Dataset Information

Using Person Fit Statistics to Detect Outliers in Survey Research.

ABSTRACT: Context: When working with health-related questionnaires, outlier detection is important. However, traditional methods of outlier detection (e.g., boxplots) can miss participants with "atypical" responses to the questions that otherwise have similar total (subscale) scores. In addition to detecting outliers, it can be of clinical importance to determine the reason for the outlier status or "atypical" response. Objective: The aim of the current study was to illustrate how to derive person fit statistics for outlier detection through a statistical method examining person fit with a health-based questionnaire. Design and Participants: Patients treated for Cushing's syndrome (n = 394) were recruited from the Cushing's Support and Research Foundation's (CSRF) listserv and Facebook page. Main Outcome Measure: Patients were directed to an online survey containing the CushingQoL (English version). A two-dimensional graded response model was estimated, and person fit statistics were generated using the Zh statistic. Results: Conventional outlier detections methods revealed no outliers reflecting extreme scores on the subscales of the CushingQoL. However, person fit statistics identified 18 patients with "atypical" response patterns, which would have been otherwise missed (Zh > |±2.00|). Conclusion: While the conventional methods of outlier detection indicated no outliers, person fit statistics identified several patients with "atypical" response patterns who otherwise appeared average. Person fit statistics allow researchers to delve further into the underlying problems experienced by these "atypical" patients treated for Cushing's syndrome. Annotated code is provided to aid other researchers in using this method.

SUBMITTER: Felt JM

PROVIDER: S-EPMC5445123 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Using Person Fit Statistics to Detect Outliers in Survey Research.

Felt John M JM Castaneda Ruben R Tiemensma Jitske J Depaoli Sarah S

Frontiers in psychology 20170526

<b>Context:</b> When working with health-related questionnaires, outlier detection is important. However, traditional methods of outlier detection (e.g., boxplots) can miss participants with "atypical" responses to the questions that otherwise have similar total (subscale) scores. In addition to detecting outliers, it can be of clinical importance to determine the reason for the outlier status or "atypical" response. <b>Objective:</b> The aim of the current study was to illustrate how to derive ...[more]

PMID: 28603512

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Project description:ObjectivesPeople interact with their work environment through being, to a greater or lesser extent, compatible with aspects of their setting. This interaction between person and environment is particularly relevant in healthcare settings where compatibility affects not only the healthcare professionals, but also potentially the patient. One way to examine this association is to investigate person-organisation (P-O) fit and person-group (P-G) fit. This systematic review aimed to identify and synthesise knowledge on both P-O fit and P-G fit in healthcare to determine their association with staff outcomes. It was hypothesised that there would be a positive relationship between fit and staff outcomes, such that the experience of compatibility and 'fitting in' would be associated with better staff outcomes.DesignA systematic review was conducted based on an extensive search strategy guided by Preferred Reporting Items for Systematic review and Meta-Analyses to identify relevant literature.Data sourcesCINAHL Complete, EMBASE, Ovid MEDLINE, PsycINFO and Scopus.Eligibility criteriaArticles were included if they were empirical studies, published in peer-reviewed journals in English language, set in a healthcare context and addressed the association that staff outcomes have with P-O and/or P-G fit.Data extraction and synthesisIncluded texts were examined for study characteristics, fit constructs examined and types of staff outcomes assessed. The Quality Assessment Tool was used to assess risk of bias.ResultsTwenty-eight articles were included in the review. Of these, 96.4% (27/28) reported a significant, positive association between perception of fit and staff outcomes in healthcare contexts, such that a sense of compatibility had various positive implications for staff, including job satisfaction and retention.ConclusionAlthough the results, as with all systematic reviews, are prone to bias and definitional ambiguity, they are still informative. Generally, the evidence suggests an association between employees' perceived compatibility with the workplace or organisation and a variety of staff outcomes in healthcare settings.

Dataset Information

Using Person Fit Statistics to Detect Outliers in Survey Research.

Publications

Using Person Fit Statistics to Detect Outliers in Survey Research.

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