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https://github.com/mattansb/structural-equation-modeling-for-psychologists

Lesson files used in the Structural Equation Modeling for Psychologists.
https://github.com/mattansb/structural-equation-modeling-for-psychologists

bgu-university lavaan psychologists rstats sem statistics teaching-materials

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Lesson files used in the Structural Equation Modeling for Psychologists.

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README 

        
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output: github_document

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```{r setup, include=FALSE}

library(knitr)



opts_chunk$set(echo = TRUE)



`%>%` <- dplyr::`%>%`

```



# Structural Equation Modeling foR Psychologists



[![](https://img.shields.io/badge/Open%20Educational%20Resources-Compatable-brightgreen)](https://creativecommons.org/about/program-areas/education-oer/)

[![](https://img.shields.io/badge/CC-BY--NC%204.0-lightgray)](http://creativecommons.org/licenses/by-nc/4.0/)  

[![](https://img.shields.io/badge/Language-R-blue)](http://cran.r-project.org/)



*Last updated `r Sys.Date()`.*



This Github repo contains all lesson files for *SEM - Practical Applications in R*. The goal is to impart students with the basic tools to construct, evaluate and compare **Structural Equation Models (SEM; w/ plots), using [`lavaan`](http://lavaan.ugent.be/)**.



These topics were taught in the graduate-level course ***Structural Equation Modeling*** (Psych Dep., Ben-Gurion University of the Negev). This course assumes basic competence in R (importing, regression modeling, plotting, etc.), along the lines of [*Practical Applications in R for Psychologists*](https://github.com/mattansb/Practical-Applications-in-R-for-Psychologists).



**Notes:**  



- This repo contains only materials relating to *Practical Applications in R*, and does not contain any theoretical or introductory materials.  

- Please note that some code does not work *on purpose*, to force students to learn to debug.



## Setup



```{r, echo=FALSE}

extract_pkgs <- function(fl) {

  `%>%` <- dplyr::`%>%`

  

  if (length(fl) == 1) {

    txt <- read.delim(fl, header = FALSE) %>% 

      .[[1]] %>% 

      paste0(collapse = "\n")

    

    pkg_lib <- stringr::str_extract_all(txt, pattern = "(?<=library\\().{1,}(?=\\))")

    

    pkg_req <- stringr::str_extract_all(txt, pattern = "(?<=require\\().{1,}(?=\\))")

    

    pkg_name <- stringr::str_extract_all(txt, pattern = "[a-z|A-Z|0-9]{1,}(?=\\:\\:)")

    

    pkgs <- c(pkg_lib, pkg_req, pkg_name)

    

  } else if (length(fl) > 1) {

    pkgs <- sapply(fl, extract_pkgs)

  }

  

  

  pkgs %>%

    unlist(recursive = TRUE) %>%

    unique()

}



make_pkg_table <- function(pkgs) {

  `%>%` <- dplyr::`%>%`

  

  pkgs <- pkgs[sapply(pkgs, function(x) length(x) > 0)]

  

  ps <- sapply(pkgs, function(x){

    paste0(

      glue::glue("[`{x}`](https://CRAN.R-project.org/package={x})"),

      collapse = ", "

    )

  })

  

  glue::glue("|[{folder}](/{folder})|{ps}|\n\n",

             folder = names(pkgs)) %>% 

    c("|Lesson|Packages|\n|----|----|\n", .) %>% # header

    paste0(collapse = "")

}

```



You will need:



1. A fresh installation of [**`R`**](https://cran.r-project.org/) (preferably version 4.0 or above).

2. [RStudio IDE](https://www.rstudio.com/products/rstudio/download/) (optional, but recommended).

3. The following packages, listed by lesson:



```{r, echo=FALSE, message=FALSE, warning=FALSE}

r_list <- list.files(pattern = ".(R|r)$", recursive = TRUE, full.names = TRUE) %>% 

  .[!stringr::str_detect(., pattern = "(SOLUTION|logo)")] %>% 

  .[stringr::str_detect(., pattern = "^./[0-9]")]



lesson_names <- stringr::str_extract(r_list, pattern = "(?<=(/)).{1,}(?=(/))")



r_list <- split(r_list, lesson_names)



pkgs <- lapply(r_list, extract_pkgs)



print_pkgs <- make_pkg_table(pkgs)



```



`r print_pkgs`



You can install all the packages used by running:



```{r echo=FALSE, comment = "", warning=FALSE}



pkgs <- pkgs %>% 

  unlist(recursive = TRUE) %>% 

  unique() %>% sort()



cat("# in alphabetical order:")



pkgs %>% {

  capture.output(dput(.))

} %>% {

  capture.output(cat("pkgs <-", ., fill = 80))

} %>%

  styler::style_text()



cat("install.packages(pkgs, dependencies = TRUE)")

```



Package Versions

The package versions used here:

```{r, echo=FALSE}

packinfo <- installed.packages(fields = c("Package", "Version"))



get_src <- function(pkg) {

  pd <- packageDescription(pkg)

  if (is.null(src <- pd$Repository)) {

    if (!is.null(src <- pd$GithubRepo)) {

      src <- paste0("Github: ",pd$GithubUsername,"/",src)

    } else {

      src <- "Dev"

    }

  }

  return(src)

}



V <- packinfo[pkgs,"Version"]

src <- sapply(pkgs, get_src)

# setNames(paste0(V, " (", src,")"), pkgs)



v_info <- paste0(glue::glue(" - `{pkgs}` {V} (*{src}*)"), collapse = "\n")

```



`r v_info`



## Other Useful Resources



### Other Courses



In addition to [`lavaan`'s toutorials](http://lavaan.ugent.be/tutorial/index.html), you might find the following online courses useful: 



- Sacha Epskamp's [online course](http://sachaepskamp.com/SEM2020) and [YouTube lectures](https://www.youtube.com/playlist?list=PLliBbGBc5nn3m8bXQ4CmOep3UmQ_5tVlC).  

  - See also the [`psychonetrics`](http://psychonetrics.org/) package for psychometric network modeling.  

- Michael Hallquist's [course](https://psu-psychology.github.io/psy-597-SEM/).



### Selected Quantitude (podcast) Episods



These are selected episodes from the [Quantitude podcast](https://quantitudepod.org/) related to SEM:



- [S2E08: **Mediation** and the Art of Squid Spleening](https://www.buzzsprout.com/639103/5817541-s2e08-mediation-and-the-art-of-squid-spleening)

- [S2E24: The **Equivalent Models** Problem](https://www.buzzsprout.com/639103/7983649-s2e24-the-equivalent-models-problem)

- [S2E26: MLM vs. **SEM**: Opportunities for Growth](https://www.buzzsprout.com/639103/8084578-s2e26-mlm-vs-sem-opportunities-for-growth)