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It matters how you start: Early numeracy mastery predicts high school math course-taking and college attendance

dc.contributor.authorDavis-Kean, Pamela E.
dc.contributor.authorDomina, Thurston
dc.contributor.authorKuhfeld, Megan
dc.contributor.authorEllis, Alexa
dc.contributor.authorGershoff, Elizabeth T.
dc.date.accessioned2022-05-06T17:27:23Z
dc.date.available2023-04-06 13:27:22en
dc.date.available2022-05-06T17:27:23Z
dc.date.issued2022-03
dc.identifier.citationDavis-Kean, Pamela E. ; Domina, Thurston; Kuhfeld, Megan; Ellis, Alexa; Gershoff, Elizabeth T. (2022). "It matters how you start: Early numeracy mastery predicts high school math course- taking and college attendance." Infant and Child Development 31(2): n/a-n/a.
dc.identifier.issn1522-7227
dc.identifier.issn1522-7219
dc.identifier.urihttps://hdl.handle.net/2027.42/172290
dc.description.abstractUsing data from the Applied Problems subtest of the Woodcock-Johnson Tests of Achievement (Woodcock & Johnson, 1989/1990, Woodcock-Johnson psycho-educational battery-revised. Allen, TX: DLM Teaching Resources) administered to 1,364 children from the National Institute of Child Health and Human Development (NICHD) Study of Early Childcare and Youth Development (SECCYD), this study measures children’s mastery of three numeric competencies (counting, concrete representational arithmetic and abstract arithmetic operations) at 54 months of age. We find that, even after controlling for key demographic characteristics, the numeric competency that children master prior to school entry relates to important educational transitions in secondary and post-secondary education. Those children who showed low numeric competency prior to school entry enrolled in lower math track classes in high school and were less likely to enrol in college. Important numeracy competency differences at age 54 months related to socioeconomic inequalities were also found. These findings suggest that important indicators of long-term schooling success (i.e., advanced math courses, college enrollment) are evident prior to schooling based on the levels of numeracy mastery.
dc.publisherWiley Periodicals, Inc.
dc.publisherPsychological Corporation
dc.subject.otherlongitudinal
dc.subject.othermathematical achievement
dc.subject.othernumeracy
dc.titleIt matters how you start: Early numeracy mastery predicts high school math course-taking and college attendance
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelPsychology
dc.subject.hlbsecondlevelSocial Work
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/172290/1/icd2281.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/172290/2/icd2281_am.pdf
dc.identifier.doi10.1002/icd.2281
dc.identifier.sourceInfant and Child Development
dc.identifier.citedreferenceNational Center on Education Statistics. ( 2019 ). NAEP Report Card: Mathematics. Retrieved 12/7/19 from https://www.nationsreportcard.gov/mathematics/nation/achievement?grade=4
dc.identifier.citedreferenceMann, H. ( 1848 ). Twelfth annual report to the Massachusetts Board of Education. The Republic and the School: Horace Mann and the Education of Free Men.
dc.identifier.citedreferenceMatthews, P. G., & Fuchs, L. S. ( 2020 ). Keys to the gate? Equal sign knowledge at second grade predicts fourth-grade algebra competence. Child Development, 91 ( 1 ), e14 – e28. https://doi.org/10.1111/cdev.13144
dc.identifier.citedreferenceMatthews, P. G., Lewis, M. R., & Hubbard, E. M. ( 2016 ). Individual differences in nonsymbolic ratio processing predict symbolic math performance. Psychological Science, 27 ( 2 ), 191 – 202. https://doi.org/10.1177/0956797615617799
dc.identifier.citedreferenceMerkley, R., & Ansari, D. ( 2016 ). Why numerical symbols count in the development of mathematical skills: Evidence from brain and behavior. Current Opinion in Behavioral Sciences, 10, 14 – 20. https://doi.org/10.1016/j.cobeha.2016.04.006
dc.identifier.citedreferenceMullis, I. V. S., Martin, M. O., Foy, P., Kelly, D. L., & Fishbein, B. ( 2020 ). TIMSS 2019 international results in mathematics and science. Retrieved from Boston College, TIMSS & PIRLS International Study Center website. Retrieved from https://timssandpirls.bc.edu/timss2019/international-results/
dc.identifier.citedreferenceMurnane, R. J., Willett, J. B., & Levy, F. ( 1995 ). The growing importance of cognitive skills in wage determination (no. w5076). National Bureau of Economic Research. https://doi.org/10.3386/w5076
dc.identifier.citedreferenceMuthén, L. K., & Muthén, B. O.. ( 2016 ). Mplus: Statistical analysis with latent variables. Los Angeles, CA: Muthén & Muthén.1988–
dc.identifier.citedreferenceNational Institute of Child Health and Human Development Early Child Care Research Network. ( 2002 ). Early child care and children’s development prior to school entry: Results from the NICHD study of early child care. American Educational Research Journal, 39, 133 – 164. https://doi.org/10.3102/00028312039001133
dc.identifier.citedreferenceNational Institute of Child Health and Human Development Early Child Care Research Network, & Duncan, G. J. ( 2003 ). Modeling the impacts of child care quality on children’s preschool cognitive development. Child Development, 74, 1454 – 1475. https://doi.org/10.1111/1467-8624.00617
dc.identifier.citedreferenceOECD. ( 2019 ). PISA 2018 results (volume III): What school life means for Students’ lives, PISA. Paris: OECD Publishing. https://doi.org/10.1787/acd78851-en
dc.identifier.citedreferencePurpura, D. J., & Simms, V. ( 2018 ). Approximate number system development in preschool: What factors predict change? Cognitive Development, 45, 31 – 39. https://doi.org/10.1016/j.cogdev.2017.11.001
dc.identifier.citedreferenceReardon, S. F. ( 2011 ). The widening academic achievement gap between the rich and the poor: New evidence and possible explanations. Whither Opportunity. 91 – 116.
dc.identifier.citedreferenceRupp, A. A., Templin, J., & Henson, R. A. ( 2010 ). Diagnostic assessment: Theory, methods, and applications. New York: Guilford.
dc.identifier.citedreferenceSarama, J., & Clements, D. H. ( 2009 ). Early childhood mathematics education research: Learning trajectories for young children. New York: Routledge.
dc.identifier.citedreferenceSiegler, R. S. ( 2016 ). Magnitude knowledge: The common core of numerical development. Developmental Science, 19 ( 3 ), 341 – 361. https://doi.org/10.1111/desc.12395
dc.identifier.citedreferenceSiegler, R. S., Duncan, G. J., Davis-Kean, P. E., Duckworth, K., Claessens, A., Engel, M., … Chen, M. ( 2012 ). Early predictors of high school mathematics achievement. Psychological Science, 23 ( 7 ), 691 – 697. https://doi.org/10.1177/0956797612440101
dc.identifier.citedreferenceSokolowski, H. M., Fias, W., Ononye, C. B., & Ansari, D. ( 2017 ). Are numbers grounded in a general magnitude processing system? A functional neuroimaging meta-analysis. Neuropsychologia, 105, 50 – 69. https://doi.org/10.1016/j.neuropsychologia.2017.01.019
dc.identifier.citedreferenceSusperreguy, M. I., & Davis-Kean, P. E. ( 2016 ). Maternal math talk in the home and math skills in preschool children. Early Education and Development, 27 ( 6 ), 841 – 857. https://doi.org/10.1080/10409289.2016.1148480
dc.identifier.citedreferenceTemplin, J., & Bradshaw, L. ( 2013 ). Measuring the reliability of diagnostic classification model examinee estimates. Journal of Classification, 30 ( 2 ), 251 – 275.
dc.identifier.citedreferencevon Hippel, P. T., & Hamrock, C. ( 2019 ). Do test score gaps grow before, during, or between the school years? Measurement artifacts and what we can know in spite of them. Sociological Science, 6, 43 – 80. https://doi.org/10.15195/v6.a3
dc.identifier.citedreferencevon Hippel, P. T., Workman, J., & Downey, D. B. ( 2018 ). Inequality in reading and math skills forms mainly before kindergarten: A replication, and partial correction, of “are schools the great equalizer? ”. Sociology of Education, 91 ( 4 ), 323 – 357. https://doi.org/10.1177/0038040718801760
dc.identifier.citedreferenceWatts, T. W., Duncan, G. J., Clements, D. H., & Sarama, J. ( 2018 ). What is the long-run impact of learning mathematics during preschool? Child Development, 89 ( 2 ), 539 – 555. https://doi.org/10.1111/cdev.12713
dc.identifier.citedreferenceWatts, T. W., Duncan, G. J., Siegler, R. S., & Davis-Kean, P. E. ( 2014 ). What’s past is prologue: Relations between early mathematics knowledge and high school achievement. Educational Researcher, 43, 352 – 360. https://doi.org/10.3102/0013189X14553660
dc.identifier.citedreferenceWoodcock, R. W., & Johnson, M. B. ( 1989 /1990). Woodcock-Johnson psycho-educational battery-revised. Allen, TX: DLM Teaching Resources.
dc.identifier.citedreferenceAttewell, P., & Domina, T. ( 2008 ). Raising the bar: Curricular intensity and academic performance. Educational Evaluation and Policy Analysis, 30 ( 1 ), 51 – 71.
dc.identifier.citedreferenceBarnard, W. M. ( 2004 ). Parent involvement in elementary school and educational attainment. Children and Youth Services Review, 26 ( 1 ), 39 – 62. https://doi.org/10.1016/j.childyouth.2003.11.002
dc.identifier.citedreferenceBayley, N. ( 1993 ). Bayley scales of infant development ( 2nd ed. ). New York, NY: Psychological Corporation.
dc.identifier.citedreferenceBradley, R. H., & Caldwell, B. M. ( 1979 ). Home observation for measurement of the environment: A revision of the preschool scale. American Journal of Mental Deficiency, 84, 235 – 244.
dc.identifier.citedreferenceBradley, R. H., & Corwyn, R. F. ( 2002 ). Socioeconomic status and child development. Annual Review of Psychology, 53 ( 1 ), 371 – 399. https://doi.org/10.1146/annurev.psych.53.100901.135233
dc.identifier.citedreferenceBrooks-Gunn, J., & Duncan, G. J. ( 1997 ). The effects of poverty on children. The Future of Children, 7 ( 2 ), 55 – 71. https://doi.org/10.2307/1602387
dc.identifier.citedreferenceBurris, C. C., Heubert, J. P., & Levin, H. M. ( 2004 ). Math acceleration for all. Educational Leadership, 61 ( 5 ), 68 – 72.
dc.identifier.citedreferenceCai, L. ( 2016 ). flexMIRT version 3: Flexible multilevel multidimensional item analysis and test scoring. Seattle, WA: Vector Psychometric Group.
dc.identifier.citedreferenceCard, D. ( 1999 ). The causal effect of education on earnings. In Handbook of labor economics (Vol. 3, pp. 1801 – 1863 ). Amsterdam, Netherlands: Elsevier.
dc.identifier.citedreferenceChoi, H. J., Rupp, A. A., & Pan, M. ( 2012 ). Standardized diagnostic assessment design and analysis: Key ideas from modern measurement theory. In Self-directed learning oriented assessments in the Asia-Pacific (pp. 61 – 85 ). Dordrecht: Springer. https://doi.org/10.1007/978-94-007-4507-0_4
dc.identifier.citedreferenceClaessens, A., Engel, M., & Curran, F. C. ( 2014 ). Academic content, student learning, and the persistence of preschool effects. American Educational Research Journal, 51 ( 2 ), 403 – 434. https://doi.org/10.3102/0002831213513634
dc.identifier.citedreferenceConnor, C. M., Morrison, F. J., Fishman, B. J., Schatschneider, C., & Underwood, P. ( 2007 ). Algorithm-guided individualized reading instruction. Science, 315 ( 5811 ), 464. https://doi.org/10.1126/science.1134513
dc.identifier.citedreferenceConnor, C. M., Morrison, F. J., & Katch, L. E. ( 2004 ). Beyond the reading wars: Exploring the effect of child-instruction interactions on growth in early reading. Scientific Studies of Reading, 8 ( 4 ), 305 – 336. https://doi.org/10.1207/s1532799xssr0804_1
dc.identifier.citedreferenceConnor, C. M., Morrison, F. J., Schatschneider, C., Toste, J. R., Lundblom, E., Crowe, E. C., & Fishman, B. ( 2011 ). Effective classroom instruction: Implications of child characteristics by reading instruction interactions on first graders’ word reading achievement. Journal of Research on Educational Effectiveness, 4 ( 3 ), 173 – 207. https://doi.org/10.1080/19345747.2010.510179
dc.identifier.citedreferenceDavis-Kean, P. E. ( 2005 ). The influence of parent education and family income on child achievement: The indirect role of parental expectations and the home environment. Journal of Family Psychology, 19 ( 2 ), 294 – 304. https://doi.org/10.1037/0893-3200.19.2.294
dc.identifier.citedreferenceDavis-Kean, P. E., & Jager, J. ( 2014 ). Trajectories of achievement within race/ethnicity: “Catching up” in achievement across time. The Journal of Educational Research, 107 ( 3 ), 197 – 208. https://doi.org/10.1080/00220671.2013.807493
dc.identifier.citedreferenceDe La Torre, J., & Douglas, J. A. ( 2004 ). Higher-order latent trait models for cognitive diagnosis. Psychometrika, 69 ( 3 ), 333 – 353. https://doi.org/10.1007/BF02295640
dc.identifier.citedreferenceDunn, L., & Dunn, L. ( 1981 ). PPVT-R Manual. Circle Pines, MN: American Guidance Service.
dc.identifier.citedreferenceGierl, M. J., Alves, C., & Majeau, R. T. ( 2010 ). Using the attribute hierarchy method to make diagnostic inferences about examinees’ knowledge and skills in mathematics: An operational implementation of cognitive diagnostic assessment. International Journal of Testing, 10 ( 4 ), 318 – 341. https://doi.org/10.1080/15305058.2010.509554
dc.identifier.citedreferenceGraham, J. W., Olchowski, A. E., & Gilreath, T. D. ( 2007 ). How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prevention Science, 8 ( 3 ), 206 – 213. https://doi.org/10.1007/s11121-007-0070-9:10.1007/s11121-007-0070-9
dc.identifier.citedreferenceHeckman, J. J., Humphries, J. E., & Veramendi, G. ( 2018a ). The nonmarket benefits of education and ability. Journal of Human Capital, 12 ( 2 ), 282 – 304. https://doi.org/10.1086/697535
dc.identifier.citedreferenceHeckman, J. J., Humphries, J. E., & Veramendi, G. ( 2018b ). Returns to education: The causal effects of education on earnings, health, and smoking. Journal of Political Economy, 126 ( S1 ), S197 – S246. https://doi.org/10.1086/698760
dc.identifier.citedreferenceHeckman, J. J., Moon, S. H., Pinto, R., Savelyev, P. A., & Yavitz, A. ( 2010 ). The rate of return to the HighScope Perry preschool program. Journal of Public Economics, 94 ( 1–2 ), 114 – 128. https://doi.org/10.1016/j.jpubeco.2009.11.001
dc.identifier.citedreferenceHu, L., & Bentler, P. M. ( 1999 ). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1 – 55. https://doi.org/10.1080/10705519909540118
dc.identifier.citedreferenceKuhfeld, M., Gershoff, E. T., & Paschall, K. W. ( 2018 ). The development of racial/ethnic and socioeconomic achievement gaps during the school years. Journal of Applied Developmental Psychology, 57, 62 – 73. https://doi.org/10.1016/j.appdev.2018.07.001
dc.identifier.citedreferenceLee, J. S., & Bowen, N. K. ( 2006 ). Parent involvement, cultural capital, and the achievement gap among elementary school children. American Educational Research Journal, 43 ( 2 ), 193 – 218. https://doi.org/10.3102/00028312043002193
dc.identifier.citedreferenceLevine, S. C., Suriyakham, L. W., Rowe, M. L., Huttenlocher, J., & Gunderson, E. A. ( 2010 ). What counts in the development of young children’s number knowledge? Developmental Psychology, 46 ( 5 ), 1309. https://doi.org/10.1037/a0019671
dc.identifier.citedreferenceLibertus, M. E., Odic, D., Feigenson, L., & Halberda, J. ( 2016 ). The precision of mapping between number words and the approximate number system predicts children’s formal math abilities. Journal of Experimental Child Psychology, 150, 207 – 226. https://doi.org/10.1016/j.jecp.2016.06.003
dc.identifier.citedreferenceLong, M. C., Conger, D., & Iatarola, P. ( 2012 ). Effects of high school course-taking on secondary and postsecondary success. American Educational Research Journal, 49 ( 2 ), 285 – 322. https://doi.org/10.3102/0002831211431952
dc.identifier.citedreferenceLyons, I. M., Bugden, S., Zheng, S., De Jesus, S., & Ansari, D. ( 2018 ). Symbolic number skills predict growth in nonsymbolic number skills in kindergarteners. Developmental Psychology, 54 ( 3 ), 440. https://doi.org/10.1037/dev0000445
dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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