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

Standard Deviation01:10

Standard Deviation

17.7K
The most commonly used measure of variation is the standard deviation. It is a numerical value measuring how far data values are from their mean. The standard deviation value is small when the data are concentrated close to the mean, exhibiting slight variation or spread. The standard deviation value is never negative, it is either positive or zero. The standard deviation is larger when the data values are more spread out from the mean, which means the data values are exhibiting more...
17.7K
Calculating Standard Deviation01:08

Calculating Standard Deviation

11.0K
The standard deviation is the most common measure of variation. It is a value that tells us how far a data value is from the mean value in a dataset. Further, the standard deviation is always a positive value or zero.
The standard deviation value is small when all the data is concentrated close to the mean. Here the data exhibits low variation. The standard deviation value is larger when the data values are more spread out from the mean. Here, the data displays high...
11.0K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.1K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.1K
Standard Deviation of Calculated Results01:14

Standard Deviation of Calculated Results

9.1K
Standard deviation measures the spread of data around the mean value. Many large data sets follow a Gaussian distribution, also known as a normal distribution. This distribution is bell-shaped curved, with the most frequently observed value (mean or central value) in the middle. The farther away from the central value, the greater the deviation from the central value, and the lower the frequency.
A broad Gaussian distribution curve has a wider standard deviation, representing a data set with...
9.1K
Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

24.7K
In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
24.7K
Chebyshev's Theorem to Interpret Standard Deviation01:15

Chebyshev's Theorem to Interpret Standard Deviation

4.4K
Chebyshev’s theorem, also known as Chebyshev’s Inequality, states that the proportion of values of a dataset for K standard deviation is calculated using the equation:
4.4K

You might also read

Related Articles

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

Sort by
Same author

Facile Synthesis of Biocompatible Fluorescent Nanoparticles for Cellular Imaging and Targeted Detection of Cancer Cells.

ACS applied materials & interfaces·2015
Same author

Item exposure control for multidimensional computer adaptive testing under maximum likelihood and expected a posteriori estimation.

Behavior research methods·2015
Same author

[Electric-heat needling for 56 cases of cervical spondylosis with the neck type].

Zhongguo zhen jiu = Chinese acupuncture & moxibustion·2015
Same author

[Effects of GLP-1 Agonist Exenatide on Cardiac Diastolic Function and Vascular Endothelial Function in Diabetic Patients].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2015
Same author

Evaluation of The Cervista HPV A9 group In Screening Patients for Cervical Cancer.

Journal of medical screening·2015
Same author

Porous carbon derived from a metal-organic framework as an efficient adsorbent for the solid-phase extraction of phthalate esters.

Journal of separation science·2015
Same journal

Predicting Multilevel Growth Trajectories Using a Random-Effect Diagnostic Classification Model.

Psychometrika·2026
Same journal

Regularized Joint Maximum Likelihood Estimation for Exploratory Multidimensional Item Response Theory Models.

Psychometrika·2026
Same journal

Capturing Heterogeneity in Levels, Variability, and Couplings across Persons and Time with a Hierarchical Time-Varying Coefficient Formulation of the Multivariate Normal.

Psychometrika·2026
Same journal

BAYESIAN MIXED MULTIDIMENSIONAL SCALING FOR AUDITORY PROCESSING.

Psychometrika·2026
Same journal

Testing linear hypotheses in repeated measures generalized linear models using external information.

Psychometrika·2026
Same journal

When Do Unifactorial Items Increase the Reliability?

Psychometrika·2026
See all related articles

Related Experiment Video

Updated: May 5, 2026

Testing Tactile Masking between the Forearms
08:05

Testing Tactile Masking between the Forearms

Published on: February 10, 2016

6.1K

Does standard deviation matter? Using "standard deviation" to quantify security of multistage testing.

Chun Wang1, Yi Zheng, Hua-Hua Chang

  • 1University of Minnesota at Twin-Cities, 75 East River Road, Elliott Hall N658, Minneapolis, MN, 55403, USA, wang4066@umn.edu.

Psychometrika
|December 11, 2013
PubMed
Summary
This summary is machine-generated.

Multistage testing (MST) security analysis reveals that the standard deviation of test overlap is crucial for understanding item exposure. A larger SD in MST, compared to computerized adaptive testing (CAT), indicates greater variability in item sharing among test-takers.

More Related Videos

Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition
14:01

Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition

Published on: May 22, 2015

42.2K
Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator
03:49

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

Published on: May 19, 2023

1.8K

Related Experiment Videos

Last Updated: May 5, 2026

Testing Tactile Masking between the Forearms
08:05

Testing Tactile Masking between the Forearms

Published on: February 10, 2016

6.1K
Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition
14:01

Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition

Published on: May 22, 2015

42.2K
Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator
03:49

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

Published on: May 19, 2023

1.8K

Area of Science:

  • Educational Measurement
  • Psychometrics
  • Computer-Based Testing

Background:

  • Online testing is prevalent in large-scale assessments, posing security risks due to potential information sharing among test-takers.
  • Existing security indices, like the average test-overlap rate, primarily focus on the mean and were developed for computerized adaptive testing (CAT).
  • Multistage testing (MST) is a growing alternative to CAT, necessitating new security assessment methods.

Purpose of the Study:

  • To introduce the standard deviation (SD) of test overlap rate as a vital metric for assessing test security in multistage testing (MST).
  • To analytically derive the lower bounds of the SD of test overlap rate under MST and compare it with CAT.
  • To investigate the impact of single-pool versus multiple-pool designs on MST security.

Main Methods:

  • Analytical derivation of the lower bounds for the standard deviation of test overlap rate in MST.
  • Comparison of MST and CAT security metrics using derived bounds.
  • Simulation study to provide empirical evidence for analytical findings.
  • Comparative analysis of single-pool and multiple-pool designs in MST.

Main Results:

  • For a given mean overlap rate, the standard deviation of test overlap rate tends to be larger in MST compared to CAT.
  • A non-overlapping multiple-pool design in MST was found to slightly increase security risks.
  • The standard deviation of test overlap rate provides additional, crucial information beyond the mean for evaluating test security.

Conclusions:

  • The standard deviation of test overlap rate is an essential metric for comprehensive test security analysis in MST.
  • MST security profiles differ from CAT, with potentially higher variability in item overlap.
  • Careful consideration of pool design in MST is necessary to mitigate security risks.