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Heywood cases: possible causes and solutions

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  • Rayees Farooq

Abstract

The purpose of the study is to identify the causes and recommend possible solutions to the Heywood cases. The study reviews the literature from 1960-2021 using the keyword search, 'Heywood cases,' 'Improper solutions,' and 'Negative variance'. The studies were explored from selected databases viz. Google Scholar, Scopus and Web of Science. The study has found that fixing the negative variance to zero is the most widely used solution to the Heywood cases. The study also found that multivariate normality, small sample size with a large number of indicators, factor loadings of less than 0.5, and model misspecification are the possible causes of Heywood cases. The study suggests novel solutions to overcome the possibility of the Heywood cases, including fixing the negative variance to zero, maintaining the large sample size, and increasing the number of indicators in the construct. The study can be beneficial to the researchers who validate the model using CB-SEM. The study offers a basic understanding of the possible causes and novel solutions to the Heywood cases to help the researchers better develop the constructs/scales. The present research guides the researchers through the various effects of Heywood cases on the study's findings.

Suggested Citation

  • Rayees Farooq, 2022. "Heywood cases: possible causes and solutions," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 14(1), pages 79-88.
    • Handle: RePEc:ids:injdan:v:14:y:2022:i:1:p:79-88
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    Cited by:

    1. Ünsal-Altuncan, Izel & Vanhoucke, Mario, 2024. "A hybrid forecasting model to predict the duration and cost performance of projects with Bayesian Networks," European Journal of Operational Research, Elsevier, vol. 315(2), pages 511-527.
    2. Monia Ranalli & Roberto Rocci, 2024. "Composite likelihood methods for parsimonious model-based clustering of mixed-type data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(2), pages 381-407, June.

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