Call for Papers Session: `Proper and robust multiple imputation of complex data' Organizer: Kristian Kleinke, Martin Spiess & Jost Reinecke

Dear Colleagues,

we would like to draw your attention to the call for papers for our session

'Proper and robust multiple imputation of complex data'

at the 7th Conference of the European Survey Research Association, 17th-21st July 2017 in Lisbon, Portugal.

********************************************************** Summary:

Allison (2001) states that the best solution to the missing data problem is prevention. This is especially true for complex data sets like multilevel data. Here, missingness may occur at various levels: in the outcome variable(s), in level-1 predictors, level-2 predictors, or even higher levels, and finally even in the group identifier(s). Many researchers still handle missingness (e.g. in multilevel data in level-1 and level-2 predictors) by excluding the incomplete cases from the analysis – a wasteful practice, which may lead to biased inferences. On the other hand, also none of the currently existing multiple imputation solutions for complex data can be described as optimal, as they either rely rather heavily upon strong distributional assumptions, often including homoscedasticity, which are frequently violated in “real life” situations. On the other hand, non- or semiparametric imputations methods often lack justification. Recent papers that contrast and review various strategies to impute complex or multilevel data are Drechsler (2015) and Enders, Mistler and Keller (2016). Shortcomings of some imputation techniques or consequences of misspecifications even in simple data sets are considered, e.g. in de Jong, van Buuren and Spiess (2016) or He and Raghunathan (2009). All in all, missing data in complex data structures is a field where a lot of research still has to be done. Feasible and robust software solutions need to be developed that work, even when empirical data do not exactly follow the convenient statistical distributions assumed by the respective procedures. We invite colleagues to present their research on multiple imputation solutions for complex data structures (e.g. clustered data, longitudinal data, panel data, cohort-sequential designs, etc.). We especially encourage proposals for robust procedures for “non-normal” missing data problems, i.e. when convenient distributional assumptions of standard MI procedures (normality, homoscedasticity) are violated. Also simulations that evaluate/compare different MI procedures regarding their robustness against violated assumptions are highly welcome.

References

Allison, P. D. (2001). Missing data. Thousand Oaks: Sage.

de Jong, R., van Buuren, S. & Spiess, M. (2016) Multiple Imputation of Predictor Variables Using Generalized Additive Models. Communications in Statistics – Simulation and Computation, 45(3), 968–985.

Drechsler, J. (2015). Multiple Imputation of Multilevel Missing Data – Rigor Versus Simplicity. Journal of Educational and Behavioral Statistics, 40(1), 69–95.

Enders, C. K., Mistler, S. A., & Keller, B. T. (2016). Multilevel multiple imputation: A review and evaluation of joint modeling and chained equations imputation. Psychological Methods, 21(2), 222–240.

He, Y. & Raghunathan, T. (2009). On the Performance of Sequential Regression Multiple Imputation Methods with Non Normal Error Distributions. Communications in Statistics – Simulation and Computation, 38(4), 856–883.

*********************************************************** To submit an abstract, please visit the conference website at http://www.europeansurveyresearch.org/conference, sign up/log in and follow the instructions. You can select this session from the list of sessions provided in the submission form. [Proposals must be submitted online by 4th December 2016.]

If you have any question, please feel free to contact us:

Kristian Kleinke (kristian.kleinke@fernuni-hagen.de) Martin Spiess (martin.spiess@uni-hamburg.de)