MODEL-FREE MEASUREMENT OF CASE INFLUENCE IN STRUCTURAL EQUATION MODELING

This study introduces a new model-free case influence measure (DOCR) to the SEM field and evaluates its performance compared to that of Mahalanobis Distance (MD) and Generalized Cook’s Distance (gCD) when the sample size, proportion of target cases to non-target cases, and type of model used to gene...

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Main Author: Jaffari, Fathima
Format: Others
Published: OpenSIUC 2019
Online Access:https://opensiuc.lib.siu.edu/dissertations/1689
https://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=2693&context=dissertations
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spelling ndltd-siu.edu-oai-opensiuc.lib.siu.edu-dissertations-26932019-08-16T03:22:56Z MODEL-FREE MEASUREMENT OF CASE INFLUENCE IN STRUCTURAL EQUATION MODELING Jaffari, Fathima This study introduces a new model-free case influence measure (DOCR) to the SEM field and evaluates its performance compared to that of Mahalanobis Distance (MD) and Generalized Cook’s Distance (gCD) when the sample size, proportion of target cases to non-target cases, and type of model used to generate the data are manipulated. The findings suggest the DOCR measure generally performed better than MD and gCD in identifying the target cases across all simulated conditions. However, the performance of the DOCR measure under small sample size was not satisfactory, and it raised a red flag about the sensitivity of this measure to small sample sizes. Therefore, researchers and practitioners should only use the DOCR measure with sufficiently large sample sizes, but not larger than 600. 2019-05-01T07:00:00Z text application/pdf https://opensiuc.lib.siu.edu/dissertations/1689 https://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=2693&context=dissertations Dissertations OpenSIUC
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format Others
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description This study introduces a new model-free case influence measure (DOCR) to the SEM field and evaluates its performance compared to that of Mahalanobis Distance (MD) and Generalized Cook’s Distance (gCD) when the sample size, proportion of target cases to non-target cases, and type of model used to generate the data are manipulated. The findings suggest the DOCR measure generally performed better than MD and gCD in identifying the target cases across all simulated conditions. However, the performance of the DOCR measure under small sample size was not satisfactory, and it raised a red flag about the sensitivity of this measure to small sample sizes. Therefore, researchers and practitioners should only use the DOCR measure with sufficiently large sample sizes, but not larger than 600.
author Jaffari, Fathima
spellingShingle Jaffari, Fathima
MODEL-FREE MEASUREMENT OF CASE INFLUENCE IN STRUCTURAL EQUATION MODELING
author_facet Jaffari, Fathima
author_sort Jaffari, Fathima
title MODEL-FREE MEASUREMENT OF CASE INFLUENCE IN STRUCTURAL EQUATION MODELING
title_short MODEL-FREE MEASUREMENT OF CASE INFLUENCE IN STRUCTURAL EQUATION MODELING
title_full MODEL-FREE MEASUREMENT OF CASE INFLUENCE IN STRUCTURAL EQUATION MODELING
title_fullStr MODEL-FREE MEASUREMENT OF CASE INFLUENCE IN STRUCTURAL EQUATION MODELING
title_full_unstemmed MODEL-FREE MEASUREMENT OF CASE INFLUENCE IN STRUCTURAL EQUATION MODELING
title_sort model-free measurement of case influence in structural equation modeling
publisher OpenSIUC
publishDate 2019
url https://opensiuc.lib.siu.edu/dissertations/1689
https://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=2693&context=dissertations
work_keys_str_mv AT jaffarifathima modelfreemeasurementofcaseinfluenceinstructuralequationmodeling
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