Generalizability Theory ICC toolbox

Purpose: Calculate ICC coefficients including multiple sources of error (i.e., facets) and perform Decision Study.

Getting Started

Prerequisites

Matlab (tested with Matlab 2018b).

Usage

1. Create data and factor table variables. This can be accomplished using the load_reliability_data script, which automatically creates a factor table from the filenames. This can be changed for your data or data can be loaded manually (try "help load_reliability_data" for more info on the format).

2. Run reliability analysis. Example: [icc,var,stats,sigmask]=run_reliability(data',data,'factors',ftbl). Try "help run_reliability" for more info. This will result in average D- and G-coefficients, a Decision Study visualization, and a variable "icc" which contains detailed ICC results.

References:

Noble, S., Spann, M. N., Tokoglu, F., Shen, X., Constable, R. T., & Scheinost, D. (2017). Influences on the test–retest reliability of functional connectivity MRI and its relationship with behavioral utility. Cerebral Cortex, 27(11), 5415-5429.

Noble, S., Scheinost, D., Finn, E. S., Shen, X., Papademetris, X., McEwen, S. C., ... & Mirzakhanian, H. (2017). Multisite reliability of MR-based functional connectivity. Neuroimage, 146, 959-970.

Webb, N.M., Shavelson, R.J. and Haertel, E.H., 2006. Reliability coefficients and generalizability theory. Handbook of statistics, 26, pp.81-124.