THE RELATIONSHIP BETWEEN REGISTRATION TIME AND …

APPROVED: V. Barbara Bush, Major Professor Beverly Bower, Committee Member Pu-Shih Daniel Chen, Committee Member Christy Crutsinger, Committee Member Janice Holden, Chair of the Department of

Counseling and Higher Education Jerry Thomas, Dean of College of

Education Mark Wardell, Dean of the Toulouse

Graduate School

THE RELATIONSHIP BETWEEN REGISTRATION TIME AND MAJOR STATUS AND

ACADEMIC PERFORMANCE AND RETENTION OF FIRST-TIME-IN-COLLEGE

UNDERGRADUATE STUDENTS AT A FOUR-YEAR, PUBLIC UNIVERSITY

Marian Ford Smith

Dissertation Prepared for the Degree of

DOCTOR OF EDUCATION

UNIVERSITY OF NORTH TEXAS

August 2014

Smith, Marian Ford. The Relationship Between Registration Time and Major

Status and Academic Performance and Retention of First-Time-In-College

Undergraduate Students at a Four-Year, Public University. Doctor of Education (Higher

Education), August 2014, 97 pp., 16 tables, references, 76 titles.

This quantitative study utilized secondary data from one large four-year, state

university in the southwestern US. The relationship between registration time and

academic performance was examined as well as the relationship between registration

time and retention of first-time-in-college (FTIC) undergraduate students during their

first semester of enrollment at the university. The differences between decided and

undecided students were tested regarding students’ academic performance and

retention of the same population. The study population for the fall 2011 semester

included 6,739 freshmen, and the study population for the fall 2012 semester included

4,454 freshmen. Through multiple and logistic regression models, registration time was

shown to statistically have a relationship with academic performance and retention (p <

.05). Later registrants showed to have a negative relationship with GPA and were less

likely to return the following spring semester. The explained variance (R2) for both

measures of academic performance and retention along with descriptive statistics are

also presented. A Mann Whitney U test and chi square test indicated that a statistically

significant association between decided and undecided students exists for academic

performance and retention (p < .05). Decided major students performed better as

measured by semester GPA performance and were more likely to return the following

spring semester. Recommendations and implications are issued regarding future

research, policy, and practice.

Copyright 2014

by

Marian Ford Smith

ii

iii

ACKNOWLEDGEMENTS

There have been countless individuals in my life that have contributed to me

achieving this lifelong academic goal. - I’m remorseful at not being able to recognize

them all in print. My hope is that they know how they have supported me through

encouragement, understanding and prayer. I will be forever grateful for the wonderful

family, friends, colleagues and mentors the Lord has blessed me with. I wish to

acknowledge my mentor and boss Dr. Christy Crutsinger and my major professor Dr. V.

Barbara Bush especially for guiding me through this entire process by providing advice,

support and most importantly not allowing me to quit when the going got tough. I wish

to thank my mother, Cheryl Warrick Ford for all the years of proofreading my countless

papers and, most importantly, by believing in me; I never knew unconditional love like

she has shown me until I became a mother myself last year. When I began this

program I had a different last name and still wish to include that name on this degree

because the first Dr. Ford I knew was the reason I started this journey and strived to

achieve the same degree that he had. I’ve looked up to my dad my whole life and I

completed this degree because of the example of academic excellence and career

attainment he set. The number one person I could never have completed this journey

without however is my husband Dan Smith. He gave me the gift of my new last name

and the most perfect little girl a mother could ever hope and pray for, Rebecca Lee

Smith. He allowed me to focus on my degree completion and career change with

unconditional support, love, and his unwavering belief in my ability to see this through to

the end. I will be forever grateful to him for everything he has sacrificed for our family

and for this chapter in my life to be completed.

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TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS ............................................................................................... iii LIST OF TABLES ........................................................................................................... vii CHAPTER 1. INTRODUCTION ...................................................................................... 1

Problem Statement ............................................................................................... 5

Purpose of Study .................................................................................................. 6

Research Questions ............................................................................................. 6

Significance of the Study ...................................................................................... 6

Definition of Key Terms ........................................................................................ 7 CHAPTER 2. LITERATURE REVIEW .......................................................................... 10

Enrollment and Admission Procedures ............................................................... 10

Freshman Students ............................................................................................ 14

Orientation Practices and Procedures ................................................................ 15

Academic Performance and Registration Timing ................................................ 18

Retention ............................................................................................................ 20

Students with Undecided Major Status ............................................................... 26

Summary ............................................................................................................ 29

Conceptual Framework ...................................................................................... 31 CHAPTER 3. METHODOLOGY ................................................................................... 34

Overview ............................................................................................................ 34

Research Design ................................................................................................ 35

Site Selection and Population of Study ............................................................... 35

Sample and Participants ..................................................................................... 37

Instrumentation ................................................................................................... 39

Data Collection and Analysis Procedures ........................................................... 39

Limitations .......................................................................................................... 42

Delimitations and Assumptions ........................................................................... 43 CHAPTER 4. RESULTS ............................................................................................... 44

Overview ............................................................................................................ 44

v

Data on Institution Included in Study .................................................................. 44

Results for Research Questions ......................................................................... 45

Results for Research Question 1: Relationship between Registration Time and Academic Performance ..................................................................... 45

Results for Research Question 2: Relationship between Registration Time and Retention .......................................................................................... 53

Results for Research Question 3: Differences between Decided and Undecided Students on Academic Performance ..................................... 60

Results for Research Question 4: Differences between Decided and Undecided Students on Retention ........................................................... 62

CHAPTER 5. SUMMARY, DISCUSSION AND CONCLUSIONS ................................. 65

Overview ............................................................................................................ 65

Discussion .......................................................................................................... 65

Discussion for Research Question 1: Relationship between Registration Time and Academic Performance ............................................................ 66

Discussion for Research Question 2: Relationship between Registration Time and Retention ................................................................................. 68

Discussion for Research Question 3: Differences between Decided and Undecided Students on Academic Performance ..................................... 71

Discussion for Research Question 4: Differences between Decided and Undecided Students on Retention ........................................................... 72

Summary Discussion .......................................................................................... 73

Implications for Policy ......................................................................................... 74

Required Major Status ............................................................................. 74

Mandatory Orientation Sessions and Students Excluded from the Study 76

Implications for Practice ..................................................................................... 78

Academic Advising .................................................................................. 78

Freshman Preparation Courses ............................................................... 80

Student Services Related to Registration ................................................ 81

Implications for Future Research ........................................................................ 82

Returning Students .................................................................................. 82

Transfer Students .................................................................................... 82

Qualitative Study ...................................................................................... 84

Closing ............................................................................................................... 85

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APPENDIX A. ORIENTATION SESSIONS .................................................................. 87 APPENDIX B. DATA FILE FIELDS .............................................................................. 89 REFERENCES .............................................................................................................. 92

vii

LIST OF TABLES

Page

1. FYU All Student Demographics ............................................................................... 36

2. FYU First-time in College Student Demographics.................................................... 38

3. Methodology Variables ............................................................................................ 41

4. Descriptive Statistics Question 1 Fall 2011 .............................................................. 46


6. Multiple Regression Statistical Significance Question 1 Fall 2011 ........................... 49

7. Multiple Regression Statistical Significance Question 1 Fall 2012 ........................... 50



10. Logistic Regression Model Summary Question 2 Fall 2011 .................................... 57

11. Logistic Regression Model Summary Question 2 Fall 2012 .................................... 58

12. Descriptive Statistics Question 3............................................................................. 61

13. Mann Whitney U test Results Question 3 ............................................................... 61

14. Cross tabulation Question 4 Fall 2011 .................................................................... 63

15. Cross tabulation Question 4 Fall 2012 .................................................................... 63

16. Chi-Square Test Results Question 4 ....................................................................... 64

1

CHAPTER 1

INTRODUCTION

Accountability with regard to student retention has become increasingly important

as shown by the fact that universities are examining more closely the matriculation,

retention and graduation of students. Universities are responding to the 2013 statistic

showing that nearly 46% of students who enroll in an institution of higher learning do not

graduate with a degree within six years of enrollment (HCM Strategists, 2013). In 2007,

the average student retention rate among all U.S. institutions of higher education from

the first year of enrollment to the second year of enrollment was 68.7% (Jamelske,

2009). According to the American College Testing (ACT) report (2013), there has been

little progress or growth in higher education in first to second-year retention rates and

graduation (or persistence to degree) rates, but instead, there has been a slight decline.

In 2013, the national first to second year retention rate was 64.9% for students

attending a public institution to attain a BS (bachelor of science) degree or BA (bachelor

of arts) degree, and 65.8% for all institution types. This data does not reflect, however,

an increase in retention rates from the year 2000 when they were 31.8% and 32.7%

respectively. Graduation rates have not shown much change in the past decade. In

2000, ACT reported the graduation rate as 41.6% for students attending a public

institution and attaining a BS or BA degree and 45.4% for all institution types.

According to the ACT report (2013), the graduation rate has actually fallen to 36% for

students attending a public institution and attaining a BS or BA degree and 43.3% for all

institution types in 2013. Student retention and graduation are two major areas of

concern because factors that contribute to student retention can affect every aspect of

2

higher education, resulting in financial loss for institutions and for individual students as

well. This is perhaps why student retention is so widely researched in higher education

(Tinto, 2006). Recruiting students is very expensive for institutions. Therefore,

programs have been implemented for the retention and benefit of the qualified students

(Lau, 2003).

From the student perspective, dropping out of college limits career options and

earning potential over the student’s lifetime. The median U.S. weekly salary in 2012 for

an individual with only a high school diploma was $647, while an individual with a

bachelor’s degree earned a weekly salary of $1193 (Bureau of Labor Statistics, 2012).

Also, according to U.S. Census data from 2012, individuals who earn a bachelor’s

degree earn, on average, about one-third more than individuals who do not finish

college and twice as much as individuals who possess only a high school diploma.

Universities have evaluated various activities designed to improve student

retention rates. These activities may include recruitment and admission strategies, new

student orientation, academic advising and support, learning communities, and first-year

seminars (Hunter, 2006). The National Resource Center for the First Year Experience

will host its 33rd annual conference in early 2014, which shows the demand and

attention surrounding first-year programs and the need for best practices that aid in

student persistence from the first to second year (National Resource Center, 2013).

Freshmen students are different from their upperclassman peers who are not

new to the university because they are enrolling and matriculating into the university for

the first-time. Researchers such as Hornik et al. (2008) found that class and school

withdrawals can be more common with freshmen than upperclassmen, and Elkins,

3

Braxton, and James (2000) reported that 17% of students leave during their first

semester of college. Burgette and Magun- Jackson (2008) found that a relationship

among long-term persistence does exist with GPA and freshman orientation courses

because they are created to help students new to the university become familiar with

campus resources and transition into the university successfully. These factors can be

the strongest predictors of student retention and graduation for freshman students

enrolling at a university for the first time (Wang, 2009).

Students who matriculate into the university without a chosen major of study are

often referred to as undecided students, and literature that examines undecided student

persistence is sparse. There are contradictory findings in literature regarding whether

or not undecided students reach higher levels of academic achievement and are more

likely to persist to graduation than students who have decided their major of study.

Older studies such as Astin (1975) and Noel, Levitz, and Richter (1999) referred to

undecided students as “at risk” for attrition. Leppel (2001) found that differences in

persistence rates can be explained by the subject areas chosen by students. Studies

such as John et al. (2004) investigated the influence a college major field can have on

persistence for specific groups of students, but major field was not shown to be

statistically significant for all groups because only one ethnicity was studied. Graunke

and Woosley (2005) found that commitment to choosing a major was a positive

predictor of GPA and, therefore, attaining academic achievement. Leppel (2001) found

that students who have chosen a major that is specific for their future career have

higher retention rates than those students who have not yet decided on their future

career and, therefore, have not decided on a major of study.

4

The matriculation processes at universities include registration. Studies

conducted at the community college and university levels explore the relationship the

timing of registration can have on student academic performance. Smith, Street, and

Olivarez (2002) found that time of registration significantly affected students’ academic

success and retention, but this study was conducted at a community college. Wang

and Pilarzyk (2007) found that students who applied late to a program were less

prepared for the academic term and their academic success was negatively impacted.

Freer-Weiss (2004) showed that students applying late and, therefore, registering later

than other students who applied and registered early or on time may be at a

disadvantage before they even begin their semester coursework.

Nationally, only 54% of students who enroll in a university graduate with their

degree within six years of enrollment (including transferring to multiple institutions), and

the first to second-year retention rate is approximately 65% for students attending a

public institution. However, the institution of this study, Four-Year University (FYU)

reports graduation rates higher than the national average. The 2005 cohort (first-time,

full-time freshmen) graduated 50.1% of its students in six years from FYU with 8.6%

graduating in six years from other state public institutions, for a total graduation rate of

58.7% (FYU 2012-2013 Fact Book). Graduation rates are important because they

reflect a student’s successful academic performance and retention throughout their

tenure at the institution. Additionally, FYU’s retention rate for first-time in college new

from high school population enrolled fall 2011 and returning fall 2012 was 75.8%, and

enrolled fall 2010 and returning fall 2011 was 78.5%, both well over the national

average. FYU is unique because it is a large, public institution with higher than national

5

average graduation and retention rates and can be viewed as a leading institution in

these areas.

Problem Statement

FYU’s freshman (first-time in college) population is approximately 15.4% of its

total undergraduate population according to the FYU 2012-2013 fact book. For the fall

2011 and fall 2012 approximately, 24% of FYU’s freshman student population is listed

as having an undecided major. Research and current trends in higher education have

shown that students with undecided majors have lower retention rates and lower

academic performance than do students with decided majors. FYU has been

concerned with maintaining and increasing its graduation rate as well as its first to

second-year retention rates. Because of this large, student population with undecided

majors, FYU may soon experience lower retention rates and lower levels of academic

performance, based on national trends.

FYU schedules a mandatory orientation session for all new students, freshman

and transfer, admitted to the university. The orientation sessions are offered at various

set times (shown in Appendix A), and during these sessions, students register for the

upcoming semester. In the rare instances that a freshman student cannot attend one of

the designated orientation sessions specifically for freshmen, they may have attended a

transfer orientation in order to register for the upcoming semester. At FYU, registration

is a key part of students’ introduction to the university. Enrollment management

professionals hold timely registration and orientation as keys to the academic success

and retention of new students (Smith, 2001). However, FYU has not conducted an

6

analysis of how national student enrollment trends may relate to current practice in new

student matriculation.

Purpose of Study

This study has two purposes. The first purpose is to examine the relationship

between registration time and academic performance and retention of first-time in

college (FTIC) undergraduate students during their first semester of enrollment at a

comprehensive, four-year state research university. The second purpose is to examine

the differences between major status with regard to academic performance and

retention of FTIC undergraduate students during their first semester of enrollment at the

university.

Research Questions

The following research questions are addressed in this study.

1. What is the relationship between registration time and academic performance of FTIC (first-time in college) students after controlling for high school academic achievement, gender, ethnicity and SES factors?

2. What is the relationship between registration time and retention of FTIC students after controlling for high school academic achievement, gender, ethnicity and SES factors?

3. Are there differences between decided and undecided FTIC students with regard to academic performance?

4. Are there differences between decided and undecided FTIC students with regard to retention?

Significance of the Study

In order to retain students and contribute to strong student academic

performance, universities need to offer assistance and programming that meets the

needs of students regarding the timing of registration and other enrollment processes.

Studies have been conducted that evaluate the relationship between registration time

7

and academic performance and retention but mostly at community colleges level (such

as Smith’s et al. 2002 study). There are limited studies of four-year universities that can

be used as models for investigating registration times as well the relationship between

registration time and academic performance and the comparison of undecided majors

vs. decided majors at different colleges. Student academic performance is linked to

retention and graduation rates as shown by Smith et al. (2002) Mendiola- Perez (2004)

and Neighbors (1996). Evaluating those factors that show an existing relationship

between student academic performance and course completion is vital to the mission of

a university. It is also important to understand that services can assist students when

choosing programs of study, courses and majors to help reduce the dropout rates and

elevate success rates.

Definition of Key Terms

The terminology used in this study is defined below:

Academic Performance

Current GPA is used to measure academic performance.The semester

credit hours (SCH) completed percentage is also be used to measure

academic performance.

Current Grade Point Average (GPA)

The weighted mean value of all grade points the student earned by

enrollment for semester.

First-time in College (FTIC)

Students who are enrolled for the first-time in college: For the purposes of

this study it is the same as freshman students

8

High School Achievement

For purposes of this study, high school achievement is measured by the

variable SAT score.

Major Status

Students who have not decided on a major or chosen a field of study at

the initial time of registration have a major status of undecided, all other

students are decided majors.

Registration Time

The time when students first enroll at the university; this takes place

during the student’s designated orientation session

Retention

Two different ways were used as a measure or retention. One way used

to measure retention was whether the student completed the first current

semester of enrollment, and the second way used to measure retention

was whether the student returned for enrollment the following academic

semester.

SCH Completed

Semester credit hours completed

SCH Ratio

Semester credit hours taken/semester credit hours completed

Socioeconomic (SES) factors

The following two variables were used to measure SES factors for this

study:

9

First generation students: students whose parents never graduated

from college.

Pell grant eligibility: The Pell Grant is a need-based grant for

students enrolled in a post-secondary institution. Eligible Pell Grant

applicants are usually considered low-income or face financial

hardship.

10

CHAPTER 2

LITERATURE REVIEW

This chapter examines the literature related to institutional enrollment,

orientation, and registration practices, and the relationships these practices can have on

a student’s academic performance and retention. Student characteristics that are

covered include classification (i.e., FTIC) and major status (i.e., decided and

undecided). Finally, the conceptual framework is presented.

An unstable economy in the past few years, as well as questions arising about

the value of an education, has resulted in added pressure on the recruitment and

enrollment of students at institutions of higher education (Newman, 2013). A survey of

436 private colleges and state universities found that nearly half of the respondents had

missed their enrollment or net-tuition revenue goals for the past year (Newman, 2013).

It is becoming a reality that in today’s society, the more education a person receives,

the greater are chances of retaining employment and earning more money, thereby

increasing the importance of higher education and obtaining a college degree (Duggan

& Pickering, 2007). A college degree has now become a standard that is linked to long-

term benefits (financial, social and cognitive) and enhances the quality of life of all those

that attain one (Kuh et al., 2008).

Enrollment and Admission Procedures

The goals of most universities are to enroll, retain, educate and graduate their

students. These goals can be unrealized, however, if enrollment management practices

and decisions are not informed by detailed enrollment data that addresses how the

timing of student decisions affects their academic performance, academic success and

11

retention through graduation (Wang & Pilarzyk, 2007). Older studies from Levitz and

Noel (1990) support the belief that student experience during the first year, perhaps

even the first six weeks at the institution, is vital for student retention and persistence to

graduation.

Enrollment management professionals are particularly concerned that late-

applying students are at a greater risk of not succeeding academically. When setting

deadlines and timelines for admissions and registration enrollment, administrators look

at various internal and external factors that include the fiscal implications of losing

enrolled students as well as examining their academic performance (Wang & Pilarzyk,

2007). In Wang’s and Pilarzyk’s study, the relationships between program application,

financial aid applications and awards, and registration time were evaluated. The results

showed that the earlier students apply to a program and apply for financial aid, the

earlier students will register for classes. This relationship illustrates that factors other

than administrator processes can affect registration time for students and needs to be

considered when predicting student success based on academic performance.

Additionally, they found the later students apply and register, the less likely they are to

complete their coursework, and GPAs were lower than those who apply and register

earlier. In the study, a term GPA of 2.0 and credit completion rate of 67% were the

standards used to determine good academic standing. Students who delayed their

registration, therefore, may be less prepared for the academic semester.

Wang and Pilarzyk (2007) also found that students who applied late to a

program were more often older, part time, requiring financial aid, and fitting the profile of

an at-risk student. Students who applied late to their programs also applied late for

12

financial aid and registered late, thereby making themselves less prepared for the

academic term and threatening their academic success. Wang and Pilarzyk determined

that to help eliminate barriers for the students regarding lack of financial assistance and

course preparedness a firm application deadline was warranted.

Open door policies adopted by colleges are those that allow any student with a

high school diploma or GED certificate to enroll and attend. An open admissions policy,

therefore, gives any student who has completed a high school diploma the opportunity

to pursue a college degree, regardless of their previous academic background or ability

(Bailey et al. 2005). Like much of the research that exists on how registration time

affects academic success and retention, the Wang and Pilarzyk (2007) study was

conducted at a two-year community college where the open door admission policy

allows for leaner deadlines and can contribute to later registration times. Most of the

research on late registration timing and its relationship with academic performance has

been done at two-year community colleges, therefore, gaps in the literature allows for

studies to be done at larger four-year institutions .

Institutional factors can contribute to the success of students and research that

affects student outcomes is necessary, but those studies done at the community college

level can only offer examples of research and provide a template for future studies; but

the models used in those studies are inappropriate for the analysis of four-year

institutions. Community colleges have different operating and fiscal structures than

four-year counterparts (Bailey et al., 2005).

Institutions have integrated many aspects of the enrollment management model

into their retention programs because they recognize that if students’ needs are

13

addressed, then it is more likely a student will remain at the institution (Smith, 2001).

Smith conducted a survey of 500 enrollment managers at two-year and four -year

undergraduate institutions, both private and public. The study examined factors related

to the institutions’ enrollment performance. Smith found that the more integrative

strategies institutions have among departments that serve students and the more

support activities that improve integration, the larger the institutional enrollment

performance. Pascarella and Terenzini (1998) concluded that multiple forces influence

student learning and student retention; some of these include student precollege

characteristics and experiences, the college experience through organizational context,

and their peer environment. Organizations that function systematically can positively

influence student retention because the institution aligns its policies and procedures for

student success (Reason, 2009). Although Smith’s study (2001) was targeted for

helping enrollment managers, the recommendations and conclusions from the study

could be used by all institutional leaders, including student affairs professionals who

assist with registration, orientation, and admissions and advising. This study helped

illustrate the need for evaluation of administrative policies, for they can affect student

success.

In a study of rural community colleges, Gray and Hardy (1986) examined the

effect of application timing on academic performance and found that time was an

indicator of academic performance as measured by GPA. The GPAs of those students

that applied earlier were significantly higher than the GPAs of those students that

applied late to the institution. This study was conducted at the community college level,

however, where admission requirements, deadlines and procedures could be vastly

14

different than that of a larger university. A similar study would need to be conducted at

a four-year university to help strengthen the findings of the research conducted by Gray

and Hardy. This study also only examined first-time, full-time students.

Freshman Students

Freshmen new to a university are unique and can exhibit traits different from

those of their upperclassmen peers. Hornik et al. (2008) found that class and school

withdrawals can be more common with freshmen than with upperclassmen. Adelman

(1999) showed that the number of students who intend to complete a bachelor’s degree

and earn fewer than 20 credit hours in their first year has a negative relationship to

degree completion. Moore and Shulock (2009) concluded that excessive course

withdrawals (or uncompleted semester credit hours) have a negative impact on degree

completion. Although this research gives strength to the need for measuring students’

rate of successfully completing courses in order to help indicate academic success,

there appear to be many challenges in measuring student progress and success.

Programs aimed at freshman students, such as freshman seminars and

freshman orientation courses, have been created over time to help students in topic

areas such as critical thinking and problem solving, communication and relationship

skills, and introduction to campus resources including academic planning and advising

(Burgette & Magun-Jackson, 2008). In an exploratory study of full- time students at a

large, state institution, Burgette and Magun-Jackson (2008) found that the relationship

among long-term persistence, GPA was a major variable and that taking the freshman

orientation course had a significant impact on the students’ first year GPA. Their study

controlled for gender, race, high school GPA, and all students had decided on their

15

majors. This study failed to show that the course had an impact on the second

academic year or enrollment, however, and it failed to look at those students with an

undecided major, which could have helped control individual differences among the

student population. Additional research could have been done by expanding the period

of study to six years, because its primary intention was to determine the impact of the

freshman orientation course on persistence(less than one percent of the students had

graduated through the five fall semesters of the cohort). Students have highest

departure rates in their first year of college, making the task of understanding which

factors contribute to the risk of leaving an important one to study during a student’s

freshman year at the university (Herzog, 2005).

Orientation Practices and Procedures

Orientation practices can vary widely from institution to institution. Moore and

Shulock (2009) examined how first-year experience programs demonstrate the benefit

to students, regardless of taking an orientation course upon initial enrollment in college

or prior to enrollment. Freshman orientation courses often address specific topics, such

as time management skills, stress management, academic planning and advising and

campus integration services such as college procedures and resources available to

students that they otherwise would not have been aware of had they not attended or

enrolled in a session (Burgette & Magun-Jackson, 2008). Orientation programs provide

new students information on support services, like advising and enrollment practices to

help acclimate them to the university climate. Community colleges are using results

from analyzing institutional data regarding academic progress in order to change

policies and practices, such as imposing mandatory orientation courses or programs

16

prior to enrollment, as well as setting limits on late registration as a way of improving

retention (Moore & Shulock, 2009). Derby and Smith’s 2004 study of over 7,000

community college freshman students, found that a significant association exists

between orientation courses and degree attainment. There was a greater number of

students in the study who attended the orientation course and obtained their associates

degree than students who did not take the course.

Advising is an important part of the enrollment process for new students entering

a university, whether they are first-time in college (freshman) or transfer students.

According to Goomas (2012), a positive relationship exists between retention and

academic advising, and students are often dissatisfied with the advising they receive

prior to enrollment. Likewise, Tinto (2005) found that students will persist and graduate

in settings that provide clear and consistent information about the institutional

requirements and effective advising as an important piece of the orientation process

prior to enrollment. Students who are undecided about their major of study need to

understand the “road map to completion” and know what courses they need to be

successful. Without a model of collaboration that closes the gap between faculty and

student-affairs professionals, such as orientation or transfer offices, students might not

receive the benefits such as academic advising that they need to succeed (Goomas,

2012).

In a pilot study at a community college, performed by Goomas (2012), new

students were offered an intervention intended to enhance the academic advising

process. This intervention provided in-class career services advising, degree selection,

degree planning, course selection, course scheduling, and training in the college’s on-

17

line registration system. Results of the study suggested that the intervention assisted

the students with setting up their own schedules, degree plans and registration, which

made them more accountable for their education. Further, by improving the academic

advising and other registration activities, it allowed for better utilization of faculty, staff

and administration in helping students become more accountable and knowledgeable

about the registration and advising process. Although this study reflected a small

sample size, it does provide some useful procedures processes and suggests the need

for further research at a larger universities to help address the gap that exists between

advising and enrollment and registration policies and procedures at the community

college level.

The Ford (2008) study tracked registration in five undergraduate classes at a

public four-year university and found that students who registered early for their classes

tended to perform better in those classes. Ford stated that “a correlation of registration

latency with GPA for the semester and overall GPA support the interpretation that

higher-performing students are more likely to pre-register for classes” (p. 406). He also

gave recommendations for orientation and enrollment practices. Ford emphasized the

importance of pre-registration during freshman orientation and academic advising and

how this might reduce the negative implications, such as low academic performance.

He recommended that universities consider offering earlier registration or orientation

times to students that are identified as at-risk (first generation students, lower scoring

on admission tests, lower transfer GPA) in order to combat the connections between

late registration and low academic performance.

18

Academic Performance and Registration Timing

With the advancement of technology, the options students have to complete the

registration process have also advanced. Fewer than three decades ago, experts

thought that the touchtone telephone revolutionized how institutions handled the

registration process (Spencer, 1991). Today, students can register in person at the

registrar’s office or at an orientation session, through their advisors, over the telephone,

or most commonly now through online registration. Although the method of registration

was not evaluated in this study, it could be a variable to be researched further in future

studies, to examine the correlation between method of registration, registration time and

retention and academic success. Because online registration is a fairly recent

phenomenon and the technology can vary based on the institutions’ database used,

there are not many studies that investigate this area.

Neighbors (1996) held in his review of the duties and responsibilities of the

admissions and registrar offices that the primary goal of every institution should be the

effectiveness in all of its education programs, delivery systems, and support structures

for the betterment of the university. Although most colleges have early, regular, and

late registration, late registration has been the most targeted area for research, but

discrepancies have existed and a multitude of variables have been included and

excluded to provide inconclusive results for universities to use to help facilitate and

adapt their registration process. Most often community colleges have been studied

because of the open door policy and the larger number of students who are allowed to

register late. Neighbors (1996) explored the three phases of registration for one

semester at a community college, private university, and public four-year university. He

19

found that late registration is detrimental to student success and those students that

register later did not achieve a high rate of academic success at all three institutions.

Smith, Street, and Olivarez (2002) evaluated the differences between students

who enroll in the three different phases of registration at a community college They

found that registration time significantly affected students in terms of academic success

and retention. Smith et al. (2002) recommended flexible payment schedules,

registration advertising and easy-access registration to help encourage early

registration. This finding adds to the research on registration time and effect on

retention and academic success, but it was conducted at a community college where

the student population could be quite different from a four-year research or liberal arts

institution. Several variables are suggested that could have influenced the findings,

including major or types of courses taken.

Late registration is used in community colleges because of their open door policy

which allows all students who apply to be eligible for admittance to the college. An

institution’s state funding is based in part on enrollment, which is also the case in state

institutions. Private universities depend on tuition revenue as well but not as much for

funding; tuition revenue is heavily what keeps the operations of the facility alive

(Summers, 2003). Street (2000) concluded that late registration was a deterrent to the

academic success and retention for new and returning students, due to the fact that

retention was significantly lower within the semester and from one semester to another

as well as lower academic success, as measured by semester GPA and successful

completion rates. The study did not evaluate the majors or courses taken by students

20

to determine if program of study was a variable that correlated with registration time and

academic success.

Mendiola-Perez (2004) evaluated the effects on early, regular, and late

registration on academic success and retention of first-time students enrolled at a

community college. This study narrowly focuses on first-time students and reviews

registration patterns for the students’ second year of college. Through her study,

Mendiola-Perez identified several patterns suggesting that students who register late

are more likely to have less academic success and lower retention rates than those

students who register earlier. This finding indicates a need to evaluate registration

procedures and their connection to the administrative functions that could prevent or

delay registration, such as admission timelines or procedures. A study conducted by

Tincher-Ladner found that at Mississippi Gulf Coast Community College a student who

registered during pre-registration or regular registration had on average, a 26.53%

higher GPA than that of a student who registered during late registration (Windham et

al., 2014). This study was quite limited, however, with its population. Many variables

could have contributed to the results from the study, such as it being specific to a

community college, the study’s location, student population, policies and practices and

student enrollment.

Retention

Increasingly low graduation rates and scarce financial resources drive

universities to pay close attention to student success and retention (Burgette & Magun-

Jackson 2008). As a result, universities are examining their admission, enrollment, and

orientation practices to improve the overall student experience and ultimately retain and

21

graduate students. Students’ successful integration into the academic world of the

university affects their persistence to their second semester of college and ultimately

their degree attainment. Students may attain their degree but that does not mean that

they are staying at the first university in which they enroll. Although most often

associated with community college students, the phenomenon of students jumping

around from institution to institution has become a more common enrollment pattern in

recent years (Borden, 2004). As reported by Adelman (2004), over a third of all 1992

high school graduates, who earned a bachelor’s degree by the year 2000, earned the

degree at a different institution than the one they first attended. Over 73% of the

students who started at a four-year institution and graduated from the institution in

which they first enrolled, also enrolled at another institution. Therefore, it is imperative

for institutions to understand factors that help students persist for the benefit of the

student, but also for the university that wants to retain the students to graduation.

According to Stillman (2009), institutions need to identify those variables that

show a relationship between academic performance and low retention rates and

implement policies and procedures that contribute to increasing students’ performance

and retention. Institutions are also hurt by low retention rates, not only from loss of

tuition income, but also alumni contributions, government assistance and reputation.

Student recruiting is also an area that requires sustainable institutional resources, such

as staff, marketing, travel and funds. It has been measured that it takes 3-5 times as

much money to recruit a new student than it does to retain the student that is already

currently enrolled (Cuseo, 2010).

22

Registration timing is one area that could affects academic performance and

retention as shown in studies such as Burgette and Magun-Jackson (2008) that

evaluated the impact of a freshman orientation course and its relationship with college

achievement as measured by GPA. The Kiser and Price (2007) study of first-year

college GPA on persistence of college freshman to their sophomore year helps show

that a student’s first year or semester is vital to further academic success.

Administrators need to consider what contributes to retention in new students and as

Reason (2003) supports, “student achievement in college, as measured by first-

semester grade point average, proves to be a significant variable in retention” (p. 495).

Student retention is the primary goal for higher education institutions and to this

end much research effort has been focused on this topic (Reason, 2009). Because the

terminology used to define students leaving the university has changed over time,

Seidman (2005) provided practical definitions to help distinguish between retention,

attrition, departure and academic persistence. According to Seidman, retention is the

institution’s ability to keep students from admission to graduation. Conversely, attrition

is when students fail to reenroll at an institution in consecutive semesters, although

these students may enroll at another institution to complete their degree.

The concern for student retention by administrators is evident by the past

proliferation of publications, conferences, seminars, forums and discussions dedicated

to this topic (Lang, 2001). Even as early as the late seventies and early eighties, interest

in student retention was gaining momentum, as evidenced by several national studies

conducted by the American College Testing Program (ACT) and the National Center for

Higher Education Management Systems (NCHEMS ) that yearly perform studies on

23

student retention, graduate rates and other student data. One area investigated to help

explain student retention issues is the student’s institutional experience. Tinto (2006)

proposed his model of institutional departure to help further explain the student retention

process’s dependency on the student’s institutional experiences.

In Tinto’s (2006) model, he explained that the more students learn and value

their learning, the more likely they are to stay at the institution and continue their

education and eventually graduate. Students enter their institution with diverse abilities,

skills, attributes, attitudes, values, knowledge and external commitments. Tinto’s model

argues that institutional commitment provides the overall context for institutional action

and that those institutions committed to student success are more likely to generate

success than those that are not or do not have student success as a top priority.

Further, (Lau, 2003) states that the student retention process is dependent on the

student’s institutional experiences and whether or not the student is satisfied with the

institution’s supportive academic and social systems.

The 2009 Organization for Economic Co-operation and Development (OECD)

data showed that the United States is one of the world leaders with respect to college

participation rates, but it ranks near the bottom among OECD nations in college

completion rates (Moore & Shulock, 2009). Stillman (2009) holds that because of low

student completion and retention rates, university administrators need to show an

increased interest in evaluating student progress and success.

Although registration time has been shown to impact student success in several

studies, Smith (2001) suggests other factors that may contribute to student retention.

Studies such as Smith, Street and Olivarez’s (2002) study on early, regular, and late

24

registration and student success and Burgette and Magun-Jackson’s (2005) study of

freshman orientation, persistence, and achievement have investigated GPA, course

completion, college attrition, and student characteristics. They compared late

registrants with those students who register during regular time schedules. Academic

performance, which can be measured by college GPA and credits earned are examples

of factors that include retention at the individual level. Reason (2009) identifies GPA

and credits earned as predictors for college persistence that can contribute strongly to

college retention. In a longitudinal study, Makuakane – Drechsel and Hagedorn, (2000)

found that GPA was the most significant predictor of persistence for college students.

This study examined students’ persistence at four community colleges over a five-year

period and focused on factors that promoted persistence. Ishitani and DesJardins

(2002) found in a longitudinal study that the higher a student’s first year GPA, the less

likely the student was to drop out of college.

Summers’ 2000 study at a small rural community college in the Midwest found

that students who persisted enrolled for fall semester classes nearly 30 days earlier

than students who did not complete the semester. This study also found that fall

semester GPA related to attrition in that there was a statistically significant difference in

the fall semester GPA of students who did not enroll in classes for the following spring

semester and those that did not enroll. Summer’s study, however, was conducted on a

very small scale, with a limited population of full-time students which may limit her

findings as not generalizable to larger four -year state institutions with a larger

population of students both part-time and full-time. The statistical tests used in the

study also relied on the mean dates of enrollment.

25

Two prominent scholars, Beal and Noel (1980) identified leading factors in earlier

studies that affected student retention. They studied campus action programs and

efforts for improving student retention. Their studies identified student characteristics

such as academic factors, aspirations and motivations, demographic factors and

financial factors. They also showed that environmental factors such as the type of

school, housing, advising services and academic support services and retention

services all affected student retention. The early research results, however, were

inconclusive in determining whether factors such as school policies and regulations

contributed to retention rates.

Lang (2001) stressed the need to continue to examine student retention in higher

education from conceptual and programmatic perspectives. In general, Lang suggested

that if administrative duties do not relate to the student needs or help the student, then

retention can be impacted negatively. Therefore, administrative operation such as

admissions or registration need to be evaluated to improve retention.

Astin (2005) studied GPA and its relationship to student retention. College GPA

has repeatedly been shown to be one of the strongest predictors for student retention.

Wang and Pilarzyks (2009) conducted analyses of program registrations in the fall

terms for two academic years and gave special attention to the timing of student

registration in order to highlight issues that could contribute to retention. More

specifically, Freer-Weiss (2004) showed that the timing of when students apply to

college and the timing of when they register can relate to their academic standing.

Based on this research, deadlines could potentially affect the accumulation and

retention of enrolled students. Students who applied late were at a disadvantage

26

because they could incur challenges not experienced by their peers who applied on

time or early.

Freer-Weiss (2004) also investigated poor academic performance by students

who apply late and are less likely to re-enroll. The study was conducted once again at a

two-year institution with an open admissions policy. Only students new to post-

secondary education were used in the sample population. This study examined 785

admissions files of first-time enrolled college freshman. Data were collected regarding

the demographics, characteristics, and academic performance of these newly enrolled

students. Late applicants were found to exhibit different characteristics from the

students who applied earlier; and the late applicants had higher rates of attrition. The

study further illustrated that institutions allowing late admissions and, in effect, late

registration, may be doing harm to students who are not prepared for college life. (Tinto,

1975). Roueche and Roueche (1993) recommend that community colleges eliminate

late registration because retention and academic performance would be improved with

its abolishment. Although this study focused more on late applicants, other research

established the strong connection between the application process and the registration

process. The study also found that students who apply late will have a higher rate of

attrition.

Students with Undecided Major Status

A gap exists in the literature regarding the relationship between academic

performance and retention with regards to major status of students. Older studies

conducted by the American College Testing (ACT) program found that the ACT scores

of undecided and students who decided a major in their college program were not

27

significantly different from each other (Wikoff & Kafka, 1978). Other tests, however,

have also been used, and the results were inconclusive leading to investigations using

personality factors as well as academic potential. The study conducted by Wikoff and

Kafka at the University of Nebraska at Omaha investigated the relationship of academic

potential and personality factors to the choice of major by students. The purpose was to

evaluate any significant differences between students so that counselors could guide

them in academic studies as well as career choices. This study showed that the

relationship between academic potential measures and the ACT were correlated to

personality, but overall measures of academic potential were not reliable predictors of

academic success based on major statistics.

Another older study done by Lewallen (1993) contributes to the previous findings

and assumptions in higher education that the differences between decided and

undecided students do not have an effect on predicting retention, and his 1995 study of

over 20,000 decided and undecided students at six different types of institutions found

that undecided students were shown to have higher average GPAs and persist to

graduation (Lewallen, 1995). There has been a historic interest for researchers and

institutions about whether students who are decided or undecided effect their retention

and academic performance, but other questions such as when and how students

decided on their major is also an important area. Establishing policies and processes

that impact undecided students is important with current studies that have shown that

retention rates and academic performance could be lower than those students who

have a decided major (Cuseo, 2005).

28

Leppel (2001) investigated the connection between college retention and the

student’s choice of major. She found that one of the main reasons students attend

college is for career preparation. An additional finding in the study was that differences

in retention rates are explained by differences in subject interest. Students whose

major is geared towards a specific career are presumed to have higher retention rates

than those whose future plans are undecided. Leppel found that college retention rates

vary with major field when all other variables are static. This study was conducted from

the student’s first year of college to the second year of college and evaluated six

categories for major field of study. The results showed that those students with

undecided majors have low academic performance as well as low retention rates.

John et al. found in their 2004 study that major choice was an important decision

for students in the study because of the impact on persistence. The study’s results also

showed that freshman students having an undecided major was negatively associated

with the probability of the student persisting. This study helps add to the literature that

supports the association of academic program or major status is important for freshman

students in regards to persistence and retention. The study measured the outcome

variable of retention as whether the students persisted for the entire academic year.

Students who enrolled in the fall and returned the following spring semester were

counted as students who persisted.

Social forces, as well as administrative processes can affect why students with

undecided majors may be prone to lower retention rates and academic performance.

As the ACT Policy Report (2006) found, one of the primary factors affecting college

retention is the quality of interaction that a student has with a concerned person on

29

campus. Academic advising is one of the ways the university generates this interaction

between students and members of the university. As Goomas found in his 2012 study

at a community college district, an intervention of offering advising services to students

resulted in a higher degree of satisfaction for students with the registration environment.

This intervention could help bridge gaps that do exists between advising services and

the student affairs processes at the universities (Allen and Smith, 2008).

Tinto (2005) found that students will persist and graduate in university

environments that provide clear and consistent information about the institutional

requirements. Advising is an important piece of the orientation process prior to

enrollment for students who are undecided about their major of study. Providing

programs that support academic decision-making have shown to provide benefits to

students and institutions by promotion and increase student retention and students’

overall satisfaction with the university and its processes (Cuseo, 2005). Therefore a

need exists to investigate if differences between undecided and decided students exists

in regards to academic performance and retention, and also, the need for programming

if those differences show a positive relationship between decided students and

successful academic performance and high retention rates. The university environment

can help undecided students choose a major and connect with a person on campus

through programming and policy instituted for the assistance of undecided students.

Summary

A review of the literature on student retention and persistence, registration timing,

and academic performance shows that the topic of retention and successful academic

performance is very important to the growth and mission of institutions in higher

30

education. There is research being done on the connection between administrative

processes and its effect on students’ success and retention in college and universities.

Wang and Pilarzyk (2007) showed that a relationship between program application,

financial aid applications, and registration time are related. They also found that

students who applied later and therefore registered later in their program were less

likely to complete their coursework and have lower GPAs. This study, as well as

Smith’s 2001 study which examined factors related to institutions’ enrollment

performances, was conducted at a two-year community college. Gray and Hardy

(1986) also studied the effect of application timing on academic performance and found

GPA to be an indicator of academic performance: but this study was conducted at only

rural community colleges. These studies helped show the relationship between

different institutional programs’ timing on academic performance, but they were

conducted at the two-year community college level, and factors such as admission

requirements, deadlines and procedures can be vastly different for four-year larger

institutions. These studies helped illustrate the need for an evaluation of policies and

procedures, but the findings are not generalizable to larger institutions.

Burgette and Magun-Jackson (2008) investigated programs aimed at freshman

students, such as orientation courses and how they positively impact GPA. Further

research needs to be done however, because these studies failed to evaluate how the

timing of orientation and consequent registration time affects GPA and the retention of

the students.

The impact of registration timing on academic performance has been evaluated

by researchers like Smith, Street and Olivarez (2002) but only at a community college

31

level. Studies such as Smith, et al. and Mendiola-Perez (2004) evaluated the effects of

registration timing on academic success and retention, but did not evaluate the major

status of students to determine if program of study is a variable that is correlated with

registration time and academic success. Leppel (2001) did investigate the connection

between college persistence and the student’s choice of major, but the study did not

evaluate the student’s successful completion of credit hours for specific semesters, and

majors could change from year to year. Because there do not appear to be studies in

the literature that take into account such variables as major status and registration time

and their relationship to new student academic performance and retention, the present

study may help to fill that gap.

Conceptual Framework

Several student retention models including Tinto’s model of college student

departure (1993), Astin’s theory on student involvement (1999), and Bean and

Metzner’s non-traditional student attrition model (1995) can help explain what student

and institutional variables contribute or impact student retention directly or indirectly

(Bean, 2001). Tinto’s model of college student departure and Bean’s student attrition

model provide a comprehensive framework on college departure, but for purposes of

this study, Astin’s (1999) input-environment-outcome (I-E-O) model of assessment

provides the best fit. Conceptually, his model provides the basic foundation for

understanding the effects of college on students (Burgette & Jackson, 2008). Although

both Tinto and Astin’s theories examine the persistence and involvement in the

environment of college students and how this contributes to college student persistence

and success, Tinto focuses more on the departure of the student from the university.

32

Astin’s theory explains the entire student process of entrance into a university, the

experience while at the university, and the exit of the student from the university.

Another theorist, Gordon (1981) suggests that students develop in identifiable phases;

therefore administrative practices (such as registration) can be influenced by students’

needs. Gordon suggests the need for the administrative structure to be a supportive

environment because it can help students feel less pressured when choosing an

academic major and allows them to develop naturally instead of forcing the

developmental process.

Astin’s (1984) I-E-O model (input, environment, and output) suggests that the

college environment not only plays a role in student satisfaction but also in retention. A

student’s input factors will interact with the environment a student encounters. In this

study, input, addressed characteristics students possess when they are admitted to the

institution. These include the controlled independent variables of gender, ethnicity, SAT

score, Pell grant eligibility, and first generation student status. Environment is

represented by orientation sessions because a student’s interaction with the university

begins prior to even taking a step into the classroom. The entire enrollment and

matriculation process is the student’s first introduction to the university’s environment

and contributes to how they become involved and saturated into that environment.

Output refers to the skills and abilities that a student exhibits while studying at the

institution and includes the dependent variables of current semester grade point

average and semester credit hours completed percentage as well as whether the

student was retained for the following semester and completed any semester credit

hours for the current semester of enrollment.

33

Astin (1984) used his theory of involvement to define a student’s experience at

the institution in terms of the input, environment and outcome. He found that individual

students bring experience with them when they matriculate into the institution-- the

input. The environment the student encounters while at the institution includes

academic and non-academic activities the student participates in during their tenure at

the institution starting with orientation and registration. The output is a combination of

the input and environment that will or will not lead to the completion of a college degree

(Astin, 1993). Because institutions are ranked based on their degree completion rates,

educators need to investigate how they facilitate degree completion and explore

retention programs or programs that can contribute positively to student retention (Astin,

2005). By utilizing the I-E-O model as a guide, this study can provide insight as to how

students academically succeed at the institution and help identify factors associated

with student retention.

34

CHAPTER 3

METHODOLOGY

Overview

This quantitative study had two purposes. The first purpose was to examine the

relationship between registration time and academic performance and retention of first-

time in college (FTIC) undergraduate students during their first semester of enrollment

at a comprehensive, four-year state research university. The second purpose was to

examine the differences between major status with regard to academic performance

and retention of FTIC undergraduate students during their first semester of enrollment

at the university. In order to achieve these purposes, the following research questions

were addressed in the study.

1. What is the relationship between registration time and academic performance of FTIC (first- time in college) students after controlling for high school academic achievement, gender, ethnicity and SES factors?




This chapter includes the research design of the study, site selection and

population of the study, sample and participants, instrumentation used in the study, the

data collection and data analysis process as well as validity of the study and limitations,

delimitations and assumptions.

35

Research Design

Quantitative research is the approach used in research when theories are tested

by examining relationships between variables (Creswell, 1998). By using this

methodology, the researcher was able to examine a larger amount of secondary student

data for in-depth analysis and generalize results to a larger population. For this study, a

correlational research design was used because the relationship of several variables

that measure academic performance and retention was examined in relation to another

variable (registration time). The differences between two groups (undecided students

and decided students) were also examined in regards to the measures of academic

performance and retention. A quantitative analysis allows the researcher to identify

evidence regarding cause and effect relationships (Creswell, 2013).

Site Selection and Population of Study

The site of this study was “Four -Year University” (FYU), a large,

doctoral/research institution located in Texas. For the fall 2012 semester, FYU enrolled

28,911 undergraduates and awarded credentials from baccalaureate through doctoral

degrees. Its enrollment is 53% female and 47% male. About 5,523 of its

undergraduate students are enrolled part-time while the remaining 23,388

undergraduates are full-time students. FYU has a freshman (first-time-in college)

population of 4,546. The freshman persistence rates for full-time, first-time new from

high school enrolled in the fall 2011 semester and returning in fall of 2012 was 75.8%,

and those enrolled in fall 2010 and returning in fall 2011 was 78.5%. The 2005 cohort

of first-time full- time freshman who graduated in 6 years from FYU was 50.1% with an

additional 8.6% graduating from other Texas public institutions, for a total graduation

36

rate of 58.7%. Although not the measure used for this study, graduation rates are

important statistics for the institution because they are another measure of the student

retention and successful academic performance.

FYU was chosen as the site of this study for two main reasons. First FYU

enrolls a large freshman class each fall semester of over 4,000 students and second,

FYU has a large population of FTIC students (approximately 25% of their new

undergraduate student population) who enroll without a chosen major prior to their

enrollment. Table 1 illustrates the student demographics for FYU for the fall 2011 and

fall 2012 semesters that were used in the study.

Table 1

FYU All Student Demographics

Characteristics Fall 2011 Fall 2012 Gender

Male 54% 53% Female 46% 47%

Average Age (Undergraduate) 22.5 22.5 Ethnicity Of Students

White 58.1% 55.9% African-American 12.7% 13.0% Hispanic 15.5% 17.0% Native American/Alaskan 1.4% 1.4% Asian & Pacific Islander 6.1% 6.1% Non-Resident Alien 5.0% 5.4% Unknown 1.2% 1.1%

SAT Average 1106 1105 Full-time Undergraduate 22,487 23,388 Part-time Undergraduate 5,795 5,523 Total 28,282 28,911

37

Sample and Participants

The participants were selected from a data file obtained from the Director of

Institutional Research (IR) at FYU. The researcher obtained permission to use the

institutional electronic data sets via the institutional review board of FYU. The identities

of the participants were numerically coded by the IR office so they were anonymous to

the researcher. The participants’ registration data, as determined by specific

orientation session are listed in Appendix A, as well as all other individual information

contained in the data fields of the file obtained from the director are listed in Appendix B.

The field entitled SESSION contained the orientation sessions for fall 2011 and fall

2012. This file included all freshman (FTIC) students registered for the fall 2011 and fall

2012 semesters.

New freshman students entering the fall semester attend a two-day new student

orientation session that includes assistance with class scheduling, campus-life

sessions, placement testing, academic advising, early registration, among other

individual tailored activities for the students. Students who are not able to attend the

regular two-day orientation have the option to attend a half-day session in conjunction

with transfer students. Students leave their respective orientation session with a class

schedule for the upcoming semester. The students’ orientation sessions fall between

specific registration dates as set by the office of orientation and transfer programs (as

shown in Appendix A).

Table 2 illustrates the first-time in college student demographics for the fall 2011

and fall 2012 semesters. There were 247 participants in the population file for fall 2011

that were blank for the SESSION field and 236 participants in the population file for fall

38

2012 that were blank in the SESSION field; therefore, they were excluded from the

sample used for the study. An additional 53 students and 50 students were also

exclude from analysis for the fall 2011 and fall 2012 semester respectively , because

the students did not attend the designated freshman orientation session, but attended a

transfer orientation session instead. This resulted in observations of less than 30 for the

each transfer orientation sessions. Therefore the total number of first-time in college

students included in the analysis was 6,439 for fall 2011 and 4,168 for fall 2012.

Table 2

FYU First-time in College Student Demographics

Characteristics Fall 2011 Fall 2012 Gender

Male 43.4% 45.5% Female 56.6% 54.5%

Ethnicity Of Students White 52.7% 49.7% African-American 15.6% 14.3% Hispanic 20.9% 23.6% Native American/Alaskan 1.6% 1.5% Asian & Pacific Islander 8.2% 8.7% Non-Resident Alien 0.7% 1.3% Unknown 0.2% 0.9%

SAT Average 1105 1105 Full-time Undergraduate 6,618 4,380 Part-time Undergraduate 121 74 Total 6,739 4,454 SES Factors

First generation status 40.3% 39.4% Pell grant eligibility 37.0% 37.6%

39

Instrumentation

The study is quantitative using an internal university database, and the data used

by the researcher was secondary data. The database contained student registration

data which included time of registration, GPA, semester credit hours completed and

whether the student returned to enroll in the following semester. The database also

included majors if those majors have been decided prior to or at time of enrollment. A

list of all data fields used in this study can be found in Appendix B. If the data field

entitled ACAD_PLAN is listed as an undecided (see Appendix B for full list of undecided

majors for all colleges), the student was coded as an undecided major for purposes of

the study.

Data Collection and Analysis Procedures

All newly-accepted freshman students admitted to FYU are required to attend an

orientation session which includes the ability to register for classes. Students who are

admitted after the semester begins do not attend orientation. For purposes of this

study, these students were not evaluated because their time of registration was not

known. They were not included in the sample that was analyzed. The data file received

from IR included the orientation sessions for all new freshman students. The orientation

session represents registration time for purposes of this study. All students were

grouped by their respective orientation sessions and coded accordingly for analysis.

In order to illustrate the relationship between registration time and academic

performance, two separate measures were used for academic performance. The

current GPA of each student was used to measure academic performance as well as

semester credit hours completed percentage. The students were grouped and coded

40

by their respective orientation sessions, and an analysis was done to evaluate the

percentage of courses students attempted compared to the courses that they completed

at the end of the semester of enrollment. Academic success was measured not only

GPA, but also whether or not the student successfully completed a high percentage of

the courses attempted. Two measures of retention were used based on semester credit

hours completed (or not) and another based on the student’s return the next semester.

The student data was also coded based on the variable of academic plan. All data that

had one of the undecided majors (see Appendix B) in the academic plan field were

coded as undecided and all other data that was not was coded as decided.

For Research Questions 1 and 2, the independent variable was the registration

time for the student, determined by the orientation session that they attended. For

Research Questions 3 and 4 the independent variable was the academic plan or also

referred to as major status (undecided and decided) that was reported in the data file.

The dependent variables for Questions 1 and 3 were the academic performance of

current GPA as calculated and reported in the data file as well as the percentage of

semester credit hours completed. The dependent variables for Questions 2 and 4 were

whether the student completed the semester and whether the student returned the next

spring semester. For Questions 1 and 2 only, the controlled independent variables that

impact student GPA were included. These were high school academic achievement as

measured by SAT score, gender, race/ethnicity, and other SES indicators, including first

generation status and Pell grant eligibility information. Table 3 shows the independent

and dependent variables used for all research questions in the study.

41

Table 3

Methodology Variables

Independent Variable

Independent Variable (Input Factors)

Dependent Variable

Question 1 Session SAT Score, Gender, Ethnicity, First generation status, Pell grant

eligibility

Current GPA Semester Credit Hours

Completed % Question 2 Session SAT Score, Gender, Ethnicity,

First generation status, Pell grant eligibility

Completed Semester Returned Spring Semester

Question 3 Academic Plan Current GPA Semester Credit Hours

Completed % Question 4 Academic Plan Completed Semester Returned Spring Semester

For Research Question 1, a multiple regression analysis was run to test the

relationship of the independent variables listed above with each of the dependent

variables (Field, 2009). Two separate multiple regression analyses were run for each of

the dependent variables (current GPA and SCH completed percentage). All orientation

sessions were compared to each other through descriptive statistics listed in Tables 4

and 5 for each fall semester. The eight assumptions needed perform multiple

regression appropriately were met (as detailed in the results section of the study);

therefore, multiple regression was used. The proportion of variability in the dependent

variables that could be explained by the independent variables was determined.

Statistical significance was determined for each independent variable, based on

corresponding p value. Additional descriptive statistics were also provided, including

mean, standard deviation and median numbers. For Research Question 2, logistic

regression was run instead of multiple regression because the dependent variables of

42

current semester completed and returning the following spring semester, that measured

retention were dichotomous variables and not continuous as in research Question 1.

For Research Question 3, a Mann-Whitney U test was run, and for Research

Question 4, a chi square analysis was done (because the variables that measured

retention were dichotomous variables) in order to examine the differences between the

academic performance and retention of decided and undecided students (Field, 2009).

The Mann-Whitney U test was used instead of an independent t test because the data

was not normal when a normality test was conducted during initial analysis. Additional

summary statistics are also provided for Question 3 including mean, standard deviation,

and median numbers, which can be found in Tables 12 and 13.

The data file with participant information used in the study was secondary data

collected by FYU. Variables were recoded from string to number format, and frequency

reports were run to ensure that the data management process was not jeopardized and

the original data file and its content remained intact. The researcher used secondary

data because the data for both fall semesters had been collected, calculated and

inputted into FYU’s database and was readily available by the Institutional Research

Office. The researcher obtained descriptions for each of the fields in the data set to

insure that all terms used were clear and not misinterpreted for the study’s analysis.

Limitations

There was a limitation on the registration timing for the sample of students

because new freshman students are not allowed to register earlier due to the

restrictions of only being allowed to register during designated freshman orientation

sessions. Because freshman students only register during their designated orientation

43

times, (unless exceptions are made) which do not begin until May for the fall semesters,

it may be difficult to compare whether or not there is a significant difference in academic

performance and registration timing between new students and returning students

because returning students can begin the registration process as early as March for the

fall semester.

Delimitations and Assumptions

The researcher chose to only include one university in the study; therefore, the

results may not be generalizable to an institution that does not have similar processes,

demographics or programs. It is assumed that the data collected from the university

reporting system was accurate and reliable. The researcher also chose to limit this

study to the fall 2011 and fall 2012 semesters. A longitudinal research study would

capture more information on students’ retention as they progress to graduation.

The study also only included the fall semesters for analysis and excluded the

spring semesters from the analysis. As evidenced by the descriptive statistics analyzed

for first-time in college students enrolled in the spring semesters, the sample size was

very low for each orientation session. There was also a lack of input factors in the file

related to SAT score, and SES factors. One reason for this could be the number of

students reported as non-resident alien students. International students would not be

able to receive financial aid in the spring semester or have an SAT score to report. The

fall semesters traditionally have a larger number of first-time in college students enroll

compared to spring because traditional students are entering into college immediately

following the completion of their high school career.

44

CHAPTER 4

RESULTS

Overview

This study had two purposes. The first purpose of the study was to examine the

relationship between registration time and academic performance and retention of first-

time in college undergraduate students during their first semester of enrollment at a

comprehensive, four-year state research university. The second purpose was to

examine the differences between major status with regard to academic performance

and retention for the same population of students, for the same period of time. In order

to achieve these purposes the following research questions were addressed in the

study through quantitative methods to help evaluate the data.

1. What is the relationship between registration time and academic performance of FTIC (first- time in college) students after controlling for high school academic achievement, gender, ethnicity and SES factors?




In this chapter, key findings and results of the study are presented as well as

descriptive statistics that help to answer the research questions examined.

Data on Institution Included in Study

As previously reported, the institution in this study is a four-year

doctoral/research university located in Texas, referred to as FYU. The study utilized

secondary institutional data from the fall 2011 and fall 2012 academic semesters for

45

first-time in college students only. The researcher was given the secondary data by the

Institutional Research Office at the university with all student-specific identifying

information excluded from the file. FYU had a total freshman population of 6,739 for the

fall 2011 semester and 4,454 for the fall 2012 semester. A snapshot of the

demographics of all FYU students for the fall 2011 and fall 2012 academic semesters as

reported by the FYU Fact Book is listed in Table 1 in the site selection and population of

study section of chapter 3 above. Table 2 provides a comparison of the demographics

of the first-time in college student population for the fall 2011 and fall 2012 semesters

that were used for this study.

Results for Research Questions

This study included the first-time in college student population for two academic

semesters. The results for each research question are presented below.

Results for Research Question 1: Relationship between Registration Time and Academic Performance

To examine the relationship between registration time and academic

performance, a multiple regression test was conducted. Registration time occurs during

the student’s specific orientation session indicated in Appendix A. A multiple regression

allows the researcher to predict the dependent variables based on multiple independent

variables (Johnson & Christensen, 2008). In this test, the multiple independent

variables were the orientation session and the controlled variables of gender, ethnicity,

SAT score (used as a measure of high school performance), first generation status and

Pell grant eligibility (used as measures of SES factors). Academic performance was

measured by the dependent variables of current GPA and the SCH (semester credit

hours) completed percentage. Because there were two dependent variables used to

46

measure academic performance, the multiple regression test was run twice for each

academic semester.

Table 4 Descriptive Statistics (Fall 2011) - Question 1

Current GPA Semester Credit Hours Completed %

Variable N Mean Std Dev Median N Mean Std Dev Median Orientation Session Freshman 1 984 2.91 0.927 3.12 984 97.84 7.828 100.00 Freshman 2 953 2.92 0.956 3.20 953 97.97 9.330 100.00 Freshman 3 923 2.74 0.977 3.00 923 97.76 8.930 100.00 Freshman 4 903 2.72 0.928 2.81 903 97.64 9.958 100.00 Freshman 5 892 2.60 1.076 2.80 892 97.35 11.335 100.00 Freshman 6 878 2.39 1.074 2.50 878 97.20 9.699 100.00 Freshman 7 811 2.40 1.134 2.60 811 96.98 11.356 100.00 Late 95 2.15 1.228 2.25 95 96.49 13.680 100.00 Gender Male 2755 2.53 1.071 2.75 2755 97.07 10.341 100.00 Female 3684 2.78 0.992 3.00 3684 97.89 9.471 100.00 Ethnicity White 3452 2.81 1.004 3.00 3452 97.44 10.065 100.00 African-American 1027 2.32 1.031 2.40 1027 97.76 8.774 100.00 Hispanic 1395 2.55 1.038 2.75 1395 97.68 9.467 100.00 Native American/Alaskan 106 2.70 0.957 2.79 106 98.24 6.648 100.00 Asian & Pacific Islander 404 2.85 1.006 3.20 404 97.64 10.303 100.00 Non-Resident Alien 42 2.71 1.228 2.90 42 91.86 23.737 100.00 Unknown 13 2.63 0.998 2.75 13 98.46 5.547 100.00 First Generation Status Yes 2673 2.52 1.065 2.71 2673 97.40 10.181 100.00 No 3456 2.82 0.977 3.00 3456 97.79 9.023 100.00 Unknown 310 2.41 1.129 2.62 310 95.85 14.673 100.00 Pell Grant Eligibility Yes 2426 2.46 1.060 2.60 2426 97.56 9.657 100.00 No 4013 2.81 0.997 3.00 4013 97.52 9.982 100.00

In order to compare each orientation session’s GPA and the semester credit

hours completed percentage with one another and to provide the mean, number of

observations, standard deviation and median, descriptive statistics were run for the fall

47

2011 and fall 2012 semesters and are displayed in Tables 4 and 5. The average SAT

score for fall 2011 was 1095 and the average SAT score for fall 2012 was 1089.


Current GPA Semester Credit Hours Completed %

Variable N Mean Std Dev Median N Mean Std Dev Median Orientation Session Freshman 1 567 2.83 1.004 3.07 567 97.89 9.560 100.00 Freshman 2 572 2.73 1.071 3.00 572 98.01 8.086 100.00 Freshman 3 574 2.81 0.974 3.00 574 98.45 7.732 100.00 Freshman 4 581 2.82 1.022 3.00 581 97.70 11.174 100.00 Freshman 5 592 2.71 0.992 3.00 592 98.04 8.914 100.00 Freshman 6 606 2.34 1.101 2.60 606 97.61 9.154 100.00 Freshman 7 598 2.38 1.153 2.55 598 97.54 11.835 100.00 Late 78 2.14 1.366 2.25 78 94.07 21.544 100.00 Gender Male 1873 2.45 1.136 2.69 1873 97.46 10.339 100.00 Female 2295 2.80 0.993 3.00 2295 98.10 9.654 100.00 Ethnicity White 2144 2.78 1.052 3.00 2144 97.98 9.239 100.00 African-American 622 2.28 1.089 2.50 622 97.69 9.943 100.00 Hispanic 1026 2.56 1.040 2.78 1026 98.00 9.533 100.00 Native American/Alaskan 64 2.64 1.033 2.78 64 96.48 13.706 100.00 Asian & Pacific Islander 237 2.82 1.052 3.00 237 96.67 13.630 100.00 Non-Resident Alien 42 2.68 1.315 1.729 42 95.24 21.554 100.00 Unknown 33 2.36 1.240 2.77 33 98.09 6.154 100.00 First Generation Status Yes 1724 2.52 1.077 2.75 1724 98.19 8.218 100.00 No 2076 2.78 1.044 3.00 2076 97.77 10.102 100.00 Unknown 368 2.49 1.127 2.73 368 96.34 15.223 100.00 Pell Grant Eligibility Yes 1630 2.49 1.089 2.69 1630 97.49 11.019 100.00 No 2538 2.75 1.052 3.00 2538 98.02 9.230 100.00

Multiple regression allows the researcher to determine the overall fit (or variance

explained) of the model and the relative contribution (if any) of each of the independent

48

variables to the total variance explained. Therefore, the tests helped examine whether

academic performance (as measured by GPA and SCH completed percentage) could

be predicted based on the independent variables of session (registration timing) along

with the five other control variables of gender, ethnicity, SAT score, first generation

status and Pell grant eligibility.

Before the statistical test can be run, eight assumptions must be met to ensure

the data to be analyzed can be analyzed through multiple regression. All eight

assumptions were met prior to running the tests for multiple regression. The first and

second assumptions met, i.e. verification that the dependent variables are continuous

and there are more than two independent variables. The third assumption, i.e. an

independence of residuals (each student is only accounted for once in the data file) was

assessed by a Durbin-Watson statistic of 0.879 (current GPA) and 1.460 (SCH

completed percentage) for fall 2011 and 1.811 (current GPA) and 1.910 (SCH

completed percentage) for fall 2012. The fourth assumption, i.e. that the independent

variables are collectively and individually linearly related to the dependent variables,

was verified by plotting the residuals against the predicted values. The residuals

formed a horizontal band, indicating that the relationship between the dependent

variable and independent variables are likely to be linear. The fifth assumption, i.e. the

residuals are equally spread over the predicted values of the dependent variable

(homoscedasticity) was met by utilizing the same plot created to check for linearity, and

showing the spread of the residuals were not increasing or decreasing across the

predicted values. The sixth assumption checked was that multicollinearity did not occur.

This assumption was met by examining the Pearson correlation and verifying that none

49

of the independent variables have correlations greater than 0.7 as well as verifying the

collinearity statistics of Tolerance is greater than 0.1. The seventh assumption was met

by verifying that no significant outliers existed in the data. The statistical test did not

produce a casewise diagnostic table highlighting any cases contacting outliers. The

eighth and final assumption was met by verifying that the residuals were normally

distributed producing a histogram with a superimposed normal curve.

The Multiple Regression Statistical Significance Tables 6 and 7 presented show

several results, including the contribution of each independent variable to the model and

its statistical significance. For each fall semester a baseline was chosen for each

categorical variable (i.e. freshman 4 was chosen as the baseline for orientation session)

in order to compare the change in the odds ratio for each increase in one unit of the

independent variable. The chosen baseline for each fall semester is noted next to the

respective categorical variable.

Table 6

Multiple Regression Statistical Significance (Fall 2011) – Question 1

Current GPA Variable B SE B t p

Orientation Session (Freshman 4 was used as baseline) Freshman 1 0.112 0.052 0.038 2.141 0.032 Freshman 2 0.034 0.052 0.012 0.656 0.512 Freshman 3 0.043 0.052 0.015 0.828 0.408 Freshman 5 -0.093 0.052 -0.032 -1.804 0.071 Freshman 6 -0.224 0.052 -0.076 -4.297 <0.001 Freshman 7 -0.267 0.054 -0.085 -4.922 <0.001 Late -0.498 0.115 -0.061 -4.313 <0.001

Gender (Male was used as baseline) Female 0.335 0.028 0.161 11.864 <0.001

Ethnicity (African-American was used as baseline) White 0.131 0.043 0.063 3.038 0.002 Hispanic 0.158 0.045 0.065 3.482 0.001 Native American/Alaskan 0.236 0.124 0.027 1.894 0.058 Asian & Pacific Islander 0.295 0.065 0.072 4.557 <0.001 Non-Resident Alien 0.884 0.228 0.056 3.880 <0.001 Unknown 0.156 0.304 0.007 0.513 0.608

(table continues)

50

Table 6 (continued).


First Generation Status (No was used as baseline) Yes -0.105 0.031 -0.050 -3.372 0.001 Unknown -0.255 0.071 -0.052 -3.611 <0.001

Pell Grant Eligibility (No was used as baseline) Yes -0.089 0.032 -0.042 -2.785 0.005

SAT Score 0.002 0.000 0.289 19.593 <0.001 Note: R2 = 0.161 (N=4728, p <0.001)

Semester Credit Hours Completed % Variable B SE B t p

Orientation Session (Freshman 4 was used as a baseline) Freshman 1 0.676 0.519 0.025 1.302 0.193 Freshman 2 0.315 0.518 0.012 0.608 0.543 Freshman 3 0.774 0.519 0.028 1.493 0.135 Freshman 5 -0.260 0.516 -0.010 -0.503 0.615 Freshman 6 -0.507 0.518 -0.019 -0.977 0.329 Freshman 7 0.815 0.541 0.028 1.505 0.132 Late -1.359 1.150 -0.018 -1.182 0.237 Gender (Male was used as baseline) Female 0.919 0.282 0.048 3.262 0.001 Ethnicity (African-American was used as baseline) White -0.353 0.430 -0.019 -0.821 0.412 Hispanic -0.142 0.453 -0.006 -0.314 0.754 Native American/Alaskan 0.123 1.240 0.002 0.009 0.921 Asian & Pacific Islander -0.348 0.645 -0.009 -0.539 0.590 Non-Resident Alien 2.646 2.270 0.018 1.166 0.244 Unknown 0.806 3.025 0.004 0.267 0.790 First Generation Status (No was used as baseline) Yes -0.584 0.311 -0.030 -1.879 0.060 Unknown -1.575 0.705 -0.035 -2.235 0.025 Pell Grant Eligibility (No was used as baseline) Yes 0.277 0.320 0.014 0.864 0.388 SAT Score 0.002 0.001 0.023 1.439 0.150 Note: R2 = 0.007 (N=4728, p = 0.009)

Table 7

Multiple Regression Statistical Significance (Fall 2012) – Question 1


Orientation Session (Freshman 4 was used as baseline) Freshman 1 0.022 0.076 0.007 0.288 0.773 Freshman 2 -0.067 0.075 -0.021 -0.887 0.375 Freshman 3 -0.016 0.074 -0.005 -0.218 0.828 Freshman 5 -0.010 0.074 -0.003 -0.132 0.895 Freshman 6 -0.317 0.073 -0.105 -4.319 <0.001 Freshman 7 -0.273 0.075 -0.088 -3.653 <0.001 Late -0.621 0.151 -0.078 -4.120 <0.001

(table continues)

51

Table 7 (continued).


Gender (Male was used as baseline) Female 0.414 0.040 0.189 10.456 <0.001

Ethnicity (African-American was used as baseline) White 0.087 0.062 0.040 1.388 0.165 Hispanic 0.146 0.065 0.058 2.258 0.024 Native American/Alaskan 0.053 0.211 0.005 0.251 0.802 Asian & Pacific Islander 0.347 0.096 0.075 3.633 <0.001 Non-Resident Alien 1.071 0.263 0.077 4.072 <0.001 Unknown 0.011 0.210 0.001 0.052 0.959

First Generation Status (No was used as baseline) Yes -0.156 0.045 -0.070 -3.490 <0.001 Unknown -0.128 0.077 -0.032 -1.666 0.096

Pell Grant Eligibility (No was used as baseline) Yes -0.033 0.045 -0.015 -0.725 0.468

SAT Score 0.002 0.000 0.254 13.062 <.001 Note: R2 = 0.141 (N = 2751, p <0.001)

Semester Credit Hours Completed % Variable B SE B t p Orientation Session (Freshman 4 was used as a baseline) Freshman 1 -0.195 0.716 -0.007 -0.273 0.785 Freshman 2 0.371 0.710 0.031 0.523 0.601 Freshman 3 0.291 0.697 0.011 0.417 0.617 Freshman 5 0.336 0.697 0.012 0.482 0.630 Freshman 6 -0.114 0.690 -0.004 -0.165 0.869 Freshman 7 0.326 0.704 0.012 0.463 0.643 Late -3.959 1.419 -0.057 -2.789 0.005 Gender (Male was used as baseline) Female 0.522 0.373 0.027 1.399 0.162 Ethnicity (African-American was used as baseline) White 0.015 0.588 0.001 0.026 0.980 Hispanic 0.204 0.610 0.009 0.334 0.738 Native American/Alaskan 2.273 1.984 0.022 1.145 0.252 Asian & Pacific Islander -1.487 0.900 -0.037 -1.653 0.098 Non-Resident Alien 3.388 2.476 0.028 1.368 0.171 Unknown -0.003 1.977 0.000 -0.002 0.999 First Generation Status (No was used as baseline) Yes 0.478 0.422 0.021 0.967 0.334 Unknown -0.958 0.726 -0.027 -1.320 0.187 Pell Grant Eligibility (No was used as baseline) Yes -0.479 0.424 -0.024 -1.130 0.258 SAT Score 0.002 0.002 0.029 1.365 0.172 Note: R2 = 0.010 (N = 2751, p =0.060)

To determine how well the model fits for each academic semester, several

measures were evaluated after running the multiple regression test. The R2 listed in

52

Tables 6 and 7 represents the coefficient of determination, or the proportion of variance

in the dependent variables that can be explained by the independent variables. For the

fall 2011 semester for the dependent variable of current GPA the model showed R 2

value of 0.161. Therefore, this test shows that the independent variable of orientation

session, along with the controlled variables included (gender, ethnicity, SAT score, first

generation status and Pell grant eligibility) explain 16.1% of the variability of the

dependent variable of current GPA for the fall 2011 academic semester. The following

fall 2012 semester produced a lower R 2 of 0.141, indicating that the independent

variable of orientation session, along with the controlled variables, explained 14.1% of

the variability of current GPA.

Tables 6 and 7 also show that all six variables are statistically significantly, p <

.05 for both fall 2011 and fall 2012 for the variable of current GPA. The tables also

show that the six variables are not statistically significant, p > .05 for both fall 2011 and

fall 2012 for the variable of SCH completed percentage. The unstandardized coefficient

B indicates how much the respective dependent variable varies with each independent

variable when all other independent variables are held constant. Each of the six

independent variables’ statistical significance are shown by the t-value and

corresponding p-values for each dependent variable in the tables. The results show

that when compared to the baseline of freshman 4 orientation the later orientation

sessions (6, 7 and late) are all statistically significant and the coefficients are negative,

indicating that the later orientation sessions have a negative relationship with GPA for

both fall 2011 and fall 2012 semesters.

53

The beta value, β, is a measure of how strongly each independent variable

influences the dependent variable. It is measured in units of standard deviation and the

higher the beta value, the greater the relationship the independent variable has on the

dependent variable, and it allows for comparisons and the ability to assess the strength

of the relationship between each independent variable to the dependent variable. The

descriptive statistics reported in Tables 4 and 5 illustrate which orientation session

produced a higher GPA and SCH completed percentage for the semesters found to be

statistically significant according to the model.

In summary, a multiple regression was run to evaluate the relationship of current

GPA and SCH completed percentage from orientation session, gender, ethnicity, first

generation status, Pell grant eligibility, and SAT score. The assumptions of linearity,

independent of errors, homoscedasticity, unusual points and normality of residuals were

met. For GPA, these variables statistically showed a relationship with GPA, p < .001,

R 2 = .161 (fall 2011) and R 2 = .141 (fall 2012). The regression coefficients and

standard errors are found in the Tables 6 and 7 above. Once again the multiple

regression model did not show a statistically significant relationship between the six

independent variables and the dependent variable of SCH completed percentage for

both fall 2011 and fall 2012 academic semesters.

Results for Research Question 2: Relationship between Registration Time and Retention

In order to examine the relationship between registration time and retention, a

logistic regression was conducted. Once again, registration time occurs during the

student’s specific orientation session indicated in Appendix A. A logistic regression

allows the researcher to predict the probability that a student will fall into one of the two

54

categories of the dichotomous dependent variable (Agresti, A, 2002). These tests

helped examine whether retention (as measured by SCH completed for the current

semester or return for the following academic semester) could be predicted based on

the independent variables of session (registration timing) along with five other variables

(gender, ethnicity, SAT score, first generation status, Pell grant eligibility). The tests

also allowed the researcher to determine if the model was statistically significant, a

good model for the data, and the relative contribution of each of the independent

variables (if any) on the variance explained (Johnson & Christensen, 2008).

Retention was measured by two dependent variables; therefore, the logistic

regression test was run separately for each of these dependent variables for each

semester and each academic year. The first variable measured whether the student

completed any semester credit hours for the current semester. The second variable

measured whether the student returned the following academic semester. The logistic

regression model was used instead of the multiple regression model because each of

the two dependent variables are dichotomous and could not be measured on a

continuous scale. In order to compare each orientation session’s retention variables

with one another and to provide the number of observations for each variable,

descriptive statistics for each fall academic semester are displayed in Tables 8 and 9.

As in multiple regression, logistic regression requires that assumptions must be

met in order to use the statistical model. The six assumptions were met including the

first assumption of having an independence of cases (each student is only accounted

for once in the data file as assessed by the Durbin-Watson statistic, also used when

testing this assumption for multiple regression as stated above).

55



Variable Yes No Yes No Orientation Session Freshman 1 982 2 945 39 Freshman 2 948 5 918 35 Freshman 3 919 4 879 44 Freshman 4 898 5 864 39 Freshman 5 884 8 835 57 Freshman 6 874 4 834 44 Freshman 7 806 5 757 54 Late 94 1 84 11 Gender Male 2739 16 2590 165 Female 3666 18 3526 158 Ethnicity White 3432 20 3241 211 African-American 1023 4 994 33 Hispanic 1389 6 1333 62 Native American/Alaskan 106 101 5 Asian & Pacific Islander 402 2 396 8 Non-Resident Alien 40 2 41 1 Unknown 13 10 3 First Generation Status Yes 2657 16 2548 125 No 3443 13 3289 167 Unknown 305 5 279 31 Pell Grant Eligibility Yes 2415 11 2329 97 No 3990 23 3787 226

The second and third assumptions are that a linear relationship between the continuous

independent variables exist as verified by plotting the residuals, as in the test above for

multiple regression and checking that the continuous independent variables are linearly

related to the logit of the dependent variables. This was tested by using the Box-Tidwell

procedure, requiring a transformation of the dependent variable. The fourth assumption

is that there is no multicollinearity as verified by examining the Pearson correlation and

the collinearity statistics of Tolerance as done above for multiple regression. The fifth

assumption is that there are no significant outliers or influential points.

56



Variable Yes No Yes No Orientation Session Freshman 1 564 3 530 37 Freshman 2 571 1 548 24 Freshman 3 572 2 543 31 Freshman 4 575 6 546 35 Freshman 5 589 3 560 32 Freshman 6 604 2 556 50 Freshman 7 592 6 535 63 Late 75 3 63 15 Gender Male 1862 11 1719 154 Female 2280 15 2162 133 Ethnicity White 2134 10 1985 159 African-American 618 4 588 34 Hispanic 1020 6 958 68 Native American/Alaskan 63 1 56 8 Asian & Pacific Islander 234 3 225 12 Non-Resident Alien 40 2 38 4 Unknown 33 31 2 First Generation Status Yes 1718 6 1600 124 No 2062 14 1942 134 Unknown 362 6 339 29 Pell Eligibility Grant Yes 1616 14 1532 98 No 2526 12 2349 189

Like ordinary multiple regression, the casewise list table produced in the

statistical test results will highlight if there are any cases where outliers may exists. The

sixth and final assumption is that the categories are mutually exclusive. This

assumption is true because a student either completes the semester or they do not

complete the semester, and the student will either return the following semester or they

will not return.

57

Table 10

Logistic Regression Model Summary (Fall 2011) – Question 2

Completed Semester Variable B SE OR 95% CI Wald

statistic p

Orientation Session (Freshman 4 was used as baseline) Freshman 1 16.075 1509.7 9.58E6 0.000 0.992 Freshman 2 0.062 0.717 1.064 (0.261,0.4337) 0.007 0.931 Freshman 3 0.711 0.869 2.035 (0.371,11.182) 0.669 0.414 Freshman 5 -0.382 0.649 0.682 (0.191,2.437) 0.346 0.556 Freshman 6 -0.019 0.713 0.982 (0.243,3.970) 0.001 0.982 Freshman 7 15.986 1620.5 8.76E7 0.000 0.992 Late -1.008 1.143 0.365 (0.039,3.427) 0.778 0.378

Gender (Male was used as baseline) Female -0.101 0.451 0.904 (0.373,2.188) 0.050 0.823

Ethnicity (African-American was used as baseline) White 0.513 0.660 1.671 (0.458, 6.094) 0.605 0.437 Hispanic 0.002 0.662 1.002 (0.274, 3.668) 0.000 0.998 Native American/Alaskan 16.385 4685.9 1.31E7 0.000 0.997

Asian & Pacific Islander -0.145 0.903 0.865 (0.147, 5.078) 0.026 0.873 Non-Resident Alien 16.323 7558.4 1.23E7 0.000 0.998 Unknown 15.794 10919 7.23E6 0.000 0.999

First Generation Status (No was used as baseline) Yes -1.202 0.802 0.301 (0.062, 1.446) 2.249 0.134 Unknown -0.525 0.498 0.592 (0.223, 1.569) 1.113 0.291

Pell Grant Eligibility (No was used as baseline) Yes 0.818 0.534 2.265 (0.796, 6.448) 2.348 0.125

SAT Score -0.001 0.002 0.999 (0.995, 1.002) 0.487 0.485 Note: CI = Confidence Interval for odds ratio (OR)

Returned Spring Semester

Variable B SE OR 95% CI Wald Statistic p

Orientation Session (Freshman 4 was used as a baseline) Freshman 1 -0.053 0.290 0.949 (0.537, 1.675) 0.033 0.856 Freshman 2 -0.013 0.297 0.987 (0.552, 1.766) 0.002 0.987 Freshman 3 -0.067 0.293 0.935 (0.527, 1.662) 0.052 0.820 Freshman 5 -0.631 0.265 0.532 (0.317, 0.895) 5.667 0.017 Freshman 6 -0.331 0.281 0.718 (0.414, 1.247) 1.384 0.239 Freshman 7 -0.485 0.285 0.616 (0.352, 1.076) 2.902 0.088 Late -1.470 0.430 0.230 (0.099, 0.534) 11.701 0.001 Gender (Male was used as baseline) Female 0.231 0.143 1.260 (0.952, 1.666) 2.618 0.106 Ethnicity (African-American was used as baseline) White -0.657 0.234 0.518 (0.328, 0.820) 7.886 0.005 Hispanic -0.319 0.250 0.727 (0.445, 1.188) 1.622 0.203 Native American/Alaskan 1.005 1.045 2.733 (0.352, 21.191) 0.926 0.336 Asian & Pacific Islander 0.161 0.417 1.175 (0.519, 2.663) 1.622 0.203 Non-Resident Alien 19.465 8770.6 2.84E8 0.000 0.998 Unknown -2.018 0.802 0.133 (0.028, 0.640) 6.327 0.012 First Generation Status (No was used as baseline) Yes -1.034 0.258 0.356 (0.214, 0.590) 16.010 <0.001 Unknown -0.121 0.160 0.886 (0.647, 1.213) 0.572 0.449 Pell Grant Eligibility (No was used as baseline) Yes 0.397 0.168 1.487 (1.070, 2.067) 5.573 0.018 SAT Score 0.002 0.001 1.002 (1.001, 1.003) 9.688 0.002 Note: CI = Confidence Interval for odds ratio (OR)

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Table 11

Logistic Regression Model Summary (Fall 2012) – Question 2

Completed Semester Variable B SE OR 95% CI Wald

statistic p

Orientation Session (Freshman 4 was used as baseline) Freshman 1 0.336 0.920 1.399 (0.231, 8.486) 0.133 0.715 Freshman 2 16.273 2052.7 1.17E7 0.000 0.994 Freshman 3 0.391 0.922 1.478 (0.242, 9.013) 0.180 0.672 Freshman 5 0.510 0.923 1.665 (0.273, 10.175) 0.305 0.581 Freshman 6 0.675 0.928 1.964 (0.319, 12.097) 0.530 0.467 Freshman 7 0.546 0.930 1.726 (0.279, 10.677) 0.344 0.557 Late -1.289 0.962 0.276 (0.042, 1.815) 1.796 0.180

Gender (Male was used as baseline) Female -0.174 0.541 0.841 (0.291,2.429) 0.103 0.748

Ethnicity (African-American was used as baseline) White -0.138 0.804 0.871 (0.180,4.212) 0.029 0.864 Hispanic -0.070 0.798 0.933 (0.195,4.456) 0.798 0.930 Native American/Alaskan 15.944 7528.5 8.40E7 0.000 0.998

Asian & Pacific Islander -0.884 0.953 0.413 (0.064,2.675) 0.860 0.354 Non-Resident Alien 16.636 9292.4 1.68E7 0.000 0.999 Unknown 15.965 7718.7 8.58E6 0.000 0.998

First Generation Status (No was used as baseline) Yes -0.496 0.739 0.609 (0.143,2.593) 0.450 0.502 Unknown 0.594 0.637 1.812 (0.520,6.318) 0.870 0.351

Pell Grant Eligibility (No was used as baseline) Yes -0.817 0.595 0.442 (0.138,1.419) 1.883 0.170

SAT Score 0.002 0.002 1.002 (0.998,1.007) 1.067 0.302 Note: CI = Confidence Interval for odds ratio (OR)

Returned Spring Semester

Variable B SE OR 95% CI Wald Statistic p

Orientation Session (Freshman 4 was used as baseline) Freshman 1 -0.052 0.296 0.949 (0.531, 1.694) 0.031 0.859 Freshman 2 0.464 0.332 1.591 (0.830, 3.047) 1.959 0.162 Freshman 3 0.259 0.308 1.295 (0.709, 2.367) 0.707 0.400 Freshman 5 0.309 0.316 1.362 (0.733, 2.530) 0.957 0.328 Freshman 6 -0.281 0.280 0.804 (0.465, 1.391) 0.609 0.435 Freshman 7 -0.472 0.273 0.624 (0.365, 1.064) 2.996 0.083 Late -1.518 0.410 0.219 (0.98, 0.489) 13.720 <0.001 Gender (Male was used as baseline) Female 0.413 0.157 1.511 (1.110,2.056) 6.881 0.009 Ethnicity (African-American was used as baseline) White -0.875 0.288 0.417 (0.237,0.733) 9.211 0.002 Hispanic -0.595 0.299 0.551 (0.307,0.990) 3.970 0.046 Native American/Alaskan -1.665 0.618 0.189 (0.056,0.636) 7.253 0.007 Asian & Pacific Islander -0.633 0.408 0.531 (0.239,1.182) 2.404 0.121 Non-Resident Alien 18.914 9498.8 1.64E8 0.000 0.998 Unknown 0.177 1.061 1.193 (0.149,9.556) 0.028 0.868 First Generation Status (No was used as baseline) Yes -0.214 0.287 0.807 (0.460,1.416) 0.558 0.455 Unknown -0.213 0.176 0.809 (0573,1.141) 1.462 0.227 Pell Grant Eligibility (No was used as baseline) Yes 0.431 0.182 1.538 (1.076,2.198) 5.585 0.018 SAT Score 0.002 0.001 1.002 (1.000,1.003) 7.242 0.007 Note: CI = Confidence Interval for odds ratio (OR)

59

The model summary table presented in Tables 10 and 11 above show several

results, including the contribution of each independent variable to the model and its

statistical significance. For each fall semester, a baseline was chosen for each

categorical variable in order to compare the change in the odds ratio for each increase

in one unit of the independent variable. The chosen baseline for each fall semester is

noted next to the respective categorical variable.

The Wald test is used to determine statistical significance for each of the

independent variables and the significance of the test is found in the ‘p’ column. Based

on these results for the dependent variable of semester completed, both fall 2011 and

fall 2012 showed that the relationship between all of the independent variables and the

dependent variable of semester completed were not significant. The coefficients (‘B’

column in the tables above) are used in the equation to predict the probability of the

student completing the semester and the odds ratio of each independent variable in the

‘OR ‘column, along with the confidence intervals shows the change in the odds ratio for

each increase in one unit of the independent variable. Because the model for fall 2011

and fall 2012 showed no statistical significance regarding the dependent variable of

completed semester, the odds ratio is not used. As indicated in Tables 10 and 11 the

fall 2011 and fall 2012 semester did show a statistically significant relationship between

the independent variable of session and students returning the following semester. For

example, for the fall 2011 and fall 2012 semester, late orientation shows a statistically

significant relationship and students enrolled during this session are approximately 4.5

times more likely to not return the following semester, than those students who enroll

during freshman orientation session 4.

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In summary, the logistic regression was performed to show if the relationship of

session, gender, ethnicity, first generation status, Pell grant eligibility, and SAT score

can predict whether a student completes the semester, and whether the student returns

the following semester. Based on the results of the logistic regression model it was

determined that there is not a relationship between the independent variables and the

whether the student completed the semester the dependent variable. There is,

however, a statistically significant relationship between session and the controlled

variables of gender, ethnicity, SAT, first generation status and Pell grant eligibility and

retention as measured by students returning the following academic semester as

indicated in the table.

Results for Research Question 3: Differences between Decided and Undecided Students on Academic Performance

In order to evaluate the differences in academic performance between the two

independent groups of students (undecided and decided students), a Mann-Whitney U

test was used. The Mann-Whitney U test was used because after testing for normality

the researcher determined the independent t test could not be used as the statistical

test because in all cases the p value for the normality test was less than .05 (with a

confidence level of 95%). Prior to running the test, four assumptions were met. Each

test run used one dependent variable at a time (GPA and SCH completed percentage)

and the one independent variable of major status (which was dichotomous), and there

was an independence of observations. The final assumption that was met was

determining that the shape of the distributions of the two groups were the same. This

allows the researcher to determine whether the median score of the two groups of

undecided and decided students for the independent variable of major status are

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different in terms of the dependent variables of GPA and SCH completed percentage

and how much the difference the median between the two groups are. Descriptive

statistics, including the median for the two groups and for every academic year and

semester are presented in Tables 12 and 13.

Table 12 Descriptive Statistics – Question 3

Current GPA Semester Credit Hours Completed % Fall 2011 N Mean Std Dev Median N Mean Std Dev Median

Decided 4885 2.70 1.036 2.94 4885 97.71 9.780 100.00 Undecided 1607 2.61 1.037 2.80 1607 96.90 10.563 100.00

Fall 2012 N Mean Std Dev Median N Mean Std Dev Median Decided 3148 2.67 1.074 3.00 3148 97.78 10.138 100.00 Undecided 1070 2.56 1.075 2.77 1070 97.63 10.464 100.00

Table 13 Mann Whitney U Test Results – Question 3

Current GPA

Decided Undecided Variable Mean SD Mean SD U p

Fall 2011 2.70 1.036 2.61 1.037 3693382 <0.001 Fall 2012 2.67 1.074 2.56 1.075 1563871 <0.001

Semester Credit Hours Completed % Decided Undecided

Variable Mean SD Mean SD U p Fall 2011 97.71 9.780 96.90 10.563 3785039 <0.001 Fall 2012 97.78 10.138 97.63 10.464 1674150 0.532

Table 13 above presents the U statistic, and the statistical significance of the

Mann-Whitney test for each of the fall semesters. For the dependent variable of GPA

and the fall semesters 2011 and 2012, the p values are less than .05, indicating there is

a statistically significant difference in median GPA between decided and undecided

students. For the fall 2011 semester there is also a statistically significant difference in

median SCH completed percentage between decided and undecided students.

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Also reported in Table 13 is the mean rank for decided students and undecided

students. For the fall semesters, the mean rank indicates which group is higher than

the other group. It was also determined that the distribution of scores for both groups of

the independent variables of GPA and SCH completed percentage had the same

shape; therefore by examining the descriptive statistics in Table 12 for the fall

semesters, the researcher can determine which group’s value is higher. For example,

based on results of the Mann-Whitney model, there are differences in academic

performance of decided and undecided students enrolled in the fall 2012 semester, and

decided students have a 0.23 higher median score than undecided students and have a

0.11 higher mean value that undecided students.

Results for Research Question 4: Differences between Decided and Undecided Students on Retention

In order to evaluate the differences in retention between the two independent

groups of students (decided and undecided) a chi-square test was run. This test

determines whether two variables have a statistically significant association. Tables 14

and 15 report the cross tabulation of observed frequencies for each of the independent

(major status) and dependent variables (completed semester and returned spring

semester).

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Table 14

Cross Tabulation (Fall 2011) – Question 4

Completed Semester Returned Spring Semester Decided Yes No Total Yes No Total

Count 4858 27 4885 4655 230 4885 % Within Major Status 99.4 0.6 100.0 95.3 4.7 100.0 % Within Variable 75.2 75.0 75.2 75.5 69.9 75.2 % of Total 74.8 0.4 75.2 71.7 3.5 75.2

Undecided Count 1598 9 1607 1508 99 1607 % Within Major Status 99.4 0.6 100.0 93.8 6.2 100.0 % Within Variable 24.8 25.0 24.8 24.5 30.1 24.8 % of Total 24.6 0.1 24.8 23.2 1.5 24.8

Total Count 6456 36 6492 6163 629 6492 % Within Major Status 99.4 0.6 100.0 94.9 5.1 100.0 % Within Variable 100.0 100.0 100.0 100.0 100.0 100.0 % of Total 99.4 0.6 100.0 94.9 5.1 100.0

Table 15

Cross Tabulation (Fall 2012) – Question 4

Completed Semester Returned Spring Semester Decided Yes No Total Yes No Total

Count 3128 20 3148 2948 200 3148 % Within Major Status 99.4 0.6 100.0 93.6 6.4 100.0 % Within Variable 74.7 71.4 74.6 75.1 68.0 74.6 % of Total 74.2 0.5 74.6 69.9 4.7 74.6

Undecided Count 1062 8 1070 976 94 1070 % Within Major Status 99.3 0.7 100.0 91.2 8.8 100.0 % Within Variable 25.3 28.6 25.4 24.9 32.0 25.4 % of Total 25.2 0.2 25.4 23.1 2.2 25.4

Total Count 4190 28 4218 3924 294 4218 % Within Major Status 99.3 0.7 100.0 93.0 7.0 100.0 % Within Variable 100.0 100.0 100.0 100.0 100.0 100.0 % of Total 99.3 0.7 100.0 93.0 7.0 100.0

As mentioned previously, the chi-square test shows whether two categorical

variables are associated, and Table 16 illustrates whether or not decided and undecided

students are statistically associated. For the dependent variable of completed semester

the test shows that there is not a statistically significant association between decided

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and undecided students for fall 2011 and fall 2012. The p value reported for the fall

semesters was greater than .05. For the semesters fall 2011 and fall 2012, the test

showed that there was a statistically significant association between decided and

undecided students and whether the students would return the following semester. The

Phi Value is reported to provide measures of effect size. In conclusion, there are

differences between decided and undecided students on retention, as measured by the

variable of Returned, for the referenced semesters.

Table 16

Chi-Square Test Results – Question 4

Fall 2011 Decided Undecided

Variable n % N % χ2 p Completed Semester 4858 75.2 1598 24.8 0.001 0.973 Returned Spring Semester 4655 75.5 1508 24.5 5.301 0.021

Fall 2012 Decided Undecided

Variable n % N % χ2 p Completed Semester 3128 74.7 1062 25.3 0.153 0.696 Returned Spring Semester 2948 74.6 976 25.4 7.283 0.007

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CHAPTER 5

SUMMARY, DISCUSSION AND CONCLUSIONS

Overview

Higher education administrators need to assess how they can assist students at

every entry point into the university in order to help them successfully persist and attain

their degrees (Marling, 2013). Society, institutions and students benefit from high

student academic performance and retention rates. As stated by Schnell and Doetkott,

(2003) “Student experience during the first year and particularly the first six weeks is

critical for persistence to graduation” (p. 377). This present study was designed to

examine the relationship between registration timing and academic performance and

retention for first- time in college students, as well as bring attention to institutional

processes and how they can contribute to the environment a student encounters

throughout their educational pursuits, including the matriculation and registration

process. The study was also designed to bring attention to the differences between

having a declared major and having an undecided major can have on a student’s

academic performance and retention for the first semester in college.

Discussion

Astin’s I-E-O model (input, environment, and output) was used as a conceptual

guide for this study. The first- time in college students entered FYU with several input

factors that were utilized as controlled variables (gender, ethnicity, SES factors, SAT

score) in the analysis of the data for Research Questions 1 and 2. The environment

was their orientation session, which represented the students’ interaction with the

university through the enrollment and matriculation process and contributed to how they

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finished the academic semester. Their output variables were measured by the students’

academic performance (their current semester grade point average and semester credit

hours completed percentage) as well as their retention rates for the current semester

and whether they returned the following semester to FYU. Astin stressed the need for

focus on resources in the first year of undergraduate work (Schnell & Doetkott, 2003).

Freshman orientation is one of the first encounters a student has with the university, its

policies, procedures and matriculation into the university; therefore, an analysis of the

relationship between timing of a student’s registration and their academic performance

and retention is important to study. By utilizing the I-E-O model as a guide, this study

helped provide insight as to how students’ input factors interact with their environment

and the relationship it can have on their academic performance and retention at the

university. A discussion of each research question and its findings in relation to the

literature as well as the implications they can have on future policies and practices at

FYU is discussed in the remainder of this chapter.

Discussion for Research Question 1: Relationship between Registration Time and Academic Performance

The study found that a good model does not exist to predict the output factor of

academic performance in Astin’s model, however the relationship between registration

timing and academic performance, as measured by the variables of current GPA and

SCH completed percentage, is statistically significant. This indicates that registration

time (as represented by orientation session), along with the multiple input factors,

(gender, ethnicity, SAT score, first generation status, Pell grant eligibility), cannot

predict current GPA or the semester credit hours percentage that a freshman completes

in the first semester of enrollment. Registration timing is just one of the factors used as

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a variable in the study because students’ multiple input factors can also interact with

their environment to show a relationship with academic performance. There are other

variables, however, unaccounted for in this model that can contribute to the variation of

GPA and SCH completed percentage that would help explain more of the variation of

the dependent variables. As this model shows, registration timing (along with the

control input factors) have a relationship with current GPA and SCH completed

percentage, therefore, as registration timing changes, GPA and SCH completed

percentage will be different between the registration times. The model cannot,

however, use registration time to predict what a student’s current GPA or the

percentage of semester credit hours the students complete will be.

The descriptive statistics included in Tables 4 and 5 help illustrate that the earlier

orientation sessions show a higher mean GPA for students who registered during that

time in comparison with the later orientation session times. For example, for fall 2012,

students who enrolled during freshman orientation sessions 1,2,3,4 had mean GPAs

higher than freshman orientation sessions 5, 6 and 7. The results indicate that the

orientation session a student attends does not having any influence on the percentage

of credit hours a student will complete during the fall 2011 and fall 2012 semester since

the results indicated no statistical significance. For both fall 2011 and fall 2012, and the

designated freshman orientation sessions (listed in Appendix A), late registration shows

the lowest mean GPA, as well as the lowest SCH completed percentage. Tables 10

and 11 illustrate the contribution of each independent variable to the model and its

statistical significance for the fall 2011 and fall 2012 semesters. Freshman orientation

session 4 was used as the baseline to compare all other orientation sessions. One

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significant finding from the multiple regression model indicates that students who

registered later than those in freshman orientation session 4 will have a lower GPA.

The study’s findings help support previous research such as Neighbors’ (1996)

study which found that students who registered later did not achieve a high rate of

academic success. This study also is consistent with Street’s (2000) study that found

late registration to be a deterrent to academic success for new students, because the

students had lower academic success as measured by semester GPA and successful

completion rates. Mendiola-Perez (2004) also evaluated the effects of registration

timing on academic success for first-time students and found through her review that

students who register later are more likely to have less academic success than those

students who register earlier. Her study, however, was completed mostly at the

community college level. In contrast, the present study of the relationship between

registration timing and academic success for first-time in college students was

conducted at a four-year university where the registration, enrollment and matriculation

processes could be very different regarding mandatory orientation sessions and student

input factors prior to interaction with the institutional environment. Combined with the

previous literature focusing on community colleges what the present study of university

students illustrates is, regardless of institution type or enrollment processes, first- time in

college students who register earlier are found to have higher GPAs for their first

semester of enrollment.

Discussion for Research Question 2: Relationship between Registration Time and Retention

The study found that a good model does exist to predict the output factor of

retention in Astin’s model but only when measuring whether or not students returned the

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following academic semester. The model was a poor fit for the dependent variable of

completed semester, and the relationship between registration timing and retention as

measured by this variable was not statistically significant. One explanation could be

that there was a high percentage of students in the population who completed the

semester: therefore, the variable was not a reliable indicator as a measure of retention.

The relationship between registration timing and retention as measured by the variable

of ‘returned spring semester’, is statistically significant. This indicates that registration

time (as represented by orientation session), along with the multiple input factors,

(gender, ethnicity, SAT score, first generation status, Pell grant eligibility) can predict

whether first-time students at FYU will return the following academic semester after they

complete their first semester of enrollment at FYU.

As indicated in the discussion for Research Question 1, registration timing is just

one of the factors used as a variable in the study because students’ multiple input

factors can also interact with their environment to show a relationship with retention.

There are other variables, however, unaccounted for in this model that can contribute to

the variation of students returning the following academic semester that would help

explain more of the variation of the dependent variables. As this model shows,

registration timing (along with the control input factors) have a relationship with ‘returned

spring semester’, but there is only a small reliable relationship between the dependent

variable of ‘returned spring semester’ and the independent variables in this study.

Therefore, as registration timing changes, so does the probability that the student will

return the following academic semester.

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Tables 10 and 11 illustrate the contribution of each independent variable to the

model and its statistical significance for the fall 2011 and fall 2012 semesters.

Freshman orientation session 4 was used as the baseline to compare all other

orientation sessions. One significant finding from the logistic regression model indicates

that students who registered during late orientation were approximately 4.5 times more

likely to not return the following academic semester for both the fall 2011 and fall 2012

semesters than if they registered during freshman orientation session 4.

This finding agrees with other studies found in literature regarding the

relationship between registration timing and retention. Smith, Street and Olivarez

(2002) found that registration time significantly affected students in terms of retention:

and they recommended easy-access registration to encourage early registration. Lang

(2001) and Wang and Pilarzyk (2009) also analyzed the timing of student registration in

order to evaluate how retention can be effected based on deadlines and institutional

policies and procedures. Roueche and Roueche recommended in their 1993 study that

community colleges eliminate late registration completely because of how retention

could be improved with the removal of late registration. While this recommendation

may seem extreme and perhaps not practical for all institutions, the results for this study

do indicate how much more likely a late registering, first-time enrolling student is to not

return. Summer’s 2000 study found that persistent students enrolled for fall semester

classes almost 30 days earlier than students who did not complete their semester or

who did not enroll the following academic semester. The literature does support this

study’s finding that later registration times could predict lower retention rates, but as

Stillman (2009) recommended, each institution needs to identify the variables that show

71

relationships with retention in order to implement policies and procedures to increase

them. Registration timing is just one variable that does have a relationship with

retention of first-time in college students at FYU.

Discussion for Research Question 3: Differences between Decided and Undecided Students on Academic Performance

When examining the differences between decided and undecided students on

academic performance, the study found that decided students have a statistically higher

GPA than undecided students for the fall 2011 and fall 2012 semesters. Decided

students also have a statistically higher SCH completed percentage than undecided

students for fall 2011 semester. The results indicate that first-time students at FYU who

have decided their major prior to enrollment will have a higher GPA and/or complete a

higher percentage of their semester credit hours at the end of the semester than the

first-time students at FYU who are undecided about their major prior to enrollment.

Table 13 helps illustrate the differences between the mean GPA of decided students

and the mean GPA of undecided students as well as how much higher the mean GPA

score is for decided students compared to the mean GPA of undecided students.

The present study contributes to literature that investigated any connection

between academic performance and a student’s choice of major. Older studies such

as Wikoff and Kafka (1978) have showed that major statistics were not reliable

predictors of academic success, but more current research, such as Leppel (2001),

have showed the connection between a student’s choice of major on their academic

success and/or retention rates. Because the results of this study indicate that decided

students will have a higher GPA than undecided students, FYU should investigate

policies or practices that could contribute to helping undecided students choose a major

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prior to enrollment. Limited research has been conducted regarding registration time,

academic success, and retention as they relate to decided and undecided students, but

this finding for first-time students at FYU, illustrates the need for further studies that

could help explain the importance of choosing a major for a first-time in college student.

Discussion for Research Question 4: Differences between Decided and Undecided Students on Retention

Regarding retention, there was not a statistically significant association between

major status (i.e. decided or undecided), and whether or not the students completed

their current semester of enrollment. One explanation could be that there was a high

percentage of students in the population who completed the semester; therefore, the

variable was not a reliable indicator as a measure of retention. The test did show,

however, that there was an association between decided and undecided students and

whether the students would return the following semester.

This study showed that first-time students at FYU who decided their major prior

to enrollment were more likely to return the following academic semester than the first-

time students who were undecided about their major when they enrolled for their first

semester at FYU. This finding contributes to other research findings that students who

have decided majors will have higher retention rates than students who have undecided

majors. Therefore, as discussed later in the implications for policy, FYU should

consider requiring all first-time students to decide upon a major prior to enrollment, if the

reason they retained is attributed to the fact they have chosen a major area of study.

Leppel (2001) also studied the differences between student retention and a

student’s choice of major. She found that the differences in subject interest could help

explain retention rates. The study was also conducted during the student’s first year of

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college and showed that students with undecided majors have lower academic

performance and retention rates. Advising a student is an important part of the

enrollment process for new students according to Goomas (2012). He showed that a

positive relationship does exist between retention and academic advising. Therefore,

investing in academic advising activities that help students decide their major as a part

of students’ enrollment process could promote higher retention rates.

In conclusion, there are differences between decided and undecided students on

academic performance and retention for the referenced semesters. A model does exist

that can measure how much higher a decided student’s GPA is in comparison to the

GPA of an undecided student, as well as how much more likely they are to return the

following semester based on their major status, i.e. decided or undecided.

Summary Discussion

Academic performance and retention are important factors to measure for FYU

and many other universities concerned with retaining and graduating students. As

studies such as Hornik et al. (2008) have showed, school withdrawals are more

common with freshmen than upperclassman. Programs such as freshman orientation

are created to help with academic planning and advising as Burgette and Magun-

Jackson (2008) found. Their study also found that there is a relationship between long-

term persistence and GPA. By studying the environment of the university, which

includes programs and processes such as freshman orientation, registration,

matriculation and enrollment procedures, FYU and others can examine the relationship

between the environment and the student’s academic success or output during their

tenure at the university. Evaluating the relationship between registration timing and

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differences in major status of students with regard to academic performance and

success is important when making new policies and practices for the university.

Implications for Policy

Required Major Status

As this study showed, there were differences in academic performance and

retention between decided and undecided students. FYU has considered the

introduction of a policy that would require all entering FTIC students to the university to

declare a major prior to enrollment, and therefore, undecided FTIC students would not

exist. FYU recently eliminated the department of undergraduate studies that supports

the indication that the policy will soon reach fruition. If students are required to declare

a major upon admission to the university, they will need to work with their academic

advisor closely in order to choose coursework that is specific for their chosen degree

path because deciding a major, especially before taking one course can be

overwhelming for most students.

If the policy of requiring a major status for every FTIC student prior to enrollment

at FYU is created and enforced, the specific colleges at FYU should consider investing

in academic advising areas and/or increasing the number of program coordinators for

each discipline in order to help students decide a major prior to enrollment at the

university. As illustrated in the results, differences do exist between undecided and

decided students with regards to academic performance and retention. Decided

students have a statistically higher GPA than undecided students, as well as a higher

percentage of semester completed hours. It is likely that the students who had decided

on their major prior to enrollment would have had additional interaction with the

75

university environment prior to orientation if they chose their major at a time other than

orientation. These students may have received information and/or guidance from a

university office or person on campus. As literature and reports such as the ACT Policy

Report (2006) have shown, one of the primary factors affecting college retention is the

quality of interaction that a student has with a concerned person on campus. An

academic advisor would be an example of a connection a student would have with a

member of the university prior to ever stepping foot inside a classroom. Based on the

results of this study and with support of past literature, requiring FTIC students to

declare a major and meet with an academic advisor prior to enrollment could be a

positive policy change for FYU and for FYU future student academic success and

retention.

Students will need assistance in researching what is required of potential majors

in order to maximize their preparation and enroll in courses that will be necessary to

fulfill their requirements and return the following semester. A 25- year longitudinal study

of approximately 20,000 first-year students found that 85% of undecided students were

anxious about choosing a major and need a supportive institutional environment in

order to assist the decision making process of choosing a major (Gordon & Steele,

2003). As Burgette and Magun-Jackson (2008) also found, freshman orientation

courses should address topics such as academic planning and advising. In FYU’s

case, with 24% of its FTIC students enrolling without a chosen major, they need to

address the finding that decided students will have a higher mean GPA and have a

higher retention rate than undecided students. Providing a supportive advising

environment prior to registration could help increase the number of decided students,

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students who may perform better and be retained at a higher rate, as well as connect

students to a university contact that can help provide guidance and assistance.

If a policy requiring FTIC students to decide on a major prior to enrollment is

written at FYU, then administration will need to communicate and create a strategic plan

for all colleges and support them with the allocated resources necessary to enforce the

policy successfully. Researching peer institutions that are comparable to FYU and have

promulgated a similar policy provide an opportunity for benchmarking and reference.

Mandatory Orientation Sessions and Students Excluded from the Study

Currently, FYU does not enforce a policy requiring FTIC students to attend a

mandatory orientation session, although procedures do state that an orientation session

is required for all new students before enrollment into the university. This study did

show that registration timing was one of a multitude of factors that does relate to

academic performance and retention, but it is a small relationship. A concern resulting

from the study was the lack of orientation sessions listed for some new students as well

as the number of FTIC students attending the orientation sessions reserved for transfer

students.

The students in the data file who did not have an orientation session listed were

excluded from the study for analysis. The assumption was made by the researcher that

those students who had the SESSION field blank did not attend an orientation session

for reasons unknown. Institutional Research reported to the researcher that the

orientation term came from a separate orientation file in FYU’s database, so there was

also the possibility of an error in the merging of the data files, and the students could

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have attended a specific orientation session, but without the correct data, it was best to

exclude these students from the study.

With students excluded from the study because of a lack of orientation session

listed, it raises the issue of a need for an exception to the required procedure that

orientation is mandatory for all incoming new students to the university. Currently, if a

FTIC student cannot attend one of the reserved orientation sessions for FTIC students

specifically, they attend one of the transfer student orientation sessions. There appears

to be a need for a specific policy to address the students who did not attend one of the

required orientation sessions but still were able to matriculate and enroll into the

university for the semester. Orientation session presents the opportunity to capture the

initial registration timing of FTIC students. If students do not attend an orientation

session it is difficult to examine the relationship initial registration timing will have on the

academic performance or retention of these students.

Moore and Shulock (2009) reported that students who take an orientation course

complete their courses at higher rates and maintain higher GPAs than those students

who do not take a freshman orientation course. Zeidenberg, Jenkins, and Calcagno

(2007) studied the impact of orientation courses on freshman students and found that

the community college students who attended orientation courses in his study were

eight percent more likely to remain enrolled in their institution after five years and they

were more likely to complete their degree than those who did not. Derby and Smith

(2004) also found that associations do exist between taking an orientation course and

student retention. First-year experience programs, including first-year seminars are a

different variety of programs than orientation courses, but they also provide students

78

early support and indicate a positive relationship with persistence and success (Moore

& Shulock, 2009). They are mentioned below as an implication for practice at FYU.

Additionally, the study conducted at FYU did not analyze specific differences

between those FTIC students that attended orientation sessions designed for transfer

students instead of the designated freshmen orientation sessions because the sessions

had less than 30 observations in each session. Once again, because FTIC students

are not required to attend their designated orientation session, students were excluded

from analysis, limiting the findings of this study and future evaluations of FYU

orientation, registration and enrollment processes. Also, because the study did not

address the differences between transfer orientation and freshman orientation it is not

known if freshman students who attended the transfer orientation received information

appropriate to assist in their adaptation to the university environment.

If special allowances are made for FTIC students that cannot attend sessions

that are specific for FTIC students, then there needs to be a process and policy in place

to ensure those who do not attend or those that attend a transfer orientation receive all

pertinent information required and needed for all FTIC students. As the literature shows

in the discussion later in this chapter, transfer students have unique characteristics from

FTIC students and their orientation sessions and matriculation into the university is

different. Specific orientation sessions should be kept separate for FTIC and transfer

students.

Implications for Practice

Academic Advising

As mentioned in implications for policy and in studies conducted on students with

79

undecided majors, academic advising is a critical in the student services’ area at

universities. As the ACT Policy Report (2006) found, one of the main factors that can

affect college retention of all students is the quality of an interaction between a student

and a person who is on campus; every student is required to at least have an interaction

(and hopefully relationship) with their academic advisor. Robbins et al. (2009) found

that students who utilize the resource of advising were positively associated with GPA

and/or retention, as well as more prominently used more for at risk students. Since

research has shown that academic advising can play a role in a student’s decision to

persist or drop out, academic advising is an area that needs to be heavily invested in by

FYU and other universities nationwide.

If FYU decides to implement the policy of requiring all FTIC students to decide on

an academic plan or major prior to enrollment, the orientation process and registration

and enrollment practices and procedures will have to be altered to account for the time

and planning of academically advising all new incoming freshmen to the university.

Literature such as Goomas’ 2012 study showed that a positive relationship exists

between retention and academic advising. Also, Tinto (2005) stated that students will

persist in settings that provide clear and consistent information about institutional

requirements and effective academic advising can become an important piece of the

orientation process. This study’s results show that a statistically significant difference in

median GPA between decided and undecided students does exist and the results could

be used as an example to help support why a policy of requiring all FTIC students to

have a decided major prior to enrollment in their first semester is important in their

academic performance and retention.

80

Freshman Preparation Courses

Freshman preparation courses have shown to have a positive relationship with

the persistence of students, as well as a positive relationship with college achievement.

Required freshman preparation courses, whether they happen before (freshman

orientation or summer preparation courses) or after matriculation are also an area that

FYU needs to allocate time and resources to help strengthen support for freshman

students. Pascarella and Terenzini (2005) found that student persistence can have a

positive relationship with multiple areas, including first-year seminars. Barefoot et al.

(2005) reports that first-year seminars are the most common for freshman students.

As previously mentioned, Burgette and Magun-Jackson (2008) found there is a

positive relationship among long-term persistence, GPA, and students who took a

freshman orientation course, but first year programs for freshman can be just as

beneficial. With current literature such as Jamelske’s (2009) study of a first year

experience program, he found that on average the students who participated in the

program earned higher GPAs than those students who did not. The program was

designed to integrate students into the university community by offering academic and

non-academic activities. First year seminars can also provide contributions to higher

retention rates for freshman students, as Schnell and Doetkott (2003) found in their

examination of the impact of students enrolled in a first year seminar and retention over

a four-year period. This longitudinal study’s results showed that the “students who

enrolled in the first year seminar were consistently retained in significantly greater

numbers” (p. 367) than those who did not enroll.

81

With literature showing the positive relationship a freshman preparation course

can have regarding persistence, whether it is taken prior to enrollment or during the first

year at the university, FYU needs to incorporate courses that will include topics covering

freshman concerns and needs. These include areas such as academic advising and

academic planning (especially if an upcoming policy requiring a mandatory major

declaration by freshman prior to enrollment).

Student Services Related to Registration

The enrollment and matriculation process at a university is one step in a

sequential line of processes students complete before they even step foot in a

classroom to begin their academic career. Other student services related to enrollment

can have an influence on a student’s interaction with their environment at the university.

These services include admission, and recruitment offices, financial aid and financial

planning offices, orientation offices and academic advising services. These only

represent a small group of people with whom FTIC students will have interaction prior to

their enrollment in the university. Others that are not visited by every student can

include housing offices, international student offices and offices with disability

accommodation, to name just a few.

If FYU adapts and enforces a policy that every new student at FYU is required to

attend an orientation session to register, then there is an opportunity for offices integral

to the academic success and retention of the students to be a part of the student’s

environment during registration. As literature has shown, when a student has contact or

a relationship with a person on campus, they are more likely to persist if it is a positive

82

relationship and the student is satisfied with the institution’s support, both academic and

non-academic (Lau, 2003).

Implications for Future Research

Returning Students

This study focused on first-time in college students only and not those students that

were returning to FYU. Returning students have separate registration timelines

depending on several factors, including, but not limited to, semester, academic

standing, academic classification, and any non-academic restrictions or holds on

accounts that would affect the ability to enroll for courses. As mentioned previously in

chapter two, studies such as Neighbors (1996) Smith, Street, and Olivarez (2002) and

Mendiola-Perez (2004) have been conducted at the community college and university

levels to explore registration timing’s effect on academic performance and retention.

Individual case studies at an institutional level could be studied in order to help

investigate if the student classification has a significant effect on the relationship

between registration timing and academic success and retention. Returning students

often have a larger timeframe in which to prepare for enrollment for the next academic

semester. Preparation time for registration is a variable that could be evaluated as well

as other environmental factors such as financial planning and academic advising.

Whether or not a positive relationship between timing and academic success and

retention exists could also be evaluated.

Transfer Students

Transfer students are a unique population of students that was not included in

this research study at FYU. Transfer students have many distinctive characteristics

83

because they are also entering their specific university for the first time just like their

freshman counterparts; they have expectations and additional barriers when enrolling

and matriculating for the first time at their chosen transfer school. The characteristics

that have been shown to be associated with transfer student academic success and

retention include student characteristics such as prior academic performance and first

semester GPAs (McGuire & Belcheir, 2013). These characteristics are the strongest

predictors of college student persistence and graduation (Wang, 2009). Therefore,

evaluating students’ academic performance as it relates to their registration times for

those students who have entered the university for the first semester is important

regarding their long term persistence and graduation. Transfer students differ from

FTIC students because they represent a wide range of experiences and baggage and

bring a variety of characteristics to the university with the transfer. These

characteristics include, such as which college they are transferring from, the number of

credits they transfer, part-time vs. full-time employment, and traditional transfer students

vs. nontraditional students that vary in age and marital status (Duggan & Pickering,

2007). These input factors could be used as controlled independent variables for a

future study on transfer students and modeled after this study which pertains to first-

time in college students.

At FYU, transfer students are also required to attend a mandatory orientation

session where they will register and matriculate into the university for the first time. In

terms of strong academic performance, students are influenced by how well they are

prepared for the transfer process. Gaining access to resources and knowledge about

the process through programs such as orientation helps transfer students prepare for

84

the academic term, and they are subsequently better prepared to succeed

academically. A similar study with this group of students new to FYU could help show

relationships between registration timing and academic performance and retention, as

well as the differences between transfer students with undecided majors and decided

majors.

Qualitative Study

This quantitative study allowed the researcher to investigate the general

relationship that registration timing can have on academic performance and retention,

as well as the differences between undecided and decided students with regard to

academic performance and retention. This methodology was chosen in order to

analyze multiple student records for several semesters with multiple variables. It

allowed the researcher to evaluate these records in order to interpret the relationship of

multiple factors on the FTIC student population’s output during the respective semester

analyzed. As the results show, there are multiple factors that can predict GPA or

retention, and registration time is only one of those factors.

A qualitative study can also be done in order to explore individual student

experiences with their environment during their first semester at FYU and show the

other student factors and student perspectives through individual student interviews.

(Creswell, 2009). Quantitative methods allowed measurement of the relationship

between timing and academic performance and retention and major status regarding

academic performance and retention, but these methods do not explain new student

experiences and other factors that also have a relationship with the variables (Creswell,

2009). Through qualitative inquiry, a researcher can seek to understand student

85

perspectives and how the environment at FYU contributes student performance and

persistence at FYU. A qualitative inquiry for freshman students would be descriptive

and provide a richer more detailed, individualized perspective of the registration process

and the student’s introduction to FYU (Merriam, 2009). Student’s experiences and

interaction with their new environment will be different. A qualitative study would

complement the results of this study for the freshman population at FYU.

Closing

Institutional effectiveness is often judged by student retention and graduation

rates. If universities can identify the factors or obstacles that inhibit a student’s potential

for high academic performance and subsequent retention, then the university will fulfill

its mission of retaining and graduating students. Likewise, universities will profit

financially from increased retention and graduation rates. Researchers have found that

college GPA is a significant predictor of student success (Moore & Schulock, 2009).

Cabrera, La Nasa and Burkum, (2003) found that every one-point increase in GPA

correlated to an increase of attaining a bachelor’s degree by 32%. Beyond the benefits

to the university, students who matriculate and succeed contribute to society in

meaningful ways. Students who successfully matriculate will help contribute to society

through higher paying jobs, greater engagement in civil service, and healthier lifestyle

choices (Stillman, 2009).

As mentioned in the introduction to this study, statistics from 2013 show that

nearly 46% of students who enroll in a higher education institution do not graduate with

a degree within six years of enrollment (HCM Strategists, 2013). Also, students who

have enrolled are not remaining in school. The report from the National Student

86

Clearinghouse Research Center also shows that there is decrease in student enrollment

in higher education nationwide. The overall college enrollment for 2013 fell from 20.2

million students in the fall of 2012 to about 19.9 million for fall of 2013. FYU

experienced a decline in its number of enrolled freshman students as well. There was a

significant decrease in the number of enrolled freshman in fall 2011 semester compared

to the fall 2012 semester. This decrease illustrates not only a similarity with nationwide

trends but also a need for FYU to analyze the factors that could relate to academic

performance and retention if the number of students attending FYU continues to

decrease. FYU and other universities nationwide need to focus on retaining the

students that have enrolled at their universities, and evaluate relationships between

college matriculation efforts such as orientation and earlier registration times as well as

whether helping students decided their major through the assistance of academic

advising will have a positive relationship with successful academic performance and

retention.

87

APPENDIX A

ORIENTATION SESSIONS

88

Freshman Fall 2011

Freshman Orientation 1 (June 15-17)



Freshman Orientation 4 (June 29-July 1)

Freshman Orientation 5 (July 11-13)


Freshman Orientation 7 (August 17-18)

Late Orientation (August 28, 2011)

Freshman Fall 2012




Freshman Orientation 4 (July 9 - 11)



Freshman Orientation 7 (August 22-23)

Late Orientation (August 24, 2012)

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APPENDIX B

DATA FILE FIELDS

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• ACAD_TERM_DESC

o 2011 Fall

o 2013 Fall

• ACAD_PLAN

o CUND = Undetermined – College of Public Affairs and Community Service

o AUND = Undetermined – College of Arts and Sciences

o BUND = Business Undetermined – College of Business

o EUND = Undetermined – College of Education

o ENUN = Engineering Undetermined – College of Engineering

o MUND = Undetermined – College of Music

o VADU = Undeclared – Undergraduate Studies

o VUND = Undetermined – College of Visual Arts and Design

o HUND = Undetermined – College of Merchandising, Hospitality and

Tourism

o UUND = University Undecided – Undergraduate

o DBUND = Business Undetermined

o DUNDECIDED = Undecided Liberal Arts & Life Sciences

• PLAN_DESCR

• GROUP_DESCR = Colleges

• GENDER

• ETHNIC_GROUP2_DESC

• ADMIT_N_DESC

• FULLPART

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• CUR_GPA

• SCH_TAKEN

• SCH_COMPLETED

• SCH_SUCCESS

• ORIENTATION TERM

• SESSION

• SAT SCORE

• PELL GRANT ELIGIBILITY

• FIRST GENERATION STATUS

• RETURNED

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