Data pooling is a powerful strategy in empirical research. However, combining multiple datasets often results in a large amount of missing data, as variables that are not present in some datasets effectively contain missing values for all participants in those datasets. Furthermore, data pooling typically leads to a mix of continuous and…

Recurrent event analyses have found a wide range of applications in biomedicine, public health, and engineering, among others, where study subjects may experience a sequence of event of interest during follow-up. The R package reReg offers a comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events,…

Sociologists increasingly face choices among competing algorithms that represent reasonable approaches to the same task, with little guidance in choosing among them. We develop a strategy that uses simulated data to identify the conditions under which different methods perform well and applies what is learned from the simulations to predict which…

Exploratory graph analysis (EGA) is a commonly applied technique intended to help social scientists discover latent variables. Yet, the results can be influenced by the methodological decisions the researcher makes along the way. In this article, we focus on the choice regarding the number of factors to retain: We compare the performance of the…

Since the 1990s, European school policy has been steered by management dreams that systematic monitoring and assessment would guide schools and society toward a future of greater quality, efficiency, and growth. This article, drawing on Jean Baudrillard, explores whether it makes sense to rearticulate this dream of optimization by assessment in…

This paper describes an innovative approach for teaching the challenges in the management of data warehouse development. The approach contains lecture material providing conceptual background about the management of data warehouse development, a simulation game supporting experiential learning, and a post-play debriefing to support synthesis of…

Simulated standardized patients (SPs) are trained individuals who pose incognito as people seeking treatment in a health care setting. With the method's increasing use and popularity, we propose some standards to adapt the method to contextual considerations of feasibility, and we discuss current issues with the SP method and the experience of…

This paper details the anticipated impact of synthetic "big" data on learning analytics (LA) infrastructures, with a particular focus on data governance, the acceleration of service development, and the benchmarking of predictive models. By reviewing two cases, one at the sector-wide level (the Jisc learning analytics architecture) and…

This review explores the integration and effects of the Internet of Things (IoT) in education, highlighting its importance in transforming traditional teaching and learning techniques. It examines the early uses and historical growth of IoT, its development, and the turning points in its adoption. It explores IoT platforms, tools, and technologies…

Agent-based models combine data and theory during both development and use of the model. As models have become increasingly data driven, it is easy to start thinking of agent-based modelling as an empirical method, akin to statistical modelling, and reduce the role of theory. We argue that both types of information are important where the past is…

Recurrent event data arise frequently in various fields such as biomedical sciences, public health, engineering, and social sciences. In many instances, the observation of the recurrent event process can be stopped by the occurrence of a correlated failure event, such as treatment failure and death. In this article, we propose a joint scale-change…

With this article, we propose using a Bayesian multilevel latent class (BMLC; or mixture) model for the multiple imputation of nested categorical data. Unlike recently developed methods that can only pick up associations between pairs of variables, the multilevel mixture model we propose is flexible enough to automatically deal with complex…

Following the onset of the novel coronavirus disease 2019 (COVID-19) pandemic, daily life significantly changed for the population. Accordingly, researchers interested in examining patterns of change over time may now face discontinuities around the pandemic. Researchers collecting in-person longitudinal data also had to cancel or delay data…

In this article we present a general approach not relying on item response theory models (non-IRT) to detect differential item functioning (DIF) in dichotomous items with presence of guessing. The proposed nonlinear regression (NLR) procedure for DIF detection is an extension of method based on logistic regression. As a non-IRT approach, NLR can…

This article describes (1) the survey methodological and statistical characteristics of the nonrandomized method for surveying sensitive questions for both cross-sectional and panel survey data and (2) the way to use the incompletely observed variable obtained from this survey method in logistic regression and in loglinear and log-multiplicative…