Impact of a Confounding Variable on a Regression Coefficient

By Kenneth A. Frank. 2000

887 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/db729c36f978c136e08d869c7b91638e39326abb

How researchers see this paper

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

In Statistics and Probability

Citations in the corpus

The sensitivity of linear regression coefficients' confidence limits to the omission of a confounder (2009, background): “…Rubin (1983), Rosenbaum (1988), Copas and Li (1997), Robins, Rotnitzky and Scharfstein (2000), Scharfstein and Irizarry (2003), Marcus (1997), Lin, Psaty and Kronmal (1998), Frank (2000) and Imbens (2003), among others, the last four bearing closest resemblance to the approach to be presented here.”
Consumption-based versus production-based accounting of CO2 emissions: Is there evidence for carbon leakage? (2018, methodology): “Moreover, we tested for the influence of omitted variables using the method suggested by Frank (2000).”
Exploring Colleagues’ Professional Influence on Mathematics Teachers’ Learning (2014, background): “Furthermore, the prior scores “absorb” the influence of other unmeasured and sustaining characteristics of teachers, such as personal values and motivation to collaborate with other teachers (Frank, 2000).”
Investor Relations and Information Assimilation (2018, methodology): “We test the sensitivity of our results to possible correlated omitted variables using the impact threshold for a confounding variable (ITCV) procedure recommended by Larcker and Rusticus (2010) and Frank (2000).”
On the Use of Instrumental Variables in Accounting Research (2010, methodology): “significant, we examine the potential impact of unobserved confounding variables using the approach in Frank (2000). This method is based on the notion that for an unobserved variable to affect the results it needs to be correlated with both the x-variable and the yvariable (controlling for the othe”
Corporate governance and the profitability of insider trading (2016, background): “independent variable of interest and the confounding variable), at which the impact of a confounding variable would alter the statistical inference about the predictor if the confounding variable were controlled in the regression model (Frank, 2000).”
Endogeneity: A Review and Agenda for the Methodology-Practice Divide Affecting Micro and Macro Research (2020, background): “We recommend Angrist & Pischke (2008), Kennedy (2008), and Wooldridge (2010) as additional sources for the majority of these topics.”
The association between book-tax conformity and earnings management (2014, positive): “First, as argued by its proponents, high levels of book-tax conformity might mitigate the incentive to 7 A contemporaneous working paper by Watrin et al. (2012) also tests for the relation between book-tax conformity and earnings management in a European setting using book-tax conformity measures ba”
Omitted Variables in Multilevel Models (2006, background): “Among the extensive literature dealing with these types, we refer to Heckman and Singer (1982), Chamberlain (1985), Yamaguchi (1986), Palta and Yao (1991), Vermunt (1997), Frank (2000) , Halaby (2004), and Frees (2004) for further readings.”
The Role of School-Based Colleagues in Shaping the Commitment of Novice Special and General Education Teachers (2013, methodology): “We quantified the robustness of our inferences with respect to concerns about omitted confounding variables using Frank’s (2000) impact threshold for a confounding variable (ITCV).”
What Would It Take to Change an Inference? Using Rubin’s Causal Model to Interpret the Robustness of Causal Inferences (2013, background): “Technically, this can be dealt with by conducting dual sensitivity (Gastwirth, Krieger, & Rosenbaum, 1998) or by characterizing sensitivity analysis in terms of the product of the two relationships (Frank, 2000; Hirano & Imbens, 2001; Imai, Keele, & Yamamoto, 2010).”
7. Assessing Bias in the Estimation of Causal Effects: Rosenbaum Bounds on Matching Estimators and Instrumental Variables Estimation with Imperfect Instruments (2004, methodology): “It is important to note, however, that the approach here differs from a sensitivity analysis for omitted variables in ordinary regression, as shown, for example, in Frank (2000).”
Financial Reporting Frequency, Information Asymmetry, and the Cost of Equity (2012, positive): “Using the approach in Frank (2000) we calculate the Impact Threshold for a Confounding Variable (hereafter ITCV).”
Does the Age That Children Start Kindergarten Matter? Evidence of Long-Term Educational and Social Outcomes (2006, background): “Frank (2000) offers a strategy to test the sensitivity of regression estimates by assessing the impact of either a confounding variable or a suppressor variable on the significance of regression coefficients.”
Omitted Variable Bias: Examining Management Research With the Impact Threshold of a Confounding Variable (ITCV) (2021, methodology): “…to highlight that the ITCV is useful to examine the causal inference and statistical significance of relationships between independent and dependent variables, but it does not address inaccuracies of the parameter estimate itself (Frank, 2000; Frank et al., 2013; Larcker & Rusticus, 2010).”
Internal Control and Management Guidance (2009, methodology): “Following Frank (2000) and Larcker and Rusticus (2008), we next assess how closely an unobservable confounding variable would have to be correlated with ABSERROR and MW to change the OLS results.”