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Reliability and Validity

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.

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Related Experiment Video

Updated: Jun 25, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Interrater and intermethod reliability of default mode network selection.

Alexandre R Franco¹, Aaron Pritchard, Vince D Calhoun

¹The Mind Research Network, Albuquerque, New Mexico.

Human Brain Mapping

|February 12, 2009

Summary

This summary is machine-generated.

Reliability in identifying the default mode network (DMN) using automated methods was assessed. Human agreement was high, but human-machine agreement was moderate, improving with specific brain region weighting.

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Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

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Area of Science:

Neuroimaging
Cognitive Neuroscience
Brain Network Analysis

Background:

Resting-state neuroimaging reveals the default mode network (DMN), active during passive mental states.
Independent Component Analysis (ICA) enables DMN analysis but requires component selection.
Current methods for DMN component selection lack reliability assessment.

Purpose of the Study:

To investigate the reliability of human versus automated methods for identifying DMN activation.
To assess interrater (human-human) and intermethod (human-machine) reliability.
To explore improvements in automated DMN identification.

Main Methods:

Utilized independent component analysis (ICA) on resting-state fMRI data from 42 healthy controls.
Evaluated DMN component selection reliability using human raters and automated techniques.
Compared interrater and intermethod reliability, with adjustments using anterior and posterior cingulate nodes.

Main Results:

Near-perfect interrater reliability was observed for DMN identification.
Intermethod reliability between human and automated methods was only moderate.
Weighting anterior and posterior cingulate nodes significantly improved intermethod reliability.

Conclusions:

Human assessment of DMN components is highly reliable.
Automated DMN identification requires refinement for consistent results.
Weighted node approaches show promise for improving automated DMN selection accuracy.