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The development and investigation of a theoretical model for clinical decision making in physical therapy

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The development and investigation of a theoretical model for clinical decision making in physical therapy
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Day, Jane A., 1946-
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x, 94 leaves : ill. ; 29 cm.

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Decision making ( jstor )
Graduates ( jstor )
Heuristics ( jstor )
Investigational therapies ( jstor )
Modeling ( jstor )
Nursing ( jstor )
Nursing students ( jstor )
Physical therapy ( jstor )
Problem solving ( jstor )
Reasoning ( jstor )
Medical logic ( lcsh )
Physical therapy -- Decision making ( lcsh )
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Thesis:
Thesis (Ph. D.)--University of Florida, 1991.
Bibliography:
Includes bibliographical references (leaves 87-93).
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Typescript.
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Vita.
Statement of Responsibility:
by Jane A. Day.

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Copyright [name of dissertation author]. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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THE DEVELOPMENT AND INVESTIGATION OF A THEORETICAL MODEL
FOR CLINICAL DECISION MAKING IN PHYSICAL THERAPY










By

JANE A. DAY


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY


1991




THE DEVELOPMENT AND INVESTIGATION OF A THEORETICAL MODEL
FOR CLINICAL DECISION MAKING IN PHYSICAL THERAPY
By
JANE A. DAY
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
1991


Dedicated with love to my Mom and Dad
they taught me the true value of love and sacrifice
and they never quit believing in me.


ACKNOWLEDGEMENTS
After all these years, with absolute pleasure, I have an opportunity to
thank people for their support. I express sincere appreciation to the
members of my committee: Dr. James W. Hensel, chairman, Dr. Mary Kay
Dykes, and Dr. Forrest W. Parkay for their encouragement and guidance
throughout this process. I owe a special thanks to Dr. Hensel for remaining
positive at all times, for never giving up on me no matter how long the effort
took. I also thank Dr. Margaret K. Morgan for her support and for her advice
regarding this manuscript.
I am especially grateful and indebted to two of my mentors in physical
therapy education. Dr. Dorothy Pinkston faithfully supported me through this
dissertation experience, and her professionalism and knowledge continually
encouraged and motivated me. Dr. Katherine Shepard also served as an
inspiration, providing assistance when asked, always with warmth and
friendship.
I thank the people who helped with the data collection for this study
and all the students who voluntarily participated in the study. Without these
people the dissertation would only have been a dream.
I extend special thanks to my mom, Ruth, for her love, faith,
understanding and pride, and to my dad, Warren, who is not here to see me
finish, but whose love and support are with me even in his absence.


I also give heartfelt thanks to Sarah Chesrown, with whom I have
shared a house and a wonderful friendship during these years of graduate
work. I appreciate her unfailing support, understanding, and love during this
process. Additionally, I thank all my other friends for believing in me and for
their love and repeated encouragement. I especially thank Martha Wroe for
willingly being my sounding board on almost a daily basis. I share my
accomplishment with these special people.


TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS iii
LIST OF TABLES viii
ABSTRACT ix
CHAPTERS
1INTRODUCTION 1
Background of the Problem 1
Statement of the Problem
Purpose of the Study
Research Questions
Significance of the Study.
Assumptions
Limitations of the Study....
Definition of Terms
2 REVIEW OF LITERATURE 11
Problem Solving, Critical Thinking, and Decision
Making-A Clarification of Terms 11
Problem Solving and Clinical Decision Making in
Physical Therapy and Other Health Related
Fields 13
Measurement of Problem-Solving and Clinical
Decision-Making Ability 22
Instruments Selected for Use in Study 25
Clinical Decision Making in Nursing Scale 25
Graduate Record Examination 27
Summary 29
3 MODEL DEVELOPMENT 31
Introduction 31
Three Theories of Required Attributes For Problem
Solving and Decision Making 32
1. Knowledge as the Key 32
v
CD CD 00 00 "vi O Ol


2. Heuristic Process as the Key 34
3. Heuristic process, Knowledge, and
Analytical Reasoning Combined 34
A Heuristic Process for Decision Making 35
The Theoretical Model for Clinical Decision Making
in Physical Therapy 36
Components of the Model 38
Testing the Model 39
Summary 39
4 METHODOLOGY 41
Questions and Hypotheses 43
Subjects 44
Procedure 44
Data Analysis 46
Instrumentation 47
Clinical Decision Making in Nursing Scale 47
Graduate Record Examination 49
5 RESULTS 51
Introduction 51
Relationship between GRE-VQ Scores, GRE-A
Scores, and GPAs, and CDMS Total and
Subscale Scores 52
Difference in Performance of Students on the
CDMS by Physical Therapy Program 57
Descriptive Data for Total Students and for
Students From Each Physical Therapy Program 58
Summary 59
6 DISCUSSION, CONCLUSIONS, AND
RECOMMENDATIONS 60
Discussion 60
Conclusions and Recommendations 65
APPENDICES
A COVER SHEET FOR STUDENT'S NAME OR
IDENTIFICATION NUMBER 71
B THE CLINICAL DECISION-MAKING SCALE 72


C ANSWER SHEET FOR CLINICAL DECISION
MAKING SCALE 78
D BIOGRAPHICAL INFORMATION FORM 79
E DATA RECORDING FORM 80
F DESCRIPTIVE DATA SUMMARY FOR PROGRAM
A STUDENTS 81
G DESCRIPTIVE DATA SUMMARY FOR PROGRAM
B STUDENTS 82
H DESCRIPTIVE DATA SUMMARY FOR PROGRAM
C STUDENTS 83
I DESCRIPTIVE DATA SUMMARY FOR PROGRAM
D STUDENTS 84
J DESCRIPTIVE DATA SUMMARY FOR PROGRAM
E STUDENTS 85
K DESCRIPTIVE DATA SUMMARY FOR PROGRAM
F STUDENTS 86
REFERENCES 87
BIOGRAPHICAL SKETCH 94


LIST OF TABLES
TABLE Page
5-1 MULTIPLE REGRESSION OF CDMS TOTAL
SCORE MODEL 53
5-2 MULTIPLE REGRESSION OF CDMS-A SCORE
MODEL 54
5-3 MULTIPLE REGRESSION OF CDMS-B SCORE
MODEL 54
5-4 MULTIPLE REGRESSION OF CDMS-C SCORE
MODEL 55
5-5 MULTIPLE REGRESSION OF CDMS-D SCORE
MODEL 55
5-6 CORRELATION MATRIX FOR ALL EIGHT
VARIABLES 56
5-7 FIVE ONE-WAY ANOVAS COMPARING SIX
PHYSICAL THERAPY PROGRAMS ON CDMS
TOTAL AND SUBSCALE SCORES 58
5-8 DESCRIPTIVE DATA SUMMARY FOR ALL
STUDENTS 59
v i i i


Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
THE DEVELOPMENT AND INVESTIGATION OF A THEORETICAL MODEL
FOR CLINICAL DECISION MAKING IN PHYSICAL THERAPY
By
Jane A. Day
May 1991
Chairman: Dr. James W. Hensel
Major Department: Educational Leadership
The primary purpose of this study was to investigate the relationship
between components of the Theoretical Model for Clinical Decision Making
in Physical Therapy and the decision-making ability of students in masters
degree entry-level physical therapy programs. This investigator developed
the model after a review of literature revealed various theories regarding
attributes required for quality decision making.
A secondary purpose was to determine any significant differences in
decision-making ability among students from different physical therapy
programs. Although the programs selected for the study were representative
of both geographic location and institutional funding source (state versus
private), each program was unique regarding the approach to teaching
clinical decision making.


The subjects were 244 postbaccalaureate students from six entry-
level master's degree physical therapy programs. This investigator tested
four hypotheses. Three related to the model were tested by analyzing data
from the six programs with five multiple regression analyses to determine the
relation between a measure of verbal and quantitative knowledge, a
measure of professional knowledge, a measure of analytical reasoning
ability, and a measure of clinical decision-making ability. The independent
variables for each analysis were the students' Graduate Record Examination
Verbal plus Quantitative scores, Analytical scores, and final grade-point
averages in the physical therapy program. The dependent variable was
either the total score obtained on the Clinical Decision-Making Scale or the
score obtained on Subscale A, B, C, or D of that instrument. These
analyses, as well as Pearson product-moment correlations, revealed no
significant relation (p < .05) between the predictor and criterion variables.
The researcher tested the fourth hypothesis by determining whether
differences in decision-making ability were present in students from various
physical therapy programs. The investigator analyzed data from the six
programs with five one-way ANOVAs. The results indicated no significant
differences, at the .05 alpha level, in performance on the CDMS among
students from the six programs.
The researcher also analyzed descriptive data for all programs
combined and for each separately. The study includes possible
explanations for the results of all analyses and recommendations for future
investigations.
x


CHAPTER 1
INTRODUCTION
Background of the Problem
Physical therapy faculty have long been interested in how students
acquire problem-solving and clinical decision-making skills (Barr, 1977;
Day, 1986; May, 1977; Myers & Rose, 1989; Shepard, 1977; van der Sijde,
Sellink & Wurms,1987). Educators in other disciplines (Bloom & Broder,
1950; Ennis, 1982; Glaser, 1984; Gordon, 1966; Janis & Mann, 1977;
Jenkins, 1985; Newell & Simon, 1972; Polya, 1957; Whimbey & Lochhead,
1982; Wickelgren, 1974) have shared this interest and their discussions and
findings are relevant for the physical therapy educator.
Interest in decision making in physical therapy has recently
heightened nationally because of the trend toward practice without
practitioner referral or "direct access." Durant, Lord, and Domholdt (1989)
stated: "Since 1968, 21 states have passed laws enabling physical
therapists to evaluate and treat patients without a practitioner's referral. This
legislation gives consumers "direct access" to the services of physical
therapists" (p. 850). Magistro (1989) reminded physical therapists that this
expansion of practice into more independent modes makes it imperative that
1


2
the profession educate current and future practitioners in the methods of
clinical decision making.
This issue is of primary importance to the profession of physical
therapy. Myers and Rose (1989) have indicated that it is time to address
clinical decision making in a more formal sense, and Rothstein (1990) said
that the profession seems committed to improving the clinical decision
making process, as well as to training future generations of therapists to be
better decision makers. With these thoughts in mind, physical therapy
faculty are striving to teach clinical decision making and problem solving.
Physical therapy programs differ, however, in the number of
classroom hours devoted to helping students acquire problem-solving and
clinical decision-making skills, and a few programs actually have separate
courses on these topics. The document, Evaluative Criteria for Accreditation
of Education Programs for the Preparation of Physical Therapists
(APTA,1990a ) does not mandate methods or number of hours required to
cover problem solving and decision making. Faculty may design their
curricula in any manner, resulting in a variety of approaches to these topics
and major differences among programs.
Problem solving and clinical decision making are interrelated and
often confused with each other. Taylor (1965) perhaps oversimplified the
difference between the two terms when he stated that "problem solving is
that thinking which results in the solution of problems," and "decision making
is that thinking which results in the choice among alternative courses of
action . ." (p. 48). For the purposes of this paper, the researcher uses an
expansion of Taylor's definitions: Problem solving is the process of
analytical reasoning and knowledge recall necessary for the effective
solution of problems, and clinical decision making is the steps one goes


3
through, mentally and physically, to gather and analyze necessary
information and to choose among alternative courses of action to make a
decision regarding a client. Although problem solving and clinical decision
making are clearly two different concepts, they definitely have overlapping
aspects and problem solving is implicit in clinical decision making.
A review of the literature revealed that educators differ in theories
about what abilities are necessary for effective problem solving and decision
making. Some believe that skill in the heuristic process is the key element
(Whimbey & Lochhead, 1982; Wickelgren, 1974), whereas others (Chase &
Simon, 1973; Glaser, 1984) purport knowledge to be the alpha element.
Polya (1957) and Taylor (1965) discussed a third theory that indicates
content knowledge and the heuristic process are equally important.
Additionally, Glaser (1984) stated that "high-aptitude individuals appear to
be skillful reasoners because of the level of their content knowledge as well
as because of their knowledge of the procedural constraints of a particular
problem form, such as inductive or analogical reasoning" (p. 99).
These relationships between logical and analytical reasoning ability,
level of knowledge, and skill in the heuristic process are important for
physical therapists to understand because both problem-solving and
decision-making abilities are considered required skills for the effective
physical therapy clinical practitioner (May, 1984; Tammivaara & Yarbrough,
1984).
To illustrate and investigate these relationships, the investigator
developed and tested the Theoretical Model for Clinical Decision Making in
Physical Therapy (Figure 3-1). The model grew out of various theories
purported in the literature regarding the key attributes for decision making.
Chapter 3 contains a detailed explanation of the development of the model,


4
a model that incorporates all three attributes discussed in the literature-
knowledge, analytical reasoning, and the heuristic process.
Evaluation of clinical decision-making skill for the physical therapist is
difficult because, to date, no one has published an instrument to measure
clinical decision-making ability in physical therapy and other health care
disciplines have perfected few such measures. Faculty in physical therapy
have traditionally measured students' knowledge related to problem solving
and decision making by using content examinations at intervals throughout
the students' course of study. Measurement of the clinical decision-making
process, though, is more complex than measurement of content. Physical
therapists have, however, devoted much energy to the process of
understanding what is involved in clinical decision making and how to teach
it. Wolf (1985) included chapters devoted to decision analysis, clinical
decision making, and decision making in various physical therapy speciality
areas. Although Wolf and others discussed numerous aspects of the clinical
decision-making process, they presented no tools or procedures for
quantifying the level of skill in clinical decision making.
Burnett and Pierson (1988) discussed classroom activities designed
to enhance problem-solving skills in physical therapy, and Rothstein and
Echternach (1986) introduced the hypothesis-oriented algorithm for physical
therapy clinicians (HOAC). They designed this device to help physical
therapists in clinical decision making and patient management. Payton
(1985) published results of a study showing that the clinical problem-solving
sequence used by physical therapists was similar to a method that
physicians have used. None of these authors included a tool that could be
used to measure the clinical decision-making process.


5
Jenkins (1985), however, published a tool to measure the clinical
decision-making ability of nurses. She developed the Clinical Decision
Making in Nursing Scale (CDMNS) to measure how nursing students
perceived their own clinical decision-making ability. Jenkins later revised
the Likert-type answer scale descriptors to better reflect behavior rather than
perceived ability. Other nurses have since used the CDMNS to investigate
the decision-making process and published these studies in a master's
thesis (Engberg, 1987) and a doctoral dissertation (McFadden, 1987).
Although developed for use in nursing, the CDMNS is not specific to
nursing professionals, and therefore could be used to evaluate clinical
decision-making ability for other health professionals. No investigators have
published studies involving the use of the CDMNS by other health
professional faculty.
Statement of the Problem
The problem has two aspects. Decision making is considered a
required skill for effective clinical practice in physical therapy, and a tool for
measuring clinical decision-making abilities of physical therapists has not
been documented. The second aspect is that educators differ regarding the
importance of various attributes as they relate to problem-solving and
decision-making abilities. To date no theorist has developed and tested a
theoretical model for clinical decision making in physical therapy. This
investigator addressed both aspects of the problem in this prospective
cohort study.


6
Purpose q the.Study
In order to understand the factors influencing the process of clinical
decision making in physical therapy, this investigator developed a
theoretical model (Figure 3-1) that included the following components: (a)
general and professional knowledge, (b) analytical reasoning skill, and (c)
heuristic process skill. The purpose of the study was to investigate the
relation between these components of the model and the clinical decision
making ability of students in master's degree entry-level physical therapy
programs. The independent variables for the study were (a) a measure of
analytical reasoning, (b) a measure of verbal and quantitative knowledge,
and (c) a measure of professional knowledge; the dependent variable was a
measure of clinical decision-making ability. The independent variables
were operationally defined, respectively, as (a) students' scores on the
Analytical (GRE-A) portion of the Graduate Record Examination, (b) Verbal
and Quantitative (GRE-VQ) scores on the general portion of the Graduate
Record Examination (verbal plus quantitative score), and (c) final grade-
point averages (GPA) in the master's degree entry-level physical therapy
professional education program. The students' scores on the Clinical
Decision Making in Nursing Scale (CDMNS) became the operational
definition of the dependent variable.
A secondary purpose of the study was to determine whether
graduates from different physical therapy programs and, therefore, with
different degrees of formal instruction in problem solving and clinical
decision making performed differently on the CDMNS.


7
Research Questions
This investigator designed the first three research questions posed in
this study to investigate the relation between components of the Theoretical
Model for Clinical Decision Making in Physical Therapy (Figure 3-1) and the
clinical decision-making ability of physical therapy students. A fourth
research question was designed to determine whether graduates from these
various physical therapy programs differed in decision-making ability. Note
that the variables chosen to represent components of the model are as
follows: (a) students' CDMNS scores represent clinical decision- making
ability; (b) students' GRE-A scores represent analytical reasoning ability; (c)
students' GRE-VQ scores represent verbal and quantitative knowledge; and
(d) students' GPAs represent the didactic portion of professional knowledge.
Looking at students just prior to graduation from master's degree
entry-level physical therapy programs, the investigator asked these four
research questions:
1. Is there a relation between posttraining CDMNS scores (total or
individual subscale) and pretraining GRE-A scores after controlling for GRE-
VQ scores and GPA?
2. Is there a relation between posttraining CDMNS scores (total or
individual subscale) and pretraining GRE-VQ scores after controlling for
GRE-A scores and GPA?
3. Is there a relation between posttraining CDMNS scores (total or
individual subscale) and final GPA obtained for the physical therapy
program after controlling for GRE-VQ and GRE-A scores?
4. Is there a difference in performance on the CDMNS among
students of selected master's degree entry-level physical therapy programs?


8
Significance of the Study
The heightened interest in problem solving and clinical decision
making among physical therapy educators and the continuing debate
among educators regarding the important attributes for quality decision
making, intensified the need for the development and testing of the
Theoretical Model for Clinical Decision Making in Physical Therapy (Figure
3-1). The study was performed to end the debate over which attributes-
knowledge, analytical reasoning ability, and/or heuristic process-are most
important for quality decision making in physical therapy. This study also
adds to the current knowledge regarding clinical decision-making ability of
physical therapy students by documenting that ability with a quantifiable
instrument. The analysis of performance among students from different
programs is relevant for curriculum design and for clinical practice.
Assumptions
The assumptions underlying this study were as follows:
1. Graduates of physical therapy programs need to be able to
problem solve and to make sound clinical decisions.
2. Analytical reasoning is a necessary element for problem solving
and clinical decision making in physical therapy.
3. Verbal and quantitative knowledge are necessary elements for
problem solving and clinical decision making in physical therapy.
4. Professional knowledge is a necessary element for problem
solving and clinical decision making in physical therapy.


9
5. Scores on the CDMNS represent clinical decision-making
heuristic process ability.
Limitations of the Study
Three specific limitations have a direct impact on this study:
1. Random selection of subjects was impossible. An attempt was
made to obtain a representative sample by using subjects from six physical
therapy programs from various regions of the country, but since
randomization was not used, representation cannot be absolutely assured.
2. Generalization is limited to that population of individuals who are
at the point of completing a master's degree as the first professional degree
in physical therapy. (The first professional degree can be either a
baccalaureate or a master's degree, and the choice varies among
programs.)
3. Because the CDMNS is a self-report instrument, there was
concern about respondents indicating their perceived versus actual patterns
of decision-making behavior. To help alleviate this problem, the investigator
assured respondents of their anonymity and requested that they respond on
the basis of their actual behavior while in a clinical setting. She assured
them that the instrument had no "right" or "wrong" answers.
Definition of Terms
The investigator defined the following terms for use in this study:
1. Problem solving is the process of analytical reasoning and
knowledge recall necessary for the effective solution of problems.


1 o
2. Client refers to a health care consumer.
3. Clinical decision making is defined as the steps one goes
through, mentally and physically, to gather and analyze necessary
information and to choose among alternative courses of action to make a
decision regarding a client.
4. Heuristic process is the use of rules or steps when making a
clinical decision.
5. Professional knowledge is the physical therapy knowledge
acquired while attending a physical therapy program and any time
thereafter.
6. Analytical reasoning is the ability to understand the relations
between persons, places, things or events and to deduce new information
from those relations. Additionally, it is the ability to think analytically and
logically, including the ability to understand, analyze, and evaluate
arguments.
7. Grade-point average is the average for all didactic work
accomplished in the physical therapy program with a possible range of
1.00 4.00.


CHAPTER 2
REVIEW OF LITERATURE
This chapter contains a review of literature relevant to problem
solving and decision making. This review is to clarify related terms,
demonstrate relevance to the health professions, especially to physical
therapy, and describe published efforts to measure problem-solving and
clinical decision-making ability. Additionally, this chapter contains a review
of applicable literature on the use of the Graduate Record Examination and
the Clinical Decision Making in Nursing Scale. For clarity Chapter 3, Model
Development, contains a review of literature pertaining to theories of
required attributes for problem solving and decision making.
Problem Solving. Critical Thinking, and Decision Making A Clarification of
Terms
The literature contains many definitions of problem solving, critical
thinking, and decision making. Because the public often uses these terms
interchangeably, leading to confusion, mention of all of them is appropriate.
Chipman (1985) defined problem solving as the use of previously acquired
knowledge and skill to deal with new situations. Bruner (1964) described
problem solving as a cycle that includes formulating a testing procedure,


operating a testing procedure, and comparing the results of the test with
some criterion.
Kurfiss (1988) discussed both problem solving and critical thinking,
stating that the two are interrelated and discussing the differences between
them. She defined critical thinking as a form of problem solving that involves
reasoning about open-ended problems or problems with no single solution,
while she considered problem solving to be more narrow in scope because
a correct solution usually exists. She said that problem solvers analyze the
current state of the situation, "identify constraints, gather information,
generate one or more hypotheses, and test their hypotheses until the goal is
achieved" (p. 29).
Ennis (1982) described critical thinking as "the correct assessing of
statements" (p. 83). He further listed what he believed to be 12 aspects of
critical thinking:
1. Grasping the meaning of a statement.
2. Judging whether there is ambiguity in a line of reasoning.
3. Judging whether certain statements contradict each other.
4. Judging whether a conclusion follows necessarily.
5. Judging whether a statement is specific enough.
6. Judging whether a statement is actually the application of
a certain principle.
7. Judging whether an observation statement is reliable.
8. Judging whether an inductive conclusion is warranted.
9. Judging whether the problem has been identified.
10. Judging whether something is an assumption.
11. Judging whether a definition is adequate.
12. Judging whether a statement made by an alleged authority
is acceptable, (p. 84)
Ennis considered these twelve overlapping aspects to be characteristic of a
critical thinker and he saw the need for further study in how to integrate the
teaching of critical thinking into a curriculum. These aspects of critical


thinking are clearly related to problem solving and involve the knowledge
and mental skills needed for the solution of problems.
Champagne and Klopfer (1977) discussed reflective thinking as yet
another term related to problem solving. They advocated that innovative
problem solving is the external manifestation of reflective thinking. Shulman
and Elstein (1975 ) stated that "the essence of learning is not merely doing,
but thinking about what one is doing" (p. 37). Taylor (1965) also discussed
the complexities of decision making, problem solving, and creative thinking
and stated that "creativity is that thinking which results in the production of
ideas (or other products) that are both novel and worth while" (p. 48). He
defined problem solving simply as "that thinking which results in the solution
of problems" (p. 48) and decision making as "that thinking which results in
the choice among alternative courses of action" (p. 48). A more thorough
definition of these terms is as follows: Problem solving is defined as the
process of analytical reasoning and knowledge recall necessary for the
effective solution of problems. Clinical decision making is defined as the
steps one goes through, mentally and physically, to gather and analyze
necessary information and to choose among alternative courses of action to
make a decision regarding a client.
Problem Solving and Clinical Decision Making in Physical Therapy and
Other Health Related Fields
Physical therapists have commented on the importance of effective
problem-solving and clinical decision-making abilities for the physical
therapist. Tammivaara and Yarbrough (1984) interviewed 22 persons
regarding the field of physical therapy. One particular topic involved the


perceived characteristics of competent physical therapists. They found
problem-solving ability to be a general category common to most
descriptions of a competent physical therapist. One particular description of
competent physical therapists is as follows:
They are excellent problem solvers. They take their background
knowledge, theories, and principles and can, in a relatively short
period of time, come up with and generate, numerous alternatives
to a problem, whether that problem be an administrative,. . man
agerial, ... or patient problem .. Based on their problem solving
abilities they can recognize when something is or is not working
and can ... revise the program without having anxiety that it's not a
good idea to try something new. (p. 26)
Shepard and Jensen (1990 ) alluded to the importance of a special
kind of problem solving in their discussion of the physical therapist as a
"reflective practitioner." They emphasized that therapists must use a
"reflective" or "intuitive" knowledge in order to practice in what Schon called
"the indeterminate zones of practice." Schon (1987 ) stated that these
indeterminate zones of practice are "uncertainty, uniqueness, and value
conflict" ( p. 6 ). Day (1986) identified "an assumption that the ability to solve
problems and to analyze new situations is paramount to good performance
by a physical therapist" (p. 1555). She investigated the use of the Graduate
Record Examination (GRE) analytical scores as predictors of success in
physical therapy programs. Her results indicated the GRE analytical scores
were significant predictors of final grade-point averages for master's degree
entry-level physical therapy students.
May and Newman (1980) stated that "students enter physical therapy
curricula with a developed approach to problem solving and unconsciously
use that approach in solving problems related to physical therapy practice
with varying degrees of success" (p. 1140). May (1984) later emphasized
that students need learning experiences involving problem solving, self-


direction, and self-learning in order to prepare them to be competent
physical therapy practitioners.
Because physical therapists need to problem solve and make clinical
decisions, several physical therapists have published in the areas of
problem-solving curriculum design and problem-solving methods or
strategies for use in the classroom. Johnson ( 1974 ) wrote about curriculum
design for physical therapy education and predicted that physical therapists
might need to prepare for independent practice. To enable the physical
therapist to practice independently of the physician, she indicated the need
to prepare the physical therapist "as a health care practitioner responsible
and accountable for decisions and actions in delivering care" (p. 384).
May (1977) discussed a design for an integrated problem-solving
curriculum for physical therapy education. She identified the need for
problem-solving learning experiences to help students develop skills in
problem solving. Students who graduated from her program commented
that they felt well prepared for any situation and that they felt they knew how
to problem solve when in the clinic. May (1988) later commented on
problem-based learning, saying that one of the goals of such a curriculum
design is to help students function as effective problem solvers. She stated:
structuring learning experiences around real-life clinical
situations enables students to learn decision-making skills
in the relative safety of the classroom. Problem solving and
decision making are not identical processes, and the use of a
problem-based approach can provide students with greater
opportunities for decision making than the more traditional
subject-matter approach, (p. 528)
Barr (1977) described another problem-solving curriculum design in
physical therapy that included such advantages as preparing the learner to


deal effectively with unknown physical therapy problems In the future.
Chlpman (1985), a general educator, supported this concept by stressing
that problem solving is the use of previously acquired knowledge and skill to
deal with new situations. Barr (1977) identified seven basic assumptions
that underlie a problem-solving curriculum in physical therapy:
1. The physical therapist is a problem solver.
2. Problem-solving skills utilized by physical therapists can be
taught and learned.
3. Problems from physical therapy can be used as organizing
centers for learning activities.
4. Problem-solving and affective abilities can be applied in any
of the settings or roles in which physical therapists function.
5. There can be concurrent learning of content and process, as
well as development of affective abilities.
6. A problem-solving approach will facilitate transfer of knowledge
and continued learning which are dependent upon application
to and practice with similar, but different problems.
7. A problem-solving curriculum design in physical therapy will
overcome some of the inadequacies of the traditional, subject-
centered design, (p. 263)
Olsen (1983) described an operational problem-solving model
developed and used by the faculty in the physical therapy curriculum at the
University of Puget Sound to help provide problem-based learning in a
subject oriented curriculum. Students and faculty believed the model to be
useful in achieving that goal. Olsen's model involved the following
components:
1. Problem
Identify the patient's problem.
2. Cause
Describe the causes of the problem.
3. Principle
The principle is divided into three parts:
a) Method-Select a way to deal with the problem.


b) Solution-Identity the mechanism the method will
have on the cause.
c) Product -Identify expected outcomes.
4. Modality
Use a therapeutic intervention.
5. Goal
Set a goal for the patient. The patient meets it or does not
meet it.
May and Newman (1980) wrote that "problem solving is an integral
part of effective physical therapy practice" (p. 1140) and presented an
operational model that depicted the cognitive, affective, and psychomotor
behaviors of the problem solver when solving problems. This is the
sequence of activities of the model:
1. Problem recognition
2. Problem definition
3. Problem analysis
4. Data management
a) Data collection methods selection
b) Data collection
5. Solution development
a) Data analysis
b) Alternative solution determination
c) Solution selection
6. Solution implementation
7. Outcome evaluation
Burnett and Pierson (1988) discussed activities designed to help first-
year physical therapy students develop problem-solving skills in the


classroom. The format of their class sessions and activities evolved over a
three-to-four year period. They found that many students were
unenthusiastic and uncomfortable with problem solving and suggested that
the student's level of cognitive development was one of the main influences
of that attitude .
Rothstein and Echternach (1986) introduced an hypothesis-oriented
algorithm for clinicians (HOAC) that was designed to help physical therapists
with clinical decision making and patient management. The HOAC requires
the therapist to document all actions taken as well as all underlying
rationales and the instrument consists of two parts. "The first part is a
sequential guide to evaluation and treatment planning. The second part
involves a branching program that anticipates the clinical decisions that
must be made" (p. 1389).
Burnett, Mahoney, Chidley, and Pierson ( 1986 ) described the use of
a problem-solving method to structure a clinical experience for first-year
physical therapy students. Their method included the presentation of patient
case studies to seven groups of 10 students. The clinical instructor helped
the students in identifying problems, in goal setting, and in developing
alternative treatment plans for the patient. The authors did not collect
statistical data to demonstrate improvement of problem-solving skills of the
students. However, both students and clinicians subjectively reported that
the experiences were worthwhile.
Slaughter, Brown, Gardner, and Perritt (1989) reported the results of a
study in which they used the Watson Glaser Critical Thinking Appraisal
(CTA) and participant feedback to assess the effectiveness of a model for
teaching problem-solving skills to first-year physical therapy students. They
used a pretest-posttest control design. Students in the experimental group


used a problem-solving model during a four-week clinical practicum.
Although the investigators found no significant difference in performance on
the CTA between the experimental and control groups, subjectively students
and clinical instructors in the experimental group found the model to be
effective in aiding students' understanding of patient assessment and
treatment program planning. The authors emphasized the need for a valid
instrument to measure progress in physical therapy students' problem
solving skills.
Van der Sijde, Sellink, and Wurms ( 1987 ) developed an
audiovisual training program in problem-solving skills for physical therapy
students. They used a series of structured videotaped case histories at three
levels of learning in their curriculum-year one, year two, and years three
and four. These case histories were designed to introduce different
elements of problem solving at the three levels with the third level requiring
the most skill. They reported favorably regarding the effectiveness of the
audiovisual approach as a tool for physical therapy education.
These publications in the physical therapy literature are
documentation of the interest in problem solving and clinical decision
making in physical therapy. The relative newness of the concept of clinical
decision making to physical therapy is noteworthy. Echternach and
Rothstein (1989) reviewed the physical therapy literature in preparation for
an article related to clinical decision making. They found that clinical
decision making was not a subject topic used in the Sixtv-Five Year Index to
Physical Therapy (APTA. 1986a) or in the yearly subject index of Physical
Therapy through 1988 (APTA, 1986b-1988). Physical therapists have
indeed published in the area of problem solving and have delivered lectures
at professional meetings on the issues of problem solving and decision


20
making, but clinical decision making has not been considered a main topic
as evidenced by the sparsity of entrees in the major physical therapy
indices.
This topic, however, is one that physical therapists now believe
should be examined in a more formal sense (Myers & Rose, 1989), and one
that has become of national interest due to the increasing numbers of states
that allow physical therapists to evaluate and treat patients without a
practitioner's referral. At a conference on Clinical Decision Making in
Physical Therapy Practice, Education, and Research, Myers and Rose
(1989) commented on the importance of formal instruction in clinical
decision making when they stated that" the primary objective of the
conference was to begin the preparation for the introduction of clinical
decision making into the curricula of education programs for the physical
therapist" (p. 523).
Magistro (1989) emphasized the importance for all physical
therapists to be able to make sound clinical decisions. He believed skill in
decision making to be necessary because of the trend for the profession of
physical therapy to move toward a role of increased independence or "direct
access." He also pointed to the need for physical therapists to gain a greater
understanding of the clinical decision-making process itself.
Several investigators in the fields of medicine, nursing and physical
therapy have provided some insight into this process, but none have
published a theoretical model that demonstrates the elements necessary for
quality decision making. Payton (1985) reported on a study in which ten
skilled physical therapist clinicians were observed as they performed an
initial interview with a patient. These interviews were audiotaped and later
analyzed. Payton found that the clinical problem-solving sequence used by


21
these therapists was comparable to methods reported in the literature by
Barrows and Bennett (1972) and Elstein, Kagan, Shulman, Jason, and
Loupe (1972) that physicians use. He recommended further study to
determine whether less skilled therapists use the same clinical reasoning
process as the skilled therapists.
The clinical reasoning process Elstein, Shulman, and Sprafka (1978)
described included four steps: cue acquisition, hypothesis generation, cue
interpretation, and hypothesis evaluation. These authors stated that
differences in the content knowledge stored may distinguish the stronger
from the weaker problem solver, but that medical problem solving does not
depend solely upon mastery of content recall. Also, medical problem
solving requires gathering additional data, evaluating those data, redefining
problems, and exploring alternative interpretations.
Wales, Nardi and Stager (1986) gave another description of
professional decision making. They emphasized a pattern of four operations
for professional decision making: (a) state the goal, (b) generate ideas, (c)
prepare a plan, (d) take action. The authors combined these operations with
a hierarchy of thinking that included analysis, synthesis and evaluation, into
a 12-step process designed to help one understand how to solve problems.
Watts (1989) reported on a systematic method for making clinical
decisions in physical therapy that she called clinical decision analysis.
Raiffa (1968) originated the method at Harvard Business School. It has been
adapted for use in the medical field. As adapted for physical therapy, the
clinical decision analysis involves six major steps:
1. Defining the decision problem
2. Defining successful and unsuccessful outcomes
3. Describing alternative approaches and their consequences


22
4. Estimating and analyzing probabilities
5. Estimating costs
6. Selecting a preferred strategy
Watts said that this method is not practical for making most decisions in
everyday clinical practice, but should be used selectively "for decisions that
are made frequently, have important consequences, and provoke some sort
of controversy, uncertainty, or discontent with the results less formal decision
making achieves" (p. 576). She also believed that decision analysis does
not always need to include all component steps to be useful.
Measurement of Problem-Solving and Clinical Decision-Making Ability
Another concern in the health professions is how to measure
problem-solving or decision-making ability. Marshall (1977) described the
use for physicians of patient management problems (PMPs) which are
produced in booklet format with the information needed to solve the problem
concealed through the use of invisible ink. He said that the highest marks in
PMPs are achieved by those candidates who arrive at the correct diagnosis
in the most efficient fashion and, further, that the PMP may then be
measuring efficiency in problem-solving ability.
Goran, Williamson, and Gonnella (1973) reported the results of a
study that raised questions of the validity of the PMPs. In their study they
focused on a single medical problem and involved 22 university clinic
teams. The investigators compared teams' performance with actual patients
with their performance on a simulated PMP. The investigators found that the
PMP did not provide a valid discrimination of adequate clinical performance.
Marshall (1983) confirmed problems with PMPs. He suggested that PMPs


23
are not a measure of total clinical competence, but "should be used to
address only one aspect of this complex issue--that of problem solving per
se" (p. 321). Newbie, Hoare, and Baxter (1982) also cautioned users of the
PMPs to remember "that written PMPs cannot yet be regarded as a valid
simulation of clinical performance" (p.137). They stated that while content
validity is high, this is not true for construct validity or concurrent validity.
Barrows and Tamblyn (1980) discussed the evaluation of problem
based learning and clinical reasoning ability. They emphasized the
importance of matching the tool for evaluation to the behavior or competency
to be tested and recommended several tools to look at the intermediate
steps and the total product of the clinical reasoning process. One example
was time-out discussions during the use of simulated patients where the
student's thought processes could be dissected. Barrows and Tamblyn
(1980) also noted the paucity of "evidence that the amount of factual
knowledge possessed by a student, as scored by objective examinations,
correlates in any way with clinical competence (p. 6).
Heifer and Slater (1971) used another type of instrument called the
Diagnostic Management Problem (DMP) to measure the problem-solving
process used by medical students. They compared the scores received on
the DMP by a group of students with scores received on Patient
Management Problems by the same group of students and found the
correlation for this comparison to be .60 (p = .01). They concluded that the
instrument was reliable for measuring the process a student uses to arrive at
a clinical diagnosis.
Vu (1980) published a thorough review of the medical literature on
problem solving and discussed several important issues. Two of these
were the possibility of identifying potential deficiencies in problem-solving


24
ability early in the medical curriculum, and identification of instruments that
could be used to predict these deficiencies. He found a need for future
studies since the studies he reviewed used different predictors and defined
their criteria differently. For these reasons, he felt that broad generalizations
and comparisons were not feasible.
McGuire (1985) critiqued the literature on medical problem solving
and commented that researchers believed it was not possible to generalize
problem-solving performance across problems. She felt that a large portion
of the cognitive process involved in decisions about patient care is still
unknown.
Aspinall and Tanner (1981) developed a model of the thinking
processes of clinical problem solving in nursing. Their problem-solving
process included the generation of multiple alternatives and the systematic
testing of those alternatives against additional information gathered from the
patient and other sources. On the other hand, del Bueno (1983) used
simulations to teach and assess the clinical decision-making skills of nurses.
Her study involved both experienced and inexperienced registered nurses
who had graduated from three types of educational programs-bachelor of
science in nursing, associate degree in nursing, and diploma in nursing.
She reported that both experience and the baccalaureate preparation
correlated positively with the correct decisions.
To date, no one has published an instrument to measure problem
solving and/or clinical decision-making skills used in physical therapy. Wolf
(1985) included chapters devoted to decision analysis by Watts, clinical
decision making by Hislop, and decision making in various physical therapy
speciality areas by other authors. Although these authors discussed
numerous aspects of the clinical decision-making process they presented


25
no tools or procedures for quantifying the level of skill in clinical decision
making.
The nursing profession has made progress in this area. Jenkins
(1985) published information about the Clinical Decision Making in Nursing
Scale (CDMNS) that she developed to measure how nursing students
perceive their own clinical decision-making ability. She later revised the
Likert-type answer scale descriptors to better reflect behavior rather than
perceived ability. Other nurses have since used the CDMNS to investigate
the decision-making process. Engberg (1987) published such a study in a
master's thesis and McFadden (1987) in a doctoral dissertation.
Instruments Selected for Use in Study
Clinical Decision Making in Nursing Scale
The Clinical Decision Making in Nursing Scale (CDMNS) was
developed by Jenkins (1985) to study the perceptions of clinical decision
making in nursing students. The instrument was developed with normative
decision making and self-perception theory as the theoretical base. The
work of Janis and Mann (1977) was the basis for tool construction and the
model of decision making they chose was a normative one. Jenkins (1985)
condensed Janis and Mann's seven criteria into four categories of decision
making:
1. search for alternatives and options
2. canvassing of objectives and values
3. evaluation and reevaluation of consequences
4. search for information and unbiased assimilation


26
These four categories became the four subscales of the instrument. Items
for the CDMNS were gathered from relevant managerial and nursing
literature and from decision theory and, after the items were generated, they
were subjected to validity and reliability procedures (Jenkins, 1985).
Jenkins (1985) reported on a study that involved pretesting, pilot
testing, and formal testing of the instrument. The formal testing included 111
nursing students who were engaged in clinical practice at the end of a
semester of study. Of the 111 students, 27 were sophomores, 43 were
juniors, and 41 were seniors. The author found no significant differences
among sophomores, juniors, and seniors except on Subscale A--Search for
Alternatives or Options. The difference in mean scores on this subscale
between juniors and seniors was significant, with seniors having the higher
mean score. Jenkins (1989) recommended further study in this area and
suggested replication of the study using the CDMNS with other groups and
comparing the CDMNS with other measures.
McFadden (1986) used the CDMNS to investigate 153 senior nursing
students' perceptions about their clinical decision making and the relation of
these perceptions to learning style, personality type, and age, sex,
education, college career choice, and nursing work experience. She
reported no significant relation between CDMNS scores and learning style,
age, sex, education, or work experience. She did find a weak positive
relation between the Sensing/Intuitive scale of the Myers Briggs Type
Indicator and CDMNS scores and an inverse relation between the
Extravert/lntrovert scale and CDMNS scores. Her reported Cronbach's
alpha was 0.80.
Engberg (1987) studied the relation between scores obtained on the
CDMNS and accuracy in solving a videotape simulation of a clinical


27
problem for 31 registered nurses. She found no significant relation between
the two, and reported a Cronbach's alpha of 0.93.
Jenkins gave written permission for this investigator to change the
name of the CDMNS for this study to Clinical Decision Making Scale
(CDMS). The rationale was that omitting the word Nursing would make the
instrument less confusing for physical therapists.
Graduate Record Examination (GRE)
The Educational Testing Service developed and publish the GRE.
Graduate schools use scores on it, along with other data, to predict
academic success. "Research to date indicates that GRE scores are valid
predictors of success in the first year of graduate school for all students" (p.
14, ETS, 1990).
Traditionally, the GRE General Test has included verbal (GRE-V) and
quantitative (GRE-Q) measures. The analytical (GRE-A) measure is the
newest of the three measures of the current General Test. The ETS
instituted the analytical measure in October, 1977 and revised it for use
beginning in October, 1981. The revised instrument includes items to test
analytical and logical reasoning (ETS, 1990).
Several investigators have considered the predictive validity of the
analytical measure. Mowsesian and Hays (1982) examined the efficacy of
the GRE-A score as a predictor for admission to a graduate program in
educational psychology. They reported that when GRE-A scores were used
in combination with a student's grade-point averages, GRE-V scores and
GRE-Q scores, the GRE-A contributed little to the total predictive ability of the
variables. Independently, however, the GRE-A scores were as reliable as
any of the other scores.


28
Mowsesian and Hays (1984) reported the GRE-A scores to be a
useful predictor for the selection of doctoral candidates in an educational
psychology department. Using a step-wise regression analysis with 23
predictor variables, the authors found the GRE-A score to be in a group of
nine variables that accounted for 70 % of the variance in the predictors
(r2 = .697). When the investigators analyzed only quantitative predictors, the
GRE-A score and the grade-point average, taken together, accounted for 23
% of the total variance (r2= .225).
Mowsesian and Hays (1985) compared the earlier experimental
format of the GRE-A with the current format. They analyzed data on 407
students who applied for admission to a graduate program in educational
psychology. They used regression analysis of the independent variables,
GRE-V, GRE-Q and GRE-A measures, along with sex, ethnic status, area of
specialization, faculty evaluation of Ph.D. qualification, and demographic
data to attempt to account for a significant variance associated with
admissions decisions. They found the GRE-V and GRE-Q scores accounted
for more variance than the GRE-A score, but that the GRE-A score did add to
the prediction of advancement to Ph.D. candidacy and they concluded that
the GRE-A measure had predictive utility for admissions decisions
regardless of format.
Kingston (1985) reported results of a study of 2,146 students in 158
departments. He concluded that the analytical measure was not significant
over the GRE-V and GRE-Q measures in predicting graduate first-year GPA
with the possible exception of students in engineering and physical-
mathematical sciences programs.
Day (1986), in a study of four master's degree entry-level physical
therapy programs, analyzed data on a total of 121 students who had taken


29
the 1981 revised format of the GRE-A measure. Analysis of results
indicated the GRE-A score to be significant (p < .05) in predicting the final
GPA of students in this group. The GRE-A score was also a significant
predictor in Program A (p < .05) when the researcher analyzed data from
programs individually.
In an unpublished follow-up study of the same four master's degree
entry-level physical therapy programs, Day (1987) analyzed data on a total
of 234 students. She found that when she combined data from all programs
the GRE-A score and overall GPA remained significant predictors of final
GPA. In two of the four individual programs the GRE-A score was also a
significant predictor.
Summary
This review of literature in the area of problem solving and decision
making contains the following points:
1. Many persons confuse the terms problem solving, decision
making, reflective thinking, critical thinking, and clinical reasoning. The
literature contains many examples of the interchangeable use of the terms.
2. Problem-solving and clinical decision-making skills are important
to the field of physical therapy, and physical therapy educators have devoted
great effort to curriculum design and teaching methods to help students
acquire these skills.
3. Physical therapists have recently become increasingly interested
in clinical decision making, in part due to the movement of physical
therapists toward practice without physician referral (direct access).


30
4. To date, investigators have documented no theoretical models for
clinical decision making in physical therapy.
5. To date, they have documented no instrument for measuring
clinical decision-making ability in physical therapy.
6. Problem solving and decision making are topics of interest in other
health professions and nursing has developed an instrument for measuring
clinical decision-making ability for nurses.
This chapter does not contain a review of theories regarding required
attributes for quality decision making. As stated in the introduction to this
chapter, this review is in Chapter 3, Model Development.


CHAPTER 3
MODEL DEVELOPMENT
Introduction
As discussed in the review of literature in Chapter 2, physical
therapists believe skills in problem solving and clinical decision making to
be paramount for quality practice. Although the two skills are separate by
definition, to separate them in actual practice is difficult and problem solving
is usually implicit in clinical decision making. Educators have published
much about problem solving and decision making but they differ in opinion
regarding the required attributes for these skills.
The review of the literature revealed three primary theories about the
required attributes for problem solving and decision making. Proponents of
each of these theories emphasized certain attributes and the debate
appeared to be centered over the importance of knowledge versus heuristic
process, or a combination of these two attributes. One author (Glaser, 1984)
also mentioned the importance of analogical reasoning. The following
section contains these three major theories.
31


32
Three Theories of Required Attributes for Problem Solving and Decision
Making
1. Knowledge as the Kev
Some social scientists theorize that knowledge is the primary element
in problem solving and decision making. Newell & Simon (1972) supported
this theory:
Repeatedly, we have found ourselves concerned with the content
of the problem solver's knowledge. Behavior is not simply a
function of a few aggregative features of this content--of how
much content there is, or how it is expressed. Behavior is a
function of the specific detail of content, of the actual facts of the
particular task in hand. (p. 867)
Their work on human problem solving included a theory of man as an
information processing system. They used the computer for problem-solving
simulations.
Glaser (1984) reported that evidence from several sources supports
the conception that a major component of thinking is the possession of
accessible and usable knowledge. He (Glaser, 1985) advocated that rich,
domain-specific knowledge is of key importance in problem solving. Glaser
(1984) further stated that "the problem-solving difficulty of novices can be
attributed largely to the inadequacies of their knowledge bases and not to
limitations in their processing capabilities such as the inability to use
problem-solving heuristics" (p. 99).
Other supporters of knowledge as a requirement for problem solving
were Green, McCloskey, and Caramazza (1985). Their work was in physics
and they wrote that many students who take physics courses come to the
courses without correctly understanding motion and, therefore, do not
correctly solve problems. Hayes (1989) also spoke to the importance of


33
knowledge of the material in solving problems and wrote a manual to
provide the reader with skills to become a better problem solver and to give
current information about the psychology of problem solving.
Chase and Simon (1973) discussed theories related to chess skill.
They determined that chess skill was dependent upon a vast long-term
memory of "knowledge" of information about chessboard patterns. They
stated that the most important processes for chess mastery were immediate
visual-perceptive processes that called upon this knowledge base and not
logical-deductive thinking processes.
Barrows and Feltovich (1987), in support of knowledge, have
described the clinical reasoning process for the physician as a model with
the following components: (a) patient presentation, (b) generation of
multiple hypotheses, (c) hypothesis-oriented inquiry, and (d) problem
syntheses. They suggested that students' reasoning difficulties are often
due to a deficiency in the knowledge base.
Kurfiss (1988) combined the thoughts of Larkin, Heller, and Greeno
(1980), and Simon (1980) when she stated that, "While some general
strategies for problem solving may exist, skill in solving most problems
depends a great deal on the extent and organization of the knowledge base
available to the problem solver" (p. 29). She discussed the fact that
knowledge takes many forms and defined one form as "declarative
knowledge." Kurfiss stated that this form includes principles, stories,
concepts, and other knowledge that is used to make inferences. She
defined another type of knowledge as "procedural or strategic knowledge"
that she said includes knowing how and when to use declarative
knowledge. This type of knowledge really describes what a person can
actually do. Examples of this kind of knowledge include knowing how to use


34
a computer or how to drive a car. Both types of knowledge are useful for
problem solving.
2) Heuristic Process as the Kev
Wickelgren (1974) believed problem-solving methods to be the
important element for solving problems. In his book, he provided examples
of various kinds of problems with step-by-step solutions. The text was
specifically designed to help the reader increase ability in solving scientific,
mathematical, and engineering problems. He believed that general
problem-solving methods were especially useful to students in subjects
where they did not completely understand the relevant material.
Other proponents of the heuristic process are Whimbey and
Lochhead (1982). They developed a program that required thinking aloud
to a partner about the steps taken in solving problems. They assumed that
subjects made relatively few errors in problem solving due to a lack of
knowledge. They believed errors were due to a lack of correct reasoning or
failure to approach the problem in a step-by-step manner.
3) Heuristic Process. Knowledge, and Analytical Reasoning Combined
Other writers have emphasized the contributions of a combination of
knowledge and the heuristic process in solving problems. Taylor (1965)
discussed decision making and problem solving and commented that
rationality in decision making is dependent upon both knowledge and the
processes of the thinker. Polya (1957), in a book directed toward students
and teachers of mathematics, recommended that attention be given to
heuristic processes as well as to content. The book guides the reader in a
study of the methods of solving mathematical problems. Kurfiss (1988)
summarized the ideas of Bransford, Sherwood, Vye, and Rieser (1986);
Perfetto, Bransford, and Franks (1983); and Simon (1980), by stating that


35
"Declarative knowledge alone is necessary but not sufficient for the
development of skilled performance. Students must also learn strategies or
procedures for using their knowledge and conditions under which specific
knowledge is relevant" (p. 29).
Glaser (1985) advocated the importance of both knowledge and
process when he stated,
There appears to be an overemphasis on the instructability
of general processes, when recent research also shows the
importance of domain-specific and knowledge structure
influences on exercising significant forms of problem solving
and learning. This course needs to be corrected. Knowledge
fosters process, and process generates knowledge. The tale
begins and ends with both. (p. 574)
Another idea Glaser (1984) discussed is the importance of inductive
or analogical reasoning. He stated that an important component of aptitude
and intelligence is knowledge of the solution procedures required for solving
a certain task type and he called that ability analogical reasoning. He
hypothesized that individuals who have high levels of content knowledge
and who also have this analogical reasoning ability are high aptitude
individuals.
A Heuristic Process for Decision Making
Janis and Mann (1977) listed seven major characteristics of quality
decision making. These heuristic characteristics were extracted from the
literature on effective decision making. The authors advocated that if
decisions were based on these seven procedural criteria, decision makers
would increase the frequency of attainment of their objectives.


36
The decision maker, to the best of his ability and within his infor
mation-processing capabilities
1. thoroughly canvasses a wide range of alternative courses
of action;
2. surveys the full range of objectives to be fulfilled and the
values implicated by the choice;
3. carefully weighs whatever he knows about the costs and
risks of negative consequences, as well as the positive
consequences, that could flow from each alternative;
4. intensively searches for new information relevant to further
evaluation of the alternatives;
5. correctly assimilates and takes account of any new information
or expert judgment to which he is exposed, even when the
information or judgment does not support the course of action
he initially prefers;
6. reexamines the positive and negative consequences of all
known alternatives, including those originally regarded as
unacceptable, before making a final choice;
7. makes detailed provisions for implementing or executing the
chosen course of action, with special attention to contingency
plans that might be required if various known risks were to
materialize, (p. 11)
They stated that if a person does not meet one or more of the seven criteria
when making an important decision, the decision-making process is
defective. The more defects in the process, the more likelihood the decision
maker will later experience postdecisional regret.
The Theoretical Model for Clinical Decision Making in Physical Therapy
This debate by educators over the required attributes for problem
solving and decision making, plus the documented importance of these
skills for physical therapists, led to the development of this model. The
model (depicted in Figure 3-1) was designed on the premise that all three
theories for problem solving and decision making described earlier have


Figure 3-1 THEORETICAL MODEL FOR CLINICAL DECISION MAKING IN PHYSICAL THERAPY
Items 1-7 are adopted from Janis, I. L., & Mann, L. (1977). Decision Making:
A Psychological Analysis of Conflict. Choice & Commitment New York:
The Free Press.


38
merit. Physical therapists need an adequate background in general verbal
and quantitative knowledge, a strong professional knowledge base, and
analytical reasoning ability. This model illustrates that physical therapists
must possess these attributes, in addition to having skill in the heuristic
process, in order to make quality clinical decisions.
Components of the Model
KNOWLEDGE-
a) Verbal and Quantitative--A combination of general
knowledge of words and reading comprehension and
mathematical skills, including data interpretation.
b) Professional-Knowledge of physical therapy acquired by
participating in a physical therapy program or gained at
any time thereafter.
ANALYTICAL REASONING-
The ability to think analytically and logically, including the
ability to understand, analyze, and evaluate arguments.
Additionally, the ability to understand relationships between
persons, places, things, or events and to deduce new
information from those relationships.
CLINICAL DECISION MAKING-
The process of using knowledge and analytical reasoning to
problem solve and to go through steps or heuristic process to
choose among alternative courses of action to make a decision
regarding a client.


39
HEURISTIC PROCESS-
The investigator derived the heuristic process for this model
from the work of Janis and Mann (1977) and it includes the
following seven steps:
1) Canvasses alternatives
2) Surveys objectives and values
3) Weighs consequences
4) Searches for new information
5) Assimilates new information
6) Reexamines consequences
7) Plans for implementation
Testing the Model
This study was designed to determine the strength of the relation
among the model components. Chapter 4, Methodology contains details of
the design and the procedure fortesting.
Summary
This chapter contains three theories regarding necessary elements
for problem solving and decision making. The investigator developed the
Theoretical Model for Clinical Decision Making in Physical Therapy to


40
incorporate the primary elements of each theory because of the belief that
knowledge, analytical reasoning, and skill in the heuristic process are
equally important in making a clinical decision regarding a client.


CHAPTER 4
METHODOLOGY
This prospective cohort study is an effort to explore the relation
among components of the Theoretical Model for Clinical Decision Making in
Physical Therapy, developed by this investigator and described in Chapter
3. In order to investigate these relationships, the researcher needed an
instrument that would measure decision-making ability. The Clinical
Decision Making Scale (CDMS) was chosen for this purpose, with scores
obtained on it representing a measure of the heuristic process and
decision-making ability.
The investigator also needed quantitative measures of verbal,
mathematical and professional knowledge, and analytical reasoning. She
used scores obtained on the Graduate Record Examination (GRE) as
measures of verbal and quantitative knowledge and analytical reasoning.
The General Test portion of the GRE "yields separate scores for the
general verbal, quantitative, and analytical abilities related to success at the
graduate level of education" (p. 7, ETS, 1990). The verbal (GRE-V) and
quantitative (GRE-Q) subtests of the GRE are measures of verbal and
mathematical abilities. Scores from these two measures were combined to
provide a composite or GRE-VQ score, representing a global measure of
verbal and quantitative knowledge for this study.
41


42
The analytical subtest of the Graduate Record Examination
(ETS,1990) is a measure of analytical and logical reasoning abilities and the
investigator used the score obtained on this subtest to represent analytical
reasoning ability. As stated in the GRE Guide to the Use of the Graduate
Record Examinations Program (ETS,1990)
Analytical reasoning questions test the ability to understand
a given structure of arbitrary relationships among fictitious
persons, places, things, or events and to deduce new information
from the relationships given. Logical reasoning questions
test the ability to understand, analyze, and evaluate arguments:
recognizing the assumptions on which an argument is based,
drawing conclusions and forming hypotheses, identifying methods
of argument, evaluating arguments and counterarguments,
and analyzing evidence, (p. 7)
The Educational Testing Service regularly reports analytical scores as one
of the three major component scores of the GRE. They are contained in the
same report with the scores of the verbal and quantitative components.
In this study, the physical therapy students' final grade-point averages
(GPAs) were the measure of professional knowledge. The final GPA
constitutes the cumulative GPA achieved for didactic work in the two years of
the physical therapy program.
The investigator then determined the strength of the relationship
among the attributes of verbal and quantitative knowledge, professional
knowledge, analytical reasoning and the heuristic process or decision
making ability for physical therapy students by using students' GRE-A
scores, GRE-VQ scores and students' GPAs as independent variables and
students' CDMS scores as the dependent variable. This chapter contains
the sample, design, and procedures for data collection and analyses.


43
Questions and Hypotheses
The questions and hypotheses posed for this study for students about
to graduate from masters degree entry-level physical therapy programs
were as follows:
1. Is there a relation between posttraining CDMS scores (total or
individual subscale) and pretraining GRE-A scores after controlling for GRE-
VQ scores and GPA?
Operational Null Hypothesis A
No significant relation exists between GRE-A scores and scores
obtained on the CDMS after controlling for effects of other predictor
variables.
2. Is there a relation between posttraining CDMS scores (total or
individual subscale) and pretraining GRE-VQ scores after controlling for
GRE-A scores and GPA?
Operational Null Hypothesis B
No significant relation exists between GRE-VQ scores and scores
obtained on the CDMS after controlling for effects of other predictor
variables.
3. Is there a relation between posttraining CDMS scores (total or
individual subscale) and final GPA for work accomplished in the physical
therapy program after controlling for GRE-VQ and GRE-A scores?
Operational Null Hypothesis C
No significant relation exists between final GPA for work
accomplished in the physical therapy program and scores obtained
on the CDMS after controlling for effects of other predictor variables.


44
4. Is there a difference in performance on the CDMS instrument
among students of various master's degree entry-level physical therapy
programs?
Operational Null Hypothesis D
No significant differences exist in scores obtained on the CDMS
instrument among students of various masters degree physical
therapy programs.
Subjects
The subjects were 244 postbaccalaureate students from six
universities that offer master's degree entry-level physical therapy programs.
The students were enrolled in their last semester of graduate work and were
less than one week from graduation. These subjects were predominantly
white and female with a total of 186 women and 58 men. Of the 186 women,
170 were white, 9 were Asian, 3 were Hispanic, 2 were native American, 1
was black and, 1 was oriental. Of the 58 men, 56 were white, 1 was native
American, and 1 was Hispanic. The average age of the subjects was
26-3.55 years.
Procedure
Directors of six master's degree entry-level physical therapy
education programs agreed to have their students participate in the study.
The investigator selected six programs from the 31 established programs
because they represented a cross section of the United States and were


45
also representative of the specific areas of the United States where the
majority (68%) of the 31 programs were located.
Additionally, 23 (74%) of the 31 programs were located in private
universities or colleges while eight (26%) were located in state universities.
These six programs selected for inclusion in the study reflected the national
funding patterns. Four of the six (67%) are housed in private universities
and two of the six (33%) are housed in state universities. The programs that
participated in this study were the University of Alabama at Birmingham,
Birmingham, Alabama; Emory University, Atlanta, Georgia; the University of
Southern California, Los Angeles, California; Duke University Medical
Center, Durham, North Carolina; Columbia University, New York, New York;
and the University of Iowa, Iowa City, Iowa.
After the study was in progress, an additional 13 programs were
accredited, elevating the total number of programs in the United States to
44 (APTA, 1990b). The six programs that participated in the study remain
representative of both geographical location and institutional funding
source. These six programs now represent areas of the country where 88 %
of the programs are located. The pattern of institutional funding is currently
27 (62%) privately sponsored programs and 17 (38%) state-supported
programs.
Prior to graduation at the respective universities, the investigator sent
to the program director or designated representative for each program a
packet of materials for each student who had finished didactic and clinical
work. Each student packet contained the following items:
1. A cover sheet (Appendix A) for the student's name or identification
number. This sheet was removed, to assure the anonymity of each student,
before the rest of the forms were returned to the investigator.


46
2. A copy of the Clinical Decision Making Scale (CDMS) with
instructions (Appendix B).
3. An answer form for the CDMS (Appendix C).
4. A biographical information form (Appendix D).
5. A data recording form for reporting final GPA, GRE-Q, GRE-V and
GRE-A scores (Appendix E).
The director or a designated faculty representative of each program
arranged a meeting of the graduating class during the week before
graduation and at that time administered the CDMS to the volunteer
students. The students also completed the cover sheet and biographical
information forms during that meeting.
The faculty representative collected the forms from the students and
recorded the requested GRE and GPA data on the data recording forms.
Before returning all completed forms to this investigator for analysis, the
faculty representative removed the cover sheets to assure student
anonymity. Upon receiving the materials, the investigator coded the data by
university.
Data Analysis
The researcher performed data management and statistical analyses
using the StatView 512 + (1986) statistical analysis system. The
analyses generated descriptive data on students from all programs
combined and for each program individually. The relation between the three
independent variables, students' GRE-A scores, GRE-VQ scores and GPAs,
and the dependent variable, students' CDMS scores, was determined with
multiple regression analyses. The computer program generated five


4 7
independent multiple regression analyses using the three independent
variables in each and using the CDMS total scores or the CDMS subscales
A, B, C, and D scores respectively as the dependent variable. Pearson
product-moment correlations were also computed between the independent
variables and the CDMS scores. The alpha levels for the tests of
significance were set at .05.
The investigator determined differences in student performance
among programs on the CDMS (total scores and the four subscale scores)
using five one-way ANOVAs. The alpha level for the test of significance for
the CDMS total score was set at .05. Because the ANOVAs were repeated
for each of the four subscales, the alpha levels for the tests of significance of
the subscales were set at .01.
Instrumentation
The researcher used scores from two different instruments in the
study. This section contains a description of these two instruments.
Clinical Decision Making in Nursing Scale (CDMNS)
The CDMNS is a 40-item scale with a range of possible scores of 40-
200. A higher score represents a higher quality of clinical decision making.
The instrument was designed to measure decision-making process as
compared with an effective, normative process described by Janis and Mann
(1977). The CDMNS was first published and copyrighted by Jenkins in
1983 as the Clinical Decision Making in Nursing Scale. The author
(Jenkins, 1983) granted permission to this investigator to change the name
to Clinical Decision Making Scale (CDMS) for the purpose of this study to
avoid confusion when used by a health professional other than a nurse. No


48
changes were made in the 40 items because the items, as presently
constituted, are appropriate for assessing clinical decision-making process
for anyone in a health related area. The CDMS is not a timed test but has
been shown to require between 20-25 minutes for completion.
The instrument was originally designed to measure how nursing
students perceived their own clinical decision-making ability. The Likert-
type scale had choices ranging from 5 (strongly agree) to 1 (strongly
disagree). Jenkins later revised the scale descriptors from 5 (always) to 1
(never), to better reflect self-perceived decision-making behavior rather than
perceived ability.
The CDMS is subdivided into four subscales of the decision-making
process:
Subscale A: Search for Alternatives and Options
Subscale B: Canvassing of Objectives and Values
Subscale C: Evaluation and Reevaluation of Consequences
Subscale D: Search for Information and Unbiased Assimilation
of New Information
The range of possible scores for each subscale is 10-50.
Jenkins first established content validity of the CDMS by designing
the instrument based on the literature regarding decision making. She then
pretested the instrument, critiqued it forcongruity and clarity of test items,
and had nurse education experts evaluate and rate each item again for
content validity. She used a specification matrix with criteria formulated by
Nunnally and Durham (1975). She retained each item receiving a total
matrix score of 70-77 % agreement (Jenkins, 1985).
The instrument was determined to have an internal consistency
reliability of 0.83 as assessed using Cronbach's alpha. The standardized-


4 9
item alpha is 0.85 (Jenkins, 1985). Two other investigators have used the
instrument and reported Cronbach's alphas of 0.80 (McFadden, 1986) and
0.93 (Engberg, 1987) for their studies.
The scoring system for the instrument involves a weighting scale with
22 of the 40 CDMS items weighted as positive and 18 of the items weighted
as negative. The 40 items are further separated into the four subscales.
Using the scoring key provided by Jenkins, this investigator scored the
instrument for each participant in the study. Each participant received a
CDMS total score and a score for each of the four subscales,
graduate Record Examination (GRE)
The Educational Testing Service (ETS, 1990) developed and
publishes the GRE. The General Test portion of the GRE "yields separate
scores for the general verbal, quantitative, and analytical abilities related to
success at the graduate level of education" (p. 7). The GRE General Test
requires 3 hours and 30 minutes of testing time, and scores are based on
the number of correct answers. Each examinee receives a separate score
within a range of 200-800 for each of the three measures.
The analytical measure is the newest of the three measures. ETS
developed it to test abilities other than verbal and quantitative. Investigators
have shown that analytical ability is related to success in graduate work
(ETS, 1990). The measure was instituted in October, 1977 and revised in
October, 1981. This revised measure includes items to test analytical and
logical reasoning (ETS, 1990).
The test makers designed the verbal measure to test knowledge of
words and reading comprehension. They employed four types of questions
in this measure: antonyms, analogies, sentence completion, and reading
comprehension (ETS, 1990).


50
ETS designed the quantitative measure to test mathematical skills.
They used three types of questions in this measure: discrete quantitative
questions, data interpretation questions, and quantitative comparison
questions (ETS, 1990).
Reliability data for the General Tests were determined using the
Kuder-Richardson Formula (20). Reliability coefficients for the General
Tests, based on a sample of 2,435 examinees, were 0.91 for the verbal
measure, 0.92 for the quantitative measure, and 0.89 for the analytical
measure (ETS, 1990).


CHAPTER 5
RESULTS
Introduction
This study was designed to investigate the relation among
components of the Theoretical Model for Clinical Decision Making in
Physical Therapy and the decision-making ability of students in master's
degree entry-level physical therapy programs. This investigator developed
the model after a review of literature revealed a variety of theories regarding
attributes required for quality decision making. Chapter 3 contains a
discussion of the model and Figure 3-1 depicts it. The investigator
examined the physical therapy students' GRE-VQ scores, GRE-A scores,
final GPAs for work in the physical therapy program, and students' CDMS
total and subscale scores to determine possible relationships. She used the
data from the six physical therapy programs to apply Pearson correlation
procedures and five multiple regression models to address this issue.
A second purpose of the study was to determine whether a significant
difference existed among the performances of students from various
physical therapy programs on the CDMS instrument. The investigator used
five one-way analysis of variance procedures to determine any differences
on the total and four subscale CDMS scores.
51


52
Relationships Between GRE-VQ Scores. GRE-A Scores. GPAs. and CDMS
Total and Subscale Scores
The investigator tested for the relation between a measure of verbal
and quantitative knowledge, a measure of analytical skill, a measure of
professional knowledge, and a measure of clinical decision-making process
ability, by computing five multiple regression analyses. The same three
independent variables-students' GRE-VQ scores, GRE-A scores and
physical therapy program GPAs--made up each of the five multiple
regression analyses. The dependent variable was different for each
analysis and was the students' CDMS total scores in the first analysis and
subscale A, B, C, or D scores respectively in the other four analyses. The
researcher computed Pearson product-moment correlations to compare
each independent variable with each dependent variable.
These analyses were used to test the following null hypotheses:
Null Hypothesis A
No significant relation exists between GRE-A scores and
scores obtained on the CDMS after controlling for effects
of other predictor variables.
Null Hypothesis B
No significant relation exists between GRE-VQ scores and
scores obtained on the CDMS after controlling for effects
of other predictor variables.
Null Hypothesis C
No significant relation exists between final GPA for work in the
physical therapy program and scores obtained on the CDMS
after controlling for effects of other predictor variables.


53
As indicated in Tables 5-1 5-5, the multiple regression analyses with 241
subjects did not yield significant ( p < .05 ) predictor variables of clinical
decision making ability. Consequently, the investigator could not reject any
of the three null hypotheses. Beta coefficients were not reported due to lack
of significance.
Table 5-1
Multiple Regression of CDMS Total Score Model
Source
SS
df
MS
F
P
R2
Model
145.8
3
48.6
.569
.64
.007
Error
20251.9
237
85.5
Total
20397.7
240
GRE-VQ
Score
113.8
1
113.8
1.3
.25
GRE-A
Score
.9
1
.9
.01
.92
GPA
31.1
1
31.1
.36
.55


54
Table 5-2
Multiple Regression of CDMS-A Score Model
Source
SS
df
MS
F
P
R2
Model
12.3
3
4.1
.609
.61
.008
Error
1595.9
237
6.7
Total
1608.2
240
GRE-VQ
Score
6.2
1
6.2
.918
.34
GRE-A
Score
.5
1
.5
.082
.77
GPA
5.6
1
5.6
.827
CO
CO
Table 5-3
Multiple Regression of CDMS-B Score Model
Source
SS
df
MS
F
P
R2
Model
11.3
3
3.8
.344
.79
.004
Error
2591.2
237
10.9
Total
2602.5
240
GRE-VQ
Score
6.1
1
6.1
.556
.46
GRE-A
Score
4.2
1
4.2
.385
.54
GPA
1.0
1
1.0
.091
.76


55
Table 5-4
Multiple Regression of CDMS-C Score Model
Source
SS
df
MS
F
P
R2
Model
15.1
3
5.0
.404
.75
.005
Error
2952.2
237
12.5
Total
2967.3
240
GRE-VQ
Score
14.4
1
14.4
1.2
.28
GRE-A
Score
.01
1
.01
.001
.98
GPA
.70
1
.70
.056
.81
Table 5-5
Multiple Regression of CDMS-D Score Model
Source
SS
df
MS
F
P
R2
Model
15.9
3
5.3
.71
.55
.009
Error
1772.9
237
7.5
Total
1788.8
240
GRE-VQ
Score
3.7
1
3.7
.492
.48
GRE-A
Score
2.9
1
2.9
.394
.53
GPA
9.3
1
9.3
1.2
.27


56
The Pearson product-moment correlations between the independent
vahables-students' GRE-VQ scores, GRE-A scores and physical therapy
GPAs and the dependent vahables-students' CDMS total and subscale
scores were not significant at the .05 alpha level, further substantiating the
inability to reject any of these three null hypotheses. As indicated in Table
5-6, the GRE-VQ scores were negatively, but not significantly, correlated with
CDMS total and subscale scores. A negative nonsignificant correlation was
Table 5-6
Correlation Matrix for all Eight Variables
GRE-
A
GPA
GRE-
VQ
CDMS
T
CDMS
A
CDMS
B
CDMS
C
CDMS
D
GRE-
A
1
GPA
.183
CO 04)
1
GRE-
VQ
.506
(.0001)
.085
(.19)
1
CDMS
T
-.044
(.50)
.024
(.71)
-.075
(.25)
1
CDMS
A
-.047
(.46)
.041
(.53)
-.062
(.34)
.752
(.0001)
1
CDMS
B
-.059
(.36)
.002
(.97)
-.048
(.46)
.776
(.0001)
.42
(.0001)
1
CDMS
C
-.034
(.60)
-.019
(.77)
-.070
(.28)
.788
(.0001)
.488
(.0001)
.456
(.0001)
1
CDMS
D
.012
(.85)
.063
(33)
-.045
(.48)
.722
(.0001)
.463
(.0001)
.439
(.OOO1')
.368
(0001N)
1
p value in ( )


57
found between GRE-A scores and CDMS total, subscale A, subscale B and
subscale C scores, and a positive nonsignificant correlation was found
between GRE-A scores and CDMS subscale D scores. Correlations
between physical therapy students' GPAs and CDMS scores were all
positive and nonsignificant with the exception of GPA and CDMS subscale
C scores which was negative and nonsignificant.
The correlation between the independent variables GRE-VQ scores
and GRE-A scores was positive and significant, as were the correlations
between the dependent variables CDMS total and subscale scores. The
independent variables GPA and GRE-VQ scores were positively, but not
significantly, correlated. However, a positive significant correlation was
demonstrated between GPA and GRE-A scores.
Difference in Performance of Students on the CDMS Instrument bv Physical
Therapy Program
The investigator used five one-way analysis of variance procedures
to determine whether students from the various physical therapy programs
performed differently on the CDMS. She ran the first procedure comparing
programs with CDMS total scores using an alpha level of .05 and the
subsequent analyses comparing programs with CDMS subscale scores
using an alpha level of .01. She used these analyses to test Null Hypothesis
C--there will be no significant relation between final GPA for work in the
physical therapy program and scores obtained on the CDMS. As indicated
in Table 5-7, the analyses revealed no significant differences in performance


58
on the CDMS among students from various programs; thus this null
hypothesis could not be rejected.
Table 5-7
Five One-Wav ANOVAS Comparing Six Physical Therapy Programs
on CDMS Total and Subscale Scores
Source
df
SS
MS
F
P
CDMS-T
5
254.067
50.813
0.587
0.709
ERROR
238
20590.261
86.514
CDMS-A
5
28.365
5.673
0.842
0.521
ERROR
238
1604.373
6.741
CDMS-B
5
37.765
7.553
0.693
0.629
ERROR
238
2595.395
10.905
CDMS-C
5
52.388
10.478
0.843
0.520
ERROR
238
2959.841
12.436
CDMS-D
5
58.711
11.742
1.575
0.167
ERROR
238
1773.957
7.454
Descriptive Data for Total Students and for Students From Each Physical
Therapy Program
The descriptive data for all students in the study revealed an average
age of 26 + 3.50 years; an average GRE-VQ score of 1152 125.5; an
average GRE-A score of 613 + 87.0; an average GPA of 3.50 + 0.26; an
average CDMS-T score of 151 + 9.26; an average CDMS-A score of


59
38 i 2.60; an average CDMS-B score of 38 3.30; an average CDMS-C
score of 38 3.52; and an average CDMS-D score of 38 2.75. These data
are reported in Table 5-8. The descriptive data for students from each of the
six individual programs are reported in Appendices F K.
Table 5-8
Descriptive Data Summary for All Students
VARIABLE
N
M
SD
Mode
Low
High
Range
AGE
242
26
3.50
24
22
42
20
GRE-VQ
241
1152
125.5
1150
770
1560
790
GRE-A
241
613
87.0
630
370
800
430
GPA
244
3.50
0.26
3.60
2.82
4.00
1.18
CDMS-T
244
151
9.26
156
123
176
53
CDMS-A
244
38
2.60

31
46
15
CDMS-B
244
38
3.30
37
28
47
19
CDMS-C
244
38
3.52
38
26
47
21
CDMS-D
244
38
2.75
38
30
45
15
Summary
The results of the data analyses did not support rejection of any of the
four null hypotheses formulated for testing. Chapter 6 contains a discussion
of these findings, their implications, and suggestions for further research.


CHAPTER 6
DISCUSSION, CONCLUSIONS, AND RECOMMENDATIONS
Discussion
A review of the relevant literature revealed the need for a better
understanding of the attributes required for quality decision making in
physical therapy. Based on the literature, the investigator developed the
Theoretical Model for Clinical Decision Making in Physical Therapy,
discussed in detail in Chapter 3. The present investigation was designed to
study the relation between components of this model-knowledge, analytical
reasoning ability, and decision-making abilityfor students approximately
one week away from graduation from a masters degree entry-level physical
therapy program. In addition, the researcher intended this investigation to
answer the question of whether students from various physical therapy
programs with varying numbers of classroom hours in instruction in problem
solving and decision making would perform differently on an instrument
designed to measure decision-making ability.
The analysis was an examination of the relationships between the
components of the model investigated in two ways. Five multiple regression
analyses used the same three independent variables-students' GRE-VQ
scores, GRE-A scores, and GPAs for work in the physical therapy program.
The dependent variables for the analyses were the students' CDMS total
60


61
scores or subscale A, B, C, or D scores respectively. These analyses
revealed no significant relation between the independent and dependent
variables. Pearson product-moment correlations were also computed
between the independent and dependent variables and were not significant
at the .05 alpha level.
Before discussion of these results, the researcher wishes to share an
excerpt from an article by Shulman and Elstein (1975):
The tendency of intuitive statisticians to overemphasize positive
information, information that supports a belief or reflects a positive
co-occurrence of two variables, is also consistent with findings from
research in concept attainment. Failure to give adequate weight to
negative instances of a concept has been noted by Bruner et al.,
(1956), Bourne (1966), and, most convincingly, by Wason (1968).
Recognition of these human tendencies for self-deception in the
processing of information are not new discoveries by contemporary
psychologists. They have merely provided experimental confirmation
of the observations made several centuries ago by Francis Bacon in a
discussion of the "idols of the mind."
"The human understanding when it has adopted an opinion,
either as being the received opinion or as being agreeable to itself,
draws all things else to support and agree with it. And though there
be a greater number and weight of instances to be found on the other
side, yet these it either neglects and despises, or else by some
distinction sets aside, and rejects; in order that by this great and
pernicious predetermination the authority of its former conclusions
may remain inviolate. And therefore it was a good answer that was
made by the man who was shown hanging in a temple a picture of
those who had paid their vows as having escaped shipwreck. They
would have him say whether he did not now acknowledge the power
of the gods--'Aye', asked he again, 'but where are they painted that
were drowned after their vows?'
... it is the peculiar and perpetual error of the human intellect
to be more moved and excited by affirmatives than by negatives;
whereas it ought properly to hold itself indifferently disposed toward
both alike. Indeed in the establishment of any true axiom, the
negative instance is the more forcible. (Bacon, cited in Curtis and
Greenslet, 1962, pp. 16-17.)". (p. 24)
With these thoughts in mind, although no significant relation was
discovered among the variables in the model, several explanations for the
findings may apply. Determining adequate measures of the various model


62
components was a challenge and one or more of the quantitative variables
used to represent the components verbal and quantitative knowledge,
professional knowledge, analytical reasoning ability, and decision-making
ability, may not accurately represent those components.
1. Verbal and quantitative scores obtained on the Graduate Record
Examination (GRE-VQ), while accepted as valid predictors for success in the
first year of graduate school (ETS, 1990), may not accurately represent
verbal and quantitative knowledge for this model. Additionally, although
GRE-A scores were shown to be valid predictors of success in master's
degree entry-level physical therapy programs (Day, 1986 ), they may
not accurately represent analytical reasoning ability in this model.
2. Although a student's physical therapy program final GPA can be
considered a quantitative measure of professional knowledge, it does not
represent every aspect of the student's professional knowledge. For
example, knowledge gained on clinical rotations that are graded on a
pass/fail basis is not included in the GPA. Also, GPA cannot account for life
experiences.
3. The scores obtained on the CDMS may not be an accurate
representation of clinical decision-making ability.
(a) These scores are based on self-report and, even though
students were assured anonymity, they may have answered as
they felt the investigator would want them to answer.
(b) The students may not be aware of their real decision
making behavior.
(c) The physical therapy curriculum may not have emphasized
decision-making terms and process, resulting in poor
perception and understanding by the physical therapy students.


63
Additionally, the students may have had little actual opportunity for
independent decision making while in the physical therapy program. Even
though all had completed their clinical internships and were about to
graduate, many students may not have been allowed to make clinical
decisions while on internships, or may have taken decision making lightly,
knowing that the ultimate decision always rested with the clinical instructor.
Students may also have had poor role models in the clinics. No studies
have documented the expertise of clinical instructors in the decision-making
process.
Another explanation for the nonsignificant findings of the study may
be the fact that little variability existed in the sample. The sample was made
up of graduate physical therapy students who were quite homogeneous by
virtue of the fact that each student was competitively selected from a large
applicant pool. They, in fact, have passed through more than one selection
process before making it into the physical therapy program. They were
selected for admission to a four-year institution to obtain their bachelor's
degrees, then selected into graduate school, and finally selected into the
physical therapy program. The narrow range of GPAs for this sample must
be considered when interpreting the results. The final GPA range for
didactic course work in the physical therapy program for all students
combined was 2.82 4.00 with a mean of 3.50 t 0.26 and a mode of 3.60.
This narrow range makes prediction difficult, as Willingham (1974) has
documented.
The results of five one-way ANOVAs indicated no significant
differences in performance on the CDMS among students from different
physical therapy programs. These findings are interesting because the
programs do not cover the topics of problem solving and decision making in


64
the same way. When asked to list the number of classroom hours devoted to
these topics, the program directors were unable to do so, and indicated that
the topics are covered in parts of many of the courses. Only one program
had an entire course devoted to these topics at the time of the study and one
other program has since instituted such a course. There may be several
reasons for the fact that no significant differences in student performance
were found among programs.
1. Clinical decision making may actually be learned only during
clinical internships when students have real opportunities to practice the
skill. If this is the case, then not much difference between students' clinical
decision-making abilities would be expected because programs are similar
with regard to practice time. Each of the six programs includes a minimum of
18 weeks of full-time clinical experiences for the students and the programs
offer comparable internships. Even though the didactic portions of the
programs are different, the actual clinical decision practice time in the clinic
in the various programs is similar.
2. The students' scores on the CDMS may reflect an innate decision
making ability that has nothing to do with the physical therapy program. May
and Newman (1980) postulated that students enter the physical therapy
program with a developed approach to problem solving. Perhaps the time
spent in the program does not affect the decision-making ability of the
students.
3. Another possible explanation is that because problem solving and
decision making are considered prerequisites for quality practice in physical
therapy, and because these topics are interwoven throughout the curricula
of these physical therapy programs, the students may be getting continual
exposure to both regardless of the physical therapy program.


65
Conclusions and Recommendations
Because physical therapists are now able to practice independently
without practitioner referral in 24 states, being able to problem solve and
make quality clinical decisions has become increasingly important. A better
understanding of the clinical decision-making process and attributes
required for quality decision making is imperative. Although the data in this
study did not substantiate the Theoretical Model for Clinical Decision Making
in Physical Therapy that this investigator developed, many possible reasons
for these findings were discussed earlier. One important conclusion that
must not be ignored is that, for this sample, there was no significant relation
between components of this model and clinical decision-making ability.
Although a believer in the importance of these non-significant results, this
researcher would advocate strongly for more study of this model based on
the previous discussion. The investigation of other possible models in the
future is also important.
One recommendation would be to investigate other quantitative
measures of knowledge and analytical reasoning to use in evaluating this
model. For example, perhaps some quantitative measure could be
developed for the professional knowledge component that would include the
important clinical internship work that is graded on a pass/fail basis and
therefore not included in a student's GPA.
The use of the licensure examination for the physical therapist is one
such idea because this instrument has a section that requires incorporation
of clinical knowledge. Using the licensure examination score as the
measure of professional knowledge would necessitate that the study be


66
done after graduation instead of immediately prior to graduation, but it would
incorporate a quantitative measure that represents the knowledge required
to practice physical therapy. All states require that graduates of physical
therapy programs pass this examination before being allowed to practice
physical therapy. These licensure examination scores are confidential and
participants in the study would have to agree to release them to the
investigator.
Another idea would be to study those physical therapists who have
become certified clinical specialists in an area of physical therapy. The
American Physical Therapy Association currently recognizes seven clinical
specialties:
1. Pediatrics
2. Orthopedics
3. Neurology
4. Sports
5. Electrophysiology
6. Cardiopulmonary
7. Geriatrics
The use of a sample of these specialty certified physical therapists in testing
the model would enable the investigator to use scores that these participants
achieved on the Clinical Specialist Examination as the quantitative measure
for professional knowledge in the model. It would also enable the
researcher to test for differences in decision-making abilities of therapists in
different clinical speciality areas.
The Watson Glaser Critical Thinking Appraisal (CTA) is another
instrument that might be used to measure analytical reasoning ability.
Scores obtained on this instrument could then replace the GRE-A score as


67
the quantitative measure of analytical reasoning for the model. The CTA
could be administered to those agreeing to participate in the study at the
same time they take the CDMS.
More research with the CDMS is also recommended as follows:
1. Administer the CDMS to the same subjects that participated in this
study after they have completed one year of full-time employment as
physical therapists to determine whether clinical decision-making ability
changes after actual "on the job" experience. Experience may improve
decision-making ability.
2. Administer the CDMS to a group of physical therapists who have
practiced physical therapy for at least ten years, and who are considered by
their peers to be experts in the field, to determine whether these therapists
score significantly higher than did the new graduates. This would further
delineate the role of experience in clinical decision making and help
establish baseline scores for different levels of therapists.
3. Administer the CDMS to a sample of clinical instructors who, by
definition, have at least two years of clinical experience, to determine
whether a significant difference exists in scores for this group compared with
the new graduates. These clinical instructors are supposed to be role
models for students and should therefore be better decision makers than the
new graduates.
4. Administer the CDMS to graduate students in other disciplines to
compare performances among students in a variety of health disciplines.
For example, studying graduate students from occupational therapy
programs, nursing programs, and physician assistant programs as well as
physical therapy programs would be interesting.


68
5. Administer the CDMS to a group of students about to graduate
from entry-level master's degree physical therapy programs and to a group
about to graduate from entry-level baccalaureate programs. This would
allow the investigator to determine differences in decision-making ability
among graduates of these two types of entry-level physical therapy
programs. The avenues of entry into the physical therapy profession are
currently just the two types of programs. Forty-four entry-level master's
degree programs and 87 baccalaureate programs are currently accredited.
Whether or not the first professional degree for the physical therapist should
be at the graduate level is a controversial topic in the physical therapy
profession. Evaluating differences in clinical decision-making ability among
graduates of these two types of professional programs would be of interest
to physical therapy educators.
6. Administer a teaching module with guidance in problem solving
and decision making to half of a physical therapy class and not to the other
half and, at the end of the semester, compare results obtained on the CDMS
between groups. This would help determine the role of "teaching" in clinical
decision making.
7. Design a final eight-week clinical internship for physical therapy
students in which they are given structured opportunities to practice clinical
decision making. Administer the CDMS to this group of physical therapy
students before the final internship and again after the eight-week
experience to see if scores on the instrument change significantly post
internship. Before this internship could be arranged, the researcher would
have to educate the clinical staff in the process of decision making and set
specific objectives to be covered, to assure that members of the staff felt
comfortable with this specific role of mentorship for decision-making skill.


69
Another recommendation for future research is that someone develop
another instrument to measure clinical decision-making process or ability.
This would allow comparison of performance on both instruments. The new
instrument would also provide an alternative quantitative measure of
decision-making ability for use in further investigation of the Theoretical
Model for Clinical Decision Making in Physical Therapy.
The final recommendation for further study is in the area of admission
criteria for physical therapy programs. Interest in prediction of successful
applicants to programs in physical therapy has developed because of the
many qualified applicants competing for each of the available slots and
because of the current shortage of physical therapists. Because the need for
physical therapists keeps increasing, graduation of each student admitted to
a physical therapy program is imperative. These students must also become
effective problem solvers and clinical decision makers.
Gross (1989) examined multiple physical therapy admission criteria
and their value for predicting didactic, clinical, and licensure performance.
He studied academic and licensure records of three classes of graduates of
three baccalaureate programs. He reported that preprofessional GPAs and
standardized measures of general verbal and mathematic aptitude were
moderate predictors of physical therapy GPA and weak predictors of
licensure performance. None of the admission criteria significantly predicted
clinical performance. Because clinical decision making is inherent in clinical
performance, perhaps these admission criteria could be investigated for
their ability to predict performance on the CDMS or another instrument
designed to measure clinical decision-making ability.
Many ideas for future investigations have been generated by this
study. Although implementing some of the ideas would be difficult, the task


70
should be undertaken due to the importance of gaining a better
understanding of the clinical decision-making process in physical therapy
The challenge of independent practice by physical therapists is a reality.
The profession must continue to make every effort to guarantee that each
physical therapist is ready to meet the challenge.


APPENDIX A
COVER SHEET FOR STUDENT'S NAME
OR IDENTIFICATION NUMBER
NAME
NUMBER
This sheet will be removed before the Clinical Decision Making Scale is sent
to me. It is necessary for you to put your name on this sheet so that a
member of your faculty can provide me with your GRE scores and your GPA.
I will not ever know you by name. Thank you for helping me with my
dissertation.
Sincerely,
Jane A. Day, PT, MA
PhD Candidate
71


APPENDIX B
THE CLINICAL DECISION MAKING SCALE
Adapted from The Clinical Decision Making in Nursing Scale with
permission of Helen M. Jenkins, PhD.
Copyright 1983
72


73
Directions for the Clinical Decision Making Scale
For each of the following statements, think of your behavior while
caring for clients. Answer on the basis of what vou are doing now in the
clinical setting.
There are no "right" or "wrong" answers. What is important is your
assessment of how you ordinarily operate as a decision maker in the clinical
setting. None of the statements cover emergency situations.
Statements are listed beginning on the following page. Use the
answer sheet provided. Do not dwell on responses. Circle the answer that
comes closest to the way you ordinarily behave.
Answer all items. About twenty minutes should be required to
complete this exercise.
Scale for the CDMS
Circle whether you would likely behave in the described way:
A Always What you consistently do every time.
F Frequently What you usually do most of the time.
O Occasionally What you sometimes do on occasion.
S Seldom What you rarely do.
N Never What you never do at any time.
Sample statement: I mentally list options before making a decision.
Key: A O S N
The circle around response F means that you usually mentally list options
before making a decision.


Clinical Decision Making Scale
Note: Be sure you respond in terms of what you are doing in the clinical
setting at the present time.
1. If the clinical decision is vital and there is time, I
conduct a thorough search for alternatives.
2. When a person is ill, his or her cultural values and beliefs
are secondary to the implementation of health services.
3. The situational factors at the time determine the number of
options that I explore before making a decision.
4. Looking for new information in making a decision is more
trouble than it's worth.
5. I use books or professional literature to look up things I
don't understand.
6. A random approach for looking at options works best for me.
7. Brainstorming is a method I use when thinking of ideas for
options.
8. I go out of my way to get as much information as possible to
make decisions.
9. I assist clients in exercising their rights to make
decisions about their own care.
10. When my values conflict with those of the client, I am
objective enough to handle the decision making required for
the situation.
11. I listen to or consider expert advice or judgment, even
though it may not be the choice I would make.
12. I solve a problem or make a decision without consulting
anyone, using information available to me at the time.
13. I don't always take time to examine all the possible
consequences of a decision I must make.
14. I consider the future welfare of the family when I make a
clinical decision which involves the individual.


75
Note: Be sure you respond in terms of what you are doing in the clinical
setting at the present time.
15. I have little time or energy available to search for
information.
16. I mentally list options before making a decision.
17. When examining consequences of options I might choose, I
generally think through "If I did this, then...".
18. I consider even the remotest consequences before making
a choice.
19. Consensus among my peer group is important to me in making
a decision.
20. I include clients as sources of information.
21. I consider what my peers will say when I think about possible
choices I could make.
22. If an instructor recommends an option to a clinical decision
making situation, I adopt it rather than searching for
other options.
23. If a benefit is really great, I will favor it without
looking at all the risks.
24. I search for new information randomly.
25. My past experiences have little to do with how actively
I look at risks and benefits for decisions about clients.
26. When examining consequences of options I might choose,
I am aware of the positive outcomes for my client.
27. I select options that I have used successfully in similar
circumstances in the past.
28. If the risks are serious enough to cause problems, I
reject the option.
29. I write out a list of positive and negative consequences
when I am evaluating an important clinical decision.


76
Note: Be sure you respond in terms of what you are doing in the
clinical setting at the present time.
30. I do not ask my peers to suggest options for my clinical
decisions.
31. My professional values are inconsistent with my personal
values.
32. My finding of alternatives seems to be largely a matter
of luck.
33. In the clinical setting I keep in mind the course objectives
for the day's experience.
34. The risks and benefits are the farthest thing from my mind
when I have to make a decision.
35. When I have a clinical decision to make, I consider the
institutional priorities and standards.
36. I involve others in my decision making only if the situation
calls for it.
37. In my search for options, I include even those that might
be thought of as "far out" or non-feasible.
38. Finding out about the client's objectives is a regular
part of my clinical decision making.
39. I examine the risks and benefits only for consequences that
have serious implications.
40. The client's values have to be consistent with my own, in
order for me to make a good decision.
Thank you for being a participant in this study. Do you have any
ideas about decision making in physical therapy that were not covered by
the scale that you would like to share? You can speak to specific items or
give any general comments you would like. Feel free to use this last page
or the back of the answer sheet.


APPENDIX C
ANSWER SHEET FOR THE CLINICAL DECISION MAKING SCALE


Answer Sheet for the Clinical Decision Making Scale
Directions: After reading each statement, circle the response which comes
closest to the way you act or behave. Please do not skip any of the items.
Remember that:
1. A F O
2. A F O
3. A F O
4. A F O
5. A F O
6. A F O
7. A F O
8. A F O
9. A F O
10. A F O
11. A F O
12. A F O
13. A F O
14. A F O
15. A F O
16. A F O
17. A F O
18. A F O
19. A F 0
20. A F O
A Always
F Frequently
0 Occasionally
S Seldom
N Never
S N
21.
S N
22.
S N
23.
S N
24.
S N
25.
S N
26.
S N
27.
S N
28.
S N
29.
S N
30.
S N
31.
S N
32.
S N
33.
S N
34.
S N
35.
S N
36.
S N
37.
S N
38.
S N
39.
S N
40.
A F O S N
A F O S N
A F O S N
A F O S N
A F O S N
A F O S N
A F O S N
A F O S N
A F O S N
A F O S N
A F O S N
A F O S N
A F O S N
A F O S N
A F O S N
A F O S N
A F O S N
A F O S N
A F O S N
A F O S N
78


APPENDIX D
BIOGRAPHICAL INFORMATION SHEET
Last 4 Digits of SS#
BIOGRAPHICAL INFORMATION
SEX: MALE FEMALE
AGE: YEARS
RACE:
AMERICAN INDIAN
ASIAN
BLACK
CAUCASIAN
HISPANIC
ORIENTAL
OTHER (write in)
PRIOR DEGREE(S): MAJOR AREA(S)(ie: lib.arts;psychology;etc):
(Check and write in all that apply)
AA
AS
BA
BS
MA
MS
OTHER (write in)
79


APPENDIX E
DATA RECORDING FORM
To be filled out by school faculty:
Final GPA
GRE Quantitative Score.
GRE Verbal Score
GRE Analytical Score
THANK YOU !!
80


APPENDIX F
DESCRIPTIVE DATA SUMMARY FOR PROGRAM A STUDENTS
VARIABLE
N
M
SD
Mode
Low
High
Range
AGE
92
26
2.77
24
22
39
17
GRE-VQ
94
1161
110.0

980
1500
520
GRE-A
94
606
79.2
550
420
800
380
GPA
94
3.42
0.26

2.82
3.90
1.08
CDMS-T
94
152
10.3
156
123
176
53
CDMS-A
94
39
2.76
40
31
46
15
CDMS-B
94
38
3.51
37
28
47
19
CDMS-C
94
38
4.02
38
26
47
21
CDMS-D
94
37
2.80
38
31
45
14
81


APPENDIX G
DESCRIPTIVE DATA SUMMARY FOR PROGRAM B STUDENTS
VARIABLE
N
M
SD
Mode
Low
High
Range
AGE
22
26
3.98
24
23
41
18
GRE-VQ
19
1101
126.3

850
1340
490
GRE-A
19
599
103.2

380
740
360
GPA
22
3.61
0.25

3.04
4.00
0.96
CDMS-T
22
149
7.78

135
164
29
CDMS-A
22
38
2.04

34
42
8
CDMS-B
22
37
2.57
39
33
42
9
CDMS-C
22
37
2.97

31
42
11
CDMS-D
22
38
3.06
36
32
45
13
82


APPENDIX H
DESCRIPTIVE DATA SUMMARY FOR PROGRAM C STUDENTS
VARIABLE
N
M
SD
Mode
Low
High
Range
AGE
36
27
3.54
25
24
38
14
GRE-VQ
36
1095
144.5

770
1460
690
GRE-A
36
603
92.6

370
800
430
GPA
36
3.66
0.20

3.00
3.97
0.97
CDMS-T
36
152
8.72
153
138
173
35
CDMS-A
36
39
2.88
36
32
45
13
CDMS-B
36
37
2.77
36
32
43
11
CDMS-C
36
38
3.52
36
30
45
15
CDMS-D
36
38
2.19

34
44
10
83


APPENDIX I
DESCRIPTIVE DATA SUMMARY FOR PROGRAM D STUDENTS
VARIABLE
N
M
SD
Mode
Low
High
Range
AGE
30
26
2.54
24
24
34
10
GRE-VQ
30
1118
124.0

910
1410
500
GRE-A
30
613
91.0
590
420
760
340
GPA
30
3.59
0.26

2.93
3.97
1.04
CDMS-T
30
150
8.40
156
132
163
31
CDMS-A
30
39
2.42
38
33
44
11
CDMS-B
30
37
3.04
38
30
43
13
CDMS-C
30
37
2.69
37
33
43
10
CDMS-D
30
37
3.22
38
31
43
12
84


APPENDIX J
DESCRIPTIVE DATA SUMMARY FOR PROGRAM E STUDENTS
VARIABLE
N
M
SD
Mode
Low
High
Range
AGE
25
27
4.91
23
23
39
16
GRE-VQ
25
1188
132.0

930
1540
610
GRE-A
25
600
93.0

460
800
340
GPA
25
3.37
0.22
...
2.93
3.74
0.81
CDMS-T
25
153
8.94
...
135
167
32
CDMS-A
25
39
2.75
38
34
45
11
CDMS-B
25
38
4.22
34
30
45
15
CDMS-C
25
38
3.25

30
42
12
CDMS-D
25
38
1.96
39
35
42
7
85


APPENDIX K
DESCRIPTIVE DATA SUMMARY FOR PROGRAM F STUDENTS
VARIABLE
N
M
SD
Mode
Low
High
Range
AGE
37
26
4.55

23
42
19
GRE-VQ
37
1218
102.0
1200
1030
1560
530
GRE-A
37
656
76.3
680
410
790
380
GPA
37
3.48
0.21

3.00
3.85
0.85
CDMS-T
37
152
8.77
162
133
166
33
CDMS-A
37
38
2.17

33
43
10
CDMS-B
37
38
3.12

31
43
12
CDMS-C
37
38
3.24

33
43
10
CDMS-D
37
37
2.82
36
30
43
13
86


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criteria in predicting didactic, clinical, and licensure performance.
Journal Physical Therapy Education. 3,7-14.


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