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Longitudinal joint modeling for assessing parallel interactive development of latent ability and processing speed
Peida Zhan1,2,3, Qipeng Chen4, Shiyu Wang5
1School of Psychology, Zhejiang Normal University, Jinhua, China. pdzhan@gmail.com.
Behavior Research Methods
|April 14, 2023
Summary
This study introduces three new models to track how latent ability and processing speed develop together over time. Analyzing response time (RT) data alongside accuracy (RA) reveals parallel development difficult to see with accuracy alone.
Area of Science:
- Psychometrics
- Cognitive Psychology
- Developmental Psychology
Background:
- Understanding the intertwined development of cognitive abilities like latent ability and processing speed is crucial.
- Existing methods often analyze accuracy (RA) or response time (RT) data separately, potentially missing complex developmental interactions.
Purpose of the Study:
- To propose and evaluate three longitudinal joint modeling approaches for simultaneously measuring the development of latent ability and processing speed.
- To investigate the parallel interactive development of these two constructs using structural equation modeling.
- To assess the utility of integrating response time (RT) data with response accuracy (RA) data.
Main Methods:
- Development of three longitudinal joint modeling approaches: unstructured-covariance-matrix-based, latent growth curve-based, and autoregressive cross-lagged.
- Application of these models to analyze longitudinal response accuracy (RA) and response time (RT) data from two empirical studies.
- Utilizing a Bayesian Markov chain Monte Carlo estimation algorithm for model parameter recovery in a simulation study.
Main Results:
- All three proposed models demonstrated practical applicability and yielded consistent conclusions regarding the developmental trajectories of ability and speed.
- Integrating response time (RT) data provided insights into parallel interactive development phenomena that were less apparent with response accuracy (RA) data alone.
- The simulation study confirmed the accuracy of the Bayesian Markov chain Monte Carlo estimation algorithm for parameter recovery across all models.
Conclusions:
- Longitudinal joint modeling offers a robust framework for understanding the parallel development of latent ability and processing speed.
- Incorporating response time (RT) data is essential for a comprehensive understanding of cognitive development dynamics.
- The proposed models and estimation algorithm provide valuable tools for researchers and practitioners in cognitive and developmental psychology.
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