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Proposing Necessary but Not Sufficient Conditions Analysis as a Complement of Traditional Effect Size Measures with an Illustrative Example

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  • Ana M. Greco

    (Departament de Psicologia Social i Psicologia Quantitativa, Facultat de Psicologia, Universitat de Barcelona, 08035 Barcelona, Spain
    Estudis de Dret i Ciència Política, Universitat Oberta de Catalunya (UOC), Rambla del Poblenou, 156, 08018 Barcelona, Spain)

  • Georgina Guilera

    (Departament de Psicologia Social i Psicologia Quantitativa, Facultat de Psicologia, Universitat de Barcelona, 08035 Barcelona, Spain
    Grup d’Estudis d’Invariància de la Mesura i Anàlisi del Canvi en els Àmbits Social i de la Salut (GEIMAC), Institut de Neurociències (UBNeuro), Universitat de Barcelona, 08035 Barcelona, Spain)

  • Laura Maldonado-Murciano

    (Departament de Psicologia Social i Psicologia Quantitativa, Facultat de Psicologia, Universitat de Barcelona, 08035 Barcelona, Spain)

  • Juana Gómez-Benito

    (Departament de Psicologia Social i Psicologia Quantitativa, Facultat de Psicologia, Universitat de Barcelona, 08035 Barcelona, Spain
    Grup d’Estudis d’Invariància de la Mesura i Anàlisi del Canvi en els Àmbits Social i de la Salut (GEIMAC), Institut de Neurociències (UBNeuro), Universitat de Barcelona, 08035 Barcelona, Spain)

  • Maite Barrios

    (Departament de Psicologia Social i Psicologia Quantitativa, Facultat de Psicologia, Universitat de Barcelona, 08035 Barcelona, Spain
    Grup d’Estudis d’Invariància de la Mesura i Anàlisi del Canvi en els Àmbits Social i de la Salut (GEIMAC), Institut de Neurociències (UBNeuro), Universitat de Barcelona, 08035 Barcelona, Spain)

Abstract

Even though classic effect size measures (e.g., Pearson’s r, Cohen’s d) are widely applied in social sciences, the threshold used to interpret them is somewhat arbitrary. This study proposes necessary condition analysis (NCA) to complement traditional methods. We explain NCA in light of the current limitations of classical techniques, highlighting the advantages in terms of interpretation and translation into practical terms and recognizing its weaknesses. To do so, we provide an example by testing the link between three independent variables with a relevant outcome in a sample of 235 subjects. The traditional Pearson’s coefficient was obtained, and NCA was used to test if any of the predictors were necessary but not sufficient conditions. Our study also obtains outcome and condition inefficiency as well as NCA bottlenecks. Comparison and interpretation of the traditional and NCA results were made considering recommendations. We suggest that NCA can complement correlation analyses by adding valuable and applicable information, such as if a variable is needed to achieve a certain outcome level and to what degree.

Suggested Citation

  • Ana M. Greco & Georgina Guilera & Laura Maldonado-Murciano & Juana Gómez-Benito & Maite Barrios, 2022. "Proposing Necessary but Not Sufficient Conditions Analysis as a Complement of Traditional Effect Size Measures with an Illustrative Example," IJERPH, MDPI, vol. 19(15), pages 1-14, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9402-:d:877251
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    References listed on IDEAS

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    1. Barbara Vis & Jan Dul, 2018. "Analyzing Relationships of Necessity Not Just in Kind But Also in Degree," Sociological Methods & Research, , vol. 47(4), pages 872-899, November.
    2. Douglas Bonett & Thomas Wright, 2000. "Sample size requirements for estimating pearson, kendall and spearman correlations," Psychometrika, Springer;The Psychometric Society, vol. 65(1), pages 23-28, March.
    3. Karwowski, Maciej & Kaufman, James C. & Lebuda, Izabela & Szumski, Grzegorz & Firkowska-Mankiewicz, Anna, 2017. "Intelligence in childhood and creative achievements in middle-age: The necessary condition approach," Intelligence, Elsevier, vol. 64(C), pages 36-44.
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