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Meta-Analyzing the Factor Structure of the Learning and Study Strategies Inventory
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Fong, Carlton J.; Lee, Jihyun; Krou, Megan R.; Hoff, Meagan A.; Johnston-Ashton, Karen; Gonzales, Cassandra; Beretvas, S. Natasha – Journal of Experimental Education, 2023
The Learning and Study Strategies Inventory (LASSI; Weinstein et al., "Learning and study strategies inventory." H&H Publishing, 1987) is a prominent instrument used in thousands of institutions worldwide as an educational and research tool. Despite its widespread prevalence, there are inconsistencies regarding the underlying latent…
Descriptors: Meta Analysis, Factor Structure, Learning Strategies, Measures (Individuals)
When Does the Factor-Mixture Distinction Matter?
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Loken, Eric – Measurement: Interdisciplinary Research and Perspectives, 2012
Von Davier, Naemi, and Roberts (this issue) present a nice summary of the statistical ambiguity often encountered in making distinctions between qualitative and quantitative constructs. In this commentary, the author begins with two broad points. The first is that the mixture/factor arguments are most intriguing when firmly embedded in a…
Descriptors: Models, Statistical Analysis, Classification, Goodness of Fit
Commentary on Factorial versus Typological Models: Complementary Evidence in the Model Selection Process
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Samuelsen, Karen – Measurement: Interdisciplinary Research and Perspectives, 2012
The notion that there is often no clear distinction between factorial and typological models (von Davier, Naemi, & Roberts, this issue) is sound. As von Davier et al. state, theory often indicates a preference between these models; however the statistical criteria by which these are delineated offer much less clarity. In many ways the procedure…
Descriptors: Models, Statistical Analysis, Classification, Factor Structure
A Validation of the Factor Structure of OQ-45 Scores Using Factor Mixture Modeling
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Kim, Seong-Hyeon; Beretvas, S. Natasha; Sherry, Alissa R. – Measurement and Evaluation in Counseling and Development, 2010
This study investigated the Outcome Questionnaire's (OQ-45) factor structure and demonstrated the use of factor mixture modeling (FMM) for the purpose of score validation. OQ-45 scores did not fit the one-class, one- and three-factor models. Use of FMM to identify a two-class model is detailed. Implications for OQ-45 users are provided. (Contains…
Descriptors: Validity, Factor Structure, Factor Analysis, Scores
A Causal Model Analysis Suggests Modification of the Cumulative Hierarchical Structure Assumed in Bloom's Taxonomy of the Cognitive Domain.
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Madaus, George F.; And Others – 1971
Bloom's taxonomy of the cognitive domain consists of six major levels: Knowledge, Comprehension, Application, Analysis, Synthesis, and Evaluation. The purpose of this study is to construct a quantitative causal model for a set of tests designed to operationally define these six levels in order to further explore the validity of the cumulative…
Descriptors: Achievement Tests, Classification, Cognitive Objectives, Cognitive Processes
Measurement Integrity of Scores from the Fennema-Sherman Mathematics Attitudes Scales: The Attitudes of Public School Teachers.
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Thompson, Bruce; And Others – 1993
The Fennema-Sherman Mathematics Attitudes Scales (E. Fennema and J. A. Sherman, 1976) are among the most popular measures used in studies of attitudes toward mathematics. However, the measurement integrity of the scores has not yet been established conclusively. Measurement integrity was explored by using data from 174 elementary school teachers…
Descriptors: Classification, Elementary Education, Elementary School Teachers, Elementary Schools