statistics+

On this page, I will aggregate issues related to statistics proper.  For a developed resource page on mixed regression models, go here. This page will cover facts about other statistics – effect sizes, bayesian analyses, homo/heteroskedasticity, etc. Given the need to run high powered studies to output worthwhile science, I will also be collecting links and papers here regarding: how to run power analyses, the concepts behind them, simulations, and general information about them along with issues of replication and reproducibility of analyses.This page is currently under construction.

Error management

• Family-wise error rate: Link

T Test

• The T Test
• The test statistic

Degrees of Freedom

• Link

Testing Variance homo/heterogeneity

• Understanding F-test to compare variances (R’s var.test)
• Compare Multiple Sample Variances in R
  
• Variance homogeneity in two or more groups

Effect sizes

• On standardized or unstandardized Effect sizes: Link
• Interpreting Cohen’s d effect size
• Abandoning standardised effect sizes and opening up other roads to power

• Five different “Cohen’s d” statistics for within-subject designs

NHST

• Null Hypothesis Testing: Link

• Understanding Statistical Power and Significance Testing

Moderation/Mediation

• Mediation/moderation in r

Confidence Intervals

• Interpreting Confidence Intervals

Likelihood statistics

• Link
• Link

Multivariate methods

• Factoextra R Package: Easy Multivariate Data Analyses and Elegant Visualization

Correlations

• Interpreting Correlations

• Understanding Variance, Covariance, and Correlation

• Visualizing Dendrograms in R
• Reproducing lattice dendrogram graph with ggplot2
• Correlation ‘distances’ and hierarchical clustering

• PCA, MDS, k-means, Hierarchical clustering and heatmap for microarray data

• Not another heatmap tutorial
• Correlation matrix : A quick start guide to analyze, format and visualize a correlation matrix using R software
• correlation matrix to build networks
• What techniques exists in R to visualize a “distance matrix”?
• Diagonal labels orientation on x-axis in heatmap(s)
• Adding a Dendrogram to a ggplot2 Heatmap

Bayesian

• Bayesian Statistics for Psychological Science
• Link, Link, Link, Link

Power

• Power analysis through simulation:
  Links: [tutorial1] [tutorial2] [for mixed models] [conceptual steps]
• On the need for power analysis: LinK, Link2
• GLIMMPSE

Reproducibility

• The following site is a tutorial for how to structure your analysis stream to make it more reproducible. Link

Measurement

• Intro to measurement book

Replication

• Vazire syllabus

• Everything is F*cking Nuanced: The Syllabus

P Hacking/QRPs

• The Grad Student Who Never Said “No”
  • Example of what not to do.

Machine Learning

• Machine learning in neuroscience: Link

Signal Detection Theory

• SDT and logistic regression
• d prime m choices
• Signal Detection Theory

PCA & EFA

• Principal component analysis in R : prcomp() vs. princomp()

• Principal Components and Factor Analysis
• Exploratory Factor Analysis Theory and Application
• Principal Component Analysis vs. Exploratory Factor Analysis
• Lavaan Project

• The Fundamental Difference Between Principal Component Analysis and Factor Analysis

• Exploratory factor analysis in R

SEM

• Random Intercept CLPM