Methodology

Bridging the Gap: Introducing Joint Models for Longitudinal and Time-to-Event Data in the Social Sciences

2026-04-02T05:57:03+00:00

In time-to-event analyses in social sciences, there often exist endogenous time-varying variables, where the event status is correlated with the trajectory of the covariate itself. Ignoring this endogeneity will result in biased estimates. In the field of biostatistics this issue is tackled by estimating a joint model for longitudinal and time-to-event data as it handles endogenous covariates properly. This method is underused in the social sciences even though it is very useful to model longitudinal and time-to-event processes appropriately. Therefore, this paper provides a gentle introduction to the method of joint models and highlights its advantages for social science research questions. We demonstrate its usage on an example on marital satisfaction and marriage dissolution and compare the results with classical approaches such as a time-to-event model with a time-varying covariate. In addition to demonstrating the method, our results contribute to the understanding of the relationship between marriage satisfaction, marriage dissolution and other covariates.

Bayesian Versus Frequentist Approaches in Multilevel Single-Case Designs: On Type I Error Rate and Power

2026-03-27T05:15:40+00:00

Single-case designs (SCEDs) assess intervention effects through repeated measurements on one or a few individuals. Multilevel models nest repeated measures within individuals and have gained popularity for inferential analysis in SCEDs, in combination with expert knowledge of the clinicians and applied researchers. However, researchers often face model specification challenges without knowing the true population model underlying their data. This study evaluates how model selection criteria (AIC, BIC, WAIC, LOO) conditioned on the selected model impact statistical power and Type I error rates in intervention effects, reflecting the ecological reality where practitioners do not know the true model. A Monte Carlo simulation modelled data of AB designs varying sample size, measurement points, intervention effects, and random effect structures. Competing multilevel models were then fitted and compared using AIC, BIC, WAIC, and LOO to examine the impact of model selection on statistical power and Type I error rates. Results indicated that frequentist criteria performed well in simpler models in terms of power, while Bayesian approaches showed greater robustness with respect to Type I error control. The findings provide practical insights on multilevel model selection under real-world conditions, highlighting Bayesian methods as a robust alternative for applied researchers handling small sample sizes and complex data structures.

Controlling for Time-Varying Confounding in the Longitudinal Fixed-Effects Model: A Latent Variable Approach

2026-04-02T06:37:53+00:00

Fixed-effects regression models are commonly used in longitudinal studies as a means to estimate causal effects while controlling for unobserved time-invariant confounders. However, unobserved time-varying confounding remains potentially problematic, and identifying and measuring such confounders can be resource-intensive and costly. We propose the Time-Varying Confounding Structural Equation Model (TVC-SEM), a simple longitudinal model that builds on previous “common factor” models and which can serve as a robustness check for the assumption of no unobserved time-varying confounding in the fixed-effects approach. We posit a model with a latent autoregressive variable Zit, which represents the combined influence of both time-invariant and time-varying unobservables, and which is linked to the independent and dependent variables over time. Through Monte Carlo simulations and analyses of data from the Early Childhood Longitudinal Studies Kindergarten cohort (ECLS-K) and the Rural Substance Abuse and Violence Project (RSVP), we show that, under most conditions, TVC-SEM provides less biased estimates than several variants of the traditional fixed-effects model. Our proposed approach offers applied researchers a practical check for gauging the extent to which the fixed-effects assumption of no time-varying confounding may produce bias in the estimation of causal effects.

Analyzing Group Differences and Measurement Fairness in Process Data: A Sequential Response Model With Covariates

2026-03-27T05:15:39+00:00

This article introduces the sequential response model with covariates (SRM-C) for analyzing process data, with emphasis on three key capabilities: detecting potential measurement bias in response processes, evaluating group differences in ability distributions and improving parameter estimation precision. The SRM-C combines measurement and structural components, with the measurement component modeling response sequences conditional on abilities and covariates, and the structural component characterizing group-specific ability distributions. Sparsity assumptions implemented through horseshoe prior distributions address identification issues within the Bayesian framework. Monte Carlo simulations demonstrated robust parameter recovery and effective differential item functioning (DIF) detection. An empirical analysis of PISA problem-solving data illustrated the model’s utility in distinguishing ability differences from potential measurement bias. The SRM-C offers a comprehensive framework for understanding group differences in process data while ensuring measurement fairness.

PLAViMoP and Eye Tracking: A Method to Integrate 2D Gaze Data Within a C3D File of a Point-Light Display

2025-12-18T02:13:53+00:00

Understanding how humans observe and interpret actions is vital for social interaction. Point-light displays (PLDs), which depict actions using only joint movements, are widely used to study this process. Recently, PLAViMoP — an open-access database of 3D PLDs covering everyday actions, fine-motor skills, sports movements, facial expressions, social interactions, and robotic actions — has been introduced to facilitate the use of PLDs. PLAViMoP includes a search engine and metadata for each sequence, including movement type, label, actor sex, and age. In complement to the database, here we present a novel methodology that integrates eye-tracking data into the PLD reference frame, allowing gaze behavior and action kinematics to be jointly analyzed (i.e., in a unified dataset). This combined approach offers new insights into action perception and has broad applications in health, sports, and occupational settings. It also offers a promising tool for continuous psychophysical studies of the perception of biological movement.

Evaluating the Standard Error Estimation of the Local Structural-After-Measurement (LSAM) Approach in Structural Equation Modeling

2025-12-18T02:13:51+00:00

Accurate estimation of standard errors (SEs) is essential in SEM as they quantify the uncertainty of parameter estimates, are fundamental to computing test statistics, and ensure robust population inferences. This study evaluated SEs within the Local Structural-After-Measurement (LSAM) framework, a two-step approach to SEM. Two simulation studies examined analytic and resampling-based SE methods under varying conditions, including normal and nonnormal data, different sample sizes, and both correct and misspecified models. The nonparametric bootstrap yielded near-unbiased SEs under nonnormality, even when models were misspecified, while the parametric bootstrap performed well under normal conditions with correct model specification. The analytic two-step method performed well under normal conditions but showed increased bias with nonnormal data and smaller samples. The robust two-step method reduced this bias in larger samples, though some underestimation remained in small-sample and misspecified conditions. To complement SE bias results, 90% coverage rates were assessed. Findings confirm LSAM’s capability for accurate SE estimation in challenging research contexts.

Mixture Multigroup Bayesian SEM With Approximate Measurement Invariance for Comparing Structural Relations Across Many Groups

2025-12-18T02:13:52+00:00

In social sciences, researchers often compare relations between constructs, referred to as “structural relations”, across a large number of groups. This paper proposes Mixture Multigroup Bayesian SEM (MixMG-BSEM), a novel method for comparing structural relations across many groups while accounting for approximate measurement invariance in factor loadings. Traditional methods often assume exact measurement invariance, which may not reflect real-world data where small differences in measurement parameters commonly occur across many groups. MixMG-BSEM addresses this by using Multigroup Bayesian CFA with small-variance priors to allow for these small differences, and groups are then clustered based on their structural relations using Mixture Modeling. This is done in a stepwise estimation procedure built on the structural-after-measurement approach. By combining cluster-specific structural relations with small between-group differences in measurement parameters, MixMG-BSEM obtains a clustering that is driven only by the structural relations. The robustness and effectiveness of MixMG-BSEM are demonstrated through a simulation study.

The Impact of Ignoring Random Slopes in a 1 → 1 → 1 Mediation Model in the Presence of Upper-Level Mediator-Outcome Confounding

2025-12-18T02:13:52+00:00

In behavioral sciences, researchers frequently employ mediation analysis with longitudinal data. A common scenario involves the 1 → 1 → 1 mediation model, where the Predictor X, Mediator(s) M, and Outcome Y are all measured at different occasions (Level 1) within individuals (Level 2). The standard 1 → 1 → 1 mediation model approach fits two multilevel models, one for the mediator and one for the outcome, with two random intercepts and three random slopes in total. However researchers often exclude random slopes from multilevel models and only include random intercepts to account for non-independence across observations of the same individual. We demonstrate that ignoring random slopes in the 1 → 1 → 1 mediation model can result in biased average indirect effect estimators, as well as underestimated standard errors. We provide code from open source and free statistical software that can be used by practitioners to fit the 1 → 1 → 1 mediation model.

Calibrating Items With Time Use Diaries: A Refined Method

2025-09-30T01:31:41+00:00

The aim of the article is to refine a calibration method already presented and used to improve the information provided by the scales of frequency in questionnaires by combining it with data from time use diaries. In other words, this study proposes improvements to an existing calibration method aiming at “adjusting” the data gathered through items — which is useful for the analysis of phenomena with relatively long time cycles, but also notoriously subject to bias — with the data gathered through daily diaries — which are less subject to distortion, but generally only suitable for analysing phenomena with short or very short time cycles. In some cases, in fact, the calibration model already proposed may be problematic since, as we shall see, it could introduce another possible cause of bias. Such distortion could become relevant in certain situations and can be remedied by the proposed refinement with the new calibration model under consideration here. Finally, to highlight the advantages of the proposed method, we will develop with practical applications, comparisons by applying the presented models on data on religious practice collected in a large survey conducted in Italy in 2008. It should be specified, however, that the applicability of the proposed model is not limited to this example and can be extended to other contexts and types of data.

SUSHIJA Framework: A New Paradigm for Scoping Reviews

2025-09-30T01:31:41+00:00

This paper presents the SUSHIJA (Scoping, Updated, Systematic, Holistic, Interpretive, Joint, and Adaptive) Framework for conducting scoping reviews in an innovative manner. The framework is developed to address limitations of traditional scoping reviews. The SUSHIJA framework has several inclusive features such as Artificial Intelligence (AI) driven automation of literature reviews and data extraction, critical appraisal of the involved literature, iterative thematic mapping of the included articles, and even a living review component to extract and synthesize new studies in real-time. SUSHIJA framework also considers stakeholder input and incorporates visual tools.