Conventional estimators for indirect effects using a difference in coefficients and product of coefficients produce the same results for continuous outcomes. However, for binary outcomes, the difference in coefficient estimator systematically underestimates the indirect effects because of a scaling problem. One solution is to standardize…

Employing nested sequences of models is a common practice when exploring the extent to which one set of variables mediates the impact of another set. Such an analysis in the context of logistic regression models confronts two challenges: (1) direct comparisons of coefficients across models are generally biased due to the changes in scale that…

Investigation of peer effects on achievement with sample survey data on schools may mean that only a random sample of the population of peers is observed for each individual. This generates measurement error in peer variables similar in form to the textbook case of errors-in-variables, resulting in the estimated peer group effects in an OLS…

Many Chicagoans are getting shortchanged, particularly when it comes to money exchange between the Illinois Lottery (IL) and Illinois State Board of Education (ISBE). A significant portion of lottery sales is earmarked for education in Illinois. Because these revenues are not generated equally, however, some contribute more to education via the…

This article describes a simulation activity that can be used to help students see that the estimator "S" is a biased estimator of [sigma]. The activity can be implemented using either a statistical package such as R, Minitab, or a Web applet. In the activity, the students investigate and compare the bias of "S" when sampling from different…

This study contributes a novel perspective on grade retention by empirically examining how classroom composition relates to the standardized-testing performance of grade-retained students in their post-retained years. This evaluation employed a sample of entire cohorts of urban elementary school children in the Philadelphia School District over 6…

For the past 25 years, methodological advances have been made in missing data treatment. Most published work has focused on missing data in dependent variables under various conditions. The present study seeks to fill the void by comparing two approaches for handling missing data in categorical covariates in logistic regression: the…

A basic question of instructional interventions is how effective it is in promoting student learning. This paper presents a study to determine the relative efficacy of different instructional strategies by applying an educational data mining technique, learning decomposition. We use logistic regression to determine how much learning is caused by…

When survey data are statistically analyzed, many times some of the data is missing. If the missing values are not correctly handled, results of the analysis may be dubious and publication may jeopardize the credibility of the organization preparing the report. This study examined four of the more commonly used methods of handling missing data.…

Both structural equation models and hierarchical linear models (HLMs) have been commonly used in multilevel analysis. This study utilized simulated data to investigate the power difference among 3 multilevel models: HLM, deviation structural equation models, and a hybrid approach of HLM and structural equation models. Two factors were examined:…

Evaluation of large-scale programs is problematical because of inherent bias in assignment of treatment and control groups, resulting in serious regression artifacts even with the use of analysis of covariance designs. Nonuniformity of program implementation across sites and classrooms is also a problem. (Author/GSK)

A study explored which nonexperimental comparison group methods provide the most accurate estimates of the impacts of mandatory welfare-to-work programs and whether the best methods work well enough to substitute for random assignment experiments. Findings were compared for nonexperimental comparison groups and statistical adjustment procedures…