At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study

By E. Hamaker, T. Asparouhov, Annette Brose, Florian Schmiedek, B. Muthén. Multivariate Behavioral Research, 2018

407 citations recorded by the source corpus.

Subfields: Applied Psychology, Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/7e685e42068bb8e047a3556109ed2a889adb655a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Psychology

1. T. Asparouhov
2. B. Muthén
3. E. Hamaker
4. Daniel J. Bauer
5. Kristopher J Preacher

In Experimental and Cognitive Psychology

1. B. Muthén
2. T. Asparouhov
3. E. Hamaker
4. Satoshi Usami
5. Alexandre J. S. Morin
6. Herbert W. Marsh
7. Mårten Schultzberg
8. Daniel J. Bauer

In Social Psychology

1. B. Muthén
2. T. Asparouhov
3. Daniel J. Bauer

Citations in the corpus

• Comparison of Models for the Analysis of Intensive Longitudinal Data (2019, methodology): “Equations (7-8) can also be viewed as the definitions for Ŷ1,it and η̂1,it.”
• Social Media Use and Adolescents’ Well-Being: Developing a Typology of Person-Specific Effect Patterns (2020, neutral): “Before interpreting the estimates, we checked whether the model converged, following the procedure of Hamaker et al. (2018).”
• Dynamic Structural Equation Models (2018, methodology): “The easiest way to model random covariance in this framework is to model the covariance through a random variance of a common factor (cf. Hamaker et al., 2017).”