PMID- 35092575
OWN - NLM
STAT- MEDLINE
DCOM- 20220908
LR  - 20221004
IS  - 1860-0980 (Electronic)
IS  - 0033-3123 (Print)
IS  - 0033-3123 (Linking)
VI  - 87
IP  - 3
DP  - 2022 Sep
TI  - Frequentist Model Averaging in Structure Equation Model With Ordinal Data.
PG  - 1130-1145
LID - 10.1007/s11336-021-09837-3 [doi]
AB  - In practice, it is common that a best fitting structural equation model (SEM) is 
      selected from a set of candidate SEMs and inference is conducted conditional on 
      the selected model. Such post-selection inference ignores the model selection 
      uncertainty and yields too optimistic inference. Using the largest candidate 
      model avoids model selection uncertainty but introduces a large variation. Jin 
      and Ankargren (Psychometrika 84:84-104, 2019) proposed to use frequentist model 
      averaging in SEM with continuous data as a compromise between model selection and 
      the full model. They assumed that the true values of the parameters depend on 
      [Formula: see text] with n being the sample size, which is known as a local 
      asymptotic framework. This paper shows that their results are not directly 
      applicable to SEM with ordinal data. To address this issue, we prove consistency 
      and asymptotic normality of the polychoric correlation estimators under the local 
      asymptotic framework. Then, we propose a new frequentist model averaging 
      estimator and a valid confidence interval that are suitable for ordinal data. 
      Goodness-of-fit test statistics for the model averaging estimator are also 
      derived.
CI  - (c) 2022. The Author(s).
FAU - Jin, Shaobo
AU  - Jin S
AUID- ORCID: 0000-0001-6538-3477
AD  - Department of Statistics, Uppsala University, Uppsala, Sweden. 
      shaobo.jin@statistik.uu.se.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20220129
PL  - United States
TA  - Psychometrika
JT  - Psychometrika
JID - 0376503
SB  - IM
MH  - *Models, Theoretical
MH  - Psychometrics/methods
MH  - Sample Size
PMC - PMC9433363
OTO - NOTNLM
OT  - confidence interval
OT  - goodness-of-fit test
OT  - local asymptotic framework
OT  - mean squared error
OT  - model selection uncertainty
OT  - pseudo maximum likelihood
EDAT- 2022/01/30 06:00
MHDA- 2022/09/09 06:00
PMCR- 2022/01/29
CRDT- 2022/01/29 12:06
PHST- 2019/05/23 00:00 [received]
PHST- 2021/09/08 00:00 [revised]
PHST- 2022/01/30 06:00 [pubmed]
PHST- 2022/09/09 06:00 [medline]
PHST- 2022/01/29 12:06 [entrez]
PHST- 2022/01/29 00:00 [pmc-release]
AID - 10.1007/s11336-021-09837-3 [pii]
AID - 9837 [pii]
AID - 10.1007/s11336-021-09837-3 [doi]
PST - ppublish
SO  - Psychometrika. 2022 Sep;87(3):1130-1145. doi: 10.1007/s11336-021-09837-3. Epub 
      2022 Jan 29.