EVIDENCE-BASED ADMISSIONS: CORRELATES OF STUDENTS’ ACADEMIC
SUCCESS IN VETERINARY MEDICAL SCHOOL
By
Hilda Mejia Abreu

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirement
for the degree of
Higher, Adult, And Lifelong Education Program—Doctor Of Philosophy
2013

ABSTRACT
EVIDENCE-BASED ADMISSIONS: CORRELATES OF STUDENTS’
ACADEMIC SUCCESS IN VETERINARY MEDICAL SCHOOL
By
Hilda Mejia Abreu
Literature on the admission criteria and processes related to academic success of
professional students at any of the twenty-eight veterinary medical schools in the United States is
limited and research on what predicts students’ academic success has not been regularly
examined (Pappaioanou, 2010). The current study investigated the extent to which traditional
academic characteristics (i.e., cumulative grade point average, science grade point average, and
graduate record examination scores) and non-traditional student characteristics (i.e.,
communication and interpersonal skills, the essay or self-assessment survey, prior knowledge of
the profession, and community service as assessed via the interview process) predicted students’
academic performance at Michigan State University (MSU) College of Veterinary Medicine
(CVM). Several analyses were performed to determine the contribution of traditional and nontraditional factors as predictors of students’ academic performance in veterinary medical school
as measured by their grade point average in the veterinary school program at two points: (a) the
completion of the didactic portion of the curriculum (the first five semesters) and (b) the
completion of the clinical portion of the curriculum (the last four semesters).
The interpretation of the results yielded significant findings in several areas: (a) the
identification of characteristics of traditional and non-traditional students that were predictive of
academic performance in the veterinary school relative to students’ cumulative grade point
average in the clinical and didactic portion of the curriculum; (b) the identification of traditional
academic characteristics that were predictive of student placement status of academic probation;

and (c) support for the expectation that the cumulative didactic grade point average was
predictive of the cumulative clinical grade point average. Through the identification of variables
related to both traditional and non-traditional characteristics, variables that were predictive of
students’ academic performance in one veterinary school, administrators at other programs for
health care professional programs may be encouraged to review and evaluate the strength of their
admission criteria to ensure the reliability and validity of their applicant selection processes.

DEDICATION
To my father, Octavio Mejia Flores and my mother, Catalina Reynoso. They both instilled in me
the importance of education while providing much love and support.
To my husband, Scott Burgess who has adjusted his entire life and career to support me during
this journey. Your support, love, and devotion made this dissertation possible.
To my siblings, Octavio Mejia Abreu, Albis Mejia Abreu, Digna Mejia Abreu, Marcia Mejia
Ware, Victor Reynoso, Miguel Reynoso, and Leticia Mejia Balbuena—thank you for inspiring
me every day. Yes, the “little paper” is finally completed!!
To my aunts, Dr. Daisy Mejia, Perfa Mejia De Liogier, to my goddaughter, Savanna Mejia Ware,
and to my niece, Siobhan Hulslander, thank you. Your love and support provided comfort.

iv

ACKNOWLEDGEMENTS

I would like to express my deepest gratitude to Dr. Matthew Wawrzynski, my
dissertation chair, for his extensive guidance and support during the completion of this degree. I
am grateful for his expertise in non-cognitive variables, student development theory, and
quantitative methods, and for his ability to guide me and to provide support during this journey.
I would also like to express my gratitude to members of my dissertation committee for
their advice and suggestions: Dr. John Dirkx, Dr. Marilyn Amey, and Dr. James Lloyd. Dr.
Dirkx provided guidance and challenged me with questions. He prompted me to be inquisitive,
and his suggestions were always thought-provoking. Dr. Amey provided the most amazing
support by recruiting me to the program, supporting me throughout with e-mail messages, and
believing in me throughout my journey. Dr. Amey, you were consistent in encouraging to me.
Thank you for always boosting my confidence and finding a way to make me see toward the
future as when you said, “I can’t wait to place that hood at graduation!” I have held onto that
vision for the last seven years. You inspired me, Dr. Amey. Dr. Lloyd has always been helpful
with guidance concerning veterinary medical education. He kept me focused and on target.
Also, I am grateful to him for the many hours of support, meetings, guidance, and coaching as I
worked on developing the study to be relevant to the veterinary profession. Finally, he paved the
way to obtain the data analyzed in this research study.
Additionally, I would like to thank Dean Christopher Brown for approving my request for
data and providing access. Thank you for your support. I will be forever grateful to Dr. Coretta
Partterson and her team at the veterinary school—Ms. Lori Headley, Ms. Pat Peterson, Ms.
Donna Grooms, and Dr. Norma Baptista. You are exceptional examples of collaboration,

v

communication, and cohesion in higher education. Without your support, this research would
not have been possible.
My most heartfelt gratitude goes to Dean Eileen Breslin at the University of Texas Health
Science Center at San Antonio, School of Nursing. Thank you for believing in me, and for your
patience, and constant support. To my team in the Office of Admissions and Student Services at
the University of Texas Health Science Center, Ms. Brandy Finck, Ms. Priscilla Reyna, and Ms.
Melissa Mireles—thank you for the many, many times you supported me.
To all members of my PhD cohort, thank you for your friendship, support, guidance and
regular conversations. I will forever be grateful to you.
On a final note, I would like to express my gratitude to Dr. Andrea Berndt, who provided
guidance whenever I had an issue with statistics. Her expertise and ability to patiently listen
could not have been more wonderful. Thank you for your invaluable and committed support to
this research.

vi

TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................. ix
LIST OF FIGURES ........................................................................................................... xi
CHAPTER 1
INTRODUCTION ...............................................................................................................1
Statement of the Problem .........................................................................................5
Purpose of Study ......................................................................................................7
Rationale ..................................................................................................................7
Significance..............................................................................................................8
Research Questions ..................................................................................................9
Definitions..............................................................................................................10
Assumptions...........................................................................................................11
Disclosure ..............................................................................................................12
Frameworks and Related Concepts ........................................................................12
Summary ................................................................................................................12
CHAPTER 2
LITERATURE REVIEW ..................................................................................................14
Conceptual frameworks .........................................................................................14
I-E-O Model ...............................................................................................15
Input ...........................................................................................................16
Environment ...............................................................................................17
Outcomes ...................................................................................................17
Sedlacek’s Non-cognitive Variables ..........................................................18
The Admission Process ..........................................................................................23
Veterinary Medical School Admission ..................................................................24
The Veterinary Medical School Curriculum..........................................................28
Professional Schools’ Selection Characteristics ....................................................31
Selection Methods ..................................................................................................32
Non-traditional Characteristics in Admission Selection ........................................33
Admission Practices and Students’ Academic Success .........................................35
Summary ................................................................................................................39
CHAPTER 3
METHOD ..........................................................................................................................41
Introduction ............................................................................................................41
Purpose of the Study ..............................................................................................42
Research Questions ................................................................................................42
Study Design ..........................................................................................................43
Limitations .............................................................................................................43
Institutional Context...............................................................................................45

vii

Operational Definitions ..........................................................................................45
Data Collection, Preparation, Sample Selection and Description..........................46
Instrumentation ......................................................................................................47
Data Collection Procedures....................................................................................51
Data Analysis Procedures ......................................................................................52
Summary ................................................................................................................55
CHAPTER 4
RESEARCH FINDINGS ...................................................................................................56
Data Screening and Merging of Cases into a Single Dataset.................................57
Descriptive Statistics and Frequencies for the Merged Dataset.............................60
Sample Equivalence Tests for Admission Characteristics and
Academic Performance ....................................................................................64
Research Question 1 ..............................................................................................69
Research Question 2 ..............................................................................................70
Research Question 3 ..............................................................................................72
Research Question 4 ..............................................................................................72
Summary ...............................................................................................................74
CHAPTER 5
DISCUSSION, IMPLICATIONS and RECOMMENDATIONS .....................................76
Purpose of the Dissertation ....................................................................................77
Discussion of Results .............................................................................................77
Traditional and Non-traditional Characteristics at Admission ..............................79
Traditional and Non-traditional Characteristics as Predictors of Academic
Performance ...........................................................................................................81
Didactic Academic Performance as an Indicator of Clinical Academic
Performance ...........................................................................................................88
Traditional and Non-traditional Characteristics that Predict Placement
On Academic Probation .........................................................................................89
Evaluation of the Conceptual Models ....................................................................91
Implications............................................................................................................93
Recommendations for Future Research .................................................................95
APPENDICES ...................................................................................................................99
APPENDIX A ADMISSION SELECTION FORMULA AT MSUCVM .........100
APPENDIX B VETERINARY MEDICAL SCHOOL CURRICULUM ...........101
APPENDIX C REQUEST FOR DATA FROM MSU CVM DEAN .................106
APPENDIX D INTERVIEW QUESTIONS AND SCORING GUIDE .............108
APPENDIX E FACULTY INTERVIEWER’S TRAINING .............................111
REFERENCES ................................................................................................................112

viii

LIST OF TABLES

Table 1: MSU CVM Application and Admission Data from 2000 to 2006 ......................47

Table 2: Traditional and Non-traditional Characteristics Collected in Veterinary Medical
Applications ...........................................................................................................47

Table 3: List of Variables Included in the Admission Dataset ..........................................51

Table 4: Demographic Characteristics of the Reduced Merged Data (N=676) .................53

Table 5: List of Variables Included in the Academic Performance Dataset ......................57

Table 6: Frequency of Cases Deleted as a Function of Removal Reason .........................58

Table 7: Distribution of Student Representation by Admission Year as a Function of
Dataset....................................................................................................................59

Table 8: Descriptive Statistics for Interval and Ratio Variables in the Merged Data
(N = 683) ................................................................................................................61

Table 9: Descriptive Statistics for Interval and Ratio Variables in the Final Merged Data
(N = 676) ................................................................................................................61

Table 10: Sample Equivalence Tests on Admission Characteristics as a Function of
Ethnicity (n=676) ...................................................................................................65

Table 11: Means, Standard Deviations, and One-way Analyses of Variance on Admission
Characteristics as a Function of Prior Degree........................................................66

Table 12: Sample Equivalence Tests on Admission Characteristics as a Function of
Residency (N=676) ................................................................................................67

ix

Table 13: Sample Equivalence Tests on Admission Characteristics as a Function of
Sex (N=676) ...........................................................................................................67

Table 14: Regression Analysis Summary for Traditional and Non-traditional Admission
Characteristics Predicting Cumulative Didactic GPA, after Removing
Non-Significant Variables (N=676).......................................................................70

Table 15: Regression Analysis Summary for Traditional and Non-traditional Admission
Characteristics Predicting Cumulative Clinical GPA, after Removing Non-significant
Variables (N=676) .................................................................................................71

Table 16: Distribution of Non-traditional Admission Characteristics as a Function of
Academic Probation ...............................................................................................73

Table 17: Group Differences for Traditional and Non-traditional Admission
Characteristics as a Function of Academic Probation (N=683).............................73

Table 18: Group Differences for Academic Performance as a Function of Academic
Probation (N=683) .................................................................................................74

x

LIST OF FIGURES

Figure 1: Astin’s (1991) I-E-O Model ...............................................................................16

Figure 2: The Three Legs of Veterinary Medical Education (Adapted from the NAVMEC
(2011) report) .........................................................................................................30

Figure 3: Design Model for this Study ..............................................................................39

Figure 4: Science GPA as a Function of Cohort Year .......................................................62

Figure 5: Interview Score as a Function of Cohort Year ...................................................63

xi

CHAPTER 1
INTRODUCTION
Members of professional program admission committees in the health professions have
an important task as they select future health care providers (Latif & Dunn, 2004; Peskun,
Detsky & Shandling, 2007). The task of selecting students who will become the best, brightest,
most competent, most ethical, and most caring health care providers is a sensitive, delicate, timeconsuming, and important activity in professions such as medicine, nursing, veterinary medicine
and dentistry as these individuals are responsible for the well-being of patients, the protection of
the public, and the reputation of their chosen profession (Lewis & Klausner, 2003).
According to the North American Veterinary Medical Education Commission’s
(NAVMEC) summary of findings, in addition to the important responsibility of the schools’
selecting exceptional future health care providers, it is expected that veterinary medical
graduates achieve competency in three areas: (a) multispecies knowledge plus clinical
competence in one or more species or disciplines; (b) “One Health” professional competency
related to the intersection of animal, human and environmental health; and (c) the development
of professional competencies. Therefore, admission selection criteria and academic curricula are
interrelated and must be aligned to address the needs of the profession and accreditation
requirements to “implant changes that allow new graduates to have the knowledge and
competencies society needs” (NAVMEC, 2011, p. 23). Further, it is stated within Standard 7 of
the American Veterinary Medical Association’s (AVMA) Council on Education (COE) that
“factors other than academic achievement must be considered for admission criteria” (AVMA,
2012, p. 34). The AVMA-COE’s Standard 9 (concerning curriculum) goes on to state that

1

effective communication shall be provided to professional students as part of the veterinary
medical school curriculum.
Admission and accreditation are critical to the assessment of outcomes for professional
veterinary medical students. The veterinary medical schools’ accreditation process speaks to the
importance of outcomes. Therefore, this study’s primary focus is to identify characteristics of
prospective students that contribute to successful academic performance at MSU CVM. In
addition, the study examined the relationship between traditional admission criteria and nontraditional criteria and students’ academic success in veterinary school. Selecting prospective
students for any professional program admission based on known factors of academic success
remains a difficult process, and performing the role of admission committee chair or member has
been one of the most important responsibilities that program administrators, faculty, and staff
accept during their careers. Of further concern related to program admission decisions is the
knowledge that students, schools, and states bear the cost of admission mistakes when students
are unsuccessful because they do not fit within the institution or program due to faulty selection
criteria (Vickers & Reeve, 1990). The importance, responsibility, and sensitive nature of
selecting the best, most capable, most caring, and most responsible future health care providers,
necessitates that admission selections procedures be regularly reviewed and updated at U. S.
schools which educate health care professional programs (Best, Diekema, Fisher, & Smith,
1971).
The literature on selection processes or admission criteria varies across veterinary
schools. For example, of the nation’s twenty-eight veterinary schools, some schools focus
primarily on traditional selection methods such as grade point average (GPA) and standardized
test scores (STS) while others use a combination of indicators, such as GPA and STS along with

2

personal interviews (Association of American Veterinary Medical Colleges (AAVMC), 2011;
Ottinger, 2011). Interestingly, the reviewed literature is more expansive in the area of admission
selection processes for allopathic medicine and dentistry as it relates to admission criteria,
selection methods used, and academic performance. Therefore, the assessment of students’
academic performance in veterinary professional programs is essential to determine which
selection criteria, processes, and methods are most useful and appropriate for predicting
academic performance. Further, the assessment of selection methods on a regular basis is a
known best practice (American Association of Collegiate Registrars and Admissions Officers,
2011) and neglecting this practice could have a negative impact on the outlined goals of both
programs and professions.
Most professional health care programs (e.g., allopathic medicine, veterinary medicine,
nursing, and dentistry) in the United States traditionally use academic data, which are often
identified at the undergraduate level by grade point average in subject areas relevant to the
professional program (e.g., natural science courses) and at the graduate level by standardized
tests such as the Medical College Admissions Test (MCAT) or the Graduate Record
Examination (GRE), to select candidates for admission (AAMCAS 2011; Ottinger, 2011).
Despite this frequent use of traditional academic data, Zwick (2002) argues that scores gathered
from standardized tests are incomplete, subject to error, and provide little information about the
knowledge of assessed individuals. Furthermore, admissions deans at professional schools have
identified standardized exams as a significant barrier to college entrance for first-generation
college students, women, and minorities (Rooney, 1998). As Zwick (2002) observes, other
critics of standardized tests argue the tests offer only limited evidence about any given person’s
capabilities. Further concerns about the reliability of traditional criteria for admission to

3

professional programs include challenges to using grade point average because grade inflation is
widespread at many academic institutions (Mansfield, 2001), thus artificially inflating the
assessment of the cognitive abilities of many prospective students. Others have argued that little
variability in test scores occur for students entering some programs such as medical school
because program admission is often highly competitive and applicants are likely to be at the top
of the potential range of scores and likely to do well in their medical school training, and the
limited variability in test scores or grade point average makes selection of top candidates
difficult, at best (Ferguson, James, & Madeley, 2002; Sedlacek, 2004). Similarly, Deary, Der,
and Ford (2001) found strong evidence supporting the need to use diverse measures of cognitive
ability to best assess general intelligence due to the challenges presented by grade inflation and
minimal variability of testing scores across programs, and to the need to include non-traditional
characteristics in selection criteria if a “best program fit” for academic success was to be
achieved.
Educators, researchers, and deans who have proposed utilizing traditional measures
(cognitive) and non-traditional measures (non-cognitive) to select appropriate student
populations for their programs of study also want to see evidence of whether combined selection
processes, criteria, or methods at one school can yield a suitable student who would be accepted
across similar institutions (Astin, 1993; Sedlacek, 2004; Tinto, 1975, 1993; Tracey & Sedlacek,
1984; Washington, 1996). These institutions describe a suitable student as one who represents
“goodness of fit” within the institution’s academic and non-academic environment and as one
who is able to successfully navigate and complete the program of study.
The combination of traditional and non-traditional attributes is seen as providing a
holistic review of applicants’ credentials, a strategy that might better forecast a student’s

4

successful academic performance in a professional program (Sedlacek, 2004). Incorporating
non-traditional attributes with traditional attributes as selection criteria could also provide useful
data for professional school selection methods because a variety of assessment strategies can be
included in the course of admission selection process. Although non-traditional components
(e.g., cultural competency, assessment of communication and interpersonal skills) have been
used in admission selection processes for professional programs, little research exists as it relates
to the use of these components specifically in veterinary medicine (Lee, Vaishnavi, Lau,
Andriole, & Jeffe, 2007). In the study entitled “The Current and Future Market of Veterinarians
and Veterinary Medical Services in the United States,” often referred to as the KPMG study or
the Mega Study, which was designed to assess whether veterinarians are meeting the needs of the
profession, Brown and Silverman (1999) found that practicing veterinarians were lacking in
positive interpersonal and communication skills. These findings support veterinary medical
school administrators’ and deans’ discussions about combining traditional and non-traditional
selection criteria and methods that would address characteristics such as skills, knowledge,
attitudes, and aptitudes desired of future veterinarians (NAVMEC, 2011).
Statement of the Problem
The veterinary profession continues to grow as more schools are created and more
students are enrolled (AAVMC, 2011). Since 2005, four new veterinary schools have been
established—one in the United States, one in Canada, and two in the Caribbean. While the
addition of four schools might not appear to be a large number, the last veterinary school prior to
this 2005 expansion was created in the 1970s; therefore, for the veterinary profession, four new
schools represent a significant expansion. With new schools being developed and the expense
and time-consuming preparations needed to inaugurate a new, credible veterinary program,

5

determining valid and reliable criteria for new-student enrollment is of paramount concern.
Providing additional guidelines to identify pertinent student characteristics predicting program
success in a veterinary medicine program will be useful to schools. Further, results from the
current study could provide new veterinary schools with identified characteristics of students
who can succeed academically in a veterinary curriculum (Ottinger, 2012). Moreover, most of
the research relative to academic success has focused on undergraduate education and in other
professional fields. The time is appropriate for veterinary education professionals to identify
characteristics that influence academic performance.
Gaining admittance to one of the few established veterinary schools in the country is a
prestigious accomplishment given that there are over 5000 applicants for approximately 2000
positions (Ottinger, 2012). Further, academically successful veterinary school graduates with
exceptional performance are actively pursued by prestigious internships and residency programs,
and these opportunities along with employment by major veterinary and corporate clinics are
highly competitive in nature (Brown & Silverman, 1999; NAVMEC, 2011). As a result, the
selection process, with clearly defined and tested criteria and methods, should be completed with
the utmost confidence. Given the importance of accomplishing this goal, academic and nonacademic abilities plus a determination of prospective student “fit” with the environment must be
included in all program admission selection processes. The process of identifying a student’s fit
to the institution is referred to in the industry as “ethical admission selection.” It is important for
schools to know which characteristics predict and positively influence academic performance
and graduation rates to ensure success in the highly competitive post-graduation world of
employment, internships, and residency programs.

6

Purpose of the Study
The purpose of this study is to identify characteristics defined within an applicant pool of
prospective students that predict academic performance in veterinary school. After more than
four decades of research and supporting evidence in allopathic medicine, Adams and Frankle
(2007) demonstrated that non-traditional student characteristics (i.e., communication and
relationship building) influenced the quality of care outcomes, which, in turn, affected both
academic success during program enrollment and professional success after graduation. It is
critical for veterinary medical education to incorporate traditional and non-traditional
characteristics to predict academic success during the didactic and clinical years. Since
allopathic medicine has successfully used traditional and non-traditional characteristics to select
future health care providers and veterinary medicine programs are not so different from
allopathic medicine, it seems reasonable for veterinary medical schools considering applicants to
incorporate both types of characteristics to identify variables predicting students’ academic
success during the didactic and clinical years.
Rationale
Since the founding of the first veterinary school in Lyon, France in 1761, veterinary
medicine has evolved. In the United States, veterinary medicine practice started during colonial
times and developed as a formal profession with the establishment of the University of
Pennsylvania Veterinary School in 1883. During the 1950s, people who worked with animal
diseases started to be called veterinarians. The profession is respected, and the number of
veterinarians in the United States has grown due to demands for services (AVMA, 2012).
Further, the profession experienced increasing demands for services in the 1980s and 1990s, and
there was a greater need for well-educated veterinarians to meet society’s needs as the number of

7

pets per household grew (AVMA, 2007). Furthermore, excellent care from a veterinarian with
technical and non-technical skills is expected. Thus, there are a number of reasons to explore the
topic of finding appropriate criteria, processes, and methods to select veterinary applicants for
admission that predict academic success: (a) an identified gap in the literature with respect to
variables that predict academic success in veterinary school, (b) findings from the broad KPMG
study challenging institutions to revamp procedures, (c) NAVMEC Roadmap (2011)
recommendations, (d) the scholarship of evidence in admission selection practices, (e) a
professional curiosity to identify whether changes in admission selection criteria and strategies
have an influence on the type of student selected and their academic success during didactic and
clinical curriculums, and (f) the need to share findings and implications from selected criteria
and methods with the Michigan State University College of Veterinary Medicine (MSU CVM) at
which this study was conducted. To these ends, the current study investigated the diverse
selection criteria and methods adopted at one veterinary medical school as predictors of
academic success.
Significance
Limited research exists that provides evidence about successful academic performance by
prospective veterinary students based on the selection method, processes, and criteria used at the
time of admission. Therefore, this initial endeavor using prospective student characteristics that
predict academic success throughout the program of study taken at routine times of program
review could provide a foundation for others to consider when conducting further studies. From
this foundation of knowledge, veterinary medical school programs could then individualize their
selection of additional prospective student characteristics to provide the “best fit” with
institutional and program statements (i.e., mission, vision, and goals) and the variety of

8

environments (i.e., geographic, psycho-social, or economic) known to influence student
satisfaction and program progression. The use of known traditional and less-familiar nontraditional student characteristics should expand the likelihood of finding predictors of academic
success. Furthermore, a review of those characteristics that are shown to positively influence
successful academic progression but are not statistically strong enough to predict the desired
study outcome could, nevertheless, provide useful additional criteria for inclusion in admission
selection decisions. In addition, those characteristics of prospective students that were identified
in the literature as relevant to academic success but were not found to be predictors of academic
performance in this study could also provide valuable information for further consideration.
Finally, the use of two distinct points of data collection across seven cohorts of students provided
information regarding predictors of academic success for both didactic and clinical components
of the curriculum.
Research Questions
1.

To what extent do traditional and non-traditional admission characteristics contribute to
the prediction of cumulative grade point average (GPA) in the didactic portion of the
veterinary medical school curriculum?

2.

To what extent do traditional and non-traditional admission characteristics contribute to
the prediction of cumulative grade point average in the clinical portion of the veterinary
medical school curriculum?

3.

To what extent does cumulative didactic GPA contribute to the prediction of cumulative
clinical GPA in the veterinary medical school curriculum?

4.

Do group differences exist between students placed/never placed on academic probation
in terms of admission characteristics or academic performance?

9

Definitions
The following terms are used throughout this study:
1.

Academic performance: A person’s level of academic achievement in a professional
program of study as measured by GPA.

2.

Academic Success: Continued, expected progression in both the didactic and clinical
portions of the veterinary school curriculum.

3.

Didactic Curriculum: The portion of the curriculum composed of lectures.

4.

Clinical Curriculum: The portion of the curriculum that includes patient care and work
with the heath care team.

5.

Student Performance Committee: The organization responsible for the evaluation and
recommendation of students who find themselves in academic difficulty.

6.

Traditional (cognitive as well as technical) Characteristics: Grade point average, science
grade point average, Medical College Admissions Test score, and Graduate Record
Examination score.

7.

Non-traditional (non-cognitive, social, as well as non-technical) Characteristics: Personal
statement, interview score, evaluation or reference letters, types and quantity of
community service, interpersonal and communication skills, volunteer activities,
knowledge of the profession, work experience, successful leadership activities, and
knowledge of self.

8.

Criteria: Individual traditional and non-traditional student characteristics that are assigned
a numerical value for the purpose of measurement or statistical analysis.

9.

Process: A series of progressive, decision-making phases that culminates in the selection
of qualified applicants for program enrollment.

10

10.

Method: A variety of traditional (i.e., standardized test score or GPA) and non-traditional
activities (i.e., personal interview or essay review) that provide information about
prospective applicants for consideration during the program admission decision-making
process.

Assumptions
The following assumptions guided the initial design of this study:
1.

Admissions personnel across all professional programs have an ethical responsibility to
select qualified students believed to be capable of academic success within their program
of study.

2.

There is a critical need for veterinary medical school admission personnel to identify
prospective student characteristics that predict academic success.

3.

The use of traditional and non-traditional characteristics provides an opportunity for
holistic assessment of applicants to predict academic success in veterinary medical
school.

4.

Prospective student characteristics that are predictive and positively influence academic
performance and success are useful criteria for inclusion in program admission selection
processes within the veterinary program of study and possibly others.

5.

Data are presumed to be accurate and complete for study cohorts.

6.

The sample of approximately 600 veterinary students is presumed to be a representative
sample of veterinary students in the country because there are twenty-eight veterinary
schools in the United States and the applicant pool is known to be very homogenous as it
relates to applicants and enrolled student characteristics of GPA, gender, background, and
preparation.

11

Disclosure
The researcher was Assistant Dean for Admissions and Student Services at the veterinary
school in question at the time that students were being admitted.
Frameworks and Related Concepts
Astin’s (1993) I-E-O model and Sedlacek’s (2004) non-cognitive variables framework,
viewed as seminal works within the reviewed literature (Amey & Long, 1998; Cress, Astin,
Zimmer-Oster, Burkhardt, 2001) were used as conceptual underpinnings to guide the study
design.
Summary
Current opportunities in veterinary medicine are a product of results from the KPMG
study. The study by Brown and Silveman conducted in 1999, urged the veterinary profession to
select as future veterinarians students who possess technical and non-technical skills to serve the
community in their capacity as health care providers. According to the study, the solo
practitioner was vanishing and practices were changing to include a health care team approach
(Brown & Silveman, 1999). Thus, the finding proposed challenges to identifying a different
kind of veterinary student. While few studies have focused on the long-term predictive validity
of non-cognitive characteristics such as experience, interest, biographical data, and personality
variables (Lievens, Ones, & Dilchert, 2009), there have been many studies relative to cognitive
predictive values.
In this chapter, I presented the background in brief for the study, the problem statement,
the purpose of the study, and its rationale and significance. Additionally, I presented information
concerning the need for conducting this research. Finally, Astin’s (1993) I-E-O model and
Sedlacek’s (2004) non-cognitive variables were briefly introduced as the frameworks

12

underpinning this research. Astin’s model was designed to study the influence of the
undergraduate environment on retention and persistence and has been used to analyze the
environmental effects of school on graduate and professional students (Gore, 2009). Sedlacek’s
non-cognitive variables framework has been used to analyze the degree to which non-traditional
attributes influence academic success of various populations. In the next chapter, the literature
for this study is discussed.

13

CHAPTER 2
LITERATURE REVIEW
This research study examined which variables influence veterinary medical school
students’ academic success. The conceptual frameworks that guided this study were Astin’s
(1993) input, environment, and outcomes (I-E-O) model and Sedlacek’s (2004) non-cognitive
variables (NCVs). A brief background on Sternberg’s (1985) Triarchic Theory of Intelligence is
included here as the foundation for development of Sedlacek’s NCV work. Astin’s I-E-O model
and Sedlacek’s work are illustrated using findings from the literature related to research into the
application of these frameworks to undergraduate and professional students’ academic success.
A review of admission selection processes at Michigan State University (MSU) College of
Veterinary Medicine (CVM) and an outline of the veterinary medical school curriculum can be
viewed in Appendix A and Appendix B, respectively. Finally, this chapter concludes with a
review of available literature related to professional programs admission, selection, committee
members’ work, and the characteristics valued in the process as they relate to components
impacting students’ academic success.
Conceptual Frameworks
This section describes the conceptual frameworks used in this study to identify predictors
of students’ academic success at a veterinary school. Astin’s I-E-O model (1993) and Sedlacek’s
(2004) NCV framework served as the conceptual and data analysis frameworks for this research
study. Astin’s I-E-O model was conceptualized to show the influence of college on
undergraduate students. Sedlacek’s NCVs were identified to show the influence of nonacademic variables on students’ academic success in college. This research study examined the

14

influence of variables at admission on veterinary medical school students’ academic success.
Next, each conceptual framework is reviewed in greater depth.
The I-E-O Model. This research study used Astin’s (1993) input, environment, and
outcomes (I-E-O) model as one of the two conceptual and data analysis frameworks. Astin’s
model shows which input variables (i.e., traditional and non-traditional components at
admission) have an impact on the outcome variables in this study (i.e., students’ academic
success in the didactic and clinical curriculums). Student characteristics (inputs) and
participation in activities at college (environmental factors) play important roles in shaping the
outcome of a student’s college career (e.g. college GPA, graduation). Astin’s I-E-O model
allows researchers “to produce information on how outcomes are affected by different
educational policies and practices” (Astin, 1993, p. 37). Academic success in college has much
to do with students’ input at entry and with the environment a college creates for its students to
live and study in.
Astin’s I-E-O model is comprised of three parts: (a) input measures, (b) environmental
measures, and (c) outcome measures (see Figure 1). College students bring input measures or
variables at entry such as GPA, STS, their performance during an interview, and their knowledge
of self. Environmental measures refer to aspects and events that occur during the educational
program such as curriculum, support, and culture of the program. The outcome measures, as
defined by Astin, can be (a) cognitive and (b) affective. Cognitive measures are related to reason
and logic while affective measures involve attitudes and feelings.

15

Environmental
Measures Between
College and Within
College

Outcomes: Cognitive
or Affective

Input Measures

Figure 1. Astin’s (1991) I-E-O Model

To date, Astin’s I-E-O model has not been applied to veterinary medical school students
to determine which specific inputs at admission have an influence on a student’s academic
success. This study tested Astin’s model on the veterinary medical school student population at
MSU CVM and examined for several input variables that are included as pre-veterinary school
attributes such as science GPA, cumulative GPA, GRE scores, interviews, and information from
file review scores. All inputs, or experiences brought with admitted students, were considered to
determine the degree to which specific variables had an impact on students’ academic success in
school. For this study, the input variables consisted of data about academic achievements
completed prior to entering veterinary medical school.
Input. Inputs “refer to the characteristic qualities a student brings initially to the
educational program (including the student’s initial level of developed talent at the time of
entry)” (Astin, 1993, p. 18) and include both academic and non-academic experiences.
Examples of inputs can be educational background, rationale for selecting a particular institution
or profession/field, life goals, gender, ethnicity, preparation for the course of study, life
experiences, and reason for attending college (Astin, 1993). Assessment of non-academic

16

components, communication, and interpersonal skills provides information to administrators
about the range of assets applicants possess at time of admission.
Environment. Environment as defined by Astin (1993) is created at the institutional
level and includes, among other aspects, policies, pedagogical approaches, faculty, courses,
teaching, friends, events, activities, and the institutional climate. Astin (1993) postulates that if
the input is aligned with the environment, students will be engaged and find the institution
relevant to their goals. At MSU CVM, faculty and staff recognized the importance of the various
skills, knowledge, attitudes, and aptitudes veterinary students must possess at entry to fit in with
the environment. It was important, therefore, for members of the admission committee to
identify applicants who were prepared to interact with the community of scholars at MSU CVM.
Outcomes. Outcomes “refer[s] to the ‘talents’ we are trying to develop in our
educational program” (Astin, 1993, p. 18). Outcome measures are evaluated in this study as they
relate to performance in the veterinary program. Performance at MSU CVM is measured by
grade point average in both the didactic and clinical portions of the curriculum. The study did
not assess performance after graduation as post-graduate performance is most often related to
veterinary licensing examinations and work satisfaction.
The outcomes of interest for this research study were students’ veterinary medical school
GPA at two points: (a) at the completion of the didactic curriculum (five semesters) and (b) at
the completion of the clinical curriculum (four semesters). As Astin’s I-E-O model has
previously been used to study graduate students and faculty involvement (Barger, 2010; Gore,
2009), it was chosen to be one of the frameworks to study veterinary medical students’ academic
success for seven cohorts admitted from 2000 through 2006 at MSU CVM.

17

Sedlacek’s Non-cognitive Variables. The foundational work of Sedlacek’s (2004) noncognitive variables was selected as the second conceptual framework in this study to assess nontraditional variables used at admission at a veterinary medical school to determine their influence
on students’ academic success. Sterngberg’s (1985) Triarchic Theory of Intelligence was the
basis for Sedlacek’s work in non-cognitive variables’ work. Non-cognitive variables are
affective variables such as the description of feelings, perception, and are subjective in nature.
They differ from cognitive variables in that cognitive measures are defined as specific test scores
or specific grades.
According to Sedlacek (2004), three types of intelligence were suggested by Sternberg
(1985), for which some theoretical and empirical support was provided (see Sedlacek, 2004).
These types of intelligence are defined by Sternberg as (a) componential, (b) experiential, and
(c) contextual. Sedlacek (2004) describes componential intelligence as the ability to interpret
information hierarchically and taxonomically. People who perform well on standardized tests
are identified as possessing componential intelligence. Experiential and contextual intelligences
are related to interpreting information in changing contexts and to the ability to adapt.
Standardized tests do not measure these two types of intelligences; they measure componential
intelligence instead. Sternberg (1985) is a proponent of evaluating intelligence in a
comprehensive way.
In response to Sternberg (1985), who advocates for adding non-cognitive variables to
purely cognitive variables to improve the overall prediction of academic success and persistence,
Sedlacek (2004) identifies and describes non-cognitive variables as non-intellectual aspects, such
as self-concept, motivation, personality of a student, and environment that influence learning.
Further, Sedlacek (2004) identifies non-cognitive factors that influence learning as non-

18

intellectual components; these factors include a realistic self-concept, motivation, and a student’s
personality. Sedlacek (2004) cites several works which earlier identified a number of noncognitive variables as useful predictors of academic performance; these variables include
personality (Brown, 1994), self-responsibility (McConatha, 1990), self-concept (Johnson, 1993),
academic self-concept (Gerardi, 1990; Johnson, 1993), locus of control (Kanoy, Wester, & Lata,
1989), expectations and self-expectancy (Haynes & Johnson, 1983; Trippi & Stewart, 1989), and
self-efficacy (Schunk, 1991). Several studies have shown non-cognitive variables to be more
accurate predictors of academic success for minority students (e.g., Sedlacek & Adams-Gaston,
1992; Tracey & Sedlacek, 1984, 1987; Wawrzynski & Sedlacek, 2003). Woods and Sedlacek
(1988) also supported the position that non-cognitive variables were related to particular aspects
of academic success among minority students. Sedlacek and Brooks (1976), Tracey and
Sedlacek (1984), and Sedlacek (2004) proposed eight non-cognitive variables as useful to predict
academic success.
Sedlacek’s non-cognitive variables model was developed using more than thirty years of
research and practice. From this experience, Sedlacek (2004) demonstrated that measures
currently available to assess componential knowledge have been poor predictors of academic
success for racially diverse and cultural groups. His research findings on standardized tests
suggest that minorities and women tend to perform poorly on these measures. Additionally,
Sedlacek postulated that “test results might not suggest any developmental actions. Little or no
research supported many of the measures available” (2004, p. 35).
Non-cognitive variables relate to adjustment, motivation, and perception, instead of
cognitive abilities as determined by grades and test scores (Sedlacek, 2004). They also evaluate
attributes people possess and have been recognized for years by scholars. Goldberg (2001, cited

19

by Sedlacek, 2004) asserted that non-cognitive variables are personality traits. In Goldberg’s
research, the personality traits identified were (a) extraversion, (b) agreeableness,
(c) consciousness, (d) emotional intelligence, (e) intellect and (f) imagination. In identifying
non-cognitive variables, Sedlacek also considered Sternberg’s (1985, 1986, 1996) experiential
and contextual domains.
According to Sedlacek (2004), non-cognitive variables can be assessed in a number of
ways. One way is the Non-Cognitive Questionnaire (NCQ), several forms of which were
developed and employed in a variety of admission contexts. The NCQ can also be administered
online or adapted to an interview format. Tracey and Sedlacek (1984) "reported two-week testretest reliability estimates on NCQ scores ranging from .74 to .94, with a median of .85 for the
NCQ items with differing samples" (p. 49). Additionally, the NCQ has been used to select for
the Gates Millennium Scholars program and the eight non-cognitive variables were assessed
using short essay answers. Applications for the Gates Millennium Scholars were rated and interjudge agreement was found for raters (Pearson r = .83). Further, Cronbach’s alpha for all raters
in 2001 was .92 (Sedlacek, 2004).
The eight non-cognitive variables identified by Sedlacek (2004) are outlined below.
1. Positive Self-Concept: A successful individual feels confident in his or her ability to
graduate regardless of obstacles that may arise. The individual expects to do well in both
academic and non-academic areas and makes positive statements about him- or herself and
assumes an ability to handle any challenges that may come his or her way. There is a high level
of self-efficacy and self-esteem. According to Sedlacek (2004), “positive self-concept is
predictive of success in higher education for students of color and other non-traditional students.
Having a good self-concept is important for any student, but it becomes even more so for those

20

with non-traditional experiences because of the added complexity of dealing with a system that
was not designed for them” (p. 39). Further, positive self-concept was predictive of grades for
international students (Boyer & Sedlacek, 1989).
2. Realistic Self-Appraisal: The individual is able to recognize and accept background
academic deficiencies while working toward improvement. The individual recognizes criticism
or rewards as logical consequences of performance. According to Sedlacek (2004). realistic-self
appraisal is the “ability to assess one’s strengths and weakness, allowing self-development”
(p. 41). Further, Perrone, Sedlacek, and Alexander (2001), “found that White and Asian
American students perceived intrinsic interest in a field as the major barrier to achieving their
career goals, suggesting that they were not so interested in the fields they were studying as were
other students” (cited in Sedlacek, 2004, p. 41). Tracey and Sedlacek (1984) found that realistic
self-assessment positively correlated with college grades, retention, and graduation for all
students.
3. Understands and Deals with Racism: The individual has a realistic view of racism
based on personal experience and understands the role of the system in his or her life and how
the system impacts minority individuals. The individual has developed a method of assessing and
responding to cultural or racial demands. Similarly, the individual does not blame others for his
or her own struggles and reacts to injustice when appropriate.
4. Preference for Long-Range Goals to Short-Term or Immediate Needs: The individual
sets goals and progresses in a timely manner without reinforcement. The individual is able to
exhibit patience and accept partial fulfillment of a longer-term goal over a period of time. The
individual has a future and a past orientation allowing him or her to look beyond the immediate
situation to the benefits of planning for long-term academic and non-academic goals. Astin

21

(1975) noted that African Americans with lower aspirations were more likely to leave college
than were members of other groups with higher aspirations and established goals. In addition,
Tracey and Sedlacek (1984) were able to provide evidence that long-range goals impacted
grades, retention, and graduation of students.
5. Availability of a Strong Support Person: The individual is able recognize the need for
help and is willing to ask for it. The individual has identified at least one other person who
provides support and encouragement on a regular basis. The individual also recognizes the
difficulties inherent in being a loner and rarely relies solely on his or her own resources to
address problems.
6. Successful Leadership Positions: The individual has experience in leadership positions
and has influenced and assisted others in both academic and non-academic situations. The
individual is comfortable providing advice and mediation to peers. Similarly, the individual has
no problem taking appropriate action when a situation called for action.
7. Demonstrated Community Service: The individual identifies with a cultural,
geographic and/or racial group that has a specific and long-term relationship within the
community. Similarly, the individual has a history of involvement in community-based activities
and/or organizations that have accomplished specific goals in the community setting.
8. Knowledge Acquired in a Field: The individual has non-traditional, culturally and/or
racially based experience and knowledge regarding a field or area that may or may not have been
formally studied in school. Also, the individual has developed innovative and creative ways in
which to acquire information about a particular area.
There is evidence demonstrating that retention and graduation is correlated to strong
NCVs (Boyer & Sedlacek, 1989; Sedlacek, 2004; Tracey & Sedlacek, 1984). Furthermore,

22

research by Rogers (1984) and Washington (1996) supported the use of non-cognitive variables
in addition to cognitive components to predict the academic success of some students in college.
The Admission Process
This section includes a review of the literature on admission practices drawn from the
available literature about undergraduate, veterinary medicine, allopathic medicine, and pharmacy
programs. The current processes for selecting professional school applicants are discussed. Next,
the characteristics admission officers consider for selecting applicants for professional schools is
given. A background discussion on veterinary schools admission selection methods and
professional schools selection characteristics is presented, and a general discussion of nontraditional characteristics in admission to professional schools is given. Finally, the literature as
it relates to students’ academic success, and challenges and limitations when using nontraditional characteristics in admission selection practices is considered.
In the health care fields, there are various elements that seem to influence students’
academic success while in a program of study. However, there is little to no agreement among
scholars or practitioners as to which specific variables determine students’ academic success and,
therefore, should be accounted for at entry to professional schools. Traditional selection methods
such as grade point averages and standardized test scores predict performance during the didactic
curriculum but tend to be poor indicators of clinical curriculum performance. As Hughes (2002)
reminds us, admissions “selection is not an exact science but we must use what evidence we
have to ensure that we do our best by all concerned” (p. 18).
There is disagreement in the field about the extent to which strong academic preparation
contributes to the success of a veterinary medical school graduate. In addition, practitioners and
educators increasingly recognize the value of strong interpersonal skill in that success. As

23

suggested by Levetown (2008), “health care communication is a skill that is critical to safe and
effective medical practice; it can and must be taught” (p. 1). And, according to Lewis and
Klausner (2003), clearly, technical as well as non-technical skills are critical to the success of
veterinary practitioners.
Admission committees for health care programs face the difficult task of selecting
applicants with potential for success in the curriculum and clinical rotations who will meet the
needs of patients/clients and the profession once in practice. To begin with, veterinary education
is expensive, and, therefore, accurate selection and admission decisions can help minimize
educational costs by decreasing the proportion of dropouts (Brown & Silverman, 1999). On the
other hand, veterinary education is the professional training needed to acquire knowledge and
skills that will enable individuals to perform their jobs (Lloyd, King, Maccabe, & Heider, 2004).
However, unless there is a method to assess non-traditional components during applicants’
selection, admission officers and committee members may find themselves unable to determine
whether applicants to health care professions possess these important skills.
Veterinary Medical School Admission
Veterinary Medical School (VMS) admission officers and committee members seek to
select students who are most likely to be most successful at their institutions. Research related to
the selection of applicants at colleges and universities in the United States has, for the most part,
focused on traditional components to predict success while in college. Typically, most colleges
select applicants for their undergraduate degrees based on some combination of academic
records, high school GPA, class rank, and the Scholastic Achievement Test (SAT) or Academic
Collegiate Test (ACT) (McGinty, 1997). The process used in the admission of applicants to

24

advanced degree programs such as veterinary medical school is similar to that used at the
undergraduate level.
Veterinary medical schools (VMS) use traditional academic components as a way to
predict which applicants have the best chance of being successful in veterinary school, and
research that has examined the medical school admission process suggests that undergraduate
GPA is the strongest predictor of admission decisions (e.g., Montecinos & Pohlmann, 1987).
Abundant research (e.g., Confer, 1990; Confer & Lorez, 1999) has provided support for
the use of traditional components in selecting applicants and has been heavily relied upon at
VMSs and in other programs because previous GPAs and STSs have relatively high criterionrelated validities with future GPAs (Hezlett et al., 2001). Moreover, pre-veterinary grades are
viewed as the best predictor of success in a veterinary medical student’s pre-clinical years
(Chastain, Horrell, & Seay, 2007). To date, however, very little research has explored predictive
variables during the clinical years.
Empirical evidence suggests, however, that grades are not the sole predictor of
performance in college and post-graduation work. There is an ongoing debate about whether
decisions based on GPA and standardized tests provide a basis for fairness and equality in the
evaluation of applicants (Sedlacek, 2004). For example, Sedlacek (2004) suggests, “Even
though it may seem logical to hope that a single standardized test could fairly equate applicants,
expecting such an outcome is unreasonable” (p.60). Over the past two decades, veterinary
medicine school administrators have found themselves wrestling with challenges related to
characteristics that might be assessed at admission (input) that may be predictive of students’
academic success in didactic and clinical curriculums (outcomes). According to Sedlacek
(2004), “standardized tests fall short when predicting (a) grades beyond first year for any student,

25

(b) retention or graduation for any student, and (c) grades or retention for students of color and
women” (p.61).
Contributing to this debate, Chastain et al. (2007) claimed that in addition to preveterinary grades, factors such as the ability to communicate, to work in groups, and problem
solve also influence future academic success. They further argued that grades are not enough to
predict success as a professional veterinarian. During the didactic portion of the curriculum,
grades and standardized test scores have been shown to play a significant part in predicting grade
point average. The clinical portion of the medical school curriculum, however, demands that
students possess the ability to communicate with clinicians, members of the health care team,
clients, and other stakeholders, which often cannot be evaluated from grades or standardized
tests.
In a longitudinal study of 627 medical students, Lievens et al. (2009) demonstrated that
extraverted, agreeable, and conscientious medical students in Belgium tended to obtain lower
GPAs in the preclinical medical school curriculum. However, later in medical school, such
personality traits (e.g., conscientiousness, agreeableness, extraversion, openness, and emotional
stability) were positively—and more strongly related—to clinical performance. They further
suggested adding the assessment of personality traits to the medical school entrance requirements
to predict which students would be successful. To support the need for evaluations based on
non-traditional components in health care programs, in 2012, the Association of American
Medical Colleges (AAMC) adopted a new MCAT to assess for social and behavioral traits
(Jaschik, 2011).
Non-traditional skills have been suggested to have a strong relationship with a medical
student’s clinical performance. For example, though communication skills, leadership ability,

26

and interpersonal skills are not directly correlated with performance during the didactic
curriculum, it is suggested that they are critical qualities in the development of a successful
clinical student (Meredith, Dunlap, & Baker, 1982; Murden, Galloway, Reid & Colwill, 1978).
According to Lievens et al. the results of their longitudinal study have implications for practice:
Performance in professional education (e.g., law, business, administration,
pharmacy, medical school) over the years becomes less reliant on the acquisition
of declarative knowledge and incorporates more strongly interpersonal and
motivational qualities. Admission to professional education cannot be based on
only one type of predictor or on maximizing one type of criterion. (p. 1528)
Indeed, applicants to veterinary school with a combination of traditional and non-traditional
characteristics might yield the type of veterinary practitioner the profession is seeking.
However, few empirical studies have determined the extent to which non-traditional skills
assessed at the admission point have any relevance to students’ academic success in veterinary
medical school.
Veterinary medical schools (VMS) prepare graduates with the technical knowledge
necessary for success in practice (Lewis & Klaussner, 2003). Undoubtedly, the technical
preparation and capabilities in this particular health care field are important. The literature,
however, suggests that veterinary medical school graduates are lacking non-technical skills that
are also valuable in successful practices (Brown & Silverman, 1999; Lewis & Klaussner, 2003).
Because the veterinary curriculum is demanding, overloaded, and rigorous, teaching nontraditional components (e.g., interpersonal and communication skills) as part of the veterinary
curriculum is difficult. Rather, it may be more efficient and advantageous for admission
professionals at VMSs that do not currently include NCVs in their admission selection process to

27

adopt selection practices that include the assessment of these characteristics (Brown &
Silverman, 1999; Lewis & Klaussner, 2003).
Researchers such as Chuck (2008) and Tracy & Sedlacek (1984) have noted that students
in minority populations who possess these non-technical skills are successful and engaged in
various academic curricula in their selected professions. In contrast, others argue (e.g.,
Bridgeman, McCamley-Jenkins, & Ervin, 2000; Hezlett et al., 2001) that, as a whole, both GPA
and STSs have predictive validity in determining a variety of academic performance outcomes.
Indeed, the standard variable for predicting first year performance in VMS is STS (Confer,
Turnwald, & Wollenburg, 1999), but this is a one-dimensional, easy, and convenient way to
assess applicants (Sedlacek, 2004).
The Veterinary Medical School Curriculum
For the purpose of this study, some general background about veterinary medical school
curriculum is highlighted. Information about the veterinary medical school curriculum and how
it interfaces with admission practices and accreditation is presented.
Veterinary medical school curricula are not the same across the twenty-eight American
institutions educating future veterinarians (AAVMC, 2011). The duration of veterinary medical
school programs around the world is anywhere from three to six years, but, in the United States,
they are usually four years in duration (at MSU CVM, the four years include summer sessions
during the clinical curriculum). During the first two years, students are taught basic sciences,
and this part is referred to as the didactic portion of the curriculum. During the last two years,
students are expected to participate in clinical rotations delivering care to a wide range of
animals. In countries outside the United States, clinical participation is limited due to the lack of
resources in the form of teaching hospitals or clinics. Additionally, in some countries, entry to

28

veterinary medical school happens immediately after completion of high school—unlike in the
United States where college preparation is required for entrance. Finally, the veterinary
profession in the United States educates students in treating every species while in other
countries students specialize in one or two species from the beginning. In other words,
specialization to one or two species does not exist during the four-years of the American
veterinary medical curriculum.
As previously mentioned, curricula across the twenty-eight veterinary medical schools in
the United States vary. The veterinary profession is evolving, and, more than ever, graduates are
expected to possess certain competencies to treat the patient as well as the caretaker of the
patient. The NAVMEC (2011) report presents an interesting summary of society’s expectations
of veterinarians as illustrated in the oath taken at graduation:
“Being admitted to the profession of veterinary medicine, I solemnly swear to use
my scientific knowledge and skills for the benefit of society though the protection
of animal health and welfare, the prevention and relief of animal suffering, the
conversation of animal resources, the promotion of public health, and the
advancement of medical knowledge. I will practice my profession
conscientiously, with dignity, and in keeping with the principles of veterinary
medical ethics. I accept as a lifelong obligation the continual improvement of my
professional knowledge and competence.” (p. 12)
This oath implies an expected commitment from graduates to certain competencies veterinarians
must have as they relate to technical (i.e., the capacity to deliver health care in a competent way)
and non-technical (i.e., the capacity to deliver health care through communication and
leadership) characteristics that are essential to success in any discipline (Lloyd, King, Mase, &

29

Harris, 2005). The veterinary medical school curriculum is an important and critical component
of accreditation. NAVMEC recommends an engaging education, accreditation, and
testing/licensure as the three legs of veterinary medical education (see Figure 2).
The curriculum at the MSU CVM is composed of three phases. Phase I focuses on
normality, Phase II focuses on abnormality, and Phase III focuses on intervention and
prevention. In other words, the curriculum is created from building blocks, from which
knowledge and expertise are constructed as students’ progress in the curriculum. Phase I and
Phase II represent the didactic portion of the curriculum. Phase III reflects the clinical portion of
the curriculum. By focusing on normality and abnormality, the coursework in Phase I and Phase
II are designed to prepare veterinary students to move forward to the next steps of the

Educational Program
Veterinary Medical Colleges/AAVMC

Accreditation
DOE/AVMA COE*

Graduates with the
Knowledge and
Skills to Meet
Societal Needs

Testing/Licensure
NBVME/AAVSB**

Figure 2. The Three Legs of Veterinary Medical Education
(adapted from the NAVMEC (2011) report)
*
**

DOE/AVMA COE: U.S. Department of Education/American Veterinary Medical
Association Council on Education
NBVME/AAVSB: National Board of Veterinary Medical Examiners/American
Association of Veterinary State Boards

30

curriculum, which trains students in the competent delivery of health care in a teaching and
learning environment. Each of the three phases of the veterinary school curriculum are assigned
grades. In the clinical curriculum, grading of student performance takes into account technical
competencies as well as non-technical competencies. Although the clinical and didactic portions
of the curriculum both have grades assigned, the clinical portion is more subjective because a
variety of competencies are evaluated related to communications, interpersonal skills, navigating
the clinic, and working with the healthcare team, to mention just a few. The very nature of the
evaluation of students’ clinical performance opens the door for potential biases. This delivery of
care is expected to showcase competencies related to technical and non-technical skills, as
outcomes are important to the profession and impact accreditation for institutions and licensing
for graduates.
Professional Schools’ Selection Characteristics
Institutions such as allopathic medicine, pharmacy, and dentistry schools value the
importance of the personal attributes of practicing health care professionals; however, few
programs have been able to implement selection methods that include non-cognitive variables
(NCVs). The use of such criteria as interviews, file reviews, and reference letters remains an
issue in admissions due to the need to make the selection of medical students effective, fair, and
open (Oneil, Korsholm, Wallstedt, Eika, & Hartvigsen, 2009). Admission committee members at
medical schools find it difficult to use NCVs because there is a belief that this method is not as
defensible and valid as GPA and STS.
A study assessing pharmacy graduates’ personal attributes once they were in practice
reported that Doctor of Pharmacy (Pharm.D) program graduates believed their personal attributes
were positive and essential to their success in practice (Doege & Assa-Eley, 2005). At the

31

Pharm.D institution at which their study was conducted, the curriculum was specifically
enhanced to include courses focusing on patient-oriented aspects of pharmacy. Due to the
interpersonal nature of the pharmacists’ job, the pharmaceutical profession cannot leave to
chance whether graduating pharmacists will acquire these necessary skills as they mature in their
professional careers (Fjortoff & Zgarrick, 2001). The role non-cognitive characteristics play in
the clinical and post-graduate environment is important, but curricula in most professional
programs have little room for the inclusion of these skills (Lloyd & Walsh, 2002).
Selection Methods
There are a variety of admission practices among schools to select students for
professional programs, and selection methods depend on the following factors (a) the
institutional philosophy, goals, vision, mandates, or values and (b) admission committee
membership. Some programs select students using only traditional criteria (e.g., grade point
average and standardized tests). In contrast, other programs use a combination of traditional and
non-traditional criteria, which is often referred to as a holistic admission process. In addition to
evaluating test scores and GPAs, a holistic process may include an interview, a file review, a
personal statement, a personality test, and/or a review of reference letters.
Many studies have identified the value of an undergraduate GPA as a powerful predictor
of future academic success in VMSs; however, many of these findings are several decades old
(e.g., Clapp & Reid, 1976; Confer & Lorez, 1990; Confer et al., 1999; Noeh, 1974). More
recently, Turnwald, Sapfford, and Bohr (2001) reported that undergraduate GPAs and GREs
were predictive of the first two years’ performance for students in veterinary medical school.
Kunnel (2005) found that student’s undergraduate pre-pharmacy GPAs and their results from the
Pharmacy College Admission Test were a moderate predictor of graduate pharmacy GPA.

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However, no research has been conducted to identify characteristics predicting clinical
performance at veterinary medical schools. There is growing evidence that performance during
the clinical years and in practice is more related to non-traditional characteristics that applicants
possess at admission, that is, which are not learned while enrolled in a program (Lievens et al.,
2009). Recent research by Lievens et al. (2009) provided support for the inclusion of personality
factors in selection as they “have a predictive value as to the success rate of admitted medical
students. Considering personality of applicants can be quite helpful to medical school
admissions programs” (p. 1516).
On the other hand, Schwitzer, Ancis, and Griffin (1988) found academic characteristics
(e.g., standardized exams and GPA) had limited power in predicting academic and non-academic
performance for college students. Moreover, these same authors suggested traditional cognitive
variables were less useful in predicting the academic performance of African American males
relative to African American females (Schwitzer, Ancis, & Griffin, 1988; see also Sedlacek
2004). Concerns over the use of traditional selection methods warrants an investigation to
identify the best predictors of academic success for all groups and not just those traditionally
enrolled in VMS.
Non-traditional Characteristics in Admission Selection
Non-traditional characteristics in admission selections may include numerous elements.
The use of non-traditional characteristics in admission selection varies and can range from
interviewing applicants and reviewing their files to evaluating reference letters. The most typical
non-traditional selection methods are (a) a review of applicants’ files to validate data and to
evaluate written communication skills and other activities such as altruism, the applicant’s
knowledge of the profession, and his or her ability to work with others; (b) the personal

33

interview, which is used to evaluate verbal communication skills and the ability to problem-solve
quickly; and (c) the reference letters submitted at the applicants’ requests, which are often used
to evaluate interpersonal skills and breadth of character.
Assessing verbal and written communication skills in admission selection is needed in a
variety of health care professional programs because they require an evaluation of applicant’s
ability to perform critical tasks and behaviors, which include obtaining a medical history from
the patient or, in the case of a veterinarian, the patient’s owner, explaining the diagnosis and
prognosis, giving therapeutic instructions, and counseling. A well-prepared health care provider
needs good communication and interpersonal skills (Joint Commission, 2010).
The interview is another non-traditional selection characteristic. As part of the admission
selection process, an interview can provide the admission committee with an opportunity to
assess the applicant’s communication skills and level of knowledge in the particular field, as well
as their problem-solving abilities and interpersonal skills. These latter characteristics are
inherently relational and process oriented (Duffy, Gordon, Whelan, Cole-Kelly, & Frankel,
2004). Furthermore, “interpersonal competencies involve the effect that communication has on
another person such as relieving anxiety or establishing a trusting relationship” (Duffy et al.,
2004, p. 497). Since interpersonal and communication competencies are viewed as important
(Duffy et al., 2004), assessing these at entry and teaching them during coursework to future
health care professionals is critical (Murray & Foster, 2000).
Reference letters are a third component used in the selection process at some
professional programs to assess the characteristics of an applicant. Letters of reference allow
professional schools admission committees to gain insights into relevant information that may
otherwise be difficult to acquire from an applicant’s grades or STSs (McCarthy & Goffin, 2001).

34

Examples of the relevant information could range from “applicant was dependable” to “applicant
did not work well with others,” often with specific examples provided by the referee. Grades
and standardized tests are not designed to provide the type of information that can be found in a
reference letter.
Using interviews and letters of reference during the admission process has been called
into question by administrators and admission professionals as these methods are often viewed as
inherently subjective. It has been argued that if there are standardized questions and trained
interviewers, then this method might be worth implementing as long as other components such
as grade point average are also utilized. Reference letters have been under scrutiny because
referees might not want to disclose negative information about an applicant to a medical school
for fear of retribution. However, these two components are still part of the selection process at
most professional schools in the United States. In veterinary medicine, the interview is a widely
used selection tool (Lewis, van Walsum, Spafford, Edwards, & Turnwald, 2004). The veterinary
profession has tried to address the issues related to the validity and reliability of interviews;
however, some schools have decided to abandon the practice while others have continued with
the practice and updated their interviewing methods to ensure a more reliable and valid
assessment.
Admission Practices and Students’ Academic Success
In the health care field, communication and strong interpersonal attributes are catalysts
for successful patient care and practice (Joint Commission, 2010). As a result, admission
selection practices that include traditional and non-traditional characteristics have been designed
and implemented at some VMSs. However, there are few studies examining their predictive
power on students’ academic success, retention, clinical performance, or graduation (e.g., Astin,

35

1993; Tracy & Sedlacek, 1984). Tracy and Sedlacek (1984) defined non-cognitive variables as
non-intellectual aspects of an applicant, such as self-concept, motivation, and personality.
Veterinary schools using non-traditional characteristics in the admission selection of candidates
have anecdotal evidence about their usefulness, citing diverse interests in selected students and
support from their committees. There is some support for the incremental validity and practical
usefulness of non-traditional components over the more traditional components (e.g., Cress et al.,
2001; Willingham, 1985). However, critics of these methods argue they tend to be problematic
for several reasons: (a) admission committees fail to apply a standardized validity to the various
components, (b) the reliability of student reported data is questionable, and (c) the anticipated
expense of additional faculty and admission committee members to assess applicants’ files and
interview performance is considerable (Willingham, 1985).
While an applicant’s grade point average is often heavily relied upon to make admission
selections, national trends in grade inflation have also increased so that the potential to
discriminate among applicants is in question (Sedlacek, 2004). Moreover, because admission
policies are created at the admission committee level, it is imperative to acknowledge that a onesize-fits-all policy might not work. As higher education institutions continue to evolve, as
students continue to change, and as the needs of particular fields continue to transform, creating
systematic ways to meet the needs of a changing society and demands of the evolving
professions is necessary (Walsh, Osburn, & Schumacher, 2002).
Some institutions might be reluctant to abandon the false security of traditional admission
components such as GPAs and STSs because it is cheaper, easier and less labor-intensive to use
these characteristics than it is to evaluate applicants using narrative information (Sedlacek,
2004). Although test scores and grades are frequently cited as the best available predictors of

36

academic success in college, they rarely account for more than 10% of the variance in college
success (Burton & Ramist, 2001). The combination of academic performance, standardized
tests, and non-academic criteria may prove to be a better predictor of success than any of these
criteria alone (Confer et al., 1999).
Of the 28 veterinary medical schools in the United States, only Colorado State University
(CSU) reports the use of non-cognitive components in the selection of applicants. At CSU, the
selection process integrates both traditional and non-traditional components to assess applicants
(Kogan & McConnell, 2001). Each CSU admission committee member evaluates the applicant’s
files and makes a predictive judgment called a “subjective” admission. The basic premise of the
subjective admission process at CSU VMS is that it is important to view applicants holistically
and to select those who have optimal academic potential for completing the professional program
and who have the greatest potential to contribute in a positive fashion to the veterinary
profession (Kogan & McConnell, 2001).
The study of admission at CSU provides a foundation for an examination of nontraditional components in admission selection at a veterinary school as a legitimate part of the
admission process; however, there are several issues not addressed in the CSU study. Most
importantly, little was said about the influence of non-traditional admission components on
students’ academic performance during the didactic and clinical components of the curriculum.
In the selection process of undergraduate applicants, high school GPAs and STSs are
normally used. For health care professions, admission selection is most typically conducted
using undergraduate GPAs, science GPAs, and standardized tests such as the MCAT and/or the
GRE. Pikes and Saupe (2002) found that high school GPA and STS consistently predicted
college GPA. It is possible that health care professional schools have found some security in

37

using GPA as a major selection component as GPA was found to predict performance for
undergraduate students. However, health care professional programs differ from undergraduate
programs. To begin with, health care professional programs use different methods of
instructions from most undergraduate programs—most importantly, theory application in the
clinical environment. Therefore, it would be useful to assess different admission characteristics
that might predict academic success in these different circumstances.
The literature generally lacks information about factors that influence veterinary school
students’ academic success. Over the past four decades, the literature is replete with studies that
have assessed correlates of academic success. Yet there is a gap in the literature as it relates to
characteristics that predict veterinary students’ academic success in the didactic and clinical
curriculum, and the current study sought to fill this gap in the literature.
In the current study, it was found that significant attention was given by the admission
committees to cumulative GPA, science GPA, GRE, and non-traditional characteristics used to
evaluate applicants for admission and to the predictive power these variables have on veterinary
students’ academic success during the didactic and clinical components of their degree programs.
Figure 3 illustrates the design of the study and includes the information collected and assessed at
the admission entry point by the MSU CVM Office of Admissions and Student Services, which
allowed for a comparison of academic performance outcomes at completion of the veterinary
school curriculum.

38

Traditional
Characteristics

GPA in Didactic
Curriculum

Entry at
Admission

GPA in Clinical
Curriculum

Non-traditional
Characteristics
Figure 3. Design Model for this Study
Summary
The current status of American veterinary medical education is a product of many
traditions and customs. Over the past twenty years, there have been several movements at the
national level to refocus veterinary education towards a model that would address the challenges
of a constantly evolving society that demands exceptional technical and non-technical skills from
veterinarians to serve society’s needs.
The literature on student development and factors that predict students’ academic success
at the graduate and undergraduate levels seems to call for strong non-traditional characteristics of
admitted students. Astin’s (1993) I-E-O model and Sedlacek’s (2004) non-cognitive variables
were shown to be appropriate frameworks to investigate and assess the influence predictor
variables have on a student’s veterinary school GPA at the completion of the didactic and
clinical curricula. Indeed, both models have already been used to predict success at the
undergraduate level; Astin’s work has been used to study faculty and students in graduate school
while Sedlacek’s has been used to assess success of minority students at the undergraduate level.

39

Chapter Three discusses the methodology for this research study, including the study
design, the institutional context, sample selection, instruments, data collection procedures, data
analysis procedures, and other items pertaining to this research study.

40

CHAPTER 3
METHOD
Introduction
This research study analyzed how a set of variables influenced students’ academic
success as measured by grade point average (GPA) in veterinary school during the didactic and
clinical portions of the curriculum. The students’ GPA was chosen because of its acceptance as
a measure of success by the veterinary profession. This chapter is divided into the following
sections: the first part briefly restates the purposes and research questions of this study; the
second part describes the study design (see Figure 3 above), setting, and operational definitions,
and includes a sample selection procedure; and the chapter concludes with explanation of the
instrumentation, data collection, and data analysis procedures, followed by a summary. Data
collected during admission for seven cohorts of students admitted to MSU CVM from 2000
through 2006 were used in this study. These data are organized by what part of the veterinary
medical school curriculum each cohort was in for the evaluation of variables that might predict
students’ academic success during didactic and clinical learning experience. The primary
hypothesis was that the assessment of non-traditional characteristics during the veterinary
medical school application process provides value beyond that provided by traditional
characteristics with respect to students’ academic success in the program. The analysis and
interpretation of the findings related to this hypothesis may play an important role in gauging the
utility of admission characteristics and in predicting students’ academic success in veterinary
medical programs.

41

Purpose of the Study
As described in Chapter One, the purpose of this study was to identify characteristics
defined within an applicant pool of prospective students that predict admitted students’ academic
success in veterinary school. After more than four decades of research and supporting evidence
in allopathic medicine, Adams and Frankle (2007) demonstrated that non-traditional student
characteristics (i.e., communication and relationship building skills) influence the quality of
academic performance, which, in turn, influence academic success during program enrollment.
Since allopathic medicine has successfully used traditional and non-traditional characteristics to
select future health care providers and as veterinary medicine programs are similar to program in
allopathic medicine, it seems reasonable for veterinary medical schools to incorporate both sets
of characteristics when assessing students for program selection to best predict academic success
during the didactic and clinical years.
Research Questions
The following research questions were examined:
1.

To what extent do traditional and non-traditional admission characteristics contribute to
the prediction of cumulative grade point average (GPA) in the didactic portion of the
veterinary medical school curriculum?

2.

To what extent do traditional and non-traditional admission characteristics contribute to
the prediction of cumulative grade point average in the clinical portion of the veterinary
medical school curriculum?

3.

To what extent does cumulative didactic GPA contribute to the prediction of cumulative
clinical GPA in the veterinary medical school curriculum?

42

4.

Do group differences exist between students placed/never placed on academic probation
in terms of admission characteristics or academic performance?

Study Design
This non-experimental study was guided by Astin’s (1993) I-E-O model and Sedlacek’s
(2004) model of non-cognitive or non-traditional variables. The former posits that input
characteristics of applicants at the time of program admission can be predictive of program
outcomes, while the later identifies demographic and traditional prospective student
characteristics thought to best predict students’ academic success. The use of traditional
characteristics (e.g., GPA and GRE) as potential predictors of academic success are appropriate
for study inclusion based on the research by Confer et al. (1999), who found that performance in
the first year of veterinary school was positively correlated to GPA and STSs. In addition, Gore
(2009) found that the use of non-traditional characteristics (i.e., file review and interview) in
admission selection assisted in the identification of (a) students who entered with average GPA
but with the potential to perform better than expected, (b) first-generation, academically
underprepared students who did not perform well on standardized tests but who were engaged,
resilient, confident, driven and mentally prepared to succeed, and (c) targeted student services
that were helpful in supporting enrolled students.
Limitations
As with any research study, the current study must be interpreted in light of some
limitations. The first limitation included the use of the summative interview score. In order to
assess one of the variables, specifically the interview, the summative interview score was used
rather than the individual scores for each category in the interview, which was due to the absence
of individual scores in each category of the interview for each participant in the study. Future

43

research should include the scores for each interview category in the study design to provide
detailed information relative to questions that actually identified goodness of fit between
program and candidate. This information would be useful in the planning for the strong
admission selection of candidates. The second limitation relates to the research being conducted
at one institution (i.e., MSU CVM where the cohorts were admitted). Conducting a study at a
single institution limits the researcher’s ability to generalize since it would likely be difficult to
apply the results to other veterinary schools across the United States. Furthermore, the study was
conducted over a period of seven years, and may not be generalizable to future time periods
because it was conducted at one institution rather than several institutions with diverse students
and curriculum designs. Nevertheless, this research provides insight into characteristics that were
predictive of academic performance in the didactic and clinical curriculum at MSU CVM during
that time. In addition, while this study is not all encompassing, it may still provide guidance in
selecting the best applicants for the profession. The findings from this study support the need for
additional research at other institutions and the need for flawless, well-developed, and
methodically applied selection criteria during the admission process. Clients, the profession, and
administrators understand that an exceptional graduate is one who can serve society as
demonstrated by his or her technical and non-technical competencies, which are directly
associated with traditional and non-traditional characteristics. High science GPAs and
standardized test scores are only a portion of the equation in the selection process. Moreover, the
research findings suggest that, perhaps, there is a need to identify minimum cut-off scores for
traditional characteristics upon admission based on subsequent academic probation status. This
research provides insights into identifying veterinary medical students who are most likely to
perform well in the didactic and clinical curricula.

44

Institutional Context
The study was conducted at MSU CVM, a large Midwest, land grant, research institution
with three medical schools (osteopathic, allopathic, and veterinary). MSU CVM has a long
tradition of educating veterinarians since 1910 (Ottinger, 2012) and has been ranked as one of
the top veterinary schools in the United States. Although approximately 1,000 prospective
students apply, limited enrollment allows the selection of 100 to 110 admitted veterinary medical
students annually (Ottinger, 2012). The curriculum at MSU CVM has a total duration of four
years, with the first five semesters (two-and-a-half years) dedicated to didactic learning
experiences and the last four semesters (including one summer session) dedicated to clinical
applications of learned knowledge.
MSU CVM is ranked as one of the top ten veterinary schools in the United States.
During the time of the study, the school employed approximately 125 faculty members with
responsibilities for teaching, research, and service. The school also houses a teaching hospital
dedicated to service to small and large animals. During the time of the study, the average total
student body enrolled in the Doctor of Veterinary Medicine ranged from approximately 400 to
420. Applicants to the program were not required to hold a baccalaureate degree to gain
admission to the program; as a result, a vast majority (between 60% to 70%) of enrolled students
in the program are between 18 and 22 years of age. Applicants to the program are selected based
on the criteria outlined on Appendix A.
Operational Definitions
Operational definitions are given below to ensure clarity:
1.

Academic Success: Positive college grade point average at or above a passing score.

45

2.

Average GPA: The minimal passing grade allowing student progression in the veterinary
program of study. At this institution, the minimum acceptable GPA for progression was
a 2.00 on a 4.00 scale.

3.

Interview Score: A sum of numbers assigned by admission committee personnel based on
applicants’ responses to questions posed during an interview. The interview process
assessed activities related to (a) community involvement, (b) knowledge of the
profession, (c) problem-solving, (d) motivation and enthusiasm, and (e) leadership.
Details of the scoring guide appear in Appendix D.

Data Collection, Preparation, Sample Selection and Description
The population of interest in this research study consisted of seven cohorts of veterinary
program graduates who were admitted to MSU CVM from 2000 to 2006 and who completed
their degrees from 2004 to 2011. No inducements were provided for participation. The
researcher submitted a letter requesting data to the Office of the Dean at MSU CVM (Appendix
C). Permission for access to these data was granted by the Office of the Dean via letter to the
researcher. The sample was acquired after consultation with the veterinary medical school
Office of the Dean personnel responsible for these data. Following permission for access to data,
appropriate requests were submitted for Institutional Review Board (IRB) approval to conduct
this research. The IRB Office approved the research and, immediately after approval, the data
were provided to the researcher by MSU CVM. To ensure the privacy of graduates from the
veterinary medical school, no identifying information was included as stipulated by the Family
Educational Rights and Privacy Act
During the 2000 to 2006 admission cycles, students at MSU CVM were selected on the
basis of traditional and non-traditional characteristics. The yearly pool of applicants from 2000

46

through 2006 ranged from 883 in 2005 to 1253 in 2000. The data on applicants and admitted
students annually from 2000 to 2006 as reported in the MSU CVM Application and Admission
Data can be viewed in Table 1.
Table 1
MSU CVM Application and Admission Data from 2000 to 2006
Year of Application
2000 2001 2002
2003
Number of Applicants
1253 1108
974
1010
Number Admitted
105
104
104
108
Percentage Admitted
8.3%
9.3% 10.6% 10.6%

2004 2005 2006
Total
1014
883 1008
7250
107
108
108
744
10.0% 12.2% 10.7% 10.3%

During the 2000 – 2006 admission cycle, staff in the MSU CVM Admissions Office evaluated a
variety of traditional and non-traditional characteristics for applicants to the veterinary medical
school (see Table 2 for a complete list of these characteristics).
Table 2.
Traditional and Non-Traditional Characteristics Collected in Veterinary Medical Applications
Characteristics
Description/Abbreviation
Traditional
Cumulative Grade Point Average
Pre CUMUL GPA
Last 45 Credits Grade Point Average Last 45 GPA
Science Grade Point Average
SCI GPA
Graduate Record Examination
GRE Quant
Verbal and Quantitative
GRE Verbal
Total Number Credits
Total CR
Non-Traditional

Age
Sex
Ethnicity
Interview
Parent Veterinarian
Prior Degree
Residency

Age in Years
Female, Male
White, Non-White
Interview (from 15 to 35)
Yes, No
None, Non-Science, Science
Michigan, Non-Michigan

Instrumentation
The researcher evaluated traditional and non-traditional characteristics collected from
students at admission. The traditional characteristics evaluated were (a) science GPA, (b) GRE

47

Verbal score, and (c) GRE Quantitative score. Science GPA had a maximum score of 4.00, and
each component of the GRE standardized test had a maximum score of 800 during the period of
this study and prior to subsequent changes to scoring the GRE examination.
The non-traditional characteristics evaluated were interview scores with ratings that
assessed students’ (a) self-reported community service, (b) self-reported leadership activities,
and (c) knowledge of the profession. Each component of the interview was measured on a 7point scale, using a rubric and scoring sheet developed by members of the committee on student
admission, and yielding a total interview score that ranged from a minimum of 15 to a maximum
of 35. An example of the interview questions and scoring guide, which was used to score
applicants’ interview responses appears in Appendix D. Prior to participating in the interview
process and scoring interview responses, all raters were trained on the appropriate use of the
rubric and scoring sheet. During each annual interview period, approximately 20 to 26 faculty
members volunteered to serve as raters. Participants in the interview process were provided with
training at least a month prior to interviews.
The interview training involved a full-day workshop typically held in February of each
year (the interviews were held late February through early March) and given by a nationally
recognized professional in psychology and interviewer training for medical schools, using
research by Edwards, Johnson, and Molidor (1990) as a foundation. During the workshop,
questions for the interviews were developed and submitted later for approval by the Committee
on Student Admissions. Faculty members also received a list of illegal and inappropriate
interview questions; in addition, protocols for how to start and end the interviews were
discussed, with particular emphasis paid to interviews at which the prospective students
demonstrate their knowledge of other cultures. Finally, an interviewer’s guide was developed

48

and shared with interviewers for their reference (Appendix D). This guide was developed in
collaboration with the trainer and with input from faculty and the admission team.
Interview questions developed by faculty interviewers and the admission committee
members were guided initially by research by Tracey and Sedlacek (1984) and Lewis and
Klausner (2000) and were further refined taking into consideration updated work by Sedlacek
(2004), which also informed the development of the guide. Although the interview questions
developed were not tested for psychometric measures, admission committee members were
confident that, by providing structured questions—and by providing a rubric for interviewers—
the interviews would be fair and valid. On multiple occasions, it was confirmed that
interviewers were consistent with their ratings and inter-rater reliability existed; however, there
is always the possibility that interviewers did not follow the assigned script and rubric in every
case. On the other hand, there was a certain level of confidence that interviewers would follow
the assigned structure since the interviews were conducted by two faculty interviewers for each
applicant. It is certainly possible, however, that, although interviewers were trained and
provided with the questions (which remained the same from year to year), sample answers, and a
rubric, and attended a follow-up meeting after the interviews, some interviewers may have
decided to deviate from the given structure, which may have led to inconsistencies in the
interviews.
The admission selection process from the years 2000 through 2006 used traditional
selection components (Science GPA and Standardized Test scores) and non-traditional
components (the interview) to select applicants to start the veterinary medical school
professional curriculum each fall semester. Prior to the interview, a calculation of scholastic
indicators was conducted to verify that applicants had the academic preparation to move forward

49

into the interview process. The Scholastic Indicator Score was composed of (a) cumulative
GPA, (b) science GPA, and (c) GRE Verbal and Quantitative scores. The traditional
components were assigned differential weights based on their expected contribution to academic
success and were summed to create the total Scholastic Indicator score. All applicants for each
year were then rank ordered in descending order of the total score. Using that ranking, the top
200 applicants were selected to participate in the interview (usually lasting 30 to 40 minutes),
which was conducted on site. Rating scores from the interview were then included in a revised
Scholastic Indicator equation and students were again rank ordered in descending order using the
combination of traditional and non-traditional components to select a final class of 100 to 110
students annually.
The scholarship of enrollment and admission selection in veterinary medicine has
focused on the predictive power of the science grade point average (SCI GPA) and the
standardized test results (STRs), and on academic performance during the preclinical curriculum
at veterinary medical schools (Turnwald et al., 2001). The vast majority of published studies
examining the influence of academic (cognitive or traditional) and personal (non-cognitive or
non-traditional) attributes used in selection decisions on graduate academic performance in
veterinary medicine have been conducted in North America (e.g., Craven, 2004). To this
author’s knowledge, this research study is the first to study the contribution of traditional
(science GPA, STRs), and non-traditional characteristics (age, gender, residency, and interview
score) on students’ academic performance during preclinical and clinical curricula at a veterinary
medical school. Table 3 outlines a list of the variables included in the admission dataset.

50

Table 3.
List of Variables Included in the Admission Dataset
Variable
Description
Unique Identifier
An unique identification number to match cases across datasets
Admission Year
Year in which each student was accepted to veterinary school
Age
Student age at admission (in years)
Ethnicity/Race
Students’ ethnic/race classification
Cumulative GPA
Cumulative Grade Point Average at admission
Last 45 Credits GPA
Grade Point Average for last 45 completed credits at admission
Science GPA
Science Grade Point Average at admission
Graduation Year
Year in which each student graduated from veterinary school
GRE Quantitative
GRE Quantitative test score
GRE Verbal
GRE Verbal test score
GRE Total
Sum of GRE Verbal and Quantitative scores
Interview Score
Total Interview score at admission (from 15 to 35)
Biological Sciences MCAT Biological Sciences MCAT score
Physical Sciences MCAT
Physical Sciences MCAT score
Verbal Reasoning MCAT
Verbal Reasoning MCAT score
Parent Veterinarian
Parent veterinarian (Yes or No)
Pre-College status
Pre-College school location (Michigan, Non-Michigan)
Prior Degree
Prior Degree status (None, Non-science, Science, Other)
Residency status
Resident status (Michigan, Non-Michigan, International)
Scholastic Indicator
Scholastic Indicator (weighted by cumulative and science GPA and
GRE total)
Sex
Student sex (Female or Male)
Total Credits
Total Number of Completed Credits at admission

Data Collection Procedures
Data for this research were collected after obtaining permission to access student
information from the school registrar. The request for data access specifically outlined academic
and admission variables of interest for veterinary medical school graduates who were admitted to
the program between 2000 through 2006.
Data included information from interviews of seven student cohorts at the time of
admission, from the students’ scores at the time of completion of the didactic curriculum
component, and from the students’ scores at the time of completion of the clinical curriculum
component. Although individual student data were obtained within each cohort, the lack of

51

identifying information connecting individual students to student records meant that it was not
possible to clarify or correct incomplete or confusing data.
Data Analysis Procedures
For this study, a minimum required sample size of 575 was needed to avoid a Type I
error rate above a significance criterion of .05 with power set at a conventional level of .80. This
population effect size was estimated based on other non-veterinary studies related to the strength
of admission variables to predict academic performance.
All statistical analyses were computed using SPSS Version 20.0. Prior to performing
analyses related to the study questions, individual cases in the obtained data were evaluated for
accuracy. Cases were removed for two reasons: (a) data on predictor or outcome variables
reflected values that were outliers, and (b) data on predictors or outcome variables were missing.
Inspection of frequencies in the reduced merged data revealed that the majority of
students were White (89.3%) Female (83.7%) Michigan residents (74.7%) between the ages of
18 to 22 years (61.4%) who had entered with a prior degree in science (61.4%). For the purpose
of this research study, data on ethnicity was collapsed and classified as White and non-White due
to the small number of minority participants. Non-White participants were inclusive of
Hispanics, African Americans, Asians, Native Americans, and Multiracial groups. The
demographic characteristics of the reduced merged data are provided on Table 4.

52

Table 4.
Demographic Characteristics of the Reduced Merged Data (N = 676)
Characteristic
n
Percent
Age Groups
18 – 22
415
61.4%
23 – 28
218
32.2%
29 – 56
43
6.4%
Ethnicity/Race
White
Non-White

604
72

89.3%
10.7%

Prior Degree
None
Non-Science
Science

140
52
475

21.1%
7.8%
71.7%

Resident Status
Michigan
Non-Michigan

505
171

74.7%
25.3%

566
111

83.7%
16.3%

a

Sex
Female
Male
a

Note. . = 12 participants were classified as “Other”, thus the total sample size is 667. Totals of
percentages are not 100 for every characteristic due to rounding.
The questions in the study were evaluated using inferential analyses to examine
differences in dependent variables as a function of independent variables and to examine
relationships between predictors and outcome variables using linear regression. Frequencies and
descriptive statistics for all sample demographics (i.e., cumulative grade point average, science
grade point average, scores from the Graduate Record Examination components), and interview
scores (see Appendix D for interview questions and scoring guide) were computed. The
students’ results at the completion of the didactic learning experience and clinical application
components of the curriculum were selected as the best measures to assess academic success.
An alpha level (p < .05) was chosen to determine statistical significance of all results.

53

The statistical analyses selected for the study are similar to those used in other academic
success and retention studies. Previous studies have examined the predictive validity of multiple
independent variables of academic success and persistence (Boyer & Sedlacek, 1989,
Carmichael, Burke, Hunter, Labat, & Sevenair, 1986; House, 1994; Sedlacek, 2004; Tracey &
Sedlacek, 1984). Frequencies and percentages were generated to describe the sample
demographics of importance such as gender, age, and prior degree.
The researcher performed regression analysis to determine the contribution of the
selected non-cognitive variables (community service, leadership, and knowledge of the
profession) and traditional variables (science GPA and STS) to the prediction of students’
academic performance. The Statistical Package for Social Sciences (SPSS Version 20.0) was
used to conduct multiple regression concerning scores at two junctures in the curriculum using
the didactic and clinical GPAs as the outcome variables and the interview score assessed at
admission and the traditional characteristics at entry (science GPA, STS, and interview scores) as
the predictor variables.
Collected data were first categorized by the seven cohorts as defined by the date of
admission to the veterinary medical program. Next, data were categorized by the three input
categories, Demographic, Traditional, and Non-traditional Characteristics. The category of
Demographic Characteristics included information such as gender, ethnic background, age, and
undergraduate degree. The category of Traditional Characteristics aligned with Astin’s (1993)
work, which predominately examined cognitive assets such as undergraduate science grade point
average, cumulative grade point average, and Graduate Record Examination scores. The final
category, Non-traditional Characteristics, aligned with Sedlacek’s (2004) work in defining social

54

characteristics and included information assessed during the interview such as leadership,
community service, and knowledge of the veterinary profession.
Descriptive statistics and regression analysis have been used previously as statistical
techniques to explore and model predictive relationships between two or more variables (Boyer
& Sedlacek, 1989; Carmichael et. al, 1986; Pallant, 2001; Tracey & Sedlacek, 1984). As this
research examined the contribution of students’ traditional and non-traditional characteristics at
the time of program admission to their academic performance during the program, linear
regression was the appropriate analysis for testing and interpretation.
Summary
In this chapter, the theoretical frameworks, the data analysis framework, the population
and samples of interest, the data collection procedures, and the source of the data were outlined.
Data for admitted students in the years 2000 through 2006 at MSU CVM were collected and
analyzed. The data were deemed to be reliable as the institution provided them directly to the
researcher for this research study.
Results of the data analyses are provided in Chapter Four, and recommendations for
further study as well as policy implications for admission professionals at veterinary schools are
found in Chapter Five.

55

CHAPTER 4
RESEARCH FINDINGS
The information in this chapter is presented in five sections. The first section includes
information about data preparation, screening, and merging. The second section includes an
interpretation of the findings from descriptive statistics and analyses testing assumptions of
normality in the final dataset. The third section contains the interpretation of the analyses
performed to examine sample equivalence at admission. The fourth section includes the results
of regression analyses performed to examine the first three research hypotheses. The fifth section
includes an interpretation of results examining differences at admission and academic
performance as a function of academic probation. The research questions were
1.

To what extent do traditional and non-traditional admission characteristics contribute to
the prediction of cumulative grade point average (GPA) in the didactic portion of the
veterinary medical school curriculum?

2.

To what extent do traditional and non-traditional admission characteristics contribute to
the prediction of cumulative grade point average in the clinical portion of the veterinary
medical school curriculum?

3.

To what extent does cumulative didactic GPA contribute to the prediction of cumulative
clinical GPA in the veterinary medical school curriculum?

4.

Do group differences exist between students placed/never placed on academic probation
in terms of admission characteristics or academic performance?

The results concerning the characteristics that predict didactic and clinical performance at MSU
CVM are provided in this chapter. Additionally, the extent to which traditional and non-

56

traditional admission characteristics contribute to the prediction of academic performance and
explain differences in academic probation status are given.
Data Screening and Merging of Cases into a Single Dataset
The Registrar at MSU CVM provided two datasets for this research. The Admission
dataset contained 726 cases with information related to admission characteristics of students who
were admitted into MSU CVM (see Table 3 in the previous chapter for a complete list of
variables). The Academic Performance dataset contained 694 cases with information about the
academic performance by semester of students who matriculated and graduated from the
veterinary medical school (see Table 5 for a complete list of variables).
Table 5.
List of Variables Included in the Academic Performance Dataset
Variable
Description
Unique Identifier
An unique identification number to match cases across datasets
Beginning Term/Year Term and year in which each student matriculated
Cumulative GPA
Cumulative grade point average for all completed semesters
Term GPA
Grade point average for each semester of coursework
Didactic GPA
Cumulative grade point average for the first five semesters
[Coursework = 81 lecture hours, 47 laboratory hours]
Clinical GPA
Cumulative grade point average for the last four semesters
[Clerkships = 33 required hours, 27 elective hours]
Academic Probation
Academic Probation in the program (Yes or No)
Semester Status
Semester status (Extended for academic probation, leave of
absence, personal reasons; Transfer)

To test the research hypotheses, I merged the variables across the two datasets into a
single database using the shared unique identification numbers. Prior to merging the variables,
three examinations of the data were performed to identify cases that should be removed due to
issues with the dataset. The three examinations of data were necessary as the datasets were
provided by the Admissions Office and the Registrar’s Office three different times. To ensure
data integrity, careful review of data had to be performed each time. First, frequencies and

57

descriptive statistics were reviewed for each variable in each dataset. This examination
accomplished two goals: (a) to identify situations in which observed values for variables were
coded incorrectly and (b) to identify students whose academic progress in the veterinary school
curriculum was characterized by a leave of absence, an extended curriculum due to personal
reasons, or entrance as a transfer student. Second, the data were reviewed to determine if
information needed to test the research hypotheses was missing. Third, the unique identification
numbers were compared across datasets to ensure that a match was present. Based on the results
of these examinations, thirty-five cases were removed from one or both datasets prior to merging
cases into a single dataset (see Table 6 for more detailed information).
Table 6.
Frequency of Cases Deleted as a Function of Removal Reason
Removal Reason
Out-of-Range values in the Admission data
Out-of-Range values in the Academic Performance data
Student cases characterized by leave of absence, extended curriculum for
personal reasons, or entrance as transfer student
Cases with unique identification numbers without a match in the other dataset
Total Number of Cases Removed

Frequency
1
1
10
23
35

As noted in Table 1 (i.e., MSU CVM Application and Admission data from 2000 to
2006), 744 students were accepted into the school in the study period. To ensure the validity and
representativeness of the data obtained for this research, the distribution of students admitted
each year in the Admission and Academic Performance datasets and in the final merged database
were compared to the frequencies in Table 7. Although 35 cases were removed prior to merging
into a single dataset, the final sample size of 683 still represents 91.8% of the original 744 cases.
The percentage of cases by year relative to the original frequency of cases in the MSU CVM
Application and Admission data ranged from a low of 84.6% in 2001 to a high of 97.2% in 2003.

58

Table 7.
Distribution of Student Representation by Admission Year as a Function of Dataset
University Application and
Admission
Academic
Merged
a
Year
Admission Data
Data
Performance Data
Data
2000
105
104
99
99 (94.3%)
2001
104
94
93
88 (84.6%)
2002
104
101
100
98 (94.2%)
2003
108
107
104
105 (97.2%)
2004
107
106
96
101 (94.4%)
2005
108
107
98
95 (87.9%)
2006
108
107
104
97 (89.8%)
Total
744
726
694
683 (91.8%)
a

Note. . Values in the merged data column represent frequencies followed by the percentage of
available cases in the merged dataset relative to the percentage of cases reported in the
University Application and Admission data. For example, in the merged data column, the 99
cases available in 2000 represent 94.3% of the 105 cases in the University Application and
Admission data reported in 2000.

To examine the degree of sample equivalence across the seven selected cohorts in the
final dataset, a series of chi-square analyses and one-way analyses of variance were performed.
Chi-square analyses were performed to examine the proportional distributions of ethnicity, prior
degree, residency, and sex by admission year. No patterns of association were noted for ethnicity
2

or residency. A significant association was noted for sex by admission year, χ (6, N = 283) =
14.17, p < .05, φc = .14. Specifically, fewer males were admitted in the 2001 cohort (4.5%) than
in any other cohort year (range 14.1% to 22.8%). Another significant association was noted for
2

prior degree and cohort year, χ (12, N = 669) = 28.28, p < .01, φc = .15. Specifically, more
students were admitted with science degrees in 2006 (85.4%) than in all preceding years (range
62.9% to 74.0%).
One-way analyses of variance were performed to examine if admission characteristics
and/or academic performance differed as a function of cohort year. No differences emerged for
GRE Verbal, GRE Quantitative, Age, Didactic GPA, or Clinical GPA by cohort year. Two main

59

effects were noted by cohort year, however. The first main effect was noted for science GPA and
2

cohort year, F (6, 682) = 9.97, p < .01, η = .08. Interpretation of a Dunnett T3 post-hoc
analysis indicated that students who were admitted in 2005 had significantly lower science GPA
than did students admitted in 2003, 2004, or 2006. Further, students admitted in 2000, 2001, and
2002 had significantly higher science GPA than students admitted in later years, with the
exception of students admitted in 2004 (see Figure 4 for depiction of the main effect).
The second main effect was noted for interview score and cohort year, F (6, 682) = 32.38,
2

p < .01, η = .22 (see Figure 5 for depiction of the main effect). Interpretation of a Dunnett T3
post-hoc analysis indicated that students who were admitted in 2000 and 2001 had significantly
lower science interview scores than did students admitted in later years.
Descriptive Statistics and Frequencies for the Merged Dataset
Prior to performing analyses to test the research hypotheses, assumptions of normality
were assessed in interval and ratio variables by examining means, standard deviations, and skew
values and kurtosis (see Table 8). Normal distributions were exhibited across variables with the
exception of Age, which had a moderate positive skew value of 3.33. To achieve normality,
seven cases with outlier values were removed and a square root transformation was performed.
Following these changes, the skew value for Age was 1.95 and the total number of cases in the
merged data was 676. Using the reduced merged data with 676 cases, the means, standard
deviations, skew, minimum, and maximum values were recalculated for interval and ratio
variables (see Table 9).

60

Table 8.
Descriptive Statistics for Interval and Ratio Variables in the Merged Data (N = 683)
Variables
Mean Standard Deviation Skew Minimum
Maximum
Age (Square Root Transform) 23.00
4.10
3.33
18.00
56.00
GRE Verbal
505.93
89.42
.15
80.00
800.00
GRE Quantitative
642.78
78.82
-.46
50.00
800.00
Science GPA
3.39
.39
-.45
2.00
4.00
Interview
26.98
3.63
-.23
15.02
35.00
Didactic GPA
3.15
.46
-.02
2.10
4.00
Clinical GPA
3.46
.23
-.93
2.38
3.92
Table 9.
Descriptive Statistics for Interval and Ratio Variables in the Final Merged Data (N = 676)
Variables
Mean Standard Deviation Skew Minimum
Maximum
Age (Square Root Transform) 4.76
.32
1.95
4.24
56.00
GRE Verbal
505.65
89.61
.16
280.00
800.00
GRE Quantitative
643.12
78.97
-.47
350.00
800.00
Science GPA
3.39
.39
-.47
2.00
4.00
Interview
26.96
3.62
-.24
15.02
35.00
Didactic GPA
3.15
.46
-.01
2.10
4.00
Clinical GPA
3.47
.23
-.90
2.38
3.92

61

3.55

3.5

3.45

3.4

3.35

3.3

3.25

3.2

3.15

3.1
Science GPA

2000

2001

2002

2003

2004

2005

2006

3.51

3.47

3.48

3.32

3.42

3.17

3.32

Figure 4 . Science GPA as a Function of Cohort Year

62

29
28.5
28
27.5
27
26.5
26
25.5
25
24.5
24
23.5
Interview

2000

2001

2002

2003

2004

2005

2006

24.18

24.4

28.23

28.52

27.75

27.1

28.37

Figure 5. Interview Score as a Function of Cohort Year

63

Sample Equivalence Tests for Admission Characteristics and Academic Performance
To ensure appropriate interpretation of the results related to the research questions,
sample equivalence tests were performed. Ideally, values on admission characteristics should not
differ by pre-existing group classifications. If group differences are found to exist, then
subsequent findings must be interpreted cautiously. In this study, sample equivalence of interval
and ratio admission characteristics were evaluated as a function of students’ ethnicity, prior
degree, residency, and sex. Independent t-tests were performed for sample equivalence tests on
ethnicity, residency, and sex. One-way analyses of variance were performed for sample
equivalence tests on prior degree. Complete information related to these examinations is
available in Tables 10, 11, 12, and 13.
Interpretation of the results indicated that one or more significant differences in
admission characteristics existed across all group comparisons. Specifically, White students had
significantly higher GRE Verbal scores, GRE Quantitative scores, and cumulative science grade
point averages than did non-White students (see Table 10). Four main effects were found as a
function of students’ prior degree (see Table 11). In terms of traditional admission
characteristics, two differences were noted. First, students who entered without any prior degree
and students with a prior science degree both had significantly lower GRE verbal scores than
students with a non-science degree. Second, students who entered with a non-science degree or a
science degree had significantly lower cumulative science grade point averages than students
who entered without any prior degree. Further, students who entered without a prior degree had
significantly lower interview scores than students who had a science or non-science degree.
Finally, students who entered without a prior degree were significantly younger than students
with a science degree who were significantly younger than students with a non-science degree.

64

Three significant differences were noted as a function of students’ residency status. Students who
were non-Michigan residents had significantly higher GRE Quantitative score, interview scores,
and were significantly older than Michigan residents (see Table 12). Two significant differences
were noted as a function of students’ sex (see Table 13). Specifically, male students had
significantly higher GRE Quantitative scores and interview scores than did female students.
Table 10.
Sample Equivalence Tests on Admission Characteristics as a Function of Ethnicity (N = 676)
White
Non-White
n = 604
n = 72
Characteristic
Mean
SD
Mean
SD
df
t
Age
22.76
3.34
22.76
2.98
674 a
- .01
2.92**
GRE Verbal
509.59
87.15
472.64 103.00
83.56
GRE Quantitative
645.58
76.80
622.50
93.29
674
2.35*
Interview
26.91
3.64
27.42
3.43
674
-1.13
Science GPA
3.40
.38
3.26
.40
674
3.06**
a

Note. . A pooled t-test was used because the assumption of homogeneity was violated.
* p < .05. ** p < .01.

65

Table 11.
Means, Standard Deviations, and One-Way Analyses of Variance on Admission Characteristics as a Function of Prior Degree
(N = 664)

Characteristic

None
n = 140
Mean
SD

Age

20.91a

2.12

25.71c

4.79

22.92b

2.95

52.86**

.14

487.50a
638.71
26.06a

80.74
76.60
3.73

548.85b
665.96
28.06b

95.29
80.13
3.82

506.82a
642.90
27.15b

89.69
79.29
3.49

9.21**
2.27
7.64*

.03
.00
.02

Science GPA
3.52b
.37
3.34a
.42
3.35a
.38
11.08**
Note. * = p < .01. ** = p < .001. Means with different subscripts differ significantly from one another.

.03

GRE Verbal
GRE Quantitative
Interview

Non-Science
n = 52
Mean
SD

Science
n = 472
Mean
SD

66

F
(2, 663)

η

2

Table 12.
Sample Equivalence Tests on Admission Characteristics as a Function of Residency (N = 676)
Michigan
Non-Michigan
n = 505
n = 171
Characteristic
Mean
SD
Mean
SD
df
t
Age
GRE Verbal
GRE Quantitative
Interview
Science GPA

22.51
503.09
639.66
26.68
3.38

3.17
87.90
77.48
3.61
.39

23.48
513.22
653.33
27.81
3.40

3.57
94.35
82.59
3.49
.39

267.03
674
674
674
674

a

- 3.33**
-1.28
-1.96*
-3.56**
-0.52

a

Note. . A pooled t-test was used because the assumption of homogeneity was violated.
* p < .05. ** p < .01.
Table 13.
Sample Equivalence Tests on Admission Characteristics as a Function of Sex (N = 676)
Female
Male
n = 110
n = 566
Characteristic
Mean
SD
Mean
SD
df
t
Age
22.74
3.36
22.87
3.02
674
- 0.40
GRE Verbal
508.43
89.74
491.36 87.95
674
1.83
GRE Quantitative
639.98
78.92
659.27 77.56
674
-2.35*
Interview
26.84
3.63
27.58
3.49
674
-1.96*
Science GPA
3.40
.38
3.34
.42
674
1.45
Note. * p < .05.
To examine sample equivalence across nominal admission characteristics, a series of chisquare analyses were performed. Proportional distributions were compared across student
ethnicity, prior degree, residency, and sex. No patterns of association were revealed across
proportional distributions of student ethnicity, prior degree, or sex. In contrast, associations
between student residency and ethnicity, student residency and prior degree, and student
residency and sex were significant. Specifically, Michigan residents were significantly more
likely to be White (94.9%) than non-White (5.1%). When students were non-White, they were
2

proportionally more likely to be non-Michigan residents (26.9%), χ (1, N = 676) = 63.51,
p < .001, φc = .31. In addition, Michigan residents were significantly more likely to be female

67

(85.3%) than male (14.7%). When students were male, they were proportionally more likely to
2

be non-Michigan residents (21.1%), χ (1, N = 676) = 3.84, p < .05, φc = .08. Finally, while
Michigan and non-Michigan residents were both more likely to have a science degree (68.4%
versus 78.8%, respectively), there was a stronger tendency for Michigan students to have no
degree (25.7%) as compared to non-Michigan residents (7.6%). Moreover, while very few
Michigan residents had a non-science degree (5.9%), non-Michigan residents were substantially
2

more likely to have a non-science degree (13.5%), χ (2, N = 664) = 30.97, p < .001, φc = .22.
To examine sample equivalences across academic performance, several analyses were
performed. Ideally, values on academic performance should not differ by pre-existing group
classifications. If group differences are found to exist, then results from the regression analyses
examining the research questions may reflect the contribution of confounding variables. In this
study, sample equivalence of academic performance (i.e., measured by cumulative didactic GPA
and cumulative clinical GPA) was evaluated as a function of students’ ethnicity, prior degree,
residency, and sex. Independent t-tests were performed for sample equivalence tests on ethnicity,
residency, and sex. One-way analyses of variance were performed for sample equivalence tests
on prior degree.
No differences in academic performance were noted as a function of students’ residency,
sex, or prior degree. In contrast, significant differences were noted in both cumulative didactic
and cumulative clinical GPA as a function of ethnicity. Specifically, White students had
statistically significantly higher cumulative didactic GPAs (M = 3.17, SD = .46) as compared to
2

non-White students (M = 3.05, SD = .44), t (674) = 2.12, p < .05, η = .006. Furthermore, White

68

students also had significantly higher cumulative clinical GPAs (M = 3.48, SD = .23) as
2

compared to non-White students (M = 3.37, SD = .24), t (674) = 3.83, p < .001, η = .021.
The following sections report the results in relationship to the specific research questions.
Each question will be restated with its corresponding findings. Additionally, each test conducted
for the analyses will be stated and interpretation of findings provided.
Research Question 1
To what extent do traditional and non-traditional admission characteristics contribute to
the prediction of cumulative grade point average (GPA) in the didactic portion of the veterinary
medical school curriculum?
To determine if traditional and/or non-traditional characteristics contributed to didactic
GPA, a linear multiple regression analysis was performed. Variables tested as traditional
predictors included GRE Quantitative and Verbal scores and cumulative science GPA. Variables
included as non-traditional predictors included students’ age (transformed), ethnicity (dummy
coded as White or non-White), interview score, prior degree (dummy coded into two variables—
prior degree versus no degree, and science degree versus non-science or no degree), residency
status (dummy coded as Michigan or non-Michigan), sex (dummy coded as female or male). To
make the data more parsimonious, non-significant variables were deleted from the model until
the least number of significant variables that explained the most variance remained. Regression
assumptions were tested by examining normal probability plots of residuals and scatter diagrams
of residuals versus predicted residuals. Because multiple regression analyses are sensitive to
outliers, potential influential outliers were examined using the studentized residual, Mahalanobis
distance, and Cook’s distance scores (Tabachnick & Fidell, 2007).

69

The regression model for GRE Verbal, cumulative science GPA, and age (transformed)
on cumulative didactic GPA appears in Table 14. Preliminary analyses supported the hypothesis
and indicated that no violations to the assumptions of normality, linearity, homoscedasticity, and
independence of errors occurred. Interpretation of the first inverse coefficient illustrates that as
student age decreased, there was a tendency towards higher cumulative didactic GPA after
controlling for all other effects in the model. In addition, interpretation of the beta coefficients
indicates that as GRE Verbal scores and cumulative science GPA increased, there was a
tendency for mean cumulative didactic GPA scores to increase as well. In summary, the model
containing age (transformed), GRE verbal scores, and cumulative science GPA explained 34.4%
of the variance in cumulative didactic GPA among students in veterinary medical school.
Table 14.
Regression Analysis Summary for Traditional and Non-Traditional Admission Characteristics
Predicting Cumulative Didactic GPA, after Removing Non-Significant Variables (N = 676)
Variable
B
SE B
β
t
Age (Transformed)
-.141
.053
-.099
-2.65*
GRE Verbal
.001
.000
.233
6.29**
Science GPA
.257
.044
.216
5.83**
2

Note. * p < .05. ** p < .01. R = .34 (N = 676, p < .001).
Research Question 2
To what extent do traditional and non-traditional admission characteristics contribute to
the prediction of cumulative grade point average (GPA) in the clinical portion of the veterinary
medical school curriculum?
To determine if traditional and/or non-traditional characteristics contributed to clinical
GPA, a linear multiple regression analysis was performed. The same variables selected as
predictors for Research Question 1 were chosen for Research Question 2. Similarly, preliminary
analyses were performed to test for regression assumptions of normality, linearity,

70

homoscedasticity, and independence of errors. Using the rules of parsimony, non-significant
variables were deleted from the model until the least number of significant variables that
explained the most variance remained. Again, potential influential outliers were examined using
the studentized residual, Mahalanobis distance, and Cook’s distance scores (Tabachnick &
Fidell, 2007).
The regression model for the effect of age (transformed), ethnicity (dummy coded), GRE
Quantitative, interview scores, and sex (dummy coded) on cumulative clinical GPA appears in
Table 15. The overall test for the model was significant (F = 10.761, df = 5, 675, p < .001),
explaining 27.1% of the variance in cumulative clinical GPA. Six cases were identified as
potential outliers through case diagnostics; however, none of the cases exceeded the critical
values for Cook’s distance, the average leverage, Mahalanobis distance, or the covariance ratio.
Therefore, none of the cases were deleted.
Interpretation of the first negative coefficient illustrates that as student age decreased,
there was a tendency towards higher cumulative clinical GPA, after controlling for all other
effects in the model. In addition, interpretation of the beta coefficients indicate that as GRE
Quantitative and interview scores increased, there was a tendency for mean cumulative clinical
GPA scores to increase. Furthermore, the inverse beta coefficients for ethnicity and sex indicate
that White students and female students tended towards higher cumulative clinical GPA relative
to non-White and male students.

71

Table 15.
Regression Analysis Summary for Traditional and Non-Traditional Admission Characteristics
Predicting Cumulative Clinical GPA, after Removing Non-Significant Variables (N = 676)
Variable
B
SE B
β
t
Age (Transformed)
-.121
.027
-.169
-4.47**
Ethnicity
-.106
.028
-.141
-3.77**
GRE Quantitative
.001
.000
.080
2.11*
Interview
.009
.002
.137
3.63**
Sex
-.048
.024
-.076
-2.03*
2

Note. * p < .05. ** p < .01. R = .27 (N = 676, p < .001).

Research Question 3
To what extent does cumulative didactic GPA contribute to the prediction of cumulative
clinical GPA in the veterinary medical school curriculum?
To determine if cumulative didactic GPA contributed to clinical GPA, a simple multiple
regression analysis was performed. The overall test for the model was significant (F = 103.66, df
= 1, 675, p < .001), explaining 36.5% of the variance in cumulative clinical GPA. Interpretation
of the beta coefficient illustrates that as cumulative didactic GPA increased, there was a tendency
towards higher cumulative clinical GPA.
Research Question 4
Do group differences exist between students placed/never placed on academic probation
in terms of admission characteristics or academic performance?
To examine if patterns of association existed between academic probation status and
nominal admission characteristics, chi-square analyses were performed. Proportional
distributions were compared across student ethnicity, prior degree, residency, and sex as a
function of academic probation. Inspection of the findings indicated that no patterns of
association existed (see Table 16). Next, to examine if interval or ratio admission characteristics

72

differed as a function of academic probation, independent t-tests were performed (see Table 17).
A review of the results indicated that students placed on academic probation had significantly
lower GRE Verbal and GRE Quantitative scores as compared to students never placed on
academic probation. Similarly, students placed on academic probation had significantly lower
cumulative science GPA as compared to students never placed on academic probation. Finally,
to examine if cumulative didactic GPA and/or cumulative clinical GPA differed as a function of
academic probation, two additional independent t-tests were performed (see Table 18).
Table 16.
Distribution of Non-Traditional Admission Characteristics as a Function of Academic Probation
Probation
No Probation
n = 36_
n = 647
2
Characteristic
n
%
n
%
χ (df)
Age Groups
18 – 22
20
55.6%
395 61.1%
.43 (2)
23 – 28
13
36.1%
205 31.7%
29 – 56
3
8.3%
47
7.3%
Ethnicity
White
Non-White

30
6

83.3%
16.7%

581
66

89.8%
10.2%

1.51 (1)

Prior Degree
None
Non-Science
Science

9
3
22

26.5%
8.8%
64.7%

133
49
453

20.9%
7.7%
71.3%

.72 (2)

Residency
Michigan
Non-Michigan

30
6

83.3%
16.7%

480
167

74.2%
25.8%

1.51 (1)

a

Sex
Female
31
86.1%
539 83.3%
.19 (1)
Male
5
13.9%
108 16.7%
Note. The total sample size for Prior Degree is 669 because 14 participants were classified as
“Other” and were treated as missing for this comparison.

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Table 17.
Group Differences for Traditional and Non-Traditional Admission Characteristics as a Function
of Academic Probation (N = 683)
Probation
No Probation
n = 36
n = 647
Characteristic
Mean
SD
Mean
SD
df
t
Age
23.44
4.56
22.98
4.08
681
- 0.67
GRE Verbal
473.89 85.30
507.71 89.37
681
2.22*
GRE Quantitative
608.06
86.94
644.71 77.96
681
2.73**
Interview
27.88
4.35
26.93
3.59
681
-1.52
Science GPA
3.19
.32
3.40
.39
681
3.16**
Note. * p < .05. ** p < .01.
Table 18.
Group Differences for Academic Performance as a Function of Academic Probation (N = 683)
Probation
No Probation
n = 36
n = 647
Characteristic
Mean
SD
Mean
SD
df
t
Didactic GPA
Clinical GPA

2.69
3.36

.34
.23

3.18
3.47

.46
.23

42.19
681

a

8.15**
2.79*

a

Note. ** p < .01. . A pooled t-test was used because the assumption of homogeneity was
violated.

Summary
The information in this chapter reviewed the tasks and evaluation of data related to data
preparation and screening, and the procedures used to merge cases into a single dataset. In
addition, descriptive statistics for the dataset and tests evaluating assumptions of normality were
described. Next, analyses used to evaluate sample equivalence were presented and the findings
were interpreted. The research questions for the study were restated and appropriate analyses
were performed.
The current research findings provided information on the characteristics at admission of
veterinary medical students that predict performance in the didactic and clinical curricula.
Regardless of any errors in the independent and dependent variables, it is intellectually satisfying

74

that significance was still found. Additionally, the study results provided insights that traditional
characteristics at entry are predictive of didactic performance and a mix of traditional (e.g., GRE
quantitative) and non-traditional (e.g., interview) characteristics are predictive of clinical
performance. Finally, an interesting finding provided background for the trend that the younger
students in the program tended to have stronger performances in the didactic curriculum.
The implications of the findings from this chapter deserve further discussion with
attention given to future research opportunities that may contribute to the prediction of academic
performance at key junctures in veterinary medical schools. It is worth noting that data tells us
only so much and to find the remarkable relationships that were found is an exceptional
accomplishment in this research. Recommendations are presented to assist veterinary medical
colleges to engage in discussion about selection components utilize across the profession to
select future veterinarians. Further, implications of these research opportunities, concluding
thoughts, and a summary of this research are presented in Chapter Five.

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CHAPTER 5
DISCUSSION, IMPLICATIONS, AND RECOMMENDATIONS
This chapter provides a discussion of the results and presents the conclusions,
implications, and recommendations; these follow the research results. The first section provides
a discussion of the key findings and their strengths as well as their consistency with previous
research and the applicability of the conceptual models. In the second section, a summary of the
conclusions is provided with a further discussion of the implications for the scholarship of
admissions and its applicability to admission practices at veterinary medical schools. The
conclusion consists of recommendations for future research. In this study, I expanded on the
limited research available concerning the influence of traditional and non-traditional
characteristics at admission and their relationship to performance in the veterinary medical
school curriculum. Also, I explored the use of traditional and non-traditional characteristics,
specifically, which of these predicted performance in the didactic and clinical curriculum.
Various scholars (Astin 1993; Pascarella & Terenzini, 2005) have researched and
reported research based on undergraduate students and predictors of performance at time of
college entry and have concluded that both input at entry and environment are directly related to
performance in college (i.e., outcomes). Much of this reported research has focused on
undergraduate students and not on graduate students or students in professional programs.
Therefore, the current study sought to expand the application of this body of literature to
veterinary medical school admissions. Sedlacek (2004) explored performance of college
students as measured by non-traditional characteristics and found that certain non-traditional
characteristics are predictive of performance. In other work, Wawrzynski and Sedlacek (2003)
studied transfer students and their experiences. They posited that student success is significantly

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related to their expectations; in particular, “when students’ expectations are met, they are more
likely to succeed, and when students succeed, they are more likely to persist” (p. 499). In
summary, at the undergraduate and transfer level there are a plethora of research studies about
predictors of success; however, in veterinary medicine, this type of research is lacking, thus the
impetus for this study.
Purpose of the Dissertation
The purpose of this study was to identify characteristics found within an applicant pool of
prospective veterinary medical students that predict academic performance in veterinary medical
school during didactic and clinical curricula. The research literature reviewed assisted with the
identification of variables for this study and their division into traditional and non-traditional
characteristics. The identified traditional characteristics at entry were science GPA, and GRE
Verbal and GRE Quantitative. The identified non-traditional characteristics at entry were
interview score, age, gender, ethnicity, and residency.
This research study used the I-E-O model (Astin, 1993) and non-cognitive variables
(Sedlacek, 2004) as the foundation for the analysis of the data. The population of interest was
students admitted to MSU CVM from 2000 to 2006. The initial sample consisted of 744 MSU
CVM students and, after correction of items due to error in data entry, the final sample resulted
in 683 students. Regression analysis identified four variables as moderate predictors of didactic
and clinical veterinary medical school GPA: (a) science GPA, (b) GRE Verbal, (c) Age, and
(d) Interview Score.
Discussion of the Results
During data analysis of admission characteristics, two different pairs of characteristics
were moderately predictive for each of the didactic and clinical GPAs: GRE Verbal and Science

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GPA, and GRE Quantitative and the interview score respectively. Together, these four input
variables were positive predictors of GPA during veterinary medical school curricula, but at
different stages of the program.
The focus of Research Question One was to examine if traditional and non-traditional
characteristics or input measures influence veterinary medical school didactic and clinical GPA.
The results of the data analyses demonstrate that science GPA and GRE Verbal, as well as
interview scores were positive predictors of performance in the curriculum (Table 14), which is a
finding consistent with research conducted by Turnwald et al. (2001); in that study, they
hypothesized that traditional characteristics (e.g., pre-veterinary science GPA and GRE) were
predictors of performance during the didactic curriculum. Members of admission committees
normally assess for these two characteristics to help with decisions about admission to veterinary
medical school, and findings from this research study support the use of these characteristics as
valid predictors of veterinary medical students’ academic performance during the preclinical
phase of the curriculum.
Academic success in college has much to do with students’ input (grade and standardized
tests) and the environment a college creates for students to live and study in (Astin, 1993). The
findings with respect to Research Question One support the position that GPA is a strong
predictor of admission decisions (e.g., Montecinos & Pohlman, 1987). In addition, the findings
support research by Confer and Lorez (1999), who concluded that GPA and standardized test
scores (STSs) have relatively high criterion-related validity with future GPAs. In veterinary
medicine, the literature is limited about characteristics that predict performance during didactic
and clinical curricula, but a study by Chastain et al. (2007) provides further evidence that preveterinary GPA was the best predictor of veterinary medical student success in the didactic

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curriculum phase in that study. The findings in this research are further explicated below
regarding traditional and non-traditional characteristics at entry.
Traditional and Non-Traditional Characteristics at Admission
In the current study, support was found for the use of traditional and non-traditional
characteristics in admission decisions. Demographic characteristics for the MSU CVM cohorts
being studied showed that most of the students were from Michigan, White, and female. When
comparing females and males at entry, males had significantly higher GRE quantitative scores
and significantly higher interview scores. These findings are consistent with previous literature,
which found similar results (Kaczmarek & Franco, 1986). When traditional and non-traditional
characteristics of White and non-White applicants at entry were examined, non-White students
performed above their colleagues at entry during the interview. The use of the non-traditional
interview variable score as a strong predictor of performance for women and minorities is
consistent with work by Hughes (2002). Similarly, in allopathic medicine, Calkins, Arnold, and
Willoughby (1987) found that for women, interview ratings and previous relevant experience
were more predictive than previous didactic scores, and, for non-White medical students, the
locus of control and self-evaluation were predictors of performance (Webb et al., 1997). The
findings in the current study that non-White and non-Michigan residents performed higher in the
interview and that their higher interview performance is a predictor of didactic academic
performance suggests the beneficial role that the interview can have in the admission process for
some students. Since the interview is a predictor of performance for some students (i.e., women
and underrepresented students), it may serve as a tool to add diversity to an institution. Also,
higher interview scores predicted better clinical performance.

79

Basco, Gilbert, Chessman, and Blue (2000) showed that interview performance was not
predictive of clinical performance for human medicine students. However, in the current study,
interviews were found to be predictive of clinical performance at MSU CVM, which is a critical
and important finding for the veterinary profession. Since veterinarians, just like other
healthcare professionals, must possess exceptional communications and interpersonal skills,
assessing these skills is important and could be briefly and easily assessed during an interview.
It was also found that non-Michigan residents performed better than Michigan residents
in the interview while Michigan residents were significantly more likely to be female than male.
The higher scores on the interview by non-Michigan applicants at admission may be due to
maturity and life experiences as Michigan residents were significantly younger at admission than
non-Michigan residents. In addition, Michigan applicants with the highest science GPAs
performed poorly in the interview when compared with applicants in other groups. This
difference may be due to a lack of experience with interviews for the younger Michigan students.
It is also worth noting that, at admission entry, non-Michigan residents had significantly higher
non-traditional characteristics. Further, as GRE Quantitative and interview scores increased,
there was a tendency for mean cumulative clinical GPA scores to increase. As noted earlier, the
inverse beta coefficients for ethnicity and sex indicate that White and female students tended
towards higher cumulative clinical GPA relative to non-White and male students. Also, NonMichigan residents did not perform at higher level of academic performance than Michigan
residents at both didactic and clinical curricula as measured by scores assigned using input from
clinicians, other students, technicians, and animal owners considering such characteristics as
communication, and ability to relate to others.

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It was also revealed in the analysis that traditional characteristics at entry and at the
completion of the didactic and clinical curricula were significantly higher for White students
(Table 15). During the assessment of findings, the analysis demonstrated that ethnicity/race and
sex were likely to predict clinical performance. Further, ethnic/race classification alone was a
significant predictor of students’ didactic performance in veterinary school. Given the
expectation that all students would be equally successful because of the selective admissions
process, these findings should be further investigated. Finally, the findings are similar to Astin’s
(1993) research and that reported by Pascarella and Terenzini (2005) that White students had
significantly higher grades at the undergraduate level relative to non-White students. In general,
clinical performance was predicted by both traditional and non-traditional characteristics at
entry. However, in the current study, it was found that White and female students were predicted
to have higher clinical GPAs than non-White and male students. In allopathic medicine, findings
have demonstrated an association between clinical performance and interviews (Murden et al.,
1978), while other studies reveal no relationships (Basco et al., 2000).
Traditional and Non-Traditional Characteristics as Predictors of Academic Performance
The second research question explored the extent to which traditional and non-traditional
characteristics contribute to the prediction of cumulative grade point average in the clinical phase
of the veterinary medical school curriculum. The findings in this portion of the study were
derived by conducting independent sample t-tests, analyses of variance, and χ2 independence
tests. As might be expected, input characteristics such as GRE Quantitative and the interview
score of admitted veterinary medical students were different at admission, and it is not surprising
to see those differences persist in the results of outcomes after completion of didactic and clinical
curricula. The reasons for such differences, however, are probably not unique to veterinary

81

medicine. On the other hand, it is the responsibility of institutions to ensure that accepted
students will be successful irrespective of their entry characteristics. Further, accreditation
essentials in veterinary medicine are changing to assess selection process, admitted students’
retention, and graduation rates. Therefore, it is important to identify learning strategies that can
be implemented for students prior to admission or early in the program. However, this is an
opportunity to level the field by creating pre-enrollment programs for admitted students whose
characteristics are so vastly different within the admitted cohort. This is not to say that students
with less competitive traditional characteristics are worse than those with higher traditional
characteristics, but such findings may provide the institution with information to create programs
to prepare students for success.
This research study investigated inputs at admission at MSU CVM and their relationship
with outcomes upon completion of the veterinary medical school curriculum. To clarify,
applicants to MSU CVM, unlike applicants to many other professional programs (i.e., law,
business, pharmacy, or some allopathic medicine programs) may be considered for admission
without the completion of a baccalaureate degree. Applicants may apply to MSU CVM after the
completion of two years of college (that is, at the junior level or third year of college).
Applicants with the equivalent of two years of college who wish to apply to MSU CVM are
considered without a college degree if the required science courses are completed (during the
time of this study, MSU CVM requires 45 science semester credits for admission consideration).
In the cohorts studied, such applicants were younger than applicants who already held a
baccalaureate or a graduate degree. Contextual aspects of the environment were not included as
assessment variables because these data were not collected. However, including such contextual
aspects for analysis in future studies would be useful as it might provide answers to questions

82

relative to students’ academic performance. Since characteristics at entry revealed the
differences in the groups, establishing pre-entry programs for admitted students who are not as
academically strong as their admitted counterparts could help reduce the differences as they
move through the curriculum. The finding that younger students admitted to MSU CVM
performed better academically than older students suggests that students’ completion of science
requirements closer to enrollment in the MSU CVM curriculum may influence performance.
However, further investigation regarding the environment (i.e., financial aid, counseling,
advising, mentorship, leadership involvement, and institutional culture) at the start and
conclusion of the program would be helpful for future studies to consider.
No differences in performance in the didactic and clinical curricula were found as a
function of sex. However, a difference in performance was found as function of ethnicity: White
students performed at a significantly higher level than non-White students in didactic and clinical
curricula, as measured by GPA. It is possible that these findings of differences in performance
between these two groups can be explained as follows: (a) the majority of admitted students were
White; (b) the non-White students may not have the support systems necessary to feel
comfortable in a new environment as many of the non-White students were from other states; (c)
the data indicated that non-White students were older than White students, and it is possible that
being away from a science curriculum longer than their White counterparts precluded them from
performing better (results indicated that younger admitted veterinary medical students performed
better than older students in the curriculum); (d) the possibility of a long adjustment period in the
new environment precluded them from engaging and performing as well as their White
counterparts; and (e) it is possible that, since the majority of faculty were White, faculty as well
as students did not know how to navigate their respective worlds due to cultural differences.

83

Furthermore, poor clinical performance of non-White students could have been the result of a
hostile, unfamiliar, and culturally deficient clinical faculty who did not know how to interact
with non-White students.
Work by Gurin, Dey, Hurtado, and Gurin (2002), among others, has espoused the view
that diversity is of compelling interest at all levels, and they have eloquently observed that
“helping faculty develop a pedagogy that makes the most of the diverse perspectives and student
backgrounds in their classrooms can foster active thinking, intellectual engagement, and
democratic participation” (p. 362). For faculty to not have these perspectives can hinder the
teaching and learning experience. Indeed, colleges and universities should provide a supportive
environment in which disequilibrium and experimentation can occur by increasing interaction
among diverse peers and help faculty and students manage conflict when individuals share
different points (Gurin et. al., 2002 p. 362). Finally, Sedlacek (2004) has demonstrated the role
that strong support persons and community may play in helping non-traditional students be
successful. At MSU CVM, students were mentored by other students, faculty, and practitioners;
unfortunately, because the potential mentors, reflexive of the veterinary profession as a whole,
are not very diverse, they may not have had the background or experience to provide needed
assistance.
At MSU CVM, students were matched with a “big sib” or peer mentor who helped new
students navigate the environment and were available to answer questions or provide support.
Future research might want to examine the contributions of the big sib mentorship program to
students’ academic success. The findings do not suggest that White students will always
perform better than non-White students or that preference in admission should be given to
students of any group.

84

The findings relative to performance of White and non-White students, supports the
inclusion of the assessment of traditional and non-traditional characteristics at entry. It is clear
that if admissions were based only on traditional characteristics, it would reduce the diversity of
the admitted pool. However, it is important to recognize that while these differences were
present, students with lower scores on traditional characteristics did, on the whole, graduate on
time and met the required GPA for graduation.
Wightman (1997) demonstrated that admission decisions based on traditional
components would negatively impact the diversity of an applicant pool. More recently, Elmore
(2003) provided a compelling account of the lack of racial diversity in veterinary medicine and
the need for proactive recruitment and selection processes. In fact, limiting or excluding nontraditional characteristics during the admission selection can negatively impact the diversity of
the veterinary profession. Finally, a possible reason for the differences in performance between
White and non-White is that White students from Michigan, which were the majority of admitted
students, were younger and might have completed science courses more recently than did the
non-White students. As shown in Table 10, White students arrived to campus with significantly
higher science GPA, GRE Verbal, GRE Quantitative scores, and were younger and
predominantly from Michigan while non-White students at admission had higher interview
scores. However, as mentioned above, the environment, which is a component of the I-E-O
model, was not fully examined due to the lack of data on the environment. (To recap,
environment refers to organizational factors such as support systems for students at the
veterinary medical school that might include financial aid services, advising, mentorship, campus
life, and other support systems.) Including various aspects of the environment in future research

85

of such populations should be assessed to determine if aspects of the environment have an
influence on performance.
GRE Verbal and science GPA predicted approximately 34.4% of the sample variance (R)
in didactic GPA and age was less likely to significantly predict didactic GPA performance. Of
the three input variables, GRE Verbal was the most important predictor of didactic performance,
followed by science GPA. These predictors of didactic GPA performance could be related to the
emphasis in the didactic curriculum that requires completion of 18 to 22 semester credit hours
with heavy reading, test-taking, and laboratory work. Students’ performance in GRE Verbal
may be indicative of their reading and critical analysis capabilities, which is likely to help them
navigate large amounts of information, books, and journals and likely to help them in their
performance in examinations and synthesizing information.
Additionally, performance in the didactic curriculum could be explained by the more
recent preparation of some veterinary medical students through completing science courses
shortly before enrollment in the veterinary medical school curriculum. Students who completed
their science courses closer to enrollment in the veterinary medical school curriculum had an
advantage over their colleagues as their science knowledge was more current and more easily
accessible for the heavy and demanding science curriculum.
As noted above, age as a non-traditional characteristic was less likely to predict didactic
performance. Nonetheless, in general, younger students have been known in science to perform
better than older students in an intensive science curriculum. Their higher performance may be
attributed to enrollment in science programs immediately after completion of these courses.
While this may explain the current findings, it is important to note that majority of students in
these data were younger than 28 years old. In fact, only 6.4% of the students were between the

86

ages of 29 to 56. As a result, this may actually be an artifact of the data and not generalizable.
However, age as a predictor of higher cumulative clinical GPA is surprising, and it does not align
with previous research findings. The higher the science GPA and GRE Verbal, the higher was
the didactic GPA. Science GPA, GRE Verbal, and age moderately positively related to didactic
performance. Interview score and age weakly positively related to clinical GPA and age was
moderately inversely related to clinical performance. The finding that higher interview scores
were predictive of clinical performance is validated by Chuck (2008) and Sedlacek (2004), who
found that students in undergraduate, non-traditional, and minority populations with these nontraditional skills are successful and engaged in a variety of academic curricula.
It is unclear from the findings of this study why, as a student’s age increased, a lower
clinical GPA was predicted. Although the relationship between students’ age and clinical GPA
is unclear, one might speculate that some biases of expectations exist from clinical faculty and
older students’ performance. Perhaps the expectations for performance were set higher for older
students than for younger students. In this research, the findings demonstrate that students who
scored high on interviews were older, which supports research by Meredith et al. (1982) as well
as by Murden et al. (1978). Although this literature may be dated, it still holds that performance
in professional education over the years has become less reliant on the acquisition of declarative
knowledge and incorporates interpersonal and motivational qualities more strongly. Perhaps,
more students perform better during interviews due to more strongly developed interpersonal
skills and motivational qualities. Interestingly, these findings did not translate into the clinical
veterinary medical school curriculum for these students. Future research should investigate the
traits and behaviors needed to be successful in the veterinary clinical environment. A better

87

understanding of these traits and behaviors would be useful to ensure greater success among all
students.
Research on pharmacy doctoral students found that the personal attributes, which the
interviews aim to assess, are directly linked as positive and essential in their performance (Doege
& Assay-Eley, 2005). Additionally, in the current research, ethnicity/race and sex were
important predictors of students’ clinical performance. Students’ interview scores at admission
also contributed to academic clinical performance. Unfortunately, the interview score at MSU
CVM was a total value rather than individual responses to each interview category. Therefore, it
is unclear how specific personal attributes measured by interviews are related to students’
performance.
Didactic Academic Performance as an Indicator of Clinical Academic Performance
The third research question explored whether didactic performance was a predictor in the
clinical curriculum. The traditional and non-traditional variables used for this regression
analysis were science GPA, GRE Verbal, GRE Quantitative, age, ethnicity, and interview.
Regression analyses suggested that didactic GPA significantly predicts approximately 36.5% of
the sample variance (R) in clinical GPA, and would predict approximately 13.2% of the
population variance (Adjusted R²). Higher didactic GPA predicted higher clinical GPA.
Regression analysis indicated positive prediction of performance in the clinical when didactic
GPA was high, which was expected.
The traditional and non-traditional characteristics at entry were very different for
admitted students. There were incoming students who started the program with low science
GPA, GRE Verbal, and GRE Quantitative scores in comparison with their counterparts with
higher traditional characteristics. As this research study demonstrated, these three variables were

88

moderately to strong predictors of positive performance in the didactic curriculum. To assist
students with less competitive admission scores, McMaster University in Ontario invests time
and effort in their selection process and also offers ‘remediation’ support programs for students
with academic deficiencies (Hughes, 2002). While this approach appears to work well for
McMaster University, concerns about the constitutionality of admission processes at public
institutions may limit its application in this country. Pre-entry enrichment programs might
reduce the number of students placed on academic probation from the approximately 5.3% for
the cohorts in the research study, and potentially improve retention. Currently, two medical
schools (Newcastle, New South Wales; and McMaster University, Ontario) have demonstrated
that careful selection and good support programs can have a positive impact on retention and
graduation (Hughes, 2002).
In conclusion, the findings that didactic GPA predicted clinical performance is aligned
with research findings that previous performance predicts future performance. Several studies
(Clapp & Reid, 1976; Confer & Lorez, 1990, Confer et al., 1999) have demonstrated that
previous GPA predicts future GPA for veterinary medical students. The relationship of the
didactic performance as a predictor of clinical performance is contrary to findings in allopathic
and osteopathic medicine. A study by Turnwald et al. (2001) reported that undergraduate GPAs
were predictive of the first two years’ performance for students in veterinary medical school,
which support the findings in the current study that didactic GPA is predictive of clinical GPA in
the veterinary medical school curriculum. Finally, this finding might be due the fact that the
MSU CVM curriculum has deliberately included hands-on activities during the didactic
curriculum that would allow future clinical students to learn using cases, problem-based learning,
and clinical application before arriving to clinical rotations.

89

Traditional and Non-Traditional Characteristics that Predict Placement on Academic
Probation
The fourth research question explored the question of whether differences existed
between students placed or never placed on academic probation in terms of admission
characteristics. It was important to examine characteristics that predict academic probation
because (a) veterinary medical school is expensive for the student; (b) there is an investment of
time, money, and resources on the part of both institution and the student when a student is
placed on probation; and (c) there might be some characteristics identified via this study that
could help in the selection of students to reduce the negative impact of being placed on academic
probation. Additionally, when students are placed on probation, there is usually delayed
program completion, which may have financial implications and may impact the psychological
confidence about the students’ ability to complete the degree or their preparation and skills at
completion of the degree.
In order to investigate this question, a comparison of students as a function of academic
probation was performed to determine if students who were placed on academic probation during
their program differed in their input characteristics from students who were never placed on
academic probation. Independent t-test analysis indicated that 36 students or (5.3%) of
participants (n=683) in this study were placed on academic probation. These students had
significantly lower GRE Verbal scores at admission than students never placed on academic
probation. They also had significantly lower GRE Quantitative scores and science GPAs.
Finally, the students who entered with lower traditional characteristics performed poorly in both
the didactic and clinical portions of the curriculum; however, the negative performance was
much more apparent in the didactic curriculum. At the end of their studies, students who had

90

been placed on probation had significantly lower didactic and clinical GPAs than those never
placed on probation. This result could be a factor of the required or needed interpersonal aspects
of performance as measured by faculty supervisors or mentors during the MSU CVM
curriculum.
Finally, the research findings demonstrate that there is, statistically, a moderate
relationship between traditional and non-traditional characteristics at entry with performance in
the didactic and clinical curricula. However, in line with previous studies, the strongest
predictors of performance for the didactic curriculum were science GPA and GRE Verbal. A
positive relationship to performance in the clinical curriculum included GRE Quantitative and
interview scores. It is important to note that there has been no previously published research
regarding the predictive value of interview, age, residency, or ethnicity in the clinical veterinary
medical curriculum; as a result, additional research related to the predictive value of nontraditional components on clinical GPA is warranted.
Evaluation of the Conceptual Models
Astin’s (1993) I-E-O model and Sedlacek’s (2004) non-cognitive variables were the
models identified as the starting points for the design of the study. These provided a structure for
understanding the traditional and non-traditional variables that influence academic performance
during the didactic and clinical curricula in veterinary medical school. Astin’s model assisted in
guiding the research with respect to input variables at admission and outcomes variables at
completion of the program. Sedlacek’s non-cognitive variables model guided the research by
identifying non-traditional variables at entry as possible influences during didactic and clinical
curricula.

91

Sedlacek’s non-cognitive variables work was included to guide the research design with
respect to non-traditional variables assessed at entry, which were embedded in the interview tool
used at MSU CVM. For the purpose of interviewing candidates for admission, MSU CVM
Admissions Committee members used NCV categories based on Sedlacek’s (2004) work to
create a tool with categories important to the institution for assessment. More specifically, the
NCVs included and examined as part of the interview tool were (a) knowledge of the veterinary
profession; (b) leadership activities, and (c) community service. The findings from the current
research supported the importance of the interview score as a contributor to clinical academic
performance.
The models created for this study were designed to develop an understanding of what
inputs at entry to a veterinary medical program influences academic performance in the didactic
and clinical curricula and were found to be helpful in identifying factors that influence academic
performance in the veterinary medical school curriculum. The model created for this study may
be useful in designing future research into academic performance of students. Recommendations
for future research are provided later in this chapter.
In the evaluation of Astin’s (1993) I-E-O model, I examined individual traditional and
non-traditional characteristics at admission as they related to my research questions. Astin’s
model suggested that students with strong traditional characteristics would have better academic
performance in the didactic portion of the curriculum, which is consistent with other studies
previously mentioned. The results of the current research support that position, while nontraditional characteristics were positive predictors of clinical academic performance.

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Based on the findings of this study, the following conclusions about the relationship
between input characteristics and relationship to performance in the didactic and clinical
curricula were established:
1.

The input characteristics of admitted veterinary medical students for the cohort studies at
entry were Science GPA, GRE Verbal, GRE Quantitative, interview, gender, ethnicity,
and residency status.

2.

The majority of students in the sample were female, White, Michigan residents and most
were young (18-22).

3.

White students had significantly higher science GPAs, GRE Verbal, GRE Quantitative
than non-White students but significantly lower interview scores.

4.

Michigan residents had significantly lower interview scores and were significantly
younger at admission.

5.

The strongest predictors of didactic GPA at entry were science GPA and GRE Verbal.

6.

Moderate predictors of clinical GPA were age and interview score.

7.

Higher interview scores were predictive of clinical performance.

8.

The characteristics of students placed on academic probation were a function of low
science GPAs, GRE Verbal, and GRE Quantitative. They also had significantly lower
didactic GPAs than students who were never placed on academic probation.

Implications
This research study confirms the importance of traditional characteristics as predictors of
didactic performance and suggests a relationship between certain non-traditional characteristics
and performance in some parts of the veterinary medical school curriculum. Findings in this
study show the relationship that traditional and non-traditional characteristics have to veterinary

93

medical students’ academic performance in the didactic and clinical curriculum (Tables 14 and
15). From the research findings, it can be seen that students admitted with high traditional and
non-traditional characteristics performed better academically than those who had lower
traditional and non-traditional inputs at entry. Additionally, at entry, admitted veterinary
medical students with higher scores on traditional and non-traditional characteristics were less
likely to be placed on academic probation. These findings are important because, as veterinary
medical schools review admission criteria, they may consider identifying processes to select
students who are less likely to be placed on probation and more likely to complete the program.
Furthermore, since the AVMA COE has indicated an expectation for accreditation that schools
will maintain both technical/traditional skills and non-technical/non-traditional skills, the
findings that both characteristics contribute to the performance of veterinary students is helpful.
Finally, as the cost of veterinary medical education continues to increase, it is important to
establish traditional and non-traditional thresholds for academic success and program
completion. Cost of attendance to and completion of a veterinary medical program are forecast
to be important accreditation essentials. Veterinary medical schools should be at the forefront of
creating an environment for reducing student debt and for creating a tool that demonstrates that
assessment at entry is appropriate for yielding better performance of graduates.
Admissions professionals, deans, administrators, and admission committee members
should consider how to identify criteria for admission and what characteristics to include as
criteria for admission to yield the desired outcomes. Is there a way institutions of higher
education can implement admission criteria and thresholds that will prevent students’ placement
on academic probation? Because AVMA COE expects graduates to be prepared with
exceptional traditional and non-traditional skills, it is important for institutions to seriously

94

evaluate the tools they use in admission selection. It is important, then, for administrators and
faculty to work collaboratively to identify best policies and practices for admission selection in
veterinary medical schools. As we know, the application process is highly competitive, and
tuition is expensive at veterinary schools in the United States. The implementation of sound,
well-orchestrated, and well-researched admission criteria will only help to create and implement
appropriate admission criteria.
Finally, admissions professionals, administrators, faculty members, and deans are
continually evaluating the best admission practices at their respective schools. However, the
evaluation and implementation of these practices are implemented with an awareness that
institutions are unable to invest sufficient time in this process because faculty members do not
have much spare time for service. The evaluation of traditional input components such as
science GPA and STRs are easily quantifiable and assessed without much investment of time on
the part of faculty and administrators. The challenges encountered are the time and cost needed
to assess NCVs via written or verbal communication (e.g., letters of recommendation or the
interview). Unfortunately, in many programs, there has been a tendency to eliminate interviews
due to concerns about their validity, reliability, and legality. The reliability and validity of
interviews have been called into question as it compares to other traditional components with
higher precision (Kreiter, Yin, Solow, & Brenna, 2004). Overall, leaders in the field of
veterinary medicine should consider how the inclusion of non-traditional characteristics in the
admission selection process would positively benefit the profession.
Recommendations for Future Research
It is widely acknowledged that admission selection is neither science nor art. What is
known is that implementing the right selection polices to select the best and brightest health care

95

professionals are critical to serve society and animals. As noted in Chapter Two, much of the
research on academic performance has been conducted at undergraduate institutions, allopathic
and dental schools, and other professional programs. In veterinary medicine, the most
comprehensive study was completed by Confer et al. (1999) almost 15 years ago and that only
examined the contribution of traditional characteristics to didactic performance. Additional
research studies on the performance of veterinary medical students in didactic and clinical
curricula and the characteristics that predict performance would extend the findings of the
current study and create a platform for further discussion on the future of admission selection
characteristics for veterinary medical schools.
At present, it would be important for admissions officers at veterinary medical schools to
include in their assessments of applicants the use of GRE, GPA, and interview since this study
focuses on one school only and findings from this study must be interpreted with caution.
Applicants should be encouraged to apply and enroll in veterinary medical school upon recent
completion of their bachelor’s degree or, for those with no completed degree, to enroll in science
refresher courses prior to admission. To assess whether such selection methods are appropriate
at other veterinary medical schools in the United States, I recommend conducting similar studies
using past admitted cohorts and evaluating the performance and retention of students admitted.
Finally, veterinary medical schools are encouraged to monitor the performance of students
during the didactic curriculum and provide supportive services to students who are not
performing well. This monitoring measure might assure good clinical performance and
experience.
As the results of this study directly relate to the program at MSU CVM, it is important to
conduct additional research comparing the cohorts admitted in 2000 through 2006, when

96

interviews were part of the assessment process, with the performance of subsequent cohorts
starting in 2007 through the present, when interviews were not included. A study comparing the
two groups would assess any differences in performance during the didactic and clinical
curricula of these two groups..
Furthermore, I recommend that the Association of American Veterinary Medical
Colleges (AAVMC) champion a large national study that (a) examines selection methods at
veterinary schools, (b) organizes schools with similar admission criteria for the study, and
(c) identifies a particular school that will have the responsibility for the research project to
examine the characteristics at admission (input) that predict academic performance (outcomes) in
the didactic and clinical curricula at veterinary schools across the United States. In addition, I
recommend that this endeavor be a longitudinal study because that would provide the basis for
assessment over a period of time, which would enhance the study’s validity and reliability for
others to replicate. Moreover, I strongly urge that quantitative and qualitative methodologies be
used to assess characteristics that predict academic performance in veterinary medical schools.
Finally, the data can be used to conduct a multiple-year study on graduates and their performance
once in practice. Astin’s (1993) I-E-O model and Sedlacek’s (2004) NCV work should be used
for this proposed study as frameworks to guide it. While I was not able to include environmental
factors in this study, I suggest such variables be examined in future research to delineate specific
variables that influence academic performance. Specifically, the inclusion of environmental
factors influencing academic performance would be useful because placement on academic
probation was not a function of groups by ethnicity, age, or residency, so it was unclear why
these students were on probation. Perhaps personal challenges, illness, finances, or other factors

97

influenced being placed on academic probation. However, without the examination of the
environment, it is difficult to conclusively determine the causes.
In summary, this chapter included a restatement of the purpose of this study, a discussion
of the results with highlights of the characteristics predicting didactic and clinical performance at
MSU CVM, the traditional and non-traditional characteristics of participants in this study, the
results of the predictive value of traditional and non-traditional characteristics, and limitations of
and recommendations for future research. This study validates the importance of traditional
characteristics as a predictor of didactic performance and confirms that certain non-traditional
variables are predictive of performance in veterinary medical school. It is hoped that
characteristics identified in this study to predict a certain level of performance are utilized to
formulate policy at the MSU CVM and to create intervention programs that will help limit the
likelihood of placement on academic probation. Since this research provides solid evidence of
characteristics that predict performance, pre-enrollment programs can be created to help students
prior to entry to maximize success in a rigorous veterinary medical school curriculum.

98

APPENDICES

99

APPENDIX A
ADMISSION SELECTION FORMULA AT MSU CVM
MSU CVM considered the factors listed below in the admissions process to the Doctor of
Veterinary Medicine:

Five Steps in the Admission Selection Process
Step I

Scholastic Indicator Score: Cumulative Grade Point Average (GPA), GPA
required for Math and Science, GPA for last 45 Credit Hours, MCAT or GRE
Scores

Step II

Secondary Application

Step III

File Review

Step IV

Interview

Step V

Final Selection
Scholastic Score (10%)
File Review (40%)
Interview (50%)

100

APPENDIX B
VETERINARY MEDICAL SCHOOL CURRICULUM

1.
2.
3.
4.
5.

Goals of the New Curriculum
(Implemented 9/2/92)
Modify instruction to emphasize principles rather than esoteric details.
Make learning interactive using technology where appropriate.
Integrate both vertically and horizontally what is taught.
Reduce lecture time and use diverse teaching methods to stimulate lifelong learning.
Give greater visibility to non-practice career options through new applied clerkship
experiences.
BASIC VETERINARY SCIENCE
(2 1/2 years − 5 semesters)
Phase I − Normality
Year 1

Semester 1 (Fall)
ANS511 Animal Science for Veterinarians
PDI515 Comparative Veterinary Gross Anatomy
PDI516 Veterinary Histology & Cell Biology
MMG561 Veterinary Immunology
SCS511 Veterinary Radiology
VM511 Veterinary Perspectives (VP) I
VM512 VIPS I*

Semester 2 (Spring)
ANS513 Animal Nutrition for Veterinarians
PDI517 Veterinary Neuroanatomy
MMG567 Veterinary Microbiology and Infectious Diseases I
PSL511 Veterinary Physiology
PDI551 General Pathology
VM521 Veterinary Perspectives (VP) II
VM522 VIPS II*

101

2 (2-0)
6 (2-10)
4 (3-2)
2 (2-0)
1 (1-0)
2 (1-2)
1 (1-0)
18 (12-14)

2 (2-0)
1 (1-0)
5 (4-3)
5 (5-0)
3 (2-2)
2 (2-0)
3 (3-0)
21 (19-25)

Phase II − Abnormality
Year 2
Semester 3 (Fall)
MMG569 Veterinary Microbiology and Infectious Diseases II
PHM556 Veterinary Pharmacology
PDI553 Clinical and Systemic Pathology
VM532 VIPS III*
VM533 Veterinary Epidemiology
Semester 4 (Spring)
PHM557 Veterinary Toxicology
VM541 Veterinary Perspectives (VP) III
VM542 VIPS IV*
VM543 Cardiovascular Diseases
VM544 Veterinary Public Health
VM545 Principles Anesthesia and Surgery
VM546 Musculoskeletal Diseases
VM547 Respiratory Diseases

5 (4-3)
5 (5-0)
5 (4-2)
3 (1-4)
3 (3-0)
21 (17-9)
2 (2-0)
2 (2-0)
3 (2-3)
2 (2-0)
2 (2-0)
4 (3-2)
5 (5-0)
2 (2-0)
22 (20-5)

The 4-year professional program leading to the DVM degree requires completion of 163
semester credits with an overall GPA of no less than 2.0.
Phase III − Intervention and Prevention
Year 3
Semester 5 (Fall)
VM552 VIPS V*
VM553 Theriogenology and Urinary Diseases
VM554 Hematological, Oncological and Dermatological Diseases
VM555 Neurological and Ophthalmological Diseases
VM556 Digestive, Metabolic and Endocrinological Diseases
VM557 Operative Surgery
* VIPS − Veterinary Integrative Problem Solving
• Capstone course for the semester.
• Integrates material taught for that semester.
• Non-lecture format.
• Problem-solving focus.
• Designed to utilize communication skills.

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3 (2-3)
5 (4-2)
3 (3-0)
3 (3-0)
5 (5-0)
2 (1-3)
21 (18-8)

CLINICAL AND APPLIED VETERINARY MEDICAL SCIENCE
(Years 3 and 4 − Semesters 6, 7, 8, 9)
Professional students must complete 33 required clerkship credits and 27 credits from elective
clerkships and other approved courses. Students may take up to 3 vacation blocks of 3 weeks
each.
Each clerkship will be 3 weeks and 3 credits.
REQUIRED CLERKSHIPS (33 credits)
Students must complete the following required clerkships:
LCS620
PDI630
SCS611
SCS625
SCS626
SCS646
SCS647
SCS648
SCS695

Equine Clinical Clerkship I
Diagnostic Pathology Clerkship
Diagnostic Imaging Clerkship
Small Animal General Medicine Clerkship
Small Animal Soft Tissue Surgery Clerkship
Small Animal Orthopedic Surgery Clerkship
Small Animal Internal Medicine Clerkship
Anesthesia Clerkship
Emergency and Critical Care Medicine Clerkship

Students must also select one experience from each of the following pairs of clerkships:
LCS621 Equine Practice Clerkship
or
LCS631 Food Animal Practice Clerkship
LCS630 Food Animal Medicine & Surgery Clerkship
or
LCS647 Concepts in Agricultural Practice Clerkship
ELECTIVE CLERKSHIPS (27 credits)
Students planning to use more than 6 elective credits in any clerkship toward graduation
requirements must request approval from the CVM Curriculum Committee. All clerkship
schedules are subject to the approval of the Professional Programs Coordinator.
PDI610
PDI611
LCS627
LCS628
LCS632
LCS638
LCS639

Veterinary Gross Anatomy Dissection
Research Problems in Veterinary Anatomy
Advanced Equine Medicine Clerkship
Techniques in Equine Anesthesia and Surgery
Adv Food Animal Medicine & Surgery Clerkship
Beef Production Medicine Clerkship
Small Ruminant Medicine & Mgmt Clerkship
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LCS640
LCS641
LCS646
LCS677
LCS678
LCS680
LCS681
LCS682
LCS683
LCS690
LCS691
MMG690
PHM658
PDI631
PDI632
PDI634
PDI635
PDI636
SCS613
SCS637
SCS640
SCS641
SCS642
SCS644
SCS651
SCS670
SCS690
SCS693
SCS693a
SCS654
SCS655
SCS694
VM611
VM690
VM692
LCS610
LCS610
LCS611
LCS613
LCS614
LCS615
LCS622
LCS623
LCS624

Large Animal Anesthesia Clerkship
Food Animal Theriogenology Clerkship
Equine Neonatal Medicine Clerkship
Veterinary Preceptorship (off-campus)
Government & Corporate Veterinary Practice
Food Animal Techniques
Food Animal Production Medicine 1
Food Animal Production Medicine 2
Food Animal Production Medicine 3
Veterinary Public Health Field Experience Clerkship
Veterinary Public Health Research Clerkship
Veterinary Microbiology Clerkship
Research Problems in Pharmacology or Toxicology
Necropsy Clerkship
Problems in Veterinary Pathology
Endocrinology
Special Problems in Path/Histopath & Cytology
Aquatic Medicine
Diagnostic Ultrasound Clerkship
Operative Surgery: Instruction and Review
Cardiology Clerkship
Comparative Ophthalmology Clerkship
Zoo & Wildlife Medicine Mgmt Clerkship
Dermatology Clerkship
Adv Comparative Ophthalmology Clerkship
Small Animal Clinical Nutrition
Veterinary Molecular Biology Clerkship
Problems in Small Animal Clinical Sciences
Small Animal Theriogenology
Oncology
Clinical Surgical Oncology Clerkship
Small Animal Specialty Practice (off-campus)
Veterinary Externship Clerkship (off-campus)
Special Problems in Veterinary Medicine
Career Development and Business Skills
Problems in Large Animal Clinical Sciences
Wildlife Disease Ecology and Management
Research Problems in Large Animal Clinical Sciences
Clinical Problems in LCS (off campus)
Equine Clinical Proficiency Clerkship
Equine Emergency and Critical Care Clerkship
Equine Clinical Clerkship II
Equine Musculoskeletal Diseases Clerkship
Equine Theriogenology Clerkship

104

LCS625
LCS626

Equine Herd Health Clerkship
Advanced Equine Surgery Clerkship

105

APPENDIX C
REQUEST FOR DATA FROM MSU CVM DEAN
Hilda Mejia Abreu, PhD Candidate – HALE
207 River Ridge
Boerne, TX 78006
hildamejiaabreu@gmail.com
(517) 589-0859
October 4, 2012
Christopher Brown, Dean
College of Veterinary Medicine
Michigan State University
G 100 Veterinary Medical Center
East Lansing, MI 48824
Dear Dean Brown,
I am working to complete my doctoral dissertation at Michigan State University, College of
Education in the Higher, Adult, and Lifelong Learning (HALE) program. The study is entitled
“Evidence-Based Admissions: Correlates of Students’ Academic Success in Veterinary School.”
As part of this study, I am requesting to review data from the 2000-2006 admitted cohorts of
students to the Michigan State University Veterinary Medical School. The entire study will be
provided to Michigan State University College of Veterinary Medicine to inform admissions
practices.
All information and data from the study is confidential. Admitted students’ data for cohorts
2000-2006 will be coded by the institution to maintain confidentiality and protect the College as
it relates to Family Educational Rights and Privacy Act (FERPA) that safeguards privacy of
students. Dr. James Lloyd, Associate Dean and Professor at the Veterinary Medical School is a
member of my dissertation committee and will assist with facilitation of protecting these data.
Further, there are no risks associated with participating in this study. Students’ data will be
coded for protection. The benefits of conducting this study include the increased knowledge
concerning evidence to conduct admission selection processes that might identify characteristics
for success in veterinary medical school.
I will be analyzing data collected and will protect data for this analysis consistent with university
IRB guidelines and protocols. Data have already been collected at the institution through the
Office of Medical Education Research and Development (OMERAD) as the director of the
OMERAD collaborated with me during my employment at MSU as assistant dean for
Admissions & Student Services.

106

I can schedule a time via Ms. Terrie Bunn, executive assistant to clarify any concerns and answer
any questions you have related to this request.
Thank you for considering this request.
Sincerely yours,
Hilda Mejia Abreu
CC:
Dr. Matthew Wawrzynski, HALE Professor and Dissertation Chair
Dr. Marilyn Amey, HALE Department Chair, Professor, and Dissertation Member
Dr. John Dirkx, HALE Professor and Dissertation Committee Member
Dr. James Lloyd, Veterinary Medical School Professor, Associate Dean for Budget & Planning
Dr. Coretta Patterson, Veterinary Medical School Associate Dean for Academic & Student
Affairs
IRB Documents/Request for Data Dean Brown/10/4/

107

APPENDIX D
INTERVIEW QUESTIONS AND SCORING GUIDE
Introductory comments:
The purpose of the interview is to allow us to gather some information that is not available
anywhere else in your application packet. At the end of the interview, time will be allotted for
you (candidate) to ask questions about the college, the veterinary profession, MSU, or the
surrounding community. [Be sure to inform the candidate that if he/she does not feel
comfortable with a particular question, he/she reserves the right not to answer.]
Please use at least one question during the interview process for each of the categories below.
Feel free to paraphrase or use your own style of questioning.
Interviewers are asked to use candidates’ information from their application to formulate
questions for each category.
Opening Questions:
Why MSU CVM? (Objectives: introduction, get to know the applicant, start the conversation,
to allow the applicant to feel comfortable)
1. Why MSU CVM?
2. What particular event or events led to your decision to become a veterinarian?
3. Why did you select veterinary medicine over other health professions or scientific fields?
4. What is it about the veterinary profession that is most appealing to you?
5. What was the most meaningful (or most difficult) veterinary/animal related event you have
experienced?
Knowledge and insight gained through reported experience with animals:
(Objectives: to identify knowledge of behavior, management, and basic health care of animals)
1. What are the roles of veterinarians in creation of a safe food supply?
2. Is there a particular species that interests you?
Why is this species of interest to you?
Could you describe the aggressive behavior in specie of your choice? What are the signs of
aggressive behavior in ______?
How would you handle an animal showing aggressive behavior?
Knowledge of and insight gained through reported veterinary related experience:
(Objectives: awareness of current standards, trends, and opportunities within the profession)
1. What are some of the current challenges that veterinarians face? How might those change in
the future?
2. What are some of the career options/choices that are open to you with a veterinary degree?
3. Describe a typical day in the practice or laboratory or workplace where you spent time and
describe your responsibilities.
4. What is the role of the veterinarian in:
Production animal medicine (food animal production)?
Laboratory animal medicine?
Public Health?
(use candidates’ information to ask these questions)

108

5. How do veterinarians share information and remain current in their profession?
Problem-solving skills
(Objectives: to assess candidate’s ability to identify the problem, resourcefulness, logical
approach, communicate the challenge clearly, and to use internal and external perspective)
1. Scenario: Select from several possibilities as time permits. Also, be sure to use candidate’s
application to create these questions. Each scenario may explore different aspects of the
candidate’s knowledge base and background.
a. Example: How would you deal with a disease that can be transmitted from wild animals
to domestic animals and from animals to humans?
Knowledge and understanding of the importance of the role of a professional to society and
community:
(Objectives: to assess potential for leadership in community, outreach, and professional
involvement)
1. What is the role of a veterinarian in his/her community?
2. What are some local, national, or international issues in which veterinarians may play an
important role? How might you deal with them?
3. What are some of the new roles that veterinarians are undertaking in our changing world
(bio-terrorism, food safety, government, etc.)?
4. What do you plan to contribute to your community and society within 5 years of graduating
from veterinary school?
Motivation and Enthusiasm:
(Objectives: to assess coping techniques, personal drive, determination and general enthusiasm)
1. Describe a situation in which you had to make a personal sacrifice to reach a goal.
2. What are some sacrifices you might have to make to succeed in veterinary school?
3. If not admitted to veterinary school, what is your plan?
4. You are admitted to veterinary school and you failed your first exam or your loan/scholarship
was not processed, how would you handle this situation?
5. Five years after graduation, what personal goals would you have liked to have met?
Life Experiences:
(Objectives: to assess self-reflectiveness, maturity, ability to face reality, tenacity, and ability to
bounce back)
1. What do you consider the most difficult task you will be asked to accomplish in veterinary
school, and what personal characteristics do you posses that will assist you in accomplishing
this task?
2. Describe a situation in which you had to work with/interact with someone you did not like?
How did you manage to accomplish the tasks?
3. Describe a situation in which you felt overwhelmed (by work/by school/by personal events).
How did you cope at that time? What did you learn from the situation?
5. Please discuss the most rewarding activity in which you have participated during college or
work experience and provide details about this activity.

109

6. Over the past several months, the media has covered several national or international issues
or areas of conflict give an example of one these conflicts and discuss your resolution
strategies.
Presentation skills, interpersonal communication, and overall impression of the candidate:
1. Based on your overall impression of the candidate, please rate each category: 1-5.

110

APPENDIX E
FACULTY INTERVIEWERS’ TRAINING
MEMORANDUM
TO:

CVM Faculty Interviewer's Workshop

FROM:

Dr. Lorel Anderson, Chairperson, Committee on Student Admissions
Ms. H. Mejia Abreu, Director, Office of Admissions

RE:

CVM Faculty Interviewers' Workshop: Thursday, February 10, 2005

Welcome back! Congratulations on being selected as a faculty interviewer this year and thank
you for your participation in this very important process of the College of Veterinary Medicine.
Your contribution in selecting the next generation of students and future colleagues are
appreciated by the CVM community and the profession.
Every year, the CVM Office of Admissions in collaboration with the Committee on Student
Admissions makes improvements to the Interview Process in an attempt to make it more
structured and valid. In addition, every year we offer an interviewers' training. The interviewers'
training will be held on Thursday, February 10, 2005 in room A174 VMC from 10:00 a.m. to
2:00 p.m. Again, Dr. John Molidor will be our trainer.
CVM Faculty Interviewers are required to attend this workshop. Please see attached registration
form to be returned to Ms. Patricia Peterson, curtis@cvm.msu.edu, marked either attending or
will not be attending this workshop. Please confirm your attendance by January 21, 2005.
If there are questions, please call 3-9793.
Thank you and we look forward to seeing you at the workshop

111

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