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Do Self-Report Instruments Allow Meaningful Comparisons Across Diverse Population Groups? Testing Measurement Invariance Using the Confirmatory Factor Analysis Framework
by
Gregorich, Steven E.
in
Bias
/ Black people
/ Covariance
/ Cross-Cultural Comparison
/ Data Collection - methods
/ Data Collection - statistics & numerical data
/ Discriminant analysis
/ Estimators for the mean
/ Ethnicity
/ Ethnicity - statistics & numerical data
/ Factor analysis
/ Factor Analysis, Statistical
/ FACTORIAL INVARIANCE: CONCEPTS AND EXAMPLES
/ Factorials
/ Humans
/ Medical research
/ Models, Statistical
/ Multiculturalism & pluralism
/ Parametric models
/ Population mean
/ Psychometrics
/ Psychometrics - methods
/ Public health
/ Research methodology
/ Scalars
/ Statistical discrepancies
2006
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Do Self-Report Instruments Allow Meaningful Comparisons Across Diverse Population Groups? Testing Measurement Invariance Using the Confirmatory Factor Analysis Framework
by
Gregorich, Steven E.
in
Bias
/ Black people
/ Covariance
/ Cross-Cultural Comparison
/ Data Collection - methods
/ Data Collection - statistics & numerical data
/ Discriminant analysis
/ Estimators for the mean
/ Ethnicity
/ Ethnicity - statistics & numerical data
/ Factor analysis
/ Factor Analysis, Statistical
/ FACTORIAL INVARIANCE: CONCEPTS AND EXAMPLES
/ Factorials
/ Humans
/ Medical research
/ Models, Statistical
/ Multiculturalism & pluralism
/ Parametric models
/ Population mean
/ Psychometrics
/ Psychometrics - methods
/ Public health
/ Research methodology
/ Scalars
/ Statistical discrepancies
2006
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Do Self-Report Instruments Allow Meaningful Comparisons Across Diverse Population Groups? Testing Measurement Invariance Using the Confirmatory Factor Analysis Framework
by
Gregorich, Steven E.
in
Bias
/ Black people
/ Covariance
/ Cross-Cultural Comparison
/ Data Collection - methods
/ Data Collection - statistics & numerical data
/ Discriminant analysis
/ Estimators for the mean
/ Ethnicity
/ Ethnicity - statistics & numerical data
/ Factor analysis
/ Factor Analysis, Statistical
/ FACTORIAL INVARIANCE: CONCEPTS AND EXAMPLES
/ Factorials
/ Humans
/ Medical research
/ Models, Statistical
/ Multiculturalism & pluralism
/ Parametric models
/ Population mean
/ Psychometrics
/ Psychometrics - methods
/ Public health
/ Research methodology
/ Scalars
/ Statistical discrepancies
2006
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Do Self-Report Instruments Allow Meaningful Comparisons Across Diverse Population Groups? Testing Measurement Invariance Using the Confirmatory Factor Analysis Framework
Journal Article
Do Self-Report Instruments Allow Meaningful Comparisons Across Diverse Population Groups? Testing Measurement Invariance Using the Confirmatory Factor Analysis Framework
2006
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Overview
Comparative public health research makes wide use of self-report instruments. For example, research identifying and explaining health disparities across demographic strata may seek to understand the health effects of patient attitudes or private behaviors. Such personal attributes are difficult or impossible to observe directly and are often best measured by self-reports. Defensible use of self-reports in quantitative comparative research requires not only that the measured constructs have the same meaning across groups, but also that group comparisons of sample estimates (eg, means and variances) reflect true group differences and are not contaminated by group-specific attributes that are unrelated to the construct of interest. Evidence for these desirable properties of measurement instruments can be established within the confirmatory factor analysis (CFA) framework; a nested hierarchy of hypotheses is tested that addresses the cross-group invariance of the instrument's psychometric properties. By name, these hypotheses include configurai, metric (or pattern), strong (or scalar), and strict factorial invariance. The CFA model and each of these hypotheses are described in nontechnical language. A worked example and technical appendices are included.
Publisher
Lippincott Williams & Wilkins,Lippincott Williams & Wilkins, Inc,Lippincott Williams & Wilkins Ovid Technologies
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