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Statistics for the Social Sciences
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Statistics for the Social Sciences

Third Edition


August 2005 | 632 pages | SAGE Publications, Inc
Do your students lack confidence in their ability to handle quantitative work? Do they get confused about how to enter statistical data on SAS, SPSS, and Excel programs? The new Third Edition of the best-selling Statistics for the Social Sciences is the solution to these dilemmas!

Popular in previous editions, the 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 so that students come to appreciate their usefulness rather than fear 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. An Instructor's CD-ROM containing data sets, PowerPoint slides, exercises, and answers will be available free-of-charge to professors adopting this text.

 
1. How We Reason
KEY CONCEPTS  
PROLOGUE AND INTRODUCTION  
SETTING THE STAGE  
SCIENCE  
THE SCIENTIFIC METHOD  
TESTING HYPOTHESES  
FROM HYPOTHESES TO THEORIES  
TYPES OF RELATIONSHIPS  
ASSOCIATION AND CAUSATION  
THE UNIT OF ANALYSIS  
CONCLUSION  
EXERCISES  
 
2. Levels of Measurement and Forms of Data
KEY CONCEPTS  
PROLOGUE AND INTRODUCTION  
MEASUREMENT  
NOMINAL LEVEL OF MEASUREMENT  
ORDINAL LEVEL OF MEASUREMENT  
LIKERT SCALES  
SCORES VERSUS FREQUENCIES  
INTERVAL AND RATIO LEVELS OF MEASUREMENT  
TABLES CONTAINING NOMINAL LEVEL OF MEASUREMENT  
CONCLUSION  
EXERCISES  
 
3. Defining Variables
KEY CONCEPTS  
PROLOGUE AND INTRODUCTION  
GATHERING THE DATA  
OPERATIONAL DEFINITIONS  
INDEX AND SCALE CONSTRUCTION  
VALIDITY  
RELIABILITY  
CONCLUSION  
EXERCISES  
 
4. Measuring Central Tendency
KEY CONCEPTS  
PROLOGUE AND INTRODUCTION  
CENTRAL TENDENCY  
THE MEAN  
THE MEDIAN  
USING CENTRAL TENDENCY  
THE MODE  
INTERPRETING GRAPHS  
CENTRAL TENDENCY AND LEVELS OF MEASUREMENT  
SKEWNESS  
OTHER GRAPHIC REPRESENTATIONS  
CONCLUSION  
SUMMARY OF MAJOR FORMULAS  
EXERCISES  
 
5. Measuring Dispersion
KEY CONCEPTS  
PROLOGUE AND INTRODUCTION  
VISUALIZING DISPERSION  
THE RANGE  
THE MEAN DEVIATION  
THE VARIANCE AND STANDARD DEVIATION  
THE COMPUTATIONAL FORMULAS FOR VARIANCE  
VARIANCE AND STANDARD DEVIATION FOR DATA IN FREQUENCY DISTRIBUTIONS  
CONCLUSION  
SUMMARY OF MAJOR FORMULAS  
EXERCISES  
 
6. Constructing and Interpreting Contingency Tables
KEY CONCEPTS  
PROLOGUE AND INTRODUCTION  
CONTINGENCY TABLES  
REGROUPING VARIABLES  
GENERATING PERCENTAGES  
INTERPRETING  
CONTROLLING FOR A THIRD VARIABLE  
PARTIAL TABLES  
CAUSAL MODELS  
COMPUTER APPLICATIONS  
CONCLUSION  
EXERCISES  
 
7. Statistical Inference and Tests of Significance
KEY CONCEPTS  
PROLOGUE AND INTRODUCTION  
WHAT IS STATISTICAL INFERENCE?  
RANDOM SAMPLES  
COMPARING MEANS  
THE TGEST STATISTIC  
PROBABILITIES  
DECISION MAKING  
DIRECTIONAL VERSUS NONDIRECTIONAL ALTERNATIVE HYPOTHESES (ONE-TAILED VERSUS TWO-TAILED TESTS)  
CONCLUSION  
SUMMARY OF MAJOR FORMULAS  
EXERCISES  
 
8. Probability Distributions and One-Sample z and t Tests
KEY CONCEPTS  
PROLOGUE AND INTRODUCTION  
NORMAL DISTRIBUTIONS  
THE ONE-SAMPLE z TEST FOR STATISTICAL SIGNIFICANCE  
THE CENTRAL LIMIT THEOREM  
THE NORMALITY ASSUMPTION  
THE ONE-SAMPLE t TEST  
DEGREES OF FREEDOM  
THE t TABLE  
AN ALTERNATIVE t FORMULA  
A z TEST FOR PROPORTIONS  
INTERVAL ESTIMATION  
CONFIDENCE INTERVALS FOR PROPORTIONS  
MORE ON PROBABILITY  
PERMUTATIONS AND COMBINATIONS  
CONCLUSION  
SUMMARY OF MAJOR FORMULAS  
EXERCISES  
 
9. Two-Sample t Tests
KEY CONCEPTS  
PROLOGUE AND INTRODUCTION  
INDEPENDENT SAMPLES VERSUS DEPENDENT SAMPLES  
THE TWO-SAMPLE t TEST FOR INDEPENDENTLY DRAWN SAMPLES  
ADJUSTMENTS FOR SIGMA-HAT SQUARED (^ 2)  
INTERPRETING A COMPUTER-GENERATED t TEST  
COMPUTER APPLICATIONS  
THE TWO-SAMPLE t TEST FOR DEPENDENT SAMPLES  
STATISTICAL SIGNIFICANCE VERSUS RESEARCH SIGNIFICANCE  
STATISTICAL POWER  
CONCLUSION  
SUMMARY OF MAJOR FORMULAS  
EXERCISES  
 
10. One-Way Analysis of Variance
KEY CONCEPTS  
PROLOGUE AND INTRODUCTION  
HOW ANALYSIS OF VARIANCE IS USED  
ANALYSIS OF VARIANCE IN EXPERIMENTAL SITUATIONS  
F – AN INTUITIVE APPROACH  
ANOVA TERMINOLOGY  
THE ANOVA PROCEDURE  
COMPARING F WITH t  
ANALYSIS OF VARIANCE WITH EXPERIMENTAL DATA  
POST HOC TESTING  
COMPUTER APPLICATIONS  
TWO-WAY ANALYSIS FOR VARIANCE  
CONCLUSION  
SUMMARY OF MAJOR FORMULAS  
EXERCISES  
 
11. Measuring Association in Contingency Tables
KEY CONCEPTS  
PROLOGUE AND INTRODUCTION  
MEASURES FOR TWO-BY-TWO TABLES  
MEASURES FOR n-BY-n  
CURVILINEARITY  
OTHER MEASURES OF ASSOCIATION  
INTERPRETING AN ASSOCIATION MATRIX  
CONCLUSION  
SUMMARY OF MAJOR FORMULAS  
EXERCISES  
 
12. The Chi-Square Test
KEY CONCEPTS  
PROLOGUE AND INTRODUCTION  
THE CONTEXT FOR THE CHI-SQUARE TEST  
OBSERVED VERSUS EXPECTED FREQUENCIES  
USING THE TABLE OF CRITICAL VALUE OF CHI-SQUARE  
CALCULATING THE CHI-SQUARE VALUE  
YATES’ CORRECTION  
VALIDITY OF CHI-SQUARE  
DIRECTIONAL ALTERNATIVE HYPOTHESES  
TESTING SIGNIFICANCE OF ASSOCIATION MEASURES  
CHI-SQUARE AND PHI  
COMPUTER APPLICATIONS  
CONCLUSION  
SUMMARY OF MAJOR FORMULAS  
EXERCISES  
 
13. Correlation and Regression Analysis
KEY CONCEPTS  
PROLOGUE AND INTRODUCTION  
THE SETTING  
CARTESIAN COORDINATES  
THE CONCEPT OF LINEARITY  
LINEAR EQUATIONS  
LINEAR REGRESSION  
COMPUTER APPLICATIONS  
CORRELATION MEASURES FOR ANALYSIS OF VARIANCE  
CONCLUSION  
SUMMARY OF MAJOR FORMULAS  
EXERCISES  
 
14. Additional Aspects of Correlation and Regression Analysis
KEY CONCEPTS  
PROLOGUE AND INTRODUCTION  
STATISTICAL SIGNIFICANCE FOR r AND b  
SIGNIFICANCE OF r  
PARTIAL CORRELATIONS AND CAUSAL MODELS  
MULTIPLE CORRELATION AND THE COEFFICIENT OF MULTIPLE DETERMINATION  
MULTIPLE REGRESSION  
THE STANDARDIZED PARTIAL REGRESSION SLOPE  
USING A REGRESSION PRINTOUT  
STEPWISE MULTIPLE REGRESSION  
COMPUTER APPLICATIONS  
CONCLUSION  
SUMMARY OF MAJOR FORMULAS  
EXERCISES  
 
Appendix 1: Proportions of Area Under Standard Normal Curve
 
Appendix 2: Distribution of t
 
Appendix 3: Critical Values of F for p = .05
 
Appendix 4: Critical Values of Chi-Square
 
Appendix 5: Critical Values of the Correlation Coefficient
 
Answers to Selected Exercises
 
Index
 
About the Author

Excellent presentation and discussion of difficult material using multiple platforms.

Ms Sherri Verdugo
Sociology, Cal State University Fullerton
August 25, 2014

Good introductory book for social scientists. I have made it a recommended reading

Dr Karyn Morrissey
Department of Geography, Liverpool University
October 12, 2012

Really thorough without being overcomplicated.

Mr Ashton Verdery
Sociology Dept, University of North Carolina - Chapel Hill
September 11, 2012

The organization of the text and the clarity of the writing makes this text the one to use for our graduate students!

Dr Gary Troxell
Biblical Counseling Dept, Lancaster Bible College
March 10, 2011

This book takes students beyond Fallowfield, Hale and Wilkinson to allow them to utilise more ambitious statistical analysis of their projects. It is therefore included as a joint essential read for the research methods courses that I deliver.

Mr Jamie Sims
Sport, Exercise and Health Sciences, Chichester University
January 13, 2011
Key features
NEW TO THIS EDITION:
  • Includes additional exercises to reflect the new computer coverage
  • Provides new tools to teach students how to do analysis not only through SAS and SPSS, but also using Excel descriptive statistics features
  • Offers a wide range of examples from various fields in the social sciences to demonstrate the role of statistical analysis in the research process

Sample Materials & Chapters


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