## Essential Statistics for the Social and Behavioral Sciences: A Conceptual Approach

### Walsh, Anthony; Ollenburger, Jane C.

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This book helps readers become intelligent consumers of the social/behavioral science literature and familiarizes them with the fundamental tools of research. It features a conceptual, intuitive approach that is less math-oriented (e.g., not cluttered with all sorts of sub-and superscripts, and not concerned with mathematical derivatives of the various statistics), and that clearly shows the continuity and interrelatedness of the techniques discussed. After the necessary concepts have been explained and the calculations have been performed for each statistic, the text walks readers through a line-by-line explanation of a computer printout (based on actual data) containing that statistic. "Practice Applications" provide research examples with step-by-step solutions to all statistical procedures. Describing Data. Central Tendency and Dispersion. Probability and the Normal Curve. The Sampling Distribution and Estimation Procedures. Hypothesis Testing: Interval/Ratio Data. Analysis of Variance. Hypothesis Testing with Categorical Data: Chi-Square. Measures of Association with Nominal and Ordinal Data. Elaboration and Causal Analysis. Bivariate Correlation and Regression. Multivariate Correlation and Regression. For anyone in the social/behavioral sciences who needs an accessible introduction to statistics.

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PREFACE

This text is the result of a combined total of 30 years of teaching statistics and research methods to both graduate and undergraduate students. Teaching statistics to students in the social and behavioral sciences at any level is a challenge. Each of us has tried numerous approaches over the years using a variety of statistics texts from relatively simple "cookbooks" to the more rigorous and complex texts. Students learn fairly easily from the former, but uniformly come away with no conceptual understanding of statistics. Rigorous texts are more meaningful to the better prepared students, but leave many students perplexed and frustrated. We have tried to strike a balance here between ease of learning the material and the student's obtaining a satisfactory grasp of the role of statistics in the human sciences. We propose to do this in a number of ways, beginning with providing straightforward and consistent formulas, step-by-step instructions, and thorough interpretations.

COMPUTING NUMBERS

One of the important things we have learned from our teaching is that many students come to our classes with a minimal background in mathematics. Some are math phobic and dread the idea of a class requiring them to fiddle with numbers. The only assumption we make about the mathematical backgrounds of students is that they have an elementary grasp of algebra. The calculation of each statistic is introduced with a very simple example, and students are given step-by-step instructions to reach its solution. We also provide sufficient problems at the end of each chapter so students can readily improve their calculational competence. Solutions are given for odd-numbered problems in order to provide immediate feedback and improve calculating abilities.

WHAT DOES IT MEAN?

It doesn't mean much to arrive at the correct solution (a computer can do that) if we do not know what the solution means or what to do with it. To facilitate understanding, we include a line-by-line discussion of computer printouts for the majority of statistics. These printouts are based on actual data, and thus convey the feel of real-world research problems. We interpret the findings in the printouts in the simplest language possible while still endeavoring to include the necessary formal terminology of statistics. The combination of simple prose and formal terminology will help you become statistically literate, which is necessary to become proficient in reading and understanding the professional literature. Because so many excellent statistical packages are available for use, we do not rely on any one package.

The practice applications at the end of each chapter facilitate understanding. These applications pose a social science problem and use each of the statistical techniques taught in the chapter to provide answers. If you follow the applications from start to finish, you will greatly improve both your computational and interpretation skills. These exercises further develop an appreciation for the relevance of statistics in the social science.

THE SCOPE OF THE TEXT

Statistics texts usually conform to one of two types. The first emphasizes statistical techniques that are rarely used nowadays and introduce many techniques that are beyond the understanding of undergraduates. Few of these texts give adequate treatment to multivariate techniques, such as multiple ordinary least squares regression or the increasingly popular logistic regression. Students come away from such texts with an impression that social scientists are only interested in univariate and bivariate analysis, when in. fact this is rarely the case. A basic understanding of multivariate techniques is an absolute must.

The second type of text is encyclopedic in its coverage, and includes statistics that most practicing researchers rarely, if ever, employ. While we have attempted to include only the most relevant techniques, we do include some statistical techniques not typically found in an elementary text. Because tests of significance are highly sensitive to sample size, we stress that a measure of association should always accompany such tests. Thus, we emphasize computing phi-squared and the odds ratio with the chi-square test, and eta squared with the t and F tests. Measures of association help us to decide whether the established relationship is a meaningful, substantive one.

To provide continuity throughout the text, we take examples from one of the data sets explored here and use it throughout so that we can continually refer back to earlier chapters. For instance, one of the many ways we take advantage of continuity is to show that a relationship assumed to be true in a bivariate analysis may not turn out to be the case in a multivariate analysis.

We would like to thank the reviewers, Audie Blevins of University of Wyoming, Debra S. Kelley of Longwood College, and Gwen Wittenbaum of Michigan State University.

Anthony Walsh
Boise State University—Boise, Idaho

Jane C. Ollenburger
California State Polytechnic University—Pomona, California

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