Missing data: prevalence and reporting practices
Humans; United States; Bias (Epidemiology); Statistical; Data Interpretation; Databases; Missing Data Articles; Behavioral Sciences/statistics & numerical data; Bibliographic; Data Collection/statistics & numerical data; Publishing/statistics & numerical data; Research Design/statistics & numerical data; Social Sciences/statistics & numerical data
Results are described for a survey assessing prevalence of missing data and reporting practices in studies with missing data in a random sample of empirical research journal articles from the PsychINFO database for the year 1999, two years prior to the publication of a special section on missing data in Psychological Methods. Analysis indicates missing data problems were found in about one-third of the studies. Further, analytical methods and reporting practices varied widely for studies with missing data. One may consider these results as baseline data to assess progress as reporting standards evolve for studies with missing data. Some potential reporting standards are discussed.
2006
Bodner TE
Psychological Reports
2006
Article information provided for research and reference use only. PedPalASCNET does not hold any rights over the resource listed here. All rights are retained by the journal listed under publisher and/or the creator(s).
Journal Article
<a href="http://doi.org/10.2466/pr0.99.7.675-680" target="_blank" rel="noreferrer">10.2466/pr0.99.7.675-680</a>
Best practices for missing data management in counseling psychology
Humans; Bias (Epidemiology); Statistical; Counseling/statistics & numerical data; Data Interpretation; Research Design/standards; Data Collection/standards; Likelihood Functions; Missing Data Articles; Psychology/statistics & numerical data
This article urges counseling psychology researchers to recognize and report how missing data are handled, because consumers of research cannot accurately interpret findings without knowing the amount and pattern of missing data or the strategies that were used to handle those data. Patterns of missing data are reviewed, and some of the common strategies for dealing with them are described. The authors provide an illustration in which data were simulated and evaluate 3 methods of handling missing data: mean substitution, multiple imputation, and full information maximum likelihood. Results suggest that mean substitution is a poor method for handling missing data, whereas both multiple imputation and full information maximum likelihood are recommended alternatives to this approach. The authors suggest that researchers fully consider and report the amount and pattern of missing data and the strategy for handling those data in counseling psychology research and that editors advise researchers of this expectation.
2010
Schlomer GL; Bauman S; Card NA
Journal Of Counseling Psychology
2010
Article information provided for research and reference use only. PedPalASCNET does not hold any rights over the resource listed here. All rights are retained by the journal listed under publisher and/or the creator(s).
Journal Article
<a href="http://doi.org/10.1037/a0018082" target="_blank" rel="noreferrer">10.1037/a0018082</a>