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Related Concept Videos

Bias01:22

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Common Leveling Mistakes and Errors01:17

Common Leveling Mistakes and Errors

A survey team is tasked with determining the elevation difference between points Point A and Point B, separated by uneven terrain. They use a leveling instrument and a leveling rod.Common MistakesMisreading the Rod: During a backsight reading at Point A, the instrumentman observes the rod partially obscured by tall grass. Instead of reading 1.135 m, they mistakenly record 1.735 m due to the misalignment of the crosshair with the wrong graduation. This error adds 0.600 m to all subsequent...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

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Related Experiment Video

Updated: Jul 5, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Playing statistical ouija board with commonality analysis: good questions, wrong assumptions.

W Joel Schneider1

  • 1Psychology Department, Illinois State University, Normal, IL 617790-4620, USA. wjschne@ilstu.edu

Applied Neuropsychology
|April 30, 2008
PubMed
Summary

Commonality analysis may yield flawed conclusions about intelligence (g). Simulations show the highest commonality poorly represents general intelligence, and lower components may solely comprise g.

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Area of Science:

  • Psychometrics
  • Intelligence Research
  • Statistical Analysis

Background:

  • Commonality analysis is frequently employed in psychometric research to understand the structure of psychological constructs.
  • Previous applications, such as those by Hale and colleagues, have utilized this method to interpret the general factor of intelligence (g).

Purpose of the Study:

  • To critically evaluate the validity of commonality analysis for operationalizing the general factor of intelligence (g).
  • To investigate the potential for commonality analysis to produce unjustified conclusions in intelligence research.

Main Methods:

  • Analysis of simulated datasets designed to mimic common psychometric conditions.
  • Examination of the properties of highest-order, lower-order, and unique components within commonality analysis.

Main Results:

  • The highest-order commonality is demonstrated to be an inadequate measure of general intelligence (g).
  • Simulations indicate that lower-order commonalities and unique components can be substantially or entirely composed of g under typical circumstances.
  • Identified specific issues with the analytical and interpretive approaches used in prior studies.

Conclusions:

  • The use of commonality analysis, particularly focusing on highest-order commonality, is not recommended for accurately assessing the general factor of intelligence (g).
  • Researchers should exercise caution when interpreting results from commonality analyses in intelligence studies due to potential confounds with g.
  • Alternative methods may be more appropriate for robustly estimating the general factor of intelligence.