| Summary: | This dissertation examined the accuracy of a few selected statistical approaches in evaluating invariance in measurement and mediation with the presence of planned-missing
data in the context of intensive longitudinal method (ILM). The planned-missing data
design was implemented as a three-form design where a portion of measurement scale
items were selectively removed for individuals at each measurement occasion with
the purpose of reducing participation burden and fatigue stemming from the burst of
measurements in ILM ranging from 2 to 12 measurement occasions per day. Three
simulation studies were conducted with the aim of providing insights and recommendations to applied researchers in the design, measurement, and analysis of intensive
longitudinal data. Study 1 compared two methods for testing intensive longitudinal
measurement invariance in their performance in detecting invariant and non-invariant
measurement parameters. Study 2 and Study 3 evaluated the performance of the dynamic structural equation model (DSEM) framework in estimating the time-invariant
and time-varying effects of longitudinal mediation models. Sample sizes (N), length
of measurement occasions (T), percentage of planned-missing data (PMD), and effect
sizes were manipulated in the simulation studies. The dissertation concluded with recommendations for applied researchers. Limitation and areas for future research were
also discussed.
|
|---|