Recent simulation research has demonstrated that using simple raw score to operationalize a latent construct can result in inflated Type I error rates for the interaction term of a moderated statistical model when the interaction (or lack thereof) is proposed at the latent variable level. Rescaling the scores using an appropriate item response…

In the nonequivalent groups with anchor test (NEAT) design, the standard error of linear observed-score equating is commonly estimated by an estimator derived assuming multivariate normality. However, real data are seldom normally distributed, causing this normal estimator to be inconsistent. A general estimator, which does not rely on the…

A recent article in "The Physics Teacher" describes a method for analyzing a systematic error in a Boyle's law laboratory activity. Systematic errors are important to consider in physics labs because they tend to bias the results of measurements. There are numerous laboratory examples and resources that discuss this common source of error.

Ever wondered why children have difficulty using a ruler? In this article Michael Drake investigates some of the difficulties students encounter and provides some ideas for teaching about and learning to use rulers.

This paper describes how a Many Faceted Rasch Measurement (MFRM) approach can be applied to performance assessment focusing on rater analysis. The article provides an introduction to MFRM, a description of MFRM analysis procedures, and an example to illustrate how to examine the effects of various sources of variability on test takers' performance…

The standard error of equating quantifies the variability in the estimation of an equating function. Because common items for deriving equated scores are treated as fixed, the only source of variability typically considered arises from the estimation of common-item parameters from responses of samples of examinees. Use of alternative, equally…

Utilizing planned missing data (PMD) designs (ex. 3-form surveys) enables researchers to ask participants fewer questions during the data collection process. An important question, however, is just how few participants are needed to effectively employ planned missing data designs in research studies. This article explores this question by using…

A common strategy for estimating treatment effects in observational studies using individual student-level data is analysis of covariance (ANCOVA) or hierarchical variants of it, in which outcomes (often standardized test scores) are regressed on pretreatment test scores, other student characteristics, and treatment group indicators. Measurement…

Inverse probability weighting (IPW) estimates are widely used in applications where data are missing due to nonresponse or censoring or in observational studies of causal effects where the counterfactuals cannot be observed. This extensive literature has shown the estimators to be consistent and asymptotically normal under very general conditions,…

Errors don't exist in our data, but they serve a vital function. Reality is complicated, but our models need to be simple in order to be manageable. We assume that attributes are invariant over some conditions of observation, and once we do that we need some way of accounting for the variability in observed scores over these conditions of…

Methods of uniform differential item functioning (DIF) detection have been extensively studied in the complete data case. However, less work has been done examining the performance of these methods when missing item responses are present. Research that has been done in this regard appears to indicate that treating missing item responses as…

Considering scoring validity as encompassing both reliable rating scale use and valid descriptor interpretation, this study reports on the validation of a CEFR-based scale that was co-constructed and used by novice raters. The research questions this paper wishes to answer are (a) whether it is possible to construct a CEFR-based rating scale with…

When competence tests are administered, subjects frequently omit items. These missing responses pose a threat to correctly estimating the proficiency level. Newer model-based approaches aim to take nonignorable missing data processes into account by incorporating a latent missing propensity into the measurement model. Two assumptions are typically…

This study develops a tool to identify errors in the presentation of natural laws based on the epistemology and ontology of the Scientific Realism of Mario Bunge. The tool is able to identify errors of different types: (1) epistemological, in which the law is incorrectly presented as data correlation instead of as a pattern of causality; (2)…

The authors outline the application of latent class analysis (LCA) to classroom observational instruments. LCA offers diagnostic information about teachers' instructional strengths and weaknesses, along with estimates of measurement error for individual teachers, while remaining relatively straightforward to implement and interpret. It is…