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

Confidence Intervals01:21

Confidence Intervals

10.2K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
10.2K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

10.4K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
10.4K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

9.4K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
9.4K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

8.8K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
8.8K
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

27.8K
There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
27.8K
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

5.8K
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.
5.8K

You might also read

Related Articles

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

Sort by
Same author

A sequential approach for noninferiority or equivalence of a linear contrast under cost constraints.

Psychological methods·2023
Same author

Using chitosan-stabilized, hyaluronic acid-modified selenium nanoparticles to deliver CD44-targeted <i>PLK1</i> siRNAs for treating bladder cancer.

Nanomedicine (London, England)·2023
Same author

Sample size planning for replication studies: The devil is in the design.

Psychological methods·2022
Same author

Latent Class Mediation: A Comparison of Six Approaches.

Multivariate behavioral research·2020
Same author

Improved inference in mediation analysis: Introducing the model-based constrained optimization procedure.

Psychological methods·2020
Same author

A novel measure of effect size for mediation analysis.

Psychological methods·2017
Same journal

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

Multivariate behavioral research·2026
Same journal

A Unified Framework for Jointly modelling Response Times and Item Position Effects in Computer-Based Learning Assessments.

Multivariate behavioral research·2026
Same journal

Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions.

Multivariate behavioral research·2026
Same journal

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same journal

Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population.

Multivariate behavioral research·2026
Same journal

betaselectr: Selective (and Proper) Standardization in Structural Equation Models.

Multivariate behavioral research·2026
See all related articles

Related Experiment Video

Updated: Jan 23, 2026

Models and Methods to Evaluate Transport of Drug Delivery Systems Across Cellular Barriers
18:57

Models and Methods to Evaluate Transport of Drug Delivery Systems Across Cellular Barriers

Published on: October 17, 2013

47.3K

Indirect Effects in Sequential Mediation Models: Evaluating Methods for Hypothesis Testing and Confidence Interval

Davood Tofighi1, Ken Kelley2

  • 1Department of Psychology, University of New Mexico.

Multivariate Behavioral Research
|June 11, 2019
PubMed
Summary
This summary is machine-generated.

For complex mediation models, the best statistical method depends on your goal. Some methods are better for hypothesis testing, while others are superior for constructing confidence intervals (CIs).

Keywords:
Bayesian credible intervalIndirect effectconfidence intervalsequential mediation

More Related Videos

A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.4K
Direct and Indirect Culture Methods for Studying Biodegradable Implant Materials In Vitro
14:49

Direct and Indirect Culture Methods for Studying Biodegradable Implant Materials In Vitro

Published on: April 15, 2022

5.6K

Related Experiment Videos

Last Updated: Jan 23, 2026

Models and Methods to Evaluate Transport of Drug Delivery Systems Across Cellular Barriers
18:57

Models and Methods to Evaluate Transport of Drug Delivery Systems Across Cellular Barriers

Published on: October 17, 2013

47.3K
A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.4K
Direct and Indirect Culture Methods for Studying Biodegradable Implant Materials In Vitro
14:49

Direct and Indirect Culture Methods for Studying Biodegradable Implant Materials In Vitro

Published on: April 15, 2022

5.6K

Area of Science:

  • Statistics
  • Psychometrics
  • Quantitative Psychology

Background:

  • Complex mediation models, particularly sequential two-mediator models, are increasingly used in research.
  • Evaluating indirect effects in these models requires robust statistical methods for hypothesis testing and confidence interval construction.

Purpose of the Study:

  • To compare the performance of 10 frequentist and Bayesian confidence/credible intervals (CIs) for testing indirect effects in two-mediator models.
  • To assess Type I error rates, statistical power, and CI coverage under various conditions, including normal and non-normal data distributions.

Main Methods:

  • A large-scale Monte Carlo simulation study was employed.
  • Evaluated 10 different frequentist and Bayesian CI methods, including novel and understudied approaches like Bayesian CIs with different priors and model-based bootstrap methods.
  • Simulated data under normal and non-normal distributions, examining profile-likelihood, Monte Carlo with maximum likelihood standard error (MC-ML), and Monte Carlo with robust standard error (MC-Robust).

Main Results:

  • The popular BC bootstrap method exhibited inflated Type I error rates and under-coverage.
  • MC-ML, profile-likelihood, and two Bayesian methods were recommended for testing the null hypothesis of no mediation.
  • For CI construction with multivariate normal data, MC-ML, profile-likelihood, and the two Bayesian methods were recommended; for non-normal data, the percentile bootstrap was advised.

Conclusions:

  • The optimal method for hypothesis testing in mediation analysis may differ from the optimal method for confidence interval construction.
  • Specific recommendations are provided based on the analytical goal (hypothesis testing vs. CI reporting) and data distribution (normal vs. non-normal).