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Integrating informative hypotheses into the EffectLiteR framework

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Abstract
Using the EffectLiteR framework, researchers can test classical null hypotheses about effects of interest via Wald and F-tests, while taking into account the stochastic nature of group sizes. This paper aims at extending EffectLiteR to test informative hypotheses, assuming for example that the average effect of a new treatment is greater than the average effect of an old treatment, which in turn is greater than zero. We present a simulated data example to show two methodological novelties. First, we illustrate how to use the Fbar- and generalized linear Wald test to assess informative hypotheses. While the classical test did not reach significance, the informative test correctly rejected the null hypothesis, indicating the need to take into account the order of the treatment groups. Second, we demonstrate how to account for stochastic group sizes in informative hypotheses using the generalized non-linear Wald statistic. The paper concludes with a short data example.
Keywords
treatment effects, prior order expectations, type I error, higher power, hypothesis testing, Wald tests and F-tests for informative effect hypotheses, CONSTRAINTS, INEQUALITY, EQUALITY, PACKAGE

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MLA
Keck, Caroline, et al. “Integrating Informative Hypotheses into the EffectLiteR Framework.” METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES, vol. 17, no. 4, 2021, pp. 307–25, doi:10.5964/meth.7379.
APA
Keck, C., Mayer, A., & Rosseel, Y. (2021). Integrating informative hypotheses into the EffectLiteR framework. METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES, 17(4), 307–325. https://doi.org/10.5964/meth.7379
Chicago author-date
Keck, Caroline, Axel Mayer, and Yves Rosseel. 2021. “Integrating Informative Hypotheses into the EffectLiteR Framework.” METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES 17 (4): 307–25. https://doi.org/10.5964/meth.7379.
Chicago author-date (all authors)
Keck, Caroline, Axel Mayer, and Yves Rosseel. 2021. “Integrating Informative Hypotheses into the EffectLiteR Framework.” METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES 17 (4): 307–325. doi:10.5964/meth.7379.
Vancouver
1.
Keck C, Mayer A, Rosseel Y. Integrating informative hypotheses into the EffectLiteR framework. METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES. 2021;17(4):307–25.
IEEE
[1]
C. Keck, A. Mayer, and Y. Rosseel, “Integrating informative hypotheses into the EffectLiteR framework,” METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES, vol. 17, no. 4, pp. 307–325, 2021.
@article{8737748,
  abstract     = {{Using the EffectLiteR framework, researchers can test classical null hypotheses about effects of interest via Wald and F-tests, while taking into account the stochastic nature of group sizes. This paper aims at extending EffectLiteR to test informative hypotheses, assuming for example that the average effect of a new treatment is greater than the average effect of an old treatment, which in turn is greater than zero. We present a simulated data example to show two methodological novelties. First, we illustrate how to use the Fbar- and generalized linear Wald test to assess informative hypotheses. While the classical test did not reach significance, the informative test correctly rejected the null hypothesis, indicating the need to take into account the order of the treatment groups. Second, we demonstrate how to account for stochastic group sizes in informative hypotheses using the generalized non-linear Wald statistic. The paper concludes with a short data example.}},
  author       = {{Keck, Caroline and Mayer, Axel and Rosseel, Yves}},
  issn         = {{1614-1881}},
  journal      = {{METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES}},
  keywords     = {{treatment effects,prior order expectations,type I error,higher power,hypothesis testing,Wald tests and F-tests for informative effect hypotheses,CONSTRAINTS,INEQUALITY,EQUALITY,PACKAGE}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{307--325}},
  title        = {{Integrating informative hypotheses into the EffectLiteR framework}},
  url          = {{http://doi.org/10.5964/meth.7379}},
  volume       = {{17}},
  year         = {{2021}},
}

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