Getting beyond the null

Statistical modeling as an alternative framework for inference in developmental science

K.M. Lang, Shauna J. Sweet, Elizabeth M. Grandfield

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

We describe statistical modeling as a powerful alternative to null hypothesis significance testing (NHST). Modeling supports statistical inference in a fundamentally different way from NHST which can better serve developmental researchers. Modeling requires researchers to fully articulate their beliefs about the processes under study and to communicate that understanding through the structure of a probabilistic model before testing specific hypotheses. Research hypotheses are assessed through estimated parameters of the model and by conducting model comparisons. We conclude the paper with a series of worked examples that highlight the merits of the statistical modeling approach as a tool for scientific inference.

Original languageEnglish
Pages (from-to)287-304
JournalResearch in Human Development
Volume14
Issue number4
DOIs
Publication statusPublished - 2017

Cite this

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Getting beyond the null : Statistical modeling as an alternative framework for inference in developmental science. / Lang, K.M.; Sweet, Shauna J.; Grandfield, Elizabeth M.

In: Research in Human Development, Vol. 14, No. 4, 2017, p. 287-304.

Research output: Contribution to journalArticleScientificpeer-review

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AU - Grandfield, Elizabeth M.

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AB - We describe statistical modeling as a powerful alternative to null hypothesis significance testing (NHST). Modeling supports statistical inference in a fundamentally different way from NHST which can better serve developmental researchers. Modeling requires researchers to fully articulate their beliefs about the processes under study and to communicate that understanding through the structure of a probabilistic model before testing specific hypotheses. Research hypotheses are assessed through estimated parameters of the model and by conducting model comparisons. We conclude the paper with a series of worked examples that highlight the merits of the statistical modeling approach as a tool for scientific inference.

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