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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

172
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...
172
Correlation of Experimental Data01:23

Correlation of Experimental Data

411
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
411
Regression Toward the Mean01:52

Regression Toward the Mean

6.7K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.7K
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

813
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
813
Naturalistic Observations02:30

Naturalistic Observations

16.9K
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
16.9K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.8K
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...
3.8K

You might also read

Related Articles

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

Sort by
Same authorSame journal

Development and evaluation of wrist- and thigh-worn accelerometer algorithms using self-training machine learning models for classification of activity type and posture: towards device placement-agnostic methods in the ProPASS consortium.

The international journal of behavioral nutrition and physical activity·2026
Same author

Prospective Physical Activity, Sitting and Sleep consortium (ProPASS): addressing methodological and geographical barriers to inform global public health guidelines, interventions and precision medicine.

British journal of sports medicine·2026
Same author

Questionnaires Used to Explore the Perspectives of Parents and Health Professionals on Young Children's Use of Technology: Systematic Review.

JMIR pediatrics and parenting·2026
Same author

Standardizing upper arm movement definitions across observational and sensor-based methods: A Delphi consensus study among European ergonomics experts.

Scandinavian journal of work, environment & health·2026
Same author

A permutation test of differences between externally or internally defined groupings in compositional data sets.

Statistical methods in medical research·2026
Same author

Pathology and parasite distribution in mice challenged with <i>Toxoplasma gondii</i> from different geographical origins.

Parasitology·2026

Related Experiment Video

Updated: Dec 6, 2025

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.3K

Zero problems with compositional data of physical behaviors: a comparison of three zero replacement methods.

Charlotte Lund Rasmussen1,2, Javier Palarea-Albaladejo3, Melker Staffan Johansson4

  • 1National Research Centre for the Working Environment, Lersø parkalle 105, 2100, Copenhagen, Denmark. CLR@nfa.dk.

The International Journal of Behavioral Nutrition and Physical Activity
|October 7, 2020
PubMed
Summary

For time-use data analysis, the log-ratio expectation-maximization (lrEM) method is superior for handling zero values compared to simple or multiplicative replacements. Avoid replacing zeros with values exceeding the lowest observed data point to maintain data integrity.

Keywords:
Compositional data analysisMissing dataPhysical activitySedentary timeTime-use

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.8K
Using a Comparative Species Approach to Investigate the Neurobiology of Paternal Responses
07:59

Using a Comparative Species Approach to Investigate the Neurobiology of Paternal Responses

Published on: September 19, 2011

12.9K

Related Experiment Videos

Last Updated: Dec 6, 2025

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.3K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.8K
Using a Comparative Species Approach to Investigate the Neurobiology of Paternal Responses
07:59

Using a Comparative Species Approach to Investigate the Neurobiology of Paternal Responses

Published on: September 19, 2011

12.9K

Area of Science:

  • Epidemiology
  • Data Science
  • Biostatistics

Background:

  • Compositional data analysis (CDA) is crucial for time-use data, but zero values pose challenges due to log-ratio requirements.
  • Handling zeros in physical behavior time-use data is essential for accurate epidemiological studies.

Purpose of the Study:

  • To compare the effectiveness of three zero-replacement methods in compositional time-use data: simple, multiplicative, and log-ratio expectation-maximization (lrEM).
  • To evaluate the impact of replacement values exceeding the lowest observed value on data structure.

Main Methods:

  • Simulated datasets with 5-30% zeros from a complete accelerometer dataset of 1310 adults.
  • Real data examples with imposed zeros (10-20%) and various replacement values.
  • Assessed distortion by comparing replaced datasets to the complete reference dataset.

Main Results:

  • The lrEM method demonstrated the least distortion, preserving the relative variation structure of the data.
  • Simple and multiplicative replacements introduced significant distortion, especially with >10% zeros.
  • Replacing zeros with values above the observation threshold severely impacted data structure.

Conclusions:

  • Recommend multiplicative and lrEM replacements for preserving the relative structure of physical behavior data.
  • Advise against using simple replacement methods due to introduced distortion.
  • Discourage replacing zero values with amounts higher than the minimum observed value.