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Welcome to the Statistics wiki!

Below are descriptions of different statistical approaches and the code for them in R.

T-test

• Overview of the Standard t-test: Standard t-test Overview

Converting test statistics to effect sizes

• This will cover how to calculate cohen's D from test statistics. This is very useful for papers that do not directly report means and standard deviations: Converting test statistics to Cohen's D
• This will cover how to calculate partial eta-square from test statistics. This is very useful for papers that do not directly report means and standard deviations: Converting test statistics to partial eta square from ANOVA
• Here we will also calculate partial eta-square from test statistics- specifically from repeated measures ANOVA: Converting test statistics to partial eta square from repeated measures ANOVA
• Read this one extracting effect size correctly from ANOVAS, with a focused on the most common ones which are mixed ANOVAS: Converting test statistics to partial eta squared and generalized eta squared

Regression

• Overview of Regression: Regression Overview

Regression and Effect Size

• An overview that focuses on simple and multiple regression and how to calculate effect sizes Regression and Effect Sizes

Logistic Regression

• Overview on Logistic Regression: Logistic Regression Overview
• More Logistic Regression from the 'Extending the Linear Model with R' textbook Extending the Linear Model with R: Chapter 2 Logistic Regression

Gamma Regression

• Information related to running a Gamma Regression in R: Gamma Regression in R

ANOVA

Underlying Math

• This will produce a step by step guide on what the math behind a one-way ANOVA is: Math Underlying a one-way ANOVA
• This will produce a step by by step guide on what the math behind a one-way repeated measures ANOVA is: Math Underlying a one-way repeated measures ANOVA
• This will produce a step by step guide on what the math behind a two-way ANOVA is: Math Underlying a two-way ANOVA

Interpreting Outputs

• Interpreting the outputs of a one-way repeated measures ANOVA: Interpreting the Outputs in a One-Way Repeated Measures ANOVA

ANCOVA

• Overview of analysis of covariance (ANCOVA): ANCOVA Overview

Effect Coding

• Information related to effect coding in R: Effect Coding in R
• Effect Coding and Estimated Marginal Means in R: Effect Coding and Estimated Marginal Means in R

Estimated Marginal Means

• Information related to estimated marginal means for factors in R: Estimated Marginal Means in R
• This next section is on estimated marginal means from custom contrasts in R: Estimated Marginal Means from Custom Contrasts in R

Plotting Main Effect and Estimated Marginal Means Follow-Up Tests (Asterisk Lines)

• The code and example to create graphs with the asterisk lines from your own p-values: Plotting Asterisk Lines in Follow-Up Tests

Estimated Marginal Means of Linear Trends (Slopes)

• Information related to obtaining the slopes of continuous v factor interactions in R Estimated Marginal Means of Linear Trends in R

Mediation Analysis

• This will be an introduction into mediation analysis using examples inspired by the stress, testosterone, and cognition meta Introduction into Mediation Analysis

Meta-Analysis

• Here we will be describing meta-analysis in accordance to 'Doing Meta-Analysis in R: A Hands on Guide' Meta Analysis in R
• Calculating means and standard deviations when they are not presented: Estimating means and standard deviations
• Comparing fixed effects vs random effects meta-analysis models in R: Meta Analysis Fixed Effects vs Random Effects Models
• Running moderation analyses in a meta-analysis with heterogenous data: Testing for Moderation in a Meta Analysis
• Running a multilevel meta analysis in R: How to run a multi level meta analysis in R

Path Analysis

In depth introduction into path analysis in R: Introduction to Path Analysis Using Lavaan

Linear Mixed Effects Models

• Template 1 to use: Linear Mixed Effects Models (Behavior Example)
• Template 2 to use: Linear Mixed Effects Models (ERPs Example).

Generalized Linear Mixed Effects Models

• Intro into linear mixed effects models GLMM Intro into Linear Mixed Effects Models
• Intro into generalized linear mixed effects models Intro into Generalized Linear Mixed Effects Models GLMM

Generalized Structural Equation Modeling (GSEM)

• Here we will cover specific examples of how to run generalized structural equation modeling to predict a dichotomous outcome from several predictors while introducing a mediation analysis: GSEM for Path Analysis and Nested Logistic Regression

Multivariate Mixed Effects Models

Introduction into running multivariate mixed effects models: Intro into Multivariate Mixed Models

Power Analyses

• Introduction to doing a power analysis on mixed effects models through simr: Introduction to Power Analysis Using simr for Mixed Models
• Introduction to doing a power analysis on linear mixed-effects models: Introduction into Power Analysis for Linear Mixed-Effects Models

Confirmatory Factor Analysis (CFA)

• Introduction into Confirmatory Factor Analysis using Lavaan: Confirmatory Factor Analysis with Lavaan
• Heywood Cases and Constraining Introduction: Introduction into Heywood Cases and Consequences of Constraining

Item Response Theory (ITR)

• This is information covering IRT to estimate subject performance on an assessment Two parameter IRT model (2PL)
• This is information covering IRT to estimate subject performance on an assessment across datasets with different types of missing data Two parameter IRT model (2PL) with Missing Data
• This will be exploring the theta values and comparing them to z-scores in a well generated dataset: Exploring the Theta Estimate in the IRT model (2PL)
• This will be exploring the IRT Graded Response Model (GRT), which is great at handling datasets that have more than two values if there is an ordinal structure to it: Graded Response Model (GRT)

Path Diagrams

• This is information from a YT video Path Diagrams for some common types of statistical models

SEM

• This page will contain an overview of the HolzingerSwineford1939 dataset Higher order factor model HolzingerSwineford1939

Complex Models

• This page will be used strictly to test the capabilities of a bayesian generalized (non)-linear multivariate multilevel model: Testing the Capabilities of a Bayesian Non Linear Multivariate Multilevel Model in R

Categorical Exploratory Factor Analysis (EFA)

• This page will be discussing how to run categorical exploratory factory analysis (EFA) using the lavaan syntax: Getting Started with Categorical with Exploratory Factor Analysis (EFA) in Lavaan

Categorical Confirmatory Factory Analysis (CFA)

• This page will use a basic categorical confirmatory factory analysis example. This will function as a good introductory into the concept: Getting Started with Categorical Confirmation Factor Analysis
• Here we will show an example of a hierarchical categorical confirmatory factory analysis. This will function as a good introductory into the concept: Hierarchical Categorical Confirmation Factor Analysis
• For this page we will explicitly cover invariant and non-invariant conditions in categorical CFA: Invariant and Non-Invariant categorical CFA