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

Interval Level of Measurement

Interval Level of Measurement

For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between the...

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...

Comparing Experimental Results: Student's t-Test

Comparing Experimental Results: Student's t-Test

The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...

Central Tendency: Analysis

Central Tendency: Analysis

Measures of central tendency are tools used in biostatistics to identify the average or center of a dataset. They offer a single representative value for understanding and summarizing data distribution.
The mean is one such measure, calculated by totaling all values in a dataset and dividing by the number of values. For instance, the mean blood pressure reading (120, 130, 140, 150) would be 135. However, the mean can be affected by extreme values or outliers.
The median, another measure,...

Estimating Population Mean with Known Standard Deviation

Estimating Population Mean with Known Standard Deviation

To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...

Behrens–Fisher Test

Behrens–Fisher Test

The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
This test is...

You might also read

Related Articles

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

Sort by

Same author

Development of character strengths across the deployment cycle among U.S. Army soldiers.

Journal of personality·2020

Same author

Point-biserial correlation: Interval estimation, hypothesis testing, meta-analysis, and sample size determination.

The British journal of mathematical and statistical psychology·2019

Same author

Interval estimation for linear functions of medians in within-subjects and mixed designs.

The British journal of mathematical and statistical psychology·2019

Same author

Varying coefficient meta-analysis methods for odds ratios and risk ratios.

Psychological methods·2015

Same author

CFA with binary variables in small samples: a comparison of two methods.

Frontiers in psychology·2015

Same author

Meta-analysis methods for risk differences.

The British journal of mathematical and statistical psychology·2013

Same journal

Addressing selective reporting bias in meta-analysis of dependent effect sizes: A tutorial in R.

Psychological methods·2026

Same journal

Heterogeneous variance models with Gaussian processes.

Psychological methods·2026

Same journal

Bayesian evaluation for latent variable models: A tutorial on computing information criteria and bayes factors with the r package bleval.

Psychological methods·2026

Same journal

A stochastic block prior for clustering in graphical models.

Psychological methods·2026

Same journal

Three-level vector autoregressive models.

Psychological methods·2026

Same journal

Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters.

Psychological methods·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 20, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Meta-analytic interval estimation for standardized and unstandardized mean differences.

Douglas G Bonett¹

¹Department of Statistics, Iowa State University, Ames IA 50011, USA. dgbonett@iastate.edu

Psychological Methods

|September 2, 2009

Summary

This summary is machine-generated.

New fixed-effects (FE) meta-analytic confidence intervals address limitations of existing methods. These intervals perform well with effect-size heterogeneity and nonrandomly selected studies, improving meta-analysis reliability.

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Related Experiment Videos

Last Updated: Jun 20, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Area of Science:

Biostatistics
Medical Research Methodology
Statistical Inference

Background:

Traditional fixed-effects (FE) meta-analytic confidence intervals assume homogeneity of effect sizes, leading to poor performance when violated.
Random-effects (RE) meta-analytic confidence intervals assume studies are a random sample, which is often not justifiable in typical meta-analyses with nonrandomly selected studies.

Purpose of the Study:

To propose novel FE meta-analytic confidence intervals for unstandardized and standardized mean differences.
To develop intervals that perform adequately under conditions of effect-size heterogeneity and nonrandom study selection.
To offer methods applicable to independent or dependent samples and for integrating prior study results into new research.

Main Methods:

Development of new computational formulas for fixed-effects meta-analytic confidence intervals.
Evaluation of interval performance under simulated effect-size heterogeneity and nonrandom sampling.
Presentation of an alternative method for assessing effect-size heterogeneity.

Main Results:

The proposed FE confidence intervals are easy to compute.
These new intervals demonstrate proper performance even when effect sizes are heterogeneous and studies are nonrandomly selected.
The methodology accommodates various study designs, including independent and dependent samples, and facilitates meta-analytic integration.

Conclusions:

The proposed fixed-effects meta-analytic confidence intervals offer a more realistic and robust approach compared to traditional FE and RE methods.
These intervals provide reliable estimates for mean differences in meta-analyses with heterogeneous or nonrandomly selected studies.
The new methods enhance the validity and applicability of meta-analysis in diverse research contexts.