Can you "see" a group mean difference, just by eyeballing the data? Is your gut feeling aligned to the formal index of evidence, the Bayes factor?
Can you "see" a group mean difference, just by eyeballing the data? Is your gut feeling aligned to the formal index of evidence, the Bayes factor?
Visualizing the Bayes factor (quantification of evidence supporting a null or altermative hypothesis) using the urn model.
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.
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
Plot the theoretical p-value distribution and power curve for an independent t-test based on the effect size, sample size, and alpha.
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!
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.
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:
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.
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.
The Research Methods Knowledge Base is a comprehensive web-based textbook that addresses all of the topics in a typical introductory undergraduate or graduate course in social research methods. It covers the entire research process including: formulating research questions; sampling (probability and nonprobability); measurement (surveys, scaling, qualitative, unobtrusive); research design (experimental and quasi-experimental); data analysis; and, writing the research paper. It also addresses the major theoretical and philosophical underpinnings of research including: the idea of validity in research; reliability of measures; and ethics. The Knowledge Base was designed to be different from the many typical commercially-available research methods texts. It uses an informal, conversational style to engage both the newcomer and the more experienced student of research. It is a fully hyperlinked text that can be integrated easily into an existing course structure or used as a sourcebook for the experienced researcher who simply wants to browse.
Navigate this source: http://www.socialresearchmethods.net/kb/contents.php