Introduction: Statistical and Methodological Myths and Urban Legends

By Robert J. Vandenberg. 2006

244 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Social Psychology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/94721e3e4be11e7d04ff3deb153a11dda320cca3

How researchers see this paper

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

In Computer Networks and Communications

1. C. Lance
2. Marcello M. Mariani
3. W. Czakon
4. Brian J. Hoffman
5. J. Edwards
6. Joerg Evermann
7. J. Conway
8. Mikko Rönkkö

In Social Psychology

1. Brian J. Hoffman
2. Marcello M. Mariani
3. W. Czakon
4. J. Edwards
5. Joerg Evermann
6. J. Conway
7. Mikko Rönkkö
8. John Antonakis

In Sociology and Political Science

1. John Antonakis
2. Mikko Rönkkö
3. Joerg Evermann
4. Marcello M. Mariani
5. W. Czakon
6. Brian J. Hoffman
7. J. Edwards
8. J. Conway

Citations in the corpus

• What Reviewers Should Expect from Authors Regarding Common Method Bias in Organizational Research (2010, background): “…without careful consideration of the origin and rationale for the rules and their application is very problematic, and is instrumental in creating and perpetuating what Vandenberg (2006) and Lance and Vandenberg (2009) have referred to as ‘‘statistical and methodological myths and urban legends.’’”
• A Critical Examination of Common Beliefs About Partial Least Squares Path Modeling (2013, methodology): “…not based on statistical theory or simulation studies but are based on beliefs about the method that earlier, similar articles have presented, leading to the perpetuation of commonly held beliefs that have not been demonstrated, that is, methodological myths and urban legends (Vandenberg, 2006).”
• Behavioral antecedents of coopetition: A synthesis and measurement scale (2020, methodology): “…of 9.273 (df = 4; p = 0.05) and CMIN/Df = 2.318 show the model as significant, we assess the model properly fits our data as CMIN is sensitive to the sample size (Vandenberg 2006, p. 197) and may give statistically significant chi-square values for non-small (n > 250) samples (Marsh et al., 2004).”
• Method Effects, Measurement Error, and Substantive Conclusions (2010, background): “Thus, there appears to be a common belief, one might even say an ‘‘urban legend’’ (Lance & Vandenberg, 2009; Vandenberg, 2006) that correlations among variables that are measured using the same measurement method are distorted (typically inflated) as compared to correlations among the constructs…”
• Partial least squares path modeling: Time for some serious second thoughts (2016, background): “As Vandenberg (2006) vividly puts the matter: “Doctoral students may be taught or told something to do within the research process as if it were an absolute truth when in reality it is not, and yet, being who they are, they accept that presumed fact as the ‘truth.’”