Influences on Data Quality in Developmental Children Studies
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| Title: | Influences on Data Quality in Developmental Children Studies |
|---|---|
| Language: | English |
| Authors: | Stephanie Wermelinger (ORCID |
| Source: | Infant and Child Development. 2025 34(3). |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 8 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Information Analyses Reports - Research |
| Descriptors: | Infants, Young Children, Research Problems, Factor Analysis, Data Analysis, Research Methodology, Predictor Variables |
| DOI: | 10.1002/icd.70035 |
| ISSN: | 1522-7227 1522-7219 |
| Abstract: | Children's fuzziness leads to increased variance in the data, data loss, and high dropout rates in developmental studies. This study investigated the importance of 20 factors on the person (child, caregiver, experimenter) and situation (task, method, time, and date) level for the data quality as indicated via the number of valid trials in 11 studies with N = 727 infants and children (aged 5 months to 8 years). A random forest model suggests that the duration of the study, the children's age, and the age, gender, and experience of the experimenters are the most important predictors in explaining differences in children's data quality in this sample of children. Other researchers may consider shortening studies and ensuring extensive training for experimenters to help increase the probability of data retention. |
| Abstractor: | As Provided |
| Notes: | https://osf.io/32ge5 |
| Entry Date: | 2025 |
| Accession Number: | EJ1475005 |
| Database: | ERIC |
| Abstract: | Children's fuzziness leads to increased variance in the data, data loss, and high dropout rates in developmental studies. This study investigated the importance of 20 factors on the person (child, caregiver, experimenter) and situation (task, method, time, and date) level for the data quality as indicated via the number of valid trials in 11 studies with N = 727 infants and children (aged 5 months to 8 years). A random forest model suggests that the duration of the study, the children's age, and the age, gender, and experience of the experimenters are the most important predictors in explaining differences in children's data quality in this sample of children. Other researchers may consider shortening studies and ensuring extensive training for experimenters to help increase the probability of data retention. |
|---|---|
| ISSN: | 1522-7227 1522-7219 |
| DOI: | 10.1002/icd.70035 |
