Individual and School Correlates of DIT-2 Scores Using a Multilevel Modeling and Data Mining Analysis

Meghan Bankhead, Youn Jeng Choi, Yogendra Patil, Stephen J. Thoma

Research output: Contribution to journalArticlepeer-review

Abstract

Moral reasoning was investigated with respect to individual characteristics (i.e., education level, political orientation and sex) and school-related (i.e., university/college) factors using multilevel modeling and data mining analysis. We used the multilevel modeling to detect school effects on moral reasoning as well as individual effects for 16,334 students representing 79 different higher education institutions across the U.S. The school-related factors, such as the racial composition, student–faculty ratio, average SAT score, institution type, institutions’ geographical region, frequencies of morally relevant words in college course catalog, college mission and value statements were collected through website searches. Data mining analysis was utilized to extract and calculate the frequencies of morally relevant words from the website content. There were significant effects for the individual characteristic of political orientation. Additionally, all school-related factors were significant. Only main effects were observed for some school-related factors (i.e., average SAT score, institution type, frequency of morally relevant words in mission statements, value statements and course catalogs). For other school-related factors (i.e., the region, student–faculty ratio and racial composition), main effects were also observed; however, these effects were particularly illuminating given their interactions with political orientation. Implications for educational communities are discussed.

Original languageEnglish
Article number4573
JournalApplied Sciences (Switzerland)
Volume12
Issue number9
DOIs
StatePublished - 1 May 2022

Keywords

  • college students
  • data mining analysis
  • Defining Issues Test-2
  • moral judgment development
  • multilevel modeling

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