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Two-Method Measurement Planned Missing Data with Purposefully Selected Samples

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Bibliographic Details
Title: Two-Method Measurement Planned Missing Data with Purposefully Selected Samples
Language: English
Authors: Menglin Xu (ORCID 0000-0001-7895-0733), Jessica A. R. Logan
Source: Educational and Psychological Measurement. 2024 84(6):1232-1244.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 13
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Descriptors: Research Design, Research Methodology, Monte Carlo Methods, Statistical Analysis, Research Problems, Effect Size, Statistical Studies, Data Use
DOI: 10.1177/00131644231222603
ISSN: 0013-1644
1552-3888
Abstract: Research designs that include planned missing data are gaining popularity in applied education research. These methods have traditionally relied on introducing missingness into data collections using the missing completely at random (MCAR) mechanism. This study assesses whether planned missingness can also be implemented when data are instead designed to be purposefully missing based on student performance. A research design with purposefully selected missingness would allow researchers to focus all assessment efforts on a target sample, while still maintaining the statistical power of the full sample. This study introduces the method and demonstrates the performance of the purposeful missingness method within the two-method measurement planned missingness design using a Monte Carlo simulation study. Results demonstrate that the purposeful missingness method can recover parameter estimates in models with as much accuracy as the MCAR method, across multiple conditions.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1447347
Database: ERIC
FullText Text:
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  Data: Two-Method Measurement Planned Missing Data with Purposefully Selected Samples
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  Data: <searchLink fieldCode="AR" term="%22Menglin+Xu%22">Menglin Xu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7895-0733">0000-0001-7895-0733</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jessica+A%2E+R%2E+Logan%22">Jessica A. R. Logan</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Educational+and+Psychological+Measurement%22"><i>Educational and Psychological Measurement</i></searchLink>. 2024 84(6):1232-1244.
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Data: 13
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  Data: 2024
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Research+Design%22">Research Design</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Methodology%22">Research Methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+Methods%22">Monte Carlo Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+Analysis%22">Statistical Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Problems%22">Research Problems</searchLink><br /><searchLink fieldCode="DE" term="%22Effect+Size%22">Effect Size</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+Studies%22">Statistical Studies</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Use%22">Data Use</searchLink>
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  Data: 10.1177/00131644231222603
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  Data: 0013-1644<br />1552-3888
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  Data: Research designs that include planned missing data are gaining popularity in applied education research. These methods have traditionally relied on introducing missingness into data collections using the missing completely at random (MCAR) mechanism. This study assesses whether planned missingness can also be implemented when data are instead designed to be purposefully missing based on student performance. A research design with purposefully selected missingness would allow researchers to focus all assessment efforts on a target sample, while still maintaining the statistical power of the full sample. This study introduces the method and demonstrates the performance of the purposeful missingness method within the two-method measurement planned missingness design using a Monte Carlo simulation study. Results demonstrate that the purposeful missingness method can recover parameter estimates in models with as much accuracy as the MCAR method, across multiple conditions.
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  Data: 2024
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  Data: EJ1447347
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