Abstract
Although used frequently in related fields such as K-12 education research, educational psychology, sociology, and social survey research, latent class analysis (LCA) has been infrequently used in higher education. This article provides higher education researchers with a pedagogical application of LCA to classify entering freshmen based on their pluralistic orientation. This study utilized data on entering freshmen at a racially diverse institution on the West coast. LCA was used to estimate latent profile probabilities, classify freshmen into latent classes, and relate latent class probabilities to covariates. The findings indicated that a four-class model was the best fitting model: high pluralistic orientation; high-disposition, low-skill; low-disposition, high-skill; and low pluralistic orientation. Similar to previous research, the findings indicated that the probability of being classified into one group versus the other was dependent upon a student’s race/ethnicity and intended major. This approach can aid college administrators in their program planning and targeted interventions around issues of diversity.
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Notes
For example, only 4 students (<1 %) indicated that they were in the lowest 10 % in terms of their ability to see the world from someone else’s perspective. Due to the low number of respondents endorsing particular categories and an interest in identifying students who were the most positive in their opinions about their abilities, we decided to dichotomize the response options. Continuous response options are also possible using a latent profile analysis (LPA). LPA is an extension of LCA (see Masyn 2013 for additional details). Both are used to classify respondents into latent groups based on their observed response patterns. The difference between LPA and LCA is that LCA uses dichotomous items and thus models the probability of endorsing an item, while LPA uses continuous measures and thus models the means and variance of items to classify respondents into categorical latent groups. We ran the analysis both ways, using the original continuous response options and compared them to the re-coded dichotomous response options. The results were identical in terms of the number of classes identified. For pedagogical purposes, we focus on LCA; however, interested readers can refer to Pastor et al. (2007) for an excellent example of LPA in higher education.
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Appendix
Appendix
Mplus syntax for latent class analysis predicting four classes.
TITLE: Latent Class Analysis.
DATA: file is diversity2009.dat;
VARIABLE: names are subjid divrate1 divrate2 divrate3 divrate4 divrate5;
categorical are divrate1 divrate2 divrate3 divrate4 divrate5;
idvar is subjid;
missing are all (999);
classes = c(4);
ANALYSIS: type = mixture;
starts = 200 50;
process = 4(starts);
OUTPUT: tech11 tech14;
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Denson, N., Ing, M. Latent Class Analysis in Higher Education: An Illustrative Example of Pluralistic Orientation. Res High Educ 55, 508–526 (2014). https://doi.org/10.1007/s11162-013-9324-5
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DOI: https://doi.org/10.1007/s11162-013-9324-5