Jove
Visualize
Contact Us

Related Concept Videos

Statistical Analysis: Overview01:11

Statistical Analysis: Overview

6.6K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.6K
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

24.1K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
24.1K
Ratio Level of Measurement00:54

Ratio Level of Measurement

18.1K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
18.1K
Common Leveling Mistakes and Errors01:17

Common Leveling Mistakes and Errors

76
A survey team is tasked with determining the elevation difference between points Point A and Point B, separated by uneven terrain. They use a leveling instrument and a leveling rod.Common MistakesMisreading the Rod: During a backsight reading at Point A, the instrumentman observes the rod partially obscured by tall grass. Instead of reading 1.135 m, they mistakenly record 1.735 m due to the misalignment of the crosshair with the wrong graduation. This error adds 0.600 m to all subsequent...
76
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

1.5K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.5K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

103
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
103

You might also read

Related Articles

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

Sort by
Same author

Evaluating Equating Methods for Varying Levels of Form Difference.

Educational and psychological measurement·2024
Same author

Extended Multivariate Generalizability Theory With Complex Design Structures.

Educational and psychological measurement·2022
Same author

Simple-Structure Multidimensional Item Response Theory Equating for Multidimensional Tests.

Educational and psychological measurement·2020
See all related articles
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 Experiment Video

Updated: Jul 11, 2025

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

10.8K

Evaluating the Effects of Missing Data Handling Methods on Scale Linking Accuracy.

Tong Wu1,2, Stella Y Kim1, Carl Westine1

  • 1University of North Carolina at Charlotte, USA.

Educational and Psychological Measurement
|November 17, 2023
PubMed
Summary
This summary is machine-generated.

Handling missing data in large-scale assessments is crucial for accurate scale linking. Response function imputation, multiple imputation, and full information likelihood estimation methods performed best, minimizing errors in item response theory scale linking.

Keywords:
common item nonequivalent group designitem response theorymissing datascale linking

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.4K

Related Experiment Videos

Last Updated: Jul 11, 2025

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

10.8K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.4K

Area of Science:

  • Educational Measurement
  • Psychometrics
  • Statistics

Background:

  • Large-scale assessments frequently encounter missing data, impacting the reliability of results.
  • Item response theory (IRT) is widely used, but its application with missing data in scale linking remains underexplored.
  • Scale linking ensures comparability across test forms, essential for longitudinal studies and program evaluation.

Purpose of the Study:

  • To evaluate the impact of six different missing data handling methods on the accuracy of IRT scale linking.
  • To compare the performance of these methods under various simulation conditions and missing data mechanisms.
  • To identify the most effective strategies for addressing missing responses in common items during scale linking.

Main Methods:

  • Simulated data were generated under a common-item nonequivalent group design (CINEG).
  • Six methods were tested: listwise deletion (LWD), treating missing as incorrect (IN), corrected item mean imputation (CM), response function imputation (RF), multiple imputation (MI), and full information maximum likelihood (FIML).
  • Linking accuracy was assessed based on the errors in estimated linking coefficients.

Main Results:

  • Response function imputation (RF), multiple imputation (MI), and full information maximum likelihood (FIML) demonstrated superior performance, yielding the lowest linking errors.
  • Listwise deletion (LWD) resulted in the highest linking errors across all tested conditions.
  • The choice of missing data handling method significantly influenced the accuracy of scale linking.

Conclusions:

  • RF, MI, and FIML are recommended for handling missing data in IRT scale linking to ensure accurate and reliable results.
  • Listwise deletion should be avoided due to its detrimental effect on scale linking accuracy.
  • Effective missing data strategies are vital for maintaining the validity and comparability of large-scale assessments.