{
  "creator": [
    "Kim, Jaejin",
    "Li, Johnson Ching-Hong"
  ],
  "date": [
    "2023-12-22"
  ],
  "description": [
    "Ordinary least squares (OLS) regression is widely employed for statistical prediction and theoretical explanation in psychology studies. However, OLS regression has a critical drawback: it becomes less accurate in the presence of outliers and non-random error distribution. Several robust regression methods have been proposed as alternatives. However, each robust regression has its own strengths and limitations. Consequently, researchers are often at a loss as to which robust regression method to use for their studies. This study uses a Monte Carlo experiment to compare different types of robust regression methods with OLS regression based on relative efficiency (RE), bias, root mean squared error (RMSE), Type 1 error, power, coverage probability of the 95% confidence intervals (CIs), and the width of the CIs. The results show that, with sufficient samples per predictor (n = 100), the robust regression methods are as efficient as OLS regression. When errors follow non-normal distributions, i.e., mixed-normal, symmetric and heavy-tailed (SH), asymmetric and relatively light-tailed (AL), asymmetric and heavy-tailed (AH), and heteroscedastic, the robust method (GM-estimation) seems to consistently outperform OLS regression."
  ],
  "format": [
    "application/pdf",
    "text/html",
    "text/xml"
  ],
  "identifier": [
    "https://meth.psychopen.eu/index.php/meth/article/view/8285",
    "10.5964/meth.8285"
  ],
  "language": [
    "eng"
  ],
  "publisher": [
    "PsychOpen GOLD / Leibniz Institut for Psychology (ZPID)"
  ],
  "relation": [
    "https://meth.psychopen.eu/index.php/meth/article/view/8285/8285.pdf",
    "https://meth.psychopen.eu/index.php/meth/article/view/8285/8285.html",
    "https://meth.psychopen.eu/index.php/meth/article/view/8285/8285.xml"
  ],
  "rights": [
    "Copyright (c) 2023 Jaejin Kim, Johnson Ching-Hong Li",
    "https://creativecommons.org/licenses/by/4.0"
  ],
  "source": [
    "Methodology; Vol. 19 No. 4 (2023); 323-347",
    "1614-2241",
    "1614-1881",
    "10.5964/meth.v19i4"
  ],
  "subject": [
    "robust regression",
    "OLS regression",
    "outliers",
    "Type I error",
    "power"
  ],
  "title": [
    "Which Robust Regression Technique Is Appropriate Under Violated Assumptions? A Simulation Study"
  ],
  "type": [
    "info:eu-repo/semantics/article",
    "info:eu-repo/semantics/publishedVersion",
    "Peer-reviewed article"
  ]
}