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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 0000-0002-6500-9108), Marco Bleiker, Moritz M. Daum (ORCID 0000-0002-4032-4574)
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
FullText Text:
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  Data: Influences on Data Quality in Developmental Children Studies
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  Data: <searchLink fieldCode="AR" term="%22Stephanie+Wermelinger%22">Stephanie Wermelinger</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6500-9108">0000-0002-6500-9108</externalLink>)<br /><searchLink fieldCode="AR" term="%22Marco+Bleiker%22">Marco Bleiker</searchLink><br /><searchLink fieldCode="AR" term="%22Moritz+M%2E+Daum%22">Moritz M. Daum</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4032-4574">0000-0002-4032-4574</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Infant+and+Child+Development%22"><i>Infant and Child Development</i></searchLink>. 2025 34(3).
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  Data: 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
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  Data: Y
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  Data: 8
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  Data: 2025
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  Data: Journal Articles<br />Information Analyses<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Infants%22">Infants</searchLink><br /><searchLink fieldCode="DE" term="%22Young+Children%22">Young Children</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Problems%22">Research Problems</searchLink><br /><searchLink fieldCode="DE" term="%22Factor+Analysis%22">Factor Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Methodology%22">Research Methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink>
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  Data: 10.1002/icd.70035
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  Data: 1522-7227<br />1522-7219
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  Data: 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.
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  Data: As Provided
– Name: Note
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  Data: https://osf.io/32ge5
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  Data: 2025
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      – SubjectFull: Infants
        Type: general
      – SubjectFull: Young Children
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      – SubjectFull: Research Problems
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      – SubjectFull: Factor Analysis
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      – SubjectFull: Data Analysis
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      – SubjectFull: Research Methodology
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      – SubjectFull: Predictor Variables
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      – TitleFull: Influences on Data Quality in Developmental Children Studies
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              Y: 2025
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