Statistics for the social sciences / R. Mark Sirkin.

Format:

Book

Author/Creator:

Sirkin, R. Mark.

Language:

English

Subjects (All):

Social sciences--Statistical methods.

Social sciences.

Statistics.

Physical Description:

xxi, 610 pages : illustrations ; 23 cm

Edition:

Third edition.

Place of Publication:

Thousand Oaks, Calif. : Sage Publications, [2006]

Summary:

Do your students lack confidence in their ability to handle quantitative work? Do they get confused about how to enter statistical data in SAS, SPSS, and Excel programs? The new Third Edition of the best-selling Statistics for the Social Sciences is the solution to these dilemmas.

Like previous editions, this Third Edition continues to help build students' confidence and ability in doing statistical analysis by slowly moving from concepts that require little computational work to those that require more. Author R. Mark Sirkin once again demonstrates how statistics can be used to help students come to appreciate real-world applications rather than fearing them. Statistics for the Social Sciences emphasizes the analysis and interpretation of data to give students a feel for how data interpretation is related to the methods by which the information was obtained. The book includes lists of key concepts, chapter exercises, topic boxes, and more.

Statistics for the Social Sciences is an excellent text for advanced undergraduate and graduate students studying statistics across the social sciences. It can also be used in research methods courses that cover quantitative applications in some depth.

Contents:

1 How We Reason 1

Science 5

The Scientific Method 9

Testing Hypotheses 11

From Hypotheses to Theories 16

Types of Relationships 18

Association and Causation 22

The Unit of Analysis 25

2 Levels of Measurement and Forms of Data 33

Measurement 34

Qualitative and Quantitative Data 35

Nominal Level of Measurement 36

Forms of Nominal=Level Data 38

Ordinal Level of Measurement 40

Forms of Ordinal-Level Data 40

Likert Scales 43

Scores Versus Frequencies 45

Interval and Ratio Levels of Measurement 45

Forms of Interval-Level Data 48

Tables Containing Nominal Level of Measurement Variables 54

3 Defining Variables 63

Gathering the Data 64

Operational Definitions 65

Index and Scale Construction 69

Validity 73

Reliability 75

4 Measuring Central Tendency 83

Central Tendency 84

The Mean 85

The Median 90

Grouped Data 94

Using Central Tendency 98

The Mode 98

Interpreting Graphs 104

Central Tendency and Levels of Measurement 105

Skewness 106

Other Graphic Representations 112

Stem and Leaf Displays 112

Boxplots 113

5 Measuring Dispersion 127

Visualizing Dispersion 128

The Range 129

The Mean Deviation 130

The Variance and Standard Deviation 132

The Computational Formulas for Variance and Standard Deviation 136

Variance and Standard Deviation for Data in Frequency Distributions 139

6 Constructing and Interpreting Contingency Tables 149

Contingency Tables 150

Regrouping Variables 151

Generating Percentages 155

Interpreting 159

Controlling for a Third Variable 164

Partial Tables 167

Causal Models 172

Computer Applications 175

SPSS 175

SAS 177

7 Statistical Inference and Tests of Significance 191

What Is Statistical Inference? 192

Random Samples 195

Comparing Means 198

Comparing a Sample Mean to a Population Mean or Other Value 200

Comparing a Sample Mean to Another Sample Mean 202

Comparing More Than Two Sample Means 202

The Test Statistic 203

Probabilities 206

Decision Making 206

Review 208

Directional Versus Nondirectional Alternative Hypotheses (One-Tailed Versus Two-Tailed Tests) 210

Setting the Level of Significance 212

Degrees of Freedom 214

Steps in Significance Testing 214

8 Probability Distributions and One-Sample z and t Tests 225

Normal Distributions 226

The One-Sample z Test for Statistical Significance 234

The Central Limit Theorem 238

Review 243

The Normality Assumption 244

The One-Sample t Test 246

Degrees of Freedom 249

The t Table 250

An Alternative t Formula 252

A z Test for Proportions 253

Interval Estimation 254

Confidence Intervals for Proportions 256

More on Probability 258

The Addition Rule 258

The Multiplication Rule 260

Permutations and Combinations 261

9 Two-Sample t Tests 271

Independent Samples Versus Dependent Samples 272

The Two-Sample t Test for Independently Drawn Samples 275

Adjustments for Sigma-Hat Squared ([sigma superscript 2]]) 288

Interpreting a Computer-Generated t Test 288

Computer Applications: Independent Samples t Tests 290

SPSS 290

SAS 291

Excel 294

The Two-Sample t Test for Dependent Samples 297

Computer Applications: Dependent Samples t Test 301

SPSS 301

SAS 301

Excel 303

Statistical Significance Versus Research Significance 303

Statistical Power 306

10 One-Way Analysis of Variance 317

How Analysis of Variance Is Used 318

Analysis of Variance in Experimental Situations 319

F: An Intuitive Approach 322

ANOVA Terminology 326

The ANOVA Procedure 330

Comparing F With t 337

Analysis of Variance With Experimental Data 338

Post Hoc Testing 340

Computer Applications 343

SPSS 343

SAS 345

Excel 348

Two-Way Analysis of Variance 348

11 Measuring Association in Contingency Tables 359

Measures for Two-by-Two Tables 360

Yule's Q 362

The Phi Coefficient 365

Measures for n-by-n Tables 367

Goodman and Kruskal's Gamma ([gamma]) 367

Goodman and Kruskal's Lambda ([lambda]) 371

Lambda-Column Variable Dependent 372

Lambda-Row Variable Dependent 375

Curvilinearity 377

Other Measures of Association 380

Interpreting an Association Matrix 381

12 The Chi-Square Test 397

The Context for the Chi-Square Test 398

Expected Frequencies 400

Observed Versus Expected Frequencies 405

Using the Table of Critical Values of Chi-Square 408

Calculating the Chi-Square Value 412

Yates's Correction 415

Validity of Chi-Square 417

Directional Alternative Hypotheses 422

Testing Significance of Association Measures 425

Association Versus Significance 426

Chi-Square and Phi 429

Computer Applications 431

SPSS 431

SAS 433

The Limits of Statistical Significance 435

13 Correlation and Regression Analysis 443

Cartesian Coordinates 447

The Concept of Linearity 451

Linear Equations 455

Linear Regression 460

The Correlation Coefficient 468

The Coefficient of Determination 472

Finding the Regression Equation 474

Computer Applications 479

SPSS 479

SAS 484

Excel 485

Correlation Measures for Analysis of Variance 486

14 Additional Aspects of Correlation and Regression Analysis 497

Statistical Significance for r and b 498

Significance of r 506

Partial Correlations and Causal Models 508

The Role of the Partial Correlation Coefficient 511

Multiple Correlation and the Coefficient of Multiple Determination 516

Multiple Regression 520

An Example From Judicial Behavior 524

The Standardized Partial Regression Slope 528

Using a Regression Printout 530

Stepwise Multiple Regression 533

Computer Applications 538

Partial Correlations-SPSS 538

Partial Correlations-Other Programs 540

Multiple Regression-SPSS 540

Multiple Regression-SAS 543

Multiple Regression-Excel 547

Stepwise Multiple Regression-SPSS 547

Stepwise Multiple Regression-SAS 552

Appendix 1 Proportions of Area Under Standard Normal Curve 561

Appendix 2 Distribution of t 565

Appendix 3 Critical Values of F for p = .05, .01, and .001 566

Appendix 4 Critical Values of Chi-Square 569

Appendix 5 Critical Values of the Correlation Coefficient 570.

Notes:

Includes bibliographical references and index.

ISBN:

141290546X

OCLC:

58526353

Online:

Publisher description