Illusions in Regression Analysis

By Armstrong, J. Scott. 2012

387 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/33d7843fbc2e537f5bf86429e2174f79ab5628ba

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Examining the role of CSR skepticism using fuzzy-set qualitative comparative analysis (2014, background): “Armstrong (2012) posits that researchers who use regression analysis falsely assume that by entering variables into the equation, they somehow control for these variables.”
Simple versus complex forecasting: The evidence (2015, neutral): “If the data are non-experimental, no matter how “big” they are, there is little reason to expect the process to identify causal relationships (Armstrong 2012; Armstrong, Green, and Graefe, this issue).”
Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory (2013, background): “(Armstrong, 2012, p. 690) Armstrong’s (2012) observations are valuable for referencing in particular when looking at an MRA table with six-to-twenty independent terms in the attempt to control for influences beyond the focal independent variables.”
Effects of Corporate Social Responsibility and Irresponsibility Policies (2013, neutral): “The same applies to mandated disclaimers (Green and Armstrong 2012).”
Golden Rule of Forecasting: Be conservative (2015, neutral): “Most sports teams have now learned that they can either adopt evidence-based forecasting procedures for selecting players, or lose more games (Armstrong, 2012b).”
Tackling the Complexity of the Pro-environmental Behavior Intentions of Visitors to Turtle Sites (2019, result): “Assessing the net effect while the causal interactions are complex will lead to a false sense of confidence that offers misleading results regarding the complex process of decision-making (Armstrong 2012).”
Embrace•perform•model: Complexity theory, contrarian case analysis, and multiple realities (2014, background): “Typically, fit improves as complexity increases, while ex ante forecast accuracy decreases as complexity increases, a conclusion that Zellner (2001) traces back to Sir Harold Jeffreys in the 1930s (Armstrong, 2012).”
Sustainability Matter and Financial Performance of Companies (2017, background): “Armstrong [83] advocates against the use of MRA in complex situations.”
Strategic and tactical price decisions in hotel revenue management (2016, methodology): “Besides the R-squared, the Median Absolute Percentage Error (MdAPE) complements the analysis comparing the predictive power of the models (Armstrong, 2012).”
Economic Recession, Job Vulnerability, and Tourism Decision Making: A Qualitative Comparative Analysis (2017, neutral): “In fact, the standard assumption in regression analysis is that the addition of new variables increases goodness of fit (Armstrong 2012); nonetheless, the usual covariance predictors in nonexperimental studies do not provide any related supportive evidence (Skarmeas, Leonidou, and Saridakis 2014).”