Confirmatory Factor Analysis
Oxford University Press, USA, 2009 - 122 halaman
Measures that are reliable, valid and can be used across diverse populations are vital to social work research, but the development of new measures is an expensive and time-consuming process. An array of existing measures can provide a cost-effective alternative, but in order to take this expedient step with confidence, researchers must ensure that the existing measure is appropriate for the new study. Confirmatory factory analysis (CFA) is one way to do so, and in this clearly written pocket guide Donna Harrington provides social work researchers with an essential roadmap to the highlights of CFA's powers and how to harness them.CFA has four primary functions-- psychometric evaluation of measures, construct validation, testing method effects, and testing measurement invariance-- all of which Harrington makes exceedingly accessible. She includes an easy-to-follow overview of the method, step-by-step guides to creating a CFA model and assessing its fit, and clear explanations of the requirements for using CFA, as well as underscoring the issues that are necessary to consider in alternative situations, such as when multiple groups are involved. Real-world examples, screenshots from the Amos software program that can be used to conduct CFA, and reading suggestions for each chapter make the material accessible for even the greenest novice.This pocket guide is ideally suited for readers who plan to conduct CFA analyses and need a brief, non-technical introduction to the topic to get them started before getting into the more detailed and technical literature, as well as readers who do not plan to conduct CFA analyses, but want to be knowledgeable consumers of research literature that uses CFA.
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2 Creating a Confirmatory Factor Analysis Model
4 Assessing Confirmatory Factor Analysis Model Fit and Model Revision
5 Use of Confirmatory Factor Analysis with Multiple Groups
6 Other Issues
Brief Introduction to Using Amos
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Abbott Amos 7.0 Graphics Amos 7.0 Output assessing Brown burnout CFA model conducted Confirmatory Factor Analysis constrained construct validity correlations data analysis data set direct ML error covariances estimation methods examine factor loadings factor structure FAP sample fit indices Group Number 1—Default handling missing data identified Independence model Internet sample Intrinsic job satisfaction JSS1 JSS2 JSS4 JSS5 JSS6 JSS7 JSS8 JSS9 Kline Koeske kurtosis latent variables listwise MacCallum maximum likelihood Measurement intercepts measurement invariance Measurement residuals Measurement weights method effects ML estimation Model Fit Summary modification indices Mplus multiple imputation multiple-group CFA multivariate nested model non-normality normally distributed Number 1—Default Model observed variables options Organizational outliers pairwise deletion parameters principal components analysis recommended respecified RMSEA Root Mean Square Salary/Promotion sample size Saturated model social work literature software packages SPSS standardized Structural covariances structural equation modeling subscale three-factor Unconstrained