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

Two-Way ANOVA01:17

Two-Way ANOVA

2.8K
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...
2.8K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

296
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...
296
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

86
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
86
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

683
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
683
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

397
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
397
Regression Analysis01:11

Regression Analysis

6.0K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.0K

You might also read

Related Articles

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

Sort by
Same author

Psychometric Properties of the Dutch Short Form of the Five-Factor Narcissism Inventory.

Personality and mental health·2026
Same author

Advancing Dimensional Models of Psychopathology in Cancer: Insights From Applying the Hierarchical Taxonomy of Psychopathology (HiTOP).

Psycho-oncology·2026
Same author

The development and course of youth psychopathology: a longitudinal study of prevalence and continuity from early childhood through late adolescence.

European child & adolescent psychiatry·2026
Same author

The "dark triad" may be popular, but more importantly, it is irresponsible, moralizing, trivializing, and ultimately replaceable: Reply to Borráz-León et al. (2026).

Journal of psychopathology and clinical science·2026
Same author

UPPS-P Impulsivity, Momentary Affect, and Gambling: An Experience Sampling Method Study.

Journal of psychopathology and behavioral assessment·2026
Same author

From controversy to confusion: A commentary on how Marcus et al.'s (2025) Psychopathic Boldness Scale further muddies the boldness construct.

Psychological assessment·2026
Same journal

Time-Related Considerations for Modeling Event-Based Data Collected via Ecological Momentary Assessment.

Advances in methods and practices in psychological science·2026
Same journal

When Do Interaction/Moderation Effects Stabilize in Linear Regression?

Advances in methods and practices in psychological science·2026
Same journal

Multilab Direct Replication of Flavell, Beach, and Chinsky (1966): Spontaneous Verbal Rehearsal in a Memory Task as a Function of Age.

Advances in methods and practices in psychological science·2025
Same journal

A Delphi Study to Strengthen Research-Methods Training in Undergraduate Psychology Programs.

Advances in methods and practices in psychological science·2025
Same journal

A Tutorial on Analyzing Ecological Momentary Assessment Data in Psychological Research With Bayesian (Generalized) Mixed-Effects Models.

Advances in methods and practices in psychological science·2025
Same journal

Putting Psychology to the Test: Rethinking Model Evaluation Through Benchmarking and Prediction.

Advances in methods and practices in psychological science·2024
See all related articles

Related Experiment Video

Updated: Sep 11, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.1K

Tutorial: Power analyses for interaction effects in cross-sectional regressions.

David A A Baranger1, Megan C Finsaas2, Brandon L Goldstein3

  • 1Department of Psychiatry, Washington University in St. Louis.

Advances in Methods and Practices in Psychological Science
|August 13, 2025
PubMed
Summary
This summary is machine-generated.

Performing power analyses for interaction effects in regression is complex. The R package InteractionPoweR simplifies this, enabling researchers to easily conduct power analyses for interactions, even with correlated variables.

Keywords:
InteractionsRmoderationopen materialspower analysis

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.4K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.0K

Related Experiment Videos

Last Updated: Sep 11, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.1K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.4K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.0K

Area of Science:

  • Statistics
  • Psychometrics
  • Quantitative Psychology

Background:

  • Interaction analyses, also known as moderation or moderated multiple regression, assess how the relationship between two variables changes based on a third variable.
  • Performing accurate power analyses for interactions is challenging, especially with correlated and continuous variables, and existing software often lacks flexibility.
  • Key factors influencing statistical power, such as main effects, their correlations, and variable reliability, are not always clearly incorporated into power analyses.

Purpose of the Study:

  • To introduce the R package InteractionPoweR and its associated Shiny apps for conducting power analyses of interaction effects.
  • To provide researchers with a user-friendly tool for both analytic and simulation-based power analyses, requiring minimal programming experience.
  • To demonstrate how factors like main effects, correlations, reliability, and variable distributions impact statistical power for interaction analyses.

Main Methods:

  • Utilizing the R package InteractionPoweR for power analyses of interaction effects.
  • Employing both analytic and simulation-based approaches within the package.
  • Demonstrating the use of parameters such as Pearson's correlation, sample size, reliability, and variable distribution (e.g., binary, Likert scale).

Main Results:

  • The InteractionPoweR package facilitates power analyses for interaction effects, accommodating correlated and continuous variables.
  • The tutorial illustrates how main effects, their correlations, variable reliability, and distributions influence statistical power.
  • The package allows for flexible incorporation of various parameters to enhance the accuracy of power estimations.

Conclusions:

  • The R package InteractionPoweR offers a valuable and accessible tool for researchers to conduct robust power analyses for interaction effects.
  • Understanding the impact of main effects, correlations, and reliability is crucial for accurate power analysis in moderated regression.
  • The package empowers researchers to better plan studies and interpret findings involving interaction effects in statistical models.