Sample Data Gathering Research Paper - SIU

STUDENT RETENTION IN TWO-YEAR CONSTRUCTION MANAGEMENT PROGRAMS

by

Gary A. Spoerre

B.S., University of Iowa, 1992

A Research Paper Submitted in Partial Fulfillment of the Requirements for the

Master of Science in Education Degree

Department of Workforce Education and Development in the Graduate School

Southern Illinois University Carbondale April 16, 2010

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TABLE OF CONTENTS

Chapter Page

1. INTRODUCTION

Background of the Study .................................................................................... 1

Purpose of the Study ........................................................................................... 4

Statement of the Problem .................................................................................... 5

Research Questions ............................................................................................. 5

Limitations .......................................................................................................... 5

Definition of Terms ............................................................................................. 6

2. REVIEW OF RELATED LITERATURE

Overview ............................................................................................................. 7

Academic/Career Goals ...................................................................................... 7

Persistence, Social Capital, Self-Efficacy and Hardiness ................................... 9

Retention ........................................................................................................... 11

Summary ........................................................................................................... 14

3. RESEARCH METHOD

Introduction ....................................................................................................... 16

Description of Research Type ........................................................................... 16

Participant Data ................................................................................................. 17

Data Collection Instrument ............................................................................... 17

Procedures ......................................................................................................... 18

Data Analysis .................................................................................................... 19

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4. RESEARCH RESULTS

Introduction ....................................................................................................... 22

Research Question 1 ......................................................................................... 23



5. SUMMARY, CONCLUSIONS, RECOMMENDATIONS

Summary ........................................................................................................... 28

Findings ............................................................................................................ 29

Conclusions ....................................................................................................... 30

Recommendations ............................................................................................. 31

REFERENCES ............................................................................................................. 34

APPENDICES

A. Permission Documentation ........................................................................ 40

B. Human Subjects Committee Approval ....................................................... 42

C. JALC Construction Management Program Flyer....................................... 45

D. Data ............................................................................................................ 47

VITA ........................................................................................................................... 53

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LIST OF TABLES

Table Page

1. Research Questions and Data Collection Tools ............................................... 18

2. Program Year Retention Rates and Graduation Rates ...................................... 24

3. Summary Correlations for Retention Score and Student Characteristics ......... 26

4. Summary Correlations for Graduation and Student Characteristics ................. 27

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CHAPTER 1

INTRODUCTION

Background of the Study

Management models in residential construction have changed several times since

the invention of the balloon frame in the early 1800s. Changes in materials and assembly

methods, as well as changing economic pressures, have led to changes in the way that

work crews were managed (Monteyne, 2004). Today, as the result of advances in

building materials and construction methods, management models continue to change.

Construction managers are in demand due to population increases, business growth, and

the increasing complexity of construction projects. The number of job openings for

construction managers in the United States exceeds the number of qualified applicants

and is expected to drive faster than average employment growth. The growing number of

energy efficient structures will further add to the demand for construction managers,

leading to excellent job opportunities for qualified management personnel (U.S.

Department of Labor, 2010).

Despite the recent down-turn in the housing market and the increasing cost of

energy, population increases will out-strip the available housing and new construction

requiring more efficient building practices will be needed. During a conference on the

future of the built environment, Caulfield (2007) related several surprising statistics. The

built environment is the largest asset component in the United States, consuming 40 to

45% of the energy output and producing 60% of the CO2 emissions. Between the years

2000 and 2030, the population will increase by one-third, to 400 million, and the country

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will need 100 billion square feet of new residential space to accommodate that growth,

which equates to approximately 59 million new and replacement housing units.

In the pre-WWII construction model, a builder was a senior carpenter who trained

on the job and assumed the responsibilities of directing the work of the entire project. In

addition to managing the building process, builders were responsible for developing

experienced and highly motivated work crews. This process produced individuals who

were ready to take the builder’s place, or move on to start their own companies and

develop crews of their own. In time, the standardization of building materials and the

availability of long-term mortgages combined to bring drastic changes to the building

process, which forced the builder’s model to change.

The skillful marketing of the American Dream after WWII led to a construction

boom that forced productivity increases on the carpenters and pressured builders into

becoming specialized, mass-production supervisors of an increasingly deskilled

workforce (Monteyne, 2004). These narrowly trained builder/crew combinations, along

with continued pressure from the industry to increase productivity, led to the

development of the general contractor/subcontractor model. This general contractor

typically rose through the ranks of the craft, and maintained a close relationship with the

processes, obstacles, and lives of the workforce. As productivity and resource pressures

continued, many of the craftspeople who previously worked for the general contractors

were shifted to specialty trade subcontractors. Some of the duties of the general

contractor were transitioned to a new specialist known as the construction manager

(Erlich & Grabelsky, 2005) whose expertise in schedule maintenance and resource

efficiency did not necessarily originate from experience in the trade, or exposure to the

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lives of trade workers. The efficiencies gained through this model change have worked

well for commercial construction and for high-volume residential construction, but using

this model in a down-turn economy makes it difficult for residential construction

companies to maintain profitability. For residential construction companies to remain

viable in this environment, a broadly-trained construction manager is needed, one similar

to the general contractor from the old model.

Construction industry growth requires a steady supply of managers. Normal

channels, such as formal trade apprenticeships and postsecondary education, continue to

supply managers but are not meeting the demand (U.S. Department of Labor, 2010).

Trade worker shortages in commercial construction have decreased the availability of

manager candidates, and for those who do become managers, readily available work in

commercial construction often routes them away from the residential market.

Additionally, an emphasis on having a college degree has been institutionalized by the

secondary education system, a direction that tends to provide a negative perception of the

trades, further limiting the number of manager candidates available. For an industry that

largely depends on knowledge that is passed down on the job, this lack of available

workers could eventually cripple the process. At a loss for skilled work-crews, the

industry’s transition to greener, more energy conscious initiatives will be at risk (Rolph,

2008).

One way to abate the construction manager shortage is for educational institutions

offering construction management programs to graduate more students into residential

construction. Since 2006, the National Housing Endowment (the philanthropic arm of the

National Association of Home Builders) has been working to increase the number of

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qualified graduates entering the residential construction profession. Through their

Homebuilding Education Leadership Program, grants are awarded for the purpose of

helping two- and four-year colleges create and expand residential construction programs

that work to increase the number of qualified graduates entering the residential

construction profession (National Housing Endowment, 2009). These new and expanded

programs are intended to offset manager losses due to industry attrition as well as losses

to the commercial construction industry. This creation and expansion initiative will

benefit the industry in the future, but near-term solutions must also be investigated.

Current programs, focusing on retention and recruitment, are a primary candidate for

providing an immediate and cost-effective solution for increasing the number of

management graduates into the residential construction industry. Though it costs less to

keep the current students in these programs, educational institutions often focus on

student recruitment rather than student retention (Fike & Fike, 2008). By shifting the

focus to analyzing and increasing student retention rates in qualified construction

management programs, institutions can graduate more candidates sooner and possibly

forestall the negative effects of the shortage.

Purpose of the Study

The purpose of this study was to contribute to the understanding of the continued

loss of construction management personnel in the residential construction industry. More

specifically, the study attempted to identify significant factors associated with retention

in postsecondary construction management education programs.

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Statement of the Problem

There are not enough construction management graduates entering the residential

construction industry to off-set future population growth and current industry attrition.

Understanding the relationship between an institution’s retention rate and the

characteristics of the current student body will allow educational institutions with

construction management programs to better supply the residential construction industry

with qualified graduates. Therefore, the problem of this research study was to determine

the relationship between retention and selected student academic factors in a community

college construction management program.

Research Questions

The following research questions were used for this study:

1. What are the term retention rates of community college construction

management students for the program years 2002 through 2006?

2. Do selected student academic factors influence construction management

student retention in a community college construction program?

3. Do selected student academic factors influence graduation in a community

college construction program?

Limitations

As a sample population, it is uncertain how closely this group represents the

national population of construction management students in two-year programs, so the

results of this study should not be used to predict results in other programs. This study

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was used to identify relationships between retention and selected student academic

factors. Because the data were shown to be nonnormal, causality could not be determined

and predictive measures such as regression analysis could not be used. Additionally, no

distinction was made in this study between nongraduating students, students who chose

to continue their course of study at another institution, or students who left the program

of study for gainful employment.

Definition of Terms

Retention rate: The percentage measure of students who have chosen to continue

instruction into the next term as compared to the number of students who started the

current term.

Construction management: The study and practice of the managerial and

technological aspects of the construction industry.

Residential construction industry: Companies that design, develop, and/or

provide general contracting, remodeling, and renovation services for residential

buildings, including single-family houses, multifamily housing, townhomes, apartments,

and modular housing.

Program year: The year in which the student begins the program of study. A

student in the 2002 program year would begin their two-year program in the Fall of 2002

and would span the academic terms of Fall 2002, Spring 2003, Fall 2003, and Spring

2004.

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CHAPTER 2

REVIEW OF RELATED LITERATURE

Overview

The problem of this research study was to determine the relationship between

retention and selected student academic factors in a community college construction

management program. The residential construction industry is predicted to experience a

skilled management short-fall for the next eight years (U.S. Department of Labor, 2010),

and increasing the retention rate of existing programs will provide an effective and

efficient means of increasing the number of qualified graduates entering the field.

This literature review investigated the social stigma that exists between academic

and career goals, the importance of self-efficacy to persistence in educational goals, and

the role of the institution on student retention. This is by no means an exhaustive study of

the available literature, but a broad view of three aspects that are contributors to the

management shortages the industry is experiencing.

Academic/Career Goals

The academic/career split that has characterized the American education system

for nearly a century has developed into a societal perception of head work and hand

work. This very depiction leads many young people to view trade work as something one

does if they cannot go to college. In the U.S. since WWII, the focus and energy shift to

obtaining a four-year college degree has been very strong. Current demand for trade

workers continues to rise, and despite the fact that trade professions offer the potential to

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make a comfortable upper-middle-class living almost immediately, young people are still

put off by the negative perception of trade work (Rolph, 2008). A report on the future of

work (Mandel, 2007) continued this negative perception. It was reported that the

percentage of 25 to 29-year-olds with at least a bachelor’s degree had fallen this decade,

which raised “the real possibility that this generation of young Americans may actually

be less educated than the previous one, creating a growing gap between the kinds of

people companies need and the workers who are actually available” (p. 46). While it is

doubtful this national writer intended to disparage workers without college degrees, it

demonstrates the systemic nature of the head/hand mentality and its acceptance in this

society.

Career and technical education saw an early separation from the traditional

academic elite. In 1917, the Smith-Hughes Act mandated separate funding streams,

governing boards and instructional programs for vocational teacher education. This

separation led to problems, as well as solutions for workforce education. It was practiced

and believed that the purpose of the school system was to efficiently guide people into a

position where they were likely to provide the best citizenship. Immigrant and working-

class children were thus trained for manual labor. The head/hand mentality quietly

became institutionalized and continues to shape perceptions about work today. Changes

need to be made to the policies and language of vocational education to alter the way

trade knowledge is viewed by society (Rose, 2008).

Society’s view of trade knowledge is persistent because it is viewer specific.

There is currently no accepted framework for the uniform management of knowledge and

as a result, misunderstandings persist regarding the relative importance of different types

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of knowledge (Dueck, 2001). Individual personality types have specific temperaments

(Keirsey, 1998) and not surprisingly have adopted their own differing points of view

regarding knowledge management. What is critical to one may be important to another

and only a curiosity to someone else.

Persistence, Social Capital, Self-Efficacy and Hardiness

The ability to continue in a course of study is known as persistence, and is

broadly affected by many social factors, collectively described as social capital. Personal

networks, socioeconomic status and social class are included in social capital, and are

described as the community and personal relationships that a person has to assist their

personal development and advancement (Wells, 2008). This social capital brings to bear

specific influences on a student’s confidence as well as on their ability to cope with and

overcome obstacles.

Self-efficacy is the belief that a person is able to exercise influence over events in

their life, that they are confident in their ability to bring about the desired effect or

outcome. A strong sense of self-efficacy will enhance a student’s accomplishments and

personal well-being. Conversely, students lacking self efficacy doubt their ability and

may fall victim to stress and depression when failure occurs. Devonport and Lane (2006)

investigated the relationship between self-efficacy, coping and student retention and

found a significant relationship between self-efficacy and academic performance. Larson

and Borgen (2006) found that personality mattered significantly in career self-efficacy,

job performance, and job satisfaction. Self-efficacy can be developed in several ways: (a)

through the experience of overcoming progressively difficult obstacles, (b) by watching

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someone like themselves work hard and succeed, (c) by someone giving them genuine

verbal support, and (d) by using positive reinforcement to change their negative reactions

to stress (Bandura, 1994). Persistence is the result of social capital, self-efficacy, and at

least one more quality, hardiness.

Hardy individuals have a robust approach to life; hardiness allows them to stay

committed to tasks, despite apparent hardships. These individuals believe their work to

be valid, and that their efforts may actually influence the outcome of the situation at

hand. Students with higher levels of hardiness tend to persist in their academic

challenges. Maddi, Harvey, Khoshaba, Lu, Persico, and Brow (2006) described hardiness

as a personality characteristic that is a combination of commitment, control and

challenge. Hardy individuals also believe that challenges provide opportunities for

growth, regardless of the outcome.

Among healthcare professionals, hardiness is studied to gauge its effects on

coping and burnout, factors that also affect student persistence. Rowe (1997) conducted a

study of 448 healthcare professionals to investigate the relationships among hardiness,

stress, temperament, coping and burnout. It was found that perceived stress most

significantly influences burnout, and particular dimensions of temperament showed

higher levels of adaptability, leading to more personal accomplishment and less burnout.

Also, when subjects were instructed to develop proactive intervention strategies, they

were more effectively able to cope with stressors and felt a greater sense of personal

accomplishment.

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These four qualities – persistence, social capital, self-efficacy and hardiness all

come together to help students adjust and respond to the challenges they encounter in

their transition from high school to postsecondary education.

Retention

Retention initiatives have been in use for many years and much of the retention

research has been concentrated around first-year students and their emotional,

interpersonal, and financial condition (Giaquinto, 2009-2010). Recent research has

recognized this limitation and studies have included other elements of the student-

institution relationship such as new student perceptions of the institution, interactions

between student characteristics and intervention types, the effects of instruction, and the

benefits of success courses.

Oriano-Darnell (2008) described the benefits of using the Survey of Entering

Student Engagement (SENSE), an instrument designed to provide information about each

student’s initial college experience. From this data, participating colleges were able to

develop action plans that more effectively served new students’ needs during this

transition, and lead to a successful learning experience.

Valuable lessons can also be learned from colleges that have implemented

successful minority retention programs. Forde (2008) described the Office of

Multicultural Affairs (OMA) at Wake Forest University, which has worked to develop an

operational infrastructure that is part of the campus community and demonstrates a strong

interest in each student’s success. Now, more than 20 years later, they boast one of the

highest graduation rates of Black students in the country. The lesson: colleges need to be

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more responsible for the success of their students. Roach (2008) agreed. Students must

know they are being cared for and that everyone is concerned about their success. By

comparing the six-year graduation rates across racial and gender lines, programs had a

better understanding of the situation and were able to target these differences with

effective interventions. Student monitoring programs help to do this by providing early

intervention opportunities, before the student’s academic condition becomes critical.

Given the popularity of community colleges and the unique function they serve in

society, it is understandable that an institution’s administration act in an almost hyper-

vigilant capacity to regard each student as important. Open enrollment policies mean that

everyone can walk through the door, and a college needs to be structured to help

everyone succeed. Noonan-Terry and Waiwaiole (2008) recognized the retention

challenges presented by this environment, and the broad interventions that are required to

abate them. College environments must be structured to encourage students’ participation

with peers and faculty, as well as providing support and guidance. From the faculty to the

grounds-keeper, everyone on campus is responsible for keeping interest in success at a

very high level. By creating and maintaining an environment that promotes access to

positive opportunities, the college has demonstrated a genuine interest in helping the

students succeed. This serves to create learning communities where students are able to

make valid self-assessments and act to improve their opportunity for success.

Emotional Intelligence (EI) is a grouping of social and emotional factors that have

been shown to affect retention in first-year students, as shown by Parker, Hogan,

Eastabrook, Oke, and Wood (2006). Students who persisted into their second year of

postsecondary study were shown to have significantly higher EI competencies as

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compared to students who withdrew from the university prior to completing their first

year of study. EI competencies that were measured included modifying and maintaining

previous relationships (interpersonal abilities), developing new friendships (intrapersonal

abilities), adjusting to a new learning environment (adaptability), and functioning

independently as an adult (management abilities). This is consistent with the findings of

Devonport and Lane (2006) indicating a strong relationship between self-efficacy and

retention. While this study was comprised mainly of white females and is therefore not

representative of all postsecondary education, it supplies a strong indication of EI’s role

in student retention.

Social anxiety was yet another factor to be considered with regard to retention.

Strahan (2002) conducted a two-year longitudinal study to examine the role of social

anxiety as it relates to academic performance and found that while many students

experienced high levels of anxiety during their first-year transition to an academic

environment, the study did not suggest that they were incapacitated by it. For students

who experience very high degrees of distress, treatment groups could be formed where

they could express their feelings, improve their comfort level, and function socially. This

has been shown to be true by Fava and Tomba (2009), in their study of the effects of

well-being therapy on mood and anxiety disorders. Their findings indicate that

psychological distress and vulnerability to life events can be managed and even

prevented by interventive measures that increase an individual’s level of well-being.

While the subject groups in both studies differ in origin, they share their degree of

distress and present comparable results.

In an effort to improve first-year retention and student satisfaction, intervention

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programs of some type are commonly offered by educational institutions. Bai and Pan

(2009) recognized that past research focus had been placed on main-effects analysis, and

little research had explored the interaction between student characteristics and

intervention types. From their results, it can be inferred that intervention effectiveness

can be improved by selecting intervention strategies based on student characteristics,

rather than subjecting everyone to a general orientation.

Giaquinto (2009) added another element to the body of retention studies, that of

an instructional mismatch between the perceptions of first-year students and their

instructors. By concentrating on the students as information is received (or not),

instructors can better understand where a student’s cognitive development exists and can

be clear about the work that is required to close the gap. Many strategies were outlined to

engage first-year students, with the expressed suggestion that instructors actively teach

students methods for managing their own learning. An extension of this perspective is

seen in Loui, Robbins, Johnson, and Venkatesan’s (2008-2009) use of peer-led study

sessions. It was found that in two of the study’s three terms, there was a statistically

significant association between final exam scores and study session attendance.

Differences in sample size and student demographic may have contributed to the lack of

significance for the third term. For the same period, no statistically significant association

was demonstrated between student persistence and study session attendance.

Summary

Related literature indicates that many factors affect retention rates at two-year

colleges. Social stigma involving trade and trade-related work has a contributory effect

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(Rolph, 2008), as does a student’s emotional intelligence (Parker, Hogan, Eastabrook,

Oke, & Wood, 2006), self-efficacy (Devonport & Lane, 2006), and social anxiety if the

level is high enough (Strahan, 2002). But the bulk of the potential for improvement lies

within the administrative and environmental structure that has been set up by each

educational institution. Though the methods for improvement are unique to each

institution (Roach, 2008), creating opportunities for students to genuinely participate in

college communities is at the heart of increasing graduation rates.

Looking closely at initiatives that were specifically designed to improve minority

student retention will reveal methods that drive improvements across all student

demographics (Forde, 2008). Leadership and mentoring programs, a strong interest in

student success, and the development of learning communities (Noonan-Terry &

Waiwaiole, 2008) are several steps that can be made to improve retention rates. By

carefully analyzing who is staying and who is leaving, educational institutions can begin

to take more ownership in each student’s success (Forde, 2008).

Management shortfalls in residential construction will negatively affect the

industry’s ability to aggressively respond to energy conservation goals. From a largely

deskilled workforce, two and four-year colleges are being challenged to provide a highly

skilled workforce that has the mettle to persevere in the current economic down-turn. By

paying close attention to improving graduation rates among qualified institutions,

colleges and universities can minimize the impact of this management short-fall.

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CHAPTER 3

RESEARCH METHOD

Introduction



management program. More specifically, the study attempted to identify significant

factors associated with retention and graduation in postsecondary construction

management education programs.

The number of construction management graduates entering the residential

construction industry is insufficient to off-set future population growth and current

industry attrition. Understanding the relationship between an institution’s retention rate

and the characteristics of the current student body will allow educational institutions with

construction management programs to better supply the residential construction industry

with qualified graduates.

Description of Research Type

This quantitative, secondary data analysis looked at student enrollment data from

an accredited, two-year postsecondary construction management program. The

curriculum for this program is to be completed in four semesters, not including summer

sessions, and the load per student is approximately 18 credit hours per semester.

Enrollment data from Fall 2002 through Fall 2006 were used to evaluate the program’s

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retention rates and identify associations that can lead to a higher graduation rate for this

program.

Participant Data

The data used for this study spanned the academic years 2002 through 2006 and

represented 233 full-time students in their first year of study in the Construction

Management Technology Program at John A. Logan College in Carterville, Illinois. John

A. Logan College is an open-enrollment institution that requires proof of high school

graduation or GED. Students must also take the ACT or ASSETT test to determine their

placement in math and English courses. While most students are from district high

schools, this is a state-wide program that also recruits nontraditional students from the

construction industry. Coursework is semester-specific and involves approximately 50

new students each Fall semester.

Data Collection Instrument

John A. Logan College uses the APECS system to manage student data, as well as

registration and graduation requirements. Program specific enrollment information by

semester was not yet available through the APECS system, so an alternative method was

used for identifying first semester enrollees. All students enrolled in the Construction

Management program are required to take CMG 110 Wood Frame Construction in their

first semester. This course is only offered in the Fall and for the purpose of this study was

chosen as the query instrument for identifying first semester enrollees. Once the subject

population was extracted from APECS and imported into Excel, individual data sets were

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formed that would allow comparisons of student results in each program year. Table 1

illustrates the tools used for each research question.

Table 1

Data Collection Tools ______________________________________________________________________

Research Question Data Source ______________________________________________________________________

RQ1 MS Excel and APECS Student Information System

RQ2 MS Excel and SPSS Statistical Analysis Tools

RQ3 MS Excel and SPSS Statistical Analysis Tools ______________________________________________________________________

Procedures

Because this research included student academic data, approval for use was

granted by the Human Subjects Committee at Southern Illinois University Carbondale

(Appendix A). The APECS system was queried for the CMG 110 rosters for each

academic year beginning in 2002, and academic information for each student on the

roster was recorded in a data set. In order to provide anonymity while preserving

traceability, all student identifiers were removed and replaced with subject numbers that

signify year and subject. Data gathered for each student included six variables: (a)

consecutive semesters of registration; (b) grade point average; (c) whether or not the

student had previous exposure to postsecondary coursework; (d) the number of courses

that were dropped, withdrawn from, or not completed by the student; (e) the number of

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courses that were failed by the student; and (f) course completion rate (the number of

credit hours earned as compared to the credit hours attempted).

Correlation coefficients between retention scores and student academic factors

were calculated using SPSS (Version 17) and MS Excel (Version 2007) statistical

processing tools. Retention scores are numerical values assigned to consecutive terms of

study and are designed to indicate academic advancement toward graduation. In this

four-term program, one term would equal 0.25, two terms would equal 0.50, three terms

would equal 0.75, and four terms would equal 1.00. As an example, a student who is

enrolled for the first two consecutive semesters, who skips the third semester, and enrolls

for the fourth semester would be given a retention score of 0.50 because only two

consecutive semesters were completed. A student with previous coursework who

completes their degree in one, two or three consecutive semesters of enrollment would be

given a retention score of 1.00 because: (a) they have completed their degree, and (b) a

score of 0.25, 0.50, or 0.75 would indicate these students have not completed their course

of study and would falsely lower the retention score sample.

Data Analysis

Term retention rates were calculated for each program year and correlated with

academic factors for each student. To answer Research Question 1, term retention rates

were calculated for consecutive terms only. Retention rates for each term were found by

dividing the number of students who registered for the next consecutive term by the

number of students who began the program in that academic year. Following is an

example: if 50 students began the program in the Fall semester of 2002 but only 46

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registered for courses in Spring 2003, the retention rate for the first semester (SEM1) of

program year 2002 would be (46/50)*100 = 92%. If 45 of those students registered for

classes the following Fall, the retention rate for the second semester (SEM2) of program

year 2002 would be (45/50)*100 = 90%. If a student failed to register for courses the

following semester, any future registrations would not be included in the retention rate

calculations because those terms would not be consecutive. For students with previous

coursework whose degrees were awarded prior to completing four terms, their remaining

terms would be counted in that semester’s retention rate. No distinction was made

between graduating students and students with insufficient credit hours who continued

their studies past the fourth semester; both were included in the graduation rate (GRAD

Rate) if their degree was completed prior to January 2009.

For Research Questions 2 and 3, retention scores were assigned to each student

based on their number of consecutive terms of enrollment, and retention scores for all

program years were combined to provide a stronger result. To ensure that variance within

each program year was similar, the Kruskal-Wallis test for nonparametric data (Pearson,

2010) was used. This test assumes the test groups are from the same population, and a

significant result of p < .05 would indicate that at least one of the groups was different

from another group. The Kruskal-Wallis test was used instead of the one-way ANOVA

because the data sets proved to be nonnormal.

The Spearman’s rank order correlation was used to compare each term’s retention

rate with student academic factors such as grade point average, whether or not the student

had previous exposure to postsecondary coursework, the number of courses that were

dropped/withdrawn from/not completed by the student, the number of courses that were

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failed by the student, the course completion rate, and whether or not a student graduated.

Spearman’s rank order correlation, rather than Pearson’s correlation, was chosen because

the data sets are not normally distributed and the variables have a large number of

categories. Additional requirements for use of Spearman’s rank order correlation are that

variables are ordinal, independent and not causally related (Pearson, 2010).

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CHAPTER 4

RESEARCH RESULTS

Introduction



management program. Creating new programs or recruiting more students would

eventually add graduates to the industry, but increasing the retention rate of current

programs is a more efficient method for solving this problem. Understanding the

relationship between retention rate and student body characteristics will help programs

retain more students and increase graduation rates.

The data collected for this study came from the academic records of 233 full-time

students in their first years of study, spanning five academic years from 2002 through

2006. These students were enrolled in the Construction Management Technology

Program at John A. Logan College in Carterville, Illinois. Coursework was semester-

specific, required four semesters to complete, and did not include Summer coursework.

Throughout the five years of this study, the curriculum, faculty, and facility were held

constant with only minor changes taking place in the part-time faculty.

Graphing the means for retention score, grade point average, and course

completion rate showed them all to be very close and combining these five groups would

not likely drive an incorrect inference about their association with the other variables. A

Kruskal-Wallis test was conducted comparing the student retention scores for each of the

program years. No significant difference was found (H(4) = 3.53, p > .05), indicating that

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the groups did not differ significantly from each other. Being in different program years

did not seem to influence the students’ retention scores and the five data groups were

combined.

The results for each variable are ordinal, meaning their value represents a level

that is higher or lower than the other subjects in the sample. Research results are

presented according to each research question.

Research Question 1

What are the term retention rates of community college construction management

students for the program years 2002 through 2006? Table 2 data reveal that term

retention rates consistently decrease throughout a program year, and that less term-to-

term reduction is seen between semesters 3 and 4 than for all other semesters. Table 2

also shows that term-to-term variability across program years is not consistent.

24

Table 2

Program Year Retention Rates (RRate) for included terms and Graduation Rates (GRADRate), as well as Term-to-Term Reductions (Δ) ______________________________________________________________________

RRate RRate RRate GRAD Year n SEM1 Δ1 SEM2 Δ2 SEM3 Δ3 Rate Δ4 ______________________________________________________________________

2002 39 90% 10% 62% 31% 56% 8% 51% 9% 2003 50 88% 12% 70% 20% 60% 14% 38% 37% 2004 44 89% 11% 77% 13% 70% 9% 55% 23% 2005 50 86% 14% 76% 12% 70% 8% 48% 31% 2006 50 82% 18% 66% 20% 56% 15% 16% 71% ______________________________________________________________________

Avg. Term Reduction 13% 19% 11% 34% ______________________________________________________________________ Note. Δ = term-to-term reductions only and does not account for the number of students who started the program year, as seen in RRate.

Research Question 2

Do selected student academic factors influence construction management student

retention in a community college construction program? Table 3 contains correlation

coefficients and indications of statistical significance. Generally, correlations greater than

0.7 are considered strong, correlations between 0.7 and 0.3 are considered moderate, and

correlations less than 0.3 are considered weak. The asterisks indicate significant

relationships, meaning they are reliable at the given levels, but provide no indication of

relationship strength (Cronk, 2004).

Information in Table 3 reveals that two variables, GPA (student grade point

average) and CCR (course completion rate), show an association to student retention

scores. A moderate positive correlation was found between GPA and RS (retention

scores) (r(231) = .40, p < .01), indicating a significant relationship between the two

25

variables. Students with higher GPA’s tend to continue in their studies. A moderate

positive correlation was also found between CCR and RS (r(231) = .48, p < .01),

indicating a significant relationship between the two variables. Students who complete

the courses they register for tend to continue in their studies. Additionally, a strong

positive correlation was seen between GPA and CCR (r(231) = .75, p < .01), indicating a

significant relationship between those variables. Students with higher GPA’s tend to

complete the courses in which they enroll. Table 3 also shows that previous coursework

and courses dropped, withdrawn from or incomplete had no significant relationship with

a student’s retention score.

Because the Spearman correlation coefficient was used, it was not possible to

determine causality between the variables. If the data sets were normal, causality could

be determined, and expected results could be calculated for various levels of student

characteristics using regression analysis.

26

Table 3

Summary Correlations, Means (M) and Standard Deviations (SD) for Retention Score and Student Characteristics ______________________________________________________________________

Measure 1 2 3 4 5 6 M SD ______________________________________________________________________ 1. RS --- .40** - .02 - .04 - .26** .48** .78 .29 2. GPA .40** --- .18** - .35** - .67** .75** 2.52 .93 3. PCW - .02 .18** --- - .17** - .07 .11 .70 .46 4. CDWI - .04 - .35** - .17** --- .20** - .45** 1.25 1.66 5. CF - .26** - .67** - .07 .20** --- - .73** 1.12 2.08 6. CCR .48** .75** .11 - .45** - .73** --- .82 .30 ______________________________________________________________________ Note. RS = retention score; GPA = grade point average; PCW = previous coursework; CDWI = courses dropped, withdrawn or incomplete; CF = courses failed; CCR = course completion rate. n = 233. ** p<.01.

Research Question 3

Do selected student academic factors influence graduation in a community

college construction program? Table 4 reveals that three variables, RS, GPA and CCR,

show an association to student graduation in this construction management program. A

moderate positive correlation was found between RS and Graduation (r(231) = .55, p <

.01), indicating a significant relationship between the two variables. Students with higher

retention scores tend to graduate from this program.

A moderate positive correlation was found between GPA and Graduation (r(231)

= .48, p < .01), indicating a significant relationship between the two variables. Students

with higher GPA’s tend to graduate from this program.

A moderate positive correlation was found between CCR and Graduation (r(231)

= .57, p < .01), indicating a significant relationship between the two variables. Students

with higher course completion rates tend to graduate from this program.

27

Table 4

Summary Correlations, Means (M) and Standard Deviations (SD) for Construction Management Graduation and Student Characteristics ______________________________________________________________________

Measure Graduation M SD ______________________________________________________________________ 1. RS .55** .78 .29 2. GPA .48** 2.52 .93 3. PCW .13 .70 .46 4. CDWI -.26** 1.25 1.66 5. CF -.40** 1.12 2.08 6. CCR .57** .82 .30 ______________________________________________________________________ Note. RS = retention score; GPA = grade point average; PCW = previous coursework; CDWI = courses dropped, withdrawn or incomplete; CF = courses failed; CCR = course completion rate. n = 233. ** p<.01.

28

CHAPTER 5

SUMMARY, CONCLUSIONS AND RECOMMENDATIONS

Summary



management program. Not everyone agrees that construction managers have made the

industry better (Erlich & Grabelsky, 2005), but improving retention will lead to

graduation increases, and industry shortages of construction managers will be efficiently

decreased.

A review of literature indicated that a new level of management resource will be

required as construction methods and materials became more sophisticated, and that

construction managers will be in demand for several years to come. Increasing student

retention rates at current educational institutions provides an effective and efficient

solution to this increased demand. Several factors are known to affect student retention

rates, but recent studies involving student, institutional and curricular characteristics have

also revealed significant relationships. By addressing these new factors, increased

graduation rates will follow the rise in student retention.

Data for the study were extracted from the institutional student information

system and comprised five program years of registration information for a two-year

construction management program at John A. Logan College in Carterville, Illinois.

Participants were full-time students, typically male, with an average graduation rate of

41.6% during the span of the study.

29

Using SPSS, the data sets were analyzed and found to be nonnormal. Retention

rates were calculated for each term as well as the degree of change in enrollment.

Because it was found that no significant difference existed in the retention scores for each

program year, the data sets were grouped into one large set before testing for association.

Findings

For the program years 2002 through 2006, no appreciable difference was

observed in the means of the student characteristics, indicating that institutional factors

such as faculty, curriculum and facility were stable from year to year, and changes

observed in program retention rates were primarily due to changes in the student body or

possibly the way students were advised in scheduling their coursework. While retention

rates were variable from group to group, all five groups showed the lowest decrease in

term-to-term enrollment between the third and fourth semesters.

From the 233 students making up the participant pool for this study, several

significant relationships were highlighted. It was found that students with higher grade

point averages and students with higher course completion rates tended to be the same

students who continued in the program. It was also found that students with high GPA’s

were also the same students who tended to complete the courses they enrolled in. Lastly,

it was found that students who graduated from this program tended to be the students

who: (a) attended consecutive semesters, (b) had higher grade point averages, and (c)

completed the courses they were enrolled in. No significant relationship existed between

retention and students with previous coursework or the number of courses dropped,

withdrawn, or incomplete. Students with failed courses showed a predictably significant

30

negative correlation with grade point average and course completion rates, but only a

weak negative correlation to retention.

Term-to-term student enrollment reductions between Fall and Spring semesters

are comparable, but markedly higher between Spring and Fall semesters. Summer takes

place between the Spring and Fall semesters, and it is possible that students have a better

opportunity to reassess their career choice during this time.

The average graduation rate for students enrolled during this study was 41.6%. Of

those graduates, 52.6% required more than the prescribed four semesters to complete the

program of study.

Conclusions

The following conclusions were derived from the results of this study:

1. Student academic factors, such as grade point average and course completion rate,

are significant factors in student retention and graduation.

2. Retention rates varied depending on semester and program year, and graduation

rates varied depending on program year.

3. Previous exposure to postsecondary coursework did not influence student

retention or graduation rates.

4. Based on graduation rates and the number of semesters enrolled by graduates, the

prescribed program of study may be too aggressive for many of the students enrolling in

this program.

5. Of the students who did not graduate, many did well in the construction courses

but failed to pass the math and communications courses.

31

Recommendations

Based on the findings of this study, the following recommendations are offered in

the separate categories of (a) Practice and (b) Research:

Practice

1. Set appropriate expectations for students entering the program. Assemble a list of

best practices and ensure that students recognize the risk associated with deviating from

the prescribed course of study.

2. Advise students in this program using the same academic advisor to maintain a

close relationship between student and advisor. Students should be required to maintain a

portfolio and work with their advisor to investigate placement opportunities.

3. Strengthen the enrollment in professional internships during the summer session.

Construction Management students do not live on campus, and often work in

construction while attending classes. Both of these factors lead to heavier emphasis being

placed on time spent away from school. Students who know they will have the

opportunity to exercise their knowledge and develop professional relationships during

this period will likely maintain a closer adherence to the course requirements and follow

through with graduation. Activities during the school year would include membership in

professional organizations, construction activities in volunteer groups, and outreach

activities in local schools that promote careers in the industry. These activities would be

monitored by a faculty member or student advisor. Consideration should also be given to

evening coursework offerings if students are required to work during the day.

32

4. Formalize a placement process that is linked to the summer internships. This

exposure lets students experience various phases of the construction process and allows

them to draw on their strengths very early on.

5. Actively recruit both male and female students with architectural, math, science,

and engineering interests. This would add positive inertia to the classes and likely benefit

many of the students who possess lower levels of confidence in these areas. Student

records indicated that while many of the students did well in the building science courses,

those who did not continue in the program had difficulty with the math and

communications requirements.

Research

1. Investigate the percentage of students who are proficient in building science but

lack the basic math and communication skills to continue in the program. Construction

Managers must be confident using numbers and are required to communicate with many

different groups of people. An increased emphasis in these areas would benefit all

students.

2. Investigate the order of coursework in the curriculum. Term to term enrollment

reductions were the greatest between Spring and Fall terms. This may be due a higher

percentage of abstract coursework during the Spring semester which requires a broader

understanding of the construction process. Students lacking confidence in problem

solving that draws from several sources may be uncomfortable continuing in the program

and may prematurely terminate their enrollment. A block curriculum may benefit this

33

group of students. Their confidence may stay higher and proficiency gains may be greater

with this type of offering.

3. Investigate the relationship between student success courses and retention in this

program. These courses indoctrinate students to the institution, the program and the

industry, and emphasize the resources available in each. A course of this type may help

students develop stronger connections between their coursework and industry use, and

would likely reinforce the perception that maintaining a high level of accountability leads

to success.

4. Investigate personality type as it pertains to successful program completion. What

personality types comprise the construction management industry and do students in this

program share those traits?

5. Investigate methods for strengthening student involvement in the construction

student community. The construction student often works in construction and typically

does not have the same opportunities available for study and problem investigation as

students in other disciplines.

34

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APPENDICES

40

Appendix A

Permission Documentation

41

42

Appendix B

Human Subjects Committee Approval

43

44

45

Appendix C

JALC Construction Management Program Flyer

46

47

Appendix D

Data

48

Retention 90% 62% 56% 49% 51% PROGRAM YEAR 2002

n= 39 35 24 22 19 27 20Prev CM # Sem CM

Subject FA02 SP03 FA03 SP04 5th RR' CWork Grad to Grad GPA Enrolled D/W/I Failed Attempt Earned201 1 1 1 1 1 1 1 4 2.244 20 0 0 58 58202 1 1 1 1 1 1 1 1 5 3.000 18 3 0 42 42203 1 1 1 0.25 1 3.038 4 0 0 12 12204 1 1 1 1 1 1 1 1 6 2.290 21 0 4 57 45205 1 1 0.5 1 3.834 6 0 0 40 40206 1 1 0.5 2.518 8 1 0 26 26207 1 1 0.5 1 3.000 6 3 0 42 30208 1 1 1 1 1 1 1 1 5 3.374 24 0 0 70 70209 1 1 1 1 1 1 1 1 5 2.436 20 5 0 51 51210 1 1 0.5 1 1.088 10 4 6 30 2211 1 1 1 0.75 1.852 11 7 0 24 18212 1 1 1 1 1 1 1 1 5 3.407 19 2 0 49 48213 1 1 1 1 1 1 4 3.113 20 0 0 58 58214 1 0.25 1 0.667 3 1 1 6 2215 1 1 0.5 3.336 9 1 0 25 25216 1 1 1 0.75 1 3.275 11 1 0 29 29217 1 1 1 1 1 1 1 5 2.501 17 0 0 51 51218 1 1 1 1 1 1 1 1 5 3.060 23 1 2 56 53219 1 1 0.5 1 1.700 6 0 3 19 10220 1 1 1 1 1 1 3.224 14 0 0 42 42221 1 1 1 1 1 1 1 1 7 3.269 19 0 0 54 54222 1 1 1 1 1 1 1 1 5 3.709 19 0 0 57 57223 1 1 0.5 1 1 3 2.893 9 0 0 28 28224 1 1 0.5 1.000 7 2 5 13 0225 1 0.25 0.000 3 3 0 9 0226 1 1 0.5 3.282 9 1 0 25 25227 1 1 1 1 1 1 1 6 2.571 4 0 0 14 14228 1 1 1 1 1 1 1 5 2.454 21 1 0 62 62229 1 1 1 1 1 1 1 1 5 3.816 14 0 0 42 42230 1 1 1 1 1 1 1 6 2.249 21 3 0 54 54231 1 1 1 1 1 1 1 1 6 2.948 18 0 0 52 52232 1 1 1 1 1 1 1 4 3.324 17 0 0 49 49233 1 0.25 1 0.000 2 2 0 5 0234 1 1 0.5 1 1.007 9 0 3 21 13235 1 1 0.5 1 3.083 8 4 0 12 12236 1 1 1 1 1 1 2.463 18 1 5 38 29237 1 1 1 1 1 1 1 1 6 2.409 18 2 0 46 46238 1 1 1 1 1 1 4 2.955 22 0 0 66 66239 1 1 0.5 1.911 8 0 1 25 21

indicates items that were added to "n" aboveindicates items that were subtracted from "n" above

Semesters Enrolled Courses Hours

49


n= 50 44 35 30 19 29 19Prev CM # Sem CM

Subject FA03 SP04 FA04 SP05 5th RR' CWork Grad to Grad GPA Enrolled D/W/I Failed Attempt Earned301 1 1 0.5 1.084 10 2 5 25 9302 1 1 1 1 1 1 1 4 3.348 19 0 0 58 58303 1 1 1 1 1 1 3.619 13 0 0 39 39304 1 1 1 1 1 1 3.158 19 2 0 52 52305 1 1 1 1 1 1 2.063 15 0 1 42 39306 1 1 1 0.75 1.215 16 5 2 28 20307 1 1 1 1 1 2.180 20 4 0 52 49308 1 1 1 1 1 1 1 1 5 3.932 14 0 0 44 44309 1 1 1 1 1 1 1 4 3.713 17 0 0 51 51310 1 1 1 1 1 1 1 5 2.339 21 0 0 64 64311 1 1 1 1 1 1 1 1 5 3.567 17 0 0 50 50312 1 1 1 1 1 1 1 4 3.382 21 1 0 60 60313 1 1 1 1 1 1 2.201 21 4 6 48 34314 1 1 1 1 1 1 4 3.612 20 0 0 60 60315 1 1 0.5 1 1.525 7 0 7 17 0316 1 1 1 1 1 2.892 16 1 0 43 43317 1 1 1 1 1 1 1 6 3.089 17 2 0 43 43318 1 1 0.5 1 1.222 8 2 3 19 9319 1 1 0.5 0.625 12 3 1 30 27320 1 1 1 1 1 1 1 4 2.831 20 1 0 59 59321 1 1 1 1 1 1 3.550 23 1 1 52 51322 1 1 1 1 1 1.145 15 1 4 38 27323 1 0.25 1 1.000 4 0 4 13 0324 1 0.25 3.063 5 0 0 16 16325 1 1 1 1 1 1 1 6 2.617 17 4 0 40 40326 1 1 1 1 1 1 1 4 3.062 13 0 0 38 38327 1 1 0.5 1 3.462 8 1 0 22 22328 1 1 1 1 1 1.836 17 0 1 46 44329 1 0.25 1 1.000 5 0 3 9 0330 1 1 1 1 1 1 1 5 2.733 22 0 0 66 66331 1 1 1 1 1 3.137 20 0 0 60 60332 1 1 1 1 1 1 3.373 16 0 0 48 48333 1 1 0.5 1 2.167 9 1 4 25 12334 1 1 1 1 1 1.274 21 3 7 53 35335 1 1 1 0.75 2.111 12 0 1 41 36336 1 1 1 0.75 1 3.409 7 0 0 21 21337 1 1 1 1 1 1 1 5 3.191 20 0 0 61 61338 1 0.25 1.000 4 1 3 8 0339 1 1 1 0.75 1 2.733 13 0 2 39 33340 1 1 1 1 1 1 1 4 2.756 20 1 0 58 58341 1 0.25 1 2.667 5 0 0 9 9342 1 1 1 1 1 1 1 4 3.682 17 1 0 45 45343 1 0.25 0.667 4 1 1 9 5344 1 1 1 0.75 1 1 4 3.719 14 0 0 43 43345 1 1 0.5 1 1.334 10 2 3 24 15346 1 1 0.5 2.250 8 2 0 19 19347 1 1 1 1 1 1 1 4 2.298 21 0 0 63 63348 1 1 1 1 1 1 1 1 5 2.280 22 2 0 61 61349 1 1 1 1 1 1 1 4 2.741 19 2 0 48 48350 1 1 0.5 1.278 8 1 4 23 9


Courses HoursSemesters Enrolled

50


n= 44 39 34 31 24 32 24Prev CM # Sem CM

Subject FA04 SP05 FA05 SP06 5th RR' CWork Grad to Grad GPA Enrolled D/W/I Failed Attempt Earned401 1 1 0.5 1 1.091 9 3 4 22 7402 1 1 1 1 1 1 3.026 22 1 0 64 64403 1 0.25 1 1.600 2 0 1 5 4404 1 1 1 1 1 1 1 4 2.944 19 1 0 55 55405 1 1 1 1 1 1 2.500 18 4 0 46 43406 1 1 1 1 1 2.217 19 4 2 42 39407 1 1 1 1 1 2.665 20 0 0 61 61408 1 1 1 1 1 1 1 1 6 2.877 21 0 0 56 56409 1 1 1 1 1 1 1 1 5 3.058 21 0 1 66 61410 1 1 1 1 1 1 1 5 2.798 16 1 0 45 45411 1 1 1 1 1 1 1 4 3.286 16 0 0 48 48412 1 1 0.5 2.046 9 2 4 24 11413 1 0.25 1 1.000 4 4 0 5 0414 1 1 1 1 1 1 1 4 3.384 22 2 0 61 61415 1 1 1 1 1 1 1 4 2.659 19 0 0 57 57416 1 1 1 1 1 1 2.471 22 1 1 60 60417 1 1 1 1 1 1 1 3 3.530 15 1 0 42 42418 1 1 1 1 1 1 1 3 3.889 11 0 0 31 31419 1 1 1 1 1 3.291 13 0 0 39 39420 1 1 1 1 1 1 1 1 5 2.330 18 1 1 48 48421 1 1 0.5 2.693 9 4 0 26 16422 1 1 1 1 1 1 1 1 5 3.030 20 1 0 58 58423 1 1 1 1 1 1 1 4 3.582 18 0 0 50 50424 1 1 1 1 1 1 2.088 22 0 1 64 61425 1 1 1 1 1 1 1 1 5 2.150 18 0 1 48 48426 1 1 1 1 1 1 1 4 3.693 17 1 0 48 48427 1 1 1 1 1 2.651 21 2 0 57 57428 1 1 1 1 1 1 4 2.486 24 1 0 70 70429 1 1 0.5 1 1.278 11 4 3 18 10430 1 1 1 1 1 1 1 7 3.464 18 4 0 54 41431 1 1 1 0.75 1 1 3 3.644 13 0 0 39 39432 1 1 1 1 1 1 4 3.194 25 0 0 75 75433 1 1 1 1 1 1 1 2 2.488 11 0 0 33 33434 1 1 1 0.75 1 1.819 17 2 4 43 31435 1 1 1 1 1 1 4 3.316 24 0 0 71 71436 1 1 1 1 1 1 1 4 4.000 12 0 0 38 38437 1 1 1 1 1 1 2.897 20 2 1 49 49438 1 1 1 1 1 1 1 4 3.832 20 0 0 62 62439 1 0.25 1 2.250 4 0 1 12 9440 1 1 1 0.75 1.256 14 0 6 31 25441 1 0.25 1 1.000 4 1 3 9 0442 1 1 1 1 1 1 1 1 5 2.705 19 1 0 54 54443 1 1 0.5 1 1 4 3.100 6 0 0 18 18444 1 0.25 1 0.000 5 5 0 16 0



51


n= 50 43 38 35 24 39 24

Prev CM # Sem CMSubject FA05 SP06 FA06 SP07 5th RR' CWork Grad to Grad GPA Enrolled D/W/I Failed Attempt Earned

501 1 1 0.5 1.500 11 2 6 20 10502 1 1 1 1 1 1 1 1 5 3.211 18 1 0 51 51503 1 1 1 1 1 2.264 27 4 7 66 47504 1 1 1 1 1 1 3.905 19 0 0 58 58505 1 1 1 1 1 1 2.864 21 1 0 60 60506 1 0.25 1 2.800 5 0 0 15 15507 1 1 1 1 1 1 1 1 5 2.688 19 1 2 48 48508 1 1 1 1 1 1 1 5 3.722 18 0 0 54 54509 1 1 1 1 1 1 2.948 19 1 0 55 52510 1 1 1 1 1 1 1 1 6 1.709 18 1 4 43 40511 1 1 1 1 1 1 1 4 3.474 24 2 0 67 67512 1 1 1 1 1 1 1 1 5 2.572 21 1 1 57 54513 1 1 1 1 1 1 1 5 3.486 18 0 0 53 53514 1 1 1 1 1 1 1 4 2.354 23 2 1 57 57515 1 1 1 1 1 1 1 1 6 2.263 22 1 2 60 57516 1 1 1 1 1 1 1 1 6 2.518 24 1 0 66 66517 1 0.25 1 2.750 5 0 0 12 12518 1 1 1 1 1 1 1 1 5 3.104 20 0 0 61 61519 1 1 0.5 1.529 8 0 1 25 22520 1 1 1 1 1 1 1 4 3.581 25 3 0 69 63521 1 1 1 1 1 1 2.083 18 3 1 48 42522 1 0.25 1 1.000 4 0 4 12 0523 1 1 1 1 1 1 1 1 5 2.723 16 0 0 48 48524 1 1 1 1 1 1 1 4 2.305 20 0 0 59 59525 1 1 0.5 1 3.382 10 1 0 28 28526 1 1 1 1 1 1 1 1 5 3.752 19 0 0 58 58527 1 1 1 1 1 1 4 3.565 24 2 0 67 67528 1 1 1 1 1 1 2.130 23 2 0 70 64529 1 1 1 1 1 1 1 4 2.649 25 1 0 70 70530 1 1 1 0.5 1 1 6 3.725 14 0 0 39 39531 1 0.25 1 1.000 5 1 4 14 0532 1 0.25 1 1.000 3 0 3 9 0533 1 1 1 1 1 1 1 1 5 2.445 20 0 0 61 61534 1 1 1 1 1 2.603 18 1 1 58 52535 1 1 1 1 1 1 1 5 2.826 24 1 0 67 67536 1 1 1 0.75 3.251 21 6 0 44 32537 1 1 1 1 1 1 2.605 18 7 0 42 31538 1 1 1 1 1 1 1.046 19 9 2 57 24539 1 1 1 1 1 1 2.532 16 2 0 47 44540 1 1 1 0.75 1 3.174 13 2 0 40 34541 1 1 1 1 1 1 1 3 4.000 15 0 0 44 44542 1 1 1 1 1 1 2.497 26 1 0 68 65543 1 1 1 0.75 1 1.667 13 1 5 40 22544 1 1 1 1 1 1 1 4 2.624 17 0 1 51 48545 1 0.25 1 1.778 4 1 0 14 9546 1 0.25 1.308 6 1 0 17 13547 1 1 0.5 1 2.285 10 2 4 30 12548 1 1 1 1 1 1 1 4 2.539 19 3 1 57 45549 1 1 1 1 1 1 1 1 5 3.495 14 1 0 41 38550 1 1 1 1 1 1.839 17 7 1 48 28



52


n= 50 41 33 28 8 37 8Prev CM # Sem CM

Subject FA06 SP07 FA07 SP08 5th RR' CWork Grad to Grad GPA Enrolled D/W/I Failed Attempt Earned601 1 0.25 0.444 3 0 2 9 4602 1 0.25 1 0.593 4 0 2 13 5603 1 1 1 1 1 1 1.580 19 6 1 58 40604 1 1 1 1 1 1 1 4 3.460 16 0 0 47 47605 1 1 0.5 1 3.107 9 2 0 30 22606 1 1 0.5 1.341 8 1 3 27 13607 1 1 1 1 1 1 3.177 24 0 0 69 69608 1 1 1 1 1 1 2.279 21 2 0 49 45609 1 1 1 1 1 1 1 1 5 3.703 20 0 0 62 62610 1 1 1 1 1 1 1.989 23 2 8 71 41611 1 1 1 1 1 1 1 4 3.938 15 0 0 45 45612 1 0.25 1.000 4 0 4 13 0613 1 1 1 0.75 1.519 12 4 1 25 22614 1 1 1 1 1 2.108 18 1 1 51 45615 1 0.25 1 0.000 4 4 0 13 0616 1 1 1 0.25 1 3.359 12 0 0 35 35617 1 1 1 1 1 1 2.880 19 2 0 55 51618 1 1 1 1 1 1 2.393 18 0 2 53 48619 1 1 1 1 1 1 3.144 18 1 0 57 53620 1 1 0.5 1 2.215 9 0 1 26 23621 1 0.25 1 3.385 5 0 0 13 13622 1 1 0.5 1 3.032 12 6 1 37 13623 1 1 1 1 1 1 2.933 20 2 0 61 54624 1 1 1 1 1 3.335 17 0 0 50 50625 1 1 1 0.75 1 1.659 13 2 4 37 18626 1 1 1 1 1 1 1.347 21 0 8 61 39627 1 1 1 1 1 1 1.278 17 0 11 53 15628 1 1 1 1 1 1 1 1 5 3.015 19 0 0 54 54629 1 1 1 1 1 2.436 18 1 0 54 51630 1 0.25 1 2.000 4 3 0 12 3631 1 0.25 1 3.231 4 0 0 13 13632 1 1 1 1 1 1.081 19 7 6 59 16633 1 1 1 1 1 1 2.804 18 0 0 54 54634 1 1 1 1 1 1 1 4 3.410 23 0 0 70 70635 1 1 1 0.75 3.433 17 2 1 49 40636 1 1 1 0.5 1 2.531 14 0 0 42 42637 1 1 1 1 1 1 2.536 19 0 0 60 60638 1 1 1 1 1 1 1 1 5 3.334 21 0 0 67 67639 1 1 1 1 1 0.810 16 3 13 50 0640 1 1 1 1 1 2.738 22 1 0 66 63641 1 1 1 1 1 1 1 4 4.000 15 0 0 45 45642 1 1 0.5 1 1.000 8 4 4 22 10643 1 1 1 1 1 1 1 5 3.322 25 2 0 72 68644 1 1 1 1 1 1 3.597 17 0 0 51 51645 1 1 1 1 1 2.030 20 2 0 57 50646 1 1 1 1 1 1 1.095 23 2 7 71 43647 1 1 0.5 1 2.800 8 0 0 24 24648 1 1 0.5 1 1.417 8 2 2 23 12649 1 1 1 0.75 1 3.329 10 0 0 29 29650 1 0.25 1 3.000 4 0 0 12 12



53

VITA

Graduate School Southern Illinois University

Gary A. Spoerre Date of Birth: November 3. 1962

1003 Meadowlark Drive, Carterville, Illinois 62918

University of Iowa Bachelor of Science, Industrial Engineering, May 1992

Research Paper Title: Student Retention in Two-Year Construction Management Programs

Major Professor: Marcia A. Anderson

Sample Data Gathering Research Paper - SIU

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