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