Ten Difference Score Myths

By J. Edwards. 2001

719 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d65459d88369b3d7ec0352f589c07b9d85993bd1

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Testing Similarity Effects with Dyadic Response Surface Analysis (2018, background): “Note that only including the main effects and a multiplicative interaction term in a regression is not sufficient to model a similarity effect (Edwards, 2001, p. 269), although such a model frequently has been used in the literature to test for similarity effects.”
Does Seeing “Eye To Eye” Affect Work Engagement and Organizational Citizenship Behavior? A Role Theory Perspective on LMX Agreement (2014, methodology): “…have been criticized in the literature for their limitations, including low reliability, discarded information, ambiguous interpretation, confounded results, and unrealistically restrictive and often untested constraints (e.g., Edwards, 1994, 2001; MacCallum, Zhang, Preacher, & Rucker, 2002).”
Conceptualizing models using multidimensional constructs: a review and guidelines for their use (2012, methodology): “A multidimensional construct ‘refers to several distinct but related dimensions treated as a single theoretical concept’ (Edwards, 2001a, p. 144).”
The Thought Worlds of Marketing and Sales: Which Differences Make a Difference? (2007, methodology): “An alternative approach, polynomial regression, cannot be applied in our case because it assumes measurement without error (Edwards 1994, 2001; Smith and Tisak 1993; Tisak and Smith 1994).”
Leader–member exchange (LMX) differentiation and work outcomes: conceptual clarification and critical review (2018, background): “Difference scores are notoriously difficult to interpret, and recent advances in this area recommend the use of polynomial regression and surface plotting as a way to mitigate against many of these problems (see Edwards, 2001).”
Scale Coarseness as a Methodological Artifact (2009, background): “Organizational Research Methods, 11, 54-78.”
Seven deadly myths of testing moderation in organizational research (2008, background): “Such studies have fallen victim to the myths discussed here, and the problems generated by the myths are compounded by those associated with difference scores (Edwards, 1994; Johns, 1981), which have spawned myths of their own (Edwards, 2001).”
The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences (2017, background): “Generally speaking, the difference between two measures is less reliable than the individual measures themselves when the measures are highly correlated and have similar variance (Edwards, 2001; Rogosa, 1988, 1995; Willet, 1988; Zimmerman & Williams, 1998; Zumbo, 1999).”
Individual ambidexterity: the duality of exploration and exploitation and its relationship with innovative performance (2017, background): “Moreover, Edwards (2001) argued that concerns about multicollinearity in polynomial regression are largely unfounded.”
The Paradox of Meritocracy in Organizations (2010, methodology): “Given the known problems of using difference scores as dependent variables (Edwards, 2001), we interpreted the ANOVA results using bonus amount as the dependent variable: we hypothesized that the meritocracy manipulation would interact with the gender of the employees who were being evaluated to…”
Family Environment, Educational Aspirations, and Academic Achievement in Two Cultural Settings (2002, background): “Weighing the value of this information against methodological criticism of the use of difference scores (Edwards, 2001), we opted for the difference conceptualization of demandingness.”
Applicant Faking, Social Desirability, and the Prediction of Counterproductive Work Behaviors (2011, methodology): “Although the reporting and use of raw change scores as operationalizations of faking behavior is acceptable in some cases, the general use of difference scores as predictors or criteria in regression analysis has been criticized for a number of reasons (Edwards, 1993, 2001).”