The University of Tokyo · Psychology
Professor Satoshi Usami's research lab specializes in advanced longitudinal data analysis methods in the behavioral and social sciences, with a focus on modeling reciprocal relationships and causal inference between variables over time. The lab develops and compares statistical models such as the cross-lagged panel model (CLPM), random-intercept CLPM (RI-CLPM), general cross-lagged panel model (GCLM), latent change score (LCS) models, and autoregressive cross-lagged (ARCL) models to improve the validity of causal inferences in psychological and medical research. A central theme is the distinction between within-person and between-person effects, and the lab emphasizes methodological rigor in model selection and interpretation. The lab also explores innovative measurement techniques, including faking-resistant personality assessments using item response theory and pairwise preference-based designs.
Figures are computed from collected data and may differ slightly.
Inferring reciprocal effects or causality between variables is a central aim of behavioral and psychological research. To address reciprocal effects, a variety of longitudinal models that include cross-lagged relations have been proposed in different contexts and disciplines. However, the relations between these cross-lagged models have not been systematically discussed in the literature. This lack of insight makes it difficult for researchers to select an appropriate model when analyzing longit
Many methods have been developed to infer reciprocal relations between longitudinally observed variables. Among them, the general cross-lagged panel model (GCLM) is the most recent development as a variant of the cross-lagged panel model (CLPM), while the random-intercept CLPM (RI-CLPM) has rapidly become a popular approach. In this article, we describe how common factors and cross-lagged parameters included in these models can be interpreted, using a unified framework that was recently develope
Longitudinal designs provide a strong inferential basis for uncovering reciprocal effects or causality between variables. For this analytic purpose, a cross-lagged panel model (CLPM) has been widely used in medical research, but the use of the CLPM has recently been criticized in methodological literature because parameter estimates in the CLPM conflate between-person and within-person processes. The aim of this study is to present some alternative models of the CLPM that can be used to examine
This research focuses on the problem of model selection between the latent change score (LCS) model and the autoregressive cross-lagged (ARCL) model when the goal is to infer the longitudinal relationship between variables. We conducted a large-scale simulation study to (a) investigate the conditions under which these models return statistically (and substantively) different results concerning the presence of bivariate longitudinal relationships, and (b) ascertain the relative performance of an
The present paper focuses on the relationship between latent change score (LCS) and autoregressive cross-lagged (ARCL) factor models in longitudinal designs. These models originated from different theoretical traditions for different analytic purposes, yet they share similar mathematical forms. In this paper, we elucidate the mathematical relationship between these models and show that the LCS model is reduced to the ARCL model when fixed effects are assumed in the slope factor scores. Additiona
Recent years have shown increased awareness of the importance of personality tests in educational, clinical, and occupational settings, and developing faking-resistant personality tests is a very pragmatic issue for achieving more precise measurement. Inspired by Stark (2002) and Stark, Chernyshenko, and Drasgow (2005), we develop a pairwise preference-based personality test that aims to measure multidimensional personality traits using a large-scale statement bank. An experiment compares the re
When conducting longitudinal research, the investigation of between-individual differences in patterns of within-individual change can provide important insights. In this article, we use simulation methods to investigate the performance of a model-based exploratory data mining technique—structural equation model trees (SEM trees; Brandmaier, Oertzen, McArdle, & Lindenberger, 2013)—as a tool for detecting population heterogeneity. We use a latent-change score model as a data generation model and
Behavioral researchers have shown growing interest in structural equation model trees (SEM Trees), a new recursive partitioning-based technique for detecting population heterogeneity. In the present research, we conducted a large-scale simulation to investigate the performance of latent growth curve model (LGCM)-based SEM Trees for uncovering between-individual differences in patterns of within-individual change. Simulation results showed that the correct estimation rates of the number of classe
Behavioral science researchers have shown strong interest in disaggregating within-person relations from between-person differences (stable traits) using longitudinal data. In this paper, we propose a method of within-person variability score-based causal inference for estimating joint effects of time-varying continuous treatments by controlling for stable traits of persons. After explaining the assumed data-generating process and providing formal definitions of stable trait factors, within-pers
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