Unknown

Dataset Information

0

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

altmetric image

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]

Similar Datasets

| S-EPMC5933773 | biostudies-literature
| S-EPMC6512164 | biostudies-literature
| S-EPMC7049783 | biostudies-literature
| S-EPMC6765459 | biostudies-literature
| S-EPMC8016711 | biostudies-literature
| S-EPMC6989430 | biostudies-literature