sumstats_micro: Perform a summary statistics microanalysis of identification...

In fsotoc/grtools: General Recognition Theory tools for the analysis of perceptual independence

Description

Performs a summary statistics (i.e., MSDA) microanalysis (see Kadlec, 1995; Kadlec & Townsend, 1992) of data from a 2x2 identification experiment. This analysis should be performed together with the macroanalysis implemented by the function sumstats_macro

Usage

sumstats_micro(cmat, use_kadlec = T)

Arguments

cmat	A 4x4 confusion matrix (see Details).
use_kadlec	If TRUE (default), uses a definition of the decision bound parameter c from Kadlec (1999). If FALSE, it uses the definition from MacMillan and Creelman (2005).

Details

For an introductory tutorial on the summary statistics macroanalyses, see Ashby & Soto (2005), particularly pages 22-28.

A 2x2 identification experiment involves two dimensions, A and B, each with two levels, 1 and 2. Stimuli are represented by their level in each dimension (A1B1, A1B2, A2B1, and A2B2) and so are their corresponding correct identification responses (a1b1, a1b2, a2b1, and a2b2).

The data from a single participant in the experiment should be ordered in a 4x4 confusion matrix with rows representing stimuli and columns representing responses. Each cell has the frequency of responses for the stimulus/response pair. Rows and columns should be ordered in the following way:

• Row 1: Stimulus A1B1

• Row 2: Stimulus A2B1

• Row 3: Stimulus A1B2

• Row 4: Stimulus A2B2

• Column 1: Response a1b1

• Column 2: Response a2b1

• Column 3: Response a1b2

• Column 4: Response a2b2

Value

An object of class "sumstats_macro"

The function summary is used to obtain a summary of conclusions from the analysis about perceptual independence and decisional separability, (see Table 2 in Kadlec, 1995).

References

Ashby, F. G., & Soto, F. A. (2015). Multidimensional signal detection theory. In J. R. Busemeyer, J. T. Townsend, Z. J. Wang, & A. Eidels (Eds.), Oxford handbook of computational and mathematical psychology (pp. 13-34). Oxford University Press: New York, NY.

Kadlec, H. (1995). Multidimensional signal detection analyses (MSDA) for testing separability and independence: A Pascal program. Behavior Research Methods, Instruments, & Computers, 27(4), 442-458.

Kadlec, H., & Townsend, J. T. (1992). Signal detection analyses of multidimensional interactions. In F. G. Ashby (Ed.), Multidimensional models of perception and cognition (pp. 181–231). Hillsdale, NJ: Erlbaum.

Macmillan, N. A., & Creelman, D. (2005). Detection theory: A user’s guide (2nd ed.). Mahwah, NJ: Erlbaum.

See Also

sumstats_macro

Examples

# Create a confusion matrix
# Inside the c(...) below, we enter the data from row 1 in the 
# matrix, then from row 2, etc.
cmat <- matrix(c(140, 36, 34, 40,
                 89, 91, 4, 66,
                 85, 5, 90, 70,
                 20, 59, 8, 163),
                 nrow=4, ncol=4, byrow=TRUE)

# Perform the summary statistics microanalysis
micro_results <- sumstats_micro(cmat)

# See a summary of the results
summary(micro_results)

# Print to screen the details of each test
micro_results

fsotoc/grtools documentation built on Nov. 15, 2020, 5:14 a.m.