University and Student Segmentation: Multilevel Latent-Class Analysis of Students' Attitudes towards Research Methods and Statistics

Background: It is often claimed that psychology students' attitudes towards research methods and statistics affect course enrolment, persistence, achievement, and course climate. However, the inter-institutional variability has been widely neglected in the research on students' attitudes towards research methods and statistics, but it is important for didactic purposes (heterogeneity of the student population). Aims: The paper presents a scale based on findings of the social psychology of attitudes (polar and emotion-based concept) in conjunction with a method for capturing beginning university students' attitudes towards research methods and statistics and identifying the proportion of students having positive attitudes at the institutional level. Sample: The study based on a re-analysis of a nationwide survey in Germany in August 2000 of all psychology students that enrolled in fall 1999/2000 ("N" = 1,490) and "N" = 44 universities. Methods: Using multilevel latent-class analysis (MLLCA), the aim was to group students in different student attitude types and at the same time to obtain university segments based on the incidences of the different student attitude types. Results: Four student latent clusters were found that can be ranked on a bipolar attitude dimension. Membership in a cluster was predicted by age, grade point average (GPA) on school-leaving exam, and personality traits. In addition, two university segments were found: universities with an average proportion of students with positive attitudes and universities with a high proportion of students with positive attitudes (excellent segment). Conclusions: As psychology students make up a very heterogeneous group, the use of multiple learning activities as opposed to the classical lecture course is required. (Contains 6 tables and 3 figures.)