SOCIAL STATISTICS FOR A DIVERSE SOCIETY SEVENTH EDITIONPDF|Epub|txt|kindle电子书版本网盘下载

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1.The What and the Why of Statistics1

The Research Process2

Asking Research Questions3

The Role of Theory4

Formulating the Hypotheses5

Independent and Dependent Variables：Causality7

Independent and Dependent Variables：Guidelines9

Collecting Data10

Levels of Measurement11

Nominal Level of Measurement11

Ordinal Level of Measurement12

Interval-Ratio Level of Measurement12

Cumulative Property of Levels of Measurement13

Levels of Measurement of Dichotomous Variables13

Discrete and Continuous Variables16

A Closer Look 1.1：A Cautionary Note：Measurement Error16

Analyzing Data and Evaluating the Hypotheses17

Descriptive and Inferential Statistics17

Evaluating the Hypotheses18

Looking at Social Differences19

A Closer Look 1.2：A Tale of Simple Arithmetic：How Culture May Influence How We Count20

A Closer Look 1.3：Are You Anxious About Statistics？21

2.Organization of Information：Frequency Distributions27

Frequency Distributions28

Proportions and Percentages29

Percentage Distributions31

Comparisons32

Statistics in Practice：Labor Force Participation Among Foreign Born33

The Construction of Frequency Distributions35

Frequency Distributions for Nominal Variables37

Frequency Distributions for Ordinal Variables37

Frequency Distributions for Interval-Ratio Variables38

Cumulative Distributions43

A Closer Look 2.1：Real Limits，Stated Limits，and Midpoints ofClass Intervals44

Rates47

Statistics in Practice：Civilian Labor Force Participation Rates Over Time48

Reading the Research Literature：Statistical Tables48

Basic Principles49

Tables With a Different Format51

Conclusion52

3.Graphic Presentation65

The Pie Chart：Race and Ethnicity of the Elderly66

The Bar Graph：Marital Status of the Elderly68

The Statistical Map：The Geographic Distribution of the Elderly70

The Histogram71

Statistics in Practice：Gender and Age73

The Line Graph75

Time-Series Charts77

A Closer Look 3.1：A Cautionary Note：Distortions in Graphs79

Statistics in Practice：The Graphic Presentation of Education81

4.Measures of Central Tendency96

The Mode97

The Median100

Finding the Median in Sorted Data101

An Odd Number of Cases101

An Even Number of Cases103

Finding the Median in Frequency Distributions104

Statistics in Practice：Gendered Income Inequality105

Locating Percentiles in a Frequency Distribution106

The Mean108

Calculating the Mean109

A Closer Look 4.1：Finding the Mean in a Frequency Distribution110

Understanding Some Important Properties of the Arithmetic Mean113

Interval-Ratio Level of Measurement113

Center of Gravity113

Sensitivity to Extremes113

The Shape of the Distribution：Television，Education，and Siblings116

The Symmetrical Distribution116

The Positively Skewed Distribution117

The Negatively Skewed Distribution119

Guidelines for Identifying the Shape of a Distribution120

Considerations for Choosing a Measure of Central Tendency121

Level of Measurement121

Skewed Distribution121

Symmetrical Distribution122

A Closer Look 4.2：A Cautionary Note：Representing Income122

5.Measures of Variability135

The Importance of Measuring Variability136

The Index of Qualitative Variation：A Brief Introduction138

Steps for Calculating the IQV139

A Closer Look 5.1：Statistics in Practice：Diversity at Berkeley Through the Years140

Expressing the IQV as a Percentage141

Statistics in Practice：Diversity in U.S.Society142

The Range143

The Interquartile Range：Increases in Elderly Population145

The Box Plot147

The Variance and the Standard Deviation：Changes in the Elderly Population151

Calculating the Deviation From the Mean152

Calculating the Variance and the Standard Deviation154

Focus on Interpretation：GDP for Selected Countries156

Considerations for Choosing a Measure of Variation159

Reading the Research Literature：Differences in College Aspirations and Expectations Among Latino Adolescents160

6.The Normal Distribution177

Properties of the Normal Distribution178

Empirical Distributions Approximating the Normal Distribution178

An Example：Final Grades in Statistics179

Areas Under the Normal Curve180

Interpreting the Standard Deviation181

Standard （Z） Scores182

Transforming a Raw Score Into a Z Score182

Transforming a Z Score Into a Raw Score184

The Standard Normal Distribution185

The Standard Normal Table185

The Structure of the Standard Normal Table186

Transforming Z Scores Into Proportions （or Percentages）187

Finding the Area Between the Mean and a Specified Positive Z Score188

Finding the Area Between the Mean and a Specified Negative Z Score188

Finding the Area Above a Positive Z Score or Below a Negative Z Score189

Transforming Proportions （or Percentages） Into Z Scores190

Finding a Z Score Bounding an Area Above It191

Finding a Z Score Bounding an Area Below It192

Working With Percentiles in a Normal Distribution193

Finding the Percentile Rank of a Score Higher Than the Mean193

Finding the Percentile Rank of a Score Lower Than the Mean194

Finding the Raw Score Associated With a Percentile Higher Than 50195

Finding the Raw Score Associated With a Percentile Lower Than 50196

A Final Note197

7.Sampling and Sampling Distributions206

Aims of Sampling207

Some Basic Principles of Probability209

Probability Defined209

The Relative Frequency Method209

The Normal Distribution and Probabilities210

Probability Sampling211

The Simple Random Sample211

The Systematic Random Sample213

The Stratified Random Sample214

A Closer Look 7.1：Disproportionate Stratified Samples and Diversity215

The Concept of the Sampling Distribution216

The Population217

The Sample218

The Dilemma219

The Sampling Distribution219

The Sampling Distribution of the Mean219

An Illustration219

Review222

The Mean of the Sampling Distribution222

The Standard Error of the Mean223

The Central Limit Theorem224

The Size of the Sample227

The Significance of the Sampling Distribution and the Central Limit Theorem227

Statistics in Practice：The Central Limit Theorem229

8.Estimation237

Estimation Defined238

Reasons for Estimation239

Point and Interval Estimation239

Procedures for Estimating Confidence Intervals for Means240

A Closer Look 8.1：Estimation as a Type of Inference241

Determining the Confidence Interval242

Calculating the Standard Error of the Mean243

Deciding on the Level of Confidence and Finding theCorresponding Z Value243

Calculating the Confidence Interval243

Interpreting the Results244

Reducing Risk244

Estimating Sigma246

Calculating the Estimated Standard Error of the Mean247

Deciding on the Level of Confidence and Finding theCorresponding Z Value247

Calculating the Confidence Interval247

Interpreting the Results247

Sample Size and Confidence Intervals247

A Closer Look 8.2：What Affects Confidence IntervalWidth？Summary249

Statistics in Practice：Hispanic Migration and Earnings250

Confidence Intervals for Proportions253

Procedures for Estimating Proportions254

Calculating the Estimated Standard Error of the Proportion255

Deciding on the Desired Level of Confidence and Findingthe Corresponding Z Value255

Calculating the Confidence Interval255

Interpreting the Results255

Statistics in Practice：The 2012 Benghazi Terrorist Attack Investigation256

Calculating the Estimated Standard Error of the Proportion256

Deciding on the Desired Level of Confidence and Finding theCorresponding Z Value257

Calculating the Confidence Interval257

Interpreting the Results257

A Closer Look 8.3：A Cautionary Note：The Margin of Error258

9.Testing Hypotheses267

Assumptions of Statistical Hypothesis Testing268

Stating the Research and Null Hypotheses269

The Research Hypothesis （H1）269

The Null Hypothesis （H0）269

More About Research Hypotheses：One- and Two-Tailed Tests270

Determining What Is Sufficiently Improbable：Probability Values and Alpha271

The Five Steps in Hypothesis Testing：A Summary275

Errors in Hypothesis Testing276

The t Statistic and Estimating the Standard Error278

The t Distribution and Degrees of Freedom278

Comparing the t and Z Statistics279

Statistics in Practice：The Earnings of White Women280

Testing Hypotheses About Two Samples281

The Assumption of Independent Samples282

Stating the Research and Null Hypotheses282

The Sampling Distribution of the Difference Between Means283

Estimating the Standard Error284

Calculating the Estimated Standard Error284

The t Statistic285

Calculating the Degrees of Freedom for a Difference Between Means Test285

A Closer Look 9.1：Calculating the Estimated Standard Error andthe Degrees of Freedom（df）When the Population VariancesAre Assumed to Be Unequal285

The Five Steps in Hypothesis Testing About Difference Between Means：A Summary286

Focus on Interpretation：Cigarette Use Among Teens287

Testing the Significance of the Difference Between Two Sample Proportions289

Statistics in Practice：Comparing First- and Second-GenerationHispanic Americans289

Focus on Interpretation：First- and Second-Generation Asian Americans291

A Closer Look 9.2：A Cautionary Note：Is There a Significant Difference？292

Reading the Research Literature：Reporting the Results ofStatistical Hypothesis Testing292

10.Bivariate Tables303

Independent and Dependent Variables304

How to Construct a Bivariate Table：Race and Home Ownership305

How to Compute Percentages in a Bivariate Table307

Calculating Percentages Within Each Category of the Independent Variable308

Comparing the Percentages Across Different Categories of theIndependent Variable308

A Closer Look 10.1：Percentaging a Bivariate Table309

How to Deal With Ambiguous Relationships Between Variables310

Reading the Research Literature：Place of Death in America312

The Properties of a Bivariate Relationship315

The Existence of the Relationship316

The Strength of the Relationship317

The Direction of the Relationship317

Elaboration319

Testing for Nonspuriousness：Firefighters and Property Damage320

An Intervening Relationship：Religion and Attitude Toward Abortion323

Conditional Relationships：More on Abortion328

The Limitations of Elaboration330

Statistics in Practice：Family Support for the Transition From High School331

11.The Chi-Square Test and Measures of Association347

The Concept of Chi-Square as a Statistical Test350

The Concept of Statistical Independence350

The Structure of Hypothesis Testing With Chi-Square351

The Assumptions351

Stating the Research and the Null Hypotheses351

The Concept of Expected Frequencies352

Calculating the Expected Frequencies352

Calculating the Obtained Chi-Square354

The Sampling Distribution of Chi-Square355

Determining the Degrees of Freedom356

Making a Final Decision357

Review358

A Closer Look 11.1：A Cautionary Note：Sample Size and Statistical Significance for Chi-Square359

Focus on Interpretation：Education and Health Assessment360

Reading the Research Literature：Violent Offense Onset by Gender，Race，and Age362

Proportional Reduction of Error：A Brief Introduction363

A Closer Look 11.2：What Is Strong？What Is Weak？A Guide to Interpretation366

Lambda：A Measure of Association for Nominal Variables367

A Method for Calculating Lambda368

Some Guidelines for Calculating Lambda369

Cramer’s V：A Chi-Square-Related Measure of Association for Nominal Variables370

Focus on Interpretation：Gamma and Kendall’s Tau-b370

Using Ordinal Measures With Dichotomous Variables372

Focus on Interpretation：The Gender Gap in Gun Control373

12.Analysis of Variance388

Understanding Analysis of Variance389

The Structure of Hypothesis Testing With ANOVA391

The Assumptions391

Stating the Research and the Null Hypotheses and Setting Alpha392

The Concepts of Between and Within Total Variance392

A Closer Look 12.1：Decomposition of SST394

The F Statistic395

Making a Decision397

The Five Steps in Hypothesis Testing：A Summary397

A Closer Look 12.2：Assessing the Relationship Between Variables399

Focus on Interpretation：Are Immigrants Good for America’s Economy？399

Reading the Research Literature：Self-Image and Ethnic Identification400

Reading the Research Literature：Stresses and Strains Among Grandmother Caregivers402

13.Regression and Correlation413

The Scatter Diagram415

Linear Relations and Prediction Rules417

Constructing Straight-Line Graphs420

Finding the Best-Fitting Line422

Defining Error423

The Residual Sum of Squares （∑e2）423

The Least Squares Line424

Review424

Computing a and b for the Prediction Equation424

Interpreting a and b427

Statistics in Practice：Median Household Income and Criminal Behavior429

A Closer Look 13.1：Understanding the Covariance429

A Closer Look 13.2：A Note on Nonlinear Relationships430

Methods for Assessing the Accuracy of Predictions432

Prediction Errors434

The Coefficient of Determination （r2） as a PRE Measure437

Calculating r2439

Testing the Significance of r2 Using ANOVA441

Making a Decision443

Pearson’s Correlation Coefficient （r）443

Characteristics of Pearson’s r444

Statistics in Practice：Teen Pregnancy and Social Inequality445

Focus on Interpretation：The Marriage Penalty in Earnings448

Multiple Regression450

ANOVA for Multiple Linear Regression453

A Closer Look 13.3：A Cautionary Note：Spurious Correlations and Confounding Effects454

Appendix A.Table of Random Numbers477

Appendix B.The Standard Normal Table480

Appendix C.Distribution of t484

Appendix D.Distribution of Chi-Square486

Appendix E.Distribution of F487

Appendix F.A Basic Math Review489

Learning Check Solutions494

Answers to Odd-Numbered Exercises504

Glossary546

Notes551

Index556