Related Experiment Video
Updated: Jun 13, 2026
06:35
Basics of Multivariate Analysis in Neuroimaging Data
Published on: July 24, 2010
Sampling Variability Is Not Nonreplication: A Bayesian Reanalysis of Forbes, Wright, Markon, and Krueger
Payton J Jones1, Donald R Williams2, Richard J McNally1
1Department of Psychology, Harvard University.
Multivariate Behavioral Research
|August 1, 2020
Summary
Psychopathology network analysis shows good replicability, contrary to prior claims. A Bayesian re-analysis confirms network stability, offering a reliable method for future research.
Area of Science:
- Psychology
- Network Science
- Psychopathology Research
Background:
- Prior research questioned the replicability of psychopathology network characteristics.
- This critique relied on direct metrics applied to observational data, potentially overlooking statistical nuances.
Purpose of the Study:
- To critically evaluate the claims of limited psychopathology network replicability.
- To propose and apply a more robust statistical approach for assessing network stability.
Main Methods:
- Critique of direct metrics for network comparison, highlighting limitations in handling sampling variability and inferring method validity.
- Application of a Bayesian re-analysis framework to quantify uncertainty and assess evidence for replication versus nonreplication of network edges.
Main Results:
- The critique identified three key flaws in the original assessment: misinterpretation of nonreplication across datasets, incorrect assumptions about shared variance and reliability, and failure to account for sampling variability.
- The Bayesian re-analysis provided substantial evidence supporting the replication of network structures and found scant evidence for nonreplication.
Conclusions:
- The original critique of psychopathology network replicability is undermined by methodological limitations.
- A Bayesian approach offers a principled and statistically sound method for evaluating network stability and replicability in psychopathology research.
Related Concept Videos
01:20Random and Systematic Errors
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
01:12Sampling Distribution
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
01:10Propagation of Uncertainty from Systematic Error
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
01:20Random and Systematic Errors
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
01:11Variability: Analysis
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
The range is a simple measure of variability, indicating the difference between the highest and...
01:21Friedman Two-way Analysis of Variance by Ranks
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...