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Triangulating on Developmental Models with a Combination of Experimental and Nonexperimental Estimates

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Title: Triangulating on Developmental Models with a Combination of Experimental and Nonexperimental Estimates
Language: English
Authors: Wan, Sirui (ORCID 0000-0002-8750-0977), Brick, Timothy R., Alvarez-Vargas, Daniela, Bailey, Drew H.
Source: Developmental Psychology. Feb 2023 59(2):216-228.
Availability: American Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org
Peer Reviewed: Y
Page Count: 13
Publication Date: 2023
Sponsoring Agency: Institute of Education Sciences (ED)
Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (DHHS/NIH)
Contract Number: R305K05157
R305A120813
R305K050004
R01HD053714
R37HD0459M
HD15052
Document Type: Journal Articles
Reports - Research
Education Level: Elementary Education
Early Childhood Education
Preschool Education
Descriptors: Mathematics Skills, Early Intervention, Models, Randomized Controlled Trials, Urban Schools, Elementary Schools, Low Income Students, Mathematics Achievement, Preschools, Prediction
Geographic Terms: Massachusetts, New York, Kentucky, California (Sacramento)
DOI: 10.1037/dev0001490
ISSN: 0012-1649
1939-0599
Abstract: Plausible competing developmental models show similar or identical structural equation modeling model fit indices, despite making very different causal predictions. One way to help address this problem is incorporating outside information into selecting among models. This study attempted to select among developmental models of children's early mathematical skills by incorporating information about the extent to which models forecast the longitudinal pattern of causal impacts of early math interventions. We tested for the usefulness and validity of the approach by applying it to data from three randomized controlled trials of early math interventions with longitudinal follow-up assessments in the United States (Ns = 1,375, 591, 744; baseline age 4.3, 6.5, 4.4; 17%-69% Black). We found that, across data sets, (a) some models consistently outperformed other models at forecasting later experimental impacts, (b) traditional statistical fit indices were not strongly related to causal fit as indexed by models' accuracy at forecasting later experimental impacts, and (c) models showed consistent patterns of similarity and discrepancy between statistical fit and models' effectiveness at forecasting experimental impacts. We highlight the importance of triangulation and call for more comparisons of experimental and nonexperimental estimates for choosing among developmental models.
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2023
Accession Number: EJ1367130
Database: ERIC
FullText Text:
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DbLabel: ERIC
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PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Triangulating on Developmental Models with a Combination of Experimental and Nonexperimental Estimates
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22Wan%2C+Sirui%22">Wan, Sirui</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8750-0977">0000-0002-8750-0977</externalLink>)<br /><searchLink fieldCode="AR" term="%22Brick%2C+Timothy+R%2E%22">Brick, Timothy R.</searchLink><br /><searchLink fieldCode="AR" term="%22Alvarez-Vargas%2C+Daniela%22">Alvarez-Vargas, Daniela</searchLink><br /><searchLink fieldCode="AR" term="%22Bailey%2C+Drew+H%2E%22">Bailey, Drew H.</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Developmental+Psychology%22"><i>Developmental Psychology</i></searchLink>. Feb 2023 59(2):216-228.
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  Data: American Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org
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  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
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  Data: 13
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2023
– Name: SourceSuprt
  Label: Sponsoring Agency
  Group: SrcSuprt
  Data: Institute of Education Sciences (ED)<br />Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (DHHS/NIH)
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Elementary+Education%22">Elementary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Early+Childhood+Education%22">Early Childhood Education</searchLink><br /><searchLink fieldCode="EL" term="%22Preschool+Education%22">Preschool Education</searchLink>
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  Label: Descriptors
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  Data: <searchLink fieldCode="DE" term="%22Mathematics+Skills%22">Mathematics Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Early+Intervention%22">Early Intervention</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Randomized+Controlled+Trials%22">Randomized Controlled Trials</searchLink><br /><searchLink fieldCode="DE" term="%22Urban+Schools%22">Urban Schools</searchLink><br /><searchLink fieldCode="DE" term="%22Elementary+Schools%22">Elementary Schools</searchLink><br /><searchLink fieldCode="DE" term="%22Low+Income+Students%22">Low Income Students</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics+Achievement%22">Mathematics Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Preschools%22">Preschools</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink>
– Name: Subject
  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22Massachusetts%22">Massachusetts</searchLink><br /><searchLink fieldCode="DE" term="%22New+York%22">New York</searchLink><br /><searchLink fieldCode="DE" term="%22Kentucky%22">Kentucky</searchLink><br /><searchLink fieldCode="DE" term="%22California+%28Sacramento%29%22">California (Sacramento)</searchLink>
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  Data: 10.1037/dev0001490
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  Data: 0012-1649<br />1939-0599
– Name: Abstract
  Label: Abstract
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  Data: Plausible competing developmental models show similar or identical structural equation modeling model fit indices, despite making very different causal predictions. One way to help address this problem is incorporating outside information into selecting among models. This study attempted to select among developmental models of children's early mathematical skills by incorporating information about the extent to which models forecast the longitudinal pattern of causal impacts of early math interventions. We tested for the usefulness and validity of the approach by applying it to data from three randomized controlled trials of early math interventions with longitudinal follow-up assessments in the United States (Ns = 1,375, 591, 744; baseline age 4.3, 6.5, 4.4; 17%-69% Black). We found that, across data sets, (a) some models consistently outperformed other models at forecasting later experimental impacts, (b) traditional statistical fit indices were not strongly related to causal fit as indexed by models' accuracy at forecasting later experimental impacts, and (c) models showed consistent patterns of similarity and discrepancy between statistical fit and models' effectiveness at forecasting experimental impacts. We highlight the importance of triangulation and call for more comparisons of experimental and nonexperimental estimates for choosing among developmental models.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: CodeSource
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  Label: Entry Date
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  Data: 2023
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  Data: EJ1367130
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1367130
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      – Type: doi
        Value: 10.1037/dev0001490
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 216
    Subjects:
      – SubjectFull: Mathematics Skills
        Type: general
      – SubjectFull: Early Intervention
        Type: general
      – SubjectFull: Models
        Type: general
      – SubjectFull: Randomized Controlled Trials
        Type: general
      – SubjectFull: Urban Schools
        Type: general
      – SubjectFull: Elementary Schools
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      – SubjectFull: Low Income Students
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      – SubjectFull: Mathematics Achievement
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      – SubjectFull: Preschools
        Type: general
      – SubjectFull: Prediction
        Type: general
      – SubjectFull: Massachusetts
        Type: general
      – SubjectFull: New York
        Type: general
      – SubjectFull: Kentucky
        Type: general
      – SubjectFull: California (Sacramento)
        Type: general
    Titles:
      – TitleFull: Triangulating on Developmental Models with a Combination of Experimental and Nonexperimental Estimates
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            NameFull: Wan, Sirui
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            NameFull: Brick, Timothy R.
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            NameFull: Alvarez-Vargas, Daniela
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            NameFull: Bailey, Drew H.
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              Type: published
              Y: 2023
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              Value: 0012-1649
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              Value: 1939-0599
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              Value: 59
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            – TitleFull: Developmental Psychology
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