index.Rmd

--- 
title: "Theory Construction and Statistical Modeling"
author: "Kyle M. Lang"
date: "Last updated: `r Sys.Date()`"
site: bookdown::bookdown_site
---

\newcommand{\va}{\\[12pt]}
\newcommand{\vb}{\\[6pt]}
\newcommand{\vc}{\\[3pt]}
\newcommand{\vx}[1]{\\[#1pt]}

```{r global_options, include = FALSE}
library(knitr)
library(dplyr)

opts_chunk$set(warning = FALSE, message = FALSE)

dataDir  <- "../../data/"
```

# Course Information {-}

In order to test a theory, we must express the theory as a statistical model and 
then test this model on quantitative (numeric) data. In this course we will use 
datasets from different disciplines within the social sciences (educational 
sciences, psychology, and sociology) to explain and illustrate theories and 
practices that are used in all social science disciplines to statistically model 
social science theories.

This course uses existing tutorial datasets to practice the process of 
translating verbal theories into testable statistical models. If you are 
interested in the methods of acquiring high quality data to test your own theory, 
we recommend following the course *Conducting a Survey* which is taught from 
November to January.

Most information about the course is available in this GitBook. Course-related 
communication will be through <https://uu.blackboard.com> (Log in with your 
student ID and password).

<!----------------------------------------------------------------------------->

## Acknowledgement {-}

This course was originally developed by 
[dr. Caspar van Lissa](https://www.tilburguniversity.edu/staff/c-j-vanlissa). 
I ([dr. Kyle M. Lang](https://www.kylemlang.com)) have modified Caspar's original 
materials and take full responsibility for any errors or inaccuracies introduced 
through these modifications. Credit for any particularly effective piece of 
pedagogy should probably go to Caspar.

- You can view the original version of this course 
[here](https://github.com/cjvanlissa/TCSM) on Caspar's GitHub page.

<!----------------------------------------------------------------------------->

## Instructors {-}

**Coordinator:**

[dr. Kyle M. Lang](https://www.kylemlang.com)

**Lectures:**

[dr. Kyle M. Lang](https://www.kylemlang.com)

**Practicals:**

[Rianne Kraakman](https://www.uu.nl/staff/amkraakman)

[Daniëlle Remmerswaal](https://www.uu.nl/staff/DMRemmerswaal)

[Danielle McCool](https://www.uu.nl/staff/DMMcCool)

<!----------------------------------------------------------------------------->

## Course overview {-}

This course comprises three parts:

1. *Path analysis*: You will learn how to estimate complex path models of observed 
   variables (e.g., linked linear regressions) as structural equation models.
1. *Factor analysis*: You will learn different ways of defining and estimating
   latent (unobserved) constructs.
1. *Full structural equation modeling*: You will combine the first two topics to 
   estimate path models describing the associations among latent constructs.

Each of these three themes will be evaluated with a separate assignment. 

- The first two assignments will be graded on a pass/fail basis. 
- Your course grade will be based on your third assignment grade.

<!----------------------------------------------------------------------------->

## Schedule {-}

```{r print_schedule, echo = FALSE}
options(knitr.kable.NA = "")

readxl::read_excel("data/schedule.xlsx") %>%
  knitr::kable(col.names = c("Course Week",
                             "Calendar Week",
                             "Lecture/Practical Topic",
                             "Workgroup Activity",
                             "Assignment Deadline")
  )
```

*NOTE:* The schedule (including topics covered and assignment deadlines) is
subject to change at the instructors' discretion.

<!----------------------------------------------------------------------------->

## Learning goals {-}

In this course you will learn how to translate a social scientific theory into a 
statistical model, how to analyze your data with these models, and how to 
interpret and report your results following APA standards.

After completing the course, you will be able to:

1. Translate a verbal theory into a conceptual model, and translate a conceptual 
model into a statistical model.
1. Independently analyze data using the free, open-source statistical software R.
1. Apply a *latent variable model* to a real-life problem wherein the observed 
variables are only indirect indicators of an unobserved construct.
1. Use a *path model* to represent the hypothesized causal relations among
several variables, including relationships such as mediation and moderation.
1. Explain to a fellow student how *structural equation modeling* combines 
latent variable models with path models and the benefits of doing so.
1. Reflect critically on the decisions involved in defining and estimating
structural equation models.

<!----------------------------------------------------------------------------->

## Resources {-}

---

### Literature {-}

**You do not need a separate book for this course!** 

Most of the information is contained within this GitBook and the course readings 
(which you will be able to access via links in this GitBook). 

All literature is freely available online, as long as you are logging in from 
within the UU-domain (i.e., from the UU campus or through an appropriate VPN). 
All readings are linked in this GitBook via either direct download links or DOIs. 

If you run into any trouble accessing a given article, searching for the title 
using [Google Scholar][gs] or the [University Library][uu_lib] will probably due 
the trick.

---

### Software {-}

You will do all of your statistical analyses with the statistical programming 
language/environment [R][r] and the add-on package [`lavaan`][lavaan].

- If you want to expand your learning, you can follow this optional 
[`lavaan` tutorial](https://lavaan.ugent.be/tutorial/). 

<!----------------------------------------------------------------------------->

## Reading questions {-}

Along with every article, we will provide reading questions. You will not be 
graded on the reading questions, but it is important to prepare the reading 
questions before every lecture. The reading questions serve several important 
purposes:

-	Provide relevant background knowledge for the lecture
-	Help you recognize and understand the key terms and concepts
-	Make you aware of important publications that shaped the field
- Help you extract the relevant insights from the literature

<!----------------------------------------------------------------------------->

## Weekly preparation {-}

Before every class meeting (both lectures and practicals) you need to do the 
assigned homework (delineated in the GitBook chapter for that week). This course 
follows a *flipped classroom* procedure, so you must complete the weekly homework 
to meaningfully participate in, and benefit from, the class meetings.

---

### Background knowledge {- #background}

We assume you have basic knowledge about multivariate statistics before entering 
this course. You do not need any prior experience working with R. If you wish to 
refresh your knowledge, we recommend the chapters on ANOVA, multiple regression,
and exploratory factor analysis from Field’s [*Discovering Statistics using R*][field]. 

If you cannot access the Field book, many other introductory statistics textbooks 
cover these topics equally well. So, use whatever you have lying around from 
past statistics courses. You could also try one of the following open-access options:

- [*Applied Statistics with R*][stats_with_r]
- [*Introduction to Modern Statistics*][modern_stats]
- [*Introduction to Statistical Learning*][isl]

<!----------------------------------------------------------------------------->

## Evaluation {-}

Your grade for the course is based on a “portfolio” composed of the three 
take-home assignments:

1. Path modeling 
    - Deadline: **Wednesday 2024-10-02 at 23:59**
    - Group assignment
    - Pass/Fail

1. Confirmatory factor analysis
    - Deadline: **Wednesday 2024-10-16 at 23:59**
    - Group assignment
    - Pass/Fail 

1. Full structural equation modeling 
    - Deadline: **Sunday 2024-11-03 at 23:59**
    - Individual assignment
    - Comprises your entire numeric course grade

**The specifics of the assignments will be explicated in the 
[Assignments](assignments.html) chapter of this GitBook**

<!----------------------------------------------------------------------------->

## Attendance {-}

Attendance is not mandatory, but we strongly encourage you to attend all lectures 
and practicals. In our experience, students who actively participate tend to pass 
the course, whereas those who do not participate tend to drop out or fail. The 
lectures and practicals build on each other, so, in the unfortunate event that 
you have to miss a class meeting, please make sure you have caught up with the 
material before the next session.

<!----------------------------------------------------------------------------->

[gs]: https://scholar.google.com/
[uu_lib]: https://www.uu.nl/en/university-library
[r]: https://www.r-project.org/
[lavaan]: https://www.lavaan.ugent.be/
[field]: https://www.discoveringstatistics.com/books/discovering-statistics-using-r/
[stats_with_r]: https://book.stat420.org/
[modern_stats]: https://openintro-ims.netlify.app/
[isl]: https://www.statlearning.com