A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 143

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 143
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 209
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 994
Function: getPubMedXML

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3134
Function: GetPubMedArticleOutput_2016

File: /var/www/html/application/controllers/Detail.php
Line: 574
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 488
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

Heywood Cases in Unidimensional Factor Models and Item Response Models for Binary Data. | LitMetric

AI Article Synopsis

  • Heywood cases in factor analysis occur when variables have communalities over 1.00, leading to issues like negative residual variances.
  • The study explores how the problem manifests in different methods of analysis, particularly through delta and theta parameterizations in factor models and item response theory (IRT).
  • Through simulations and real data analysis, the researchers validate their theoretical findings, demonstrating consistent problems across various estimation techniques (WLS, WLSMV, ULS).

Article Abstract

Heywood cases are known from linear factor analysis literature as variables with communalities larger than 1.00, and in present day factor models, the problem also shows in negative residual variances. For binary data, factor models for ordinal data can be applied with either delta parameterization or theta parametrization. The former is more common than the latter and can yield Heywood cases when limited information estimation is used. The same problem shows up as non convergence cases in theta parameterized factor models and as extremely large discriminations in item response theory (IRT) models. In this study, we explain why the same problem appears in different forms depending on the method of analysis. We first discuss this issue using equations and then illustrate our conclusions using a small simulation study, where all three methods, delta and theta parameterized ordinal factor models (with estimation based on polychoric correlations and thresholds) and an IRT model (with full information estimation), are used to analyze the same datasets. The results generalize across WLS, WLSMV, and ULS estimators for the factor models for ordinal data. Finally, we analyze real data with the same three approaches. The results of the simulation study and the analysis of real data confirm the theoretical conclusions.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9979198PMC
http://dx.doi.org/10.1177/01466216231151701DOI Listing

Publication Analysis

Top Keywords

factor models
24
heywood cases
12
models
8
item response
8
binary data
8
models ordinal
8
ordinal data
8
theta parameterized
8
simulation study
8
real data
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!