Because of the continuing debates about statistics, many researchers may feel confused about how to analyze and interpret data. Current guidelines in psychology advocate the use of effect sizes and confidence intervals (CIs). However, researchers may be unsure about how to extract effect sizes from factorial designs. Contrast analysis is helpful…

Specialized imputation routines for multilevel data are widely available in software packages, but these methods are generally not equipped to handle a wide range of complexities that are typical of behavioral science data. In particular, existing imputation schemes differ in their ability to handle random slopes, categorical variables,…

This article presents findings on the consequences of matrix sampling of context questionnaires for the generation of plausible values in large-scale assessments. Three studies are conducted. Study 1 uses data from PISA 2012 to examine several different forms of missing data imputation within the chained equations framework: predictive mean…

Students in a junior- and senior-level Anatomy and Physiology course have the opportunity to correct missed exam questions ("regrade") and earn up to half of the original points missed. The three objectives of this study were to determine if: (a) performance on the regrade assignment was correlated with scores on subsequent exams, (b)…

Modeling the distribution of site level effects is an important problem, but it is also an incredibly difficult one. Current methods rely on distributional assumptions in multilevel models for estimation. There it is hoped that the partial pooling of site level estimates with overall estimates, designed to take into account individual variation as…

Identifying collaboration between learners in a course is an important challenge in education for two reasons: First, depending on the courses rules, collaboration can be considered a form of cheating. Second, it helps one to more accurately evaluate each learners competence. While such collaboration identification is already challenging in…

Doubly bounded continuous data are common in the social and behavioral sciences. Examples include judged probabilities, confidence ratings, derived proportions such as percent time on task, and bounded scale scores. Dependent variables of this kind are often difficult to analyze using normal theory models because their distributions may be quite…

Student modeling is a widely used approach to make inference about a student's attributes like knowledge, learning, etc. If we wish to use these models to analyze and better understand student learning there are two problems. First, a model's ability to predict student performance is at best weakly related to the accuracy of any one of its…

Researchers who make tutoring systems would like to know which sequences of educational content lead to the most effective learning by their students. The majority of data collected in many ITS systems consist of answers to a group of questions of a given skill often presented in a random sequence. Following work that identifies which items…

Bayesian methods for analyzing longitudinal data in social and behavioral research are recommended for their ability to incorporate prior information in estimating simple and complex models. We first summarize the basics of Bayesian methods before presenting an empirical example in which we fit a latent basis growth curve model to achievement data…

An interactive computer-based system for assisting investigators in the use of Bayesian analysis using the two parameter normal model is described. An important feature of this program is that it interacts with the investigator in the English language; he need not be familiar with computer languages or with the internal workings of the computer.…

Concludes that for most researchers trained in classical statistics, the use of the Bayesian approach requires substantial retooling. Observes that the technical details of the Bayesian approach are formidable, and will require studying textbooks, applications, and computer packages, as well as consulting colleagues. (Author/JDH)

Statistical significance is not a sufficient condition for claiming a hypothesis has been supported. Constructive replications are more important. Statistically significant results may be meaningless while a sequence of nonsignificant results may be quite important. Gives advice on how to overcome some limitations of classifical statistical…

The Computer-Assisted Data Analysis (CADA) Monitor is a set of conversational-language interactive computer programs that permit relatively inexperienced persons to perform relatively complex statistical data analysis. The Monitor leads the user through an analysis on a step-by-step basis providing the necessary direction, information, and…

The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…