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Weighting for item non‐response in attitude scales by using latent variable models with covariates

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  • Irini Moustaki
  • Martin Knott

Abstract

We discuss the use of latent variable models with observed covariates for computing response propensities for sample respondents. A response propensity score is often used to weight item and unit responders to account for item and unit non‐response and to obtain adjusted means and proportions. In the context of attitude scaling, we discuss computing response propensity scores by using latent variable models for binary or nominal polytomous manifest items with covariates. Our models allow the response propensity scores to be found for several different items without refitting. They allow any pattern of missing responses for the items. If one prefers, it is possible to estimate population proportions directly from the latent variable models, so avoiding the use of propensity scores. Artificial data sets and a real data set extracted from the 1996 British Social Attitudes Survey are used to compare the various methods proposed.

Suggested Citation

  • Irini Moustaki & Martin Knott, 2000. "Weighting for item non‐response in attitude scales by using latent variable models with covariates," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 445-459.
    • Handle: RePEc:bla:jorssa:v:163:y:2000:i:3:p:445-459
    DOI: 10.1111/1467-985X.00177
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    Cited by:

    1. Metaxas, Theodore & Economou, Athina, 2012. "Assessing the determinants of Firms’ Competitiveness in Greece: A Structural Equation Modeling Analysis," MPRA Paper 42794, University Library of Munich, Germany.
    2. Jiwei Zhang & Zhaoyuan Zhang & Jian Tao, 2021. "A Bayesian algorithm based on auxiliary variables for estimating GRM with non-ignorable missing data," Computational Statistics, Springer, vol. 36(4), pages 2643-2669, December.
    3. Jouni Kuha & Myrsini Katsikatsou & Irini Moustaki, 2018. "Latent variable modelling with non‐ignorable item non‐response: multigroup response propensity models for cross‐national analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1169-1192, October.
    4. Steffi Pohl & Esther Ulitzsch & Matthias Davier, 2019. "Using Response Times to Model Not-Reached Items due to Time Limits," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 892-920, September.
    5. Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2021. "On the Treatment of Missing Data in Background Questionnaires in Educational Large-Scale Assessments: An Evaluation of Different Procedures," Journal of Educational and Behavioral Statistics, , vol. 46(4), pages 430-465, August.
    6. Norman Rose & Matthias Davier & Benjamin Nagengast, 2017. "Modeling Omitted and Not-Reached Items in IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 795-819, September.
    7. Sandip Sinharay, 2022. "Reporting Proficiency Levels for Examinees With Incomplete Data," Journal of Educational and Behavioral Statistics, , vol. 47(3), pages 263-296, June.
    8. Jinxin Guo & Xin Xu & Zhiliang Ying & Susu Zhang, 2022. "Modeling Not-Reached Items in Timed Tests: A Response Time Censoring Approach," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 835-867, September.
    9. Theodore METAXAS & Athina ECONOMOU, 2016. "Assesing The Determinantts Of Firms’ Competitiveness In Greece: A Structural Equation Modeling Analysys," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 17, pages 91-113, June.

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