README.md

Mistuning Perception Test (MPT)

DOI

The MPT is an adaptive test of mistuning perception abilities.

We invite you to try the MPT here!

Want to use this test in your own online study, but don't want to go to the trouble of setting up your own web server? You are invited to host your study on the DOTS testing server of the German Society for Music Psychology (DGM). This service is free of charge, but requires a current membership of the GDM, which currently costs 45 euros per year (22 euros for students and the unemployed). For more information please contact Klaus Frieler (kgf@omniversum.de).

Citation

The MPT is introduced in the following paper:

Larrouy-Maestri, P., Harrison, P. M. C., & Müllensiefen, D. (2019). The mistuning perception test: A new measurement instrument. Behavior Research Methods, 51(2), 663–675. https://doi.org/10.3758/s13428-019-01225-1

You can also cite this specific implementation here:

Larrouy-Maestri, P., Harrison, P. M. C., & Müllensiefen, D. (2018). Mistuning Perception Test, psychTestR implementation. Zenodo. https://doi.org/10.5281/zenodo.1415363

We also advise mentioning the software versions you used, in particular the versions of the mpt, psychTestR, and psychTestRCAT packages. You can find these version numbers from R by running the following commands:

library(mpt)
library(psychTestR)
library(psychTestRCAT)
if (!require(devtools)) install.packages("devtools")
x <- devtools::session_info()
x$packages[x$packages$package %in% c("mpt", "psychTestR", "psychTestRCAT"), ]

Acknowledgements

We are grateful to the following individuals for translating the MPT into new languages:

Installation instructions (local use)

  1. If you don't have R installed, install it from here: https://cloud.r-project.org/

  2. Open R.

  3. Install the ‘devtools’ package with the following command:

install.packages('devtools')

  1. Install the MPT:

devtools::install_github('pmcharrison/mpt')

  1. Tell Shiny to use your computer's default browser, instead of the RStudio browser, which doensn't play well with this test. First, open your .Rprofile file:
install.packages("usethis")
usethis::edit_r_profile()

Add the following lines:

options(shiny.launch.browser = .rs.invokeShinyWindowExternal)

Save the file, then restart your R session (in the RStudio toolbar, click Session then Restart R).

Usage

Quick demo

You can demo the MPT at the R console, as follows:

# Load the mpt package
library(mpt)

# Run a demo test, with feedback as you progress through the test,
# and not saving your data
demo_mpt()

# Run a demo test, skipping the training phase, and only asking 5 questions
demo_mpt(num_items = 5, take_training = FALSE)

Testing a participant

The standalone_mpt() function is designed for real data collection. In particular, the participant doesn't receive feedback during this version.

# Load the mpt package
library(mpt)

# Run the test as if for a participant, using default settings,
# saving data, and with a custom admin password
standalone_mpt(admin_password = "put-your-password-here")

You will need to enter a participant ID for each participant. This will be stored along with their results.

Each time you test a new participant, rerun the standalone_mpt() function, and a new participation session will begin.

You can retrieve your data by starting up a participation session, entering the admin panel using your admin password, and downloading your data. For more details on the psychTestR interface, see http://psychtestr.com/.

The MPT currently supports English (EN), French (FR), German (DE), formal German (DE_F), Russian (RU), Danish (DA), Spanish (ES), Latvian (LV), and Italian (IT). If you are interested in adding a new language, contact us and we can add it for you. You can select one of these languages by passing a language code as an argument to standalone_mpt(), e.g. standalone_mpt(languages = "DE"), or alternatively by passing it as a URL parameter to the test browser, eg. http://127.0.0.1:4412/?language=DE (note that the p_id argument must be empty). Please note that the demo version of the test (demo_mpt) only supports English.

Results

The main output from the MPT is an ability score, corresponding to the ability estimate for the participant. It is computed from the underlying item response model and ranges approximately from -4 to +4. A secondary output is an ability_sem score, corresponding to the standard error of measurement for the ability estimate; again, it is computed from the underlying IRT model. For most applications you would only use the ability value, unless using a statistical analysis technique that allows you to specify measurement error explicitly. For more information about item response theory, see the Wikipedia article; for more information about MPT scores, see Larrouy-Maestri et al. (2019).

psychTestR provides several ways of retrieving test results (see http://psychtestr.com/). Most are accessed through the test's admin panel.

x <- readRDS("output/results/your-output-file.rds")
attr(x$MPT$ability, "metadata")

Installation instructions (Shiny Server)

  1. Complete the installation instructions described under 'Local use'.
  2. If not already installed, install Shiny Server Open Source: https://www.rstudio.com/products/shiny/download-server/
  3. Navigate to the Shiny Server app directory.

cd /srv/shiny-server

  1. Make a folder to contain your new Shiny app. The name of this folder will correspond to the URL.

sudo mkdir mpt

  1. Make a text file in this folder called app.R specifying the R code to run the app.

  2. To open the text editor: sudo nano mpt/app.R

  3. Write the following in the text file:
library(mpt)
standalone_mpt(admin_password = "put-your-password-here")

sudo chown -R shiny mpt

where shiny is the username for the Shiny process user (this is the usual default).

  1. Navigate to your new shiny app, with a URL that looks like this: `http://my-web-page.org:3838/mpt

Implementation notes

By default, the MPT implementation always estimates participant abilities using weighted-likelihood estimation. This differs from our validation study, where online abilities were scored using Bayes modal estimation. We adopt weighted-likelihood estimation for this release because this technique makes fewer assumptions about the participant group being tested. This makes the test better suited to testing with diverse participant groups (e.g. children, clinical populations).

Usage notes

Adding new languages

See data-raw/dict-russian.csv for an example of a foreign-language dictionary. Create a new dictionary for your new language following this format. Prepare a merge request for submitting your changes, perhaps using a fork of the original repository. Place the file in data-raw. Update data-raw/mpt-dict.R, adding a new four-line section for your new language, following the lines used to add previous languages. Run the file and commit the changes to Git. Update the languages section in README.md to credit the translator. Rebuild the R package locally and test that you can use the test with your new translations. Submit your changes as a merge request.



pmcharrison/mpt documentation built on May 2, 2024, 7:20 p.m.