Document Type
Dissertation
Degree
Doctor of Philosophy (PhD)
Major/Program
Higher Education
First Advisor's Name
Mido Chang
First Advisor's Committee Title
Co-Committee Chair
Second Advisor's Name
Benjamin Baez
Second Advisor's Committee Title
Co-Committee Chair
Third Advisor's Name
Norma Goonen
Third Advisor's Committee Title
Committee Member
Fourth Advisor's Name
George O'Brien
Fourth Advisor's Committee Title
Committee Member
Keywords
Remedial Math, Degree Attainment, College GPA, Propensity Score Matching, Imputation
Date of Defense
3-28-2018
Abstract
Enrollment in degree-granting postsecondary institutions in the U.S. is increasing, as are the numbers of students entering academically underprepared. Students in remedial mathematics represent the largest percentage of total enrollment in remedial courses, and national statistics indicate that less than half of these students pass all of the remedial math courses in which they enroll. In response to the low pass rates, numerous studies have been conducted into the use of alternative modes of instruction to increase passing rates. Despite myriad studies into course redesign, passing rates have seen no large-scale improvement. Lacking is a thorough investigation into preexisting differences between students who do and do not take remedial math.
My study examined the effect of taking remedial math courses in college on degree attainment and college GPA using a subsample of the Educational Longitudinal Study of 2002. This nonexperimental study examined preexisting differences between students who did and did not take remedial math. The study incorporated propensity score matching, a statistical analysis not commonly used in educational research, to create comparison groups of matched students using multiple covariate measures. Missing value analyses and multiple imputation procedures were also incorporated as methods for identifying and handling missing data.
Analyses were conducted on both matched and unmatched groups, as well as on 12 multiply imputed data sets. Binary logistic regression analyses showed that preexisting differences between students on academic, nonacademic, and non-cognitive measures significantly predicted remedial math-taking in college. Binary logistic regression analyses also indicated that students who did not take remedial math courses in college were 1.5 times more likely to earn a degree than students who took remedial math. Linear regression analyses showed that taking remedial math had a significant negative effect on mean college GPA. Students who did not take remedial math had a higher mean GPA than students who did take remedial math. These results were consistent across unmatched groups, matched groups, and all 12 multiply imputed data sets.
Identifier
FIDC006525
Recommended Citation
Clovis, Meghan A., "An Investigation of the Effects of Taking Remedial Math in College on Degree Attainment and College GPA Using Multiple Imputation and Propensity Score Matching" (2018). FIU Electronic Theses and Dissertations. 3573.
https://digitalcommons.fiu.edu/etd/3573
Included in
Educational Assessment, Evaluation, and Research Commons, Higher Education Commons, Longitudinal Data Analysis and Time Series Commons, Multivariate Analysis Commons, Other Education Commons, Secondary Education Commons
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