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Cited by 177Statistical Methods and Bayesian InferencePsychometric Methodologies and TestingSurvey Methodology and Nonresponse

The DOI matches its Crossref record; 38 of the 39 references it cites were found in a registry or an index.

Influence of Imputation and EM Methods on Factor Analysis when Item Nonresponse in Questionnaire Data is Nonignorable

Bernaards, Coen A., Sijtsma, Klaas·2000·Multivariate Behavioral Research
DOI 10.1207/s15327906mbr3503_03
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Abstract

This study deals with the influence of each of twelve imputation methods and two methods using the EM algorithm on the results of maximum likelihood factor analysis as compared with results obtained from the complete data factor analysis (no missing scores). Complete questionnaire rating scale data were simulated and, next, missing item scores were created under both ignorable and nonignorable nonresponse mechanisms. Next, imputation methods were used to fill the gaps and factor analysis was applied to both the original complete data and to the data sets including imputed scores. Each imputation method was implemented once with residual error and once without residual error. Also, one EM method estimated the factor loadings directly and the other estimated the complete data covariance matrix, which subsequently was factor analyzed. A design was analyzed with design factors Latent Trait Structure (technically called Mixing Configuration), Correlation Between Latent Traits, Nonresponse Mechanism, Percentage of Missingness, Sample Size, and Imputation Method. We found that, in general, methods that impute a score based on a respondent's mean score obtained from his/her observed item scores best recovered the factor loadings structure from the complete data. Moreover, for unidimensional data person mean methods with a residual error gave better results than the other imputation methods, either with or without a residual error component. For the EM methods a smaller design was analyzed. The conclusion was that both EM methods better recovered the complete data factor loadings than the imputation methods.

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Research works printed but not linked: 3

  1. Acock A. C., 1997, Family Science Review, 10, 76
  2. Likert R. A., 1932, Archives of Psychology, 140, 149
  3. Liu C., 1998, Statistica Sinica, 8, 729

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Metadata

Authors
Bernaards, C. A., & Sijtsma, K.
ORCID
Sijtsma, Klaas
Venue
Multivariate Behavioral Research
Publisher
Informa UK Limited
Published
2000-07
Volume
35
Issue
3
Pages
321-364
ISSN
  • 0027-3171 (ISSN-L)
  • 1532-7906
DOI
10.1207/s15327906mbr3503_03
OpenAlex
W2111095327
Cited by
177
References
39
Citations by year
2026: 4 · 2025: 6 · 2024: 5 · 2023: 12 · 2022: 13 · 2021: 14 · 2020: 4 · 2019: 7 · 2018: 8 · 2017: 4 · 2016: 9 · 2015: 6 · 2014: 6 · 2013: 5 · 2012: 6
FWCI
6.15
Type
journal article
Language
en
Open access
closed
Locations
  • Multivariate Behavioral Research (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
  • Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
Keywords
missing data imputation, nonignorable nonresponse, maximum likelihood factor analysis, expectation-maximization algorithm, questionnaire data, item nonresponse, factor loadings, residual error, simulation study

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