ANSEC: A BASIC package for lag sequential analysis of observational data

Three BASIC programs for processing observational, nonconcurrent sequential data are presented. The programs follow Sackett’s lag sequential analysis method and have the innovations of running on interactional microcomputers and of providing plots of results. The outcome of the analysis is stored on a magnetic disk, facilitating a further application of probabilistic models to the transformed data. The Allison-Liker correction for the comparison test between expected and observed lag probabilities is included in the programs.     

A multipurpose laboratory package for 0S/8 BASIC

A set of BASIC-callable assembler routines are described that support experimental programming on the PDP-8/e laboratory computer. The package includes both general-purpose laboratory utilities (e.g., for timing delays, sensing and controlling switches, etc.) and specialized functions to facilitate elementary signal processing operations. System performance is evaluated in terms of timing accuracy, optimal sampling rates, and transportability to other laboratories.     

A self-paced CAI package for selecting statistical methods

TREELAB is a package of FORTRAN IV programs, files, work sheets, and assignments for self-paced instruction in selecting statistical methods for a broad variety of empirical research purposes. It complements traditional statistics course curricula by giving students extensive practice in evaluating data sets and selecting appropriate methods according to their theoretical needs.     

How meta-analysis increases statistical power

One of the most frequently cited reasons for conducting a meta-analysis is the increase in statistical power that it affords a reviewer. This article demonstrates that fixed-effects meta-analysis increases statistical power by reducing the standard error of the weighted average effect size (T.) and, in so doing, shrinks the confidence interval around T.. Small confidence intervals make it more likely for reviewers to detect nonzero population effects, thereby increasing statistical power. Smaller confidence intervals also represent increased precision of the estimated population effect size. Computational examples are provided for 3 effect-size indices: d (standardized mean difference), Pearson's r, and odds ratios. Random-effects meta-analyses also may show increased statistical power and a smaller standard error of the weighted average effect size. However, the authors demonstrate that increasing the number of studies in a random-effects meta-analysis does not always increase statistical power.     

The power of statistical tests in meta-analysis

Calculations of the power of statistical tests are important in planning research studies (including meta-analyses) and in interpreting situations in which a result has not proven to be statistically significant. The authors describe procedures to compute statistical power of fixed- and random-effects tests of the mean effect size, tests for heterogeneity (or variation) of effect size parameters across studies, and tests for contrasts among effect sizes of different studies. Examples are given using 2 published meta-analyses. The examples illustrate that statistical power is not always high in meta-analysis.