KUKU

KU ScholarWorks

  • myKU
  • Email
  • Enroll & Pay
  • KU Directory
    • Login
    View Item 
    •   KU ScholarWorks
    • Dissertations and Theses
    • Dissertations
    • View Item
    •   KU ScholarWorks
    • Dissertations and Theses
    • Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Auto-Regressive Latent Variable Modeling: A General Framework for Bayesian Spatial Structural Equation Models

    Thumbnail
    View/Open
    Roman_ku_0099D_16927_DATA_1.pdf (2.113Mb)
    Issue Date
    2019-12-31
    Author
    Roman, Zachary Joseph
    Publisher
    University of Kansas
    Format
    218 pages
    Type
    Dissertation
    Degree Level
    Ph.D.
    Discipline
    Psychology
    Rights
    Copyright held by the author.
    Metadata
    Show full item record
    Abstract
    Spatial analytic approaches are classical models in econometric literature (LeSage & Pace, 2009). Recently, the behavioral sciences have seen an increase in their application, but spatial effects are generally still ignored (Stakhovych et al., 2012; Musafer et al., 2017; Oud & Folmer, 2008; Hogan& Tchernis, 2004). Spatial analysis models are synonymous with social network auto-regressive models which are also gaining popularity in the behavioral sciences. Structural Equation Models (SEM) are widely used in psychological research for measuring and testing multi-faceted constructs (Bollen, 1989). While SEM are widely used limitations remain, in particular latent interaction/polynomial effects are troublesome (Brandt et al., 2014). Recent work has produced methods to account for these issues (Brandt et al., 2018). Further, recent work has established methods to account for spatial and network effects in SEM (Oud & Folmer, 2008). However, a cohesive framework which can simultaneously estimate latent interaction/polynomial effects and account for spatial effects, has not been established. To accommodate this I provide a novel model, the Bayesian Spatial Auto-Regressive Structural Equation Model (SASEM). In the first chapter of this dissertation I review existing literature relevant to spatial analysis and latent interaction effects in SEM. In the next chapter I present a new modeling framework which can accommodate these effects. In the next chapter I investigate model performance with a series of Monte-Carlo studies. Results are promising particularly for one sub-model of the SASEM. I provide an empirical example using the spatially dependent extended US southern homicide data (Messner et al., 1999; Land et al., 1990) to show the rich interpretations made possible by the SASEM. Finally, I discuss results, implications, limitations, and recommendations.
    URI
    http://hdl.handle.net/1808/31511
    Collections
    • Dissertations [4474]
    • Psychology Dissertations and Theses [459]

    Items in KU ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.


    We want to hear from you! Please share your stories about how Open Access to this item benefits YOU.


    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

    Browse

    All of KU ScholarWorksCommunities & CollectionsThis Collection

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

    The University of Kansas
      Contact KU ScholarWorks
    Lawrence, KS | Maps
     
    • Academics
    • Admission
    • Alumni
    • Athletics
    • Campuses
    • Giving
    • Jobs

    The University of Kansas prohibits discrimination on the basis of race, color, ethnicity, religion, sex, national origin, age, ancestry, disability, status as a veteran, sexual orientation, marital status, parental status, gender identity, gender expression and genetic information in the University’s programs and activities. The following person has been designated to handle inquiries regarding the non-discrimination policies: Director of the Office of Institutional Opportunity and Access, IOA@ku.edu, 1246 W. Campus Road, Room 153A, Lawrence, KS, 66045, (785)864-6414, 711 TTY.

     Contact KU
    Lawrence, KS | Maps