Developing and Validating a Novel Anonymous Method for Matching Longitudinal School-Based Data
| Autor: | David Tidd, Mikyoung Jun, Daniel L Agley, Yunyu Xiao, Stephanie L. Dickinson, Steve Sussman, Ruth A. Gassman, Jon Agley, Wasantha Jayawardene, Lori Eldridge |
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| Rok vydání: | 2021 |
| Předmět: |
Matching (statistics)
longitudinal data anonymous Longitudinal data Psychological intervention Academic achievement Article Education SGIC 0504 sociology Intervention (counseling) Developmental and Educational Psychology self-generated identification code Applied Psychology Medical education Data collection Applied Mathematics matching 05 social sciences 050401 social sciences methods 050301 education methodology Information security School based Psychology 0503 education |
| Zdroj: | Educational and Psychological Measurement |
| ISSN: | 1552-3888 |
| Popis: | Prospective longitudinal data collection is an important way for researchers and evaluators to assess change. In school-based settings, for low-risk and/or likely-beneficial interventions or surveys, data quality and ethical standards are both arguably stronger when using a waiver of parental consent—but doing so often requires the use of anonymous data collection methods. The standard solution to this problem has been the use of a self-generated identification code. However, such codes often incorporate personalized elements (e.g., birth month, middle initial) that, even when meeting the technical standard for anonymity, may raise concerns among both youth participants and their parents, potentially altering willingness to participate, response quality, or generating outrage. There may be value, therefore, in developing a self-generated identification code and matching approach that not only is technically anonymous but also appears anonymous to a research-naive individual. This article provides a proof of concept for a novel matching approach for school-based longitudinal data collection that potentially accomplishes this goal. |
| Databáze: | OpenAIRE |
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