The Dynamic Measurement-Burst Model: A method for assessing psychological process features at multiple timescales.
Pia K. Andresen 
Welcome
This website is a supplement to “The Dynamic Measurement-Burst Model: A method for assessing psychological process features at multiple timescales.” by Andresen, Schuurman and Hamaker (under review). Please cite us when using this method.
All models presented on this website were tested using the empirical example presented in Andresen et al (in prep.). Mplus output files can be found here.
Please note that we do not possess ownership of the data. Researchers wishing to obtain data for further investigation or as part of this tutorial are obliged to request the data from the Emote Database.
This website contains Mplus syntax for v.8.11, to specify dynamic measurement burst models (DMBM), an approach integrating the multilevel first order autoregressive model (ML-AR(1)M) and the random intercept cross-lagged panel model (RI-CLPM) to assess stability and change in psychological processes at the micro- meso and macro-level.
On this website, we provide a simple tutorial for running these models in Mplus. Concretely, we will explain:
- Basic data-preprocessing steps required to run the DMBM
- The basic Mplus Syntax for Variance Decomposition as used in used in Andresen et al (in prep.)
- Mplus syntax of a network model for cross-burst correlations of micro-level process features
- Mplus syntax of the DMBM for assessing micro- and meso-level dynamics and macro-level stability
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