IIP-C-IRT

In this post, I provide a paper version of the Inventory of Interpersonal Problems–Circumplex Item Response Theory (IIP-C-IRT) assessment measure. This measure is a brief (32-item) version of the Inventory of Interpersonal Problems with circumplex scales that was developed by Sodano & Tracey (2011) . While similar to the related IIP-32 and IIP-SC measures, the IIP-C-IRT uses a reduced set of items selected using nonparametric item response theory. This paper version spans three pages: two for administration and one for scoring. Items are ordered by octant in four sets of eight ({PA, …, NO} x 4) to facilitate hand-scoring. Basic didactic information about the measure and interpretation recommendations are provided on the scoring page.

Click on the images above or the following link to download version 1.0 as a PDF: IIP-C-IRTv1.0

References

Sodano, S. M., & Tracey, T. J. (2011). A brief Inventory of Interpersonal Problems–Circumplex using nonparametric item response theory: Introducing the IIP–C–IRT. Journal of Personality Assessment, 93(1), 62-75. doi: 10.1080/00223891.2010.528482

Zimmermann, J., & Wright, A. G. (2017). Beyond description in interpersonal construct validation: Methodological advances in the circumplex structural summary approach. Assessment, 24(1), 3-23. doi: 10.1177/1073191115621795

Tutorial on “Statistical Methods for Affective Computing”

I had a great time presenting on “Statistical Methods for Affective Computing” at the IEEE International Conference on Automatic Face & Gesture Recognition.

Resources are available for download from: https://goo.gl/VbjHGo

Topics include inter-rater and predictive reliability (agreement indexes for categorical data and correlation indexes for dimensional data), estimation of population parameters (effect size measures and confidence intervals), and analyzing complex relationships (general linear modeling, generalized linear modeling).

Audiovisual Recording from MATLAB

I have been developing a graphical user interface (GUI) in MATLAB to present experimental stimuli to participants and record their responses in a variety of formats. MATLAB makes it pretty easy to collect self-report data using the uicontrol function, which can be configured as an edit box, drop-down box, check box, list box, button, or slider. However, I also wanted to collect behavioral data using the computer’s built-in webcam and microphone. As it turns out, recording synchronized audio and video data in MATLAB is surprisingly difficult. This post will describe the solution I found and provide source code that you can build upon.

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Gabor Visualization

Gabor filters are often used for edge detection in computer vision tasks such as optical character recognition and facial expression recognition. In this blog post, I will demonstrate and provide source code for visualizing the “Gabor Space” using MATLAB. Given my research focus on faces, I will use facial images and video in my examples.


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