1 Read in the data

Note: data need to be in the same folder as the analysis script. Otherwise, the path needs to be specified.

library(readr)
tdata <- read_delim("data.txt", 
    delim = "\t", escape_double = FALSE, 
    trim_ws = TRUE)
## Rows: 780 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (7): subj_code, desktop_conf, attent_conf, Cond_sum, explanation, gender...
## dbl (5): condition, instr_tests, Rating_CC, Rating_SE, age
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

2 Demographics

Subject demographics:

# demographics 

min(tdata$age) # minimum age 
## [1] 18
max(tdata$age) # maximum age
## [1] 75
mean(tdata$age) # mean age
## [1] 37.94231
sd(tdata$age) # standard deviation of age
## [1] 12.73167
table(tdata$gender) 
## 
##              1: male            2: female        3: non-binary 
##                  388                  378                   11 
## 4: prefer not to say 
##                    3

3 Analyses

First prepare the data set:

  • Factorize
  • reshape into long format (for graphs)
  • divide dv ratings by 10 to limit ratings to a range between 0 and 1 (this is how we scale the y-axis for the graphs)

3.1 Graphs

Ratings in all conditions:

## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
## Warning: `fun.y` is deprecated. Use `fun` instead.
## `fun.y` is deprecated. Use `fun` instead.
## `fun.y` is deprecated. Use `fun` instead.