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

Dose-Response Relationship: Selectivity and Specificity

Dose-Response Relationship: Selectivity and Specificity

Drugs exert their therapeutic effects by interacting with receptors, enzymes, or ion channels that are present throughout the human body. The strength and duration of the interaction between a drug and its target receptor are characterized by the selectivity and specificity of the drug. Selectivity refers to a drug's strong preference for its intended target over other targets. For instance, isoprenaline, a non-selective β-adrenergic agonist, interacts with both β1- and β2-adrenergic receptors...

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

One-Way ANOVA: Unequal Sample Sizes

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

Detection of Gross Error: The Q Test

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...

You might also read

Related Articles

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

Sort by

Same author

Rise of the Machine: Detecting Aberrant Response Patterns in Survey Instruments Using Autoencoder.

Applied psychological measurement·2026

Same author

Correction: Liu et al. Relationships between Grey Matter Volume in the Bilateral Superior Frontal Gyrus and Reactive Aggression Varied by Level of Traditional Masculinity. <i>Brain Sci.</i> 2024, <i>14</i>, 605.

Brain sciences·2025

Same author

Understanding social adversity and stress and mood symptoms: The Bayesian network approach.

Journal of health psychology·2020

Same author

Examining item content validity using property fitting analysis via multidimensional scaling.

International journal of methods in psychiatric research·2019

Search research articles

Related Experiment Video

Updated: Jun 5, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Beyond One-Size-Fits-All: A Differential Sensitivity Framework for Machine Learning-Based Detection of Anomalous

¹University of Missouri-St. Louis, MO, USA.

Educational and Psychological Measurement

|June 4, 2026

Summary

Detecting anomalous survey responses using machine learning (ML) is crucial for research validity. This study found that while some response anomalies are easily detected, others require specific ML methods, and straightline responding remains challenging.

Keywords:

anomaly detection benchmarking machine learning survey data quality unsupervised learning

Related Experiment Videos

Last Updated: Jun 5, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Area of Science:

Social Sciences Research Methods
Data Science
Psychometrics

Background:

Anomalous survey responses threaten research validity.
Traditional detection methods have limitations.
Machine learning (ML) offers potential alternatives.

Purpose of the Study:

Evaluate the effectiveness of 11 unsupervised ML anomaly detection algorithms.
Compare algorithm performance across six simulated anomaly types.
Identify optimal ML methods for different response anomaly patterns.

Main Methods:

Simulated six types of anomalous survey responses (random, careless, extreme, acquiescent, straightline, alternating).
Embedded anomalies in a realistic survey dataset (N = 3,000).
Applied 11 unsupervised ML algorithms from four paradigms (distance, density, reconstruction, tree/boundary-based).

Main Results:

Globally deviant patterns (random, extreme, alternating) were universally detectable.
Careless and acquiescent responding required reconstruction- or boundary-based methods.
Straightline responding was highly resistant to detection (max AUC-ROC < .70).

Conclusions:

No single ML algorithm is effective for all anomaly types.
Multimethod approaches combining ML and traditional indicators are recommended.
A framework for selecting ML detection methods based on anomaly types is provided.