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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

For instructors

Review and Desk copies for this title are available digitally via VitalSource.

Request e-review copy

If you require a print review copy, please call: (800) 818-7243 ext. 6140 or email textsales@sagepub.com.

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