Dark Desires

Ideal for social science majors, Statistical Methods for the Social Sciences applies statistical methods to the social sciences and makes methods more accessible with an emphasis on concepts. It highlights applications throughout, assuming no previous knowledge of statistics and only a minimal mathematical background. It contains sufficient material for a 2-semester course. The 5th Edition uses examples and exercises with a variety of real data. It includes more illustrations of statistical software for computations and takes advantage of the outstanding applets to explain key concepts, such as sampling distributions and conducting basic data analyses. It continues to downplay mathematics while avoiding an overly simplistic, recipe-based approach to statistics. Cover......Page 1 Title Page......Page 4 Copyright Page......Page 5 Dedication......Page 6 Contents......Page 8 Preface......Page 12 Acknowledgments......Page 14 1.1 Introduction to Statistical Methodology......Page 16 1.2 Descriptive Statistics and Inferential Statistics......Page 19 1.3 The Role of Computers and Software in Statistics......Page 21 1.4 Chapter Summary......Page 23 2.1 Variables and Their Measurement......Page 26 2.2 Randomization......Page 29 2.3 Sampling Variability and Potential Bias......Page 32 2.4 Other Probability Sampling Methods*......Page 36 2.5 Chapter Summary......Page 38 3.1 Describing Data with Tables and Graphs......Page 44 3.2 Describing the Center of the Data......Page 50 3.3 Describing Variability of the Data......Page 56 3.4 Measures of Position......Page 61 3.5 Bivariate Descriptive Statistics......Page 66 3.7 Chapter Summary......Page 70 4.1 Introduction to Probability......Page 82 4.2 Probability Distributions for Discrete and Continuous Variables......Page 84 4.3 The Normal Probability Distribution......Page 87 4.4 Sampling Distributions Describe How Statistics Vary......Page 95 4.5 Sampling Distributions of Sample Means......Page 100 4.6 Review: Population, Sample Data, and Sampling Distributions......Page 106 4.7 Chapter Summary......Page 109 5.1 Point and Interval Estimation......Page 118 5.2 Confidence Interval for a Proportion......Page 121 5.3 Confidence Interval for a Mean......Page 128 5.4 Choice of Sample Size......Page 135 5.5 Estimation Methods: Maximum Likelihood and the Bootstrap*......Page 141 5.6 Chapter Summary......Page 145 6 Statistical Inference: Significance Tests......Page 154 6.1 The Five Parts of a Significance Test......Page 155 6.2 Significance Test for a Mean......Page 158 6.3 Significance Test for a Proportion......Page 167 6.4 Decisions and Types of Errors in Tests......Page 170 6.5 Limitations of Significance Tests......Page 174 6.6 Finding P(Type II Error)*......Page 178 6.7 Small-Sample Test for a Proportion—the Binomial Distribution*......Page 180 6.8 Chapter Summary......Page 184 7.1 Preliminaries for Comparing Groups......Page 194 7.2 Categorical Data: Comparing Two Proportions......Page 197 7.3 Quantitative Data: Comparing Two Means......Page 202 7.4 Comparing Means with Dependent Samples......Page 205 7.5 Other Methods for Comparing Means*......Page 208 7.6 Other Methods for Comparing Proportions*......Page 213 7.7 Nonparametric Statistics for Comparing Groups*......Page 216 7.8 Chapter Summary......Page 219 8.1 Contingency Tables......Page 230 8.2 Chi-Squared Test of Independence......Page 233 8.3 Residuals: Detecting the Pattern of Association......Page 240 8.4 Measuring Association in Contingency Tables......Page 242 8.5 Association Between Ordinal Variables*......Page 248 8.6 Chapter Summary......Page 253 9.1 Linear Relationships......Page 262 9.2 Least Squares Prediction Equation......Page 265 9.3 The Linear Regression Model......Page 271 9.4 Measuring Linear Association: The Correlation......Page 274 9.5 Inferences for the Slope and Correlation......Page 281 9.6 Model Assumptions and Violations......Page 287 9.7 Chapter Summary......Page 292 10.1 Association and Causality......Page 302 10.2 Controlling for Other Variables......Page 305 10.3 Types of Multivariate Relationships......Page 309 10.4 Inferential Issues in Statistical Control......Page 314 10.5 Chapter Summary......Page 316 11.1 The Multiple Regression Model......Page 322 11.2 Multiple Correlation and R2......Page 331 11.3 Inferences for Multiple Regression Coefficients......Page 335 11.4 Modeling Interaction Effects......Page 340 11.5 Comparing Regression Models......Page 344 11.6 Partial Correlation*......Page 346 11.7 Standardized Regression Coefficients*......Page 349 11.8 Chapter Summary......Page 352 12.1 Regression Modeling with Dummy Variables for Categories......Page 366 12.2 Multiple Comparisons of Means......Page 370 12.3 Comparing Several Means: Analysis of Variance......Page 373 12.4 Two-Way ANOVA and Regression Modeling......Page 377 12.5 Repeated-Measures Analysis of Variance*......Page 384 12.6 Two-Way ANOVA with Repeated Measures on a Factor*......Page 388 12.7 Chapter Summary......Page 393 13.1 Models with Quantitative and Categorical Explanatory Variables......Page 402 13.2 Inference for Regression with Quantitative and Categorical Predictors......Page 409 13.3 Case Studies: Using Multiple Regression in Research......Page 412 13.4 Adjusted Means*......Page 416 13.5 The Linear Mixed Model*......Page 421 13.6 Chapter Summary......Page 426 14.1 Model Selection Procedures......Page 434 14.2 Regression Diagnostics......Page 441 14.3 Effects of Multicollinearity......Page 448 14.4 Generalized Linear Models......Page 450 14.5 Nonlinear Relationships: Polynomial Regression......Page 454 14.6 Exponential Regression and Log Transforms*......Page 459 14.7 Robust Variances and Nonparametric Regression*......Page 463 14.8 Chapter Summary......Page 465 15.1 Logistic Regression......Page 474 15.2 Multiple Logistic Regression......Page 480 15.3 Inference for Logistic Regression Models......Page 485 15.4 Logistic Regression Models for Ordinal Variables*......Page 487 15.5 Logistic Models for Nominal Responses*......Page 492 15.6 Loglinear Models for Categorical Variables*......Page 495 15.7 Model Goodness-of-Fit Tests for Contingency Tables*......Page 499 15.8 Chapter Summary......Page 503 16.1 Missing Data: Adjustment Using Multiple Imputation*......Page 512 16.2 Multilevel (Hierarchical) Models*......Page 516 16.3 Event History Models*......Page 518 16.4 Path Analysis*......Page 521 16.5 Factor Analysis*......Page 525 16.6 Structural Equation Models*......Page 530 16.7 Markov Chains*......Page 534 16.8 The Bayesian Approach to Statistical Inference*......Page 535 Appendix: R, Stata, SPSS, and SAS for Statistical Analyses......Page 542 Answers to Selected Odd-Numbered Exercises......Page 580 Bibliography......Page 594 Credits......Page 598 C......Page 600 G......Page 601 M......Page 602 P......Page 603 S......Page 604 Z......Page 606 Statistical Methods Applied To Social Sciences, Made Accessible To All Through An Emphasis On Concepts Statistical Methods For The Social Sciences Introduces Statistical Methods To Students Majoring In Social Science Disciplines. With An Emphasis On Concepts And Applications, This Book Assumes You Have No Previous Knowledge Of Statistics And Only A Minimal Mathematical Background. It Contains Sufficient Material For A Two-semester Course. The 5th Edition Gives You Examples And Exercises With A Variety Of “real Data.” It Includes More Illustrations Of Statistical Software For Computations And Takes Advantage Of The Outstanding Applets To Explain Key Concepts, Such As Sampling Distributions And Conducting Basic Data Analyses. It Continues To Downplay Mathematics–often A Stumbling Block For Students–while Avoiding Reliance On An Overly Simplistic Recipe-based Approach To Statistics.-- Introduction -- Sampling And Measurement -- Descriptive Statistics -- Probability Distributions -- Statistical Inference: Estimation -- Statistical Inference: Significance Tests -- Comparison Of Two Groups -- Analyzing Association Between Categorical Variables -- Linear Regression And Correlation -- Introduction To Multivariate Relationships -- Multiple Regression And Correlation -- Regression With Categorical Predictors: Analysis Of Variance Methods -- Multiple Regression With Quantitative And Categorical Predictors -- Model Building With Multiple Regression -- Logistic Regression: Modeling Categorical Responses -- An Introduction To Advanced Methodology. Alan Agresti, University Of Florida. Includes Index. Includes Bibliographical References And Index. "Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for the Social Sciences introduces statistical methods to students majoring in social science disciplines. With an emphasis on concepts and applications, this book assumes you have no previous knowledge of statistics and only a minimal mathematical background. It contains sufficient material for a two-semester course. The 5th Edition gives you examples and exercises with a variety of "real data." It includes more illustrations of statistical software for computations and takes advantage of the outstanding applets to explain key concepts, such as sampling distributions and conducting basic data analyses. It continues to downplay mathematics-often a stumbling block for students-while avoiding reliance on an overly simplistic recipe-based approach to statistics"-- Provided by Publisher