week4.Rmd

# EFA

```{r week4_global_options, include = FALSE}
source("code/chapter_setup.R")
partDir <- paste0(partDir0, "week4/")
```

This week will be a general introduction to latent variables and scaling 
procedures. We will discuss several different aspects of exploratory factor 
analysis (EFA). Most notably:

- The differences between Principal Component Analyses (PCA) and Factor Analysis
- Model estimation and factor extraction methods
- Factor rotations

You will have to make decisions regarding each of these aspects when conducting
a factor analysis. We will also discuss reliability and factor scores as means 
of evaluating the properties of a scale.

**Homework before the lecture**

1. Watch the [Lecture Recording](lecture-3.html) for this week.
1. Complete the [Reading](reading-3.html) for this week, and answer the 
associated reading questions.

**Homework before the practical**

Complete the [At-Home Exercises](at-home-exercises-3.html). 

**Practical content**

During the practical you will work on the [In-Class Exercises](in-class-exercises-3.html).

```{r, echo = FALSE, message = FALSE, warning = FALSE, results = "asis"}
knit_child(paste0(partDir, "lecture.Rmd"), quiet = TRUE) %>% cat(sep = "\n")
# knit_child(paste0(partDir0, "lecture-placeholder.Rmd"), quiet = TRUE) %>% cat(sep = "\n")
knit_child(paste0(partDir, "reading.Rmd"), quiet = TRUE) %>% cat(sep = "\n")
knit_child(paste0(partDir, "home.Rmd"), quiet = TRUE) %>% cat(sep = "\n")
knit_child(paste0(partDir, "class.Rmd"), quiet = TRUE) %>% cat(sep = "\n")
```