Would logistic regression with a dichotomous variable indicating pre- or post-training provide an adequate model? John John Sorkin Chief Biostatistics and Informatics Univ. of Maryland School of Medicine Division of Gerontology and Geriatric Medicine JSorkin at grecc.umaryland.edu -----Original Message----- From: Mike Lawrence <Mike.Lawrence at dal.ca> To: <r-sig-mixed-models at r-project.org> Sent: 11/14/2010 10:58:03 PM Subject: [R-sig-ME] Analysis of signal detection data Hi folks, Yet another query on whether traditional stats employed in psychology might be improved by mixed effects modelling... Consider a radiologist looking at a CT scan and attempting to make the binary diagnosis of cancer/no cancer. Signal detection theory suggests that the normalized difference between the radiologist's hit rate and false alarm rate provides a metric of the radiologist's discrimination skill (d'). That is: d' = qnorm(hit_rate) - qnorm(FA_rate) Now, if we wanted to see if discrimination skill was improved by some intervention, we might recruit a bunch of radiologists and measure their d' both before and after the intervention. That is, both before the intervention, each radiologist would be presented with a number of "trials" where they review CT scans, mark them as cancer/no cancer, and we experimentalists score each diagnosis as a hit, miss, false alarm, or correct rejection. Presented with data like this, most psychologists would compute a d' score for each radiologist both before and after the intervention, then submit the d' scores to a repeated-measures ANOVA, which assumes gaussian error. However, hit and false alarm rates should yield binomially distributed error distributions, and monte carlo experimentation in R leads me to believe that in cases where only a moderate number of CT scans are reviewed per session (say, 10-20), d' may be expected to be considerably non-gaussian. I know mixed effects modelling can handle binomially distributed error, but is there any way to handle this sort of signal detection data? My first thought is that glmmer with 4 categories corresponding to the hit, miss, false alarm, and correction categorization of responses, but I don't immediately see how this would properly connect the hit-vs-miss data to reflect a hit rate and the false-alarm-vs-correct-rejection data to reflect a FA rate. Thoughts? Mike -- Mike Lawrence Graduate Student Department of Psychology Dalhousie University Looking to arrange a meeting? Check my public calendar: http://tr.im/mikes_public_calendar ~ Certainty is folly... I think. ~ _______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models Confidentiality Statement: This email message, including any attachments, is for th...{{dropped:6}}
Analysis of signal detection data
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