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Rosen, Brittany L.; Goodson, Patricia; Thompson, Bruce; Wilson, Kelly L. – Journal of School Health, 2015
Background: Because human papillomavirus (HPV) vaccine rates remain low, we evaluated US school nurses' knowledge, attitudes, perceptions of their role as opinion leaders, and professional practice regarding HPV vaccine, and assessed whether knowledge, attitudes, and perceptions of being an opinion leader influenced their professional…
Descriptors: Cancer, Immunization Programs, Role, Case Studies
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Thompson, Bruce – Educational and Psychological Measurement, 1997
A general linear model framework is used to suggest that structure coefficients ought to be interpreted in structural equation modeling confirmatory factor analysis (CFA) studies in which factors are correlated. Two heuristic data sets make the discussion concrete, and two additional studies illustrate the benefits of CFA structure coefficients.…
Descriptors: Factor Analysis, Mathematical Models, Structural Equation Models
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Zientek, Linda Reichwein; Thompson, Bruce – Educational Researcher, 2009
Correlation matrices and standard deviations are the building blocks of many of the commonly conducted analyses in published research, and AERA and APA reporting standards recommend their inclusion when reporting research results. The authors argue that the inclusion of correlation/covariance matrices, standard deviations, and means can enhance…
Descriptors: Effect Size, Correlation, Researchers, Multivariate Analysis
Thompson, Bruce – 1996
A general linear model (GLM) framework is used to suggest that structure coefficients ought to be interpreted in structural equation modeling confirmatory factor analysis (CFA) studies in which factors are correlated. The computation of structure coefficients in explanatory factor analysis and CFA is explained. Two heuristic data sets are used to…
Descriptors: Ability, Correlation, Heuristics, Mathematical Models
Thompson, Bruce – 1998
This paper provides an introduction to basic issues concerning structural equation modeling (SEM), a research methodology increasingly being used in social science research. First, seven key issues that must be considered in any SEM analysis are explained. These include matrix of associations to analyze, model identification, parameter estimation…
Descriptors: Mathematical Models, Research Methodology, Social Science Research, Statistical Analysis
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Thompson, Bruce; Cook, Colleen; Heath, Fred – Structural Equation Modeling, 2003
Used confirmatory factor analysis to evaluate the score integrity of LibQUALl+, an instrument to measure perceptions of library service quality. Results for 60,027 graduate and undergraduate students suggest that the model implied by LibQUAL is reasonable and invariant across independent samples and fits all three major subgroups of library users.…
Descriptors: College Students, Evaluation Methods, Factor Structure, Higher Education
Thompson, Bruce; Melancon, Janet G. – 1996
This study investigated the benefits of creating item "testlets" or "parcels" in the context of structural equation modeling confirmatory factor analysis (CFA). Testlets are defined as groups of items related to a single content area that is developed as a unit. The strategy is illustrated using data from the administration of…
Descriptors: Statistical Distributions, Structural Equation Models, Test Construction
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Wang, Zhongmiao; Thompson, Bruce – Journal of Experimental Education, 2007
In this study the authors investigated the use of 5 (i.e., Claudy, Ezekiel, Olkin-Pratt, Pratt, and Smith) R[squared] correction formulas with the Pearson r[squared]. The authors estimated adjustment bias and precision under 6 x 3 x 6 conditions (i.e., population [rho] values of 0.0, 0.1, 0.3, 0.5, 0.7, and 0.9; population shapes normal, skewness…
Descriptors: Effect Size, Correlation, Mathematical Formulas, Monte Carlo Methods
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Fan, Xitao; Wang, Lin; Thompson, Bruce – Structural Equation Modeling, 1999
A Monte Carlo simulation study investigated the effects on 10 structural equation modeling fit indexes of sample size, estimation method, and model specification. Some fit indexes did not appear to be comparable, and it was apparent that estimation method strongly influenced almost all fit indexes examined, especially for misspecified models. (SLD)
Descriptors: Estimation (Mathematics), Goodness of Fit, Monte Carlo Methods, Sample Size
Thompson, Bruce; Fan, Xitao – 1998
This study empirically investigated bootstrap bias estimation in the area of structural equation modeling (SEM). Three correctly specified SEM models were used under four different sample size conditions. Monte Carlo experiments were carried out to generate the criteria against which bootstrap bias estimation should be judged. For SEM fit indices,…
Descriptors: Estimation (Mathematics), Goodness of Fit, Monte Carlo Methods, Sample Size