Developments in sociological methodology and in quantitative sociology have always been closely related to developments in statistical theory, methodology and computation. The same statement applies if "methodology for social research" and "quantitative social research" replace the more specific terms in this statement. Statistical methodology, including especially the battery of methods used to estimate and evaluate statistical models, has had a tremendous effect on social research in the post-war period, particularly in the United States. What is less well appreciated is the influence of sociological methodology, or methodology for social research more generally, on modern statistics. I give a brief sketch of the linkages between methodology in social research and methodology in statistics. The focus is on areas where developments in sociological methodology, or at least the scientific contexts of social research, have brought forth new methods of general significance to the practice of statistics, in both theoretical and "applied" areas. These remarks should be taken as the impressions of someone who has tried to straddle the fence between statistics and social research throughout his career, not as a careful history of statistical ideas.

We report progress on some methodological issues in meta-analysis. Evidence continues to accumulate that randomized trials show smaller gains than nonrandomized trials when innovations are compared to standard therapies. Quality scores for randomized clinical trials show that reporting has improved about 27% in three decades, to a quality level slightly over 50%. Although quality scoring could be useful, in principle, for adjusting estimates of gain from innovations, a substantial study has not found a statistical relation between gains and quality. We describe a method of blinding papers to reduce the bias of readers doing meta-analyses. For combining data for fixed effects, Greenland and Salvan recommend using Mantel-Haensztel, weighted least squares or maximum likelihood methods. For random effects, Larholt, Tsiatis and Gelber have improvements for the DerSimonian and Laird method. Eddy and his colleagues have prepared software and book-length works on Bayesian methods for technology assessment using meta-analysis. Louis has a valuable review article on Bayesian approaches. The annoying difficulties in combining $2 \times 2$ tables when some cells have zeros has been largely overcome by exact calculation methods. From diagnostic data acquired from several independent investigations, new methods have appeared for estimating receiver operating characteristic curves. An update on meta-analyses of randomized clinical trials shows about 16 meta-analyses per year in journals during 1983-1990. We expect much more methodologic work as new issues appear and findings point us toward fresh solutions.

A semi-parametric method is developed for assessing publication bias prior to performing a meta-analysis. Summary estimates for the individual studies in the meta-analysis are assumed to have known distributional form. Selective publication is modeled using a nonparametric weight function, defined on the two-sided p-value scale. The shape of the estimated weight function provides visual evidence of the presence of bias, if it exists, and observed trends may be tested using rank order statistics or likelihood ratio tests. The method is intended as an exploratory technique prior to embarking on a standard meta-analysis.

Publication selection effects arise in meta-analysis when the effect magnitude estimates are observed in (available from) only a subset of the studies that were actually conducted and the probability that an estimate is observed is related to the size of that estimate. Such selection effects can lead to substantial bias in estimates of effect magnitude. Research on the selection process suggests that much of the selection occurs because researchers, reviewers and editors view the results of studies as more conclusive when they are more highly statistically significant. This suggests a model of the selection process that depends on effect magnitude via the p-value or significance level. A model of the selection process involving a step function relating the p-value to the probability of selection is introduced in the context of a random effects model for meta-analysis. The model permits estimation of a weight function representing selection along the mean and variance of effects. Some ideas for graphical procedures and a test for publication selection are also introduced. The method is then applied to a meta-analysis of test validity studies.

When the report by the New Researchers' Committee (NRC) of the IMS was published in Statistical Science (6 163-174), readers were invited to provide additional comments. We publish here commentaries by four individuals and also two invited discussions about programs at the National Science Foundation and the National Security Agency designed to benefit new researchers (NR's). The original report by the New Researchers' Committee provides suggestions for NR's and covers the role of the IMS as an advocate for them. The new NRC, now chaired by Deborah Nolan, responds to these comments adding some new material. The NRC also recently has published the New Researcher's Survival Guide to help the professional development of NR's.

Federal and state agencies, private industry and professional societies are developing programs to encourage women to enter the education "pipeline" for careers in science and technology. To reverse female students' underrepresentation in science and to retain incoming women in these fields, especially those entering academia, requires the active support of women and men faculty as mentors. The focus of the present manuscript addresses this issue. Here, we give a profile of women in statistics and, more broadly, in the sciences, and we outline the elements of a good mentor. Statistics are presented in support of our argument, but case studies form the basis for the recommendations.