Abstract

This article has been retracted at the request of the Senior Editor. Please see Elsevier Policy on Article Withdrawal: ( http://www.elsevier.com/locate/withdrawalpolicy ). After concerns were raised about possible problems of reporting in this paper, the Senior Editor consulted with the two previous Senior Editors of The Leadership Quarterly and a methodologist (M1) (not the claimant) to assess the seriousness of the allegations and to make a preliminary determination concerning the allegations’ merits. All concurred that there were serious problems in this paper. The methodologist prepared a report outlining the problems and this report was forwarded to a second methodologist (M2) to confirm the correctness of methods used by the first methodologist to detect the problems. The second methodologist attested to the correctness of the first methodologist’s analyses. The Senior Editor then contacted the authors to inform them of the problems identified in the paper. The authors were asked to respond to concerns raised and encouraged to send the original data from this paper to the Senior Editor for reanalysis. The authors did not provide the original data, but sent a letter replying to the methodology report, along with new analytic results. These were reviewed by a third methodologist (M3) and the methodologist who prepared the report (M1). Both agreed that the reply did not ameliorate concerns about serious methodological omissions and shortcomings, and a resultant misreporting of results. The Senior Editor has concluded that in the published paper, the model that was estimated was not the one that was reported to have been estimated, which thus compromised the scientific review process. More specifically, the dfs reported do not match those that should have reported. That is, the dfs should have been 1631; however, the paper reported 56, which is not possible based on the number of manifest items used as indicators in their factor models. Following communications with the authors, re-analyses revealed additional problems in the paper. Parceling was used and not declared as such in the article; such a practice can mask psychometric deficiencies in the scales (cf. Bandalos, 2002; Bandalos & Finney, 2001; Little, Cunningham, Shahar & Widaman, 2002). Next, clustered data were analyzed at the individual level and this information also was not declared; analyzing data at the individual level, can bias estimates and tests of model fit (see Asparouhov, 2005; Cameron, Gelbach, & Miller, 2011; Muthen & Satorra, 1995; Skinner, 1989; Stapleton, 2006, 2008; Yuan & Bentler, 2000). As reported by the authors, when the data were analyzed using a cluster-robust estimator (which corrects for non-independence), the fit of the target model was substantially decreased (i.e., significant chi-square test of fit, TLI = .86 and CFI = .87). Important to note is that the authors stated in their response that the non-independence of data was not of issue; yet, in their article they justified using Hierarchical Linear Modelling for their regression results and stated they used this estimator “to account for the non-independence in our OCB data because each supervisor evaluated multiple employees,” (p. 290). There were also discrepancies with the sample size reported in the article and in re-analyses (e.g., in the re-analysis with full items they reported n=275 and for the re-analysis with parcels they reported n=264; however, they had reported n=277 in the paper). How missing data were handled was not explained in the article. Finally, the authors had reported in the article that they used 5 items for the emotional exhaustion scale and 12 items for the work withdrawal scale; yet, when submitting their reanalyzed results, they reported using 6 and 8 items respectively. The above suggests that the validity of the results (and consequently the review process) were compromised. As a consequence of the processes and concerns outlined above, the scientific trustworthiness and value of this work cannot be established. However, intentional wrongdoing should not be inferred.

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