Agent-based models (ABMs) often rely on psychometric constructs such as 'opinions', 'stubbornness', 'happiness', etc. The measurement process for these constructs is quite different from the one used in physics as there is no standardized unit of measurement for opinion or happiness. Consequently, measurements are usually affected by 'psychometric…

Multiple imputation is a popular method for addressing data that are presumed to be missing at random. To obtain accurate results, one's imputation model must be congenial to (appropriate for) one's intended analysis model. This article reviews and demonstrates two recent software packages, Blimp and jomo, to multiply impute data in a manner…

Across the evidence-based policy and practice (EBPP) community, including education, randomised controlled trials (RCTS) rank as the most "rigorous" evidence for causal conclusions. This paper argues that that is misleading. Only narrow conclusions about study populations can be warranted with the kind of "rigour" that RCTs…

The paper examines "Campbell's Law": "The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor." The examination of measurability leads to explaining the…

It is common in regression discontinuity analysis to control for high order (third, fourth, or higher) polynomials of the forcing variable. We argue that estimators for causal effects based on such methods can be misleading, and we recommend researchers do not use them, and instead use estimators based on local linear or quadratic polynomials or…

Missing data are a common occurrence in survey-based research studies in education, and the way missing values are handled can significantly affect the results of analyses based on such data. Despite known problems with performance of some missing data handling methods, such as mean imputation, many researchers in education continue to use those…

Schools and school systems throughout the nation are increasingly experimenting with using various instructional technologies to improve productivity and decrease costs, but evidence on both the effectiveness and the costs of education technology is limited. A recent report published by the Thomas B. Fordham Institute sets out to describe "the…

Three problems with stepwise research methods are explored. Computer packages may use incorrect degrees of freedom in stepwise computations. In addition, stepwise methods do not identify correctly the best variable set of a given size. A third problem is that stepwise methods tend to capitalize on sampling error. (SLD)

"How New York City's Charter Schools Affect Achievement" estimates the effects on student achievement of attending a New York City charter school rather than a traditional public school and investigates the characteristics of charter schools associated with the most positive effects on achievement. Because the report relies on an…

This repeated measures factorial design study compared the results of two procedures for identifying multivariate outliers under varying conditions, the Mahalanobis distance and the Andrews-Pregibon statistic. Results were analyzed for the total number of outliers identified and number of false outliers identified. Simulated data were limited to…

In a previous issue, Lawrence Schweinhart and David Weikart conclude that children who attended a direct-instruction preschool program had significantly greater adult felony arrests than children following the High/Scope or nursery-school curricula. Analysis of research methods suggests improbable links between preschool experience and adult…

Presents a table of cases requiring causal modeling or an equivalent technique. Reviews nine years of published research in communication journals to assess the adequacy of analysis in these situations. Offers standards for the conduct and reporting of causal modeling along with a review of their use in published causal modeling. (NKA)

To determine if the observed correlation between two variables can be "explained" by a third variable, a significance test on the partial correlation coefficient is often used. This can be misleading when the third variable is measured with error. This article shows how the problem can be partially overcome. (Author/BW)

Four things are recommended to minimize the influence or importance of statistical significance testing. Researchers must not neglect to add "statistical" to significant and could interpret results before giving p-values. Effect sizes should be reported with measures of sampling error, and replication can be built into the design. (SLD)

Reviews J. P. Rushton's data in "Race Differences in Behavior: A Review and Evolutionary Analysis" (1988), and suggests that aggregating large cohorts of methodologically weak studies results in misleading conclusions. A reanalysis of Rushton's data shows that cranial size is not a feasible indicator of intelligence and is similar in…