Handling and modelling intensive longitudinal data
9th May 2022 : 12:45 - 14:00
Research Group: Quantitative Methods Hub
Speaker: Professor Lars Malmberg (Department of Education, University of Oxford)
Location: Online & in-person - Seminar Room D, Department of Education, or via Zoom
Convener: Ariel Lindorff
All welcome to join in person. Register to join events online via Zoom
There is large interest in intensive longitudinal data analysis in the social, educational and health sciences. Datasets can include (1) self-reports or multiple-reporter data (e.g., observed on-task behaviour, self-reported situation-specific competence) collected using diaries, experience sampling, or ecological momentary assessments, (2) task-data (e.g., trace-data, executive functioning), (3) real-time ambulatory data (e.g., accelerometer, electrodermal activity, eye-tracking), or mixtures of these. In the talk I will focus on challenges researchers face when they (i) handle and aggregate data, (ii) consider the time-structure for analysis, and (iii) specify statistical models. Time-series-based Dynamic Structural Equation Models (DSEM) using the Bayesian estimator are emerging, allowing researchers to switch focus from modelling fixed and random effects, to modelling individual processes over time. In the talk, I will illustrate intensive longitudinal data handling and modelling with on-going research, in order to highlight its relevance for understanding intraindividual processes in educational research.