Comparing the Modeling Performance of Regression and Neural Networks as Data Quality Varies: A Business Value Approach

Bansal, Arun; Kauffman, Robert J.; Weitz, Rob R.

doi:10.1080/07421222.1993.11517988

Cited by 96 publications

(39 citation statements)

References 13 publications

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“…Some authors have shown that traditional statistical methods do not always yield accurate predictions and/or classifications (Bansal, Kauffman & Weitz, 1993;Everson, 1995;Duliba, 1991). Preliminary research using ANN for prediction, selection, and classification purposes suggests that this method may improve the validity and accuracy of the classifications, as well as increase the predictive validity of educational outcomes (Everson et al, 1994;Hardgrave et al, 1994;Perkins, Gupta, Tammana, 1995;Weiss & Kulikowski, 1991).…”

Section: Introductionmentioning

confidence: 99%

Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks

Musso

Kyndt

Cascallar

et al. 2013

FLR

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Section: Introductionmentioning

confidence: 99%

Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks

Musso

Kyndt

Cascallar

et al. 2013

FLR

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“…The effect of data errors on the outputs of computer-based models has been investigated by a number of researchers (e.g., Ballou and Pazer, 1985;Ballou et al, 1987;Bansal et al, 1993). This investigation builds on this prior research by examining the effect of data quality on linear regression models.…”

Section: Introductionmentioning

confidence: 99%

Data Quality in Linear Regression Models: Effect of Errors in Test Data and Errors in Training Data on Predictive Accuracy

Klein¹,

Rossin

1999

InformingSciJ

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Section: The Present Studymentioning

confidence: 99%

“…This could include varying the types of nonlinearity and the percentage error in a dataset (Bansal et al, 1993, found that linear regression were better overall in forecasting financial risk but neural networks were better with less accurate data).…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

“…True nonlinear models express nonlinearity in the parameters of the variables. Additional shortcomings of linear regression (Bansal et al, 1993; Lind & Sulek, 2001;Lowe et al, 2003) render it inappropriate for addressing nonlinear phenomena.The present study compared an inherently linear (multiple linear regression) and an inherently nonlinear method (artificial neural networks). It focused on two neural networks with one output, whose aim was to predict an outcome by learning the associations between that variable and a set of inputs.…”

mentioning

confidence: 99%

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The use of artificial neural networks and multiple linear regression in modelling work–health relationships: Translating theory into analytical practice

Karanika‐Murray¹,

Cox²

2010

European Journal of Work and Organizational Psychology

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Although psychological theory acknowledges the existence of complex systems and the importance of nonlinear effects, linear statistical models have been traditionally used to examine relationships between environmental stimuli and outcomes. The way that we analyze these relationships does not seem to reflect the way that we conceptualize them. The present study investigated the application of connectionism (artificial neural networks) to modeling the relationships between work characteristics and employee health by comparing it with a more conventional statistical linear approach (multiple linear regression) on a sample of 1003 individuals in employment. Comparisons of performance metrics indicated differences in model fit, with neural networks to some extent outperforming the linear regression models, such thati? for worn-out and job satisfaction were significantly higher in the neural networks.Most importantly, comparisons revealed that the predictors in the two approaches differed in their relative importance for predicting outcomes. The improvement is attributed to the ability of the neural networks to model complex nonlinear relationships. Being unconstrained by assumptions of linearity, they can provide a better approximation of such psychosocial phenomena. Nonlinear approaches are often better fitted for purpose, as they conform to the need for correspondence between theory, method and data. Modeling the impact of work and organizational characteristics on employee health is important in terms of decision-making for job design, re-design and organizational development. Effective management of work-related health relies on accurately prioritizing potential risks and explaining the highest variability in outcomes from a combination of work and organizational characteristics (Clarke & Cooper, 2004;Glendon, Clarke, & McKenna, 2006). In turn, this traditionally relies on correlational and linear approaches.Although such an approach has historically proven useful, it produces two sources of uncertainty for the assessment of risk to work-related health. First, it implies a linear relationship between work and organizational characteristics and health outcomes, which often conflicts with available empirical evidence (Karanika, 2006). Second, organizations themselves provide a demonstration of complexity (Cox et al, 2007;Schneider & Somers, 2006). It also renders examination of the impact of combinations of risks (as opposed to bivariate relationships) more pertinent to decision-making in relation to risk management for work-related health (Karanika-Murray, Antoniou, Michaelides, & Cox, in press). The present study tested a nonlinear connectionist model vis-a-vis a traditional linear regression model of the impact of work characteristics on work-related outcomes. The remainder of this section discussing two key sources of uncertainty in modeling work-health relationships which set the main arguments for artificial neural networks, before it briefly outlines their principles. Uncertainty 1: Nonlinear Work-H...

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Comparing the Modeling Performance of Regression and Neural Networks as Data Quality Varies: A Business Value Approach

Cited by 96 publications

References 13 publications

Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks

Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks

Data Quality in Linear Regression Models: Effect of Errors in Test Data and Errors in Training Data on Predictive Accuracy

The use of artificial neural networks and multiple linear regression in modelling work–health relationships: Translating theory into analytical practice

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