The impact of part-time faculty on student retention : a case study in higher education

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The impact of part-time faculty on student retention : a case study in higher education

Please use this identifier to cite or link to this item: http://hdl.handle.net/10355/8939

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Title: The impact of part-time faculty on student retention : a case study in higher education
Author: Smith, Curtis V. (Curtis Vinicio), 1953-
Keywords: student retention
part-time faculty
higher education
Date: 2010
Publisher: University of Missouri--Kansas City
Abstract: There has been considerable debate in community colleges over the last forty years regarding the impact of increased use of part-time faculty (PTF) on student learning. It has been argued that part-time faculty fail to provide the same level of teaching quality as full-time faculty (FTF). The purpose of this study was to determine the impact of part-time faculty on student retention at an average-sized urban community college, Kansas City Kansas Community College. Archival data obtained from the college for 2,030 first-time full-time students (FTFTS) enrolled by the first day of the Fall semester for academic years: 2003, 2004, 2005 and 2006, were retrospectively analyzed for retention to the Spring semester, and the next Fall semester in each academic year, and for all years combined. This study applies a multi-step method for model building and a quantitative, descriptive, ex post facto design. The first step involved univariable analysis of six independent variables suggested in the literature to be correlated with retention of all first-time full-time students: (1) exposure to part-time faculty, (2) ethnicity, (3) gender, (4) degree seeking status, (5) developmental or non-developmental learner status, and (6) number of credit hours enrolled during the first semester. Pearson correlations, t tests, and analysis of variance statistical methods were employed in order to obtain Chi Square, means, standard deviations, t values and significance scores. The second step involved binary logistic regression for multi-variable analysis of each academic year in order to assess the six independent variables with the dependent variable, retention, to the respective Spring, and next Fall semester. The final step employed logistic regression to determine what independent variables predicted the likelihood of student retention to the Spring, and next Fall semester with all academic years combined. Statistical results of the final logistic regression analysis predicted the likelihood of a decrease in first-time full-time student retention with increased exposure to part-time faculty to the next Fall semester and with all academic years combined.
URI: http://hdl.handle.net/10355/8939

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