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Measurement Model Misspecification in Dynamic Structural Equation Models: Power, Reliability, and Other
Hyungeun Oh1, Michael D Hunter1, Sy-Miin Chow1
1Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA 16802.
Measurement error in Dynamic Structural Equation Models (DSEMs) causes significant parameter bias, even with high reliability. Careful model specification is crucial for accurate analysis of intensive longitudinal data (ILD).
Area of Science:
- Psychometrics
- Quantitative Psychology
- Statistical Modeling
Background:
- Dynamic Structural Equation Models (DSEMs) are powerful for intensive longitudinal data (ILD).
- The impact of measurement structure misspecification in DSEMs is not well understood.
- Reliability and model complexity can influence DSEM results.
Purpose of the Study:
- To investigate the effects of measurement error and misspecification in DSEMs.
- To evaluate how reliability conditions and model complexity impact parameter estimation.
- To provide practical recommendations for DSEM design and analysis.
Main Methods:
- Conducted Monte Carlo simulations to assess DSEM performance under misspecification.
- Varied reliability conditions, number of participants, and time points.
- Compared single-indicator and multiple-indicator DSEM measurement structures.
Main Results:
- Omitting measurement errors caused severe dynamic parameter bias, irrespective of reliability.
- Increased sample size and time points reduced but did not eliminate bias.
- Single-indicator DSEMs with composite scores performed similarly to multiple-indicator DSEMs.
Conclusions:
- Measurement misspecification is a critical issue in DSEMs, leading to biased dynamic parameters.
- Design choices, including the number of indicators, significantly affect DSEM results.
- Recommendations and tools are provided for improving DSEM reliability and power analysis.
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