Literature DB >> 32109784

Matching anonymous participants in longitudinal research on sensitive topics: Challenges and recommendations.

Jane E Palmer1, Samantha C Winter2, Sarah McMahon3.   

Abstract

The purpose of this study was to examine the final analytic sample of a longitudinal randomized control trial (RCT) evaluation of a sexual violence prevention program at a university after facing challenges with the implementation of a self-generated identification code. The matched and unmatched samples (e.g., all unique surveys across all time periods) included 10,135 surveys. Eighty-eight percent of these surveys were matched into the final longitudinal dataset. Findings suggest that students with certain characteristics were more likely to be matched over time (i.e., students who participated in student government, Latino/a students, and Asian students). In addition, students who did not comply with RCT protocol were less likely to be matched. Student history of victimization or perpetration of sexual violence was not associated with being matched over time. This study provides recommendations for preventing matching problems in longitudinal studies, a process for rectifying matching issues and a critique of studies that do not address issues of matching-related sample bias in their final analytic sample.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Anonymity; Campus sexual violence; Longitudinal research; Sample matching; Self-generated identification codes

Year:  2020        PMID: 32109784     DOI: 10.1016/j.evalprogplan.2020.101794

Source DB:  PubMed          Journal:  Eval Program Plann        ISSN: 0149-7189


  2 in total

1.  Matched and Fully Private? A New Self-Generated Identification Code for School-Based Cohort Studies to Increase Perceived Anonymity.

Authors:  Maria Calatrava; Jokin de Irala; Alfonso Osorio; Edgar Benítez; Cristina Lopez-Del Burgo
Journal:  Educ Psychol Meas       Date:  2021-08-12       Impact factor: 3.088

2.  Developing and Validating a Novel Anonymous Method for Matching Longitudinal School-Based Data.

Authors:  Jon Agley; David Tidd; Mikyoung Jun; Lori Eldridge; Yunyu Xiao; Steve Sussman; Wasantha Jayawardene; Daniel Agley; Ruth Gassman; Stephanie L Dickinson
Journal:  Educ Psychol Meas       Date:  2020-07-08       Impact factor: 2.821

  2 in total

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