A primer on continuous-time modeling in educational research: an exemplary application of a continuous-time latent curve model with structured residuals (CT-LCM-SR) to PISA Data

Abstract

One major challenge of longitudinal data analysis is to find an appropriate statistical model that corresponds to the theory of change and the research questions at hand. In the present article, we argue that continuous-time models are well suited to study the continuously developing constructs of primary interest in the education sciences and outline key advantages of using this type of model. Furthermore, we propose the continuous-time latent curve model with structured residuals (CT-LCM-SR) as a suitable model for many research questions in the education sciences. The CT-LCM-SR combines growth and dynamic modeling and thus provides descriptions of both trends and process dynamics. We illustrate the application of the CT-LCM-SR with data from PISA reading literacy assessment of 2000 to 2018 and provide a tutorial and annotated code for setting up the CT-LCM-SR model.

Cracking Chicken-Egg Conundrums: Juxtaposing Contemporaneous and Lagged Reciprocal Effects Models of Academic Self-Concept and Achievement’s Directional Ordering

“…Our paper, as well as most debates on longitudinal data analysis, adhere to a predictive causality perspective-known as Granger causality-where a cause is equated with the prospective/longitudinal effect of a variable, net of confounding factors of change (Granger, 1969; also see Campbell & Stanley, 1963;Cook & Campbell, 1979;Diener et al, 2022). This framework is explicitly referenced and privileged in SEMs of longitudinal data, due to its inherent temporal focus (Hamaker et al, 2015;Zyphur et al, 2020;Lohmann et al, 2022). Nevertheless, the validity of causal interpretations remains susceptible to threats and might never be fully resolved with statistical models of longitudinal correlational data.…”

“…The continuous time model (CTM) is an evolving statistical model. Although CTMs have only been applied to evaluate how cross-lagged panel effects vary over time (e.g., Hecht & Zitzmann, 2021a, 2021bKuiper et al, 2018;Lohmann et al, 2022;Voelkle et al, 2018), treating time as a continuous variable has theoretically important implications potentially relevant to our research. In order to juxtapose our extension to traditional approaches to CLPMs with CTMs, we reanalyzed our data with CTMs, explicitly modelled time as a continuous rather than a discrete variable (see supplemental materials).…”

Beyond the average: embracing speaker individuality in the dynamic modeling of the acoustic-articulatory relationship

“…This sort of measurement allows us to gain insights of our constructs (formant contours) at each temporal interval. However, in many theories of change, it is assumed that the variables under study exist and develop continuously over time, and not solely at the measured occasions (Lohmann et al, 2022). Thus, by statistically modeling these continuously developing constructs we are able to more closely connect models with theories of change and to investigate how dynam-ic effects may develop (ibid.).…”

Role of ILSAs for Meta-Analyses in Education

“…This self-selection has several consequences, such as the varying participation across ILSAs and ILSA cycles with countries remaining, dropping out, or joining in, and the possible underrepresentation of cultures or World regions (e.g., Rutkowski & Rutkowski, 2021). Moreover, the varying participation of countries challenges the study of educational trends due to the lack of consistent longitudinal data at the level of countries (e.g., Lohmann et al, 2022).…”

“…However, to achieve this, complex meta-analytic models, such as cross-classified metaanalysis separating study from country heterogeneity or meta-analysis with autoregressive time structures, are required. To further account for possible changes in effect sizes over time, ideally, repeated participation of countries would be desired, and thus some studies considered only countries that participated continuously (e.g., Borgonovi & Pokropek, 2021;Lohmann et al, 2022). Future research could shed light on the consequences of the country self-selection into…”