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

Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Margin of Error01:27

Margin of Error

The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
Response Surface Methodology01:16

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
Nominal Level of Measurement00:56

Nominal Level of Measurement

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. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal scale is...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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...
Detection of Gross Error: The Q Test01:00

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

Monitoring caustic injuries from emergency department databases using automatic keyword recognition software.

Annals of burns and fire disasters·2011
Same author

Road safety: objective of the European Union for 2010 and assessment criteria.

Annali di igiene : medicina preventiva e di comunita·2010
Same author

[A method to estimate one's own blood alcohol concentration when the ministerial tables are not avaible].

Annali di igiene : medicina preventiva e di comunita·2010
Same author

[Prevalence estimate of three dichotomic characteristics by the analysis of pools, with an application for simulation of the joint detection of cocaine, cannabis and alcohol].

Annali di igiene : medicina preventiva e di comunita·2009
Same author

[Impact of increasing number of road alcohol controls on the prevalence of driving under the influence in Italy].

Annali di igiene : medicina preventiva e di comunita·2009
Same author

The urgency of establishing a rapid monitoring system for mortality due to traffic accidents (as well as for all mortality due to violence and accidents).

Annali di igiene : medicina preventiva e di comunita·2009

Related Experiment Video

Updated: Jul 13, 2026

Using a Classroom-Based Deese Roediger McDermott Paradigm to Assess the Effects of Imagery on False Memories
08:53

Using a Classroom-Based Deese Roediger McDermott Paradigm to Assess the Effects of Imagery on False Memories

Published on: November 14, 2018

Quantifying a phenomenon without knowledge of individual data: the erased respondent method (ERM).

F Taggi1

  • 1Dept. Environment and Prevention, Section Environment and Trauma, National Institute of Health, Rome, Italy. taggi@iss.it

Annali Di Igiene : Medicina Preventiva E Di Comunita
|July 31, 2007
PubMed
Summary

A new Erased Respondent Method (ERM) offers privacy by analyzing group data, not individual responses. This technique is useful for sensitive topics and biological sample analysis, ensuring anonymity and data confidentiality.

More Related Videos

Humor or Rationality? The Neural Mechanisms of How Agent Type and Language Style Influence Satisfaction with Ride-Hailing Service Failure Recovery
09:53

Humor or Rationality? The Neural Mechanisms of How Agent Type and Language Style Influence Satisfaction with Ride-Hailing Service Failure Recovery

Published on: March 13, 2026

An Experimental Analysis of Children's Ability to Provide a False Report about a Crime
07:36

An Experimental Analysis of Children's Ability to Provide a False Report about a Crime

Published on: May 3, 2016

Related Experiment Videos

Last Updated: Jul 13, 2026

Using a Classroom-Based Deese Roediger McDermott Paradigm to Assess the Effects of Imagery on False Memories
08:53

Using a Classroom-Based Deese Roediger McDermott Paradigm to Assess the Effects of Imagery on False Memories

Published on: November 14, 2018

Humor or Rationality? The Neural Mechanisms of How Agent Type and Language Style Influence Satisfaction with Ride-Hailing Service Failure Recovery
09:53

Humor or Rationality? The Neural Mechanisms of How Agent Type and Language Style Influence Satisfaction with Ride-Hailing Service Failure Recovery

Published on: March 13, 2026

An Experimental Analysis of Children's Ability to Provide a False Report about a Crime
07:36

An Experimental Analysis of Children's Ability to Provide a False Report about a Crime

Published on: May 3, 2016

Area of Science:

  • Statistics
  • Biostatistics
  • Survey Methodology

Background:

  • Traditional survey methods can face challenges with sensitive data due to respondent reluctance.
  • Existing techniques like Randomized Response Technique (RRT) have limitations, especially with biological sample analysis.

Purpose of the Study:

  • To introduce a novel privacy-preserving statistical method, the Erased Respondent Method (ERM).
  • To provide an alternative to RRT applicable to both survey data and biological sample pools.
  • To enable the estimation of population characteristics while ensuring individual anonymity.

Main Methods:

  • ERM utilizes binomial distribution, focusing on the probability of failure (negative result) within groups of 'n' individuals.
  • Data is collected from 'N' groups, each comprising 'n' subjects or biological samples.
  • The proportion of "negative" groups observed is used to estimate the population proportion of "positive" individuals.

Main Results:

  • The method allows for the point estimation of population proportions without direct individual data linkage.
  • ERM effectively anonymizes responses, mitigating concerns about ethical, legal, or social repercussions for individuals.
  • The proportion of "positive" individuals is calculated based on the observed proportion of "negative" groups.

Conclusions:

  • ERM provides a robust framework for collecting sensitive information and analyzing biological samples with enhanced confidentiality.
  • The method is particularly suitable for phenomena where direct observation or individual reporting is difficult or undesirable.
  • ERM facilitates the study of sensitive issues like substance abuse, genetic diseases, and public opinion polls while protecting individual privacy.