References
Statistics by Us for You
Welcome
Foreword
Preface
Authors
Introduction
The Basics
1
Variables and Distributions
2
The Best Guess
3
Dispersion and Uncertainty
4
Cleaning House
Making Inferences with Data
5
Expectation and Understanding: The Z-Distribution
6
Covariance and Association
7
Testing Hypotheses with Data
8
Statistical Power: How to Find Things That May Not Be There
Checking Our Data
9
Reliability (not Validity) of Data
10
Validity (not Reliability)
11
Measuring Like Ben Wright: Rasch in R, Python, and Julia
Preparing Our Data
12
Data Reduction: Fewer, Better Measures
Using Models
13
Introducing the General Linear Model (GLM)
14
Bridging the Gap: The Point Biserial Correlation
15
Simple Regression
16
Multiple Regression
17
ANOVA and the General Linear Model
18
Basic ANOVA
19
Coding Categorical Predictors
Latent Variable Models
20
Latent Variables: Confirmatory Factor Analysis and SEM
21
Measurement Invariance: Does the Ruler Mean the Same Thing for Everyone?
22
Latent Classes: Which Kind of Person Is This?
23
More Item Response Theory, and Generalizability
Causal Inference
24
Causal Inference with DAGs
Beyond the Normal Curve
25
Beyond the Normal Curve: Resampling, Robust, and Bayesian Statistics
Displaying Data
26
Displaying Data: Graphics
27
Displaying Data: Tables
Statistics in the Wild
28
Statistics in the Wild: The Sports Page Way
29
Case Study: When the Total Score Hides the Story
30
Case Study: Building a Measure, Study by Study
31
Case Study: The Data You Didn’t Get
The Methods in Practice
32
The Methods in Practice: The Authors at Work
Appendices
The Same Analysis in R, SPSS, and Julia
Setup and Required Packages
Changelog
References
References
Changelog