Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

Friedman Two-way Analysis of Variance by Ranks

Friedman 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...

Two-Way ANOVA

Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the means for...

One-Way ANOVA

One-Way ANOVA

One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...

One-Way ANOVA: Unequal Sample Sizes

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...

You might also read

Related Articles

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

Sort by

Same author

Oncology Nurses' Perceptions of Barriers and Facilitators to Conducting Spiritual Histories.

Journal of hospice and palliative nursing : JHPN : the official journal of the Hospice and Palliative Nurses Association·2025

Same author

Human-Centered Design Development and Acceptability Testing of a Goal Concordant Prescribing Program in Hospice.

Journal of palliative medicine·2025

Same author

A Goal-Concordant Prescribing Conceptual Framework: Implications for Practice and Measurement.

Journal of hospice and palliative nursing : JHPN : the official journal of the Hospice and Palliative Nurses Association·2025

Same author

Shared Medication PLanning In Home Hospice to Address Medication Regimen Complexity and Family Caregiver Burden: A Brief Report.

Journal of palliative medicine·2024

Same author

The Pathways to Prevention program: nutrition as prevention for improved cancer outcomes.

Journal of the National Cancer Institute·2023

Same author

Balancing Work and Hospice Caregiving-A Closer Look at Burden, Preparedness, and Mental Health.

Journal of pain and symptom management·2021

Search research articles

Related Experiment Video

Updated: Jul 3, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

AMOS versus LISREL: one data set, two analyses.

Margaret F Clayton¹, Marjorie A Pett

¹College of Nursing, University of Utah, Salt Lake City, UT 84124, USA. margaret.clayton@nurs.utah.edu

Nursing Research

|July 22, 2008

Summary

Comparing structural equation modeling (SEM) software, AMOS and LISREL, revealed highly similar path analysis results. Minor discrepancies due to rounding did not affect overall model comparability, ensuring confidence in research outcomes.

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

A User-friendly and Powerful R Analysis of Large-scale Datasets

A User-friendly and Powerful R Analysis of Large-scale Datasets

Published on: November 4, 2025

Related Experiment Videos

Last Updated: Jul 3, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

A User-friendly and Powerful R Analysis of Large-scale Datasets

A User-friendly and Powerful R Analysis of Large-scale Datasets

Published on: November 4, 2025

Area of Science:

Statistics
Quantitative Psychology
Social Sciences

Background:

Path analysis is crucial for understanding variable associations.
Limited comparative usability data exists for popular structural equation modeling (SEM) software.
Choosing between SEM programs like AMOS and LISREL can be challenging for researchers.

Purpose of the Study:

To compare path analysis outcomes between AMOS (Version 6.0) and LISREL (Version 8.80).
To evaluate the usability and comparability of two leading SEM software packages.

Main Methods:

One dataset was analyzed independently in AMOS and LISREL by blinded researchers.
Solutions were cross-replicated in the alternative software.
Reduced models were generated using modification indices and compared for similarity.

Main Results:

Both AMOS and LISREL produced highly similar, though not identical, path analysis solutions.
Modification indices suggested one additional variable and two extra paths in both programs.
Minor discrepancies in critical ratios were observed, likely due to rounding differences.

Conclusions:

Rounding differences in SEM software can cause subtle variations in model reduction.
Despite minor discrepancies, final solutions were accurately replicable across AMOS and LISREL.
Researchers can confidently use either SEM program, selecting based on programming skills and research needs.