An empirical demonstration of direct and indirect applications of mixture modeling when studying personality traits: A methodological-substantive synergy
by Kaliski, Pamela, Ph.D., JAMES MADISON UNIVERSITY, 2009, 187 pages; 3352783

Abstract:

Many personality psychology researchers have employed the person-centered approach of cluster analysis to determine how many categorical Big Five personality types exist. The majority of these researchers have suggested that three Big Five personality types exist; however, results from two recent studies suggested that five types exist. In the first part of the current study, direct mixture modeling (an alternative person-centered approach to cluster analysis), was conducted on Big Five personality variables to explore the number of Big Five personality types that exist in college students, and two methodological approaches for gathering validity evidence for the personality types were demonstrated. Although more validity evidence must be gathered, results of the direct MM suggested that three personality types may exist in college students; however, the types differed in form from the three types that are commonly reported. In the second part of the current study, the same results were used to demonstrate an application of indirect mixture modeling. As opposed to interpreting the classes as substantively meaningful discrete subgroups, they were interpreted as common configurations that best represent the aggregate dataset. Additionally, the variable-centered approach of multiple regression was conducted. A comparison of the multiple regression results and the indirect mixture modeling results reveal the similarities and differences between these two models. Implications of these results for personality psychologists, educators, and methodologists are discussed, as well as directions for future research.

 
AdviserSara Finney
SchoolJAMES MADISON UNIVERSITY
SourceDAI/B 70-04, p. , May 2009
Source TypeDissertation
SubjectsStatistics; Clinical psychology; Personality psychology
Publication Number3352783
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