The cross-classified data structure is ubiquitous in education, psychology, and health outcome sciences. In these areas, assessment instruments that are made up of multiple items are frequently used to measure latent constructs. The presence of both the cross-classified structure and multivariate categorical outcomes leads to the so-called…

This article presents methods for using extant data to improve the properties of estimators of the standardized mean difference (SMD) effect size. Because samples recruited into education research studies are often more homogeneous than the populations of policy interest, the variation in educational outcomes can be smaller in these samples than…

Research in education and behavioral sciences often involves the use of latent variable models that are related to indicators, as well as related to covariates or outcomes. Such models are subject to interpretational confounding, which occurs when fitting the model with covariates or outcomes alters the results for the measurement model. This has…

The limitations of Cohen's ? are reviewed and an alternative G-index is recommended for assessing nominal-scale agreement. Maximum likelihood estimates, standard errors, and confidence intervals for a two-rater G-index are derived for one-group and two-group designs. A new G-index of agreement for multirater designs is proposed. Statistical…

This article reviews how National Assessment of Educational Progress (NAEP) has come to collect and analyze data about cognitive and behavioral processes (process data) in the transition to digital assessment technologies over the past two decades. An ordered five-level structure is proposed for describing the uses of process data. The levels in…

The social relations model (SRM) is very often used in psychology to examine the components, determinants, and consequences of interpersonal judgments and behaviors that arise in social groups. The standard SRM was developed to analyze cross-sectional data. Based on a recently suggested integration of the SRM with structural equation models (SEM)…

I review opportunities and threats that widely accessible Artificial Intelligence (AI)-powered services present for educational statistics and measurement. Algorithmic and computational advances continue to improve approaches to item generation, scale maintenance, test security, test scoring, and score reporting. Predictable misuses of AI for…

To solve the attenuation paradox in computerized adaptive testing (CAT), this study proposes an item selection method, the integer programming approach based on real-time test data (IPRD), to improve test efficiency. The IPRD method turns information regarding the ability distribution of the population from real-time test data into feasible test…

Bounded continuous data are encountered in many applications of item response theory, including the measurement of mood, personality, and response times and in the analyses of summed item scores. Although different item response theory models exist to analyze such bounded continuous data, most models assume the data to be in an open interval and…

Interactive tasks designed to elicit real-life problem-solving behavior are rapidly becoming more widely used in educational assessment. Incorrect responses to such tasks can occur for a variety of different reasons such as low proficiency levels, low metacognitive strategies, or motivational issues. We demonstrate how behavioral patterns…

Takers of educational tests often receive proficiency levels instead of or in addition to scaled scores. For example, proficiency levels are reported for the Advanced Placement (AP®) and U.S. Medical Licensing examinations. Technical difficulties and other unforeseen events occasionally lead to missing item scores and hence to incomplete data on…

Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified…

Longitudinal data analysis has been widely employed to examine between-individual differences in within-individual changes. One challenge of such analyses is that the rate-of-change is only available indirectly when change patterns are nonlinear with respect to time. Latent change score models (LCSMs), which can be employed to investigate the…

This article discusses estimation of average treatment effects for randomized controlled trials (RCTs) using grouped administrative data to help improve data access. The focus is on design-based estimators, derived using the building blocks of experiments, that are conducive to grouped data for a wide range of RCT designs, including clustered and…

We used an unobtrusive approach, keystroke logging, to examine students' cognitive states during essay writing. Based on data contained in the logs, we classified writing process data into three states: text production, long pause, and editing. We used semi-Markov processes to model the sequences of writing states and compared the state transition…