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Handling Item-Level Missing Data: Simpler Is Just as Good
Abstract
The topic of missing data has been receiving increasing attention, with calls to apply advanced methods of handling missingness to counseling psychology research. The present study sought to assess whether advanced methods of handling item-level missing data performed equivalently to simpler methods in designs similar to those counseling psychologists typically engage in. Results of an initial preliminary analysis, an analysis using real-world data, and a series of simulation studies were used in the present investigation. Results indicated that available case analysis, mean substitution, and multiple imputation had similar results across low levels of missing data, though in data with higher levels of missing data and other problems (e.g., small sample size or scales with weak internal reliability) mean substitution produced inflation of correlation coefficients among items. The present results support the use of available case analysis when dealing with low-level item-level missingness.
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“Item-level missingness was negligible: a single item was missing from the data set, and available item analysis was used to score the scale in this case [56].”
From An Acceptance and Commitment Therapy Prototype Mobile Program for Individuals with a Visible Difference: Mixed Methods Feasibility Study (2022) · cites this paper as [56]
In the citing paper
“Item-level missingness was negligible: a single item was missing from the data set, and available item analysis was used to score the scale in this case [56].”
From An Acceptance and Commitment Therapy Prototype Mobile Program for Individuals with a Visible Difference: Mixed Methods Feasibility Study (2022) · cites this paper as [56]
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“…percentage of missing data and vidence of scale reliability, we used listwise deletion to handle issing data, rather than imputing missing data (Parent, 2013). ests of skew and kurtosis revealed that all variables were within he acceptable range, as per the guidelines of George and Mallery…”
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“Given the relatively modest amount of issing data and evidence supporting a pattern of missing comletely at random (MCAR; Little’s X2(865) = 904.8, p = .17) we used he recommended available item analysis data replacement stratgy (Parent, 2013).”
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“Of these cases, 273 (34% of total) were removed from the dataset because they were missing more than 20% of the data analyzed in this study, which exceeds the recommended level of tolerable missingness (Parent, 2013).”
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“…allows reporting item-level missing values per scale, as well as participant’s maximum number of missing items by scale, as per recommendations (Parent, 2013). extract_duplicates() creates a data frame of only observations with a duplicated ID or participant number, so they can be…”
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“Available item analysis was used for item-level missingness (Parent, 2013).”
MethodExamining moderators of discrimination and subjective well-being among LGB individuals.
Douglass, R. P., Conlin, S. E., Duffy, R. D., & Allan, B. A. (2016) · Journal of Counseling Psychology · doi:10.1037/cou0000187 · Cited by 49
“Parent (2013) demonstrated that, compared to more advanced methods of data imputation, mean imputation can be used when there is a small amount of missing data (e.g., 10%), as in this study where only 56 total items for the entire sample were missing out of a possible 27,600 items ( 0.21%).”
MethodInfluentialMinority stress and racial or ethnic minority status: A test of the greater risk perspective.
Velez, B. L., Watson, L. B., Cox, R., & Flores, M. J. (2017) · Psychology of Sexual Orientation and Gender Diversity · doi:10.1037/sgd0000226 · Cited by 45
Choma, B. L., & Prusaczyk, E. (2018) · Psychology of Women Quarterly · doi:10.1177/0361684317747845 · Cited by 49
“Where there were missing items, available item analysis was used (Parent, 2013).”
MethodLevendosky, A. A., Bogat, G. A., Lonstein, J. S., Martinez‐Torteya, C., Muzik, M., Granger, D. A., & von Eye, A. (2015) · Stress · doi:10.3109/10253890.2015.1108303 · Cited by 45
Ikizler, A. S., & Szymanski, D. M. (2018) · Journal of Clinical Psychology · doi:10.1002/jclp.22584 · Cited by 45
An Inhospitable World: Exploring a Model of Objectification Theory With Trans Women
Comiskey, A., Parent, M. C., & Tebbe, E. A. (2019) · Psychology of Women Quarterly · doi:10.1177/0361684319889595 · Cited by 48
“Any missing data present in the structural equation model were handled using full information maximum likelihood estimation (Parent, 2013).”
MethodSelf-citationRoman, E., Weininger, J., Lim, B., Roman, M., Barry, D., Tierney, P., O’Hanlon, E., Levins, K., O’Keane, V., & Roddy, D. (2020) · Brain Structure and Function · doi:10.1007/s00429-020-02069-8 · Cited by 44
“As such using the SPSS 24 “compute” command, the MEAN function was used to generate an available analysis (AIA) scale for the missing data (Parent 2013).”
MethodPichardo, C. M., Molina, K. M., Rosas, C. E., Uriostegui, M., & Sánchez-Johnsen, L. (2020) · Race and Social Problems · doi:10.1007/s12552-020-09304-1 · Cited by 45
“(Parent 2013).”
Method
Metadata
- Authors
- Parent, M. C.
- Affiliations
- University of Florida (US)
- Corresponding author
- Parent, Mike C.
- ORCID
- Parent, Mike C.
- Venue
- The Counseling Psychologist
- Publisher
- SAGE Publications
- Published
- 2012-05-10
- Volume
- 41
- Issue
- 4
- Pages
- 568-600
- ISSN
- 0011-0000 (ISSN-L)
- 1552-3861
- OpenAlex
- W2169361290
- Cited by
- 685
- References
- 19
- Citations by year
- 2026: 61 · 2025: 58 · 2024: 74 · 2023: 62 · 2022: 71 · 2021: 63 · 2020: 69 · 2019: 54 · 2018: 32 · 2017: 34 · 2016: 32 · 2015: 34 · 2014: 31 · 2013: 7 · 2012: 1
- FWCI
- 22.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Counseling Psychologist (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Keywords
- missing data handling, available case analysis, mean substitution, multiple imputation, counseling psychology research, simulation study, internal consistency, small sample size
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