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

Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.0K
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 +...
9.0K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.3K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.3K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.1K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.1K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

769
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
769
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

6.2K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
6.2K
Standard Error of the Mean01:13

Standard Error of the Mean

6.8K
The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.
The standard error of the mean is an example of a standard error. It is a unique standard deviation known as the standard deviation of the sampling distribution of the mean. The standard error of the mean is a statistic that calculates how correctly a sample distribution represents a...
6.8K

You might also read

Related Articles

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

Sort by
Same author

Large-Scale Item-Level Analysis of the Figural Matrices Test in the Norwegian Armed Forces: Examining Measurement Precision and Sex Bias.

Journal of Intelligence·2024
Same author

Erratum: A Constrained Metropolis-Hastings Robbins-Monro Algorithm for Q Matrix Estimation in DINA Models.

Psychometrika·2024
Same author

Fast estimation of generalized linear latent variable models for performance and process data with ordinal, continuous, and count observed variables.

The British journal of mathematical and statistical psychology·2024
Same author

Investigating Planning and Non-Targeted Exploration in PIAAC 2012: Validating Their Measures Based on Process Data and Investigating Their Relationships with Problem-Solving Competency.

Journal of Intelligence·2023
Same author

Longitudinal measurement properties of the Montreal Cognitive Assessment.

Journal of clinical and experimental neuropsychology·2022
Same author

Impact of Sampling Variability When Estimating the Explained Common Variance.

Applied psychological measurement·2022
Same journal

General formulae for transforming Pearson's r to the scale of Cohen's d.

Applied psychological measurement·2026
Same journal

babebi: An R Package for Bayesian Estimation and Validation in Small-N Two-Rater Pre-Post Designs.

Applied psychological measurement·2026
Same journal

A Tool for Agreement and Alignment Analysis in Binary Rating Tasks: The R Package scindex.

Applied psychological measurement·2026
Same journal

The EM Algorithm and Its Variants in Cognitive Diagnostic Models: Comparing Their Propensity for Boundaries, Extremes, Convergence, and Suboptimal Solutions.

Applied psychological measurement·2026
Same journal

When Perceptions of Social Desirability Differ: Implications for the Multidimensional Nominal Response Model of Faking.

Applied psychological measurement·2026
Same journal

csemGT: An R Package for Estimating Raw-Score Conditional Standard Errors of Measurement in Generalizability Theory.

Applied psychological measurement·2026
See all related articles

Related Experiment Video

Updated: Sep 24, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

3.4K

Standard Errors of Kernel Equating: Accounting for Bandwidth Estimation.

Kseniia Marcq1, Björn Andersson1

  • 1University of Oslo, Oslo, Norway.

Applied Psychological Measurement
|May 9, 2022
PubMed
Summary
This summary is machine-generated.

Kernel equating improves test score comparability. This study introduces a modified standard error calculation for kernel equating, accounting for bandwidth estimation variability, ensuring more accurate standard errors in standardized testing.

Keywords:
achievement testingclassical test theoryequatingitem response theorystandard errors

More Related Videos

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
12:51

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Published on: June 16, 2018

7.6K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K

Related Experiment Videos

Last Updated: Sep 24, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

3.4K
Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
12:51

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Published on: June 16, 2018

7.6K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K

Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Equating is crucial for score comparability in standardized testing.
  • Kernel equating uses smoothing bandwidths, introducing variability not accounted for in standard error calculations.
  • This omission threatens the accuracy of equating standard errors.

Purpose of the Study:

  • To derive the asymptotic variance of the bandwidth parameter estimator in kernel equating.
  • To introduce a modified method for calculating standard errors of equating that incorporates bandwidth estimation variability.
  • To evaluate the accuracy of the modified method for equivalent groups designs.

Main Methods:

  • Derivation of asymptotic variance for the bandwidth parameter estimator.
  • Development of a modified standard error calculation for kernel equating.
  • Simulation study to compare the modified method with existing methods and Monte Carlo estimates.

Main Results:

  • The modified standard errors of equating were found to be accurate under various conditions.
  • The impact of bandwidth variability on standard errors of equating was minimal.
  • Modified and existing methods yielded similar results.

Conclusions:

  • The modified method provides accurate standard errors for kernel equating.
  • The variability introduced by bandwidth estimation has a negligible impact on standard errors.
  • Accurate standard errors are essential for reliable test score interpretation.