A Monte Carlo Study of Missing Item Methods
By R. McDonald, Paul Thurston, Mark R. Nelson. 2000
59 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/c07bb9ba2de6667659cf62a0267d1c9fe2079a3a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
In Management Science and Operations Research
- Scott Tonidandel
- Morela Hernandez
- Janet P. Near
- Patrick F. McKay
- M. Hebl
- Derek R. Avery
- Mark A. Morris
- James M. LeBreton
In Statistics and Probability
- Martijn J. M. Leenknecht
- M. Köhlen
- Scott Tonidandel
- Remy M. J. P. Rikers
- L. Wijnia
- Luke K. Fryer
- Sofie M. M. Loyens
- Janet P. Near
Citations in the corpus
- Predicting employee reactions to perceived organizational wrongdoing: Demoralization, justice, proactive personality, and whistle-blowing (2012, methodology): “This method is superior to other methods of handling missing data, including listwise deletion, and it does not bias internal consistency when fewer than 20% of the items on a scale are missing (McDonald et al., 2000).”
- RACIAL DIFFERENCES IN EMPLOYEE RETENTION: ARE DIVERSITY CLIMATE PERCEPTIONS THE KEY? (2007, methodology): “This imputation method has been noted as one of the two most effective means (with regression imputation being the other) of estimating missing item responses ( McDonald, Thurston, & Nelson, 2000; Roth, Switzer, & Switzer, 1999).”
- Formative assessment as practice: the role of students’ motivation (2020, result): “Moreover, we know from previous research a discrepancy exists between teachers’ intended teaching techniques or practice and students’ perceptions of it (e.g. Skinner et al. 2008; Mulliner and Tucker 2017).”