School of Social Sciences

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Browsing School of Social Sciences by Author "Ackerman, Phillip L."

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    Trait Complex, Cognitive Ability, and Domain Knowledge Predictors of Baccalaureate Success, STEM Persistence, and Gender Differences
    (American Psychological Association, 2013-08) Ackerman, Phillip L.; Kanfer, Ruth; Beier, Margaret E.
    Prediction of academic success at post-secondary institutions is an enduring issue for educational psychology. Traditional measures of high-school grade point average and high-stakes entrance examinations are valid predictors, especially of first-year college grades, yet a large amount of individual-differences variance remains unaccounted for. Studies of individual trait measures (e.g., personality, self-concept, motivation) have supported the potential for broad predictors of academic success, but integration across these approaches has been challenging. The current study tracks 589 undergraduates from their first semester through attrition or graduation (up to 8 years beyond their first semester). Based on an integrative trait-complex approach to assessment of cognitive, affective, and conative traits, patterns of facilitative and impeding roles in predicting academic success were predicted. We report on the validity of these broad trait complexes for predicting academic success (grades and attrition rates) in isolation, and in the context of traditional predictors and indicators of domain knowledge (Advanced Placement exams). We also examine gender differences and trait complex by gender interactions for predicting college success and persistence in STEM fields. Inclusion of trait-complex composite scores and average AP exam scores raised the prediction variance accounted for in college grades to 37%, a marked improvement over traditional prediction measures. Math/Science Self-Concept and Mastery/Organization trait complex profiles were also found to differ between men and women who had initial STEM major intentions, but who left STEM for non-STEM majors. Implications for improving selection and identification of students at-risk for attrition are discussed.