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 |
| 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 |
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