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Using Lasso for Predictor Selection and to Assuage Overfitting: A Method Long Overlooked in Behavioral Sciences.

Daniel M McNeish¹

¹a Department of Human Development and Quantitative Methodology , University of Maryland , College Park.

Multivariate Behavioral Research

|November 27, 2015

Summary

Behavioral science research often uses ordinary least squares and stepwise selection, leading to overfitting. Regularization methods like Lasso offer more optimal prediction and coefficient estimation, improving model generalizability.

Keywords:

lasso overfitting regression regularization

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Area of Science:

Behavioral Science
Statistical Modeling
Machine Learning

Background:

Ordinary least squares (OLS) and stepwise selection are common in behavioral science.
These methods are prone to overfitting, inflating R-squared and coefficients while deflating standard errors and p-values.
This compromises model parsimony and generalizability.

Purpose of the Study:

Introduce regularization methods as superior alternatives to OLS and stepwise selection.
Highlight the underutilization of regularization techniques like Lasso in behavioral science.
Provide practical guidance for implementing regularization in statistical analyses.

Main Methods:

Discussed issues with traditional statistical models in behavioral research.
Introduced regularization methods, specifically Lasso and ridge regression.
Provided example analyses and R/SAS code for Lasso regression.

Main Results:

Demonstrated how regularization methods address overfitting issues inherent in OLS and stepwise selection.
Showcased the practical application and benefits of Lasso regression for predictor selection and coefficient estimation.
Highlighted the availability and implementation of these advanced methods in statistical software.

Conclusions:

Regularization methods offer more optimal solutions for prediction and inference in behavioral science compared to traditional approaches.
Encourage the adoption of Lasso and related techniques to enhance statistical rigor and the reliability of research findings.
Emphasize the need to integrate these powerful statistical tools into the standard practices of behavioral science research.