Implementation of qiat in MinnoJS
In order to use the qiat, first copy the settings file and modify it according to your needs. Documentation of all parameters can be found inline. Upload the settings file to a server and make sure you know the URL to it.
Next, in order to integrate with qualtrics, create a text question in your survey and set it up as a Multiline question.
Make sure that there is a page break before and after it.
Click the cog at the left of the question in order to see the question settings dropdown.
Choose Add Javascript, and copy-paste the code from qulatrics.js.
Change the url variable at the top of the file to point to your settings file.
That's it.
The qiat will save the results of the task as a CSV into the question column.
You can use the code in parse.R in order to parse your data.
You should run the code in R, it will add the function qiat.parse into your environment.
Then read your qulatrics data into a data frame (make sure you clean the first rows that describe the columns first).
And parse it using qiat.parse.
The arguments to the function are as follows:
| Argument | description |
|---|---|
| df | The data frame with all the data from qualtrics |
| id | The column in the df holding the unique identifier you want to keep |
| dataColumn | The column in the df holding the qiat data |
Assuming you want to use ResponseId as a unique identifier and the qiat data is held in Q4,
your code will look something like this:
df = read.csv('data.csv') # if you are on R version < 4.0 you will need to add stringsAsFactors = FALSE
parsed = qiat.parse(df, ResponseId, Q4)Each row in the reponse represents a single stimulus-response pair. The interpertation of the data exported from the qIAT is as follows:
| Column | Description |
|---|---|
| id | The value of the ID column as defined in minno.parse |
| condition | Randomization condition (can be 0 or 1) - condition 1 indicates that category1 started out on the left and vice versa |
| group | The name of the stimulus group as defined in the settings file (e.g. category1.name) |
| latency | The response latency in miliseconds |
| block | Block number |
| stimulus | The index of the stimulus as defined in the settings file |
| correct | Whether the initial response was correct (1 if yes, 0 if no) |
We provide R code in order to analyze your data.
Before you can use it, please make sure that you have tidyverse installed.
If you don't - this is how you install it:
install.packages('tidyverse')Not you can use the code in analyze.R in order to parse your data.
You should run the code in R, it will add the function qiat.analyze into your environment.
Now you can input the parsed data from qiat.parse into qiat.analyze.
results = qiat.analyze(parsed)If you want to change the default parameters, you can do that too:
results = qiat.analyze(
parsed,
cong = c(4),
incong = c(6),
exclusion_too_fast = 300,
exclusion_max_fast_perc = 0.1,
exclusion_max_error_perc = 0.2,
exclusion_max_latency = 3e4,
analyze_max_latency = 1e4,
analyze_min_latency = 400
)| Argument | Description |
|---|---|
| cong | Vector of congruent blocks (i.e. c(3,4)) |
| incong | Vector of incongruent blocks (i.e. c(6,7)) |
| exclusion_too_fast | Latency defined as too fast for computing percent of fast trials |
| exclusion_max_fast_perc | Percent of fast trials considered cause for exclusion |
| exclusion_max_error_perc | Percent of error trials considered cause for exclusion |
| exclusion_max_latency | Maximum latency of a tiral considered cause for exclusion |
| analyze_max_latency | Maximum latency of trials used for computing the dscore |
| analyze_min_latency | Minimum latency of trials used for computing the dscore |
The results from qiat.analyze is a list of 5 data.frames:
| Key | Description |
|---|---|
| trials | A data.frame with all trials with the addition of global and within block indexes |
| participants | A data.frame with all participants, their exclusion statistics and dscores |
| dscore.all | A data.frame with participants and auxilary scores for computing their dscores |
| dscore.even | A data.frame with participants and auxilary scores for computing their dscores for even trials |
| dscore.odd | A data.frame with participants and auxilary scores for computing their dscores for odd trials |