[-] Show simple item record

dc.contributor.advisorWood, Phillip K. (Phillip Karl)eng
dc.contributor.authorYou, Dongjuneng
dc.date.issued2013eng
dc.date.submitted2013 Falleng
dc.description"December 2013."eng
dc.description"A Thesis presented to the Faculty of the Graduate School at the University of Missouri In Partial Fulfillment of the Requirements for the Degree Master of Arts."eng
dc.descriptionThesis supervisor: Dr. Phillip K. Wood.eng
dc.description.abstract[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Dynamic factor model (DFM), a factor analytic approach developed for the analysis of intra-individual time series data, can be estimated using common structural equation modeling software. The thesis proposes that an independent Random Intercept is an intermediary factor structure between the traditional n and n+1 factor structures researchers could consider in the view of psychological measurement. In addition to allowing researchers to specify complex factor structures for change over time, which do not correspond to traditional notions of simple structure, the random intercept measurement model may constitute an attractive alternative to traditional factor models when data are well summarized by more parsimonious models. Simulations on random intercept DFM were conducted, and the model was applied to a real-world data..eng
dc.description.bibrefIncludes bibliographical references (pages 37-42).eng
dc.format.extent1 online resource (vii, 42 pages) : illustrationseng
dc.identifier.oclc900167292eng
dc.identifier.urihttps://hdl.handle.net/10355/44719
dc.identifier.urihttps://doi.org/10.32469/10355/44719eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsAccess is limited to the University of Missouri - Columbia.eng
dc.sourceSubmitted by the University of Missouri--Columbia Graduate Schooleng
dc.titlePsychometric dynamic factor models for the analysis of longitudinally intensive dataeng
dc.typeThesiseng
thesis.degree.disciplinePsychology (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelMasterseng
thesis.degree.nameM.A.eng


Files in this item

[PDF]
[PDF]
[PDF]

This item appears in the following Collection(s)

[-] Show simple item record