Relative Importance Analysis With Multicategory Dependent Variables:

Luchman, Joseph N.

doi:10.1177/1094428114544509

Cited by 43 publications

(36 citation statements)

References 28 publications

(45 reference statements)

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“…We utilise the user-written programme domin for Stata to calculate the relative contributions [42,43]. For this purpose, different models with all possible combinations of the five explanatory variables except the constant-only model are estimated.…”

Section: Relative Importance Analysismentioning

confidence: 99%

Reporting biases in self-assessed physical and cognitive health status of older Europeans

Spitzer

Weber

2019

PLoS ONE

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This paper explores which demographic characteristics substantially bias self-reported physical and cognitive health status of older Europeans. The analysis utilises micro-data for 19 European countries from the Survey of Health, Ageing and Retirement in Europe to compare performance-tested outcomes of mobility and memory with their self-reported equivalents. Relative importance analysis based on multinomial logistic regressions shows that the bias in self-reported health is mostly due to reporting heterogeneities between countries and age groups, whereas gender contributes little to the discrepancy. Concordance of mobility and cognition measures is highly related; however, differences in reporting behaviour due to education and cultural background have a larger impact on self-assessed memory than on self-assessed mobility. Southern as well as Central and Eastern Europeans are much more likely to misreport their physical and cognitive abilities than Northern and Western Europeans. Overall, our results suggest that comparisons of self-reported health between countries and age groups are prone to significant biases, whereas comparisons between genders are credible for most European countries. These findings are crucial given that self-assessed data are often the only information available to researchers and policymakers when asking health-related questions.

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Section: Relative Importance Analysismentioning

confidence: 99%

Reporting biases in self-assessed physical and cognitive health status of older Europeans

Spitzer

Weber

2019

PLoS ONE

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“…When there are a number of variables that affect an outcome variable, such as in a multiple regression model, researchers often want to measure their importance and rank them accordingly 15. Since relative importance analysis was developed to satisfy this desire, it quantifies the proportionate contribution of multiple predictors correlated to each other within a selected regression model 11. To date, various approaches have been proposed to evaluate relative importance.…”

Section: Discussionmentioning

confidence: 99%

“…Of those, 2 of the most outstanding are known as general dominance analysis and relative weight analysis 1617. Initially, the dominance analysis was applied only to the regression model, but recently it was extended to the logistic regression model, whether the outcome variable was binary or multicategory 1118. In this study, ED disposition, which is a dependent variable, was a multicategory variable of 3 levels; therefore, the authors performed a dominance analysis using the method proposed by Luchman.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, to analyse the relative importance of the independent variables on the outcome variable, ED disposition, in each regression model, dominance analyses were performed using Luchman's method 11. That is, if the number of independent variables in each regression model was p , then (2 p -1) reduced regression models were constructed for each one.…”

Section: Methodsmentioning

confidence: 99%

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Changes in Relative Importance of the 5-Level Triage System, Korean Triage and Acuity Scale, for the Disposition of Emergency Patients Induced by Forced Reduction in Its Level Number: a Multi-Center Registry-based Retrospective Cohort Study

et al. 2019

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Background The 5-level triage tool, the Korean Triage and Acuity Scale (KTAS), was developed based on the Canadian Triage and Acuity Scale and has been used for triage in all emergency medical institutions in Korea since 2016. This study evaluated the association between the decrease in level number and the change in its relative importance for disposition in the emergency department (ED). Methods Using the registry of the National Emergency Department Information System (NEDIS) ver. 3.1, data regarding consecutive emergency patients from March 2017 to October 2017 were reviewed retrospectively. Reconfiguring KTAS levels, a total of 15 multinomial logistic regression models (KTAS_0 to KTAS_14), including the KTAS, its variants, and covariates were constructed to determine significant factors affecting ED disposition. The relative importance of each model was obtained using a dominance analysis. Results A total of 79,771 patients were included in the analysis. In the model KTAS_0, the KTAS and 8 covariates were found to be significantly related to ED disposition. The KTAS and the decision maker of each ED visit, whether it was the physician or others, had the largest relative importance, 34.8% and 31.4%, respectively ( P < 0.001). In other models of KTAS variants, including 4-level, 3-level and 2-level, the rates of the KTAS decreased to 31.8% (interquartile range [IQR], 28.9–34.2), 26.4% (IQR, 23.2–31.0), and 18.7% (IQR, 7.5–24.9), respectively ( P = 0.016). On the other hand, the rates for covariates tended to be larger for smaller triage levels and so there was a significant interaction effect between the KTAS and the covariates according to the triage level ( P < 0.001). Conclusion The 5-level triage tool, the KTAS, had the largest relative importance among the predictors affecting ED disposition only at its original level. Therefore, it is recommended that no attempt should be made to reduce the number of levels in the triage tool.

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“…A fifth comparison condition where 1,000 cases were obtained as a simple random sample from the same population of 30,000 was also obtained. The DA was based on probit regression-based McFadden's R2s and the Stata program domin (Luchman 2013). One thousand repetitions of the simulation were conducted.…”

Section: Survey Practicementioning

confidence: 99%

Determining subgroup difference importance with complex survey designs: An application of weighted dominance analysis

Luchman¹

2015

Surv Pract

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Objective: Determining which subgroups show the most substantial differences on a measure is a common use of surveys. How to accurately and fairly determine which subgrouping is most important has not been addressed adequately in the literature. I show how dominance analysis is a useful way to identify the most important subgroup differences. Because surveys commonly employ complex sampling designs, I also provide practical guidelines for determining subgroup relative importance from complex survey data. Methods: The advantages of dominance analysis over alternative analysis procedures for determining importance are discussed using an empirical example from the political party affiliation question in the General Social Survey. Additionally, a Monte Carlo simulation was conducted to examine the accuracy of dominance analysis with complex sampling accounting for sample weights, strata, both, and neither compared to with known population values. Results: Dominance analysis clearly identifies the urbanicity subgrouping as having the most important differences on political party affiliation. Results also show the use of survey weights can have non-trivial effects on subgroup rank ordering. The simulation shows that weighed dominance statistics were more accurate than unweighted statistics. Stratified analyses had no effect on relative importance statistics. Conclusions: Dominance analysis is a useful way to identify key subgroup differences on survey measures. Survey weights are necessary to use for relative importance analysis, when available, in order to obtain an accurate representation of the rank order and magnitude of differences between subgroup indicators on a survey measure. The article concludes by outlining situations where dominance analysis is recommended. introduction Surveys are used to measure the prevalence of a behavior or attitude in a population but also to evaluate subgroup differences in prevalence. In fact, the US Census Bureau's website provides most measures collected cross-classified by demographic subgroup. Polls are used similarly, commonly testing for subgroup differences (e.g., Tables A1-A3 in Ramirez 2013). Survey research also seeks to determine which subgroup prevalence differences Institution: Fors Marsh Group LLC * Determining subgroup difference importance with complex survey designs: An application of weighted dominance analysis

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Relative Importance Analysis With Multicategory Dependent Variables:

Cited by 43 publications

References 28 publications

Reporting biases in self-assessed physical and cognitive health status of older Europeans

Reporting biases in self-assessed physical and cognitive health status of older Europeans

Changes in Relative Importance of the 5-Level Triage System, Korean Triage and Acuity Scale, for the Disposition of Emergency Patients Induced by Forced Reduction in Its Level Number: a Multi-Center Registry-based Retrospective Cohort Study

Determining subgroup difference importance with complex survey designs: An application of weighted dominance analysis

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