Human discounting research has frequently observed hyperbolic discounting of rewards which are delayed or probabilistic. However, no research has systematically combined delay and probability in one discounting procedure. Indifference points of hypothetical money rewards which are both delayed and probabilistic were determined. Odds were changed into comparable delays based on the h/k constant of proportionality based on Rachlin et al. (1991), and discounting rates were calculated. These data provided an excellent fit towards the hyperbolic type of discounting, suggesting that delay and probability could be combined right into a single metric in studies of discounting. The inclusion of the magnitude condition found the Magnitude Effect generally present in studies of temporal discounting. A temporal resolution of uncertainty condition found no effect. The current paper provides a novel record method, inside an established framework, for that analysis of information from studies of discounting that combine delay and probability.
Previous article in issue Next article in issue
How to Use SPSS- Receiver Operating Characteristics (ROC) Curve Part 1
- Cristina Sanfilippo: Someone knows ihow and if is it possible obtain Youden's index with SPSS??
- Tatsiana Leclair: Hello,
Under the Coordinates of the Curve table of this analysis, I get a footnote that says «The smallest cutoff value is the minimum observed test value minus 1, and the largest cutoff value is the maximum observed test value plus 1. All the other cutoff values are the averages of two consecutive ordered observed test values.»
How can I have the sensitivity and specificity for ALL observed values and not MEANs?
- rashmi yellappa: Crystal clear!! Thanks a tonn! 🙂
- Niveen Rizkalla: I ran a test which can determine if people have PTSD or not. I have calculated the scores of each participant, I have also read about a cut off point of 2.5 in the literature and I divided the scores into 2 categories of 0,1. But when I want to enter the two variable into spss ROC curve, I don't understand what is the second variable that I should enter into the second window!!! I want to know what is the percentage of people with or without PTSD in my study 🙁 can you help me please in answering my question….thank you 🙂
- Emmanuel Rivière: So yeah i have a question. I know when your curve is completely diagonal (going form 0,0 to 1,1), your test is completely meaningless. But how do you interpret the opposite diagonal (going from 1,0 to 0,1)?
- Jtrain Media: FANTASTIC video man. Thank you so much for explaining this to me. Totally understand it now.
- Leonardo: Amazingly clear tutorial! Thanks for your precious tips!
- William Tyler Tran: This is great! Thank you so much!
- Odelie Huet: Thanks!!! very good explanation
- Fabiano Timbó: Excelente aula. Congratulations
- hedaya alblewi: thank you so much for this video , this is really helpful.
- Robert Harvey: Thak you sooooooo much. Can not say how greatfull I am. Keep it up
- Victoria Brown: Thanks so much for this video. Really useful
- Mier: Concerning a good cut-off I recommend to look into the Youden-Index. That's at least one official way to deal with the sensitivity-specificity issue.
- TheRMUoHP Biostatistics Resource Channel: I will update the video very soon to incorporate that element. Thanks for the feedback!
- Bassem Refaat: Hi
Usually the authors in the published studies regarding new diagnostic makers report cut-off values including Senst and Specif driven from ROC. I would appreciate if you could show us how to do it using ROC in SPSS.
- TheRMUoHP Biostatistics Resource Channel: If they have the same "importance" to you, one way of calculating the cut-off is choosing that value that minimizes the distance between your ROC curve and the upper left corner of your graph. Another way is choosing as your cut-off the value that maximizes the sum of sensitivity and specificity.
- TheRMUoHP Biostatistics Resource Channel: There is not one best method in my opinion. You might weight differently the importance of sensitivity and specificity (for example, maybe you think it's more important to have a highly sensitive test even though this means having a low specific one. Or vice-versa).
- Kristen Bonistall: Is it possible to calculate an actual cut-off balance score for this example in SPSS?
- Selen Ozmen: I am very happy that I've reached this video, thank you very much for your work, this helped me a lot :)))
- TheRMUoHP Biostatistics Resource Channel: Be sure that your outcome is dichotomous (only 2 categories) and you are using "0" to denote an absence of the outcome and a "1" to indicate the presence of the outcome. Also be sure your predictor is numeric and not categorical.
- Martynas Gedminas: Huge thanks for your work! Always a big help:)