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E-raamat: Design, Evaluation, and Analysis of Questionnaires for Survey Research, Second Edition 2nd Edition [Wiley Online]

(Universitat Pompeu Fabra, Barcelona, Spain), (Universitat Pompeu Fabra, Barcelona, Spain)
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Design, Evaluation, and Analysis of Questionnaires for Survey Research, Second Edition 2nd EditionSuurem pilt
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Praise for the First Edition

“...this book is quite inspiring, giving many practical ideas for survey research, especially for designing better questionnaires.”

—International Statistical Review

Reflecting modern developments in the field of survey research, the Second Edition of Design, Evaluation, and Analysis of Questionnaires for Survey Research continues to provide cutting-edge analysis of the important decisions researchers make throughout the survey design process.

The new edition covers the essential methodologies and statistical tools utilized to create reliable and accurate survey questionnaires, which unveils the relationship between individual question characteristics and overall question quality. Since the First Edition, the computer program Survey Quality Prediction (SQP) has been updated to include new predictions of the quality of survey questions on the basis of analyses of Multi-Trait Multi-Method experiments. The improved program contains over 60,000 questions, with translations in most European languages. Featuring an expanded explanation of the usage and limitations of SQP 2.0, the Second Edition also includes:

• New practice problems to provide readers with real-world experience in survey research and questionnaire design

• A comprehensive outline of the steps for creating and testing survey questionnaires

• Contemporary examples that demonstrate the many pitfalls of questionnaire design and ways to avoid similar decisions

Design, Evaluation, and Analysis of Questionnaires for Survey Research, Second Edition is an excellent textbook for upper-undergraduate and graduate-level courses in methodology and research questionnaire planning, as well as an ideal resource for social scientists or survey researchers needing to design, evaluate, and analyze questionnaires.

Design, Evaluation, and Analysis of Questionnaires for Survey Research, Second Edition is an excellent textbook for upper-undergraduate and graduate-level courses in methodology and research questionnaire planning, as well as an ideal resource for social scientists or survey researchers needing to design, evaluate, and analyze questionnaires.Reflecting modern developments in the field of survey research, the Second Edition of
Design, Evaluation, and Analysis of Questionnaires for Survey Research continues to
provide cutting-edge analysis of the important decisions researchers make throughout the
survey design process.
The new edition covers the essential methodologies and statistical tools utilized to create
reliable and accurate survey questionnaires, which unveils the relationship between individual
question characteristics and overall question quality. Since the First Edition, the computer
program Survey Quality Prediction (SQP) has been updated to include new predictions
Näita rohkemNäita vähem
Preface To The Second Edition xiii
Preface xv
Acknowledgments xvii
Introduction 1(12)
I.1 Designing a Survey
4(8)
I.1.1 Choice of a Topic
4(1)
I.1.2 Choice of the Most Important Variables
4(1)
I.1.3 Choice of a Data Collection Method
5(1)
I.1.4 Choice of Operationalization
6(2)
I.1.5 Test of the Quality of the Questionnaire
8(1)
I.1.6 Formulation of the Final Questionnaire
9(1)
I.1.7 Choice of Population and Sample Design
9(1)
I.1.8 Decide about the Fieldwork
10(1)
I.1.9 What We Know about These Decisions
10(1)
I.1.10 Summary
11(1)
Exercises
12(1)
Part I The Three-Step Procedure To Design Requests For Answers 13(64)
1 Concepts-by-Postulation and Concepts-by-Intuition
15(15)
1.1 Concepts-by-Intuition and Concepts-by-Postulation
15(4)
1.2 Different Ways of Defining Concepts-by-Postulation through Concepts-by-Intuition
19(8)
1.2.1 Job Satisfaction as a Concept-by-Intuition
19(1)
1.2.2 Job Satisfaction as a Concept-by-Postulation
20(7)
1.3 Summary
27(1)
Exercises
28(2)
2 From Social Science Concepts-by-Intuition to Assertions
30(30)
2.1 Basic Concepts and Concepts-by-Intuition
31(1)
2.2 Assertions and Requests for an Answer
32(1)
2.3 The Basic Elements of Assertions
33(6)
2.3.1 Indirect Objects as Extensions of Simple Assertions
36(1)
2.3.2 Adverbials as Extensions of Simple Assertions
37(1)
2.3.3 Modifiers as Extensions of Simple Assertions
37(1)
2.3.4 Object Complements as Extensions of Simple Assertions
38(1)
2.3.5 Some Notation Rules
38(1)
2.4 Basic Concepts-by-Intuition
39(10)
2.4.1 Subjective Variables
40(7)
2.4.2 Objective Variables
47(2)
2.4.3 In Summary
49(1)
2.5 Alternative Formulations for the Same Concept
49(2)
2.6 Extensions of Simple Sentences
51(2)
2.6.1 Adding Indirect Objects
51(1)
2.6.2 Adding Modifiers
52(1)
2.6.3 Adding Adverbials
52(1)
2.7 Use of Complex Sentences
53(3)
2.7.1 Complex Sentences with No Shift in Concept
54(1)
2.7.2 Complex Sentences with a Shift in Concept
54(2)
2.7.3 Adding Conditions to Complex Sentences
56(1)
2.8 Summary
56(1)
Exercises
57(3)
3 The Formulation of Requests for an Answer
60(17)
3.1 From Concepts to Requests for an Answer
61(2)
3.2 Different Types of Requests for an Answer
63(6)
3.2.1 Direct Request
63(3)
3.2.2 Indirect Request
66(3)
3.3 The Meaning of Requests for an Answer with WH Request Words
69(5)
3.3.1 "When," "Where," and "Why" Requests
70(1)
3.3.2 "Who" Requests
70(1)
3.3.3 "Which" Requests
70(1)
3.3.4 "What" Requests
71(1)
3.3.5 "How" Requests
72(2)
3.4 Summary
74(1)
Exercises
75(2)
Part II Choices Involved In Questionnaire Design 77(86)
4 Specific Survey Research Features of Requests for an Answer
79(19)
4.1 Select Requests from Databases
79(2)
4.2 Other Features Connected with the Research Goal
81(2)
4.3 Some Problematic Requests
83(2)
4.3.1 Double-Barreled Requests
83(1)
4.3.2 Requests with Implicit Assumptions
84(1)
4.4 Some Prerequests Change the Concept-by-Intuition
85(1)
4.5 Batteries of Requests for Answers
86(6)
4.5.1 The Use of Batteries of Stimuli
87(1)
4.5.2 The Use of Batteries of Statements
88(4)
4.6 Other Features of Survey Requests
92(3)
4.6.1 The Formulation of Comparative or Absolute Requests for Answers
92(1)
4.6.2 Conditional Clauses Specified in Requests for Answers
93(1)
4.6.3 Balanced or Unbalanced Requests for Answers
93(2)
4.7 Special Components within the Request
95(1)
4.7.1 Requests for Answers with Stimulation for an Answer
95(1)
4.7.2 Emphasizing the Subjective Opinion of the Respondent
95(1)
4.8 Summary
96(1)
Exercises
96(2)
5 Response Alternatives
98(17)
5.1 Open Requests for an Answer
99(2)
5.2 Closed Categorical Requests
101(10)
5.2.1 Nominal Categories
103(1)
5.2.2 Ordinal Scales
104(4)
5.2.3 Continuous Scales
108(3)
5.3 How Many Categories Are Optimal?
111(1)
5.4 Summary
112(2)
Exercises
114(1)
6 The Structure of Open-Ended and Closed Survey Items
115(15)
6.1 Description of the Components of Survey Items
115(3)
6.2 Different Structures of Survey Items
118(8)
6.2.1 Open-Ended Requests for an Answer
119(1)
6.2.2 Closed Survey Items
120(4)
6.2.3 The Frequency of Occurrence
124(1)
6.2.4 The Complexity of Survey Items
125(1)
6.3 What Form of Survey Items Should Be Recommended?
126(1)
6.4 Summary
127(1)
Exercises
128(2)
7 Survey Items in Batteries
130(16)
7.1 Batteries in Oral Interviews
131(3)
7.2 Batteries in Mail Surveys
134(4)
7.3 Batteries in CASI
138(4)
7.4 Summary and Discussion
142(2)
Exercises
144(2)
8 Mode of Data Collection and Other Choices
146(17)
8.1 The Choice of the Mode of Data Collection
147(9)
8.1.1 Relevant Characteristics of the Different Modes
148(1)
8.1.2 The Presence of the Interviewer
149(2)
8.1.3 The Mode of Presentation
151(1)
8.1.4 The Role of the Computer
152(3)
8.1.5 Procedures without Asking Questions
155(1)
8.1.6 Mixed-Mode Data Collection
155(1)
8.2 The Position in the Questionnaire
156(2)
8.3 The Layout of the Questionnaire
158(1)
8.4 Differences due to Use of Different Languages
158(1)
8.5 Summary and Discussion
159(1)
Exercises
160(3)
Part III Estimation And Prediction Of The Quality Of Questions 163(80)
9 Criteria for the Quality of Survey Measures
165(25)
9.1 Different Methods, Different Results
166(7)
9.2 How These Differences Can Be Explained
173(5)
9.2.1 Specifications of Relationships between Variables in General
173(2)
9.2.2 Specification of Measurement Models
175(3)
9.3 Quality Criteria for Survey Measures and Their Consequences
178(3)
9.4 Alternative Criteria for Data Quality
181(3)
9.4.1 Test—Retest Reliability
181(1)
9.4.2 The Quasi-simplex Approach
182(1)
9.4.3 Correlations with Other Variables
183(1)
9.5 Summary and Discussion
184(1)
Exercises
185(2)
Appendix 9.1 The Specification of Structural Equation Models
187(3)
10 Estimation of Reliability, Validity, and Method Effects
190(18)
10.1 Identification of the Parameters of a Measurement Model
191(4)
10.2 Estimation of Parameters of Models with Unmeasured Variables
195(2)
10.3 Estimating Reliability, Validity, and Method Effects
197(4)
10.4 Summary and Discussion
201(1)
Exercises
202(3)
Appendix 10.1 Input of Lisrel for Data Analysis of a Classic MTMM Study
205(1)
Appendix 10.2 Relationship between the TS and the Classic MTMM Model
205(3)
11 Split-Ballot Multitrait—Multimethod Designs
208(17)
11.1 The Split-Ballot MTMM Design
209(3)
11.1.1 The Two-Group Design
209(1)
11.1.2 The Three-Group Design
210(1)
11.1.3 Other SB-MTMM Designs
211(1)
11.2 Estimating and Testing Models for Split-Ballot MTMM Experiments
212(1)
11.3 Empirical Examples
213(5)
11.3.1 Results for the Three-Group Design
213(2)
11.3.2 Two-Group SB-MTMM Design
215(3)
11.4 The Empirical Identifiability and Efficiency of the Different SB-MTMM Designs
218(3)
11.4.1 The Empirical Identifiability of the SB-MTMM Model
218(3)
11.4.2 The Efficiency of the Different Designs
221(1)
11.5 Summary and Discussion
221(1)
Exercises
222(1)
Appendix 11.1 The Lisrel Input for the Three-Group SB-MTMM Example
222(3)
12 MTMM Experiments and the Quality of Survey Questions
225(18)
12.1 The Data from the MTMM Experiments
226(3)
12.2 The Coding of the Characteristics of the MTMM Questions
229(1)
12.3 The Database and Some Results
230(7)
12.3.1 Differences in Quality across Countries
231(3)
12.3.2 Differences in Quality for Domains and Concepts
234(1)
12.3.3 Effect of the Question Formulation on the Quality
235(2)
12.4 Prediction of the Quality of Questions Not Included in the MTMM Experiments
237(4)
12.4.1 Suggestions for Improvement of Questions
239(1)
12.4.2 Evaluation of the Quality of the Prediction Models
240(1)
12.5 Summary
241(1)
Exercises
242(1)
Part IV Applications In Social Science Research 243(93)
13 The SQP 2.0 Program for Prediction of Quality and Improvement of Measures
245(18)
13.1 The Quality of Questions Involved in the MTMM Experiments
246(6)
13.1.1 The Quality of Specific Questions
246(4)
13.1.2 Looking for Optimal Measures for a Concept
250(2)
13.2 The Quality of Non-MTMM Questions in the Database
252(4)
13.3 Predicting the Quality of New Questions
256(5)
13.4 Summary
261(1)
Exercises
262(1)
14 The Quality of Measures for Concepts-by-Postulation
263(24)
14.1 The Structures of Concepts-by-Postulation
264(1)
14.2 The Quality of Measures of Concepts-by-Postulation with Reflective Indicators
264(12)
14.2.1 Testing the Models
265(3)
14.2.2 Estimation of the Composite Scores
268(2)
14.2.3 The Quality of Measures for Concepts-by-Postulation
270(4)
14.2.4 Improvement of the Quality of the Measure
274(2)
14.3 The Quality of Measures for Concepts-by-Postulation with Formative Indicators
276(7)
14.3.1 Testing the Models
278(3)
14.3.2 Estimation of the Composite Score
281(1)
14.3.3 The Estimation of the Quality of the Composite Scores
282(1)
14.4 Summary
283(1)
Exercises
284(1)
Appendix 14.1 Lisrel Input for Final Analysis of the Effect of "Social Contact" on "Happiness"
284(1)
Appendix 14.2 Lisrel Input for Final Analysis of the Effect of "Interest in Political Issues in the Media" on "Political Interest in General"
285(2)
15 Correction for Measurement Errors
287(15)
15.1 Correction for Measurement Errors in Models with only Concepts-by-Intuition
287(5)
15.2 Correction for Measurement Errors in Models with Concepts-by-Postulation
292(6)
15.2.1 Operationalization of the Concepts
292(2)
15.2.2 The Quality of the Measures
294(3)
15.2.3 Correction for Measurement Errors in the Analysis
297(1)
15.3 Summary
298(1)
Exercises
299(1)
Appendix 15.1 Lisrel Inputs to Estimate the Parameters of the Model in Figure 15.1
300(1)
Appendix 15.2 Lisrel Input for Estimation of the Model with Correction for Measurement Errors using Variance Reduction by Quality for all Composite Scores
301(1)
16 Coping with Measurement Errors in Cross-Cultural Research
302(34)
16.1 Notations of Response Models for Cross-Cultural Comparisons
303(4)
16.2 Testing for Equivalence or Invariance of Instruments
307(2)
16.2.1 The Standard Approach to Test for Equivalence
307(2)
16.3 Problems Related with the Procedure
309(9)
16.3.1 Using Information about the Power of the Test
309(6)
16.3.2 An Alternative Test for Equivalence
315(2)
16.3.3 The Difference between Significance and Relevance
317(1)
16.4 Comparison of Means and Relationships across Groups
318(6)
16.4.1 Comparison of Means and Relationships between Single Requests for Answers
318(1)
16.4.2 Comparison of Means and Relationships Based on Composite Scores
319(2)
16.4.3 Comparison of Means and Relationships between Latent Variables
321(3)
16.5 Summary
324(1)
Exercises
325(1)
Appendix 16.1 The Two Sets of Requests Concerning "Subjective Competence"
326(1)
Appendix 16.2 ESS Requests Concerning "Political Trust"
327(1)
Appendix 16.3 The Standard Test of Equivalence for "Subjective Competence"
328(1)
Appendix 16.4 The Alternative Equivalence Test for "Subjective Competence" in Three Countries
329(2)
Appendix 16.5 Lisrel Input to Estimate the Null Model for Estimation of the Relationship between "Subjective Competence" and "Political Trust"
331(2)
Appendix 16.6 Derivation of the Covariance between the Composite Scores
333(3)
References 336(16)
Index 352
WILLEM E. SARIS, PHD, is Emeritus Professor in Methodology of the University of Amsterdam and the Universitat Pompeu Fabra, Barcelona. He is Laureate of the 2005 Descartes Prize for Best Collaborative Research as member of the Central Coordinating Team of the European Social Survey (ESS) and Recipient of the World Association of Public Opinion Researchs Helen Dinerman Award in 2009 for his lifelong contribution to the methodology of Opinion Research. Dr. Saris also received the 2013 Outstanding Service Prize of the European Survey Research Association.

IRMTRAUD N. GALLHOFER, PHD, is a linguist and was senior researcher on projects of the ESS, Research and Expertise Centre for Survey Methodology at the Universitat Pompeu Fabra, Barcelona. She is Laureate of the 2005 Descartes Prize for Best Collaborative Research as a member of the Central Coordinating Team of the ESS.
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