Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

6.0K
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
6.0K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

601
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
601
Fundamental Attribution Error01:14

Fundamental Attribution Error

13.8K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
13.8K
Naturalistic Observations02:30

Naturalistic Observations

17.5K
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
17.5K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

11.0K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
11.0K
Introduction to z Scores01:06

Introduction to z Scores

11.3K
A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
11.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Associations of distinct sedentary behaviors with cortical, subcortical, and white matter hyperintensity volumes: Evidence from the ARIC study.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

The Effects of Personalized Feedback About ALDH2*2, Alcohol Use, and Associated Health Risks on Drinking Intention and Consumption: The Role of Self-Efficacy and Perceived Threat.

Alcohol, clinical & experimental research·2026
Same author

Estimating marginal effects with zero-inflated models: A tutorial with the R package mzim.

Behavior research methods·2026
Same author

Associations of sleep behaviors with white matter hyperintensity volume in middle-aged to older adults.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Disentangling the bidirectional associations of depressive symptoms, impulsivity, and substance use in early adolescence: A between- and within-person perspective.

Experimental and clinical psychopharmacology·2026
Same author

A two-stage approach to account for measurement error when using empirical Bayes estimates of random slopes.

Psychological methods·2026

Related Experiment Video

Updated: Feb 10, 2026

A Simple Composite Phenotype Scoring System for Evaluating Mouse Models of Cerebellar Ataxia
07:33

A Simple Composite Phenotype Scoring System for Evaluating Mouse Models of Cerebellar Ataxia

Published on: May 21, 2010

37.3K

Evaluation of Two Methods for Modeling Measurement Errors When Testing Interaction Effects With Observed Composite

Yu-Yu Hsiao1, Oi-Man Kwok1, Mark H C Lai2

  • 1Texas A&M University, College Station, TX, USA.

Educational and Psychological Measurement
|May 26, 2018
PubMed
Summary

Ignoring measurement errors in path models can bias interaction effect estimates. Alternative methods like reliability-adjusted product indicator (RAPI) and latent moderated structural equations (LMS) provide unbiased results, improving statistical accuracy.

Keywords:
composite scorelatent interaction effectreliabilitystructural equation modeling

More Related Videos

Wicking Tests for Unidirectional Fabrics: Measurements of Capillary Parameters to Evaluate Capillary Pressure in Liquid Composite Molding Processes
07:06

Wicking Tests for Unidirectional Fabrics: Measurements of Capillary Parameters to Evaluate Capillary Pressure in Liquid Composite Molding Processes

Published on: January 27, 2017

9.2K
Experimental Implementation of a New Composite Fabrication Method: Exposing Bare Fibers on the Composite Surface by the Soft Layer Method
06:26

Experimental Implementation of a New Composite Fabrication Method: Exposing Bare Fibers on the Composite Surface by the Soft Layer Method

Published on: October 6, 2017

8.8K

Related Experiment Videos

Last Updated: Feb 10, 2026

A Simple Composite Phenotype Scoring System for Evaluating Mouse Models of Cerebellar Ataxia
07:33

A Simple Composite Phenotype Scoring System for Evaluating Mouse Models of Cerebellar Ataxia

Published on: May 21, 2010

37.3K
Wicking Tests for Unidirectional Fabrics: Measurements of Capillary Parameters to Evaluate Capillary Pressure in Liquid Composite Molding Processes
07:06

Wicking Tests for Unidirectional Fabrics: Measurements of Capillary Parameters to Evaluate Capillary Pressure in Liquid Composite Molding Processes

Published on: January 27, 2017

9.2K
Experimental Implementation of a New Composite Fabrication Method: Exposing Bare Fibers on the Composite Surface by the Soft Layer Method
06:26

Experimental Implementation of a New Composite Fabrication Method: Exposing Bare Fibers on the Composite Surface by the Soft Layer Method

Published on: October 6, 2017

8.8K

Area of Science:

  • Psychometrics
  • Statistical Modeling

Background:

  • Path models commonly use composite scores, assuming no measurement error.
  • This assumption can lead to inaccurate interaction effect testing.

Purpose of the Study:

  • To evaluate methods for testing interaction effects in structural equation modeling (SEM) that account for measurement error.
  • To compare the reliability-adjusted product indicator (RAPI) and latent moderated structural equations (LMS) methods against traditional composite approaches.

Main Methods:

  • Review and evaluation of two SEM-based methods: RAPI and LMS.
  • Comparison of these methods with path models that ignore measurement errors.

Main Results:

  • Both RAPI and LMS methods produced unbiased estimates of interaction effects.
  • Path models ignoring measurement errors resulted in substantial bias and low confidence interval coverage.

Conclusions:

  • Accounting for measurement error is crucial for accurate interaction effect estimation in SEM.
  • RAPI and LMS are recommended alternatives to traditional composite-based path models for interaction analysis.