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Omissions by Design in a Survey: Is This a Good Choice when using Structural Equation Models?

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  • Vicente Paula C. R.

    (Lusófona University, COPELABS, Intrepid Lab, Lisbon, Portugal)

Abstract

Missing observations can arise due to the effort required to answer many questions in long surveys and the cost required to obtain some responses. Implementing a planned missing design in surveys helps reduce the number of questions each respondent needs to answer, thereby lowering survey fatigue and cutting down on implementation costs. The three-form and the two-method design are two different types of planned missing designs. An important consideration when designing a study with omissions by design is to know how it will affect statistical results. In this work, a simulation study is conducted to analyze how the usual fit measures, root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker-Lewis index (TLI) perform in the adjustment of a Structural Equation Model. The results revealed that the CFI, TLI, and SRMR indices exhibit sensitivity to omissions with small samples, low factor loadings and large models. Overall, this study contributes to our understanding of the importance of considering omissions by design in market research.

Suggested Citation

  • Vicente Paula C. R., 2024. "Omissions by Design in a Survey: Is This a Good Choice when using Structural Equation Models?," Naše gospodarstvo/Our economy, Sciendo, vol. 70(3), pages 83-91.
    • Handle: RePEc:vrs:ngooec:v:70:y:2024:i:3:p:83-91:n:1006
    DOI: 10.2478/ngoe-2024-0018
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    More about this item

    Keywords

    Omissions by design; Structural Equation Model; Survey;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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