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E-grāmata: Research with Diverse Groups: Research Designs and Multivariate Latent Modeling for Equivalence

(Associate Professor, School of Social Service, Fordham University, New York, NY, USA), (Associate Dean for Academic Affairs, School of Social Work, Rutgers University, New Brunswick, NJ, USA)
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Social work researchers often conduct research with groups that are diverse in terms of gender, sexual orientation, race or ethnic background, or age. Consequently, social work researchers must take great care to establish research-design equivalence at all phases of the research process (e.g., problem formulation, research design, sampling, measurement selection, data collection, and data analysis); otherwise, the results might reflect methodological flaws rather than true group differences and therefore lead to erroneous conclusions. This book introduces the methodological precautions that must be taken into consideration when conducting research with diverse groups. Multigroup Confirmatory Analysis (MG-CFA) using structural equation modeling (SEM) was utilized to demonstrate how to assess seven types of measurement and structural equivalence that Milfont and Fischer (2010) have deemed important for studies with diverse samples. A hypothetical example was provided to illustrate how to design a study with good research-design equivalence. A case example was provided to demonstrate how to conduct an MG-CFA for each type of measurement and structural equivalence discussed. The Mplus syntax used to conduct the MG-CFA was provided. The results from the MG-CFA analyses were written up as they would be for publication.
Acknowledgments ix
1 Introduction
1(12)
Diversity: Its Implications for Establishing Equivalence
3(4)
Problem Formulation
3(1)
Sampling Equivalence
3(1)
Measurement Selection
4(1)
African Americans
5(1)
Hispanics
5(2)
The Need to Consider Contextual Factors When Establishing Measurement Equivalence
7(1)
Organization of the Book
8(3)
Significance for Social Work
11(2)
2 Research-Design Equivalence
13(16)
Overview
13(1)
Problem Formulation
13(4)
Research Design
17(1)
Sampling Equivalence
18(3)
Measurement Selection
21(2)
Data Collection
23(2)
Data Analysis
25(1)
Summary
25(4)
3 Multi-Group Confirmatory Factor Analysis to Establish Measurement and Structural Equivalence
29(52)
Overview
29(1)
Measurement Equivalence Defined
30(2)
Overview of Multi-Group Confirmatory Factor Analysis
32(2)
Testing Measurement Equivalence Across Groups
34(13)
Model 0 Separate Group Analysis
37(2)
Model 1 Configural Equivalence
39(1)
Model 2 Weak Metric Equivalence
40(2)
Model 3 Strong (Scalar) Metric Equivalence
42(1)
Model 4 Strict Metric (Error Variance and Covariance) Equivalence
43(1)
Model 5 Equivalence of Factor Variance
44(1)
Model 6 Equivalence of Factor Covariance
45(1)
Model 7 Equivalence of Latent Means
45(2)
Illustration
47(32)
Distributional Analysis
48(1)
Baseline Measurement Models
48(1)
Separate Group Analysis
48(1)
Hispanic and African American Males, Baseline Model Analysis
48(5)
Hispanic and African American Females, Baseline Model Analysis
53(1)
Multi-Group Confirmatory Factor Analysis
54(1)
Model 1 Test of Configural Measurement Equivalence
54(6)
Model 2 Test of Weak Metric Measurement Equivalence
60(4)
Model 3 Test of Strong (Scalar) Metric Measurement Equivalence
64(2)
Model 4 Test of Strict Metric (Error-Variance and Covariance) Equivalence
66(1)
Error variance constraints
66(3)
Common error covariance
69(1)
Summary of the Results Assessing Measurement Equivalence
70(1)
Model 5 Equivalence of Factor Variance
70(2)
Model 6 Equivalence of Factor Covariance
72(4)
Model 7 Testing for Latent Mean Invariance
76(3)
Summary of the Results Testing for Structural Equivalence
79(1)
Summary
79(2)
4 Hypothetical Case Illustration
81(6)
Overview
81(1)
Hypothetical Case Illustration
81(4)
Summary
85(2)
5 Conclusion
87(6)
Qualitative Methods in Establishing Measurement Equivalence
87(1)
The Challenges of Conducting Research to Establish Equivalence Using National Datasets
88(1)
Future Directions
89(4)
Social Work Doctoral Education
89(4)
Appendix A Chi-Square Difference Testing Using the Satorra-Bentler Scaled Chi-Square: Hispanic and African American Males 93(2)
Appendix B Adjusted Chi-Square Difference Test: Configural versus Weak Factor Equivalence Model 95(2)
Appendix C Structural Equation Modeling Programs for Conducting Measurement Equivalency Analyses 97(2)
References 99(12)
Index 111
Dr. Antoinette Y. Farmer is associate professor and associate dean for academic affairs at Rutgers University's School of Social Work. Her research focuses on examining the social and interpersonal factors that affect parenting as well as how parenting practices influence adolescent high risk behaviors, such as delinquency and substance use. She co-edited a special issue of the Journal of Social Service Research, which was devoted to informing researchers of the methodological issues confronting them when conducting research with minority and oppressed populations. She has also written several chapters on this issue as well, with the most recent appearing in the Handbook of Social Work Research Methods (2nd Edition).