Mentoring Motives Measure

Ginkel, Gisbert van; Verloop, Nico; Denessen, Eddie

doi:10.1037/t49642-000

Cited by 1 publication

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“…Fortunately, the pooling of some of these statistics can be done using other software packages, or additional tools in SPSS. For example, F tests in ANOVA and regression can be pooled using the MIANALYZE procedure in SAS 9.4 (2013), the XTMIXED procedure in Stata 14.0 (StataCorp, 2015), and an SPSS macro by Van Ginkel (2016). LR tests in logistic regression can be pooled using the mice procedure in R (Van Buuren & Groothuis-Oudshoorn, 2011).…”

Section: Rebutting the Misconceptionmentioning

confidence: 99%

Reliability, Validity, and Factor Structure of the Maladaptive Daydreaming Scale (MDS–16) in an Italian Sample

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et al. 2019

Journal of Personality Assessment

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Missing data is a problem that occurs frequently in many scientific areas. The most sophisticated method for dealing with this problem is multiple imputation. Contrary to other methods, like listwise deletion, this method does not throw away information, and partly repairs the problem of systematic dropout. Although from a theoretical point of view multiple imputation is considered to be the optimal method, many applied researchers are reluctant to use it because of persistent misconceptions about this method. Instead of providing an(other) overview of missing data methods, or extensively explaining how multiple imputation works, this article aims specifically at rebutting these misconceptions, and provides applied researchers with practical arguments supporting them in the use of multiple imputation. ARTICLE HISTORY Goal and intended audienceThis article addresses applied researchers within the field of social and behavioral sciences (but possibly other fields as well) facing the problem of missing data, who have heard of multiple imputation as a method to deal with missing data, but have concerns about actually using this method. These concerns may be based on misconceptions implying that in their specific situation multiple imputation should either not be used at all, or only with much caution. This article collects several of those misconceptions, and provides grounded rebuttal-through theory and practical argumentation-to ultimately support researchers in their deliberations regarding their statistical analyses when faced with missing data.

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Section: Rebutting the Misconceptionmentioning

confidence: 99%