Dataset Information

Item Response Tree Models to Investigate Acquiescence and Extreme Response Styles in Likert-Type Rating Scales.

ABSTRACT: Item response tree (IRTree) models are recently introduced as an approach to modeling response data from Likert-type rating scales. IRTree models are particularly useful to capture a variety of individuals' behaviors involving in item responding. This study employed IRTree models to investigate response styles, which are individuals' tendencies to prefer or avoid certain response categories in a rating scale. Specifically, we introduced two types of IRTree models, descriptive and explanatory models, perceived under a larger modeling framework, called explanatory item response models, proposed by De Boeck and Wilson. This extends the typical application of IRTree models for studying response styles. As a demonstration, we applied the descriptive and explanatory IRTree models to examine acquiescence and extreme response styles in Rosenberg's Self-Esteem Scale. Our findings suggested the presence of two distinct extreme response styles and acquiescence response style in the scale.

SUBMITTER: Park M

PROVIDER: S-EPMC6713983 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Item Response Tree Models to Investigate Acquiescence and Extreme Response Styles in Likert-Type Rating Scales.

Park Minjeong M Wu Amery D AD

Educational and psychological measurement 20190215 5

Item response tree (IRTree) models are recently introduced as an approach to modeling response data from Likert-type rating scales. IRTree models are particularly useful to capture a variety of individuals' behaviors involving in item responding. This study employed IRTree models to investigate response styles, which are individuals' tendencies to prefer or avoid certain response categories in a rating scale. Specifically, we introduced two types of IRTree models, descriptive and explanatory mod ...[more]

PMID: 31488919

Similar Datasets

Project description:ObjectiveTo develop clinically meaningful improvement thresholds in both the 17-item and the 6-item Hamilton Rating Scale for Depression (HRSD) total scores in depressed outpatients.MethodsThe post-hoc analysis included all adult outpatients with non-psychotic major depressive disorder in the STAR*D trial who entered and exited the first treatment step (up to 14 weeks of citalopram) with a complete set of study measures at baseline and exit and at least one post-baseline measure. Within-patient change and linear regression anchor-based analyses were conducted to define meaningful and substantial changes in the HRSD17 and HRSD6 using three patient-reported outcomes [Work and Social Adjustment Scale (WSAS), Quality of Life Enjoyment and Satisfaction-Short Form (Q-LES-Q-SF); Mini-Q-LES-Q] obtained at baseline and exit from the first treatment step in STAR*D.ResultsLinear regression analyses identified a meaningful change threshold for the HRSD17 as 3.9 [3.7-4.1] [lower, upper 95% CI] and a substantial change as 7.8 [7.4-8.3] with the WSAS. Analogous thresholds based on the Q-LES-Q-SF were 5.8 [5.5-6.1] and 11.6 [11.0-12.2], respectively, and 4.9 [4.7-5.2] and 9.9 [9.3-10.4] for the Mini-QLES-Q, respectively. For the HRSD6, linear regression analyses with the WSAS identified a meaningful change as 2.2 [2.1-2.4], while a substantial change was 4.5 [4.2-4.7]. Analogous figures based on the Q-LES-Q-SF were 3.2 [3.0-3.4] and 6.4 [6.1-6.8]. Similarly, based on the Mini-QLESQ, results were 2.8 [2.6-2.9] and 5.6 [5.3-5.9]. For both the HRSD17 and the HRSD6, within-patient analyses produced less precise estimates of the same change thresholds with substantial overlap between groups. Based on the WSAS, a clinically meaningful change in the HRSD17 total score was 9.6 (SD = 6.5), while a substantial change was 15.0 (SD = 6.7). Analogous change thresholds based on the Q-LESQ-SF were 12.9 (SD = 6.2) and 16.8 (SD = 6.4), respectively. For the Mini-Q-LES-Q, thresholds were 10.9 (SD = 6.5) and 16.1 (SD = 6.2).ConclusionA 4-6 point change in the HRSD17 is clinically meaningful; a 7-12 point change is clinically substantial. For the HRSD6, analogous estimates were 2-3 and 4-7 point changes, respectively.

Dataset Information

Item Response Tree Models to Investigate Acquiescence and Extreme Response Styles in Likert-Type Rating Scales.

Publications

Item Response Tree Models to Investigate Acquiescence and Extreme Response Styles in Likert-Type Rating Scales.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets