[Paper Review] Fast & Easy Imputation of Missing Social Science Data

Florian M Hollenbach, Nils W. Metternich+2 more|arXiv (Cornell University)|Nov 3, 2014

Statistical Methods and Bayesian Inference3 citations

TL;DR

This paper proposes a fast, computationally efficient imputation method using copula distributions to fill in missing social science data, demonstrating performance comparable to gold-standard techniques. The approach enables rapid multiple imputation with minimal implementation overhead, making it ideal for Bayesian analyses and practical for applied researchers seeking speed without sacrificing accuracy.

ABSTRACT

Gold-standard approaches to missing data imputation are complicated and computationally expensive. We present a principled solution to this situation, using copula distributions from which missing data may be quickly drawn. We compare this approach to other imputation techniques and show that it performs at least as well as less computationally efficient approaches. Our results demonstrate that most applied researchers can achieve great speed improvements implementing a copula-based imputation approach, while still maintaining the performance of other approaches to multiple imputation. Moreover, this approach can be easily implemented at the point of need in Bayesian analyses.

Motivation & Objective

• To address the computational burden of traditional missing data imputation methods in social science research.
• To develop a principled yet efficient alternative to gold-standard imputation techniques that are slow and complex.
• To enable researchers to implement high-performance imputation quickly and easily at the point of need in Bayesian modeling.
• To maintain the statistical quality of multiple imputation while drastically reducing computation time.

Proposed method

• The method employs copula distributions to model the dependence structure between variables, enabling efficient generation of plausible imputations.
• Missing values are drawn directly from the estimated copula, bypassing iterative or complex optimization steps.
• The approach supports multiple imputation by generating multiple independent draws from the copula, preserving uncertainty.
• Copulas are fitted to observed data, capturing multivariate dependence without assuming multivariate normality.
• The method is designed to be modular and integrable into existing Bayesian analysis pipelines with minimal code changes.

Experimental results

Research questions

• RQ1 Can a copula-based imputation method achieve performance comparable to gold-standard multiple imputation techniques?
• RQ2 How does the computational efficiency of the copula approach compare to traditional imputation methods in social science datasets?
• RQ3 To what extent can researchers implement this method quickly and easily within existing Bayesian analysis workflows?
• RQ4 Does the copula method maintain statistical validity and reliability across diverse missing data patterns?

Key findings

• The copula-based imputation method achieves performance on par with more computationally intensive gold-standard imputation techniques.
• The method enables significant speed improvements, reducing computation time without compromising imputation quality.
• The approach is easily implementable in Bayesian analyses, supporting seamless integration into existing research workflows.
• The method maintains robustness across various missing data mechanisms and variable dependencies.