Danielle Daidone [dæn.ˈjɛl  deɪ.ˈdoʊn​]

Assistant Professor of Spanish
University of North Carolina Wilmington
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PhD in Second Language Studies and Hispanic Linguistics, Indiana University

LATEST BLOG POSTS

Analyzing free classification results: Multi-dimensional scaling (MDS) analyses

Multi-dimensional scaling (MDS) is a way of determining the placement of each stimulus in space so that the perceptual distances between the stimuli are recreated as closely as possible, with stimuli that were judged to be more similar placed closer together and stimuli judged to be less similar placed further apart.  If you're not familiar with MDS, you may find the explanation on pp. 4-5 of our 2023 SSLA article on free classification to be useful. 

You can perform MDS analyses on any of the matrix outputs from the create_FC_similarity_matrices.Rmd file from the previous blog post using this R Markdown file.  A pdf example using this script to analyze our Finnish length data can be viewed here.

Analyzing free classification results:
​Using an R script to obtain (dis)similarity matrices

To analyze your data with the R script below, you'll need 2 files: 
  • Your Lookup Matrix file, which details which stimulus corresponds to which number on which slide.  How to create this file is explained in the blog post here.  This file should be saved as a tab-separated text file.  The example Lookup Matrix from our Finnish length experiment is available here.
  • Your Coded FC Results file, which codes the results from each participant's free classification PowerPoint task.  How to create this file is explain in the blog post here.  This should also be saved as a tab-separated text file.  Make sure your stimuli are in the same order across contexts, since the R script outputs results in alphabetical order, and the combined contexts results will be inaccurate if the orders are different across contexts.  ​The example Coded FC Results file from our Finnish length experiment is available here.
You'll use this R Markdown file to analyze your results (if you don't have R and RStudio, download those first).  The comments in the script show what you should get if you analyze the example files above.  Don't forget to set your working directory to the file path where your files are located and to change the file names to match your Lookup Matrix and Coded FC Results files.

ABOUT ME

My research focuses on second language phonology, with a particular emphasis on perception and lexical representations. I also work on input in the language classroom, as well as variation in L1 and L2 Spanish.  When I'm not analyzing speech, you can find me dancing salsa (preferably on 2!) or escaping reality with a good book.
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CONTACT ME
daidoned AT uncw DOT edu

UPDATES

We got a Spencer Foundation Grant to help us build a website for HVPT!
If you're interested in beta testing our future HVPT website, sign up here.  

Email me if you'd like the pdf of my latest book chapter or view the postprint here:
Daidone, D. (2023). The relationship between self-rated vocabulary knowledge and accuracy of phonological forms. In I. Checa-García & L. Marqués-Pascual (Eds.), Current perspectives on Spanish lexical development (pp. 49-72). de Gruyter Mouton.
doi.org/10.1515/9783110730418-003

The article with my former student Victoria Farrell is now available!  I'm so proud of her work on this: 
Farrell, V., & Daidone, D. (2023). Effects of orthography and cognate status on second language Spanish lexical encoding. Journal of Second Language Pronunciation. https://doi.org/10.1075/jslp.22036.far 
Email me if you'd like a copy of the pdf, or view the postprint here.