An examination of parameter recovery in latent transition models with distal outcomes.

dc.contributor.advisorMorgan, Grant B.
dc.creatorMoore, Courtney A., 1994-
dc.date.accessioned2017-09-28T13:32:56Z
dc.date.available2017-09-28T13:32:56Z
dc.date.created2017-08
dc.date.issued2017-07-19
dc.date.submittedAugust 2017
dc.date.updated2017-09-28T13:32:57Z
dc.description.abstractLatent transition analysis (LTA) is an increasingly popular research method used to categorize subsets of individuals within a population. The current study is purposed to investigate parameter recovery of a distal outcome effect in an LTA model. All models have a two-class solution with two time points. Design factors of interest include sample size, class prevalences at Time 1, the transition parameter, and the distal outcome effect size. ANOVA was used to seek out any practically significant effects (η2P ≥ 0.14) of these design factors on the raw bias (RB) of the distal outcome estimate. The results revealed no practically significant effects, meaning that the LTA model accurately estimated the distal outcome effect size under all specified conditions. Future research could expand upon this study by including different numbers of classes, indicators, time points, distal outcomes, and other auxiliary variables in addition to the distal outcome(s).
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/2104/10119
dc.language.isoen
dc.rights.accessrightsWorldwide access.
dc.rights.accessrightsAccess changed 12/11/19.
dc.subjectLatent transition analysis. Distal outcome. Simulation. Monte Carlo. Model recovery.
dc.titleAn examination of parameter recovery in latent transition models with distal outcomes.
dc.typeThesis
dc.type.materialtext
local.embargo.lift2019-08-01
local.embargo.terms2019-08-01
thesis.degree.departmentBaylor University. Dept. of Educational Psychology.
thesis.degree.grantorBaylor University
thesis.degree.levelMasters
thesis.degree.nameM.A.

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