Statistical power analysis for the behavioral sciences

Reviews attrition, missing data, weights, imputation. The costs of phone versus personal interviews and the consequences of less extensive pursuit of nonrespondents are also examined. In comparing longitudinal and cross-sectional sampling designs, they conclude, contrary to common belief, that longi~dinal designs are less costly than repeated cross-sectional surveys of similar size and response rate. None of the essays require more than the equivalent of an introductory statistics background. For those interested in large scale survey research in general, or longitudinal studies in particular, these essays provide interesting and valuable reading. The conclusions of these papers should be of interest not only to those who design or conduct such studies, but also to those who fund them.

SAMPLING DESIGN FOR SURVEY RESEARCH: STATISTICAL POWER ANALYSIS Cohen, J. (1988). Statistical Power Analysisfor the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers. 567 pages, $55.00. Most books on sampling design approach the problem of determining sample size in terms of confidence intervals and confidence levels required. Most behavioral scientists and most statistics texts approach the problem of hypothesis testing as one of accepting or rejecting the null hypothesis. For more than two decades Cohen has been a leading critique of this approach as he shows that it typically leads to inadequate sample sizes. He shows that the null hypothesis is frequently rejected - the research hypothesis accepted - simply because unusually large effects would be necessary to accept the null hypothesis when small samples are used. He also notes this approach focuses attention on the statistical significance of the result and away from the size of the effect being pursued. Cohen and others have proposed an alternative approach to the dete~ination of sample size, the use of power analysis. Simply stated, the power of a statistical test is the probability that it will yield statistically significant results. With the four parameters of statistical inference - power, significance criteria, sample size, and effect size - if three are defined, the fourth can be computed. In addition to general discussions of power analysis and computational procedures, the book provides nine chapters describing the application of power analysis for specific statistical procedures, and provides power tables and sample size tables for each of the statistical approaches considered. These approaches include t-test, product moment, differences between correlation coefficients, tests of prounion, &i-square for goodness of fit and contingency tables, analysis of variance and covariance, multiple regression and correlation analysis, and set correlation and multivariate methods. The book assumes a background equivalent to one or two semesters of applied statistics. This book provides the most thorough treatment of the subject available today. This book can also serve as a handbook, with the reader referring only to the chapters on the particular statistical measures of interest. The tables of power and sample size in each of these chapters should be very useful. Their value lies not only in dete~ining sample size. The reader can also easily compare the sample sizes required for different designs and tests to aid in formulating study designs and analysis strategies. Kraemer, H.C., & Thiemann, S. (1987). How Many Subjects? Statistical Power Analysis in Research. Newbury Park, CA: Sage Publications. 120 pages, $17.95. The authors provide the reader with a concise and useful introduction to the subject of power analysis. In addition to the introduction and conclusions, seven chapters detail power analysis