Exploring Construct Measures Using Rasch Models and Discretization Methods to Analyze Existing Continuous Data
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| Title: | Exploring Construct Measures Using Rasch Models and Discretization Methods to Analyze Existing Continuous Data |
|---|---|
| Language: | English |
| Authors: | Chen Qiu (ORCID |
| Source: | Measurement: Interdisciplinary Research and Perspectives. 2024 22(1):108-120. |
| Availability: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Page Count: | 13 |
| Publication Date: | 2024 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Models, Measurement Techniques, Benchmarking, Algorithms, Data Analysis, Financial Services |
| DOI: | 10.1080/15366367.2023.2210358 |
| ISSN: | 1536-6367 1536-6359 |
| Abstract: | It is meaningful to create a comprehensive score to extract information from mass continuous data when they measure the same latent concept. Therefore, this study adopts the logic of psychometrics to conduct scales on continuous data under the Rasch models. This study also explores the effect of different data discretization methods on scale development by using financial profitability ratios as a demonstration. Results show that retaining more categories can benefit Rasch modeling because it can better inform the models. The dynamic clustering algorithm, k-median is a better method for extracting characteristic patterns of the continuous data and preparing the data for the Rasch model. This study illustrates that there is no one-way good discretization method for continuous data under the Rasch models. It is more reasonable to use the traditional algorithms if each continuous data variable has target benchmark(s), whereas the k-median clustering algorithm achieves good modeling results when benchmark information is lacking. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| Accession Number: | EJ1413309 |
| Database: | ERIC |
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Nájsť tento článok vo Web of Science | Abstract: | It is meaningful to create a comprehensive score to extract information from mass continuous data when they measure the same latent concept. Therefore, this study adopts the logic of psychometrics to conduct scales on continuous data under the Rasch models. This study also explores the effect of different data discretization methods on scale development by using financial profitability ratios as a demonstration. Results show that retaining more categories can benefit Rasch modeling because it can better inform the models. The dynamic clustering algorithm, k-median is a better method for extracting characteristic patterns of the continuous data and preparing the data for the Rasch model. This study illustrates that there is no one-way good discretization method for continuous data under the Rasch models. It is more reasonable to use the traditional algorithms if each continuous data variable has target benchmark(s), whereas the k-median clustering algorithm achieves good modeling results when benchmark information is lacking. |
|---|---|
| ISSN: | 1536-6367 1536-6359 |
| DOI: | 10.1080/15366367.2023.2210358 |