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

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

Sampling Plans

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

Random Sampling Method

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...

Stratified Sampling Method

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...

Sampling Distribution

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...

Systematic Sampling Method

Systematic Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...

You might also read

Related Articles

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

Sort by

Same author

Dyadic Coping and Communication as Predictors of 10-Year Relationship Satisfaction Subgroup Trajectories in Stable Romantic Couples.

Behavioral sciences (Basel, Switzerland)·2025

Same author

A complex systems view on physical activity with actionable insights for behaviour change.

Nature human behaviour·2025

Same author

Positive Outcomes of Long-Term Relationship Satisfaction Trajectories in Stable Romantic Couples: A 10-Year Longitudinal Study.

International journal of applied positive psychology·2024

Same author

Decomposing the True Score Variance in Rated Responses to Divergent Thinking-Tasks for Assessing Creativity: A Multitrait-Multimethod Analysis.

Journal of Intelligence·2024

Same author

Latent Reciprocal Engagement and Accuracy Variables in Social Relations Structural Equation Modeling.

Multivariate behavioral research·2024

Same author

A correlated traits correlated (methods - 1) multitrait-multimethod model for augmented round-robin data.

The British journal of mathematical and statistical psychology·2023

Search research articles

Related Experiment Video

Updated: May 26, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Automatic Item Generation Measurement Models Respecting the Stochastic Sampling Space for Cross-Classified and

Philipp Jahn¹, David Jendryczko¹, Fridtjof W Nussbeck¹

¹Department of Psychology, University of Konstanz, Konstanz, Germany.

Applied Psychological Measurement

|May 25, 2026

Summary

Item incidentals, surface features in automatic item generation (AIG), minimally impact item difficulty. Combining classical test theory models optimizes investigations into incidental-induced variance for better item generator validation.

Keywords:

automatic item generation cross-classified figural memory stochastic sampling space structural equation modeling two-level

More Related Videos

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

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

Published on: July 3, 2020

Related Experiment Videos

Last Updated: May 26, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

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

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

Published on: July 3, 2020

Area of Science:

Psychometrics
Educational Measurement
Cognitive Psychology

Background:

Automatic Item Generation (AIG) relies on distinguishing item incidentals (surface features) from item radicals (core attributes).
Empirical validation of AIG involves sampling subjects and incidentals, with cross-classified and two-level sampling being common approaches.
Understanding the influence of item incidentals is crucial for the validity of generated test items.

Purpose of the Study:

To present and evaluate an approach for scrutinizing the effect of item incidentals in AIG.
To utilize classical test theory models tailored for cross-classified and two-level sampling spaces.
To optimize the investigation of incidental-induced variance within item generators.

Main Methods:

Developed an approach using two classical test theory models representing cross-classified and two-level sampling.
Applied the approach to the figural short-term memory item generator (figumem).
Analyzed the impact of item incidentals on item difficulty and parameter generalization.

Main Results:

Item incidentals demonstrated minimal effect on item difficulty within the cross-classified model/sample.
Model parameters showed generalization to a broader set of incidentals in the two-level model/sample.
The combined use of models facilitated a more optimized investigation of incidental variance.

Conclusions:

Item incidentals have a limited impact on item difficulty, suggesting robustness in AIG.
The proposed methodology enhances the empirical validation of item generators.
Further research should explore implications and limitations for AIG development.