Document Type



Doctor of Philosophy (PhD)


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


Remedial Math, Degree Attainment, College GPA, Propensity Score Matching, Imputation

Date of Defense



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.





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