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<p>THIS PROJECT HAS BEEN ARCHIVED</p> <p>You can run the IAT-lab or Online using PsychoPy3 and Pavlovia: <a href="https://gitlab.pavlovia.org/demos/openiat" rel="nofollow">https://gitlab.pavlovia.org/demos/openiat</a> (this version is based on this OpenIAT code)</p> <h2>0.1 Credits</h2> <p>Created by Tom Stafford & Robin Scaife at the University of Sheffield <a href="http://www.tomstafford.staff.shef.ac.uk/" rel="nofollow">http://www.tomstafford.staff.shef.ac.uk/</a></p> <p>Part of the <em>Leverhulme Trust</em> funded Bias and Blame project</p> <p><a href="https://blogs.nottingham.ac.uk/biasandblame/" rel="nofollow">https://blogs.nottingham.ac.uk/biasandblame/</a></p> <p>(PI: Jules Holroyd, University of Nottingham)</p> <h2>0.2 Getting going</h2> <p>One click download: _AllFilesAndFolders.zip</p> <p>These files require installation of python 2.7+ and psychopy 1.80+</p> <p>The psychopy file runs a simple race IAT. </p> <p><strong>NOTE: This is the short IAT (5 blocks: 3 x 10 practice trials + 2 x 20 test trials), not the longer IAT (7 blocks: 3 x 20 practice trials + 4 x 20 test trials).</strong></p> <p>The analysis python files analyse the resulting data</p> <p>If you want to run a simple IAT you can probably do this by swopping out stimuli and with only basic knowledge of PsychoPy</p> <p>If you want to alter any aspects of the experiment, you'll need to get started with Python coding</p> <h1>1- General Notes</h1> <p>Developed in PsychoPy 1.80</p> <p>Should work on Windows and Linus (not tested under Mac)</p> <p>The build of the experiment allows for easy counter balancing. Order of pro-stereotype vs anti-stereotype IAT blocks is determined by the experimenter at the start. Enter 1 for pro-stereotypical - "Congruent" first or 2 for anti-stereotypical- "Incongruent first". </p> <p>This IAT has a built in gap of 1 second between the presentation of each stimulus. This discourages participants from randomly clicking through the experiment and allows for the programme to give the “Oops” feedback for incorrectly answered trials.</p> <h1>2- Stimuli</h1> <p>Both the faces and words used in this PsychoPy IAT are jpg files. This proved to be simpler than integrating text and image stimuli within the same trial.</p> <p>The face image stimuli are taken from where a number of IAT stimulus sets are made available for researchers. The following citation is appropriate for referencing use of these stimuli:</p> <blockquote> <p>Nosek, B. A., Smyth, F. L., Hansen, J. J., Devos, T., Lindner, N. M., Ratliff (Ranganath), K. A., Smith, C. T., Olson, K. R., Chugh, D., Greenwald, A. G., & Banaji, M. R. (2007). Pervasiveness and correlates of implicit attitudes and stereotypes. <em>European Review of Social Psychology,18</em>, 36-88.</p> </blockquote> <p>The word stimulus images were created using Microsoft Paint.</p> <h2>2.1- Editing the Stimuli</h2> <p>To change the stimuli to build your own IAT simply transfer the image files you wish to use to the stimuli folder and change the file names in the excel files for each condition to correspond with your new images. Remember to make sure that the CorrAns column in the excel file still reflects the correct key response for the image occupying the same row. </p> <h1>3- Data Output/IAT Scores</h1> <p>This PsychoPy IAT generates an excel file for each participant which records whether they answered correctly (1=correct 0=incorrect) and their reaction times in milliseconds.</p> <p>The scoring procedure used by the code (<a href="http://calcIAT.py" rel="nofollow">calcIAT.py</a>) to generate the overall IAT score is based on the recommendations made in the following paper:</p> <blockquote> <p>Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the Implicit Association Test: I. An improved scoring algorithm. <em>Journal of Personality and Social Psychology, 85</em>(2), 197-216.</p> </blockquote> <p>The procedure goes through the following steps:</p> <ol> <li>Eliminate scores over 10,000 ms </li> <li>Returns a score of "Too Fast" for participants who have RTs less than 300ms for more than 10% of their trials</li> <li>Calculate the Block 3 mean reaction time for correctly answered trials and the block 5 mean reaction time for correctly answered trials. </li> <li>Calculate the pooled standard deviation for all items in blocks 3 & 5 (as if they were just one block) regardless of if they were answered correctly or incorrectly: (Use N not N-1 because this is the whole sample). </li> <li>In blocks 3 & 5 replace incorrectly answered items with the mean for that group (from step 3) + 600ms and recalculate the mean for groups 3 & 5. </li> <li>Finally calculate Block 5 - Block 3 ÷ Pooled standard deviation to generate your overall IAT score/GNB Score</li> </ol>
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