Getting beyond the Null: Statistical Modeling as an Alternative Framework for Inference in Developmental Science

REFERENCES

• Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.
   PubMed Web of Science ®Google Scholar
• Cohen, B. H. (2017). Why the resistance to statistical innovations? A comment on sharpe (2013). Psychological Methods, 22, 204–210. doi:10.1037/met0000058
   PubMed Web of Science ®Google Scholar
• Collins, L. M. (2006). Analysis of longitudinal data: The integration of theoretical model, temporal design, and statistical model. Annual Review of Psychology, 57, 505–528. doi:10.1146/annurev.psych.57.102904.190146
   PubMed Web of Science ®Google Scholar
• Depaoli, S., & Van De Schoot, R. (2015). Improving transparency and replication in Bayesian statistics: The WAMBS-Checklist. Psychological Methods. Advance online publication. doi:10.1037/met0000065
   PubMed Web of Science ®Google Scholar
• Depaoli, S., & van de Schoot, R. (2017). Improving transparency and replication in Bayesian statistics: The WAMBS-Checklist. Psychological Methods, 22(2),240–261. doi: 10.1037/met0000065
   PubMed Web of Science ®Google Scholar
• Efron, B. (1979). Bootstrap methods: Another look at the jackknife. Annals of Statistics, 7(1), 1–26.
   Web of Science ®Google Scholar
• Efron, B. (1981). Nonparametric standard errors and confidence intervals. Canadian Journal of Statistics, 9(2), 139–158.
   Google Scholar
• Eidelson, R. J. (1997). Complex adaptive systems in the behavioral and social sciences. Review of General Psychology, 1(1), 42–71.
   Google Scholar
• Fisher, R. A. (1932). Inverse probability and the use of likelihood. Proceedings of the Cambridge Philosophical Society, 28, 257–261. doi:10.1017/S0305004100010094
   Google Scholar
• Fisher, R. A. (1954). Statistical methods for research workers (12th ed.). Edinburgh, Scotland: Oliver & Boyd.
   Google Scholar
• Fisher, R. A. (1955). Statistical methods and scientific induction. Journal of the Royal Statistical Society Series B, 17, 69–78.
   Google Scholar
• Foster, E. M., & Kalil, A. (2005). Developmental psychology and public policy: Progress and prospects. Developmental Psychology, 41(6),827–832. doi: 10.1037/0012-1649.41.6.827
   PubMed Web of Science ®Google Scholar
• Fisher, R. A. (1960). The design of experiments (7th ed.). Edinburgh, Scotland: Oliver & Boyd.
   Google Scholar
• Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2014). Bayesian data analysis (Vol. 2). Boca Raton, FL: Chapman & Hall/CRC.
   Google Scholar
• Gelman, A., & Loken, E. (2014). The statistical crisis in science. American Scientist, 102, 460–465.
   Web of Science ®Google Scholar
• Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7, 457–472.
   Google Scholar
• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). New York: Springer.
   Google Scholar
• Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York, NY: Guilford Press.
   Google Scholar
• Jose, P. E. (2013). Doing statistical mediation and moderation. New York, NY: Guilford Press.
   Google Scholar
• Kim, S. (2015). Ppcor: Partial and semi-partial (part) correlation (R package version 1.1). Retrieved from https://CRAN.R-project.org/package=ppcor
   Google Scholar
• Kline, R. B. (2015). Principles and practice of structural equation modeling. New York, NY: Guilford publications.
   Google Scholar
• Krueger, J. I. (2014, February 17). The quest for replicable results in psychology [web log post]. Retrieved from https://www.psychologytoday.com/blog/one-among-many/201402/the-quest-replicable-results-in-psychology
   Google Scholar
• Lee, S., & Hershberger, S. (1990). A simple rule for generating equivalent models in structural equation modeling. Multivariate Behavioral Research, 25, 313–334.
   PubMed Web of Science ®Google Scholar
• Levine, T. R., Weber, R., Hullett, C. R., Park, H. S., & Lindsey, L. (2008). A critical assessment of null hypothesis significance testing in quantitative communication research. Human Communication Research, 34, 171–187.
   Web of Science ®Google Scholar
• Lindquist, E. F. (1940). Statistical analysis in educational research. Boston, MA: Houghton Mifflin.
   Google Scholar
• Little, T. D., Widaman, K. F., Levy, R., Rodgers, J. L., & Hancock, G. R. (in press). Error, Error in my Model, Who’s the Fairest of Them All? Research in Human Development.
   Google Scholar
• Little, T. D., Gorrall, B. K., Panko, P., & Curtis, J. D. (in press). Modern Practices to Improve Human Developmental Research. Research in Human Development.
   Google Scholar
• Little, T. D. (2015). Methodological practice as matters of justice, justification, and the pursuit of verisimilitude. Research in Human Development, 12, 268–273.
   Web of Science ®Google Scholar
• Lugo-Gil, J., Lee, A., Vohra, D., Adamek, K., Lacoe, J., & Goesling, B., & U.S. Department of Health and Human Services. (2016). Updated findings from the HHS teen pregnancy prevention evidence review: July 2014 through August 2015 (Research Report). Retrieved from https://tppevidencereview.aspe.hhs.gov/pdfs/Summary_of_findings_2015.pdf
   Google Scholar
• MacCallum, R. C., Wegener, D. T., Uchino, B. N., & Fabrigar, L. R. (1993). The problem of equivalent models in applications of covariance structure analysis. Psychological Bulletin, 114, 185–199.
   PubMed Web of Science ®Google Scholar
• McElreath, R. (2016). Statistical rethinking: A Bayesian course with examples in R and Stan. Boca Raton, FL: CRC Press.
   Google Scholar
• Neyman, J. (1935). On the problem of confidence intervals. Annals of Mathematical Statistics, 6, 111–116. doi:10.1214/aoms/1177732585
   Google Scholar
• Neyman, J. (1942). Basic ideas and some recent results of the theory of testing statistical hypotheses. Journal of the Royal Statistical Society, 105, 292–327. doi:10.2307/2980436
   Google Scholar
• Neyman, J. (1955). The problem of inductive inference. Communications in Pure & Applied Mathematics, 3, 13–46. doi:10.1002/cpa.3160080103
   Web of Science ®Google Scholar
• Neyman, J., & Pearson, E. S. (1928). On the use and interpretation of certain test criteria for purposes of statistical inference. Particle I Biometrika, 20A, 175–240. doi:10.2307/2331945
   Google Scholar
• Neyman, J., & Pearson, E. S. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society of London A, 231, 289–337. doi:10.1098/rsta.1933.0009
   Google Scholar
• Nickerson, R. S. (2000). Null hypothesis significance testing: A review of an old and continuing controversy. Psychological Methods, 5, 241–301.
   PubMed Web of Science ®Google Scholar
• Nuzzo, R. (2014). Statistical errors. Nature, 506, 150–152.
   PubMed Web of Science ®Google Scholar
• Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge, England: Cambridge University Press.
   Google Scholar
• Plummer, M., Best, N., Cowles, K., & Vines, K. (2006). CODA: Convergence diagnosis and output analysis for MCMC. R News, 6, 7–11.
   Google Scholar
• Putnam, S. P., Jacobs, J., Gartstein, M. A., & Rothbart, M. K. (2010, March). Development and assessment of short and very short forms of the early childhood behavior questionnaire. Poster presented at International Conference on Infant Studies, Baltimore, MD.
   Google Scholar
• Putnam, S. P., & Rothbart, M. K. (2006). Development of short and very short forms of the children’s behavior questionnaire. Journal of Personality Assessment, 87(1), 103–113.
   Web of Science ®Google Scholar
• R Core Team. (2017). R: A language and environment for statistical computing., Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org
   Google Scholar
• Revelle, W. (2017) Psych: Procedures for personality and psychological research (R package version 1.7.5). Retrieved from https://CRAN.R-project.org/package=psych Version = 1.7.5
   Google Scholar
• Rodgers, J. L. (2010a). The epistemology of mathematical and statistical modeling: A quiet methodological revolution. American Psychologist, 65(1), 1–12. doi:10.1037/a0018326
   PubMed Web of Science ®Google Scholar
• Rodgers, J. L. (2010b). Statistical and mathematical modeling versus NHST? There’s no competition! Journal of Modern Applied Statistical Methods, 9(2), 340–347.
   Google Scholar
• Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. Retrieved from http://www.jstatsoft.org/v48/i02/
   Web of Science ®Google Scholar
• Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312. doi: 10.2307/270723
   Google Scholar
• Sparrow, S. S., Balla, D. A., & Cicchetti, D. V. (2005). Vineland-II adaptive behavior scales. Circle Pines, MN: American Guidance Service Publishing.
   Google Scholar
• Stan Development Team. (2016). The stan C++ library (version 2.15). Retreived from http://mc-stan.org/
   Google Scholar
• Stan Development Team. (2017). RStan: The R interface to stan (R package version 2.15.1). Retrieved from https://CRAN.R-project.org/package=rstan
   Google Scholar
• Stelzl, I. (1986). Changing the causal hypothesis without changing the fit: Some rules for generating equivalent path models. Multivariate Behavioral Research, 21, 309–331.
   PubMed Web of Science ®Google Scholar
• Van Buuren, S., & Groothuis-Oudshoorn, K. (2011). Mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67.
   Web of Science ®Google Scholar
• Wasserstein, R. L., & Lazar, N. A. (2016). The ASA’s statement on p-values: Context, process, and purpose. The American Statistician 70, 129–133. doi:10.1080/00031305.2016.1154108
   Web of Science ®Google Scholar
• Williams, M. N., Bååth, R. A., & Philipp, M. C. (in press). Using Bayes factors to test hypotheses in developmental research. Research in Human Development.
   Google Scholar
• Zondervan-Zwijnenburg, M., Peeters, M., Depaoli, S., & van de Schoot, R. (in press). Where do priors come from? A Bayesian evaluation of a latent growth model with informative priors. Research in Human Development.
   Google Scholar