stress_at_work_analysis/statistical_analysis/scale_reliability.rmd

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---
title: "Reliability of SAM threat and challenge and COPE"
output: html_notebook
---
```{r libraries, message=FALSE, warning=FALSE, include=FALSE, cache=FALSE}
library(conflicted)
library(here)
library(tidyverse)
library(magrittr)
library(lavaan)
library(kableExtra)
conflicts_prefer(
readr::col_factor,
purrr::discard,
dplyr::filter,
dplyr::lag,
purrr::set_names,
tidyr::extract,
kableExtra::group_rows
)
```
```{r style, include=FALSE, cache=FALSE}
styler::style_file(
here("statistical_analysis", "scale_reliability.Rmd"),
scope = "tokens",
indent_by = 4L
)
```
The data were preprocessed and cleaned using [expl_esm_labels.py](../exploration/expl_esm_labels.py) script and read as csv here.
```{r read_data}
COL_TYPES <- cols(
.default = col_double(),
participant_id = col_factor(),
username = col_factor(),
device_id = col_factor(),
esm_trigger = col_factor(),
esm_instructions = col_factor(),
double_esm_user_answer_timestamp = col_double(),
datetime_lj = col_datetime(format = ""),
date_lj = col_date(format = ""),
time = col_factor(),
esm_user_answer = col_factor()
)
df_SAM <- read_csv(here("data", "raw", "df_esm_SAM_threat_challenge.csv"), col_types = COL_TYPES)
df_COPE <- read_csv(here("data", "raw", "df_esm_COPE.csv"), col_types = COL_TYPES)
```
Demonstrate factor analysis for a single participant.
```{r}
df_COPE %>%
group_by(question_id, questionnaire_id) %>%
count()
```