2016
DOI: 10.1080/1743727x.2016.1168798
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Random forest as an imputation method for education and psychology research: its impact on item fit and difficulty of the Rasch model

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Cited by 22 publications
(19 citation statements)
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“…As noted earlier, neurotypical participants in the HPP sample filled out the TAS-16, a version of the TAS-20 in which four problematic items have been removed from the scale [ 65 ]. However, as we wished to compare total scores from the TAS-20 between HPP and SPARK samples, we conducted single imputation for missing items in both groups using a random-forest algorithm implemented in the R missForest package [ 96 98 ]. Such item-level imputation allowed for us to approximate the TAS-20 score distribution of the HPP participants, including the proportion of individuals exceeding the “high alexithymia” cutoff of 61.…”
Section: Methodsmentioning
confidence: 99%
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“…As noted earlier, neurotypical participants in the HPP sample filled out the TAS-16, a version of the TAS-20 in which four problematic items have been removed from the scale [ 65 ]. However, as we wished to compare total scores from the TAS-20 between HPP and SPARK samples, we conducted single imputation for missing items in both groups using a random-forest algorithm implemented in the R missForest package [ 96 98 ]. Such item-level imputation allowed for us to approximate the TAS-20 score distribution of the HPP participants, including the proportion of individuals exceeding the “high alexithymia” cutoff of 61.…”
Section: Methodsmentioning
confidence: 99%
“…We fit the model using a diagonally weighted least squares estimator [ 112 ] with a mean- and variance-corrected test statistic (i.e., “WLSMV” estimation), as implemented in the R package lavaan [ 113 ]. Very few of the item responses in our dataset contained missing values (0.16% missing item responses in the SPARK sample, no missing TAS-16 data in HPP sample), and missing values were singly imputed using missForest [ 96 98 ].…”
Section: Methodsmentioning
confidence: 99%
“…As noted earlier, neurotypical participants in the HPP sample lled out the TAS-16, a version of the TAS-20 in which four problematic items have been removed from the scale (64). However, as we wished to compare total scores from the TAS-20 between HPP and SPARK samples, we conducted single imputation for missing items in both groups using a random-forest algorithm implemented in the R missForest package (93)(94)(95). Such item-level imputation allowed for us to approximate the TAS-20 score distribution of the HPP participants, including the proportion of individuals exceeding the "high alexithymia" cutoff of 61.…”
Section: Measures Toronto Alexithymia Scale (Tas)mentioning
confidence: 99%
“…We t the model using a diagonally weighted least squares estimator ( 109) with a mean-and variance-corrected test statistic (i.e., "WLSMV" estimation), as implemented in the R package lavaan (110). Very few of the item responses in our dataset contained missing values (0.16% missing item responses in the SPARK sample, no missing TAS-16 data in HPP sample), and missing values were singly imputed using missForest (93)(94)(95).…”
Section: Con Rmatory Factor Analysis and Model-based Bifactor Coe Cientsmentioning
confidence: 99%
“…As noted earlier, neurotypical participants in the HPP sample filled out the TAS-16, a version of the TAS-20 in which four problematic items have been removed from the scale [65]. However, as we wished to compare total scores from the TAS-20 between HPP and SPARK samples, we conducted single imputation for missing items in both groups using a random-forest algorithm implemented in the R missForest package [96][97][98]. Such item-level imputation allowed for us to approximate the TAS-20 score distribution of the HPP participants, including the proportion of individuals exceeding the "high alexithymia" cutoff of 61.…”
Section: Toronto Alexithymia Scale (Tas)mentioning
confidence: 99%