THE EFFECTS of EARLY, REGULAR, and LATE REGISTRATION on STUDENT SUCCESS in COMMUNITY COLLEGES by MARGARET ANN STREET, B.S., M.A
THE EFFECTS OF EARLY, REGULAR, AND LATE REGISTRATION
ON STUDENT SUCCESS IN COMMUNITY COLLEGES
by
MARGARET ANN STREET, B.S., M.A.
A DISSERTATION
IN
HIGHER EDUCATION
Submitted to the Graduate Faculty of Texas Tech University in Partial FulfiUment of the Requirements for the Degree of
DOCTOR OF EDUCATION
Approved
Accepted
August, 2000 Copyright 2000, Margaret Ann Street ACKNOWLEDGMENTS
I am deeply grateful to the members of my committee, especially my co- chairmen. Dr. Albert Smith patiently provided guidance and encouragement throughout the project. Dr. Arturo Olivarez provided much needed assistance with the statistical analysis.
To my friends and colleagues at the college, go many thanks. Your
"nagging" encouragement was much needed and appreciated. Special thanks are
extended to Tracy Hilliard for the help using SPSS, and to Chuck Everett for the
assistance with data collection.
I owe everything to my parents. Their love of learning has inspired me
throughout my life. Without their support and encouragement, I could never
have reached this milestone in my own education.
u TABLE OF CONTENTS
ACKNOWLEDGMENTS ii
ABSTRACT vi
LIST OF TABLES viii
LIST OF FIGURES x
CHAPTER
L INTRODUCTION 1
Conceptual Framework 3
Problem 4
Purpose 5
Research Questions and Hypotheses 5
Need for the Study 7
Delimitations 10
Limitations 10
Assumptions 11
Definition of Terms 11
Organization of the Dissertation 13
IL REVIEW OF LITERATURE 15
Introduction 15
Models for Predicting Student Success and Retention 15
The Input-Environment Outcomes Model 17
m Summary of I-E-O Model 20
Student Success and Retention Variables 21
Input and Environmental Variables 22
Outcome Variables 27
Summary of Variables Used in the Study 28
Registration 29
Policies and Practices 29
Late Registration 31
Problems Associated with Late Registration 32
Studies Concerning Early, Regular,
and Late Registrants 33
Summary 40
IIL METHODOLOGY 41
Introduction 41
General Research Design 41
Description of Variables 42
Sample 43
Collection of Data 45
Analysis of Data 45
Sunmnary 48
IV. FINDINGS 49
Introduction 49 Sample 50
iv Differences Between New Student Registration Groups 54
Differences Between Returning Student
Registration Groups 58
Summary of Major Findings 64
V. SUMMARY, MAJOR FINDINGS, DISCUSSION, RECOMMENDATIONS, AND CONCLUSIONS 66 Summary 66
Major Findings 68
Discussion 70
Discussion of Academic Dependent Variables 70
Discussion of Retention
Dependent Variables 74
Recommendations for Future Policy and Practice 77
Recommendations for Further Research 79
Conclusions 81
REFERENCES 83
APPENDICES
A. REQUEST FOR STUDENT DATA 88
B. CODING OF DATA 91
C. PARTIAL LIST OF DATA 93 ABSTRACT
Student success is a critical issue in higher education. Using the input- environment outcomes assessment model of Astin (1993), the main problem of this study was to determine whether or not early, regular, and late registration students differed from each other in terms of their academic success.
This study had three purposes. The first purpose was to determine the differences between students enrolling during the three phases of registration
(early, regular, and late) in a two-year college. A second purpose was to suggest late registration policy and practices that might improve student success. The third purpose was to make research recommendations for further study in the area of late registration.
Registration time, academic records, and demographic information were collected from a stratified random sample of students at one community college in the fall of 1998. Students were grouped according to type (new and returning) and registration time (early, regular, and late). The sample consisted of 86 new students (55 regular and 31 late registrants) and 165 returning students (55 from each phase of registration). Analysis of covariance and chi-square tests were used to analyze the data.
The major findings were as follows. For both new and returning students, late registrants were shown to be much less likely to persist to the spring semester than were early (returning students only) or regular registrants.
VI Of the new students, 80% of regular and 35% of late registrants were retained to the next semester. For returning students, 80% of early, 64% of regular, and 42% of late registrants were retained. Differences in withdrawal rates were also significant for both new and returning students. New students who registered on time (regular) withdrew from 10% of their course hours while those who registered late withdrew from 21%. For returning students, early registrants withdrew from 5% of their course hours, regular registrants withdrew from 4%, and late registrants withdrew from 13%.
Returning students also differed significantly in their semester grade point average (GPA) and their successful completion rate based on their time of registration. Early registrants earned a fall semester GPA of 3.48 and successfully completed 96% of their course hours. Regular registrants earned a GPA of 3.33 and successfully completed 91 % of their course hours. Late registrants earned a
GPA of 2.69 and successfully completed 74% of their course hours.
Policy and practice recommendations were made based on these findings.
The researcher concluded that the practice of late registration is a deterrent to both academic success and retention of students.
vu LIST OF TABLES
1. Description of Variables 46
2. Comparison of New Student Samples and Population Selected Demographic Characteristics 52
3. Comparison of Returning Student Samples and Population Selected Demographic Characteristics 53
4. Results of ANCOVA for New Students Withdrawal Rates by Registration Time with Age and Term Hours 55
5. Frequency Table Showing Persistence of Regular and Late Registration New Students 55
6. Results of ANCOVA for New Students Term GPAs by Registration Time with Age and Term Hours b7
7. Results of ANCOVA for New Students Successful Completion Rates by Registration Time with Age and Term Hours 57
8. Results of ANCOVA for Returning Students Semester GPAs by Registration Time with Cumulative GPA and Term Hours 60
9. Results of ANCOVA for Returning Students Successful Completion Rates by Registration Time with Cumulative GPA and Term Hours 60
10. Results of ANCOVA for Returning Students Withdrawal Rates by Registration Time with Age and Term Hours 62
11. Frequency Table Showing Persistence of Early, Regular,
and Late Registering Returning Students 63
12. Significant Variables Found by Testing the Hypotheses 65
13. Comparison of New Student Dependent Variables by Registration Time 69 14. Comparison of New Student Dependent Variables by Registration Time 69
vm 15. Coded Values for Categorical Variables 92
16. Partial List of New Student Data 94
17. Partial List of Returning Student Data 95
IX LIST OF FIGURES
1. Astin's I-E-O Assessment Model 4
2. The I-E-O Model 17 CHAPTER I
INTRODUCTION
Student success is a major concern of higher education institutions for a
variety of reasons. According to the Commission on Colleges (1995) of the
Southern Association of Colleges and Schools, "effectiveness in all educational
programs, delivery systems, and support structures should be the primary goal
of every institution" (p. 21). Student pass rates are often a component of
assessments of institutional effectiveness (Alfred, Kreider, & McClenney, 1994).
Faculty and program evaluations also make use of student pass rates. Another
growing concern is the stronger push for accountability for community colleges
(Parnell, 1990). "A fiscally conservative political climate at local, state, and federal
levels has left many conmiunity colleges without adequate funds. It has also left
the institutions under the gun to be far more accountable for the money they
spend'' (Schmidt, 1998, p. A37). The impetus for performance-based funding
seems to be grow^ing. ''Right now, 11 states tie some appropriations to measures
of public institutions' performance, and 15 more are likely to follow suit within
the next five years, according to state officials and policy analysts" (Carnevale,
Johnson, & Edwards, 1998, p. B6). Retention rates are widely used as an indicator
of institutional performance (Wyman, 1997). Graduation rate is still another
performance indicator used by South Carolina's 1996 law which created a
performance-based system for financing public education (Schmidt, 1997). Therefore, student success in terms of pass rates takes on critical accreditation, accountability, and financial importance for many institutions of higher education.
Community colleges typically do not require college entrance exams.
Students who earned a general equivalency diploma (GED) are admitted equally with high school graduates (Undergraduate admissions..., 1980). Individuals may also be admitted to many two-year colleges without a GED or high school diploma if they have demonstrated "reasonable potential for success"
(Commission on Colleges, 1995, p. 23). This open door policy leads to a diverse student body that is, in general, less prepared to complete college level courses
(Warren, 1985). For these reasons, it is vital that any policy that may impede student success be examined and revised if necessary.
There is a general feeling among community college faculty, counselors, and administrators that students who register late do not do as well in their classes and tend not to complete their coursework (Roueche & Roueche, 1994a;
Sova, 1986). If this is indeed true, then the late registration policies of institutions of higher education need to be reevaluated.
There are two rationales for late registration in community colleges. The first is the underlying philosophy of ease of access as demonstrated by the open door policy stated above. The second stems from the fact that institutional state funding is based in part on enrollment. Any policy that increases the number of students enrolled is often viewed as a financial benefit to the college. Increased demands have been placed on those responsible for the registration process to make registration faster, easier, more accessible, and more convenient One by-product of this evolution has been a tendency to permit students to enroll in classes increasingly later into the term. Although allowing students to register late is a well intentioned effort to acconrmiodate student needs, the question arises, is it in the best interest of the student? (Angelo, 1990, p. 316)
This study attempted to answer Angelo's question using the input-environment-
outcome (I-E-O) assessment model of Astin (1993).
Conceptual Framework
"The I-E-O model is predicated on the assumption that the principal
means by which assessment can be used to improve educational practice is by
enlightening the educator about the comparative effectiveness of different
educational policies and practices" (Astin, 1993, p. 37). This fact, along with its
simplicity, makes the I-E-O model ideal for the study of the practice of early,
regular, and late registration.
The I-E-O model consists of three components (Figure 1). Inputs refer to
those personal qualities the student brings initially to the educational program;
the environment refers to the student's actual experiences during the educational program; and outcomes refer to the talents developed in the educational program. The arrows in Figure 1 depict the relationships and directions of the relationships among the three sets of variables. "Assessment and evaluation in education are basically concerned with relationship B — the effects of environmental variables on outcome variables" (Astin, 1993, p. 18). However, in his own research, Astin discovered that student inputs must be taken into account in order to truly understand the relationship between environmental variables and various student outcomes. Student inputs can be related to both outputs (arrow C) and environments (arrow A); therefore, affecting the observed relationship between environments and outputs. "The basic purpose of the I-E-O design is to allow us to correct or adjust for such input differences in
order to get a less biased estimate of the comparative effects of different environments on outputs" (Astin, 1993, p. 19). The I-E-O model will be described more fully in Chapter II.
Figure 1: Astin's I-E-O Assessment Model
Problem
This was a study concerning the characteristics of early, regular, and late registration students and their success in a conmnunity college. The main problem addressed in the study was to determine whether or not early, regular, and late registration students differ from each other in terms of their academic success and retention. Purpose
The study had three purposes. The first purpose was to determine the differences between students enrolling during the three phases of registration
(early, regular, and late) in two-year colleges. A second purpose was to suggest late registration policy and practices that might improve student success. The third purpose was to make research recommendations for further study in the area of student registrations.
Research Questions and Hypotheses
This study answered two research questions, each having four hypotheses. These questions and hypotheses refer to variables from each of the three categories mentioned in Astin's input-environment outcome (I-E-O) model for assessment in higher education. One input variable, one envirormiental variable, and four outcome variables are considered in each question. The first question dealt with new students and the second question dealt with returning students.
Research Question 1. Do semester grade point average, successful completion rate, withdrawal rate, and persistence (outcome variables) differ between new students according to time of registration after adjusting for age
(input variable) and current number of hours taken (environmental variable)? Hi A: Semester grade point averages (GPAs) for the fall semester of 1998 do not differ by time of registration adjusting for age and current number of hours taken.
HIB: Successful completion rates for the fall semester of 1998 do not differ by time of registration adjusting for age and current number of hours taken.
Hic: Withdrawal rates for the fall semester of 1998 do not differ by time of registration adjusting for age and current number of hours taken.
Hidi Persistence from the fall semester of 1998 to the spring semester of
1999 does not differ by time of registration.
Research Question 2. Do semester grade point average, successful completion rate, withdrawal rate, and persistence (outcome variables) differ between returning students according to time of registration after adjusting for prior cumulative grade point average (input variable) and current number of hours taken (environmental variable)?
H2A: Semester grade point averages (GPAs) for the fall semester of 1998 do not differ by time of registration adjusting for cumulative GPA and current number of hours taken.
H2B: Successful completion rates for the fall semester of 1998 do not differ by time of registration adjusting for cumulative GPA and current number of hours taken.
H2c: Withdrawal rates for the fall semester of 1998 do not differ by time of registration adjusting for cumulative GPA and current number of hours taken. H2D: Persistence from the fall semester of 1998 to the spring semester of
1999 does not differ by time of registration.
Need for the Study
Student success and retention are critical problems for community
colleges. "Politicians are responding to funding pressures and public
questions by pushing for more accountability in the higher education enterprise"
(Parnell, 1990, p. 164).
The internal and external challenges confronting our colleges mandate careful attention to effectiveness. Faculty and staff daily face the daunting task of providing high-quality, flexible education that is relevant to the needs of increasingly diverse students at the same time resources are being held constant or reduced. They are beginning to understand assessment as a tool for improving performance and allocating resources. (Alfred, Kreider, & McClenney, 1994, p. 5)
Student assessment is often used as a tool to answer the question of institutional
effectiveness and accountability. The Community College Round table (Alfred,
Kreider, & McClenney, 1994) listed student progress as one of the core indicators
of institutional effectiveness.
Community colleges must find ways to balance the issues of open access
and quality education. It is imperative that improvement of student success rates
does not come at the sacrifice of academic standards. Thompson (1985) stated
that "having both an open door admissions policy and quality outcomes is both desirable and possible" (p. 10). One way to achieve this goal is to identify and change policies and practices that hinder student success.
7 Few college studies have been published comparing success rates of early, regular, and late student registrants. Studies concerning the effects of registration time used a variety of definitions of late registration and examined different aspects of the question, making it difficult to make comparisons and reach meaningful conclusions for future policy and sound practice.
The effects of late registration on student achievement and persistence at four-year colleges were studied by Chilton (1964) and Parks (1974). Both studies
indicated that late registrants did not perform as well as those who registered on
time. Chilton (1964) found that late registrants made lower semester GPAs than
regular registrants, and that late registrants who enrolled prior to the seventh
class day made higher semester GPAs than those who registered on the seventh
to twelfth class day. Parks (1974) found that, when compared to regular
registrants, late registrants had a higher withdrawal rate from college, dropped
more hours, received more grades of F and X, and received lower GPAs. After
concluding that "to date, no published studies dealing with late enrollment and its
effects on student persistence at the community college," had been conducted,
Angelo (1990, p. 317) looked at the practice of late registration in community
colleges and found the opposite to be true. The late registrants (students
registering after the first week of classes) in Angelo's study earned higher
completion rates and higher GPAs than did regular registrants (students
registering before the end of the first week of classes). Neighbors (1996) stated
that "studies related to registration are limited and studies dealing specifically
8 with each of the three time aspects of the process of registration (early, regular,
and late) are lacking" (p. 12). She then studied the effects of early, regular, and
late registration at a community college, a private university, and a public four-
year college. The study determined that students enrolling during early and
regular registration earned statistically significantly higher semester GPAs than
did those registering late. Significant differences in withdrawal rates based on
registration time were not discovered.
Peterson (1986), Sova (1986), Stein (1984), and Snell (1996) also studied
aspects of late registration and student success. However, these studies also
either reached conflicting conclusions or were limited in scope to new students or
to a single instructor's classes. The studies conducted by Peterson, Sova, and
Stein did not employ the use of statistical analyses to determine significance of
the data collected. No other published studies could be found on this topic.
Neighbors (1996) concluded, "the nature and mission of the community
college and its specific, local clientele warranted localized research" (p. 16). Her
study analyzed three types of institutions (conmnunity college, private
university, public university) and she recommended "another study should be
conducted that compares several institutions within each category" (p. 105). This
study followed that recommendation and considered aspects of registration
practices and student success at one community college in West Texas. For the
purposes of this study the college will be referred to as West Texas Community
College. Delunitations
This study was reduced in scope to include only:
1. A stratified random sample of 251 on-campus community college students enrolled in credit courses for the fall semester of 1998 at one community college in West Texas ; and
2. The impact of registration time on student academic success and retention while excluding any consideration of financial impact on the college.
Limitations
Two major limitations of this study were:
1. As this study encompassed only students attending one community college in West Texas, it may be difficult to generalize to community colleges in other parts of the country.
2. Key input and environment variables affecting student success may have been overlooked. Variables such as major, type of courses taken
(developmental vs. college level and academic vs. vocational), number of years since last attending school (high school or college), socio-economic level, ethnicity, number of hours worked per week, level of self-confidence, and motivation may have influenced the outcome variables, but were not considered in this study.
10 Assumptions
Listed below are the major assumptions made in this study:
1. A randomly selected group of students from each of the three phases
of registration (early, regular, and late) at West Texas Community College was
representative of the student body at West Texas Community College for the fall
semester of 1998.
2. The data obtained from the registrar's office at West Texas Community
College were accurate and complete.
Definition of Terms
Definitions for terms used in this study were as follows.
Accumulated hours: Accumulated hours referred to the number of hours
a student has attempted at any college before the fall semester of 1998.
Age: Age referred to the age of the student as of September 1,1998.
Cumulative grade point average: Cumulative grade point average
(CGPA) referred to the student's cumulative grade point at all colleges at the time of registration for the fall semester of 1998.
Current hours: Current hours referred to the number of credit hours a student was enrolled for during the fall semester of 1998.
Early registration: Early registration referred to the practice of colleges permitting enrolled students to pre-enroll for subsequent semesters. In this
11 study, it referred to two periods during the spring semester of 1998 (April 28-30, and May 20-21).
Employment: In this study, employment was measured by the number of hours worked per week.
Late registration: Late registration referred to the practice of colleges permitting students to enroll in classes on or after the first class day. In this study it referred to a period covering the first eight class days (August 27-
September 8) of the fall semester of 1998.
New student: For the purposes of this study, new student referred to a student who had not previously attended any college or university.
Persistence: For the purposes of this study, persistence occurred when a student re-enrolled for one or more hours for the spring of 1999 at West Texas
Community College.
Regular registration: Regular (or walk-through) registration referred to the period of enrollment just prior to the beginning of classes. All students, new or returning, may register during this period. In this study, it referred to the three days prior to the first class day (August 24-26) of the fall semester of
1998.
Retiu-ning student: For the purposes of this study, returning student referred to a student who had previously attended a college or university.
Semester grade point average: The semester grade point average referred to the student's 1998 fall semester GPA rather than his/her cumulative
12 average. The GPA was calculated by dividing the total grade points earned in the semester by the total number of hours attempted in the semester.
Successful completion: Completing a course with a grade of A, B, or C, was considered to be successful course completion for the purposes of this study.
Successful completion rate: The successful completion rate was computed for each student by dividing the number of hours successfully completed during the fall semester of 1998 by the number of hours initially attempted during the fall semester of 1998.
Withdrawal: The dropping of a class prior to the end of the semester, regardless of the reason, was considered a withdrawal for the purposes of this study.
Withdrawal rate: The withdrawal rate was computed for each student by dividing the number of hours dropped during the fall semester of 1998 by the number of hours initially attempted during the fall semester of 1998.
Organization of the Dissertation
Introductory information and a rationale for the study have been presented in Chapter I. Chapter II contains a review of related literature including variables related to student success and retention, registration practices and problems, existing studies related to student success and time of registration, and a description of assessment models. Chapter III contains a description of the methodology that was followed in the study. Findings of the study are
13 presented in Chapter IV. A summary of the study is presented in Chapter V, along with a discussion of the major findings, recommendations, and conclusions.
14 CHAPTER II
REVIEW OF LITERATURE
Introduction
This chapter is presented in five sections. The introduction, which briefly
restates the emphasis of this study, is followed by a description of assessment
models and the one selected as a conceptual framework for this study. A review
of the literature about the variables related to student success and retention
follows. Literature and research on policies and practices of late registration are
discussed as well as problems associated with late registration. Conflicting
studies concerning the effects of early and late registration on student success are
then reviewed. This is followed by a brief summary.
Registration at Texas colleges and universities is generally conducted in
three phases: early, regular (or walk-through), and late registration. Previous
research has focused on the impact of late registration on student achievement
and retention. This study focused on the effects of time of registration on
student success and retention at one West Texas community college.
Models for Predicting Student Success and Retention
In an attempt to develop a conceptual framework for this study, several retention and assessment models were reviewed. One of the earliest and most widely used retention model was developed by Spady (1970) and expanded on
15 by Tinto (1975). Tinto's model was developed to study retention of traditional aged students attending four-year residential institutions and asserts that a student's academic and social integration at an institution are key contributors in his or her decision to stay or leave. The theory emphasized the need for a predictive model of attrition to account for the unique interactions generated by a particular individual-institution combination. This interactive view of the retention process has been validated by a number of studies. Summarizing one review of retention literature, Lenning, Sauer, and Beal (1980) noted:
While it is true that the researchers and theorists have viewed retention from various perspectives, a common conclusion has been that retention and attrition result from the interactions between persons and institutions ... The characteristics of the interaction, not the student or institution alone, affect a student's decision to stay or drop out. (p. 4)
However, after adapting Tinto's model to non-residential institutions, Pascarella,
Duby, and Iverson (1983) discovered that social integration had a negative influence on persistence. They postulated that the diverse natures of non residential institutions might cause variance of the influence of variables on retention. Bean and Metzer (1985) developed a retention model for examining the persistence of nontraditional students attending conrunuter colleges. Their model, which reduced the importance of social integration, consisted of seven sets of variables: background, academic, environmental, social integration, academic outcome, psychological outcomes, and intent to leave.
Astin (1993) developed the input-environment-outcome assessment model (Figure 2). This model has been used to assess student success (Amey &
16 Long, 1998) and retention (Kelly, 1996). "The I-E-O model captiires the
longitudinal nature of the process, highlights the interactivity between student background characteristics and the college environment, and provides a broad context in which institution-specific investigations of attrition can be conducted'
(Kelly, 1996, p. 5). For this reason, the I-E-O model was chosen to provide the conceptual framework for this study.
Figure 2: The I-E-O Model
The Input-Environment Outcomes Model
The first component in Astin's (1993) assessment model is input data on the entering student. Input data are important because they are related to both outcome and environment measures. Types of input data include fixed student attributes, cognitive functioning, aspirations and expectations, self-ratings, behavioral patterns, and educational background characteristics. Student input measures are collected in order to control for the effects of student input characteristics, but can also be used for purposes such as curriculum review, admissions and recruitment, and public information.
17 The second component of the model is the environment. According to
Astin, the environment encompasses everything that happens to a student during the course of an educational program that might conceivabh^ influence the outcomes under consideration. Environmental factors include programs,
personnel, curricula, teaching practices, facilities, and the social and institutional climate.
The third component is the outputs or outcomes. Student outcomes refer
to those aspects of the student's development that the institution either does
influence or attempts to influence through its educational programs and practices
and are often divided into three types: cognitive, affective, and psychomotor.
Each component of the model serves a vital purpose in the assessment
process. According to Astin, the basic purpose of the I-E-O design is to allow us
to correct or adjust for input differences in order to get a less biased estimate of
the effects of different environments on outputs. If any of the components are
omitted from the assessment process, the chances of making valid inferences are
decreased significantly. In order to determine whether the program or
institution being assessed is effective, it is necessary to have information about
the knowledge and skills the students bring with them when entering and the
environment to which they are exposed during the assessment period.
Otherwise, it will be unclear whether a student's gains should be attributed to the
program or institution, to previous knowledge, or to environmental factors
outside the program.
18 Considering the weaknesses of assessment strategies that are missing one
or more of the I-E-O components helps to underscore the importance of each
component. The outcome-only assessment model, while easy to implement,
produces data that are difficult to interpret. From a talent development
perspective, there is no way to know how much has been learned as a result of
the program because there is no input data with which to compare the output
data. Also, due to the lack of information on how students performed under
different envirormiental circumstances, there is no way to tell which educational
programs and practices are most effective. These problems become more
pronounced as the model is applied on a broad scale to a program or institution
rather than a single course.
Environment-outcome assessments allow for no control over differential
inputs and can lead to unwarranted causal interpretations of environmental
effects. Only when students have been assigned at random to the different
envirormients can we be justified in concluding that output differences across
different environments may be caused by those environmental differences. Due
to the enormous diversity of students entering higher education institutes, it is
difficult to reliably assess the impact of environmental experiences without input information on the characteristics of entering students.
Input-outcome models assume that any changes in the student are due to experiences in the educational program. Inferential difficulties result from the need to assume that change is equivalent to environmental impact. It is possible
19 that the program being assessed had a negative effect on student gains even though the assessment model indicated a positive gain. The gain could have been due to external environmental factors not taken into account, indicating that a different educational environment could have produced even greater gains.
The environment only assessment, often used for accreditation and curriculum evaluations, emphasizes the educational program itself. The major flaw in this model is that it provides no direct information on learning or the talent-development process. In the absence of data concerning the actual impact or effectiveness of the educational program, individuals are forced to make inferences in order to evaluate the program.
Summary of I-E-O Model
The literature on retention models stresses the importance of the interactions between the student and the institution. These interactions are both social and academic. Astin's I-E-O assessment model allows for consideration of these interactions. The student's background (input) affects interactions with the environment, leading to academic success or failure (outcomes). The specific variables to be considered in this study will be examined in the next section.
20 Student Success and Retention Variables
In 1975, Astin wrote "dropping out of college is a little like the weather: something everyone talks about but no one does anything about" (p. 1). He went on to describe his research on characteristics that may be helpful in predicting "drop-out prone" freshmen. These characteristics included "poor academic records in high school, low aspirations, poor study habits, relatively uneducated parents, and small town backgrounds" (Astin, 1975, p. 45). Chaney and Farris (1991) state that selectivity in admissions is the single most important predictor of retention at institutions of higher education, accounting for 17% to
29% of the variation in retention rates. While it may be possible for many institutions to use enrollment standards to combat high attrition rates, community colleges do not have this option. Throughout their history, community college educators have endeavored to bring higher education to the masses. In doing so, they have developed a mission that embraces the philosophy of open-access and diversity. Gleazer (1980) stated six qualities that should be requisite for the mission of any community college:
1. The college is adaptable. It is capable of change in response to new conditions and demands, or circumstances. 2. The college operates with a continuing awareness of its conununity. 3. The college has continuing relationships with the learner. 4. The college extends opportunity to the 'unserved'. 5. The college accommodates diversity. 6. The college has a nexus function in the community's learning system, (p. 132)
21 The open-access policy of the conununity college has led to a diverse student
body. Community college students "include proportionately more minority
students and older undergraduates than do their counterparts in four-year
colleges and universities" (Warren, 1985, p. 53). "In general, students who enter
community colleges instead of universities have lower academic ability and
aspirations and are from a lower socioeconomic class" (Cohen & Brawer, 1989, p.
44). The lack of ability to control these background variables by means of
selective admissions leads to an increased need for understanding of how these
and other more controllable variables affect student success and retention in
order to more effectively serve community college students.
Input and Environmental Variables
Several input and environmental variables have been identified in the
literature as being predictors of student success and retention. These inputs
included four demographic variables: (a) gender, (b) age, (c) marital status, and
(d) ethnicity, and two academic variables: (a) cumulative GPA, and (b) accumulated hours. The environmental variables included: (a) the current number of hours enrolled, (b) the level of financial aid, and (c) the time of registration. The input variables and first two environmental variables will be discussed in this section.
22 Gender and Marital Status
Although much of the literature considers gender as a variable possibly
affecting success and retention of students, its role is somewhat ambiguous. "The
interpretation of differential retention rates by gender is complicated by the
presence of other risk factors such as major, enrollment status, employment
status, and family responsibilities" (Grimes & Antworth, 1996, p. 354). Females
seem to complete degrees in higher percentages than males (Pascarella et al.,
1983; Astin, Korn, & Green, 1987). However, marital status tends to have a
negative influence on retention for females (Astin, 1975; Lenning, 1982) and a
positive influence on retention for males (Astin, 1975). Feldman (1993) found
that, when tested by itself, gender was related to persistence, with females
tenduig to have better retention, but "it did not hold up when other factors were
accounted for" (p. 508). According to the Texas Higher Education Coordinating
Board (1998), fall-to-spring persistence rates were slightly higher for females
(68.2 %) than for males (64.6 %) for all students enrolled in Texas community and technical colleges in 1994-1995.
Age
Research concerning the effects of age on retention has produced conflicting results. Although a number of studies (Astin, 1975; Feldman, 1993;
Pascarella, Duby, Miller, & Rasher, 1981) report lower retention rates for older students, other studies (Grimes & Antworth, 1996; Pascarella, Smart, &
23 Ethington, 1986) found no direct correlation between age and persistence.
Tharp's study of urban commuter campus students found "the profile of the student at-risk for drop-out tended to be a traditional-age, single male in a baccalaureate program and undecided in major" (1998, p. 288). Indirect effects of age such as part-time attendance, greater family responsibilities, and workloads may be contributing factors for lower retention rates among nontraditional students (Wade, 1995).
Ethnicity
"Differences in persistence by ethnicity are well documented but should
be interpreted carefully because these differences may be related to
socioeconomic and first-generation-in-college status" (Grimes & Antworth, 1996,
p. 355). Many studies (Feldman, 1993; Grimes & Antworth, 1996; Waggener &
Smith, 1993) found a significant relationship between ethnicity and academic
success and retention. "With the exception of Asian students, whose odds were
similar to white students, minority students showed a greater likelihood of
dropping out than whites" (Feldman, 1993, p. 510) with black students 1.75 times
more likely to drop out than white students. Among conrniunity college
students "ethnicity, more than other characteristics, is related to difficulties with
full integration into the social and academic life of the college and raises concern
about higher attrition for African American students" (Grimes & Antworth, 1996,
p. 355). Grimes (1997) also found that, among underprepared community
24 college students, African Americans scored lower on academic placement tests
but this did not translate to higher attrition rates. However, in Texas community
and technical colleges, retention rates did not differ greatiy for Blacks (62.0 %),
Whites (64.6 %), and Hispanics (69.6 %) in 1994-1995 (The Texas Higher Education
Coordinating Board, 1998).
Cumulative GPA
In 1975, Astin stated, "By far the greatest predictive factor (of freshman retention) is the student's past academic record and academic ability" (p. 45).
Twenty-three years later, Tharp reiterated this conclusion: "college grade performance has been shown to be the single-most important factor in predicting persistence" (1998, p. 282). Numerous other studies (Chaney & Farris,
1991; Feldman, 1993; Grimes & Antworth, 1996; Waggener & Smith, 1993) corroborate the statements of Astin and Tharp. In a study of community college students. Grimes (1997) found cumulative GPA to be an important predictor of future success for both college-ready and underprepared students. No studies were found to contradict the importance of prior academic success on future success and retention.
Current and Accumulated Hours
Student success and retention may be directly linked to the number of credit hours (current hours) in which a student is enrolled. Persisters tend to be
25 enrolled in a greater number of credit hours (Pascarella et al., 1983; Tharp, 1998;
Waggener & Smith, 1993). This was also shown to be true for nontraditional
students (Metzner «fe Bean, 1987; Wade, 1995). For the nontraditional student,
hours enrolled may be the only available indicator of commitment and intent to
persist (Lenning, Sauer, & Beal, 1980). In Texas community colleges, full-time
students were much more likely to persist (76.8%) than part-time students
(47.7 %) in 1994-1995 (The Texas Higher Education Coordinating Board, 1998).
Full-time students also tend to have greater academic success as evidenced by
semester GPA (Amey & Long, 1998; Grimes, 1997).
Studies have shown that the greatest attrition occurs between the
freshman year (30 accumulated hours) and the sophomore year (30-60
accumulated hours) (Chaney & Farris, 1991; Noel, 1985). However, Wade (1995)
found that for nontraditional community college students (over 25 years of age)
persisters were more likely to have accumulated fewer hours than had
nonpersisters.
Financial Aid
There are studies with conflicting results concerning the effect of financial
aid on student success and retention. Financial aid was found to have a
significant direct positive effect on students' socialization process but no effect on
academic integration, two aspects related to retention in a study by Cabrera,
Nora, and Casteneda (1992). "Financial aid may provide recipients with enough
26 freedom to engage in social activities and to become fully integrated into the social realm of the institution" (Cabrera et al., 1992, p. 589). According to Tharp
(1998, p. 281), "students have repeatedly reported that flnances are a primary reason for dropping out, but there is a consistent pattern in the literature stating that financial aid is not a significant correlate of attrition."
Outcome Variables
Four outcome (dependent) variables were chosen for this study: (a) semester GPA, (b) successful completion rate, (c) withdrawal rate, and (d) persistence. The first two deal directly with academic success as measured by grades, while the last two concern retention.
GPA and Successful Completion Rate
The GPA was the most commonly used measure of academic success throughout the literature and was shown to be closely linked to retention
(Chaney & Farris, 1991; Feldman, 1993; Grimes & Antworth, 1996; Waggener &
Smith, 1993). "College grade performance has been shown to be the single- most important factor in predicting persistence" (Tharp, 1998, p. 282).
A related variable, successful completion rate (also called pass rate), refers to the percentage of course hours completed with a grade of A, B, or C. Student pass rates are often a component of assessments of institutional effectiveness
(Alfred, Kreider, & McClenney, 1994). Faculty and program evaluations also
27 make use of student pass rates. Therefore, student success in terms of pass rates takes on critical accreditation, accountability, and financial importance for many institutions of higher education.
Withdrawal Rate and Persistence
The withdrawal rate (percentage of course hours dropped during the semester) deals with retention within the semester and persistence deals with retention from one semester to the next. Retention rates are widely used as an indicator of institutional performance (Wyman, 1997). In some performance- base systems for financing public education, graduation rate is one of the performance indicators used (Schmidt, 1997). As community colleges are pushed to be more accountable for funding (Parnell, 1990; Schmidt, 1998), these performance indicators take on added importance.
Summary of Variables Used in the Study
The I-E-O model of assessment, developed by Astin (1993), stresses the importance of considering both input and environmental variables when assessing student progress. The input variables to be considered in this study include four demographic variables: gender, age, marital status, and ethnicity, and two academic variables: prior GPA and accumulated hours. Of these inputs, previous research indicates that by far the most accurate predictor of future success and retention is prior GPA. The interactions between various
28 demographic variables make it difficult to assess the importance of these variables independently. Two of the environmental variables in this study are the current number of hours enrolled and financial aid. Of these variables, current number of hours enrolled seems to be the best predictor of persistence.
Registration
This section contains information on registration policies and procedures, with an emphasis on the practice of late registration and the problems it causes.
This is followed by a review of the literature concerning time of registration (a fourth environmental variable) as it relates to student success and retention.
Policies and Practices
Neighbors (1996) found that most Texas colleges follow a three-phase registration process:
1. Early registration, targeting the retention of students already enrolled, permits students to pre-register for the subsequent semester. 2. Regular (or walk-through) registration enrolls students either in an arena-type setting or by telephone just prior to the beginning of classes. 3. Late registration permits students to enroll after classes begin. For community colleges, this process allows a person to walk in off the street and complete an application for admission, promise to send transcripts and any other pertinent documents, and enroll-all after classes are in session. Sometimes this practice extends into the third week of classes, (p. 4)
A search of twenty Texas community college Web sites for the most part confirmed this three-phase registration process. Three of the colleges allowed
29 returning students to register early on-line or by phone. Differences existed in the length of the registration phases, most notably in early registration. Four of the colleges conducted early fall registration in the spring for returning students.
One of these colleges had an additional day for early registration at the end of
July- Three colleges conducted early registration during the summer (June or
July); one had two sessions of early registration, two days in July and two days in August; and another conducted early registration during the first week of
August. Two colleges only provided one or two days of early registration immediately prior to regular (walk-through) registration. West Texas
Community College conducted early registration in two stages, two days at the end of April and two days at the end of May. The remaining seven colleges had no early registration dates listed. All of the colleges conducted regular or walk through registration, usually for a period of two to five days, immediately prior to the first day of class. All but four of the twenty colleges reviewed allowed students to register late (on or after the first day of class). Late registration was typically conducted for a period of two to five working days. However, two of the sixteen late registration colleges extended late registration to the twelfth class day. At West Texas Community College, late registration was conducted from
August 27,1998, the first day of class, through September 8,1998, a period of eight class days.
30 Late Registration
Late registration has long been a practice of higher education institutions.
Most colleges and universities have policies that allow students to enter a class after the first day. Usually some sort of penalty in the form of a late registration fee is imposed. A survey of 96 colleges and universities conducted by the
Department of Instructional Studies at Cincinnati University (1969) found that each institution allowed late registration. Of the respondents, 39% allowed registration through the first week of classes, 28% through the second week, while only .4% did not allow registration after the first day of classes. Eight of
the institutions responding, less that 1%, did not have a deadline beyond which a
student could not register. Penalties for late registration were imposed by 93%
of the institutions, usually a fee of ten dollars or more.
More recent reports indicate that the practice of late registration is also
prevalent among community colleges. "Some colleges report that more than
20% of their student body will enroll during the late registration period"
(Roueche & Roueche, 1994a, p. 3). The leniency in late registration policies at
community colleges seems to stem in part from the open access philosophy and
in part from the funding formula that rewards colleges financially for higher
enrollments.
A study conducted at Miami-Dade Conmnunity College (Belcher, 1990)
found that 12% of all students registered in the ten days following the first day of
class. A survey of these late registrants indicated that the most frequent reasons
31 given involved "postponing the decision to attend or to simply register." The majority was aware that their classes had already started.
The prevalence of the practice of late registration in higher education
institutions, especially in community colleges, seems to warrant studies
investigating the effects of this practice on student success and retention.
Problems Associated with Late Registration
Many educators believe that the practice of late registration causes
problems, not only for the student who registers late, but also for other students
and instructors. "I can think of no academic policy more counterproductive to
student success in an open-access situation than a policy which permits (and
perhaps encourages) students to begin their studies a week or two late"
(Roueche, 1989, p. 6).
The Commission on Academic Standards for the Los Angeles Community
College District (McCuen, 1978) stated the following three problems caused by
students who register late:
1. They interrupt at the beginning of class hours (or during class) seeking admission. 2. If admitted, they frequenfly never catch up or hold back the rest of the class—or both. 3. If they gain admission to classes that had been closed, they-and others-learn the value of registering late, thereby causing increased late registration, (p. 8)
Faculty members seem to be in agreement with this assessment of the
32 problems generated by late registrants. The importance of the first few days of class in setting the students up for success was stressed throughout the literature
(Roueche & Roueche, 1994a, 1994b; Sova, 1986). Roueche, perhaps the most outspoken advocate of the abolishment of late registration, stated that "all faculty members and staff know that the first days of any course are the most important learning experiences that a student will have" (Roueche & Roueche,
1994b, p. 7).
Sova (1986) also found that late admits were a disruption and a hindrance to the instructor's attempts to get a course going smoothly.
Many instructors use the first class day to discuss requirements and expectations, establish the importance of the course, and promote student interest in the course. The late admits and section switchers continue to come and go into the second and even third week of the term. That carefully prepared introduction to the course is never heard by a significant number of the eventual course members, (p. 1)
In general, community college students enter school with lower academic ability and aspirations than their counterparts in four year colleges and universities
(Cohen & Brawer, 1989). The community college, with its philosophy of open access, can do nothing to change the academic backgrounds the students bring with them. Therefore, practices such as late registration that may hinder student progress need to be examined in more detail.
Studies Concerning Early, Regular, and Late Registrants
There have been a number of studies with conflicting results concerning the effects of early and late registration on student success and retention.
33 However, these studies used a variety of definitions of late registration and examined different aspects of the problem making it difficult to make comparisons and reach meaningful conclusions for future policy and sound practice.
Chilton (1964) compared characteristics of late and regular registrants at
Tarleton State College in Stephenville, Texas. The study considered full-time
freshmen and sophomores and spanned the seven year period from 1955
through 1962. He concluded that students who register on time (before the first
class day) achieve at a higher level, both academically and behaviorally, than do
those who register late (on or after the first class day). He also concluded that
personality adjustment, as measured by the California Test of Personality, could
not be linked with late registration.
In 1974, Parks studied late registration at East Texas State University in
Commerce, Texas in an attempt to determine characteristics of late registrants.
Parks surveyed 158 undergraduate students who registered late (on or after the
first class day) for the fall semester of 1973. Data collected included sex,
classification, military status, employment, estimated GPA, and other personal
factors. Students were also asked to give a reason why they had registered late.
A control group of 393 regular registrants (students who registered prior to the
first class day) was also surveyed. Each group contained approximately equal
numbers of freshmen, sophomores, juniors, and seniors. The study found no
significant difference between regular and late registrants based on marital statiis
34 or age. The number of absences was not significantiy different for the two groups. Late registrants were found to have statistically significantly lower high school rank than regular registrants. Late registrants were also involved in
significantly more disciplinary actions than regular registrants. For sophomore, junior, and senior students, the number of colleges previously attended was
higher for late registrants than for regular registrants. Academically, several
statistically significant differences were found. Late registrants were placed on
academic probation more often, withdrew from more courses, received more
grades of F and X, and earned lower GPAs than regular registrants. The survey
found that reasons for late registration included financial problems, incomplete
applications, and a late decision to enter college. Parks stated that these reasons,
accounting for about 50% of the student responses, "seemed to indicate the
inability to organize and make decisions" (p. 57).
Stein (1984) investigated new students who applied and registered just
before classes began or during the first five days of class. It was determined that
31% of these new late registrants earned a GPA of 0.0 as opposed to 21% of the
total student body. However, a higher percentage of new late registrants earned
a GPA of 4.0, 28% in contrast to the student body's 18%. Retention to the next
semester for new late registrants was about half that of the total student body.
Of the new late registrants, full-time students had lower rates of attrition than
part-time students did. The statistical significance of these findings was not
stated. Stein concluded that the decision whether or not late registering students
35 should be enrolled "involves a value judgment, one which will undoubtedly vary depending on whether enrollment is deemed sufflcient or deflcient" (p. 3).
Another study of new students who applied and registered late was conducted at Honolulu Community College (Peterson, 1986). Ninety-one of the
99 late registrants completed the semester, although most students who took 12 or more hours dropped or failed at least one course. Late registrants with the highest rate of success were those enrolling in a vocational program as opposed to liberal arts classes and taking only one or two courses. The statistical significance of these findings was not stated. Peterson concluded that "the acceptance of late applications and thereby late registrants which result appears to be worthwhile for both the college and the student" (p. 3).
Sova (1986) examined final grades earned by students enrolled in developmental writing courses and freshman English courses during the fall 1985 semester at Broome Community College in Binghamton, New York. Of the
1,673 students enrolled, 1,439 were regular admits and 234 were late admits.
The study found that students who enroll on or after the first day of classes were much more likely to fail or withdraw than were students who enrolled on time.
Of late admits, 50% received passing grades, 27% failed, and 19% withdrew from class. Of regular admits, 81% received passing grades, less than 2% failed, and
16% withdrew from class. As with Stein and Peterson's studies, no hypothesis testing was done to determine the statistical significance of these results.
36 Angelo (1990) investigated the relationship between late registration and student persistence and achievement among community college students at San
Bernardino Valley College in 1988. He sampled 387 late registrants and 390 timely registrants and classified students in each group as persisters or non persisters. Angelo (1990) defined late registration to include "enrollment that took place after the close of the first week of instruction" (p. 321). Persistence was defined to be completing the course. He concluded that students registering on time are not more likely to persist than are those who register late. He also found no statistically significant difference in academic achievement between on time and late registrants. He theorized that the high rate of non-persistence of timely registrants might be due to the fact that earlier registrants "simply have more time to change their minds and find other courses which may be more appealing" (Angelo, 1990, p. 326). He referred to this as academic "window shopping" and recommended career/academic counseling to help address the problem.
Snell (1996) considered 107 students enrolled in four sections of one full time faculty member's social science class and compared first day and late registrants. Students could enter the class up to four weeks into the sixteen week semester and a "make ups week" was offered for late registrants one week prior to finals. Using a chi-square test to compare students earning an A or B to those earning Cs or below, late registrants were not found to be at a disadvantage.
37 The most comprehensive study of registration time and its effect on student success was conducted by Neighbors (1996). A random sample of 441 students, grouped by time of registration (early, regular, or late) and by type of institution (community college, private university, or public university), were tracked through the spring semester of 1996. She defined late registration as the period of enrollment occurring after classes had begun to meet and regular registration as the period of enrollment immediately prior to the beginning of classes. Early registration was defined to be "the practice of colleges permitting enrolled students to pre-enroll for subsequent semesters" (Neighbors, 1990, p.
21). The community college sample contained only freshmen and sophomores, while the university groups contained freshmen, sophomores, juniors, and seniors. The study focused on an investigation of the semester GPAs and withdrawal rates earned by students in the three registration phases. Eight student characteristics, age, gender, ethnicity, type of student (full-time or part- time), classification, financial aid, resident or commuter, and day or evening classes, were analyzed. Individual t-tests were used to test eight hypotheses and to investigate gender effect. The first three hypotheses dealt with GPA, which was determined to differ significantly based on phase of registration. Students registering early earned the highest GPAs (3.02), followed by regular registrants
(2.65), and late registrants (2.05). The other hypotheses dealt with differences in withdrawal rates compared by enrollment time and GPA and withdrawal rate
38 differences between students tirom the three types of institutions. No significant differences were found.
The following conclusions were reached:
1. Registration procedures conducted prior to the beginning of classes
(both early and walk-through) are sound avenues for enrollment of
students in terms of academic success.
2. Late registration practices are detrimental to the student in terms of
academic success.
3. Student selection of classes is not entirely infallible, since students
complete only 75% of their originally selected classes.
4. The impact of the timing of registration is fairly consistent
throughout the various types of institutions of higher education in
Texas.
5. The Open Door policy followed by most community colleges
contributes to the problems of late registration by permitting students
to fUe college applications after classes are in session.
No other studies concerning early, regular, and late registration could be found.
Neighbors suggested that further study was warranted at each type of institution. She concluded that "the nature and mission of the community college and its specific, local clientele warranted localized research" (Neighbors,
1996, p. 16).
39 Summary
An initial review of literature applicable to the study of student success and registration procedures in community colleges has been presented. In conducting the review of literature, an on-line search of ERIC and Dissertation
Abstracts International was performed. The past two years of several journals.
Research in Higher Education, Community College Journal of Research and
Practice, Community College Review, and The Chronicle of Higher Education, were also examined.
Few studies have researched the area of registration periods, and those that have done so have provided contradictory observations and conclusions.
Several of the studies present only descriptive statistics and make no attempt to perform hypothesis testing in order to determine the significance of the information collected. This study adds to the body of literature regarding the topic of registration practices and their impact on students.
The next chapter contains a detailed discussion of the methodology used in the study. The general research design, sampling procedures, and collection and analysis of data are also discussed.
40 CHAPTER III
METHODOLOGY
Introduction
This chapter on methodology describes the: (a) general research design of the study, (b) description of variables, (c) sampling procedures, and (d) collection and analysis of data.
General Research Design
In this study, a causal-comparative design was used to investigate the effects of early, regular, and late registration on community college student success.
The major advantage of causal-comparative research designs is that they allow us to study cause-and-effect relationships where experimental manipulation is difficult or impossible. The major disadvantage of causal- comparative research designs is that determining causal patterns with any degree of certainty is difflcult. (Borg & Gall, 1989, pp. 539-540)
Two research questions, each having four hypotheses, were considered. First, did the output variables (semester grade point average, successful completion rate, withdrawal rate, and persistence) differ between new students according to time of registration (regular or late) adjusting for input (age) and environmental
(current number of hours taken) variables? Second, did the output variables
(semester grade point average, successful completion rate, withdrawal rate, and persistence) differ between returning students according to time of registration
41 (early, regular or late) adjusting for input (cumulative grade point average) and environmental (current number of hours taken) variables? Registration time, academic records, and demographic information were collected for a stratified random sample of students at one community college. Students were grouped according to type (new and returning) and registration time (early, regular, and late). New students were then classified by age, gender, and ethnicity.
Returning students were classified by age, gender, ethnicity, cumulative GPA,
and accumulated hours. The hypotheses stated for the two research questions
were tested for significance at the .05 level using chi-square tests and analysis of
covariance (ANCOVA) with multiple covariates. These tests and the analysis of
the data are examined in more detail in a later section.
Description of Variables
Astin's (1993) I-E-O assessment model was used as the conceptual
framework for classifying the variables in this study. The model consists of three
sets of variables: input, environmental, and outcome. The input and
envirormiental variables used in this study were identified in the literature as
being predictors of student success and retention. For new students the input
variable was age and the environmental variables were the current number of
hours enrolled and the time of registration. For returning students the input
variable was cumulative GPA and environmental variables were the current
number of hours enrolled and time of registration. For both groups, the
42 outcome variables were semester GPA, successful completion rate, withdrawal rate, and retention. Successful completion rates were computed for each student by dividing the number of hours the student completed successfully (grade of A,
B, or C) by the total hours of enrollment for that student during the fall of 1998.
Similarly, withdrawal rates were computed for each student by dividing the number of hours for which the student received a grade of W by the total hours of enrollment for that student during the fall of 1998. Retention was deflned as a student re-enrolling for one or more hours for the spring of 1999 at West Texas
Community College. For descriptive purposes, data were also collected for both groups on the input variables gender, ethnicity, and accumulated hours (for returning students only).
Sample
The population from which the sample was taken consisted of those students enrolled in on-campus credit classes at West Texas Community College for the fall semester of 1998. The college, a two-year community college, is located in a West Texas community with a population of approximately 100,000.
The total enrollment (excluding continuing education) for the fall semester of
1998 was 4,593. Of these students, 3950 were enrolled in on-campus classes and the remaining 643 were enrolled off-campus. Approximately 70% declared academic majors, while the remaining 30% were pursuing technical degrees.
Females made up 57.8% of the student population. The ethnic breakdown was
43 60.5% white, 33.3% Hispanic, 4.4% Black, 1.0% Asian, 0.7% American Indian, and
0.1% alien. The average age of students was 24.7 years.
Registration for the fall semester of 1998 was conducted in three phases:
five days of early registi-ation (April 28-30 and May 20-21), three days of regular
registration (August 24-26), and eight days of late registration (August 27-
September 8). Approximately 37% (1721 students) registered early, 51% (2331
students) registered during regular registration, and 12% (537 students)
registered late. Students who registered late were required to pay a fee of ten
dollars. Classes began at 5:00 p.m. on August 26.
A stratified random sampling technique was used to insure that each
group (early, regular, and late registrants) of students of each type (new and
returning) was equally represented. "Stratified samples are particularly
appropriate where the research problem requires comparisons between various
subgroups" (Borg & Gall, 1989, p. 225). The stratified random samples used in
this study were obtained from the college's computerized student database using
the random file generator of the mainframe's software (based on a random
number generator). Using this procedure, 55 new regular registrants were
randomly selected from the student database. Only 31 new students registered
late for on-campus credit classes so they were all used in the study. The same
procedure was used to select 55 returning students from each phase of
registration (early, regular, and late). Therefore, the sample consisted of 86 new
students and 165 returning students for a total of 251 students.
44 Collection of Data
Data were requested (see Appendix A) and collected from the registrar's office at West Texas Community College for the students in the stratified random sample. Students were identified only by social security numbers. Each student's original registration form and final grade report were used to determine registration date, semester GPA, withdrawal rate, and successful completion rate. The college's computerized student database was used to collect demographic information for each student. Registration records for the spring semester of 1998 were used to determine retention status for each student. Table 1 describes the variables for which data were collected.
Categorical data collected for the study were coded for purposes of analysis. The categorical variables and their value ranges are presented in
Appendix B. The next section will detail the analysis of the data collected in the study.
Analysis of Data
The hypotheses under consideration in this study were tested for signiflcance at the .05 level. The unit of measurement for each variable was the individual students of each type (new and returning) sampled from each phase of registration (early, regular, and late).
45 Table 1: Description of Variables
Data Description
ID No. Student social security number
Gender Male or Female
Age Age as of September 1,1998
Ethnicity White, Hispanic, Black, or Other
Cumulative GPA Cumulative GPA prior to fall 1998 (all colleges)
Accumulated Hours Hours taken prior to fall 1998 (all colleges)
Current Hours Hours of enrollment for the fall 1998
Semester GPA GPA for the fall semester of 1998
Hrs Successfully completed Number of hours of A, B, or C (fall 1998)
Hours Withdrawn Number of hours of W (fall 1998)
Persistence Retained (re-enrolls for spring 1999) or not retained
Analysis of covariance (ANCOVA) with multiple covariates was used to test hypotheses involving the continuous dependent variables: (a) semester GPA,
(b) successful completion rate, and (c) withdrawal rate. "Analysis of covariance is used to control for initial differences between groups" (Borg & Gall, 1989, p.
556). For new students the covariates considered were age and current hours taken. For returning students, the covariates were cumulative GPA and current
46 hours taken. The assumption of homogeneity of regression was checked using
SPSS to calculate the beta values for the regression equations. For returning
student data, Fisher's PLSD was used to determine which of the groups (early,
regular, or late) differed signiflcantiy.
Chi-squared tests were used to test the hypotheses involving the
categorical variable persistence. "Chi-square is a nonparametric statistical test
that is used when the research data are in the form of frequency counts" (Borg &
Gall, 1989, p. 562). In order to use the chi-square test all expected frequencies
must be one or greater, and at least 80% of the expected frequencies must be Ave
or more. Expected values were computed to ensure these assumptions for the
chi-square tests were met.
Successful completion rates (for HIB and H2B) were computed for each
student by dividing the number of hours the student completed successfully
(grade of A, B, or C) by the total hours of enrollment for that student during the
fall of 1998. The mean of these successful completion rates was determined for
each type of student group (new and returning) for each phase of registration
(early, regular, and late). An analysis of covariance (ANCOVA) was used to
determine ii these means differed significantly after adjusting for input and
environmental variables. Similarly, withdrawal rates (for Hic and H2c) were computed for each student by dividing the number of hours for which the student received a grade of W by the total hours of enrollment for that student during the fall of 1998. The mean of these withdrawal rates was computed for
47 each type of student (new and returning) for each phase of registration (early, regular, and late) and an ANCOVA was used to determine if they differed significantiy. For returning students, Fisher's PLSD (protected least significant difference) was used to determine which groups (early, regular, and late) differed. Hypotheses involving categorical data (persistence) were analyzed using chi-square tests.
Summary
The study had three purposes. The first purpose was to determine the differences between students enrolling during the three phases of registration
(early, regular, and late) in two-year colleges. A second purpose was to suggest late registration policy and practices that might improve student success. The third purpose was to make research recommendations for further study in the area of student registrations. The methods of procedure employed by this study have been presented in this chapter. The results of the study are presented in the next chapter.
48 CHAPTER IV
FINDINGS
Introduction
The main problem of this study was to determine whether or not early,
regular, and late registration students differed from each other in terms of their
academic success. The study had three purposes. The first purpose was to
determine the differences between students enrolling during the three phases of
registration (early, regular, and late) in two-year colleges. A second purpose was
to suggest late registration practices and procedures that might improve student
success. The third purpose was to make research recommendations for further
study in the area of student registrations. This study answered two research
questions, each having four hypotheses. These questions and hypotheses
referred to variables mentioned in Astin's input-environment outcome (I-E-O)
model for assessment in higher education. The first question dealt with new
students and considered the four output variables: (a) semester grade point
average (SGPA), (b) successful completion rate (SCR) for the fall of 1998, (c) withdrawal rate (WR) during the fall semester of 1998, and (d) retention to the spring semester of 1999, adjusting for the input variable age and the environmental variable current hours taken (THRS). The second question dealt with returning students and considered the three output variables: (a) semester grade point average (SGPA), (b) successful completion rate (SCR) for the fall of
49 1998, (c) withdrawal rate (WR) during the fall semester of 1998, and (d) retention to the spring semester of 1999; adjusting for the input variable prior cumulative grade point average (CGPA) from all colleges attended, and the environmental variable current hours taken (THRS).
Sample
The population from which the sample was taken consisted of those students enrolled in on-campus credit classes at West Texas Community College for the fall semester of 1998. The college (a two-year community college) is located in a West Texas community with a population of approximately 100,000.
The total enrollment (excluding continuing education) for the fall semester of
1998 was 4,593. Of these students, 3950 were enrolled in on-campus classes and the remaining 643 were enrolled off-campus. Females made up 57.8% of the student population. The ethnic breakdown was 60.5% white, 33.3% Hispanic,
4.4% Black, 1.8% Other. The average age of students was 24.7 years.
Registration for the fall semester of 1998 was conducted in three phases: five days of early registration (April 28-30 and May 20-21), three days of regular registration (August 24-26), and eight days of late registration (August 27-
September 8). Approximately 37% (1721 students) registered early, 51% (2331 students) registered during regular registration, and 12% (537 students) registered late. Students who registered late were required to pay a fee of ten dollars. Classes began at 5:00 p.m. on August 26.
50 A systematic stratified random sampling technique was used to insure that each group (early, regular, and late registrants) of students of each type
(new and returning) was equally represented. No early registration was available for new students and only 31 new students registered late. Therefore, the new student sample consisted of 86 new students {55 regular and 31 late registrants). The new student sample was made up of 40 males and 46 females.
Of these 86 students, 45 were White, 31 were Hispanic, 8 were Black, and 2 were
American Indian. The average age of new students was 22.1 years. The returning student sample consisted of 165 students (55 from each phase of registration). The returning student sample was made up of 71 males and 94 females. Of these 165 students. 111 were White, 46 were Hispanic, 6 were Black, and 2 were Asian. The average age of returning students was 34.4 years.
Table 2 and Table 3 give demographic comparisons of the samples to the population. For each group (new and returning) the total sample was similar to the population. Differences in student characteristics in the subgroups (early, regular, and late) may have been due to student characteristics that led them to choose that registration time. Appendix C contains a partial listing of all of the data collected for each sample.
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53 Differences Between New Student Registration Groups
Four dependent (output) variables were considered for each type of student (new and returning). For new students, two of these variables, persistence and withdrawal rate, proved to differ significantiy (.05 level) for the two registration groups (regular and late). In order to answer the two research questions, analyses of covariance (ANCOVA) were performed for each of the continuous variables and chi-square tests were performed on the categorical variable.
Research Question 1: Do the output variables differ between new students according to time of registration after adjusting for input and environmental variables? Yes, two of the four output variables differed according to time of registration. Withdrawal rate and persistence proved to differ significantly (.05 level) for the two registration groups (regular and late).
In answering research question one, the following hypotheses were rejected at the .05 significance level in relationship to new students:
Hic: Withdrawal rates for the fall semester of 1998 do not differ by time of registration adjusting for age and current number of hours taken.
Hid: Persistence from the fall semester of 1998 to the spring semester of 1999 does not differ by time of registration.
Table 4 and Table 5 provide the analyses of the data related to these two hypotheses.
54 Table 4: Results of ANCOVA for New Students Withdrawal Rates by Registration Time with Age and Term Hours
Source of Sum of DF Mean F Sig of F variation Squares Square
Covariate AGE .000 1 .000 .000 .985 THRS .000 1 .000 .001 .972
Main Effects REG .445 1 .445 7.037 .010
Explained .529 3 .176 2.785 .046
Residual 5.191 82 .063
Total 5.720 85 .067
Table 5: Frequency Table Showing Persistence of Regular and Late Registration New Students
Retention Regular Late Row Totals
Retained 44 11 55
Not retained 11 20 31
Column Totals 55 31 86
%2 = 17.04, p<.001
55 The ANCOVA for withdrawal rates for new students (see Table 4), resulted in an F-value of 7.04 with a significance (p-value) of .010, indicating that
Hic should be rejected at the .05 level. At the .05 level, the covariates age and current hours were not significant (p=.985 and p=.972, respectively). The chi- square test for persistence for new students (see Table 5) resulted in a chi-square value of 17.04 with a significance of .00004, indicating that Hid should be rejected.
Therefore, it was concluded that both withdrawal rates and persistence for new students differed significantly by time of registration (regular or late).
The following hypotheses were accepted in relationship to new students:
Hi A: Semester grade point averages (GPAs) for the fall semester of 1998 do not differ by time of registration adjusting for age and current number of hours taken.
HIB: Successful completion rates for the fall semester of 1998 do not differ by time of registration adjusting for age and current number of hours taken.
The analyses of the data related to these two hypotheses are shown in Table 6 and Table 7.
The analysis of covariance (ANCOVA) for semester grade point average for new students (see Table 6) resulted in an F-value of .02 and a significance (p- value) of .88. This indicated that the hypothesis (HIA) should not be rejected at the .05 level. Therefore, it was concluded that semester grade point averages for new students for the fall semester of 1998 did not differ significantiy by time of registration adjusting for age and current number of hours taken. At the .05 level, the covariates age and current hours were not significant (p=.467 and p=.178, respectively).
56 Table 6: Results of ANCOVA for New Stiadents Term GPAs by Registration Time with Age and Term Hours
Source of Sum of DF Mean F Sig of F variation Squares Square
Covariate AGE .670 1 .670 .534 .467 THRS 2.311 1 2.311 1.843 .178
Main Effects REG .029 1 .029 .023 .879
Explained 2.906 3 .969 .772 .513
Residual 102.851 82 1.254
Total 105.757 85 1.244
Table 7: Results of ANCOVA for New Students Successful Completion Rates by Registration Time with Age and Term Hours
Source of Sum of DF Mean F Sig of F variation Squares Square
Covariate AGE .011 1 .011 .088 .767 THRS .037 1 .037 .294 .589
Main Effects REG .203 1 .203 1.613 .208
Explained .237 .079 .629 .598
Residual 10.319 82 .126
Total 10.577 85 .124
57 The analysis of covariance (ANCOVA) for successful completion rate for new students (see Table 7) resulted in an F-value of 1.61 and a significance (p- value) of .21. This indicated that the hypothesis (HIB) should not be rejected at the .05 level. Therefore, it was concluded that successful completion rates for new students for the fall semester of 1998 did not differ significantiy by time of registration adjusting for age and current number of hours taken. At the .05 level, the covariates age and current hours were not significant (p=.767 and
p=.589, respectively).
Examining the descriptive statistics for the new student data provided a
greater understanding of the significant variables. The withdrawal rate for new
regular registrants was .10, while new late registrants had a withdrawal rate of
.21. In other words, on average, each regular new student registrant withdrew from about 10% of the course hours for which he or she originally enrolled and
each late new student registrant withdrew from about 21 % of his or her course
hours. The results for persistence were even more dramatic. Of the 55 regular new student registrants, 44 (80%) were retained to the spring of 1999, while only
11 (35%) of the 31 late new student registrants were retained.
Differences Between Returning Student Registration Groups
For returning students, all four variables (persistence, semester GPA, successful completion rate, and withdrawal rate) were significantiy different (.05 level) for the three registration groups (early, regular, and late). In order to
58 answer the two research questions, analyses of covariance (ANCOVA) were performed for each of the continuous variables and chi-square tests were performed on the categorical variable.
Research Question 2. Do the output variables differ between returning students according to time of registration after adjusting for input and environmental variables? Yes, all four of the output variables differed significantly (.05 level) according to time of registration.
In answering research question two, the following hypotheses were rejected at the .05 significance level in relationship to returning students:
H2A: Semester grade point averages (GPAs) for the fall semester of 1998 do not differ by time of registration adjusting for cumulative GPA and current number of hours taken.
H2B: Successful completion rates for the fall semester of 1998 do not differ by time of registration adjusting for cumulative GPA and current number of hours taken.
H2C: Withdrawal rates for the fall semester of 1998 do not differ by time of registration adjusting for cumulative GPA and current number of hours taken.
HZD: Persistence from the fall semester of 1998 to the spring semester of 1999 does not differ by time of registration.
No hypotheses were accepted in relationship to returning students. The analyses of the data for returning students are provided in Tables 8-11.
The analysis of covariance (ANCOVA) for semester grade point average for returning students (see Table 8) resulted in an F-value of 4.61 with a significance
(p-value) of .01. A least significant difference method was used in order to determine which of the groups (early, regular, and late) differed significantiy.
59 Table 8: Results of ANCOVA for Returning Students Semester GPAs by Registration Time with Cumulative GPA and Term Hours
Source of Sum of DF Mean F Sig of F variation Squares Square
Covariate CGPA 17.202 1 17.202 19.726 .000 THRS .220 1 .220 .252 .616
Main Effects REG 8.036 2 4.018 4.608 .011
Explained 37.955 4 9.489 10.881 .000
Residual 139.525 160 .872
Total 177.480 164 1.082
Table 9: Results of ANCOVA for Returning Students Successful Completion Rates by Registration Time with Cumulative GPA and Term Hours
Source of Sum of DF Mean F Sig of F variation Squares Square
Covariate CGPA .474 1 .474 6.457 .012 THRS .051 1 .051 .699 .404
Main Effects REG .923 2 .461 6.293 .002
Explained 2.140 4 .535 7.296 .000
Residual 11.733 160 .073
Total 13.873 164 .085
60 Fisher's PLSD (protected least significant difference) values were computed using
SPSS. Significant differences were found between early and late registrants
(p<.001) and between regular and late registrants (p<.001). No significant differences were found between early and regular registrants (p=.4337).
Therefore, it was concluded that semester grade point averages (GPAs) for late
registrants differed from those of early and regular registrants adjusting for
cumulative GPA and current number of hours taken. At the .05 level, the
covariate cumulative GPA was significant (p<.001), but the covariate current
hours was not significant (p=.616).
The analysis of covariance (ANCOVA) for successful completion rate for
returning students (see Table 9) resulted in an F-value of 6.30 with a significance
(p-value) of .002. Fisher's PLSD (protected least significant difference) values
indicated significant differences between early and late registrants (p<.001), and
between regular and late registrants (p<.001). No differences were found
between early and regular registrants (p=.4447). Therefore, it was concluded that
successful completion rates for late returning registrants for the fall semester of
1998 differed from those of early and regular reUirning registrants adjusting for
cumulative GPA and current number of hours taken. At the .05 level, the
covariate cumulative GPA was significant (p=.002), but the covariate current
hours was not significant (p=.404).
The analysis of covariance (ANCOVA) for withdrawal rate for returning
students (see Table 10) resulted in an F-value of 3.18 with a significance (p-value)
61 of .04. Fisher's PLSD (protected least significant difference) values indicated signiticant differences between early and late registrants (p=.0320), and between regular and late registrants (p=.0146). No differences were found between early and regular registrants (p=.7599). Therefore, it was concluded that withdrawal rates for late returning registrants for the fall semester of 1998 differed from those of early and regular returning registrants adjusting for cumulative GPA and current number of hours taken. At the .05 level, the covariate cumulative
GPA was not significant (p=.120), but the covariate current hours was significant
(p=.019).
Table 10: Results of ANCOVA for Returning Students Withdrawal Rate by Registration Time with Age and Term Hours
Source of Sum of DF Mean F Sig of F variation Squares Square
Covariate CGPA .081 1 .081 2.447 .120 THRS .185 1 .185 5.613 .019
Main Effects REG .210 2 .105 3.175 .044
Explained .571 4 .143 4.326 .002
Residual 5.280 160 .033
Total 5.851 164 .036
62 The chi-square test (see Table 11) for persistence of returning students resulted in a chi-square value of 17.10 with a significance (p-value) of .0002.
Therefore, it was concluded that persistence of returning students for the fall semester of 1998 differed by registration time adjusting for cumulative GPA and current number of hours taken.
Table 11: Frequency Table Showing Persistence of Early, Regular, and Late Registering Returning Students
Retention Early Regular Late Row Totals
Retained 44 35 23 102
Not retained 11 20 32 63
Column Totals 55 55 55 165
X2 = 17.1, p<.001
Looking at the descriptive statistics provided at better understanding of the significance of these variables. Means for late returning registrants differed significantly from those of early and regular returning registrants for all variables. The means of the semester GPAs for early and regular returning registrants were 3.48 and 3.33, respectively, while late returning registrants earned an average GPA of 2.69. Early and regular returning registrants successfully completed 96% and 91% of their courses, respectively, while late returning registrants successfully completed only 74%. On average, the early
63 returning registrants withdrew from 5% of their courses, regular returning registrants withdrew from 4%, and late returning registrants withdrew from
13%. As with new students, the difference in persistence between the groups was dramatic. Eighty percent of early returning registrants (44 out of 55) were retain to the following semester, 64% of regular returning registrants (35 out of
55) were retained, and only 42% of late returning registrants (23 out of 55) were retained.
Summary of Major Findings
The four dependent variables considered in this study were semester grade point average, successful completion rate, withdrawal rate, and persistence. Table 12 summarizes the significant variables and their p-values.
For new students (research question one), persistence and withdrawal rate, proved to differ significantly at the .05 level. No significant differences were found in semester GPA or successful completion rate for new students based on registration time. For returning students (research question two), all four of the dependent variables differed significantly at the .05 level. Fisher's PLSD
(protected least significant difference) test indicated that the late returning registrants differed from the other two groups (early and regular). No significant differences were found between early returning and regular returning registrants.
64 Table 12: Significant Variables Found by Testing the Hypotheses
Continuous Variable F P
Semester GPA 4.608 .011 Returning Students
Successful Completion Rate Returning Students 6.293 .002
Withdrawal Rate New Students 7.037 .010 Returning Students 3.175 .044
Categorical Variable X^ P
Persistence New Students 17.04250 .00004 Returning Students 17.10084 .00019
The findings of the study have been presented in this chapter. Chapter V
consists of: (a) a summary of the study and its major findings, (b) discussion
related to the purposes of the study, (c) recommendations for further research,
and (d) conclusions.
65 CHAPTER V
SUMMARY, MAJOR FINDINGS, DISCUSSION,
RECOMMENDATIONS, AND CONCLUSIONS
Summary
Chapter V is a summary of this study on the effects of registration time on student success and persistence. In this chapter the major findings are discussed, recommendations made, and conclusions drawn.
This was a study concerning student success and retention. The Input-
Environment-Outcome (I-E-O) assessment model of Astin (1993) provided the main conceptual framework for this investigation. The main problem addressed in the study was to determine whether or not early, regular, and late registration students differ from each other in terms of their academic success.
The study had three purposes. The first purpose was to determine the differences between students enrolling during the three phases of registration
(early, regular, and late) in two-year colleges. A second purpose was to suggest late registration policy and practices that might improve student success. The third purpose was to make research recommendations for further study in the area of student registrations.
This study answered two research questions, each having four hypotheses. These questions and hypotheses refer to variable categories
66 mentioned in Astin's input-environment outcome (I-E-O) model for assessment in higher education. The research questions were:
Research Question 1. Do semester grade point average, successful completion rate, withdrawal rate, and persistence (outcome variables) differ between new students according to time of registration after adjusting for age
(input variable) and current number of hours taken (environmental variable)?
Research Question 2. Do semester grade point average, successful completion rate, withdrawal rate, and persistence (outcome variables) differ between returning students according to time of registration after adjusting for prior cumulative grade point average (input variable) and current number of hours taken (environmental variable)?
For each of these research questions hypotheses that referred to the categories of variables in the Astin (1993) I-E-O assessment model were developed and tested. These hypotheses are listed on pages 6 and 7 with the research questions.
Registration time, academic records, and demographic information were collected for a sample of students at one community college. Students were grouped according to type (new and returning) and registration time (early, regular, and late). A stratified random sampling technique was used to insure that each group (early, regular, and late registrants) of students of each type
(new and returning) was equally represented. No early registration was available for new students and only 31 new students registered late. Therefore,
67 the new student sample consisted of 86 new stiidents (55 regular and 31 late registrants). The retiirning shident sample consisted of 165 students (55 from each phase of registration). Analysis of covariance and chi-square tests were used to analyze the data.
Major Findings
A sunmiary of the signiticant variables was shown in Table 12 on page 65.
Tables 13 and 14 provide a comparison of the dependent variables by registration time for new and returning students.
The major findings were as follows. For both new and returning students, late registrants were shown to be much less likely to persist than either early (returning students only) or regular registrants. Of the new students, 80% of regular and 35% of late registrants were retained to the next semester. For returning students, 80% of early, 64% of regular, and 42% of late registrants were retained. Differences in withdrawal rates were also significant for both new and returning students. New students who registered on time (regular) withdrew from 10% of their course hours while those who registered late withdrew from
21%. For returning students, early registrants withdrew from 5% of their course hours, regular registrants withdrew from 4%, and late registrants withdrew from 13%.
68 Table 13: Comparison of New Student Dependent Variables by Registration Time
Dependent Variable Registration Time Value
Semester GPA Regular 2.53 Late 2.40
Successful Completion Regular 78% Late 68%
Withdrawal Rate Regular 10% Late 21%
Persistence Regular 80% Late 35%
Table 14: Comparison of Returning Student Dependent Variables by Registration Time
Dependent Variable Registration Time Value
Semester GPA Early 3.48 Regular 3.33 Late 2.69
Successful Early 96% Completion Rate Regular 91% Late 74%
Withdrawal Rate Early 5% Regular 4% Late 13%
80% Persistence Early Regular 64% Late 42%
69 Returning stiidents also differed significantiy in their semester grade point
average (GPA) and their successful completion rate based on their time of
registration. Early registrants earned a semester GPA of 3.48 and successfullv.
completed 96% of their course hours. Regular registrants earned a GPA of 3.33
and successfully completed 91% of their course hours. Late registrants earned a
GPA of 2.69 and successfully completed 74% of their course hours.
Discussion
The first purpose was to determine the differences between students
enrolling during the three phases of registration (early, regular, and late) in two-
year colleges. Four output (dependent) variables were considered for each type
of student (new or returning). The first two variables, semester grade point
average and successful completion rate, are measures of academic success. The remaining two variables, withdrawal rate and persistence, relate to retention.
Discussion of Academic Dependent Variables
Semester grade point average and successful completion rate (the ratio of the number of hours of A, B, or C, to the total number of hours of enrollment) are both measures of academic success. The findings of this study for the effects of registration time on these two variables were similar within new and returning student groups but not across groups. For new students significant
70 differences were not found for either variable. For returning stiidents, significant differences were found for both variables. These variables will be discussed in this section.
Semester Grade Point Average
Several studies have shown that late registrants earned lower grade point averages than did those registering on time (Chilton, 1964; Neighbors, 1996;
Parks, 1975). However, other studies (Angelo, 1990; Snell, 1996) found no significant differences in academic achievement between on time and late registrants. One possible explanation for this discrepancy is the way the studies detined late registi-ation. Chilton (1964), Neighbors (1996), and Parks (1975) all defined late registration to be enrolling for classes on or after the first day of classes. Angelo (1990) defined late registration as enrollment that took place after the close of the first week of instruction. Snell (1996) defined late registration to be enrolling after the first class day. Also, in Snell's study late registrants were allowed a "make ups week" prior to finals. This, and the fact that he was comparing students earning an A or B to those earning Cs or below rather than looking at grade point averages, could account for his findings.
This study produced only a partial answer to the effects of registration time on semester grade point average. For returning students, the analysis of covariance showed statististically significant differences (.05 level) in semester grade point average based on time of registration. Fisher's PLSD test indicated
71 that there was no signiticant difference between early and regular retiirning registi-ants. However, the differences between early and late, and regular and late returning registrants were found to be significant. The mean semester GPA was 3.48 for early registrants, 3.33 for regular registrants, and 2.69 for late registrants. Although late registrants among new students earned a slightiy lower semester GPA than did regular registi-ants (2.40 as opposed to 2.53), the analysis of covariance did not indicate that this difference was signiticant at the
.05 level.
Successful Completion Rate
No previous research was found specifically dealing with differences in successful completion rate (earning a grade of A, B, or C) between early, regular, and late registrants. However, Parks (1974) and Sova (1986) found that late registrants received more grades of F than did regular registrants. Successful completion rate is closely related to semester GPA and the findings of this study for these two variables were consistent. For returning students the analysis of covariance found statistically significant differences (.05 level) based on registration time. As with semester GPA, Fisher's PLSD indicated that the late returning registrants differed significantly from the early and regular returning registrants. Early registrants successfully completed 96% of their courses, regular registrants successfully completed 91 %, and late registrants successfully completed only 74%.
72 The findings for the successful completion rate of new students also mirrored the tindings for semester GPA. New stiidents who registered on time performed slightiy better, successfully completing 78% of their courses, than did those who registered late (68%). When the analysis of covariance was performed, this difference was not found to be statistically significant.
Explanation of Findings for Academic Dependent Variables
The findings for the effects of registration time on semester grade point average and successful completion rate were similar. This was not surprising as both are measures of academic success. However, the discrepancy in the findings for new and returning students with respect to these variables was somewhat unexpected. This study found significant differences in both semester grade point average and successful completion rate based on registration time for returning students but not for new students. It is interesting to note that, on average, the new students who registered on time had lower grade point averages than did the late returning registrants. When compared to returning students, both groups (regular and late) of new students performed poorly in terms of grade point average. This could be partially due to the fact that many students with low grade point averages dropped out of school. This "weeding out" process may have led to higher grade point averages for those students who persist (returning students). The fact that the poorly performing students had not yet been "weeded out" may have resulted in more homogeneity in the
73 two groups (early and late) of new stiidents. This could account for the discrepancies in the findings for the new and retiirning stiident groups in regard to both semester grade point average and successful completion rate. Another explanation for the discrepancies could be the small number of new stiidents who registered late, a possible limitation of the stiidy.
Discussion of Retention Dependent Variables
Withdrawal rate (the ratio of the number of hours of W to the total number of hours of enrollment) and persistence are both measures of retention.
Withdrawal rate refers to retention within the semester and persistence refers to retention from one semester to the next. The findings of this stiidy for these two variables were consistent and will be discussed in this section.
Withdrawal Rate
This study found statistically significant differences (based on ANCOVAs at the .05 level) in the withdrawal rates for both new and returning students based on registration time. New students who registered on time (regular) withdrew from 10% of their courses while those who registered late withdrew from 21%. For returning students, early registrants withdrew from 5% of their courses, regular registrants withdrew from 4%, and late registrants withdrew from 13%. For returning students, Fisher's PLSD indicated that the late registrants differed significantly from the early and regular registrants. These
74 tindings were consistent with previous stiidies (Parks, 1974; Sova, 1986) tiiat found late registrants were more likely to withdraw from classes than were regular registi-ants. However, Neighbors (1996) found no significant differences in withdrawal rates for early, regular, and late registrants.
Persistence
Previous research findings concerning registration time and persistence was conflicting. Stein (1984) found that retention to the next semester for new late registrants was about half that of the total student body. Conversely,
Angelo (1990) found no significant differences in persistence between regular and late registrants. However, this discrepancy may be due to the fact that
Angelo (1990) defined late registrants to be those students who enrolled after the first week of classes. Also, Angelo (1990) defined persistence to be completing the course (within semester persistence) rather than persistence to the next semester. This study supports Stein's findings. For both new and returning students, chi-square tests conducted at the .05 level indicated that late registrants were significantly less likely to persist into the next semester than either early
(returning students only) or regular registrants. Of the new students, 80% of regular and 35% of late registrants were retained to the next semester. For returning students, 80% of early, 64% of regular, and 42% of late registrants were retained.
75 Explanation of Findings for Retention Variables
The findings for the effects of registration time on withdrawal rates and
persistence were similar. This study found significant differences in both
withdrawal rate and persistence based on registration time for new and
returning students. These findings indicated that withdrawal rates were
significantly higher and persistence was significantiy lower for both new and
returning students who registered late. "The tirst days of any course are the
most important learning experiences that a student will have" (Roueche &
Roueche, 1994b, p. 7). It is possible that these late registirants were never able to
catch up after missing one or more class days at the beginning of the semester
and they withdrew from the class or from college in order to avoid low or failing
grades.
There are several other possible explanations for these tindings. Late
registi-ants have fewer choices for classes and meeting times due to section
closings. Some late registi-ants may have higher absentee rates due to having to
sign up for early morning or night classes that were not their first choice. It
could be that the same character traits or personal reasons that caused the late
registrants to put off registering for classes also caused them to put off doing
their coursework until they were unable to catch up. It is also possible that late
registrants were less committed to their educations than were early and regular
registrants.
76 Recommendations for Future Policv and Practice
The second purpose of the study was to suggest late registration policy and practices that might improve student success. Based on the tindings of this study, it would seem that late registration is a deterrent to students' academic success and retention. For new and returning late registrants, retention was significantiy lower both within the semester (withdrawal rates) and from one semester to the next (persistence). Returning late registrants also had significantly lower academic success (measured by semester GPA and successful completion rate) than did early or regular returning registrants. New late registrants did not have significantly lower academic success than new regular registrants did. However, the average academic performance of all new students was lower than that of returning students.
This study found no significant differences between early and regular returning registrants in relation to any of the four dependent variables. This would seem to indicate that early registration provides no advantage over regular registration in terms of student success and retention. However, early registration can be of great benefit to students in other areas such as the scheduling of classes.
These findings would seem to indicate that early and regular registi-ation should be continued and perhaps expanded, and late registration should be eliminated. However, further research is needed in order to determine whether some of the differences in outcome variables could be atti-ibuted to confounding
77 variables other than the covariates used in this study. Other confounding variables might include major, type of courses taken (developmental vs. college level and academic vs. vocational), number of years since last attending school
(high school or college), socio-economic level, ethnicity, number of hours worked per week, level of self-confidence, and motivation. Also, discontinuing late registration may not be feasible for many community colleges. There are two rationales for late registration in community colleges. The first is the underlying philosophy of ease of access as demonstrated by the open door policy. The second stems from the fact that institutional state funding is based in part on enrollment (Angelo, 1990). Any policy that increases the number of students enrolled is of financial benefit to the college. However, accountability issues require that students are not only enrolled but that they also succeed and persist. Therefore, the following recommendations for policy and practices are presented:
1. Students should be encouraged to register during the early and regular enrollment periods. These registration times should be well advertised. Faculty should inform their students of early registration times. Admissions officers should make a special effort to inform high school counselors of the hazards of late registration.
2. The college should provide easy access to early and regular registration. This might be accomplished by instituting internet and touch-tone telephone registration.
78 3. Students on academic probation should be strongly discouraged from registering late. For those who do register late, special tutoring sessions should be required to assist them in catching up.
4. Flexible pay schedules for tuition and fees should be available for students who register on time. Some students may be putting off registration
until payday in order to be able to make the initial payment or buy books.
Allowing payment deferral until the 12* class day might encourage these
students to register earlier.
5. Late registrants should be required to participate in group counseling
sessions beginning the week after late registration concludes. Topics covered
could include time management skills, organizational skills, productive study
habits, and test taking skills.
These recommendations for policy and practices include incentives to
register on time and assistance for late registrants. Implementation of these
recommendations may help community colleges reduce possible negative effects
of late registration on student success. The following section contains
recommendations for further research pertaining to student success and time of
registration.
Recommendations for Further Research
All research leads to new questions and this study is no exception. The
following recommendations are made for future research.
79 1. In order to help identify solutions to the problems caused by late registration, qualitative and quantitative studies should be conducted to determine the reasons that students register late.
2. Qualitative and quantitative studies should be conducted to discover the reasons that some students consistentiy register early or on time. Methods to entice late registrants to register on time might be generated from these studies.
3. Conduct studies to determine whether advertising and incentive plans are effective deterrents to late registration.
4. This study did not distinguish between the type of courses taken.
Studies should be conducted to determine the effects of late registration on student success in various subject areas (for example, hard sciences such as mathematics, physics, and chemistry compared to other academic subjects such as English, history and the social sciences).
5. Studies should be conducted to determine whether other confounding variables such as major, type of courses taken (developmental vs. college level and academic vs. vocational), number of years since last attending school (high school or college), socio-economic level, ethnicity, number of hours worked per week, level of self-confidence, and motivation might account for some of the differences in student success and retention that were found in this study.
80 6. This study considered academic success variables for one semester and persistence only to the next semester. Longitiidinal stiidies should be conducted that tiracks students from each phase of registi-ation over a longer period of time.
7. Studies should be conducted to determine whether support services such as aid in the development of productive study habits and organizational skills would help to offset the problems caused by late registration.
8. Studies should be conducted to examine the financial consequences of eliminating late registration.
Conclusions
Student success and retention are major areas of concern for all colleges.
The Open Door policy and large numbers of under-prepared students magnify these problems for community colleges. The findings of this study contribute to the knowledge base concerning the possible impact of registration time on community college students' success and persistence.
The study found that registration time significantly affected students in terms of academic success and retention and led to the following conclusions:
1. Late registration practices seem to be detrimental to students in terms of academic success.
2. Late registration practices seem to hinder retention of students.
81 3. Registration conducted prior to the first day of class (both early and regular) seems to be a sound avenue for enrollment of students in terms of academic success and retention.
4. More research is needed in order to determine to what extent the lower academic achievement and retention of late registrants can be attributed to registration time rather than other possible confounding variables.
Colleges should provide every opportunity for students to succeed. The practice of late registration provides the initial access for students to participate in higher education but may reduce their chances for academic success and continued participation. Steps such as the policy and practices recommendations made in this study should be taken to reduce the possible negative impact of late registration.
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87 APPENDIX A: REQUEST FOR STUDENT DATA
88 January 25,1999
Dr. Vice President of Academic Affairs
Dear Dr.
As we discussed last semester, I am conducting a doctoral study concerning the effects of registration time on student success and retention. I would like to take this opportunity to thank you for your input on the study and for vour permission to collect data on students at College.
I will need data on 50 students randomly selected from each phase of registration (early, regular, and late) for the fall semester of 1998. Students will be identified by social security number only. The data will include gender, age, ethnicity, amount of financial aid received, number of hours taken during the fall of 1998, number of hours prior to the fall of 1998, semester GPA, and cumulative GPA. I will also need to collect data concerning the number of courses successfully completed (grade of A, B, or C) and the number of courses dropped during the fall of 1998. Spring 1999 registration records will also be needed in order to determine retention status.
As you requested, I am also sending a written request for this data to the registrar's office.
Thank you for your cooperation and assistance in this endeavor.
Sincerely,
Margaret Street
89 Registrar
February 9, 1999
Ms. Margaret Street
Dear Ms. Street:
The Registrar's Office will be happy to cooperate with you in your efforts to complete your doctor study.
You might want to visit with Dr. Director of Institutional Effectiveness to see if he might be able to come up with a computer program to identify students who registered during early, regular and late registration. Tliere is nothing we have here that would give us a breakdown.
In fac-,, he or someone in the Computer Center should be able to write you a program that will pull the information you are seeing . .. gender, age, ethnicity, financialai d received, fall semester hours, previous hours completed, fall GPA and a cumulative GP/^ We use the XOAZ transcript for advising purposes and it contains the GPA information as well as Spring Registration data. Once you identify your students, we would be happy to run you copies of these transcripts?
Sincerely,
Registrar
90 APPENDIX B: CODING OF DATA
91 Table 15: Coded Values for Categorical Variables
Variable Values
Retention 1 = retained 2 = not retained
Registration Period 0 = early 1 = regular 2 = late
Type of Student 1 = new 2 = returning
Gender 1 = male 2 = female
Race 1 = White 2 = Black 3 = Hispanic 4 = Other
92 APPENDIX C: PARTIAL LIST OF DATA
93 WD R 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 1.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.25 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 0 SC R 1.00 0 1.00 0 1.00 0 0.62 5 1.00 0 1.00 0 0.12 5 0.40 0 0.00 0 1.00 0 0.50 0 1.00 0 0.29 4 1.00 0 1 0.70 0 0.00 0 1.00 0 0.76 9 0.00 0 1.00 0 0.50 0 1.00 0 0.76 9 0.09 1 0.92 3 0.36 4
tN OS ^ o 00 ON ON Os OS rH CO rH o o o OS in 00 ON tN OS 00 r-t 00 OS AG E CM rH rH 00 rH CM rH rH CM CM CM CM CM rH rH rH rH r-t
rH CM rH CM T-t 1-^ rH CM CM CM r-t r^ RE T CM 1-^ rH r-* rH r-t CM rH r-t CS rH r-* f—t
^ O O O o o o O o o O o o O o O O O o vO O O o O O o
ON O O CM in in rH CO ^^ -"t o tN ON in O ts. in CN O in in O r^ CS CM AB C rH rH rH Tf rH rH
NO o CM in ON m 00 o CO O in CO rH rH ON CN rH "* in in CO CO CM CO rH rH rH rH CM rH r-t THR S rH rH 3.11 1 3.00 0 3.80 0 2.50 0 2.93 3 1.90 0 0.57 1 0.00 0 1.78 6 TGP A 1.14 3 2.33 3 1.90 0 1.00 0
CO 3.20 0 2.07 7 3.60 0 3.50 0 3.46 7 1.84 6 1.50 0 0.50 0 2.76 9 3.25 0 to . 2.00 0 Q 4-) c CO CM CO CO rH CO f^ CO rH CO CM rH RAC E cu 5 r^ rH rH rH T-H CM CM rH r—t rH rH CM CM c^ r^ CM rH CM cs r-t CM 1-^ CM CM rH z SE X
CO N/ R
••B RE G sb* r-t CO Os ON ON CO ON Os CO in in CO 1 1 CN 1 ON in CN 1 1 OS Ov CN in in CN CO CO in CN 3-39-8 1 1-55-0 9 1 CO 5-59-1 7 5-67-0 7 1-49-6 9 4-77-1 0 7-69-4 4 7-77-37 0-98-2 9 1-91-5 4 1-65-2 9 1 SS N 9-78-4 8 f2 6 lA 5-37-1 5 5 4 6 1
94 C6 o o 8 r-t o o tN o CS o o 8 d o o O O o o o d d d o o o o o o o o U o 8 CN 2 o o o cn 8 8 o 8 m 8 8 8 o
CO ON C3N CO CO ^ CS ^ ^ CO ^ CN OS Os rH PsI ^ 5 ^ ^ CM CO Tj* CO 5 P^ CO in ^ Pi in
CM CM oe; cs
CO NO CO o o
u CO CO CO 00 OS NO CO CM CQ NO CJs NO 00 SO SO ON CO NO CO NO ON so CO < cn CO CO CO 00 ON CO CM so NO ON NO SO so ON CO NO CO NO ON so CO
o o o o o o o o o o o 2 o Cs| o o o o 8 8 in o o o o OS in CM o CO 8 CO 8 CO CO S CO csi CO CO CO CO CO CO CO
in cs cs in r-t CO CO in 55 in o 00- in CO OS cs as sa 00- so- ^ P:! iS $ CM NO Sss. tN ON o QC3 4-) cs ON ro o CO- in. in^ CS in uv co^ in- IN NO c 2 CM CM 00 OS cs CO 8 ^ o CS CM o CO tN NO ON NO so ON in CN NO cu CO CO CO in * CM CO CO CO CO rH CS CO CO cs CO CO CO 8 CNl cs cs CO CO CO CO CO 2 CM CD U to 2 2 X cu Uil d cq CM CM CS cq OJ cn cs cq cs cs CS CS CS CS CM CS
CA CM CM CM CM CM CM CM Z cs c^ CM CM CM cs cs cs CM CM cs cs CM CM CM CM CM CM
PH u
(U ^ cT TFT FT cT TFT CS TNT CN i—t CO CM OS in in in m"s ^ CO CN ^ I I ^ CO 2 9 I I I op cn r^ CO tN CO rH I CO CM CN so ^ s CM CM cn rH CO i 8 I ^ uS IPS ON m i o I I 6 lA A OS OS H OS 0\ 6 I 1 CM
95