Thomas J. Linneman
Routledge | 2014 - 2ª edição | 657 páginas | rar - pdf | 9,3 Mb
link (password: matav)
Many fundamentally important decisions about our social life are a function of how well we understand and analyze DATA. This sounds so obvious but it is so misunderstood. Social statisticians struggle with this problem in their teaching constantly. This book and its approach is the ally and support of all instructors who want to accomplish this hugely important teaching goal.
This innovative text for undergraduate social statistics courses is, (as one satisfied instructor put it), a "breath of fresh air." It departs from convention by not covering some techniques and topics that have been in social stat textbooks for 30 years, but that are no longer used by social scientists today. It also includes techniques that conventional wisdom has previously thought to be the province of graduate level courses.
Linneman’s text is for those instructors looking for a thoroughly "modern" way to teach quantitative thinking, problem-solving, and statistical analysis to their students…an undergraduate social statistics course that recognizes the increasing ubiquity of analytical tools in our data-driven age and therefore the practical benefit of learning how to "do statistics," to "present results" effectively (to employers as well as instructors), and to "interpret" intelligently the quantitative arguments made by others.
BRIEF CONTENTS
Preface xxix
Acknowledgments xxxvii
Chapter 1: Life in a Data-Laden Age: Finding and Managing Datasets 1
Chapter 2: The Art of Visual Storytelling: Creating Accurate Tables and Graphs 46
Chapter 3: Summarizing Center and Diversity: Basic Descriptive Statistics 92
Chapter 4: Using Sample Crosstabs to Talk about Populations: The Chi-Square Test 141
Chapter 5: Using a Sample Mean or Proportion to Talk about a Population: Confidence Intervals 189
Chapter 6: Using Multiple Sample Means to Talk about Populations: t-Tests and ANOVA 231
Chapter 7: Give Me One Good Reason Why: Bivariate Correlation and Regression 264
Chapter 8: Using Sample Slopes to Talk about Populations: Inference and Regression 303
Chapter 9: It’s All Relative: Dichotomies as Independent Variables in Regression 326
Chapter 10: Above and Beyond: The Logic of Controlling and the Power of Nested Regression Models 348
Chapter 11: Some Slopes Are Bigger than Others: Calculating and Interpreting Beta Coefficients 384
Chapter 12: Different Slopes for Different Folks: Interaction Effects 402
Chapter 13: Explaining Dichotomous Outcomes: Logistic Regression 435
Chapter 14: Visualizing Causal Stories: Path Analysis 467
Chapter 15: Questioning the Greatness of Straightness: Nonlinear Relationships 493
Chapter 16: Problems and Prospects: Regression Diagnostics, Advanced Techniques, and Where to Go Now 532
Appendix A: Variables and Indexes from the Datasets Used in the End-of-Chapter Exercises A-1
Appendix B: 86 Articles That Use Statistics in Less Than Scary Ways B-1
Appendix C: Statistical Tables C-1
Appendix D: Answers to Selected End-of-Chapter Exercises D-1
Bibliography R-1
Glossary/Index I-1
