PMID- 34379011
OWN - NLM
STAT- MEDLINE
DCOM- 20230501
LR  - 20230501
IS  - 1532-7906 (Electronic)
IS  - 0027-3171 (Linking)
VI  - 58
IP  - 1
DP  - 2023 Jan-Feb
TI  - A Latent Auto-Regressive Approach for Bayesian Structural Equation Modeling of 
      Spatially or Socially Dependent Data.
PG  - 90-114
LID - 10.1080/00273171.2021.1957663 [doi]
AB  - Spatial analytic approaches are classic models in econometric literature, but 
      relatively new in social sciences. Spatial analysis models are synonymous with 
      social network auto-regressive models which are also gaining popularity in the 
      behavioral sciences. These models have two major benefits. First, dependent data, 
      either socially or spatially, must be accounted for to acquire unbiased results. 
      Second, analysis of the dependence provides rich additional information such as 
      spillover effects. Structural Equation Models (SEM) are widely used in 
      psychological research for measuring and testing multi-faceted constructs. So 
      far, SEM that allow for spatial or social dependency are limited with regard to 
      their flexibility, for example, when estimating nonlinear effects. Here, we 
      provide a cohesive framework which can simultaneously estimate latent 
      interaction/polynomial effects and account for spatial effects with both 
      exogenous and endogenous latent variables, the Bayesian Spatial Auto-Regressive 
      Structural Equation Model (BARDSEM). First, we briefly outline classic 
      auto-regressive models. Next, we present the BARDSEM and introduce simulation 
      results to exemplify its performance. Finally, we provide an empirical example 
      using the spatially dependent extended US southern homicide data to show the rich 
      interpretations that are possible using the BARDSEM. Finally, we discuss 
      implications, limitations, and future research.
FAU - Roman, Zachary J
AU  - Roman ZJ
AD  - Psychology, University of Zurich.
FAU - Brandt, Holger
AU  - Brandt H
AD  - Psychology, University of Zurich.
LA  - eng
PT  - Journal Article
DEP - 20210811
PL  - United States
TA  - Multivariate Behav Res
JT  - Multivariate behavioral research
JID - 0046052
SB  - IM
MH  - Latent Class Analysis
MH  - Bayes Theorem
MH  - Computer Simulation
MH  - *Models, Theoretical
MH  - *Algorithms
OTO - NOTNLM
OT  - Latent variable
OT  - network auto-regressive
OT  - nonlinear
OT  - spatial
OT  - structural equation models
EDAT- 2021/08/12 06:00
MHDA- 2023/05/01 06:42
CRDT- 2021/08/11 12:13
PHST- 2023/05/01 06:42 [medline]
PHST- 2021/08/12 06:00 [pubmed]
PHST- 2021/08/11 12:13 [entrez]
AID - 10.1080/00273171.2021.1957663 [doi]
PST - ppublish
SO  - Multivariate Behav Res. 2023 Jan-Feb;58(1):90-114. doi: 
      10.1080/00273171.2021.1957663. Epub 2021 Aug 11.