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Fundamentals of Social Research [Pehme köide]

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(George Washington University, Washington DC), (Texas A & M University), (Texas A & M University)
  • Formaat: Paperback / softback, 308 pages, kõrgus x laius x paksus: 254x178x15 mm, kaal: 680 g
  • Ilmumisaeg: 08-Sep-2022
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1107569168
  • ISBN-13: 9781107569164
Teised raamatud teemal:
Fundamentals of Social ResearchSuurem pilt
  • Formaat: Paperback / softback, 308 pages, kõrgus x laius x paksus: 254x178x15 mm, kaal: 680 g
  • Ilmumisaeg: 08-Sep-2022
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1107569168
  • ISBN-13: 9781107569164
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781107569164.html
Märksõnad:
This textbook provides an introduction to the scientific study of sociology and other social sciences. It offers the basic tools necessary for readers to become both critical consumers and beginning producers of scientific research on society. The authors present an integrated approach to research design and empirical analyses in which researchers can develop and test causal theories. They use examples from social science research that students will find engaging and inspiring and that will help them to understand key concepts. The book makes technical materials accessible to students who might otherwise be intimidated by mathematical examples. This new text, with the addition of sociologist Steven A. Tuch to the author team, follows the successful format, approach, and pedagogical features in Paul M. Kellstedt and Guy D. Whitten's bestselling text, The Fundamentals of Political Science Research, now in its third edition. Workbooks in Stata, SPSS, and R, three of the most popular statistical analysis programs, are available as separate purchases to accompany this textbook, enabling students to connect the lessons of this book to hands-on applications of the software.

This book provides a rigorous yet accessible introduction to the scientific study of sociology and other social sciences. It is designed to provide students with the basic tools needed to be both critical consumers and beginning producers of scholarly social science research.

Arvustused

'Kellstedt, Whitten, and Tuch provide an accessible, sophisticated text. With well-chosen examples, they show why a full understanding of research design, theory construction, and causal inference is essential for effective use of our modern toolkit of data analysis techniques.' Stephen L. Morgan, Johns Hopkins University 'A comprehensive and well-written introduction to the techniques and logic of social research. The logic and application of a wide range of methodological techniques are explained eloquently and clearly, and the examples used cut across social science disciplines. This book should be widely used in methods courses across the social sciences.' George Wilson, University of Miami 'This valuable textbook is unique for two reasons: first, it seamlessly integrates theory, research design, and data analysis, providing students with the foundation required to develop empirically grounded research projects that can make theoretical progress in social science. Second, it is accessible and engaging, drawing students in and showing them how stimulating and exciting social research can be.' Michael Hughes, Virginia Tech

Muu info

This text links the complementary processes of research design and statistical analysis in assessing causal relationships in the social sciences.
List of Figures
xvi
List of Tables
xviii
Preface xxi
Acknowledgments xxiv
1 The Scientific Study of Society
1(28)
Overview
1(1)
1.1 Social Science?
1(3)
1.2 Approaching Sociology Scientifically: The Search for Causal Explanations
4(4)
1.3 Thinking About the World in Terms of Variables and Causal Explanations
8(8)
1.4 Models of Society
16(1)
1.5 Rules of the Road to Scientific Knowledge about Society
16(3)
1.5.1 Make Your Theories Causal
17(1)
1.5.2 Don't Let Data Alone Drive Your Theories
17(1)
1.5.3 Consider Only Empirical Evidence
18(1)
1.5.4 Avoid Normative Statements
18(1)
1.5.5 Pursue Both Generality and Parsimony
19(1)
1.6 The Ethics of Social Research
19(5)
1.6.1 Potential Harm
20(1)
1.6.2 Informed Consent
21(1)
1.6.3 Deception
22(1)
1.6.4 Anonymity and Confidentiality
23(1)
1.7 A Quick Look Ahead
24(5)
Concepts Introduced in This
Chapter
25(1)
Exercises
26(3)
2 The Art of Theory Building
29(17)
Overview
29(1)
2.1 Good Theories Come from Good Theory-Building Strategies
29(1)
2.2 Promising Theories Offer Answers to Interesting Research Questions
30(1)
2.3 Identifying Interesting Variation
30(3)
2.3.1 Time-Series Example
31(1)
2.3.2 Cross-Sectional Example
32(1)
2.4 Learning to Use Your Knowledge
33(3)
2.4.1 Moving from a Specific Event to More General Theories
34(1)
2.4.2 Know Local, Think Global: Can You Drop the Proper Nouns?
35(1)
2.5 Examine Previous Research
36(2)
2.5.1 What Did the Previous Researchers Miss?
36(1)
2.5.2 Can Their Theory Be Applied Elsewhere?
37(1)
2.5.3 If We Believe Their Findings, Are There Further Implications?
37(1)
2.5.4 How Might This Theory Work at Different Levels of Aggregation (Micro Macro)?
38(1)
2.6 How Do I Know If I Have a "Good" Theory?
38(3)
2.6.1 Does Your Theory Offer an Answer to an Interesting Research Question?
39(1)
2.6.2 Is Your Theory Causal?
39(1)
2.6.3 Can You Test Your Theory on Data That You Have Not Yet Observed?
40(1)
2.6.4 How General Is Your Theory?
40(1)
2.6.5 How Parsimonious Is Your Theory?
40(1)
2.6.6 How New Is Your Theory?
40(1)
2.6.7 How Nonobvious Is Your Theory?
41(1)
2.7 Conclusion
41(5)
Concepts Introduced in This
Chapter
41(1)
Exercises
42(4)
3 Evaluating Causal Relationships
46(19)
Overview
46(1)
3.1 Causality and Everyday Language
46(3)
3.2 Four Hurdles along the Route to Establishing Causal Relationships
49(8)
3.2.1 Putting It All Together - Adding Up the Answers to Our Four Questions
51(1)
3.2.2 Identifying Causal Claims Is an Essential Thinking Skill
52(4)
3.2.3 What Are the Consequences of Failing to Control for Other Possible Causes?
56(1)
3.3 Why Is Studying Causality So Important? Three Examples from Sociology
57(4)
3.3.1 Intergroup Contact and Racial Tolerance
57(1)
3.3.2 Race and Political Participation in the U.S.
58(2)
3.3.3 Evaluating Whether Head Start Is Effective
60(1)
3.4 Wrapping Up
61(4)
Concepts Introduced in This
Chapter
62(1)
Exercises
62(3)
4 Research Design
65(21)
Overview
65(1)
4.1 Comparison As the Key to Establishing Causal Relationships
65(1)
4.2 Experimental Research Designs
66(11)
4.2.1 "Random Assignment" versus "Random Sampling"
72(1)
4.2.2 Varieties of Experiments and Near-Experiments
73(1)
4.2.3 Are There Drawbacks to Experimental Research Designs?
74(3)
4.3 Observational Studies (in Two Flavors)
77(5)
4.3.1 Datum, Data, Data Set
79(1)
4.3.2 Cross-Sectional Observational Studies
80(1)
4.3.3 Time-Series Observational Studies
80(1)
4.3.4 The Major Difficulty with Observational Studies
81(1)
4.4 Summary
82(4)
Concepts Introduced in This
Chapter
83(1)
Exercises
84(2)
5 Survey Research
86(13)
Overview
86(1)
5.1 Why Surveys?
86(1)
5.2 Modes of Survey Administration
87(5)
5.2.1 Face-to-Face In-Person Interviews
87(1)
5.2.2 Self-Administered Questionnaires
88(1)
5.2.3 Telephone Interviews
88(1)
5.2.4 Web-Based Surveys
89(1)
5.2.5 Survey-Based Experiments
90(2)
5.3 Already Existing Survey Data Sets
92(2)
5.3.1 General Social Survey (GSS)
92(1)
5.3.2 American National Election Study (ANES)
93(1)
5.3.3 International Social Survey Programme (ISSP)
93(1)
5.3.4 World Values Survey (WVS)
94(1)
5.4 Probability Sampling
94(5)
5.4.1 Simple Random Samples
95(1)
5.4.2 Systematic Random Samples
95(1)
5.4.3 Stratified Random Sampling
96(1)
5.4.4 Multistage Cluster Sampling
96(1)
Concepts Introduced in This
Chapter
97(1)
Exercises
98(1)
6 Measuring Concepts of Interest
99(15)
Overview
99(1)
6.1 Getting to Know Your Data: Evaluating Measurement
99(2)
6.2 Social Science Measurement: The Varying Challenges of Quantifying Humanity
101(3)
6.3 Problems in Measuring Concepts of Interest
104(5)
6.3.1 Conceptual Clarity
104(1)
6.3.2 Reliability
105(1)
6.3.3 Measurement Bias and Reliability
106(1)
6.3.4 Validity
107(1)
6.3.5 The Relationship between Validity and Reliability
108(1)
6.4 Controversy: Measuring Racial Tolerance
109(2)
6.5 Are There Consequences to Poor Measurement?
111(1)
6.6 Conclusions
111(3)
Concepts Introduced in This
Chapter
112(1)
Exercises
112(2)
7 Getting to Know Your Data
114(17)
Overview
114(1)
7.1 Getting to Know Your Data Statistically
114(1)
7.2 What Is the Variable's Measurement Metric?
115(4)
7.2.1 Categorical Variables
116(1)
7.2.2 Ordinal Variables
116(1)
7.2.3 Continuous Variables
117(1)
7.2.4 Variable Types and Statistical Analyses
118(1)
7.3 Describing Categorical Variables
119(1)
7.4 Describing Continuous Variables
119(8)
7.4.1 Rank Statistics
121(2)
7.4.2 Moments
123(4)
7.5 Limitations of Descriptive Statistics and Graphs
127(4)
Concepts Introduced in This
Chapter
128(1)
Exercises
129(2)
8 Probability and Statistical Inference
131(16)
Overview
131(1)
8.1 Populations and Samples
131(2)
8.2 Some Basics of Probability Theory
133(2)
8.3 Learning about the Population from a Sample: The Central Limit Theorem
135(6)
8.3.1 The Normal Distribution
136(5)
8.4 Example: Presidential Approval Ratings
141(3)
8.4.1 What Kind of Sample Was That?
143(1)
8.4.2 A Note on the Effects of Sample Size
143(1)
8.5 A Look Ahead: Examining Relationships between Variables
144(3)
Concepts Introduced in This
Chapter
145(1)
Exercises
145(2)
9 Bivariate Hypothesis Testing
147(30)
Overview
147(1)
9.1 Bivariate Hypothesis Tests and Establishing Causal Relationships
147(1)
9.2 Choosing the Right Bivariate Hypothesis Test
148(1)
9.3 All Roads Lead to p
149(3)
9.3.1 The Logic of p-Values
149(1)
9.3.2 The Limitations of p-Values
150(1)
9.3.3 From p-Values to Statistical Significance
151(1)
9.3.4 The Null Hypothesis and p-Values
152(1)
9.4 Four Bivariate Hypothesis Tests
152(19)
9.4.1 Example 1: Tabular Analysis
152(7)
9.4.2 Example 2: Difference of Means
159(3)
9.4.3 Example 3: Correlation Coefficient
162(6)
9.4.4 Example 4: Analysis of Variance
168(3)
9.5 Multiple Comparisons
171(1)
9.6 Wrapping Up
172(5)
Concepts Introduced in This
Chapter
172(1)
Exercises
173(4)
10 Two-Variable Regression Models
177(24)
Overview
177(1)
10.1 Two-Variable Regression
177(1)
10.2 Fitting a Line: Population -o- Sample
178(2)
10.3 Which Line Fits Best? Estimating the Regression Line
180(4)
10.4 Measuring Our Uncertainty about the OLS Regression Line
184(10)
10.4.1 Goodness-of-Fit: Root Mean-Squared Error
185(1)
10.4.2 Goodness-of-Fit: R-Squared Statistic
185(2)
10.4.3 Is That a "Good" Goodness-of-Fit?
187(1)
10.4.4 Uncertainty about Individual Components of the Sample Regression Model
187(2)
10.4.5 Confidence Intervals about Parameter Estimates
189(1)
10.4.6 Two-Tailed Hypothesis Tests
190(2)
10.4.7 The Relationship between Confidence Intervals and Two-Tailed Hypothesis Tests
192(1)
10.4.8 One-Tailed Hypothesis Tests
192(2)
10.5 Assumptions, More Assumptions, and Minimal Mathematical Requirements
194(7)
10.5.1 Assumptions about the Population Stochastic Component
194(3)
10.5.2 Assumptions about Our Model Specification
197(1)
10.5.3 Minimal Mathematical Requirements
198(1)
10.5.4 How Can We Make All of These Assumptions?
198(1)
Concepts Introduced in This
Chapter
198(1)
Exercises
199(2)
11 Multiple Regression
201(45)
Overview
201(1)
11.1 Modeling Multivariate Reality
201(1)
11.2 Adding a Z Variable to a Bivariate Tabular Analysis
202(3)
11.3 The Population Regression Function
205(1)
11.4 From Two-Variable to Multiple Regression
205(5)
11.5 Interpreting Multiple Regression
210(3)
11.6 Which Effect Is "Biggest"?
213(1)
11.7 Statistical and Substantive Significance
214(2)
11.8 What Happens When We Fail to Control for Z
216(5)
11.8.1 An Additional Minimal Mathematical Requirement in Multiple Regression
220(1)
11.9 Being Smart with Dummy Independent Variables in OLS
221(8)
11.9.1 Using Dummy Variables to Test Hypotheses about a Categorical Independent Variable with Only Two Values
221(4)
11.9.2 Using Dummy Variables to Test Hypotheses about a Categorical Independent Variable with More Than Two Values
225(3)
11.9.3 Using Dummy Variables to Test Hypotheses about Multiple Independent Variables
228(1)
11.10 Testing Interactive Hypotheses with Dummy Variables
229(3)
11.11 Dummy Dependent Variables
232(8)
11.11.1 The Linear Probability Model
232(3)
11.11.2 Binomial Logit and Binomial Probit
235(3)
11.11.3 Goodness-of-Fit with Dummy Dependent Variables
238(2)
11.12 Implications
240(6)
Concepts Introduced in This
Chapter
241(1)
Exercises
242(4)
12 Putting It All Together to Produce Effective Research
246(21)
Overview
246(1)
12.1 Two Routes Toward a New Scientific Project
246(5)
12.1.1 Project Type 1: A New Y (and Some X)
247(2)
12.1.2 Project Type 2: An Existing Y and a New X
249(1)
12.1.3 Variants on the Two Project Types
250(1)
12.2 Using the Literature Without Getting Buried in It
251(4)
12.2.1 Identifying the Important Work on a Subject - Using Citation Counts
251(1)
12.2.2 Oh No! Someone Else Has Already Done What I Was Planning to Do. What Do I Do Now?
252(1)
12.2.3 Dissecting the Research by Other Scholars
252(1)
12.2.4 Read Effectively to Write Effectively
253(2)
12.3 Writing Effectively about Your Research
255(5)
12.3.1 Write Early, Write Often (Because Writing is Thinking)
255(1)
12.3.2 Document Your Code - Writing and Thinking While You Compute
255(1)
12.3.3 Divide and Conquer - a Section-by-Section Strategy for Building Your Project
256(3)
12.3.4 Proofread, Proofread, and then Proofread Again
259(1)
12.4 Making Effective Use of Tables and Figures
260(7)
12.4.1 Constructing Regression Tables
260(4)
12.4.2 Writing about Regression Tables
264(1)
12.4.3 Other Types of Tables and Figures
265(1)
Exercises
266(1)
Appendix A Critical Values of Chi-Squared 267(1)
Appendix B Critical Values of t 268(1)
Appendix C The A Link Function for Binomial Logit Models 269(2)
Appendix D The O Link Function for Binomial Probit Models 271(2)
References 273(6)
Index 279
Paul M. Kellstedt is a professor of political science at Texas A&M University. Guy D. Whitten is a professor of political science and Director of the European Union Center at Texas A&M University. Steven A. Tuch is a professor of sociology and public policy and public administration at George Washington University.