4 volumes : illustrations ; 24 cm. - (Sage benchmarks in social research methods ) Content notes : Machine generated contents note: Volume I --
1. Regression Fundamentals for the Social Sciences / Salvatore Babones --
1. The Meaning of p-Values --
2. The Nonutility of Significance Tests: The Significance of Tests of Significance Reconsidered / Sanford Labovitz --
3. Mindless Statistics / Gerd Gigerenzer --
4. Confusion over Measures of Evidence (p's) versus Errors ([alpha]'s) in Classical Statistical Testing / M.J. Bayarri --
5. Why We Don't Really Know What Statistical Significance Means: Implications for Educators / J. Scott Armstrong --
6. Researchers Should Make Thoughtful Assessments Instead of Null-Hypothesis Significance Tests / Fiona Fidler --
2. Control Variables --
7. Explaining Interstate Conflict and War: What Should Be Controlled For? / James Lee Ray --
8. The Phantom Menace: Omitted Variable Bias in Econometric Research / Kevin A. Clarke --
9. Beyond Baron and Kenny: Statistical Mediation Analysis in the New Millennium / Andrew F. Hayes. Contents note continued: 10. Equivalence of the Mediation, Confounding and Suppression Effect / Chondra M. Lockwood --
11. Statistical Usage in Sociology: Sacred Cows and Ritual / Sanford Labovitz --
12. Stepwise Regression in Social and Psychological Research / Daniel R. Denison --
13. Return of the Phantom Menace: Omitted Variable Bias in Political Research / Kevin A. Clarke --
14. Stepwise Regression: A Caution / Michael S. Lewis-Beck --
Volume II --
3. Outliers and Influential Points --
15. Teaching about Influence in Simple Regression / Frederick O. Lorenz --
16. Regression Diagnostics: An Expository Treatment of Outliers and Influential Cases / Robert W. Jackman --
17.A Survey of Outlier Detection Methodologies / Jim Austin --
18. Practitioners' Corner: Beware of `Good' Outliers and Overoptimistic Conclusions / Vincenzo Verardi --
19. Some Observations on Measurement and Statistics / Sanford Labovitz --
4. Multicolinearity and Variance Inflation. Contents note continued: 20. Issues in Multiple Regression / Robert A. Gordon --
21.A Caution Regarding Rules of Thumb for Variance Inflation Factors / Robert M. O'Brien --
22. What to Do (and Not Do) with Multicollinearity in State Politics Research / Gregory A. Huber --
23. On the Misconception of Multicollinearity in Detection of Moderating Effects: Multicollinearity Is Not Always Detrimental / Gwowen Shieh --
24. Correlated Independent Variables: The Problem of Multicollinearity / H.M. Blalock Jr --
5. Sample Selection Biases --
25. Modeling Selection Effects / David A. Freedman --
26. An Introduction to Sample Selection Bias in Sociological Data / Richard A. Berk --
27. Models for Sample Selection Bias / Robert D. Mare --
28. Sample Selection Bias as a Specification Error / James J. Heckman --
29. How the Cases You Choose Affect the Answers You Get: Selection Bias in Comparative Politics / Barbara Geddes. Contents note continued: 30. When Less Is More: Selection Problems in Large-N and Small-N Cross-National Comparisons / Bernhard Ebbinghaus --
Volume III --
6. Imputation Techniques --
31. The Treatment of Missing Data / David C. Howell --
32.A Primer on Maximum Likelihood Algorithms Available for Use with Missing Data / Craig K. Enders --
33. What to Do about Missing Values in Time-Series Cross-Section Data / Gary King --
34. Multiple Imputation for Missing Data: A Cautionary Tale / Paul D. Allison --
35. Multiple Imputation for Missing Data: Making the Most of What You Know / Jonathon N. Cummings --
36. Imputation of Missing Item Responses: Some Simple Techniques / Mark Huisman --
37. Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation / Kenneth Scheve --
38. An Empirical Evaluation of the Predictive Mean Matching Method for Imputing Missing Values / Carl F. Pieper --
7. Interaction Models. Contents note continued: 39. Testing for Interaction in Multiple Regression / Paul D. Allison --
40. Understanding Interaction Models: Improving Empirical Analyses / Matt Golder --
41. Product-Variable Models of Interaction Effects and Causal Mechanisms / Lowell L. Hargens --
42. Limitations of Centering for Interactive Models / Richard L. Tate --
43. Decreasing Multicollinearity: A Method for Models with Multiplicative Functions / M.S. Sasaki --
44. Some Common Myths about Centering Predictor Variables in Moderated Multiple Regression and Polynomial Regression / Michael J. Zickar --
8. Longitudinal Models --
45.A General Panel Model with Random and Fixed Effects: A Structural Equations Approach / Jennie E. Brand --
46.A Lot More to Do: The Sensitivity of Time-Series Cross-Section Analyses to Simple Alternative Specifications / Daniel M. Butler --
47. Panel Models in Sociological Research: Theory Into Practice / Charles N. Halaby. Contents note continued: 48. Dynamic Models for Dynamic Theories: The Ins and Outs of Lagged Dependent Variables / Nathan J. Kelly --
49. Using Panel Data to Estimate the Effects of Events / Paul D. Allison --
Volume IV --
9. Instrumental Variable Models --
50. Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments / Alan B. Krueger --
51. Improving Causal Inference: Strengths and Limitations of Natural Experiments / Thad Dunning --
52. Instrumental Variables Estimation in Political Science: A Readers' Guide / Donald P. Green --
53. Instrumental Variables in Sociology and the Social Sciences / Kenneth A. Bollen --
54. Problems with Instrumental Variables Estimation When the Correlation between the Instruments and the Endogenous Explanatory Variable Is Weak / Regina M. Baker --
10. Structural Models --
55. Practical Issues in Structural Modeling / Chih-Ping Chou --
56. As Others See Us: A Case Study in Path Analysis / D.A. Freedman. Contents note continued: 57. Causation Issues in Structural Equation Modeling Research / Stanley A. Mulaik --
58. Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach / David W. Gerbing --
59. Structural Equation Models in the Social and Behavioral Sciences: Model Building / James G. Anderson --
11. Causality --
60. Statistical Models for Causation / David A. Freedman --
61. Structural Equations and Causal Explanations: Some Challenges for Causal SEM / Keith A. Markus --
62. The Estimation of Causal Effects from Observational Data / Stephen L. Morgan --
63. Statistical Models for Causation: What Inferential Leverage Do They Provide / David A. Freedman --
64. The Foundations of Causal Inference / Judea Pearl. Regression analysis. Social sciences - Research - Statistical methods. Statistics.

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