Evidence map›Paper›PMID 30627936›Full record

ArticlePsychometrika2019

Creating Misspecified Models in Moment Structure Analysis.

Keke Lai

Abstract read
PubMed Publisher
In one paragraph

Article in Psychometrika, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Keke LaiPsychological Sciences, University of California, Merced, CA , 95343, USA. KLai25@UCmerced.edu.ORCID 0000-0002-8501-0210

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To understand how SEM methods perform in practice where models always have misfit, simulation studies often involve incorrect models. To create a wrong model, traditionally one specifies a perfect model first and then removes some paths. This approach becomes difficult or even impossible to implement in moment structure analysis and fails to control the amounts of misfit separately and precisely for the mean and covariance parts. Most importantly, this approach assumes a perfect model exists and wrong models can eventually be made perfect, whereas in practice models are all implausible if taken literally and at best provide approximations of the real world. To improve the traditional approach, we propose a more realistic and flexible way to create model misfit for multiple group moment structure analysis. Given (a) the model [Formula: see text] and [Formula: see text], (b) population model parameters [Formula: see text], and (c) [Formula: see text] and [Formula: see text] specified by the researcher, our method creates [Formula: see text] and [Formula: see text] to simultaneously satisfy (a) [Formula: see text], (b) the mean structure's misfit equals [Formula: see text], and (c) the covariance structure's misfit equals [Formula: see text].

Indexed as

Monte Carlo MethodBiasComputer SimulationHumansModels, TheoreticalPsychometricsRegression AnalysisStatistics as Topicmodel misspecificationmoment structure analysisMonte Carlo experimentsmultiple group analysis

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.