Comparisons of means using exploratory and confirmatory approaches.

Kuiper, Rebecca M.; Hoijtink, Herbert

doi:10.1037/a0018720

Cited by 37 publications

(49 citation statements)

References 31 publications

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“…In these papers it is often claimed that with the formulation and evaluation of informative hypotheses more powerful methods are obtained. In a few papers, some examples are provided to support this claim with numbers, e.g., [19] [20]. However, so far, no systematic study of the power of different-exploratory and confirmatory-approaches was reported and this is, therefore, the main contribution of this paper.…”

Section: Discussionmentioning

confidence: 94%

Confirmatory Methods, or Huge Samples, Are Required to Obtain Power for the Evaluation of Theories

Klugkist¹,

Post²,

Haarhuis³

et al. 2014

OJS

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Experimental studies are usually designed with specific expectations about the results in mind. However, most researchers apply some form of omnibus test to test for any differences, with follow up tests like pairwise comparisons or simple effects analyses for further investigation of the effects. The power to find full support for the theory with such an exploratory approach which is usually based on multiple testing is, however, rather disappointing. With the simulations in this paper we showed that many of the common choices in hypothesis testing led to a severely underpowered form of theory evaluation. Furthermore, some less commonly used approaches were presented and a comparison of results in terms of power to find support for the theory was made. We concluded that confirmatory methods are required in the context of theory evaluation and that the scientific literature would benefit from a clearer distinction between confirmatory and exploratory findings. Also, we emphasis the importance of reporting all tests, significant or not, including the appropriate sample statistics like means and standard deviations. Another recommendation is related to the fact that researchers, when they discuss the conclusions of their own study, seem to underestimate the role of sampling variability. The execution of more replication studies in combination with proper reporting of all results provides insight in between study variability and the amount of chance findings.

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Section: Discussionmentioning

confidence: 94%

Confirmatory Methods, or Huge Samples, Are Required to Obtain Power for the Evaluation of Theories

Klugkist¹,

Post²,

Haarhuis³

et al. 2014

OJS

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“…Fourth, we deliberately chose not to make this paper a comparison between null hypothesis testing and the Bayesian evaluation of informative hypotheses. For such comparisons we refer the reader to Kuiper and Hoijtink (2010) or Van de Schoot et al (2011a). Moreover, we chose not to provide the reader with any guidelines or cut-off values to interpret the Bayesian output.…”

Section: Discussionmentioning

confidence: 99%

Illustrating Bayesian Evaluation of Informative Hypotheses for Regression Models

Kluytmans¹,

Schoot²,

Mulder³

et al. 2012

Front. Psychology

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In the present article we illustrate a Bayesian method of evaluating informative hypotheses for regression models. Our main aim is to make this method accessible to psychological researchers without a mathematical or Bayesian background. The use of informative hypotheses is illustrated using two datasets from psychological research. In addition, we analyze generated datasets with manipulated differences in effect size to investigate how Bayesian hypothesis evaluation performs when the magnitude of an effect changes. After reading this article the reader is able to evaluate his or her own informative hypotheses.

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“…The main elements of this methodology are introduced below and are described in more technical detail in Appendix 2; however, for more detailed information about this method, see also Hoijtink and Boom (2008). Comparisons between more traditional ways of analyzing data and confirmatory Bayesian approaches are described in Van de Schoot and colleagues (in press) and Kuiper and Hoijtink (2010), for example. The first step in Bayesian model selection (BMS) is to specify the inequality constraints among the latent class probabilities of interest.…”

Section: Strategy Of Analysismentioning

confidence: 97%