A Successful Strategy for Linking Anonymous Data from Students’ and Parents’ Questionnaires Using Self-Generated Identification Codes

A Successful Strategy for Linking Anonymous Data from Students’ and Parents’ Questionnaires... We conducted a feasibility study for matching children (N = 2571, average age 12 years, 50.4% female) and their parents (N = 1931, average age 41 years, 83.3% female) represented by an anonymous self-generated identification code (SGIC) and assessed its methodological properties. We used a nine-character SGIC with the children and a mirrored version of the same code with the parents. The average overall error rate in generating the SGIC was 9.7% (4.0% in the parents and 13.9% in the children). We were able to link a total of 1765 parents’ and children’s codes uniquely (94.9% of all possible dyads) with any four-character combination and the employment of the “school” variable. The overall matching quality of linking using the SGIC only is characterized by precision (positive predictive value) of 0.979, recall (sensitivity, true positive rate) of 0.934, and an F-measure (harmonic mean of precision and recall) of 0.956. The analysis of the discrepant characters in the dyads identified the paternal grandmother’s name and eye color as those varying most often. This study is the first to look at SGIC match rates and error and omission rates in linking different subjects into dyads in prevention research. We identified a high number of unique child-parent matches while guaranteeing anonymity to the participants. We provided evidence that our SGIC is a suitable tool for between-group linking procedures and has a highly successful matching rate, while maintaining anonymity in the school-based prevention study samples. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Prevention Science Springer Journals

A Successful Strategy for Linking Anonymous Data from Students’ and Parents’ Questionnaires Using Self-Generated Identification Codes

Loading next page...
 
/lp/springer_journal/a-successful-strategy-for-linking-anonymous-data-from-students-and-fpINuzMwM4
Publisher
Springer Journals
Copyright
Copyright © 2017 by Society for Prevention Research
Subject
Medicine & Public Health; Public Health; Health Psychology; Child and School Psychology
ISSN
1389-4986
eISSN
1573-6695
D.O.I.
10.1007/s11121-017-0772-6
Publisher site
See Article on Publisher Site

Abstract

We conducted a feasibility study for matching children (N = 2571, average age 12 years, 50.4% female) and their parents (N = 1931, average age 41 years, 83.3% female) represented by an anonymous self-generated identification code (SGIC) and assessed its methodological properties. We used a nine-character SGIC with the children and a mirrored version of the same code with the parents. The average overall error rate in generating the SGIC was 9.7% (4.0% in the parents and 13.9% in the children). We were able to link a total of 1765 parents’ and children’s codes uniquely (94.9% of all possible dyads) with any four-character combination and the employment of the “school” variable. The overall matching quality of linking using the SGIC only is characterized by precision (positive predictive value) of 0.979, recall (sensitivity, true positive rate) of 0.934, and an F-measure (harmonic mean of precision and recall) of 0.956. The analysis of the discrepant characters in the dyads identified the paternal grandmother’s name and eye color as those varying most often. This study is the first to look at SGIC match rates and error and omission rates in linking different subjects into dyads in prevention research. We identified a high number of unique child-parent matches while guaranteeing anonymity to the participants. We provided evidence that our SGIC is a suitable tool for between-group linking procedures and has a highly successful matching rate, while maintaining anonymity in the school-based prevention study samples.

Journal

Prevention ScienceSpringer Journals

Published: Mar 24, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off