Influence of imputation and EM methods on factor analysis when item nonresponse in questionnaire data is nonignorable
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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Missing item scores for six patients were imputed using two-way imputation.
“The only missing item score for the remaining six patients was imputed using two-way imputation (56, 57).”
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Methods for the Analysis of Explanatory Linear Regression Models with Missing Data Not at Random
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de Vroege, L., Emons, W. H. M., Sijtsma, K., & van der Feltz-Cornelis, C. M. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00292 · Cited by 17 · see citing works
“The only missing item score for the remaining six patients was imputed using two-way imputation (56, 57).”
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A test of expectancy-value theory in predicting alcohol consumption*
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2 Advances in Analysis of Mean and Covariance Structure when Data are Incomplete
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Is there fire? Executive compensation and employee attitudes
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Handling Missing Data in Principal Component Analysis Using Multiple Imputation
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Multi-method analysis of the internal structure of the Type D Scale-14 (DS14)
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Data Quality in Evaluation of an Alcohol-Related Harm Prevention Program
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Investigation of Multiple Imputation in Low-Quality Questionnaire Data
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Investigating the PHQ-9 With Mokken Scale Analysis and Cognitive Interviews
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- 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
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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