Regression Analysis: A Constructive Critique

By Richard A. Berk. 2003

465 citations recorded by the source corpus.

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Citations in the corpus

Working with Misspecified Regression Models (2018, methodology): “Causal interpretations for race have the same problem (Berk, 2003: Chapter 5).”
The Logic of Causal Inference (2010, methodology): “…has gone through several cycles of reformation and consolidation, is still viewed with suspicion and confusion by many researchers (Sørensen, 1998; Berk, 2004; Sobel, 2008; Rubin, 2010) To witness, a recent issue of Psychological Methods, whose goal was to introduce the works Donald Campbell and…”
Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: new procedures and recommendations. (2006, background): “Further recommendations for improving causal inferences can be found in Berk (1988, 2004), Cole and Maxwell (2003), Gollob and Reichardt (1987, 1991), and Hoyle and Robinson (2004).”
Statistical Models for Causation (2006, background): “For discussion, see Berk (2004), Brady and Collier (2004), Freedman (2005ab).”
Steps in Intervention Research: Designing and Developing Social Programs (2010, background): “Recent studies suggest that routine covariance control may produce erroneous treatment estimates when the assignment mechanism (usually a dummy variable in a regression-like equation) is correlated with the error term (Berk, 2004).”
New Claims about Executions and General Deterrence: Deja Vu All Over Again? (2005, background): “Consequently, it is not clear that statistical inference is appropriate (Berk, 2003).”
Identification and Inference for Econometric Models: On Specifying Graphical Models for Causation and the Identification Problem (2005, unknown): “Berk (2003) is skeptical about the possibility of inferring causation by modeling, absent a strong theoretical base.”
The Statistics of Causal Inference: A View from Political Methodology (2015, unknown): “Regression models have been strongly critiqued as a method of the estimation of causal eﬀects (Freedman 2005; Berk 2006).”
What You Can and Can’t Properly Do with Regression (2010, background): “There are several options addressed in some depth elsewhere (Berk 2003: 39–58).”
Local model uncertainty and incomplete‐data bias (with discussion) (2005, methodology): “In the notation above, this could be modelled by assuming that uR|T .r, t/=0 for some values of t1 (the ignorable cases) but allowing uR|T .r, t/ =0 for other values of t1 (the nonignorable cases). Horton and Fitzmaurice (2002) presented a sensitivity analysis for assessing their assumptions on the ”
Assumption Lean Regression (2018, background): “at are misspecied in various ways. Conventional approaches are laden with assumptions that are questionable, many of which are eectively untestable (Box, 1976, Leamer, 1878; Rubin, 1986; Cox, 1995; Berk, 2003; Freedman, 2004; 2009). This note discusses some implications of an \assumption lean" ”
The role of human, social, built, and natural capital in explaining life satisfaction at the country level: Toward a National Well-Being Index (NWI) (2006, methodology): “Tests of collinearity using tolerance and the variance inflation factor (VIF) showed no signs of collinearity, with a tolerance value close to one and a low VIF value (Draper and Smith, 1981; SPSS, 1999; Berk, 2004).”
Data Analysis Using Regression and Multilevel/Hierarchical Models: Understanding and summarizing the fitted models (2006, background): “Berk (2004) discusses the various assumptions implicit in regression analysis. For more on children’s test scores and maternal employment, see Hill et al. (2005). See Appendix B and Murrell (2005) for more on how to make the sorts of graphs shown in this chapter and throughout the book.”
Overdispersion and Poisson Regression (2008, methodology): “What exactly this entails is discussed elsewhere (Berk, 2003) and is beyond the scope of this paper.”