satisfaction allow them to be relatively less anxious about occasional unmet e xpectations and be more responsive to the incurred cost of continuing to search. In cont rast, those who frequently fall short of their expectations appeared to maximize at a higher degree, probably driven by both the disappointment that the choices at hand are rarely good enough a nd the anxiety that further maximization would reduce their distance from the desired goals. Saying this differently, having high standards is likely the initial drive and the frequent experiences of gaps between ones standards and reality lead to a dditional pressures for maximizing. Although this latter assumption appears to be a logical interpretation of the fi ndings by Bergman et al. (2007), such a conclusion could not be drawn due to the limitations in both conceptualization and validity problems associated with the PNP. In particular, item cont ent of the PNP taps one other confounding factor besides the perfectionism trait lif e experiences of failures and successes. The factor structure of the PNP has also been inconsistent across studies (e.g., see Haase & Prapavessis, 2003). Bergman et al. (2007) thus suggest ed that alternative perfectionism scales may be helpful to further illuminate the relationship be tween maximization and perfectionism. 21


The goal of the current study was to addre ss the association between perfectionism and maximization in a more valid and informative way than has been accomplished in past research. In particular, I used a well-va lidated perfectionism measuremen t, the Almost Perfect ScaleRevised (APS-R, Slaney et al., 2001), which allows detailed examination of not only the possession of high standards (measured by the HS dimension of APS-R) but also the perceived discrepancy between ones standards and actual performance (measured by the DIS dimension of APS-R). This scale also contains a third dimension Order, whic h captures individuals needs for neatness and structure. It is conceptually not closely relate d to maximization and hence not within the focus of the current investigation. I proposed that perfectionism, in this case represented by the HS and DIS dimensions, shou ld be one personality factor determining individuals tendency to maximize. The rationale is that people may often optimize their options as much as possible in service of, or in the pu rsuit of, personal perfecti on. Especially when they perceive a large gap between their expecta tion and reality, they may expect optimized alternatives would help narrow that gap. I also test whether the contributions of HS and DIS to maximization are independent from each other, or if an interaction betwee n HS and DIS leads to higher maximization more than the simple a ddition of their individual contributions. Past literature has established that perfectioni sm as a personality trait is associated with individuals psychological well-bei ng (for a review, see Stoeber & Otto, 2006). In particular, HS and DIS were both identified to have individual c ontributions to happiness/depression in opposite direction (e.g., Mobley, Sl aney, & Rice, 2005). If indee d, perfectionism is a drive behind maximization, it is possible that perfec tionism also explains the unhappiness of maximizers. In other words, I speculate that maximization does not nece ssarily cause depression, neither does depression cause maximization. Rath er, maximization and depression are related 22


through the common perfectionism confounds of HS and DIS. In particular, given the m aladaptive function of DIS, it is likely that DIS predisposes one to depression and leads to maximization strategies. Given th e adaptive functions of HS, it is likely that HS would function in an opposite way to DIS, and probably serves as a protective factor that predisposes one to happiness or buffers one to upset from advers e consequences of ma ximization. In sum, I hypothesize that both HS and DIS contribute to maximization tendency and they have distinct confounding effects on the association between maximization and psychological well-being. Maximization and Need for Cognition Need for cognition is a construct describing in dividuals proclivity to process information and enjoy thinking (for a revi ew, see Cacioppo, Petty, Feinstei n, & Jarvis, 1996). Individuals high in needs for cognition find effortful cogni tive activity intrinsically rewarding. Although there are exceptions, such as individuals who use heuristic decision-ma king or rely on other peoples opinion to maximize, in many cases maximization increases the amount of cognitive activities in information processing. It is possibl e that maximizers also tend to be high in the need for cognition and find additional enjoyment in the cognitive involvement of the decisionmaking process. If that is the case, the differe nce in need for cognition can offer an additional explanation for the differences between maximizers and satisficers, in that the cognitive burden for satisficers may not be as great a burden for maximizers. Maximizers may even engage in maximization for the sake of cognitive activities in addition to anticipating the gains from the final outcome. In contrast, satisficers may di sengage from further choice-making process and settle for a good enough option due to their av ersion towards the additional demands of the cognitive activities. This potential connection between maximization and need for cognition has received some indirect support from the past literature. For instance, individuals high in need for cognition tend to acquire high amounts of info rmation before making their buying choices 23


(Inm an, McAlister, & Hoyer, 1990), and they ofte n appear indecisive in the face of decisionmaking (Petty & Jarvis, 1996; Webster & Kr uglanski, 1994). Both phenomena had been associated with maximization (e.g., Schwartz et al., 2002). Some other theories and re search studies suggest that individuals maximization orientation may have little associ ation with their need for cognition. As previously discussed, maximization does not necessitate high engagement in independent thinki ng; the latter is only characteristic of high need for cognition indi viduals. Iyengar et al (2006) reported that maximizers tend to rely on opini ons and recommendations from family, peers, professional services, and experts. Schwartz et al. (2002) found that maximizers often refer to more heuristics processes of decision-making and external info rmation resources, such as social comparison. Similarly, Parker et al. (2007) observed maxi mizers as dependent on others when making decisions, engaging in spontane ous decision making, and ending up with worse life outcomes. None of these patterns were associated with need for cognition, which is usually considered a functional trait (Caci oppo et al., 1996). Taken together, previous theories and rese arch have lent tentative support to both distinction and connection between maximization and need for cognition. Up to date, only one study directly tested the relati onship between the two constructs and the results were mixed (Neknov et al., 2008). One out of the three versio ns of maximization measurement revealed an association between maximization and need for cognition; such associations lie in the dimensions Decision Difficulty and High Standards, but not in the dimension Alternative Research. Recall that Alternative Research was the dimension containing maximization behaviors items more closely tied to the core construct and the other two dimensions are conceptually peripheral. 24


Demographic Factors Related to Indi vidual Differences in Maximiz ation Although not the primary focus of the current study, the relationships between maximization tendency and demographic factors are also of interest. In particular, two demographic factors are relevant to the study of maximization: gender and ethni city/cultural background. In terms of gender differences, previous findings have been mixed and inconclusive. Schwartz et al. (2002) found a significantly higher tendency toward maximization among men than women in three samples but not in the other four samples. Factors such as the different population composition of the samples di d not seem to offer explanations for this pattern of inconsistency. In the current study, I proposed to use a refined scale to clarify the existence of gender differences in maximization. The implication of cultural differences in maximization has been proposed but is only at a preliminary stage of research to date. Schwartz (2004a) proposed that the mainstream culture of American society is particularly favorable towards maximization due to the ideological significance of economics and ra tional-choice theory, which sugge sts that maximization is the method to reach rational and optimal choices. Cons istent with this assumption, Rozin, Fischler, and Shield (2006) found that, compared with thei r European counterparts, U.S. individuals are more likely to prefer large numbers of choices, such as flavors of ice cream or choices of dishes at an upscale restaurant. If the values of rational-decision making and the practice of maximization are indeed culture-dependent phenomena, it is possible that maximization tendency is more pervasive among cer tain cultural groups than othe rs. It is also possible that individuals belonging to so cial groups with long-term limitation of choices may be more likely to strive for optimality than individuals who often experience abundance and accessibility of options within their social group. In the curren t study, I will explore th ese potential cultural differences by comparing the maxi mization scores among four rela tively substantial ethnic and 25


cultu ral groups in the U.S.: White/European Americans, Black/African American, Hispanic/Latino, and Asian/Sout h Pacific Islander. Although little directional hypothesis can be drawn from the existing literature, I proposed that the ethnic minority groups may exhibit a higher maximization tendency than the group of White/European Americans. The rationale is that individuals may tend to make strong optimizi ng efforts if they struggle with survival and perceive their choices as highly limited, which is a condition likely more true to the ethnic minority groups than White/European Americans in the U.S. society. Research Questions and Hypotheses In sum, in the current research I refine the measurement of maximization based on its unidimensional conceptualizati on. I also examine the relations hip between maximization with perfectionism and test perfectionism as a potential explanation for the implication of maximization on psychological well-being. I conducte d two studies to serve these research goals. In Study 1, I test the original maximization m easurement along with newly developed items and compare the fit and other psychometric propertie s of several measurement models. The goal was to identify a valid and reliable m odel of measurement with its cont ent closely tied to the central conceptualization of maximization and having little overlap with other constructs. Confirmatory Factorial Analysis (CFA) and other psychometric evaluation pr ocedures were performed to assess the adequacy of several competing models and determine a final best model. The final model was also cross-validated with the data from Study 2. In Study 2, I investigate the relationships between maximization and several variables: perfectionism, need for cognition, and psychologi cal well-being. For perfectionism, I expected that both high personal standards (HS) and large discrepancy (D IS) should be associated with high maximization tendencies. I also tested the in teraction of HS and DIS but have no directional hypothesis for it. An associated but different ques tion is whether individu als with a combination 26


27 of high of HS and DIS, i.e., maladaptive perf ectionists (Rice & Ashby, 2007) are more likely to maximize than individuals who have high HS but low DIS (adaptive perfectionists) and those who have low HS (non-perfectionists). The dimension Order is included as part of the APS-R scale but no hypothesis was proposed in regard. Psychological well-being was evaluated using three variables: dis positional happiness, depression, and life satisfaction, c onsistent with measures employe d by Schwartz et al. (2002). I test the association between maximization and ps ychological well-being firs t with a linear model and then a quadratic model. If the findings suppor t the negative implications of maximization, as consistent with Schwartz et al. (2001), I hypothesize that HS and DIS should both confound these associations in distinctive ways. In particular, HS should suppress such associations to the point that, if HS effects are controlled, the re maining variation in maximization would show a stronger relationship with psychological we ll-being. In contrast, DIS may confound the relationships between maximization and the psycho logical well-being variables that if DIS is partialled, maximization may no longer a ssociate with psychological well-being. No directional hypotheses were proposed for the other relationships examined in the current study: the association between maximization and need for cognition, and gender and ethnic/cultural differences in maximization.


CHAP TER 3 METHODS Study 1 Participants and Procedures A total of 2046 participants were recruited from the general psychology participation pool of a large university campus in the southeastern U.S. The ques tionnaire took 5 to 10 minutes to complete and was administered as part of an online prescreening survey for multiple studies (see Appendix A for the questionnaire; there was no indi vidual consent form for this survey). Data from 33 participants were excluded from the subsequent analyses due to at least one piece of missing data. The two most missed answers were ethnicity and age: 20 and 15 cases were unanswered for the two questions respectively. This reduced the sample size to 2003 participants, a number that exceeded both Ta bachnick and Fidells (2007) recommended N = 300 and Comrey and Lees (1992) recommendati on for an excellent sample size ( N = 1,000) and was sufficient to detect the f actor loadings of the scale items. Maximization tendency scores were examin ed for assumptions supporting multivariate analysis. Based on Fields (2005) recommendations on the normality of large samples (200 or more), I examined the shape of the histograms and the skewness and kurtosis values of the each item score distribution. Most of th e maximization scores appeared negatively skewed and spread out, indicating likely departures from normality. The histograms of each variable distribution showed no significant univariate outliers. Tests of multivariate outliers (Mahalanobis distance), on the other hand, identified 10 cases of significant outliers, 2 (24) = 42.98, p < .001. Closer observations of the outlying cases suggested that their response patterns tended to include both extremely high and low values, which would be inc onsistent with normal exp ected patterns. It is possible that those respo ndents either did not pay close attention to the questions or they simply 28


had atypical personality patterns. The 10 cases w ere excluded and the remaining 2003 cases were retained as the final analysis sample. In this sample, 1375 participants were wome n and 628 were men. Their ages ranged from 17 to 25 years, M = 18.77 (SD = 1.17). Slightly more than half of the sample were White/European American (56.8%, N = 1137); the remaining ethnic composition was: 14.9% Black/African American ( N = 299), 14.5% Hispanic/Latino ( N = 291), 8.0% Asian/South Pacific Islander ( N = 160), 4.2% Biraci al/Multiethnic ( N = 85), 0.4% Arab/Middle Eastern ( N = 9), 0.2% Native American ( N = 5), and 0.8% other ( N = 17). The majority (93.7 %) reported that English was their primary language. Measures Development of new maximization items I developed 6 new maximization items to provide additional item choice s that reflected the core definition of maximization as presented in the literature re view. Two of the items address the attitudes and thoughts related to the maximization tendency ye t were underrepresented in the original maximization scale: I always keep my options open so I will not miss the next best choice available in life; and Even if I see a choice I really like, I have a hard time to make the decision if I do not have a chance to check out other possible options. One item offers a behavioral description of maximization in additio nal to the scenarios in the original MS: When going to a new restaurant, I find myself reading the complete menu before narrowing down on what I want to eat. The other three new items are revisions of three items from the original maximization scale (Items 6, 7 & 8). The revisions were made so that these items would describe maximization without confounding maximization w ith the construct of indecisiveness. For example, Item 6 originally was phrased: I often find it difficult to shop for a gift for a friend. 29


That item was reworded to be: I try to do an ex tensive search when I look for a gift for a close friend. In sum, all six new items were written to be tter capture the core concept of making the best out of a situation and optimizing the outcome in choice-making (see Appendix A, Items 19-24). Similar to the original MS, partic ipants express their agreement on a 1 ( strongly disagree ) to 7 ( strongly agree ) Likert-type scale. In order to improve the items readability and content validity, items were reviewed by one Ph.D.-level psychologist and three experienced undergraduate and graduate research assistants. Reviewers were provided with the definition of maximization and asked to comment on how well the items capture the idea of maximization and the idea only (i.e., doesnt c onfound with other constructs). The reviewers independently evaluated each item and were in complete agreement that the finalized new items were conceptually relevant in describing maximization. Maximization scale The original Maximization Scale (Schwartz et al., 2002) is a 13-item measure. Participants respond to the items using a 7-point Likert scale ranging from 1 ( strongly disagree ) to 7 ( strongly agree ). A sample item is: Whenever Im faced with a choice, I try to imagine what all the other possibilities are, even ones that arent present at the moment. According to Schwartz et al. (2002), items loaded on thr ee factors in a principal-component s factor analysis (PCA): two factors were behavior examples of maximizing and one factor repr esented having high standards. Internal reliability coefficients (Cronbachs s) ranged from .60 to .73 in different samples (Schwartz et al., 2002). Scores for the individual items were averaged to create a composite maximizing score, with a higher score represen ting greater tendency to optimize ones final choice. The maximization scores in the Schwar tz et al. samples ranged from 1.15 to 6.62, with a mean of 3.88 ( SD was not reported) and a median of 3.85. Schwartz et al. (2002) identified those 30


who scored am ong the top third on the Maximi zation Scale on the maximization scores as maximizers and those who scored among the bo ttom third as satisficers. The maximizers group mean was 5.26 ( SD = 0.50), whereas the satisficers group mean was 3.49 (SD = 0.43). Regret scale Used initially as part of the Maximization scale and later as a cl osely related concept (Schwartz et al. 2002), the Regret Scale were administered together with the Maximization scale for the sake of replication. Similar to the Maximization scale, respondents indicate to what extent they agree with the statement from 1 ( strongly disagree) to 7 ( strongly agree ). The Regret Scale contained 5 items and its scores obtained from the four college student samples in Schwartzs study have reached acceptable in ternal reliabil ity (Cronbachs s range between .67 to .78). A sample item is, If I make a choice and it turns out well, I still feel like something of a failure if I find out that another choice would have turned out better. In Sc hwartz et al. (2002), participants Regret scores correlated si gnificantly with their Maximization scores ( r s ranged from .36 to .61, ps < .001, in six independent samples, four of which were samples of college students). Study 2 The major goals of Study 2 were: 1) to crossvalidate the newly cons tructed MBS; 2) to examine the association between MBS and two personality traits: perfectionism and need for cognition; and 3) to test th e effects of perfectionism (HS and DIS dimensions) on the psychological implicatio ns of maximization. Participants and Procedures A total of 447 students from the same southeaste rn university were recruited to participate in this study. The students were recruited using the same psyc hology participati on research pool procedures as in Study 1 but because the study was conducted during a different semester, the 31


pool of students was different for this st udy. The study was conducted online (see Appendix B for the online consent form and Appendix C for the questionnaire). In order to counterbalance the potential order effects of the measures, two versions of the questionnaire were used with alternated orders of the measures Participants freely chose to sign up for either but only one of the two surveys; the numbers of participation slots opened were monitored in a weekly basis in the efforts to keep equal number of particip ants for each version. I excluded data from participants who declined to answ er more than 5 survey items, approximately 5% of the survey ( N = 6), and retained those who declined to answer 1 to 5 questions ( N = 27). This data collection as well as that in St udy 1 was both conducted on the Internet. On one hand, research on methodology such as Gosling, Vazire, Srivastava, a nd John (2003) supported Internet data collection and found Internet findings are consistent with findings from traditional methods, such as the paper-and-pencil method. On the other hand, due to the relatively easy accessibility of online questionnaires in compar ison to paper-and-pencil method, Gosling et al. (2003) alerted researchers for possibilities of nonresponsiveness (unmotivated or noninterpretable responses.) One marker of nonresponsiveness is long strings of identical responses, as recommended by Johnson (2001). In the current study, five participants responded in a matter consistent to the above descripti on throughout the survey (such as the choice of neutral or the choice at one extreme end of the scale, incl uding the reverse-coded items), except for the demographics. I t hus decided to eliminate thei r data from the analysis. This final sample retained 97.54% of the orig inal sample, or 436 participants (257 women, 178 men, and one identified as queer). Their age ranged from 18 to 30 years, with mean = 19.36 ( SD = 1.31). More than half of the sample were White/European American (59.6%, N = 32


260); th e rest of the ethnic composition was1: 11.7% Black/African American ( N = 51), 11.7% Hispanic/Latino/Mexican American ( N = 51), 9.2% Asian/Asian-American ( N = 40), 4.6% Multicultural/Mixed Race ( N = 20), 1.4% Pacific Islander ( N = 6), .7% Middle Eastern ( N = 3), .5% Native American ( N = 2), and .6% chose other ( N = 3, include 1 no specification, 1 Pilipino, and 1 Egyptian). The majority (53.1%) were freshman with diverse majors (11% of the sample majored in psychology). Similar numbers of participants participat ed in each version of the study: 231 participants completed Version 1 and 205 completed Version 2. Measures Perfectionism The Almost Perfect Scale-Revised (APS-R, Slaney, et al., 2001) contains 23 items designed to measure adaptive and maladaptive dime nsions of perfectionism. The subscales are: High Standards (HS, 7 items), Discrepancy (DIS, 12 items), and Order (4 items). HS represents an individuals level of expecta tions related to his/her performance. A sample item is: I have high standards for my performa nce at work or at school. DIS represents the perception that one consistently fails to meet the high standards one has set for oneself. (Slaney et al., 2002, p. 69). A sample item is I rarely live up to my hi gh standards. Order represents individuals preferences for order and organiza tion. A sample item is I am an orderly person. Participants respond to the items using a 7-point Likert scale ranging from 1 = Strongly Disagree to 7 = Strongly Agree Higher scores across each subscale correspond with higher levels of each dimension. 1In Study 2, the cultural/ethnic categor ies provided as answer options were adapted following recent cultural sensitive categories. They are slightly different from the categories provided in Study 1, which was determined by the administrators of the overall sc reening procedure, not the author. 33


The structure and independence of the three subscales have been supported by exploratory and confirmatory factor analyses (Sla ney et al., 2002). Cronbachs coefficient s were .92 for DIS, .85 for HS, and .86 for Order (S laney, et al ., 2001). Scores from the subscales relate in expected directions with other measures of pe rfectionism and with measures of psychological adjustment, such that HS is adaptive or benign, DIS is maladaptive, and Order is often neutral (Rice & Slaney, 2002; Slaney, et al., 2001; Sudda rth & Slaney, 2001). The combination of high scores on DIS and HS tend to distinguish mala daptive perfectionists fr om the other groups (Grzegorek, Slaney, Franze, & Rice, 2004; Rice & Slaney, 2002). Need for cognition The Need for Cognition Scale (NCS) was first developed as a 34-item measure by Cacioppo and Petty (1982). A short form of th e NCS was subsequently developed by Cacioppo, Petty, and Kao (1984) on the basis of re-analyse s of data from the or iginal Cacioppo and Petty (1982) study, and a replication and extension involving 527 undergraduates. Reliability and factor analyses confirmed that the 18-item NCS was highly correlated with the original 34-item NCS ( r = .95, p < .001), possessed high internal consistency (Cronbachs a = .90), and was characterized by one dominant factor (accounting for 37% of the variance). The 18-item short form has shown high internal consistency results, typically Cronbachs as > .85, in other studies (e.g., Berzonsky & Sullivan, 1992; Kernis, Gra nnemann, & Barclay, 1992; Miller, Omens, & Delvadia, 199l). Sadowski and Gulgoz (1992) reported a test-retest correlation of .88 (p < .001) over a 7-week period in their study of 71 unde rgraduates using the 18-item NCS. Convergent validity of the measurement was demonstrated in that participants with higher NCS scores tended to reported lower scores on dogmatism (Cacioppo & Petty, 1982; Fl etcher, Danikwies, Fernandez, Peterson, & Reeder, 1986), closed -mindedness (Petty & Ja rvis, 1996; Webster & 34


Kruglanski, 1994), the tendency to ignore, avoi d, or distort new infor mation (Venkatraman, Marlino, Kardes, & Sklar, 1990), and attention to social comparison cues (Miller et al., 1991). Depression The Beck Depression Inventory Short Form (BDI-SF; Beck & Beck, 1972 ) is the 13-item short form of the commonly used Beck Depr ession Inventory (BDI, Beck, Ward, Mendelson, Mock, & Erbaugh, 1961; Beck, 1967). In respond ing to 13 individual depression symptoms related domains from emotions to social and wo rk adjustment, participants choose from a list of four statements based on which one accurately describes the way they f eel at the present day (multiple choices are allowed). Each choice of statement was assigned a corresponding score from 0 to 3, with higher score indicating higher level of depre ssion. For example, in the first dimension Sadness, participants may choose from 3 = I am so sad or unhappy that I cant stand it, 2 = I am blue or sad all the time and I cant snap out of it, 1 = I feel sad or blue. and 0 = I do not feel sad. Beck, Rial, and Rickels (1974) and Gould (1982) found high correlations between the short fo rm and the original version of BDI. The psychometric property of BDI-SF has also been adequately validat ed among college age population (Gould, 1982) as well as clinical (e.g., Beck & Beck, 1972) and large community samples (e.g., Knight, 1984). For example, Knight (1984) repor ted that the scale intern al consistency Cronbachs was .81. Gould (1982) reported the mean score was 5.67 ( SD = 5.1) among males and 4.70 among females ( SD = 3.1). Gould (1982) also reported adequate convergent validity of BDI-SF in th at participants with high BDI-SF scores also reported higher scores on the Zung Self-rating Depression scale (Zung, 1973), the UCLA Loneliness Scale (Russell, Pe plau, & Ferguson, 1978), and lower scores on Rosenberg Self-esteem Scale (Rosenberg, 1965). In Schwartz et al (2002), BDI depression scores correlated significantly wi th the original Maximization scor es consistently across samples ( r s ranged from .24 to .44, ps < .01). 35


Subjective happiness The Subjective Happiness Scale (SH S; Lyubomir sky & Lepper, 1999) is a 4-item scale. Participants respond to a 7-point Likert scale, higher scores re presenting greater happiness. A sample item is in general, I consider myself, 1 = not a very happy person, 7 = a very happy person Lyubomirsky and Lepper (1999) found support fo r a single factor loading during their scale development. Good to excellent internal consistency across samples of varying ages, occupations, and cultures ( s ranged from .79 to .94) was also reported. Test-retest reliability was reasonably high, ranged from .55 to .90 ( M = .72) in different samples, with the time lags between testing sessions ranged from 3 weeks to 1 year. Reas onable convergent validity was demonstrated in that participants with higher SH S scores are more likely to report higher selfesteem, higher optimism, and less depression. The mean scores in Lyubomirsky and Lepper (1999) range from 4.63 ( SD = 1.72) to 5.07 ( SD = 1.14) in several samples of U.S. college students. In Schwartz et al. ( 2002), the SHS scores correlated with the original Maximization scores consistently across samples but not alwa ys at a statistically si gnificant level scale ( r s ranging from -.10 to -.28). Life satisfaction The Satisfaction with Life Scale (SWLS, Di ener, Emmons, Larsen, & Griffin, 1985) is a 5item scale widely used in psychology studies. Pa rticipants respond to items using a 7-point Likert scale, with highe r scores representing greater life sati sfaction. A sample item is: In most ways my life is close to ideal. 1 = Strongly Disagree 7 = Strongly Agree In Diener et al. (1985), this measure showed reasona ble single-factor structure, in ternal consistency coefficient was .87, and two-month test-retest reliability was .82 among a sample of college students. It also demonstrated reasonable convergent validity such that participants with high scores on this measure are more likely to report high scores on several other widely us ed subjective well-being 36


37 scales, higher self-esteem and lower neuroticism. The mean SWLS score in Diener et al. (1985) was 23.5 ( SD = 6.43). In Schwartz et al (2002), the SWLS scores correlated significantly with the original Maximization scores (r = -.27, p < .01).


CHAP TER 4 RESULTS Study 1 Confirmatory Factor Analyses. The PRELIS/LISREL 8.54 (Jreskog & Srbom, 2003) program was used to generate a polychoric correlation matrix for the Confirma tory Factor Analyses (CFAs). Polychoric correlations are recommended when analyzing it em-level data because the distributional properties of item data are more likely to be ordinal and skewed than continuous and normally distributed (Nunnally & Bernstein, 1994). Polychor ic correlations are estimates of what the correlations among variables would be if the vari ables were continuous and normally distributed (Nunnally & Bernstein, 1994, p. 127). Maximum likelihood estimation was used as the estimation method for the CFAs. To evaluate model fit, I followed recommendati ons to report the chi-square statistic with degrees of freedom and significance level, th e goodness-of-fit index (GFI; Bentler, 1983; Jreskog & Srbom, 1997), the comparative fit index (CFI; e.g., Kline, 2005; Worthington & Whittaker, 2006), the standardized root-mean-square residual (SRMR), and the root mean square error of approximation (RMSEA) with 90% conf idence interval (CI). Although chi-square is expected to be small and nonsignificant acco rding to the typical recommended criteria for adequate model, chi-square value is affected by sample size and such criteria can hence lead to overrejection of adequate models when sample size is large enough (for a review, see Marsh, Balla, & McDonald, 1988). Therefor e, I applied no specific accepta nce criteria for chi-square and used it only as a reference point in comparing competing models. There are compiling discussions in methodology on cutoff criteria of various fit indexe s to evaluate model fit. In terms of specific acceptable v the GFI and CFI sh ould be close to or greater than .90 (Quintana 38


& Maxwell, 1999; W eston & Gore, 2006). The SRMR less than or equal to .08 indicates a good fitting model (Hu & Bentler, 1999). The RMSEA may be .05 or less if a clos e fit, or at least .05 to .08 if a fair fit. Hu and Bentler (1999) recommended joint c onsideration of the SRMR and RMSEA, as the best approach for managing Type I and Type II errors; the combination of an SRMR of .10 or less with an RMSEA of .06 or less resulted in the least numb er of Type I and II errors in determining adequacy of a model. The first CFA contained the 18 original maxi mization and regret items loading on four factors: Maximization Behavior I (6 items), Maximization Behavi or II (4 items), Having High Standards (3 items), and Regret (5 items). Th e relationships between the four factors were constrained to be orthogonal. This model was supported by PCA with varimax rotation in Schwartz et al. (2002). After dele tion of the outliers, the variable scores are still not normally distributed, Mardias coeffi cient = 58.02, normalized estimate = 48.39. A more robust chisquare, Satorra-Bentler Scaled 2 (S-B 2, Satorra & Bentler, 2001) was used in order to address the concerns raised by the violation of mu ltivariate normality. As shown in Table 4-1 (uncorrelated factors model), the results did no t support this model. Th e fit indexes were poor and there were additional concerns with the identification of the model. The S-B 2 difference (Satorra & Bentler, 2001) was 743.40, df = 6, p < .01, which supports the conclusion that the correlated factors model is a significant impr ovement of the uncorrelated factors model. Next, I tested the same 18 items and 4 factor s, but allowed the factors to correlate. As shown in Table 4-1 (correlated factors model), this model is a significant improvement over the orthogonal model. All the fit inde xes improved, and there was a significant chi-square difference between the correlated factors model and the uncorrelated factors model. In addition to substantial improvement in model fit, the correlations between the factors ( r s ranged from .19 to 39


.50) suggest that the original orthogonal assum ption is not an accurate reflection of the relationship between the factors. Note that a lthough the correlated factors model indicates a reasonable fit, it still has space to improve. I proposed earlier that it is theoretically more sound to adopt a unidimensional approach in maximization measure construction than a multidimen sional approach. Therefore, the next set of CFAs was conducted on several competing models, with items constrained to load on one single maximization factor. The first single factor model I tested used all the or iginal 13 items of MS. As shown in Table 4-2, the fit was fair base d on GFI but poor according to CFI, SRMR and RMSEA. It appeared that the 13 items i ndeed measured multiple constructs beyond maximization, consistent with Diab et al.s ( 2008) conclusion. This finding provided empirical support for a new model to capture the consistent and unidimensional cons truct of maximization. The next model tested consisted of only the 6 maximization behavior items as indicators of a single dimensional measure. Items selected were those that loaded on Factor 1 (Maximization Behaviors) in Schwartz et al. (2002) and displayed no conceptu al overlap with other constructs such as indecisiveness and having high standards. All the fit indexes supp orted this model. The internal reliability of th e 6-item model Cronbachs was.60, lower than the traditionally cutoff value of .70. However, Schmitt (1996) argued that th e major concern regarding low reliability is that it might attenuate the detected relationshi ps between the measured construct and other constructs. If such attenuation is not a concer n or can be corrected, a measure with other desirable properties yet a low reliability may still be valuable. Having argued that the 6-item model is acc eptable as maximization measurement, I still seek for possibilities of improving the measure. Because measures with small numbers of items are more likely to suffer low reliability than t hose with larger numbers of items (Schmitt, 1996), 40


I tested whether adding the 6 newly developed item s to the 6-item model would help improve the reliability. Indeed, the internal reliability of the 12-item measure was much enhanced (Cronbachs = .71). In regard to fit indexes, the fit indexes of the 12-item model are not as good as the 6-item model, with th e CFI slightly falls short of the .90 cutoff criterion. This is probably due to added unique variance associated with the 6 new items. However, other than CFI, all the rest fit indexes of the 12-item model indicated a good fitting model. Although the relative inferior fit indexes sugge st an increased risk of underrej ection of inadequate models (Hu & Bentler, 1999), the combination of validity and reliability indicate that the 12-item measure is statistically more adequate than the 6-item one. In an attempt to improve the 12-item model, I examined whether elimination of certain individual items may help improve the model. Item 9 and 23 were identified as candidates for removal because they had the lowest loadings on maximization (.28 and .22, respectively) and the lowest corrected inter-item correlations among all the 12 items. I therefore eliminated these two items and tested the resulting 10-item model. The comparis on of fit indexes between the 12item and 10-item models led to a mixed conclusio n. Other than that the RMSEA which exhibited a small increase, all the other fit indexes and internal reliabilities of the 10-item model are almost identical to those of the 12-item model. A detail examination of the individual item performances suggested that the 10 items presen t a more consistent and unifor m pattern across the items than the 12 items do. As shown in Figure 4-1, the item factor loadings of the 10 items ranged from .36 to .66, in slight contrast to those of the 12 items, which ranged from .22 to .64. In addition, as shown in Table 4-4, all items of the 10-item scale meet the conventional standard (.25) for corrected item-total corr elations, while two items from the 12-item scale fell short of this standard. Taken together, I decided to adopt th e 10-item scale as an adequate maximization 41


m easurement and used that version for the remaining analyses. I named this measure Maximization Behavior Scale (MBS) in order to distinguish it from the original MS and to highlight its unidimensionality. Relations of MBS to the Demographic Variables. I conducted a two-way ANOVAs to compare MBS scores among the two gender groups and the four ethnic/cultural groups, i.e, White/European Amer ican, Black/African American, Hispanic/Latino(a), and Asian/S outh Pacific Islander. The pattern is presented in Figure 4-1. There were significant main eff ects of both gender and ethnicity, F (1, 1879) = 15.16, p < .01, and F (3, 1879) = 3.29, p < .01, respectively. There was no in teraction between gender and ethnicity, p > .1. Specifically, women reported signi ficantly higher maximization scores ( M = 4.74, SE = .03) than did men ( M = 4.53, SE = .05), effect size Cohens d = .26. Recall that Schwartz et al. (2002) found the opposite patterns and other studi es did not identify any gender differences, the current finding suggests that wo men exhibit a stronger behavioral tendency to maximize their choices than do men. Regarding cultural/ethnicity, th e post-hoc analysis suggests th at the significant differences were only between White/European American ( M = 4.55, SD = .82) and the three ethnic minority groups, M Black/African American = 4.71 (SD = .91), M Hispanic/Latino = 4.71 ( SD = .93), M Asian/South Pacific Islander = 4.76 ( SD = .83), ps < .05, Cohens d = .18, .18, and .25, respectively. There were no significant differences among the three ethnic minority groups. Noting that 120 participants belonging to ethnic groups other th an the five groups were excluded from the analysis, I included them in the analysis by creating a dummy variable minority status, in which participants who belong to the White/European American group were coded as 0 and everyone else were coded as 1. A two-way ANOVA comparing gender and minority status revealed the similar pattern, that there are significant effects of both gender and mi nority status and not interaction between them, 42


ps < .001. In sum consistent with my prediction, in both the cases of gender and ethnicity the groups who traditionally received less social priv ilege were more likely to maximize than the traditionally privileged groups. Study 2 Preliminary Analyses. Table 4-5 displays the scale means, standard deviations, and internal consistency estimates for the measures. Similar to Study 1, procedures for normality screening were performed for all variables. The scores of HS, Order, Happine ss and Satisfaction were negatively skewed; the scores of DIS and Depression were positively skewed. In addition, HS and Depression scores were substantially dispersed whereas Order and DI S scores accumulated close to their respective means. Scores on the NFC, MBS, and Regret appeared normally distributed. Based on the recommendation by Field (2005), data transformati on was performed for all scores except NFC, MBS and Regret and resulted in approximately nor mal distribution for all variables. Analyses were conducted with transformed and nontransformed data and the pattern of results obtained from the transformed data displayed very minor difference from those identified by the nontransformed data. Because no important inform ation would be missed by using the original data rather than the transformed data, and for the sake of easy interpre tability, reported results are based on the original nontransformed data. Pearsons chi-square tests to compare com position of gender and ethnicity and one-way ANOVA comparing ages indica ted that there were no demographic differences among participants who elected to par ticipate in Version 1 versus those participated in Version 2. Oneway ANOVAs comparing the measure scores indi cated no significant differences between the answers from Version 1 and those from Version 2. In other words, whether maximization 43


questions w ere completed before or after perfectionism questions did not make a systematic difference in participants answers. Cross-validation of MBS and Related Preliminary Analyses. Did the MBS model we derived from Study 1 provi de a good fit for the data from Study 2? The same CFA procedure was used to test the 10-i tem model with the current data and the results are reported in the last part of Table 4-2. The model fit and item loadings were replicated with Study 2 sample, for the most part, with some reas onable variations probably reflecting variations between the two samples. T-tests comparing th e two samples showed no systematic sample differences substantial enough to raise concerns, t (2437) = -.19, p > .90. Next, I examined the impact of demographi cs variables and pro cedure variables on MBS scores. Again the mean MBS score was higher among women ( M = 4.82, SD = .77) than among men ( M = 4.68, SD = .78), the effect size of the difference Cohens d = .18, similar to that in Study 1. This difference did not reach statistica l significance in Study 2, pr obably related to the smaller sample size. There were no significant gender differences in all other major variables, although there was a trend toward women ( M = 5.83, SD = 6.88) reporting higher depression levels than men ( M = 4.50, SD = 6.53), F (2, 433) = 4.89, p = .06, Cohens d = .20. The difference between ethnic groups appeared agai n, that the members of the minority groups ( M = 4.88, SD = .73) were significantly more likely to maximize than White/European American were ( M = 4.69, SD = .79), F (1, 434) = 7.00, p = .008, Cohens d = .25. In addition, minority status was also associated with significantly lo wer life satisfaction and higher depression, p s < .05. There was a trend of possibly high er perfectionism DIS, less need for cognition, and more regret, .05 < ps < .101 among ethnic minorities. There was no signi ficant association between age and 1Given the associations between minority status and several variables under test, I also tested the minority status as a moderator in the association between maximization and psychological well-being variables, with HS or DIS as the 44


m aximization behavior, which is not surprising given the limited age variation in the current college student sample. Table 4-6 displays the correlations between the major variables. High maximization tendency was associated with hi gher perfectionism levels acro ss all three APS-R dimensions. High maximization tendency was al so linked to higher levels of regret, depression, lower levels of dispositional happiness and lif e satisfaction, consiste nt with the findings in Schwartz et al. (2002) but not the finding on life satisfaction of Diab et al. (2008). Finally, maximization tendency was not associated with need for cogn ition, suggesting that ther e was little connection between the two constructs2. Individuals who make efforts to maximize their choice do not necessarily show more interest or tolerance towards effortful cognitive activities, although the maximization process may very well be facilita ted by increased cognitive processing. In fact, high need for cognition was also associated with lower levels of regret and depression, and higher levels of dispositional happiness and life satisfaction, opposite to the patterns associated with high maximization tendency. Relations of MBS to Perfectionism. Several analyses were performed to examin e the relationship between perfectionism and maximization. Perfectionism has been conceptuali zed and measured in past research as both a score variable and as a group/ca tegorical variable. I therefore analyzed perfectionism in both terms in the current study in order to facilitate the findings to connect with the two streams in the literature. In particular, I used regression analysis first to ex amine the continuous relationship mediator. The moderated mediation models tested based on the recommendations of Edwards and Lambert (2007) were not significant and hence not discussed in the analysis and results section. 2Although not the focus of the current research, I also test ed whether maximizers with high need for cognition were happier than those with low need for cognition. The regression analyses showed no interaction between maximization and need for cognition, p s > .1, again indicating the functional independence of the two constructs. 45


between perfectionism dimensions and MBS, and then ANOVAs to examine the maximization tendency among categories of perfecti onists and non-perfectionists. In hierarchical multiple regression analysis, I entered HS in the first step, DIS the second step, Order the third step, and HS x DIS in the f ourth step to predict ma ximization. Prior to the regression analysis, all the IVs were mean-cen tered (Aiken & West, 1991). HS alone accounted for 2% of variance in maximization, p = .002, and adding DIS in the model helped explain an additional 17% of the variance, p < .001. Order and HS x DIS did not account for the variance in maximization, ps > .1. Tests of unstandardized partial regr ession coefficients at the final step revealed that both HS and DIS were signifi cant predictors of maximization behavior, B = .022 and .021, respectively, ps < .001. As I had expected, individuals with either high HS or high DIS, or both, are more likely to maximize their choice s than those with neither. The standardized coefficient Beta was .20 for HS and .41 for DIS, s uggesting that the predic ting strength of DIS is larger than HS. The finding that there was no in teraction between HS and DIS suggests that the functions of HS and DIS are independent from each other, as consistent with past finding on the distinctive function of the two dimensions. A similar pattern was identified when I ex amined individuals maximization tendency based on perfectionist membership. Following the cutoff criteria supported by Rice and Ashby (2007), 94 participants were classified as maladaptive perfectionists (HS score 42 and DIS score 42), 138 participants classified as adaptive perfect ionists (HS score 42 and DIS score < 42), and 204 were non-perfectionists (HS < 42) A one-way ANOVA comp aring perfectionist groups (Maladaptive, Adaptive perfectionists and N on-perfectionists) revealed a significant main effect for perfectionist group, F (2, 433) = 14.47, p < .001, partial eta-squared = .06. In particular, maladaptive perfectionists ( M = 5.13, SD = .75) were significantly more likely to 46


m aximize than both adaptive perfectionists ( M = 4.64, SD = .74, Cohens d = .66) and nonperfectionists (M = 4.68, SD = .76, Cohens d = .60), ps < .001. Adaptive perfectionists did not differ from non-perfectionists in their maximizat ion tendency, probably due to the high DIS among some non-perfectionists. I also conducted ANOVAs to examine the di fferences in psychological well-being among the groups, and found that, except for the diffe rence in happiness between maladaptive perfectionists and non-perfectioni sts, all the between-group di fferences were significant, ps < .05. Maladaptive perfectionists we re the most depressed and l east satisfied group among the three, whereas adaptive perfectionists were the least depressed and most satisfied group. These results are consistent with Schwartz et al.s (2002) findings regarding the relationship between maximization and psychological well-being. Quadratic Relations of MBS to Psychological Well-being. Based on the established corre lations between maximization and the three psychological well-being variables, I tested whether a quadratic model w ould provide a good fit for these associations. In three separate hierarchical multiple regression analyses, maximization scores were entered in the first step and squared maximi zation scores were entered in the second step to predict depression, happiness, and life sati sfaction. The model predicting depression was supported in that maximization alone accounted fo r 3% of variance in maximization, and adding squared maximization in the model accounted for an additional 1% of variance, ps < .05. The quadratic model is presented as following, with y representing depression and x representing maximization: 12.1818.792.2 xxy 47


Figure 4-3 provides a visual presentation of the model. Tests of standardized partial regression coefficients at the final step reveal ed that m aximization-squared is a significant predictor of depression, p = .03, and that the main effect for maximization is marginally significant, p = .08. This model supports the two-fold relationship between maximization and depression: below the maximizati on score of 3.90, an increase in ma ximization is associated with less depression; above 3.90, an increase of maxi mization is associated with an increase in depression. For happiness and satisfaction, the quadratic te rm of maximization di d not offer additional explanations beyond the linea r term to their variance. This find ing suggests that the simple linear relationships in these cases are better presentations of the associati ons than the quadratic relationships. HS as Suppressor and DIS as Confound between Maximization and Psychological Wellbeing In this section, I examined the suppressing effects of HS and confounding effects of DIS on the relationship between maximization and ps ychological well-being. A suppressor, according to the most generally accepted definition by Conger (1974, p. 36), is a variable which increases the predictive validity of another variable (or set of va riables) by its inclusion in a regression equation (see al so Tzelgov & Henik, 1991). MacKinnon, Krull, and Lockwood (2000) further explain that a suppressor likely account s for variability in the predictor. When that part of overlapping variability between a predicto r and a third variable is controlled, a stronger relationship between that pred ictor and an outcome would emerge. A confounding variable explains the association between the two variables of interest th at, if controlled, would reduce the association between the two variables (MacKinnon, et al., 2000). MacKinnon et al. (2000) conceptualized that suppression and confounding, as well as mediati on, can all be considered as 48


tests of the change in the relationship between tw o variables as the functio n of a third variable. Despite conceptual differences, they can all be estimated by the same statistical methods testing the impact of a third variable on the relationshi p between the predictor an d the outcome variable (MacKinnon et al., 2000). As shown in Figure 4-4, the indirect effect of the third variable can be calculated either by ab or the difference between c and c similar to the traditional Sobel test (1984) for mediation. The distinction between su ppressing effects and th e other two types of effects involve the signs and magnitudes of the direct and indirect e ffects. Mediating and confounding effects are not statistica lly distinguishable in this mode l; their distinction lies in the causal assumptions of the relationships in th e model: mediation assume s causal relationships while confounding does not. In the following analyses, two sets of mediation/third variable models, each using HS or DIS, were conducted. First, I tested three models all involving HS as the third variable and maximization as the primary predictor, and happiness (Model 1a, shown as an example model in Figure 4-4), depression (Model 1b) or life satisfaction (M odel 1c) as the outcome. The second set of tests involved the same pred ictor and outcomes as the first se t but a different third variable, DIS (Model 2a, 2b, and 2c). Despite the possibili ties of testing HS and DIS simultaneously in combined multiple mediation models, I chose to test them with separate simple mediation models due to concern about col linearity between HS and DIS. These two variables have shown a significant correlation in the current study, alth ough they did not correlate in the original measurement development study by Slaney et al. (2001). According to Preacher and Hayes (2008), collinearity between multiple mediator s in the same model may obscure certain mediation effects. A drawback of the simple medi ation model is that it does not directly inform us of the strength of the HS effect in competing with DIS, or visa versa. 49


Tests of the confounding/suppressi on effects used the statistical m ethods recommended by MacKinnon et al. (2000), which is a combination of the Sobel test for mediation and the confidence intervals (CIs) of the indirect effect reported by bootstrapping approach. The Sobel test was argued as a more powerful approach to address mediation (Pr eacher & Hayes, 2008) and avoid certain potential shortcomings associated with the commonly used multistep approach proposed by Baron and Kenny (1986) (MacKin non, Lockwood, Hoffman, West, & Sheets, 2002, Edwards & Lambert, 2007). The bootstrapping proce dure was used to avoid the power concern often raised by the non-normal distribution of the indirect effect (Shrout & Bolger, 2002). Table 4-6 presents the results for Models 1a, 1b and 1c. All three suppression models received support as hypothesized. For example, for Model 1a predicting depression, maximization was positively associated with HS, as indicated by a significant unstandardized regression coefficient ( B = 1.25, t = 3.11, p = .002). Also, the inverse relationship between HS and depression, controlling for maximization, was supported ( B = -0.33, t = -6.99, p < .001). And finally, maximization was found to have an indirect effect on depression; this indirect effect was negative (-0.41), as consistent with the suppression hypothesis. The formal two-tailed significance test (assuming a normal distribution) demonstrated th at the indirect effect was significant (Sobel z = -2.82, p = .005). Bootstrap results conf irmed the Sobel test, with a bootstrapped 99% CI around the indirect eff ect not containing zero (-.93, -.02). According to MacKinnon, et al. (2000), two conditions indicate suppression: 1). the direct effect is positive, the indirect e ffect is negative, and in addition the magnitude of the total effect is smaller than the direct effect; 2). the direct eff ect is negative and the indirect effect is positive. Model 1a meets the criteria for Condition 1; Mo del 1b and 1c meet the criteria for Condition 2. In sum, having high personal standards accounts fo r some variability in maximization, and, if 50


partialled out, the variation left in m aximization -and unrelated to high standards -accounts for a larger proportion of negative psychological outcomes than was evident before controlling for the overlap between maximization and high standards. Table 4-8 presents the results for Model 2a, 2b and 2c. Consistent wi th expectations, all three confounding models were supported. First, maximization was positively associated with DIS, as indicated by a significant unst andardized regression coefficient ( B = 7.38, t = 8.68, p < .001). Also, the inverse relations hips between DIS and the th ree psychological well-being outcomes, controlling for maximization, were supported ( p s < .001). And finally, maximization was found to have significant indirect effects on psychological well-b eing, all Sobel test ps < .001, with the bootstrapped 99% CIs around the indirect effects not containing zero. MacKinnon et al. (2000) suggest ed that, for a complete confounding/mediation to be supported, the direct effect should be zero and the indirect effect should not be zero. For a partial confounding/mediation, both the direct and indirect effects have to be of the same sign, and the magnitude of the direct effect should be smaller than that of the total e ffect. Model 2a meets the latter description for partial confounding, suggesting that DIS part ially explains the relationship between maximization and depression. Model 2b and 2c both closely meet the description for complete confounding (the direct effect of Model 2b is 0 and that of Model 2c is .05, very close to 0). Taken together, DIS comple tely explains the lower level of happiness and life satisfaction experienced by maximizers, as well as part of their depression. This is consistent with my hypothesis that maximization is connected with psychological well-being through DIS. Taking away the effect of DIS, maximization predicts a much reduced variation in depression and little variation in unhappiness and life satisfaction. 51


Table 4-1. F our-factor models of maximization: Fit comparison S-B 2 df S-B 2 GFI CFI SRMR RMSEA (CI) Items 1. Factors uncorrelated 2042.42 135 .86 .84 .15 .084 (.081.087) 1-18 .79 2. Factors Correlated 1344.55 129 743.40 .90 .89 .08 .069 (.065.072) 1-18 .79 Note. N = 2003. S-B 2 is Satorra-Bentler Scaled chi-square, all p < .01, S-B 2 was calculated based on the formula provided by Satorra and Bentler (2001); CI = confidence interval, represents Cronbach's coefficient alpha. Table 4-2. One-factor models of maximization: Fit comparison S-B 2 df GFI CFI SRMR RMSEA (CI) Items 1. 13-item 1773.38 65 .84 .68 .10 .11 (.11-.12) 1-13 .70 2. 6-item 88.79 9 .98 .94 .05 .067 (.054-.079) 2 3 4 5 9 13 .60 3. 12-item 664.72 54 .92 .87 .06 .075 (.070-.080) 2 3 4 5 9 13 19 20 21 22 23 24 .71 4. 10-item Study 1 562.73 35 .92 .87 .06 .087 (.081-.093) 2 3 4 5 13 19 20 21 22 24 .72 Study 2 201.95 35 .89 .75 .09 .11 (.092-.12) Same as above .68 Note. N = 2003 for Study1 and 436 for Study2. S-B 2 is Satorra-Bentler Scaled chi-square, all p < .01; CI = confidence interval, represents Cronbach's coefficient alpha. 52


Table 4-3. T he item standardized loadings and re liability of the 10-item Maximization Behavior Scale Item and number Factorial Loading Corrected item-total correlation 1. No matter how satisfied I am with my job, its only right for me to be on the lookout for better opportunities. 0.49 0.38 2. When I am in the car listening to the radio, I often check other stations to see if something better is playing, even if Im relatively satisfied with what Im listening to. 0.66 0.49 3. When I watch TV, I channel surf, often scanning through the available options even while attempting to watch one program. 0.60 0.43 4. I treat relationships like clothing: I expect to try a lot on before I get the perfect fit. 0.36 0.29 5. I often fantasize about living in ways that are quite different from my actual life. 0.38 0.30 6. I always keep my options open so I will not miss the next best choice available in life. 0.45 0.36 7. Even if I see a choice I really like, I have a hard time to make the decision if I do not have a chance to check out other possible options. 0.56 0.46 8. When going to a new restaurant, I find myself reading the complete menu before narrowing down on what I want to eat. 0.47 0.38 9. I try to do an extensive search when I look for a gift for a close friend. 0.42 0.35 10. When shopping, I often need to scan all the clothing available in a store before I decide on what to try and buy. 0.44 0.37 Note. N = 2003. All loadings were on one single maximization factor. 53


Figure 4-1. Study 1: The indi vidual factorial loadings of the 10-item model. 54


EthnicityAsian/South Pacific Hispanic/Latino Black/African Am. White/CaucasianMaxmization4.9 4.8 4.7 4.6 4.5 4.4 4.3 GenderFemale Male Figure 4-2. Maximization (MBS) scores as related to gender and ethnicity. 55


Table 4-4. S tudy 2: Means, standard deviat ions, and score internal consistencies M SD Maximization 4.77 .77 .68 Regret 4.46 1.07 .78 High Standards 40.50 6.56 .88 Discrepancy 43.69 14.85 .94 Order 19.51 5.17 .89 Need for Cognition 3.35 .64 .89 Depression 5.30 6.77 .89 Subjective happiness 5.11 1.18 .86 Life Satisfaction 4.88 1.33 .90 Note N = 436. Means and standard deviations represent raw scores, represents Cronbach's coefficient alpha. Table 4-5. Bivariate correlations 1. 2. 3. 4. 5. 6. 7. 8. 9. 1. Maximization (MBS) 1.00 2. Regret .53 ** 1.00 3. High Standards .15 ** -.02 1.00 4. Discrepancy .38 ** .41 ** -.12 ** 1.00 5. Order .11 .06 .38 ** .02 1.00 6. Need for Cognition -.06 -.13** .26** -.14** .03 1.00 7. Depression .17 ** .29 ** -. 29 ** -.40 ** -.14 ** -.15 ** 1.00 8. Subjective Happiness -.14 ** -. 36 ** .30 ** -.36 ** .09 .14 ** -.59 ** 1.00 9. Life Satisfaction -.13** -. 33 ** .39 ** -.41 ** -.13 ** .18 ** -.60 ** .64 ** 1.00 Note. N = 436. p < .05, **p < .01. Results are based on twotailed significance tests. 56


Figure 4-3. The quadratic model predicting depression by maximization (MBS). HS Maximization Depression b a c total effect (c indirect effect) Figure 4-4. Suppression/Mediation M odel 1a based on Sobel test. 57


Table 4-6. R egression results for simple mediations, APS-R HS as mediator Direct and total effects Variable B SE t p Model 1a (Depression) Maximization predicting depression 1.44 .41 3.48 .001 Maximization predicting HS 1.25 .40 3.11 002 HS predicting depression, controlling for maximization -.33 .05 -6.99 .000 Maximization predicting depression, controlling for HS 1.85 .40 4.66 .000 Model 1b (Happiness) Maximization predicting happiness -.21 .07 -2.90 .004 Maximization predicting HS 1.26 .40 3.11 .002 HS predicting happiness, controlling for maximization .06 .01 7.11 .000 Maximization predicting happiness, controlling for HS -.28 .07 -4.07 .000 Model 1c (Life Satisfaction) Maximization predicting life satisfaction -.23 .08 -2.80 .005 Maximization predicting HS 1.25 .40 3.11 .002 HS predicting life satisfaction, controlling for maximization .08 .01 9.53 .000 Maximization predicting life satisfaction, controlling for HS -.33 .08 -4.44 .000 Sobel test Indirect effect and si gnificance using normal distribution Value SE LL 95% CI UL 95% CI z p Model 1a (Depression) -.41 .15 -.70 -.12 -2.82 .005 Model 1b (Happiness) .07 .03 .02 .12 2.83 .005 Model 1c (Life Satisfaction) .11 .04 .04 .18 2.94 .003 Bootstrapped indirect effect M SE LL 99% CI UL 99% CI Model 1a (Depression) -.42 .18 -.93 -.02 Model 1b (Happiness) .07 .03 .01 .16 Model 1c (Life Satisfaction) .11 .04 .01 .22 Note. N = 436. Unstandardized regression coefficien ts are reported. Bootstrap sample size = 1000. LL = lower limit; CI = confidence interval; UL = upper limit. 58


59 Table 4-7. Regression results for simple mediations, APS-R DIS as mediator Direct and total effects Variable B SE t p Model 2a (Depression) Maximization predicting depression 1.44 .41 3.48 .001 Maximization predicting DIS 7.38 .85 8.68 .000 DIS predicting depression, controlling for maximization .18 .02 8.39 .000 Maximization predicting depression, controlling for DIS .10 .42 .24 .81 Model 2b (Happiness) Maximization predicting happiness -.21 .07 -2.90 .004 Maximization predicting DIS 7.38 .85 8.68 .000 DIS predicting happiness, controlling for maximization -.03 .00 -7.47 .000 Maximization predicting happiness, controlling for DIS .00 .07 .03 .97 Model 2c (Life Satisfaction) Maximization predicting life satisfaction -.23 .08 -2.80 .005 Maximization predicting DIS 7.38 .85 8.68 .000 DIS predicting life satisfaction, controlling for maximization -.04 .00 -8.73 .000 Maximization predicting life satisfaction, controlling for DIS .05 .08 .56 .58 Sobel test Indirect effect and si gnificance using normal distribution Value SE LL 95% CI UL 95% CI z p Model 2a (Depression) 1.34 .22 .91 1.78 6.01 .000 Model 2b (Happiness) -.21 .04 -.29 -.14 -5.64 .000 Model 2c (Life Satisfaction) -.27 .04 -.36 -.19 -6.13 .000 Bootstrapped indirect effect M SE LL 99% CI UL 99% CI Model 2a (Depression) 1.35 .24 .78 1.97 Model 2b (Happiness) -.21 .04 -.29 -.14 Model 2c (Life Satisfaction) -.28 .05 -.41 -.16 Note. N = 436. Unstandardized regression coefficien ts are reported. Bootstrap sample size = 1000. LL = lower limit; CI = confidence interval; UL = upper limit.


CHAP TER 5 DISCUSSION Summary of Results The purpose of the current study was twofold: 1). to provide a unidimensional maximization measure, more valid and reliable th an the previously existing MB, 2). to provide more in-depth understanding regarding the asso ciation between maximization and perfectionism. The first goal was partly successful. I obtained th e scale MBS with a combination of theoretical and empirical support that seems to have made it more precise than MB, although it also seems clear that the MBS also has room for im provement. The second goal was well accomplished. Other than the limitations involving causal inferences that the st atistical models can not narrow down, all the results were consistent with my hypotheses that perf ectionism is likely a personality drive associated w ith, if not directly causing, maxi mization and one responsible for the unhappiness associated with maximization. The Refined Maximization Measurement MBS In assessing a good measure, both theoretical a nd statistical criteria are important. The original MS failed to meet both cr iteria. First, it included several peripheral factors other than the core construct. Those factors did correlate w ith the core construct of maximization, but the correlations were of medium or small size. S econd, the initial orthogona l four-factor structure (including Regret) proposed by Schw artz et al. (2002) was not suppor ted. It fits poorly with the current data; allowing the factors to correlate l eads to a much better fit. Third, the 13 MS items do not adequately capture one ge neral factor of maximization and produced a poor fit for the data. In combination with the low concurrent valid ity found by Diab et al. (2008), it is clear that the so far commonly-employed MS is not an adequate measure for maximization. Future 60


researchers in this area should consider alternat ive m easurements instead of MS and should reassess the past findings based on MS. In regard to the one-factor maximization model, except for the original 13-item MS model, the other three models all provide adequate fit for the data. Admittedly, the fit indexes of the 12item and 10-item models are slightly inferior to th ose of the 6-item model, particularly in regard to CFI and RMSEA. CFI belongs to the family of incremental fit indexes (measuring the proportionate improvement in fit by comparing a target model with a more restricted, nested baseline model). RMSEA belongs to the family of absolute fit indexes (measuring how well an a priori model reproduces the sample data). Thei r slight departure from conventional cut-off criteria suggests that the risk of under-rejection of in adequate models is in creased. However, the other two absolute fit indexes, GFI and SRMR, both suggest that the models yielded adequate fit for the data. Therefore, I concluded that the tw o models as acceptable based on the clear support of GFI and SRMR and the trend support of CFI and RMSEA. The final decision favored the 10-item model because it exceeds th e 12-item model in respect to detail item performance and the 6-item model on internal reliability. Marsh and colleagues (1998) demonstrated that measures with relatively larger numbers of items per factor have a better chance than those with smaller number of items to secure consistently high internal reliability even with small samples sizes. My preference of the10-item model over the 6-item model is consistent with their recommendation. On the other hand, the 6-item model may be considered when a short version of maximizati on measurement is more suitable for particular research situations. Its low reliability calls for improvement but does not necessarily indicate measurement problems, especially at an exploratory stage when larger error terms in the items can be tolerated. 61


It is worth noting that the 10-item resoluti on obtained worse fit indexes for the smaller sample in Study 2 than for the sample in Study 1. Marsh et al. (1988) demonstrated that fit indexes such as 2, GFI, and SRMR can be substantia lly affected by sample size and the direction of such a sample sizeeffect is difficult to predict because it is determined by a combination of the index, the specific data set, and the amount size of variation across samples. If the difference in sample size is the sufficient explanation for the current fit i ndexes variation, future tests with additional samples may help to replicate the better model fit in Study 1 based on the bigger sample of Study 1. Mean while, this variation does rais e potential concerns regarding the 10-item model. Among the two other maximization scales proposed by recent research, Nenkov et al.s (2008) 6-item measure is clearly no t consistent with the unidimen sional approach as it retained the multidimensional structure similar to MS. Diab et al. (2008) agreed with the unidimensional measure yet retained the high standards items in their Maximization Tendency Scale. Clearly, the MBS follows, more faithfully than the other measures, the theory behind the maximization construct. When testing the relationship betw een maximization and other constructs, the MBS clearly narrows the findings to the functions of maximization behaviors and attitudes, and excludes other potential interpretations, such as th e functions of having high standards. This is particularly important in addressing the associ ation between MBS and perfectionism, give that the latter contains hi gh personal standards as a main feature. Perfectionism as the Drive for Maximization and Explan ation for the Associated Unhappiness Without potential confounding due to construct overlap between maximization and perfectionism, the current study demonstrated a clear connection between the perfectionism personality and maximization style of decisionmaking. Both the HS and DIS dimensions of 62


perfectionism contribute to m aximization tendenc y; their contributions are independent from each other, and there is little overlap or interaction. In other words, individuals may maximize in decision-making because of their high personal sta ndards or their perceived inadequacy, or both, as in the case of malada ptive perfectionists. Interestingly, the function of HS is different from that of DIS, both in terms of magnitude and direction: DIS is a stronger predictor for ma ximization in comparison to HS; DIS but not HS contributes to maximizers unhappi ness. Individuals with high pe rsonal standards are likely to maximize but not to an extremely high degree; maximization driven by high standards may protect individuals from depression, presumably in the prospect of approaching the high standards. In contrast, individua ls who consistently experience unm et expectations are likely to maximize and when they do, probably maximize to a high extent even in the face of excessive subsequent costs. Despite such efforts to improve their choice outcome, they may continue to be disappointed and unhappy, likely predisposed for di sappointment by their tendency to perceive flaws and imperfection with the ou tcome. This description is c onsistent with the distinctive psychological experiences of adaptive and malada ptive perfectionists. Adaptive perfectionists do not perceive as large a discrepancy between e xpectations and evaluati on as maladaptives, and they tend to be happier than maladaptive perfectionists. Past research has accumulated evidence on the negative implication of maximization. The current research provided consistent evidence w ith a more valid measurement, and went further by supplying a possible explanation for negative outcomes associated with maximization. Based on our findings, maximization alone probably does not lead to unhappiness. Rather, maladaptive perfectionism may be the underlying reason fo r unhappiness, the same reason that leads individuals to maximize in the first place. If this is true, simply avoiding or reducing maximizing 63


as recomm ended by Schwartz (2004a) may not necessarily lead to the path of happiness, if the maladaptive perfectionism tendency stays intact. On the other hand, the quadratic model predic ting depression by maximization indicates that the relationship between ma ximization and psychological we ll-being may not be a simple linear association. Instea d, individuals at the middle range of maximization were actually the least likely to be depressed, in contrast to thos e who maximize very little or very much. Thus, maximization may still be a strategy for good life choices if it is not over-used to the extent that associated costs are high. Other Features of Maximizers In regard to the relationshi p between need for cognition and maximization, the current finding supports the non-association hypothesis. Maximizers reveal neit her particular interest in rational thinking processes nor high engagement in cognitive ac tivities. In other words, maximizers do not necessarily find the maximizing process more intrinsically rewarding or less burdensome than satisficers do. Maximizers proba bly employ strategies other than intense cognitive activities to cope with the burden of a decision task, su ch as heuristic decision-making and social comparison (e.g., Iyenga r et al., 2006; Parker et al., 2007). Therefore, even though the rational choice theory camp argues that maxi mization is a rational decision-making method, individuals who frequently maximize may not n ecessarily be more rational in their decisionmaking than those who seldom maximize. The relatively high maximization tendency among women and ethni c minorities raises several possible interpretations. From a social status and res ources perspective, women, in contrast to men, and ethnic minor ities, in contrast to White European Americans, can be considered relatively disadvantaged or under-privile ged. It is possible that those individuals have a strong tendency to seek better choices, expand their option pool, and strive to get the best out 64


of situations with the hope of gaining access to choices otherwise hardly accessible to them. Using this logic, maximization may reflect an adapting strategy in response to disadvantaged social economic status. Future studies can address this possibility by testing the association between maximization and social/economic status, the latter represented by variables such as family income. From a cultural perspective, th e maximization orientation as a decision-making method may be more imbedded with in certain cultural frames than others, and hence leads to different decision-making practices. It would thus be interesting to use qualitative approaches to examine related cultural beliefs, and quantitativ ely measure whether individual differences in maximization are associated with the extent of cultural practices of et hnic minority individuals, perhaps captured by variables such as acculturation. Limitations and Directions for Future Research One limitation of the current research is that no causal relationships can be concluded due to our correlational research de sign. Theoretically it is reasonable to assume that perfectionism personality leads to maximization tendency, a nd maximization is a behavioral expression of perfectionistic pursuits, not the other way around. E xperimental or longitudinal methods can test such causal speculations. For exam ple, although trait perfectionism can be hardly altered in a lab-setting, researchers may induce high standards or perceived inadequacy in a specific domain and observe individuals tende ncy to maximize their domain-specific choice in comparison to those in the control condition. The current study employed large samples of participants ( N = 2003 and N = 436) which possibly increases the generali zability of findings. However, caution should be taken when attempting to generalize the findi ngs to populations other than young adult U.S. college students. For example, my samples probably functioned at a generally higher level than the general population, as suggested by their acc umulation on the high end scor es on variables associated 65


with high functioning and low end scores on variables indicating difficul ties. Future studies should em ploy community samples with divers e demographic backgrounds to increase generalizability of these findings. Another limitation of the current research is related to its usage of online surveys. This limitation is comparable to th e traditional pencil-a nd-paper self-report method (Gosling et al., 2003). Self-report is commonly used due to its co nvenience, yet often challenged by its potential deviation from individuals actu al behavior or experience (Ros enthal & Rosnow, 1991). Such concern is particularly relevant in regard to maximization, in which the behavioral decisionmaking is probably no less important than the co gnitive process involved. Self-report can also contribute to distortion due to la ck of self-awareness or to impression management. The latter may be particularly true for participants hi gh in perfectionism, who are often motivated to present themselves in positive light and conceal aspects of imperfection such as feeling depressed (Hewitt, Flett, Sherry, et al., 2003). Research desi gned to include behavioral observation of maximization in an actual or lab-stimulated decision-making situation would help constructing a more complete picture of maximization. The current paper examined two potential cont ributors of maximization: perfectionism and need for cognition. Perfectionism accounted for 19% of the variation in maximization, and preference for cognitive activities accounted for no significant variance. This left 81% of maximization unexplained. The literature on ma ximization can benefit from testing other potential contributors to maximization, particul arly cognitive differences such as information processing abilities, or additional personality differences such as competitiveness. Although the current paper views maximization as a trait, and therefore somewhat static, it is possible that the practice of the maximizati on is modified according to the content of the 66


decision-m aking and/or importance of the goal. Even extremely maximizing individuals are unlikely to devote as much time and cognitive effo rt in choosing their flav ors of ice-cream after dinner as they would in determining their career choices. It would be interesting to examine the situational variation of maximization among individuals, and to examine whether some individuals would display large fl exibility in response to the context and thus alter their decisionmaking method within a wide continuum of maximi zation and satisficing. In fact, this may be true with the participants scored in the middl e range of MBS. Recall th at MBS included items describing maximization in different decision contexts, and the fina l score is an average of the item responses. Perhaps, some of middle score are a product of very high scores on some items and very low on others, rather than c onsistent medium scores on all items. Such a flexible maximization approach likely reflects an ultimate maximization of maximization method, or meta-maximization, which derives the most benefit of the maximization method and avoids an excessive cost of energy and resource s. Individuals follow this approach may behave like maximizers only in the face of critical life choices, yet satisfice in trivial choice-making and be happy with good enough choices. These individuals should be free of maximization-associated psychological su ffering. Thus, future research should test situation-specific maximization, from the realm of consumer goods to more significant life choices such as choosing a job or relationship partner; or analyzing individu al variation in MBS answers to scenario-specific items. The flexibi lity or rigidity in maximization engagement in response to different contexts may shed fu rther light on the eff ects of maximization. Accordingly, individuals may be guided to cultivate maximization in respect to more realistically-founded economic and psychological benefits. 67


Concluding Remarks Despite the lim itations in the current research and the need for future study, this dissertation made a significant contribution to psychology, and specifica lly to the study of maximization, for a variety of reasons. First, it produced the MBS, a refined measure of maximization with strong theoretical adhe rence and converging va lidity support based on multiple indicators from two large samples. Given its superiority over the original maximization scale, the MBS is likely to faci litate the fast growing body of re search in this field. Second, the current study revealed the dynamic relationship between maximization and adaptive/maladaptive perfectionism, which had not been explored in a rigorous manner due to conceptualization or validity limitations associated with the other perf ectionism measures used in the past. Third, the dissertation helped to clarif y and resolve several misunders tandings regarding maximization, including the suggestion that maximization is not necessarily a rational cogn itive process, as well as the implications of maximization being a pur ely beneficial or purel y detrimental tendency. The research revealed that the outcome of maximi zation is related to its level of practice and the nature of its underlying drives. Finally, the current study pointed to several new directions of research to further specify the maximization phenomenon. Individuals strive to obtain better choices for the hope of a better life; likewise, modern Western societies grow in the direction of pr oviding individuals an abundance of choices. It would be counterintuitive and even tragic if such efforts would be the very path to unhappiness, as claimed by Schwartz (2004a). Fortunately, this study suggests that Schw artzs speculation is not necessarily true and that the path toward depression is not so simple. Granted, maximization may fail to provide satisfaction, in contrast to what many extreme maximizers may believe. This is particularly true when unrealistically high standards and perception of self-inadequacy have been present, as in the case of maladaptive perfectioni sts. I agree with Sc hwartz (2004b) that 68


69 individuals should avoid i ndiscriminant maximization and overabundance of choices. Nevertheless, maximization may still be a valuab le decision-making strategy, if individuals can utilize it judiciously with regard to decision contexts, realistica lly appraise costs and benefits, pursue the choices approximating their high standa rds, and bestow positiv e appraisals on their choices once they have been made.


APPENDIX A QUESTIONNAIRE OF STUDY 1 Maximiz ation and Regret Scale In the space next to the statem ent, please enter a number from "1" (completely disagree) to "7" (completely agree) to describe your degree of agreement with each item. 1 2 3 4 5 6 7 Completely agree Completely disagree _____ 1. Whenever Im faced with a choice, I try to imagine what all the other possibilities are, even ones that arent present at the moment. _____ 2. No matter how satisfied I am with my job, its only right for me to be on the lookout for better opportunities. _____ 3. When I am in the car listening to the ra dio, I often check other stations to see if something better is playing, even if Im rela tively satisfied with what Im listening to. _____ 4. When I watch TV, I channel surf, often scanning through the ava ilable options even while attempting to watch one program. _____ 5. I treat relationships like clothing: I expect to try a lot on be fore I get the perfect fit. _____ 6. I often find it difficult to shop for a gift for a friend. (Replaced) _____ 7. Renting videos is really difficult. Im always struggling to pick the best one. (Replaced) _____ 8. When shopping, I have a hard time findi ng clothing that I really love. (Replaced) _____ 9. Im a big fan of lists that attempt to rank things (the best movies, the best singers, the best athletes, the best novels, etc.) _____ 10. I find that writing is very difficult, even if its just writing a lett er to a friend, because its so hard to word things just right. I of ten do several drafts of even simple things. 70


_____ 11. No m atter what I do, I have th e highest standards for myself. _____ 12. I never settle for second best. _____ 13. I often fantasize about living in ways that are quite different from my actual life. _____ 14. Whenever I made a choice, Im curious about what would have happened if I had chosen differently. _____ 15. Whenever I made a choice, I try to get information about how the other alternatives turned out. _____ 16. If I make a choice and it turns out well, I st ill feel like something of a failure if I find out that another choice woul d have turned out better. _____ 17. When I think about how Im doing in life, I often assess opportunities I have passed up. _____ 18. Once I made a decision, I dont look back. Newly-developed Maximization Items _____ 19. I always keep my options open so I will not miss the next best c hoice available in life. _____ 20. Even if I see a choice I really like, I have a hard time to make the decision if I do not have a chance to check out other possible options. _____ 21. When going to a new restaurant, I find myself reading the complete menu before narrowing down on what I want to eat. _____ 22. I try to do an extensive search when I look for a gift for a close friend. (Replacement for Item 6) _____ 23. If renting video, I usually do not spend mu ch time to decide which video to rent. (Replacement for Item 7) _____ 24. When shopping, I often need to scan all the clothing available in a store before I decide on what to try and buy. (Replacement for Item 8) 71


72 Note. Reverse scored. Demographic Questions What is your age? What is your gender? I consider myself (choose one): 1 White/Caucasian, 2 Black/African American, 3 Hispanic/Latino, 4 Arab/Middle Eastern, 5 Asian/South Pacific Islander, 6 Native American, 7 Biracial/Multiethnic, 8 Other.


APPENDIX B CONS ENT FORM OF STUDY 2 Informed Consent Form The purpose of this study is to study how certain personality characteristics relate to emotional functioning. You will be asked to complete a se t of questionnaires concerning attitudes you have about yourself, decision-making, and emotional f unctioning. It should take about 30 minutes or less to complete the questionnaires There are no known risks involved in completing the study and many students may find that they learn something about themselves from participating in similar studies. Nonetheless, if being part of the study makes you feel uncom fortable, you may consider speak ing to a counselor who may be able to help you with your reactions. You can contact a couns elor through the University of Florida Counseling Center (P301 Peabody Hall, 3 92-1575). You may benefit by participating in this study through increased awareness and self-u nderstanding. You will also be contributing to knowledge regarding researchers ability to understand personality characteristics and emotional functioning. There is no compensation to you for participating in the study. Your identity will be kept confidential to the extent provided by law. Your responses on the questionnaires will be assigned a code number. The list connecting your name to this number will be kept in a password-protected computer file. When the study is completed and the data have been analyzed, the list will be destroyed. Your name will not be used in any report. You can only participate if you are 18 years of age, or older. Your partic ipation in this study is completely voluntary. There is no penalty for not participating and you have the right to withdraw from the study at anytime without consequence. If you have any questions concerning the surve y, you may contact Huan J. Ye, Department of Psychology, University of Florid a, Gainesville, FL 32611-2250, ph 392-0601, or Dr. Ken Rice, Department of Psychology, University of Florida, Gainesville, FL 32611-2250, ph 273-2119, Any questions or concerns about your rights in this study can be dire cted to the UFIRB Office, Box 112250, University of Florida, Gainesville, FL 32611-2250. I certify that I have read the precedin g or it has been read to me and that I have freely agreed to participate in this research study. I can print a copy of the consent form. Thank you for your time. We sincerely apprec iate your involvement in this research. Please enter your name in the following blank to indicate your consent of participation. ( i.e., electronic signature. ) _______________ ________________ (First name) (Last name) 73


APPENDIX C QUESTIONNAIRE OF STUDY 2 Maximiz ation and Regret Scale (Same as in Study 1.) Almost Perfect Scale-Revised (Slaney et al., 2001) The following items are designed to measure certa in attitudes people have toward themselves, their performance, and toward others. It is impor tant that your answers be true and accurate for you. In the space next to the statement, please en ter a number from "1" (strongly disagree) to "7" (strongly agree) to describe your degree of agreement with each item. STRONGLY DISAGREE 1 DISAGREE 2 SLIGHTLY DISAGREE 3 NEUTRAL 4 SLIGHTLY AGREEE 5 AGREE 6 STRONGLY AGREE 7 _____ 1. I have high standards for my perf ormance at work or at school. _____ 2. I am an orderly person. _____ 3. I often feel frustrated be cause I cant meet my goals. _____ 4. Neatness is important to me. _____ 5. If you dont expect much out of yourself you will never succeed. _____ 6. My best just never se ems to be good enough for me. _____ 7. I think things should be put away in their place. _____ 8. I have high expectations for myself. _____ 9. I rarely live up to my high standards. _____ 10. I like to always be organized and disciplined. 74


_____ 11. Doing my best never seems to be enough. _____ 12. I set very high standards for myself. _____ 13. I am never satisfied with my accomplishments. _____ 14. I expect the best from myself. _____ 15. I often worry about not measuring up to my own expectations. _____ 16. My performance rarely measures up to my standards. _____ 17. I am not satisfied even when I know I have done my best. _____ 18. I am seldom able to meet my own high standards for performance. _____ 19. I try to do my best at everything I do. _____ 20. I am hardly ever satisfied with my performance. _____ 21. I hardly ever feel that what Ive done is good enough. _____ 22. I have a strong need to strive for excellence _____ 23. I often feel disappointment after comple ting a task because I know I could have done better. 75


Subjective Happiness Scale (Lyubom irsky & Lepper, 1999) For each of the following statements and/or quest ions, please circle the point on the scale that you feel is most appropriate in describing you. 1. In general, I consider myself: 1 2 3 4 5 6 7 not a very happy person a very happy person 2. Compared to most of my peers, I consider myself: 1 2 3 4 5 6 7 less happy more happy 3. Some people are generally very happy. They en joy life regardless of what is going on, getting the most out of everything. To what extend does this characterization describe you? 1 2 3 4 5 6 7 not at all a great deal 4. Some people are generally not very happy. Although they are not depressed, they never seem as happy as they might be. To what exte nd does this characterization describe you? 1 2 3 4 5 6 7 not at all a great deal 76


Satisfaction with Life Scale (Diener, et al., 1985) Below are five statements with which you may ag ree or disagree. Usi ng the 1-7 scale below, indicate the extent of your agreement with each item by mark ing the appropriate number in the space next to each statement. STRONGLY DISAGREE 1 DISAGREE 2 SLIGHTLY DISAGREE 3 NEUTRAL 4 SLIGHTLY AGREE 5 AGREE 6 STRONGLY AGREE 7 _____ 1. In most ways my life is close to ideal. _____ 2. The conditions of my life are excellent. _____ 3. I am satisfied with my life. _____ 4. So far I have gotten the importa nt things I want in my life. _____ 5. If I could live my life over, I would change almost nothing. IS-Item Need for Cognition Scale (Cacioppo et al., 1984) 1 2 3 4 5 extremely uncharacteristic of me somewhat uncharacteristic of me neutral somewhat characteristic of me extremely characteristic of me 1. I would prefer complex to simple problems. 2. I like to have the responsibil ity of handling a situation that requires a lot of thinking. 3. Thinking is not my idea of fun.* 4. I would rather do something th at requires little thought than something that is sure to challenge my thinking abilities.* 5. I try to anticipate and a void situations where there is likely ch ance I will have to think in depth about something.* 77


6. I find satisfaction in deliberating hard and for long hours. 7. I only think as hard as I have to. 8. I prefer to think a bout sm all, daily projec ts to long-term ones.* 9. I like tasks that re quire little thought on ce I've learned them.* 10. The idea of relying on thought to ma ke my way to the top appeals to me. 11. I really enjoy a task that involves coming up with new solutions to problems. 12. Learning new ways to think doesn't excite me very much.* 13. I prefer my life to be filled w ith puzzles that I must solve. 14. The notion of thinking abstra ctly is appealing to me. 15. I would prefer a task that is intellectual, difficult, and impo rtant to one that is somewhat important but does not require much thought. 16. I feel relief rather than satisfaction after completing a task that required a lot of mental effort.* 17. It's enough for me that something gets th e job done; I don't care how or why it works.* 18. I usually end up deliberating about issues ev en when they do not affect me personally. Note. Reverse scored. 78


BECK DEP RESSION INVE NTORY, SHORT FORM Instructions: This is a questionnaire. On the questionnaire are gr oups of statements. Please read the entire group of statements in each category. Then pick out the one statement in that group which best describes that way you feel today, that is, right now Circle the number beside the statement you have chosen. If several statements in the group seem to apply equally well, circle each one. Be sure to read all the statements in ea ch group before making your choice. A. (Sadness) 3 I am so sad or unhappy that I cant stand it. 2 I am blue or sad all the time and I cant snap out of it. 1 I feel sad or blue. 0 I do not feel sad. B. (Pessimism) 3 I feel that the future is hopeless and that things cannot improve. 2 I feel I have nothing to look forward to. 1 I feel discouraged about the future. 0 I am not particularly pessimistic or discouraged about the future. C. (Sense of failure) 3 I feel I am a complete failure as a person (parent, husband, wife). 2 As I look back on my life, all I can see is a lot of failures 1 I feel I have failed more than the average person. 0 I do not feel like a failure. D. (Dissatisfaction) 3 I am dissatisfied with everything. 2 I dont get satisfaction out of anything anymore. 1 I dont enjoy things the way I used to. 0 I am not particularly dissatisfied. E. (Guilt) 3 I feel as thought I am very bad or worthless. 2 I feel quite guilty. 1 I feel bad or unworthy a good part of the time. 0 I dont feel particularly guilty. F. (Self-dislike) 3 I hate myself. 2 I am disgusted with myself. 1 I am disappointed in myself. 0 I dont feel disappointed in myself. G. (Self-harm) 3 I would kill myself if I had the chance. 2 I have definite plans about committing suicide. 1 I feel I would be better off dead. 0 I dont have any thoughts of harming myself. H. (Social withdrawal) 3 I have lost all of my interest in other people and dont care about them at all. 2 I have lost most of my interest in other people and have little feeling for them. 1 I am less interested in other people than I used to be. 0 I have not lost interest in other people. I. (Indecisiveness) 3 I cant make any decisions at all anymore. 2 I have great difficulty in making decisions. 1 I try to put off making decisions. 0 I make decisions about as well as ever. J. (Self-image change) 3 I fell that I am ugly or repulsive-looking. 2 I feel that there are permanent changes in my appearance and they make me look unattractive. 1 I am worried that I am looking old or unattractive. 0 I dont feel that I look any worse than I used to. K. (Work difficulty) 3 I cant do any work at all. 2 I have to push myself very hard to do anything. 1 It takes extra efforts to get started at doing something. 0 I can work about as well as before. L. (Fatigability) 3 I get too tired to do anything. 2 I get tired from doing anything. 1 I get tired more easily than I used to. 0 I dont get any more tired than usual. M. (Anorexia) 3 I have no appetite at all anymore. 2 MY appetite is much worse now. 1 My appetite is not as good as it used to be. 0 My appetite is no worse than usual. 79


80 Demographic Questions What is your age? _______________ (blank) What is your gender (choose one)? 1. Male 2. Female 3. Other, pls specify: ________________. I consider myself (choose one): 1. Asian or Asian-American, 2. Black, African-American, 3. Hispanic, Latino, Mexican-American 4. Pacific Islander, 5. Native American or American Indian, 6. White, European American, 7. Multicultural or Mixed Race, 8. Middle Eastern 9. Other, pls specify: ________________.


LIST OF REFERE NCES Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Thousand Oaks, CA, US, Sage Publications, Inc. Baron, R. M., & Kenny, D. A. (1986). The moderato r-mediator variable di stinction in social psychological research: Con ceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology 51 1173-1182. Beck, A. T., & Beck, R. W. (1972). Screening depr essed patients in a family practice: A rapid technique. Postgraduate Medicine, 52 81-85. Beck, A. T., Ward, C. H., Mendelson M., Mock, J., & Erbaugh, J. (1961). An inventory for m easuring depression. Archives of General Psychiatry, 4, 561-571. Beck, A. T. (1967). Depression: Clinical, Experimental, and Theoretical Aspects. New York: Harper & Row. Bentler, P.M. (1983). Some contributions to efficient statistics for structural models: Specification and estimation of moment structures. Psychometrika, 48 493. Bergman, A. J., Nyland, J. E., & Burns, L. R. (2007). Correlates with perfectionism and the utility of a dual process model. Personality and Individual Differences 43 389-399. Berzonsky, M. D., & Sullivan, C. (1992). Social-cognitive aspects of identity style: Need for cognition, experiential ope nness, and introspection. Journal of Adolescent Research, 7, 140-155. Byrne, B. M. (2004). Testing for multigroup invariance using AMOS graphics: A road less traveled. Structural Equation Modeling 11, 272. Cacioppo, J. T., & Petty, R. E. (1984). The need for cognition: Relationship to attitudinal processes. In R. P. McGlynn, J. E. Maddux, C. D. Stoltenberg, & J. H. Harvey (Eds.), Social perception in clinic al and counseling psychology (pp. 91-119). Lubbock: Texas Tech University. Cacioppo, J. T., Petty, R. E., Feinstein, J. A ., & Jarvis, W. B. G. (1996). Dispositional differences in cognitive motiva tion: The life and times of indi viduals varying in need for cognition. Psychological Bulletin 119, 197-253. Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42, 116-131. Cacioppo, J. T., Petty, R. E., & Kao, C. F. (1984). The efficient assessment of need for cognition. Journal of Personality Assessment, 48, 306-307. Comrey, A. L., & Lee, H. B. (1992). A First Course in Factor Analysis (2nd ed.) Hilldale, NJ: Lawrence Erlbaum Associates. 81


Conger, A. J. (1974). A revised defin ition for supp ressor variables: A guide to their identification and interpretation. Educational and Psychological Measurement 34, 35-46. Diab, D. L., Gillespie, M. A., & Highhouse, S. (2008). Are maximizers really unhappy? The measurement of maximizing tendency. Judgment and Decision Making 3, 364-370. Diener, E., Emmons, R. A., Larsen, R. J., & Gri ffin, S. (1985). The Satisfaction With Life Scale. Journal of Personality Assessment, 49, 71. Edwards, J. R., & Lambert, L. S. (2007). Met hods for integrating moderation and mediation: A general analytical framework us ing moderated path analysis. Psychological Methods 12, 1-22. Field, A. (2005). Discovering Statistics Using SPSS (2nd ed.) London: Sage. Fletcher, F. J. O., Danilovics, P., Fernand ez, G., Peterson, D., & Reeder, G. D. (1986). Attributional complexity: An i ndividual difference measure. Journal of Personality and Social Psychology, 51 875-884. Frost, R. O., Marten, P., Larhart, C., & Rosenbl ate, R. (1990). The dimensions of perfectionism. Cognitive Therapy and Research, 14, 449-468. Frost, R. O., Heimberg, R. G., Holt, C. S., Ma ttia, J. I., & Neubauer, A. L. (1993). A comparison of two measures of perfectionism. Personality and Individual Differences, 14 119-126. Gosling, S. D., Vazire, S., Srivastava, S., & John, O. P. (2004). Should We Trust Web-Based Studies? A Comparative Analysis of Six Preconceptions About Internet Questionnaires. American Psychologist, 59, 93-104. Gould, J. (1982). A psychometric investigation of the sta ndard and short for m Beck Depression Inventory. Psychological Reports, 51, 1167-1170. Grzegorek, J. L., Slaney, R. B., Franze, S., & Rice, K. G. (2004). Self-Criticism, Dependency, Self-Esteem and Grade Point Average Satisfa ction Among Clusters of Perfectionists and Nonperfectionists. Journal of Counseling Psychology, 51, 192-200. Haase, A. M., & Prapavessis, H. (2004). Assessi n g the factor structur e and composition of the Positive and Negative Perfectionism Scale in sport. Personality and Individual Differences, 36, 1725-1740. Hewitt, P. L., & Flett, G. L. (1990). Perfectionism and depression: A multidimentionsal analysis. Journal of Social Behavior and Personality, 5, 423-438. Hewitt, P. L., & Flett, G. L. (1991). Perfec tionism in the self and social contexts: Conceptualization, assessment, a nd association with psychopathology. Journal of Personal and Social Psychology, 60, 456-470. 82


Hewitt, P. L., Flett, G. L ., Sherry, S. B., Habke, M., Parkin, M., Lam, R. W., McMurtry, B., Ediger, E., Fairlie, P., & Stein, M. B. (2003). The interpersonal expr ession of perfection: Perfectionistic self-presentati on and psychological distress. Journal of Personality and Social Psychology, 84, 1303-1325. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria vers us new alternatives. Structural Equation Modeling 6, 1. Inman, J. J., McAlister, L., & Hoyer, W. D. ( 1990). Promotion signal: Proxy for a price cut? Journal of Consumer Research, 17 74-81. Iyengar, S. S., Wells, R. E., & Schwartz, B. (2006). Doing Better but Feeling Worse: Looking for the 'Best' Job Undermines Satisfaction. Psychological Science, 17, 143-150. Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating. Journal of Personality and Social Psychology, 79, 995. Jarvis, B., & Petty, R. E. (1996). The need to evaluate. Journal of Personality and Social Psychology, 70, 172-194. Johnson, J. A. (2001, May). Screening massively large data se ts for non-responsiveness in Webbased personality inventories. Invited talk to the joint Bielefeld-Groningen Personality Research Group, University of Groningen, The Netherlands. Joreskog, K. G., & Sorbom, D. (2003). LISREL 8.54. Chicago: Scientific Software International. Kernis, M. H., Grannemann, B. D., & Barclay, L. C. (1992). Stability of self-esteem: Assessments, correlates, and excuse making. Journal of Personality, 60, 621-644. Kline, R. B. (2005). Principles and practice of stru ctural equation modeling (2nd ed.). New York, NY, US, Guilford Press. Knight, R. G. (1984). Some general population norms for the short form Beck Depression Inventory. Journal of Clinical Psychology, 40, 751-753. Larsen, J. T., & McKibban, A. R. (2008). Is ha ppiness having what you want, wanting what you have, or both? Psychological Science, 19, 371-377. Lyubomirsky, S., & Lepper, H. S. (1999). A m easure of subjective happiness: Preliminary reliability and construct validation. Social Indicators Research, 46, 137. MacKinnon, D. P., Krull, J. L., & Lockwood, C. M. (2000). Equivalence of the mediation, confounding and suppression effect. Prevention Science 1, 173-181. MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods 7 83-104. 83


Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness-of-fit inde xes in confirm atory factor analysis: The effect of sample size. Psychological Bulletin, 103, 391-410. Marsh, Herbert W., Hau, Kit-Tai, Balla, John R ., & Grayson, David. (1998). Is more ever too much? The number of indicators per factor in confirmatory factor analysis Multivariate Behavioral Research, 33, 181-220. Miller, M. L., Omens, R. S., & Delvadia, R. (1991). Dimensions of social competence: Personality and coping style correlates. Personality and Individual Differences, 12, 955964. Mobley, M., Slaney, R. B., & Rice, K. G. (2005). Cultural validity of the Almost Perfect Scale-Revised for African American college students. Journal of Counseling Psychology 52 629-639. Nenkov, G. Y., Morrin, M., Ward, A., Schwartz, B ., & Hulland, J. (2008). A short form of the Maximization Scale: Factor structure, reliability and validity studies. Judgment and Decision Making, 3, 371-388. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York: McGraw-Hill. Parker, A. M., de Bruin W. B ., & Fischhoff, B. (2007). Maximizers versus satisficers: Decisionmaking styles, competence, and outcomes. Judgment and Decision Making, 6, 342-350. Payne, J. W. (1982). Contingent decision behavior. Psychological Bulletin, 92, 382. Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The Adaptive Decision Maker. New York: Cambridge University Press. Petty, R. E., & Jarvis, W. B. G. (1996). An individual differences perspective on assessing cognitive processes. In Schwarz, Norbert (Ed); Sudman, Seymour (Ed), Answering questions: Methodology for de termining cognitive and communi cative processes in survey research. (pp. 221-257). San Francisc o, CA, US: Jossey-Bass. Preacher, K. J., & Hayes, A. F. (2008). Asympto tic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods 40, 879-891. Quintana, S. M., & Maxwell, S. E. (1999). Implications of recent developments in structural equation m odeling for counseling psychology. Counseling Psychologist, 27, 485-527. Rassin, E. (2007). A psychological theory of indecisiveness. Netherlands Journal of Psychology, 63, 2-13. Rice, K. G., & Slaney, R B. (2002). Clusters of perfectionists: Two studies of emotional adjustment and academic achievement. Measurement and Evaluation in Counseling and Development, 35, 35-48. 84


Rice, K. G., & Ashby, J. S. (2007). An effici ent method for classifying perf ectionists. Journal of Counseling Psychology, 54, 72-85. Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, NJ: Princeton University Press. Rosenthal, R., & Rosnow, R.L. (1991). Essentials of behavioral research: Methods and data analyses (2nd Edition). Boston: McGraw-Hill. Rozin, P., Fischler C., & Shiel d, C. (2006). Attitudes towards large numbers of choices in the food domain: A cross-cultural study of fi ve countries in Europe and the USA. Appetite, 46, 304-308. Russell, D., Peplau, L. A., & Ferguson, M. L. (1978). Developing a measure of loneliness. Journal of Personality Assessment, 42, 290-294. Sadowski, C. J., & Gulgoz, S. (1992b). Internal cons istency and testretest re liability of the Need for Cognition Scale. Perceptual and Motor Skills, 74 610. Satorra, A., & Bentler, P. M. (2001). A scaled di fference chi-square test statistic for moment structure analysis. Psychometrika, 66, 507-514. Schmitt, N. (1996). Uses and abuses of coefficient alpha. Psychological Assessment, 8, 350-353. Schwartz, B., Ward, A., Monterosso, J., Lyubo mirsky, S., White, K., & Lehman, D. R. (2002). Maximizing versus satisficing: Ha ppiness is a matter of choice. Journal of Personality and Social Psychology, 83, 1178-1197. Schwartz, B. (2004a). The Tyranny of Choice. Scientific American, April, 72-75. Schwartz, B. (2004b). The Paradox of Choice: Why More Is Less New York: Harper Collins. Shafran, R., Cooper, Z., & Fairburn, C. G. (2002). Clinical perfectionism: A cognitivebehavioural analysis. Behaviour Research and Therapy, 40, 773-791. Shea, A. J., Slaney, R. B., & Rice, K. G. ( 2006). Perfectionism in Intimate Relationships: The Dyadic Almost Perfect Scale. Measurement and Evaluation in Counseling and Development, 39, 107-125. Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and none xperimental studies: New procedures and recommendations. Psychological Methods, 7, 422-445. Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 59, 99. Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63, 129. 85


86 Slaney, R. B., Rice, K. G., Mobley, M., Trippi J., & Ashby, J. S. (2001). The Revised Almost Perfect Scale. Measurement and Evaluation in Counseling and Development, 34, 130-145. Slaney, R. B., Rice, K. G., & Ashby, J. S. (2002). A programmatic approach to measuring perfectionism: The Almost Perfect Scales. In G. L. Flett & P. L. Hewitt (Eds.), Perfectionism: Theory, re search, and treatment (pp. 63-88). Washington, DC: American Psychological Association. Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and none xperimental studies: New procedures and recommendations. Psychological Methods, 7 422-445. Sobel, M. E. (1982). Asymptotic intervals for indi rect effects in structur al equations models. In S. Leinhart (Ed.), Sociological methodology, 290-312 San Francisco: JosseyBass. Stoeber, J., & Otto, K. (2006 ). Positive conceptions of perfectionism: Approaches, evidence, challenges. Personality and Social P sychology Review, 10, 295-319. Suddarth, B. H., & Slaney, R. B. (2001). An inves tigation of the dimensions of perfectionism in college students. Measurement and Evaluation in Counseling and Development, 34, 157165. Tabachnick, B. G., & Fidell, L. S. (2007). Using Multivariate Statistics Boston, MA: Pearson Education, Inc. Terry-Short, L. A., Owns, R. G., Slade, P. D., & Dewey, M. E. (1995) Perfectionism and Negative Pe rfectionism. Personality and Individual Difference, 18, 663-668. Tzelgov, J., & Henik, A. (1991). Suppression situa tions in psychological research: Definitions, implications, and applications. Psychological Bulletin 109, 524-536. Venkatraman, M. P., Marlino, D., Kardes, F. R., & Sklar, K. B. (1990). Effects of individual difference variables on response to factual and evaluative ads. Advances in Consumer Research, 17, 761-765. Von Neumann, J., & Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton, NJ: Princeton University Press. Webster, D. M., & Kruglanski, A. W. (1994). Individual differences in need for cognitive closure. Journal of Personality and Social Psychology 67, 1049-1062. Weston, R., & Gore Jr., P. A. (2006). A Brief Guide to Structural Equation Modeling. Counseling Psychologist, 34, 719-751. Worthington, R. L., & Whittaker, T. A. ( 2006). Scale Development Research: A Content Analysis and Recommendations for Best Practices. Counseling Psychologist 34, 806-838. Zung, W. W. (1973). From art to science: The diagnosis a nd treatment of depression. Archives of General Psychiatry, 29, 328-337.


BIOGR APHICAL SKETCH Huan Jacqueline Ye was born in 1976 in Guiyang, China. At Age of 18 she attended the University of International Business and Economics in Beijing, China, majoring in Business Management. After obtaining my bachelor degree, she worked for two years in business. In 2001, she decided to undertake the study of ps ychology and started as an undergraduate at University of Waterloo, Ontario, Canada. In 2002, she was admitted to the master program Social and Developmental Psycho logy at Wilfrid Laurier Univers ity, Canada, and obtaining her Master of Arts degree after tw o years of study and research. She joined the Department of Psychology at the University of Florida as a counseling doctoral student in August 2005. (She expects to complete her Doctor of Philosophy degree in August 2010.) 87