A review of techniques for treating missing data in OM survey research

By Nikos Tsikriktsis. 2005

345 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a483b6e03728f69642d45d5687b0459561b0e033

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In Economics and Econometrics

Citations in the corpus

Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph (2015, background): “However, the applications of existing techniques are limited (Tsikriktsis, 2005; Cohen et al., 2009).”
The Development and Validation of the Game User Experience Satisfaction Scale (GUESS) (2016, methodology): “Experts also are likely to recommend using maximum likelihood (ML)–based methods (e.g., EM) over other traditional methods when data are not missing completely at random and when dealing with over 10% of missing data (Roth, 1994; Tsikriktsis, 2005).”
Comparison of Performance of Data Imputation Methods for Numeric Dataset (2019, background): “However, some studies point out that the limitations of mean imputation are almost absent if less than 10% of the data is missing and when the correlations between the variables are low (Raymond 1986; Tsikriktsis 2005).”
Exploring the determinants and long-term performance outcomes of corporate carbon strategies (2017, methodology): “Energy and fuel figures that were found to be inwide variance with the regression results were corrected using regression imputation (Tsikriktsis, 2005).”
Missing value imputation: a review and analysis of the literature (2006–2017) (2019, background): “A large number of surveys of MVI from different perspectives have already appeared in the literature, such as for operation management (Tsikriktsis 2005), medical problems (Aittokallio 2009; Donders et al.”