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Graduate students

  • Use presets or change parameter values manually to explore the cost-effectiveness of different research approaches to unearth true scientific discoveries. For detailed explanation and conceptual background, see LeBel, Campbell, & Loving (in press, JPSP), Table 3. This app is an extension of Zehetleitner and Felix Schönbrodt's (2016) positive predictive value app

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  • This app allows you to derive an approximation to the difference in Bayesian information criterion and to the probability of the null and the alternative hypothesis from the sum of squares obtained in an ANOVA analysis.

    Required input

    • Number of participants
    • Df ... degrees of freedom of the effect of interest
    • Whether the effect is between or within participants
    • SSEffect ... sum of squares of the effect of interest
    • SSError ... sum of squares of the error, for within-factors the by-subject error, associated with this effect
    • SSTotal ... total sum of squares, only required for within-participant designs when using effective sample size (strongly recommended, Nathoo & Masson, 2007)
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  • Plot the theoretical p-value distribution and power curve for an independent t-test based on the effect size, sample size, and alpha.

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  • Explore the Vovk-Sellke Maximum p-Ratio, a measure that indicates the maximum diagnosticity of a given p-value. Choose your own p-value to find out how diagnostic it is for your research!

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  • This resource is designed to provide new users to R, RStudio, and R Markdown with the introductory steps needed to begin their own reproducible research. Many screenshots and screencasts (with no audio) will be included, but if further clarification is needed on these or any other aspect of the book, please create a GitHub issue here or email me with a reference to the error/area where more guidance is necessary.  It is recommended that you have R version 3.3.0 or later, RStudio Desktop version 1.0 or higher, and rmarkdown R package version 1.0 or higher. 

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  • These handouts/links give a foundational understanding of how to set up and use R

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  • This page presents a series of tutorials and interdisciplinary case studies that can be used in a variety of blended as well as brick-and-mortar courses. The materials can be used in introductory level data science courses as well as more advanced data science or statistics courses.  These materials assume that students have a basic prior knowledge of R or Rstudio.

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  • The goal of this text is to provide a broad set of topics and methods that will give students a solid foundation in understanding how to make decisions with data. This text presents workbook-style, project-based material that emphasizes real world applications and conceptual understanding. Each chapter contains:

    • An introductory case study focusing on a particular statistical method in order to encourage students to experience data analysis as it is actually practiced.
    • guided research project that walks students through the entire process of data analysis, reinforcing statistical thinking and conceptual understanding.
    • Optional extended activities that provide more in-depth coverage in diverse contexts and theoretical backgrounds. These sections are particularly useful for more advanced courses that discuss the material in more detail. Some Advanced Lab sections that require a stronger background in mathematics are clearly marked throughout the text.
    • Data sets from multiple disciplines and software instructions for Minitab and R.

    The text is highly adaptable in that the various chapters/parts can be taken out of order or even skipped to customize the course to your audience. Depending on the level of in-class active learning, group work, and discussion that you prefer in your course, some of this work might occur during class time and some outside of class. 

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  • One of the original (and still best) sources for archived data.

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  • Correspondence analysis is a method allowing you to describe synthetically a contingency table in which homogeneous individuals are classified on two criterias (or categorical variables, continuous ones being usable if discretized).  This resource tells how it can be used, graphical representations of this process, and gives examples of it in action. 

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