The Importance of Structure Coefficients in Structural Equation Modeling Confirmatory Factor Analysis
Educational and Psychological MeasurementPublished 1 February 1997
Bruce Thompson
Citations180
SJR quartileQ1
SJR score1.93
SNIP2.21
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
Abstract
A general linear model (GLM) framework is employed to suggest that structure coefficients ought to be interpreted in structural equation modeling confinmatory factor analysis (CFA) studies in which factors are correlated. The computation of structure coefficients is explained. Two heuristic data sets are used to make the discussion concrete. The benefits from using CFA structure coefficients are illustrated using two additional studies.
Keywords
Computer ScienceMathematics
Medical Entomology and ZoologyMultiple regression in behavioral research
4,886 Citations1973Fred N. Kerlinger, Elazar J. Pedhazur
Psychological BulletinEvaluation of goodness-of-fit indices for structural equation models.
2,869 Citations1989Stanley A. Mulaik, Larry R. James +4 more
A Primer of Lisrel: Basic Applications and Programming for Confirmatory Factor Analytic Models
1,193 Citations1989Barbara M. Byrne
Educational and Psychological MeasurementFactor Analytic Evidence for the Construct Validity of Scores: A Historical Overview and Some Guidelines
924 Citations1996Bruce Thompson, Larry G. Daniel
Journal of Personality and Social PsychologyA theory and method of love.
901 Citations1986Clyde Hendrick, Susan S. Hendrick
A Primer of LISREL
676 Citations1989Barbara M. Byrne
Psychological BulletinAnalysis of correlation matrices using covariance structure models.
549 Citations1989Robert Cudeck
Educational and Psychological MeasurementThe Development and Validation of the Survey of Antitudes toward Statistics
435 Citations1995Candace Schau, Joseph J. Stevens +2 more
Canonical Correlation Analysis: Uses and Interpretation
403 Citations1984Bruce Thompson
Educational and Psychological MeasurementThe Importance of Structure Coefficients in Regression Research
229 Citations1985Bruce Thompson, Gloria M. Borrello
Multivariate Behavioral ResearchCanonical Correlation Analysis As A Special Case Of A Structural Relations Model
197 Citations1981Richard P. Bagozzi, Claes Fornell +1 more
This paper illustrates how problems of canonical correlation can be resolved by expressing canonical correlation as a special case of a linear structural relations model.
The American StatisticianInterpretation of Canonical Discriminant Functions, Canonical Variates, and Principal Components
197 Citations1992Alvin C. Rencher
Multivariate Behavioral ResearchA Brief History of the Philosophical Foundations of Exploratory Factor Analysis
182 Citations1987Stanley A. Mulaik
Exploratory factor analysis derives its key ideas from many sources, and the conclusions of exploratory factor analyses are never complete without a subsequent confirmatory analysis with additional variables and new data.
Educational Measurement Issues and PracticeThemes and Variations in Validity Theory
89 Citations1995Pamela Moss
Educational and Psychological MeasurementExploring the Replicability of a Study's Results: Bootstrap Statistics for the Multivariate Case
45 Citations1995Bruce Thompson
Measurement and Evaluation in Counseling and DevelopmentChildren's Beliefs About Sources of Health: A Reliability and Validity Study
44 Citations1987Bruce Thompson, A. G. Butcher +1 more
Educational and Psychological MeasurementMeasuring Computer Anxiety in the Work Environment
37 Citations1995George A. Marcoulides, Bronston T. Mayes +1 more
Multivariate Behavioral ResearchA Canonical Cautionary
17 Citations1989Richard Harris
A cautionary tale demonstrates that relationships between linear composites of variables can be identified via canonical correlation analysis, providing that the resulting canonical variates are interpreted on the basis of the canonical-variate weights.