An Examination of Discrepancies in Multiple Imputation Procedures Between SAS® and SPSS®

Multiple imputation (MI) has become a feasible method to replace missing data due to the rapid development of computer technology over the past three decades. Nonetheless, a unique issue with MI hinges on the fact that different software packages can give different results. Even when one begins with the same random number seed, conflicting findings can be obtained from the same data under an identical imputation model between SAS® and SPSS®. Consequently, as illustrated in this article, a predictor variable can be claimed both significant and not significant depending on the software being used. Based on the considerations of multiple imputation steps, including result pooling, default selection, and different numbers of imputations, practical suggestions are provided to minimize the discrepancies in the results obtained when using MI. Features of Stata® are briefly reviewed in the Discussion section to broaden the comparison of MI computing across widely used software packages.     

“Nobody’s paying me to cry”: the causes of activist burnout in United States animal rights activists

ABSTRACT

We examine the causes of activist burnout – a condition in which the accumulative stress associated with activism becomes so debilitating that once-committed activists are forced to scale back on or disengage from their activism – in 17 United States animal rights activists. Following a phenomenological qualitative approach, analysis of interview data revealed three primary categories of burnout causes: 1) intrinsic motivational and psychological factors, 2) organizational and movement culture, and 3) within-movement in-fighting and marginalization. Implications for understandings of activist burnout and the AR movement are discussed.     

Testing for lack of fit in regression - a review

Assessment of the adequacy of a proposed linear regression model is necessarily subjective. However, the following three criteria may warrant investigation whether the distributional assumptions for the stochastic portion of the model are satisfied, whether the predictive capability of the model is satisfactory, and whether the deterministic portion of the model is adejuate in a statistical sense. The first two criteria have been reviewed in the literature to some extent. This paper reviews statistical tests and procedures which aid the experimenter in deterrmining lack of fit or functional misspecification associated with the deterministic portion of a proposed linear regression model.     

Power and inequality in global value chains: Advancing the research agenda

Power is a central, but largely undertheorized, concept for scholars of global value chains (GVCs). In this introduction to a special issue on power and inequality in GVCs, the authors summarize the key insights from the articles gathered here and explain how the collection advances our understanding of the types and forms of power operating in GVCs and their effect on different dimensions of inequality.