---
title: "A Study of Perfectionism"
author: "Dr. M. Sigal"
date: "02/03/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
```
A researcher is interested in evaluating two therapeutic interventions for perfectionism; specifically investigating whether they might be effective in reducing levels of perfectionism. Levels of perfectionism were recorded at baseline (`perf1`), 1 month (mid-intervention; `perf2`), and at 2 months (post-intervention; `perf3`) for three experimental conditions (`group`): a CBT-based intervention (1), a general stress induced intervention (2) and a control condition (3).
The researcher also collected a depression measure (`dep1`) as well as the age of the subject (`age`, where 1 = less than 50 and 2 = 50+), and saved the data as `perfect.csv`.
```{r}
dat <- read_csv("perfect.csv", col_names = TRUE) # uses the readr package, loaded via tidyverse.
dat
```
## Potential Problems to Solve
1. How to convert categorical variables to factors?
2. How to explore the data using graphs and summary statistics by group and age?
3. We might be interested in an independent sample t-test between age categories to see if they differ on mean levels of depression?
4. We might be interested in a paired sample t-test to determine whether there is a significant difference between perfectionism scores at time 1 and time 3 (ignoring group membership)
5. Might be interested in a one-way ANOVA to see whether the means differ on perfectionism scores at time 3 between the 3 groups.
6. Test a factorial ANOVA for group by age category on perfection scores at time 3.
7. Conduct a multiple regression to see whether depression predicts post-test (time 3) perfectionism scores controlling for pre-test (time 1) perfectionism.