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4

Here are a couple of suggestions: Inquisit records the timing of each response and can be run online. If you care about timing a lot and you need to run the study online, then this is quite a good option. Qualtrics can record various features of survey delivery time. In particular, you can record time spent on each survey page. If you have one question per ...


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The R package diffIRT (http://www.dylanmolenaar.nl/jss1265.pdf) estimates both the Q and the D diffusion models (see his website for the van der Maas et al. paper discussing the differences between these models). R code for the EZ2 approach, which is much faster if that is important for your applications, is http://raoul.socsci.uva.nl/EZ2/.


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You can transform RT, i.e., by log(1/RT). This makes the distribution roughly normal. The problem is that you don't usually run the ANOVA on the RT values collected at each trial, but on the average for each participant. So the distribution across participants need to be normal. A trick is to transform the single RT values, calculate the mean for each ...


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You may be able to reduce errors by telling participants to react slowly. However, by adding this instruction, you may also dilute the response time differences that your are trying to find (whether between or within subjects). If you are lucky, you are just adding random noise, however it is more likely that you will add confounds or tap into different ...


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Often, very similar phenomena have different names when studied in different modalities, because they are studied by different communities. That's why searching for perception response times + auditory doesn't yield great results (Although I did find [1] this way). Something else to try, is to pick a highly cited paper that you did find, and then search ...


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This article by Whelan (2010) is one of the best introductory papers I've found on the subject. Normalization is covered quite clearly and extensively, including the caveats and "gotchas". References Whelan, R. (2010). Effective analysis of reaction time data. The Psychological Record, 58(3), 9.


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My impression is that, recently, a consensus began to form recently that RTs should be transformed to satisfy model assumptions. This is especially true when data is analyzed with mixed models instead of ANOVAs. Concerning the stability of effects under different transformations, you may find this paper interesting: http://web.uvic.ca/psyc/masson/KMR10.pdf



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