Related Experiment Video
Updated: Jan 24, 2026
10:00
An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images
Published on: August 31, 2012
15.1K
Quantifying uncertainty in the meta-analytic lower bound estimate
Michael T Brannick1, Sean Potter1, Yuejia Teng1
1Department of Psychology.
Psychological Methods
|May 17, 2019
Summary
This study introduces new methods for calculating confidence intervals for the lower bound of credibility values in meta-analyses. Bootstrap confidence intervals for the lower bound (HOVr) are effective for correlations with sufficient studies.
Area of Science:
- Statistics
- Psychometrics
- Research Methodology
Background:
- Meta-analyses typically report confidence intervals for mean effects, but not for the standard deviation or lower bound of credibility values.
- These unquantified values are crucial for interpreting meta-analysis results.
Purpose of the Study:
- To introduce and evaluate methods for computing confidence intervals for the lower bound of credibility values in meta-analyses.
- To compare the performance of Lawless and bootstrap methods with different lower bound estimators.
Main Methods:
- Introduced two methods: Lawless and bootstrap confidence intervals.
- Utilized three lower bound estimators: Schmidt-Hunter, Schmidt-Hunter with k correction, and Morris/Hedges (HOVr/HOVd).
- Conducted two Monte Carlo simulation studies for correlations and standardized mean differences.
Main Results:
- Bootstrap confidence intervals for HOVr provided over 90% coverage for correlations with at least 25 studies.
- For standardized mean differences, all three methods with bootstrap yielded acceptable results with >= 20 studies and average sample size >= 50.
- Small study numbers (k=8-10) resulted in <90% coverage and wide intervals. Indirect range restriction and small between-studies variance also negatively impacted coverage.
Conclusions:
- Bootstrap confidence intervals offer a viable solution for estimating the lower bound of credibility values in meta-analyses.
- The effectiveness of these methods is dependent on the number of studies, average sample size, and characteristics of the data.
- Software is provided to facilitate the computation of these bootstrap confidence intervals for meta-analysts.
More Related Videos
10:22Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
Published on: September 7, 2019
8.7K
06:26Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
Published on: November 27, 2019
77.4K
Related Concept Videos
04:08The Uncertainty Principle
31.4K
Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
31.4K
02:46Uncertainty in Measurement: Reading Instruments
50.9K
Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
50.9K
00:59Uncertainty: Overview
1.7K
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
1.7K
03:34Uncertainty in Measurement: Significant Figures
80.9K
All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.
80.9K
00:54Uncertainty: 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
03:37Uncertainty in Measurement: Accuracy and Precision
100.5K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
100.5K