Community College Transfer to Four-Year Institutions: A Latent Class Structural Equation Model

Drawing on data from the nationally representative 2004/09 Beginning Postsecondary Students Longitudinal Study (BPS 04/09), this study proposed and tested a latent class measurement model of public two-year community college student transfer subtypes, and examined the latent class conditional structural relationships among student background characteristics, Remediation, First-year college GPA, Student Engagement and transfer to four-year institutions. Perhaps, most importantly, this study examined whether latent class membership moderated the relationships between malleable factors and four-year transfer likelihood. This study employed latent class analysis (LCA) to identify potential latent transfer subtypes, confirmatory factor analysis (CFA) to account for the unreliability in the indicators of the hypothesized latent student Engagement factor, and structural equation modeling (SEM), using an unbiased 3-step approach to the analysis of both predictors of latent class and latent class prediction of distal outcomes (Asparouhov & Muthén, 2014a; Vermunt, 2010), to examine the associations among the above mentioned variables and four-year transfer likelihood. Based on a comprehensive review of information criteria and fit indices, a four class solution fit the data best and provided four substantively relevant transfer classes which I labeled as follows: Class 1:High Transfer Intentions, Few Barriers, Class 2: Low Transfer Intentions, Some Barriers, Class 3: Moderate Transfer Intentions, Low Academic Resources, Class 4: Moderate Transfer Intentions, Low Academic Momentum. Controlling for latent class membership, first generation college status and exposure to remediation were negatively associated with four-year transfer likelihood, while increases in both first-year GPA and student Engagement were positively associated with transfer outcomes. However, when latent class specific slopes were estimated, exposure to Remediation and first-year GPA were statistically significantly (p<.05) related to transfer only in Class 1:High Transfer Intentions, Few Barriers, while only First Generation Status was statistically significantly related to transfer in Class 3; Moderate Transfer Intentions, Low Academic Resources and Class 4: Moderate Transfer Intentions, Low Academic Momentum; student Engagement, at an inflated alpha of .10, was statistically significantly (p=.07) related to transfer in Class 4: Moderate Transfer Intentions, Low Academic Momentum.That latent class membership moderated the relationships between malleable factors and transfer likelihood provides underfunded community colleges with a more nuanced answer as to which variables are related to transfer. Using such information, community colleges could provide class-specific advice and interventions, rather than a one size fits all approach, which may or may not be right for each transfer subtype. In this way, community colleges may increase transfer rates in an efficient and strategic manner that meets the needs of its diverse student population

Community College Transfer to Four-Year Institutions: A Latent Class Structural Equation Model

Drawing on data from the nationally representative 2004/09 Beginning Postsecondary Students Longitudinal Study (BPS 04/09), this study proposed and tested a latent class measurement model of public two-year community college student transfer subtypes, and examined the latent class conditional structural relationships among student background characteristics, Remediation, First-year college GPA, Student Engagement and transfer to four-year institutions. Perhaps, most importantly, this study examined whether latent class membership moderated the relationships between malleable factors and four-year transfer likelihood. This study employed latent class analysis (LCA) to identify potential latent transfer subtypes, confirmatory factor analysis (CFA) to account for the unreliability in the indicators of the hypothesized latent student Engagement factor, and structural equation modeling (SEM), using an unbiased 3-step approach to the analysis of both predictors of latent class and latent class prediction of distal outcomes (Asparouhov & Muthén, 2014a; Vermunt, 2010), to examine the associations among the above mentioned variables and four-year transfer likelihood. Based on a comprehensive review of information criteria and fit indices, a four class solution fit the data best and provided four substantively relevant transfer classes which I labeled as follows: Class 1:High Transfer Intentions, Few Barriers, Class 2: Low Transfer Intentions, Some Barriers, Class 3: Moderate Transfer Intentions, Low Academic Resources, Class 4: Moderate Transfer Intentions, Low Academic Momentum. Controlling for latent class membership, first generation college status and exposure to remediation were negatively associated with four-year transfer likelihood, while increases in both first-year GPA and student Engagement were positively associated with transfer outcomes. However, when latent class specific slopes were estimated, exposure to Remediation and first-year GPA were statistically significantly (p<.05) related to transfer only in Class 1:High Transfer Intentions, Few Barriers, while only First Generation Status was statistically significantly related to transfer in Class 3; Moderate Transfer Intentions, Low Academic Resources and Class 4: Moderate Transfer Intentions, Low Academic Momentum; student Engagement, at an inflated alpha of .10, was statistically significantly (p=.07) related to transfer in Class 4: Moderate Transfer Intentions, Low Academic Momentum.That latent class membership moderated the relationships between malleable factors and transfer likelihood provides underfunded community colleges with a more nuanced answer as to which variables are related to transfer. Using such information, community colleges could provide class-specific advice and interventions, rather than a one size fits all approach, which may or may not be right for each transfer subtype. In this way, community colleges may increase transfer rates in an efficient and strategic manner that meets the needs of its diverse student population