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Exploring Rating Scale Functioning for Survey Research [Pehme köide]

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Exploring Rating Scale Functioning for Survey ResearchSuurem pilt
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Püsilink: https://www.kriso.ee/db/9781071855379.html
Märksõnad:
"Items with ordered response categories are common in survey research, such as when respondents are asked how much they agree with certain statements (1=strongly agree to 5=strongly disagree). But how large are the differences between categories of response, and how well do they distinguish between respondents? This volume is the first to introduce the evaluation of rating scales to an audience of survey researchers. Evaluating Rating Scale Functioning for Survey Research provides researchers with an overview of rating scale analysis along with practical guidance on how to conduct such analyses with their own survey data. Author Stefanie A. Wind presents three categories of methods: Rasch models; non-Rasch Item Response Theory (IRT) models; and non-parametric models, together with practical examples. Tutorials, datasets, and software code (R and Facets) to accompany the book are available on the book's website"--

Items with ordered response categories are common in survey research, such as when respondents are asked how much they agree with certain statements. But how large are the differences between categories of response, and how well do they distinguish between respondents? This volume is the first to introduce the evaluation of rating scales to an audience of survey researchers. Evaluating Rating Scale Functioning for Survey Research provides researchers with an overview of rating scale analysis along with practical guidance on how to conduct such analyses with their own survey data. Author Stefanie A. Wind presents three categories of methods: Rasch models; non-Rasch Item Response Theory (IRT) models; and non-parametric models, together with practical examples. Tutorials, datasets, and software code (R and Facets) to accompany the book are available on the book’s website at https://study.sagepub.com/researchmethods/qass/wind-exploring-rating-scale-functioning.

Arvustused

This book makes a valuable contribution to the field of rating scale analysis, and one that methodologists in the field of psychometrics should strongly consider. -- Mark Ellickson Wind takes the complex topic of evaluating rating scales, and explains the core issues researchers must address throughout the process. -- Charles P. Kost II This book comprehensively covers the analytical models and techniques for rating scale analysis. The author uses plain and clear language to explain all the models, equations, and illustrations of rating scale analysis in the book. I appreciate that the author makes the data and all software scripts available. -- Yi-Hsin Chen This book includes much good content will help students and researchers with their survey research, along with some great examples. -- Jingshun Zhang

Series Editor Introduction xiv
Acknowledgments xvi
About the Author xvii
List of Acronyms
xviii
Accompanying Website xix
1 What Is Rating Scale Analysis?
1(18)
What Is Item Response Theory?
2(3)
IRT for Rating Scale Data
4(1)
What Is Rating Scale Analysis?
5(6)
How Is Rating Scale Analysis Different From Other Survey Analyses?
5(3)
What Are the Requirements for Rating Scale Analysis?
8(3)
How Should Researchers Select a Model for Rating Scale Analysis?
11(4)
What Can Be Learned From Rating Scale Analysis?
15(1)
What Will This Book Help Researchers Do With Their Data?
15(2)
Introduction to Example Data
17(1)
Resources for Further Study
18(1)
2 Rasch Models for Rating Scale Analysis
19(29)
What Is Rasch Measurement Theory?
19(8)
What Are Rasch Models?
20(3)
Polytomous Rasch Models for Rating Scale Analysis
23(2)
Why Are Polytomous Rasch Models Useful for Rating Scale Analysis?
25(2)
Rasch Models for Rating Scale Analysis
27(11)
Rating Scale Model (RSM)
27(1)
Application of the RSM to the CES-D Scale Data
28(1)
Preliminary Analysis: Model-Data Fit
29(5)
Overall RSM Results
34(4)
Partial Credit Model (PCM)
38(3)
Application of the PCM to the CES-D Data
40(1)
Extending the Rating Scale and Partial Credit Models: The Many-Facet Rasch Model (MFRM)
41(4)
Application of the PC-MFRM to the CES-D Data
44(1)
Chapter Summary
45(3)
3 Illustration of Rating Scale Analysis With Polytomous Rasch Models
48(39)
Rating Scale Analysis With the Rating Scale Model
48(13)
Rating Scale Category Ordering
50(4)
Rating Scale Category Precision
54(6)
Rating Scale Category Comparability
60(1)
Rating Scale Analysis With the Partial Credit Model
61(10)
Category Ordering Indices
61(5)
Rating Scale Category Precision
66(4)
Rating Scale Category Comparability
70(1)
Rating Scale Analyses With the Partial Credit Many-Facet Rasch Model
71(11)
Category Ordering Indices
72(2)
Rating Scale Category Precision
74(8)
Chapter Summary
82(2)
Appendix
84(3)
4 Non-Rasch IRT Models for Rating Scale Analysis
87(38)
Rating Scale Analysis Using Polytomous IRT Models With Slope Parameters
88(5)
Generalized Partial Credit Model
90(3)
Overall Model Results
93(3)
Rating Scale Category Ordering
96(3)
Average Participant Locations Within Rating Scale Categories
96(1)
Logit-Scale Location Estimates of Item-Specific Rating Scale Category Thresholds
97(1)
Ordering of Item-Specific Category Probability Curves
98(1)
Rating Scale Category Precision
99(5)
Distance Between Item-Specific Threshold Location Estimates on the Logit Scale
99(1)
Distinct Item-Specific Category Probability Curves
100(4)
Graded Response Model
104(5)
Illustration of Rating Scale Analysis With the Graded Response Model
107(2)
Overall Model Results
109(1)
Rating Scale Category Ordering
110(1)
Average Participant Locations Within Item-Specific Rating Scale Categories for Individual Items
111(1)
Rating Scale Category Precision
111(6)
Distance Between Rating Scale Category Threshold Estimates on the Logit Scale for Individual Items
111(1)
Plots of Cumulative Category Probabilities for Individual Items
112(5)
Plots of Individual Category Probabilities for Individual Items
117(1)
Chapter Summary
117(2)
Appendix
119(6)
5 Nonparametric Measurement Models for Rating Scale Analysis
125(25)
Mokken Scale Analysis
126(1)
Overview of Mokken Models for Rating Scale Data
127(11)
Polytomous Monotone Homogeneity Model
127(7)
Polytomous Double Monotonicity Model
134(4)
Rating Scale Category Ordering
138(2)
Average Participant Restscores Within Rating Scale Categories for Individual Items
138(1)
Counts of Item-Specific Violations of Category Monotonicity
139(1)
Graphical Displays of Cumulative Category Monotonicity for Individual Items
139(1)
Rating Scale Category Precision
140(5)
Distinct Cumulative Category Probabilities for Individual Items
140(4)
Discriminating Item-Specific Cumulative Category Probabilities
144(1)
Chapter Summary
145(2)
Appendix
147(3)
6 Summary and Resources for Further Study
150(21)
What Is Rating Scale Analysis?
150(1)
Summary of Previous
Chapters
151(2)
How Should a Researcher Select a Model for Rating Scale Analysis?
153(3)
Overall Modeling Goals
153(1)
Practical Goals for Rating Scale Analysis
154(2)
Considerations Related to Audience
156(1)
Practical Takeaways: How Can a Researcher Use Results From Rating Scale Analysis?
156(4)
What Should I Do If My Scale Categories Are Disordered?
156(2)
What Should I Do If My Scale Categories Are Imprecise?
158(1)
How Do I Know If My Neutral Category Is Meaningful?
159(1)
Resources for Further Study
160(3)
Methodological Research on Rating Scale Analysis
160(1)
Examples of Applications of Rating Scale Analysis to Real Survey Data
160(3)
Appendix
163(8)
Tables 171(10)
Glossary 181(4)
References 185(8)
Index 193