Useful Links
Below are a number of guides and links to help you with statistics and research methods.
This shiny app allows you to conduct meta analysis as well as inter-rater reliability, publication bias and calculate effect sizes.
Free internet sources for data
Interpreting random effects in linear mixed-effect models
This blog helps explain a difficult statistical method.
Averages and Likert Scale Data
This book presents better ways to analyze Likert scale data than averages.
Frequnetism and Bayesianism: A practical Introduction
A 5 part series on two approaches to statistics.
MorePower 6.0 is a flexible freeware statistical calculator that computes sample size, effect size, and power statistics for factorial ANOVA designs.
Power ANalysis for GEneral Anova designs
A quick experiment displaying the impact of sample size on estimates we make from the data.
Test of Insufficient Variance - TIVA
A tool to detect questionable research practice
Interactive tools for meta-analysis, power analysis and experimental planning.
An interactive visualization for interpreting Cohen's d
Data Generator for Teaching Statistics
Helpful for creating datasets for specific tests
A tools to simulate existing datasets
A tool to draw (simulate) your data
Sample size estimation by simulation! (in R)
How many trials should each participant do in an experiment?
A discussion of power looking at trials instead of participants
This site provides you with a web application to plot experimental data from an estimation statistics perspective.
This document is summarised in the table below. It shows the linear models underlying common parametric and “non-parametric”" tests.
This tutorial details a few ways Lisa Debruin simulates data. She'll be using some functions from my faux package to make it easier to generate sets of variables with specific correlations.
his post provides a discussion of best practices1 for developing code-based projects and for writing R code in a research setting with an eye toward proactively avoiding common pitfalls.
The goal of ANOVApower is to easily simulate ANOVA designs and calculate the observed power.
Professor Andy Field has a great book, An Adventure in Statistics, and R package, adventr, to help understand statistics. The A-Z list provides in-depth information on each subject.
Rice Virtual Lab in Statistics
An online stats book as well as a number of case studies with real research studies and data.
Teaching reproducible research using R
Beautiful website showing how to plot data the correct way.