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Influence of Parsimony and Work-related Psychological Constructs in Predicting Turnover Intention when Using Machine Learning VS Regression

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Title: Influence of Parsimony and Work-related Psychological Constructs in Predicting Turnover Intention when Using Machine Learning VS Regression
Authors: Figueiras, Diego
Summary: This dissertation explores the ongoing debate between traditional statistical regression models and machine learning (ML) algorithms in predictive modeling, focusing on the impact of sample size and the number of variables. Study 1 investigates the relationship between sample size and predictive accuracy, proposing hypotheses regarding the advantages of ML over regression as sample size increases. Additionally, the study examines the influence of the number of variables on predictive accuracy, emphasizing the trade-off between ML and regression models. Using data from the Federal Employee Viewpoint Survey, the research aims to contribute insights into the conditions favoring each modeling approach. Study 2 shifts the focus to incremental validity, exploring whether work-related psychological constructs enhance ML models' predictive accuracy in turnover intention when compared to biodata alone. The proposed hypotheses suggest that incorporating psychological constructs will improve predictive accuracy, addressing the "garbage in garbage out" concern prevalent in ML applications. The methods involve diverse datasets, including responses from federal employees in an online survey through Amazon's MTurk, with machine learning algorithms such as Gradient Boosting Trees, Random Forest, Neural Networks, and Support Vector Machines being compared to linear and logistic regressions. The dissertation seeks to advance understanding in the field, offering practical insights for researchers and practitioners navigating the dynamic landscape of predictive modeling.
URL: https://digitalcommons.montclair.edu/etd/1468
Database: OpenDissertations
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  Availability: 0
Header DbId: ddu
DbLabel: OpenDissertations
An: ddu.oai.digitalcommons.montclair.edu.etd.2472
AccessLevel: 6
PubType: Dissertation/ Thesis
PubTypeId: dissertation
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Influence of Parsimony and Work-related Psychological Constructs in Predicting Turnover Intention when Using Machine Learning VS Regression
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Figueiras%2C+Diego%22">Figueiras, Diego</searchLink>
– Name: Abstract
  Label: Summary
  Group: Ab
  Data: This dissertation explores the ongoing debate between traditional statistical regression models and machine learning (ML) algorithms in predictive modeling, focusing on the impact of sample size and the number of variables. Study 1 investigates the relationship between sample size and predictive accuracy, proposing hypotheses regarding the advantages of ML over regression as sample size increases. Additionally, the study examines the influence of the number of variables on predictive accuracy, emphasizing the trade-off between ML and regression models. Using data from the Federal Employee Viewpoint Survey, the research aims to contribute insights into the conditions favoring each modeling approach. Study 2 shifts the focus to incremental validity, exploring whether work-related psychological constructs enhance ML models' predictive accuracy in turnover intention when compared to biodata alone. The proposed hypotheses suggest that incorporating psychological constructs will improve predictive accuracy, addressing the "garbage in garbage out" concern prevalent in ML applications. The methods involve diverse datasets, including responses from federal employees in an online survey through Amazon's MTurk, with machine learning algorithms such as Gradient Boosting Trees, Random Forest, Neural Networks, and Support Vector Machines being compared to linear and logistic regressions. The dissertation seeks to advance understanding in the field, offering practical insights for researchers and practitioners navigating the dynamic landscape of predictive modeling.
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  Data: <link linkTarget="URL" linkTerm="https://digitalcommons.montclair.edu/etd/1468" linkWindow="_blank">https://digitalcommons.montclair.edu/etd/1468</link>
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RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: tunover intention
        Type: general
      – SubjectFull: machine learning
        Type: general
      – SubjectFull: regression
        Type: general
      – SubjectFull: parsimony
        Type: general
      – SubjectFull: biodata
        Type: general
      – SubjectFull: Industrial and Organizational Psychology
        Type: general
      – SubjectFull: Mathematics
        Type: general
      – SubjectFull: Psychology, Industrial; Labor turnover--Mathematical models; Regression analysis--Mathematical models--Evaluation; Machine learning--Mathematical models--Evaluation
        Type: general
    Titles:
      – TitleFull: Influence of Parsimony and Work-related Psychological Constructs in Predicting Turnover Intention when Using Machine Learning VS Regression
        Type: main
  BibRelationships:
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      – PersonEntity:
          Name:
            NameFull: Figueiras, Diego
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      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Type: published
              Y: 2024
ResultId 1