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index.Rmd
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---
title: Reproducible statistics for psychologists with R
subtitle: Lab Tutorials
description: Tutorials and lab exercises for learning statistics concepts using reproducible computational approaches with R
author: "Matthew J. C. Crump"
site: bookdown::bookdown_site
documentclass: book
bibliography: [references.bib,BCStats.bib]
csl: apa-fullnote.csl
link-citations: yes
---
# Welcome {-}
```{r, include=FALSE}
knitr::opts_chunk$set(cache=TRUE)
```
<img class="cover" src="imgs/cover.png" width="50%"/> This is a series of labs/tutorials for a two-semester graduate-level statistics sequence in Psychology @ Brooklyn College of CUNY. The goal of these tutorials is to 1) develop a deeper conceptual understanding of the principles of statistical analysis and inference; and 2) develop practical skills for data-analysis, such as using the increasingly popular statistical software environment R to code reproducible analyses.
The first set of 13 labs roughly tracks "Thinking with Data" [@vokeyThinkingData7th2018] and "Answering questions with data" [@crumpAnsweringQuestionsData2018]; the second set of labs roughly tracks "Experimental Design and Analysis for Psychology" [@abdiExperimentalDesignAnalysis2009].
Although the primary aim is to create lab exercises that reinforce stats concepts and also train basic R coding skills for data-analysis, there are many side goals, including showing students the advantages of using R markdown and Github for creating and communicating research products. For example, aside from these tutorials, I have been developing an R package called [vertical](https://crumplab.github.io/vertical/) [@vuorreSharingOrganizingResearch2020], that highlights the advantages of learning R for researchers in psychology. And, where possible, I hope to inject some of this broader discussion about awesome R tools and how to use them into the labs (at the same time, a deep-dive requires a separate course...maybe coming soon to a browser near you).