This book offers a unique approach to quantitative research methods. Intended as a sequel to texts that emphasize the data collection process, this book escorts readers through organization of raw data, descriptive statistics, tests of significance, drawing conclusions, and techniques for writing research reports.An Introduction to Data Analysis in the Social Sciences serves as a resource for those who desire an applied, hands-on approach without cumbersome mathematics or detailed theory. It provides instruction in determining the appropriate statistical applications for one's project as well as step-by-step guidance in performing the tests and analyzing the results. SPSS subroutines are used throughout the book in an effort to illustrate the usefulness of computer calculations in the research process.

DEBRA WETCHER-HENDRICKS, PhD, is Associate Professor in the Sociology Department at Moravian College. She has published several journal articles in her areas of research interest, which include quantitative data analysis, interpersonal communications, and gender relations.

Preface xiii

Part I. Summarizing Data 1

1. Data Organization 3

1.1 Introduction 3

1.2 Consideration of Variables 4

1.3 Coding 15

1.4 Data Manipulations 18

1.5 Conclusion 20

2. Descriptive Statistics for Categorical Data 33

2.1 Introduction 33

2.2 Frequency Tables 35

2.3 Crosstabulations 37

2.4 Graphs and Charts 45

2.5 Conclusion 50

3. Descriptive Statistics for Continuous Data 63

3.1 Introduction 63

3.2 Frequencies 64

3.3 Measures of Central Tendency 70

3.4 Measures of Dispersion 73

3.5 Standardized Scores 79

3.6 Conclusion 88

Part II. Statistical Tests 101

4. Evaluating Statistical Significance 103

4.1 Introduction 103

4.2 Central Limit Theorem 104

4.3 Statistical Significance 107

4.4 The Roles of Hypotheses 115

4.5 Conclusion 119

5. The Chi-Square Test: Comparing Category Frequencies 125

5.1 Introduction 125

5.2 The Chi-Square Distribution 126

5.3 Performing Chi-Square Tests 130

5.4 Post Hoc Testing 143

5.5 Confidence Intervals 146

5.6 Explaining Results of the Chi-Square Test 147

5.7 Conclusion 148

6. The t-Test Comparing Continuous-Variable Data Among Dichotomous Groups 159

6.1 Introduction 159

6.2 The t Distribution 160

6.3 Performing t Tests 161

6.4 Confidence Intervals 172

6.5 Explaining Results of the t Test 173

6.6 Conclusion 174

7. Analysis of Variance: Comparing Continuous-Variable Data Among Nondichotomous Groups 187

7.1 Introduction 187

7.2 The F Distribution 189

7.3 Performing ANOVAs 192

7.4 Post Hoc Testing 214

7.5 Confidence Intervals 217

7.6 Explaining Results of the ANOVA 218

7.7 Conclusion 219

8. Correlation and Regression: Comparing Changes Among Continuous-Variable Scores 231

8.1 Introduction 231

8.2 Bivariate Relationships 233

8.3 Multivariate Relationships 244

8.4 The Phi Coefficient 253

8.5 Explaining Results of Correlation-Regression Analysis 255

8.6 Conclusion 258

9. Advanced Statistical Analyses 273

9.1 Introduction 273

9.2 Repeated-Measures Analysis of Variance 274

9.3 Multiple Analysis of Variance 278

9.4 Analysis of Covariance 283

9.5 Discriminant Analysis 286

9.6 Conclusion 290

Part III. Applying Data 303

10. Drawing Conclusions 305

10.1 Introduction 305

10.2 Accepting and Rejecting Hypotheses 306

10.3 Drawing Conclusions from Results 310

10.4 Cautions 315

10.5 Conclusion 323

11. Writing Research Reports 327

11.1 Introduction 327

11.2 Tone 328

11.3 Sections of the Research Report 334

11.4. Conclusion 348

Appendixes 353

Appendix A. Z-Score Table 355

Appendix B. Table for Critical X2 Values 357

Appendix C. Table for Critical t Values 359

Appendix D. Table for Critical F Values 361

References 369

Answers to Review Questions 371

Index 391