c¶

Download [hi-res] [pdf]

class sdt_metrics.c(methodname, add_prob_method=False)¶

c: parametric measure of response bias

Generally recommended as a better measure than beta [1], [2], [3]. First reason being that d’ and c are independent [4]. Forumula from Macmillan [5]. Applies Macmillan and Kaplan correction to extreme values. [6].

See also

dprime() loglinear_c()

[1]	Banks W. P. (1970). Signal detection theory and human memory. Psychological Bulletin, 74, 81-99.

[2]	Macmillan, N. A., and Creelman, C. D. (1990). Response bias: Characteristics of detection theory, threshold theory, and nonparametric indexes. Psychological Bulletin, 107, 401-413.

[3]	Snodgrass, J. G., and Corwin, J. (1988). Pragmatics of measuring recognition memory: Applications to dementia and amnesia. Journal of Experimental Psychology: General, 117, 34-50.

[4]	Ingham, J. G. (1970). Individual differences in signal detection. Acta Psychologica, 34, 39-50.

[5]	Macmillan, N. A. (1993). Signal detection theory as data analysis method and psychological decision model. In G. Keren and C. Lewis (Eds.), A handbook for data analysis in the behavioral sciences: Methodological issues (pp. 21-57). Hillsdale, NJ: Erlbaum.

[6]	Macmillan, N. A., and Kaplan, H. L. (1985). Detection theory analysis of group data: Estimating sensitivity from average hit and false-alarm rates. Psychological Bulletin, 98, 185-199.

classmethod direct(*args)¶

Calculates metric based on hit, miss, correct rejection, and false alarm counts

static prob(*args)¶

Calculates metric based on hit rate and false alarm rate

classmethod __call__(*args)¶

based on the number of args and the availability of .prob routes call to appropriate method.