Some Reflections On Significance Testing

This essay presents a variation on a theme from my article “The use of tests of statistical significance”, which appeared in the Spring, 1999, issue of Mid-Western Educational Researcher

Was Monte Carlo Necessary?

In the critique that follows, I have attempted to summarize the principal disagreements between Sawilowsky and Roberts & Henson regarding the reporting and interpreting of statistically non-significant effect sizes, and to provide my own personal evaluations of their respective arguments

Bimodality Revisited

Degree of bimodality is an important feature of a frequency distribution, because it could suggest heterogeneity, such as polarization or two underlying distributions combined into one. The literature contains several measures of bimodality. This article attempts to summarize most of those measures, with their attendant advantages and disadvantages

Almost All Missing Data Are MNAR

Rubin (1976, and elsewhere) claimed that there are three kinds of “missingness”: missing completely at random; missing at random; and missing not at random. He gave examples of each. The article that now follows takes an opposing view by arguing that almost all missing data are missing not at random

The Use of Tests of Statistical Significance

This article summarizes the author’s views regarding the appropriate use of significance tests, especially in the context of regression analysis, which is the most commonly-encountered statistical technique in education and related disciplines. The article also includes a brief discussion of the use of power analysis after a study has been carried out

In (Partial) Defense of .05

Researchers are frequently chided for choosing the .05 alpha level as the determiner of statistical significance (or non-significance). A partial justification is provided

A primer on statistical inferences for finite populations

This primer is intended to provide the basic information for sampling without replacement from finite populations

Semi-Partial Correlations: I Don\u27t Need Them; You Can Have Them

I have been teaching statistics and associated topics (measurement, research design) for 37 years and have contributed to the methodological literature on such matters. During that time I have managed to get along without knowing or caring very much about a variety of techniques, most notably exploratory data analysis, Bayesian inference, expected values of mean squares, and item response theory. In the essay that follows I talk about another one: semi-partial correlations

Very Small p-values

Letter to the editor regarding an article by Hartz et al1 published in JAMA Psychiatry

From Gain Score t to ANCOVA F (and vice versa)

Although they test somewhat different hypotheses, analysis of gain scores (or its repeated-measures analog) and analysis of covariance are both common methods that researchers use for pre-post data. The results of the two approaches yield non-comparable outcomes, but since the same generic data are used, it is possible to transform the test statistic of one into that of the other. We derive a formula that can be used to accomplish a conversion between the two and give an example. Such a result could be helpful to meta-analysts, where the outcomes in different research reports may be of either of the two types, yet need to be synthesized. Suggestions for additional research that can improve the usefulness of the formula are offered. Accessed 30,293 times on https://pareonline.net from March 20, 2009 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right