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

Random Error01:04

Random Error

9.8K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
9.8K
Random and Systematic Errors01:20

Random and Systematic Errors

14.9K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
14.9K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

2.0K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
2.0K
Buffer Effectiveness02:19

Buffer Effectiveness

55.1K
Buffer solutions do not have an unlimited capacity to keep the pH relatively constant . Instead, the ability of a buffer solution to resist changes in pH relies on the presence of appreciable amounts of its conjugate weak acid-base pair. When enough strong acid or base is added to substantially lower the concentration of either member of the buffer pair, the buffering action within the solution is compromised.
The buffer capacity is the amount of acid or base that can be added to a given volume...
55.1K
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

6.0K
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
6.0K
Framing Effects03:26

Framing Effects

7.9K
Information is everywhere and its presentation—such as how and when items are presented—can impact our perceptions and decisions surrounding the info. This broad concept umbrellas framing effects—influences that occur due to the way information is framed in its appearance, whether it’s purely the order or the specific wording of a message. Let’s take a look at numerous ways in which two versions of something can objectively say the same thing, yet we respond in...
7.9K

You might also read

Related Articles

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

Sort by
Same author

Testing Random Effects in Nonlinear Mixed-Effects Models.

Statistics in medicine·2026
Same author

Exploring the relationship between government stringency and preventative social behaviours during the COVID-19 pandemic in the United Kingdom.

Health informatics journal·2023
Same author

MEGH: A parametric class of general hazard models for clustered survival data.

Statistical methods in medical research·2022
Same author

Psychosocial Adjustment to Illness and Its Relationship with Spiritual Wellbeing in Iranian Cancer Patients.

International journal of chronic diseases·2020
Same author

Nonlinear mixed-effects models with misspecified random-effects distribution.

Pharmaceutical statistics·2019
Same author

Permutation and Bayesian tests for testing random effects in linear mixed-effects models.

Statistics in medicine·2019
Same journal

When to Adjust for Multiple Testing: A Unifying Guiding Principle.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Ensuring Quality in Preclinical Research: The Importance of Being Human.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Addressing Cluster-Level Treatment Effect Heterogeneity in Sample Size Determination for Hierarchical 2 × 2 Factorial Designs.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

A Multiple Imputation Approach to Distinguish Curative From Life-Prolonging Effects in the Presence of Missing Covariates.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Nonparametric Estimation of the Patient-Weighted While-Alive Estimand.

Biometrical journal. Biometrische Zeitschrift·2026
See all related articles

Related Experiment Video

Updated: Jan 29, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

3.7K

Testing random effects in linear mixed-effects models with serially correlated errors.

Reza Drikvandi1,2, Sajad Noorian3

  • 1Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK.

Biometrical Journal. Biometrische Zeitschrift
|February 6, 2019
PubMed
Summary
This summary is machine-generated.

A new permutation test effectively assesses random effects in linear mixed-effects models, even with serially correlated errors. This method addresses boundary issues and is more robust than existing tests for correlated data.

Keywords:
correlated errorslinear mixed-effects modellongitudinal datapermutation testrandom effectsserial correlation

More Related Videos

Measuring the Behavioral Effects of Intraocular Scatter
05:10

Measuring the Behavioral Effects of Intraocular Scatter

Published on: February 18, 2021

3.9K
A Microplate Assay to Assess Chemical Effects on RBL-2H3 Mast Cell Degranulation: Effects of Triclosan without Use of an Organic Solvent
17:35

A Microplate Assay to Assess Chemical Effects on RBL-2H3 Mast Cell Degranulation: Effects of Triclosan without Use of an Organic Solvent

Published on: November 1, 2013

40.5K

Related Experiment Videos

Last Updated: Jan 29, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

3.7K
Measuring the Behavioral Effects of Intraocular Scatter
05:10

Measuring the Behavioral Effects of Intraocular Scatter

Published on: February 18, 2021

3.9K
A Microplate Assay to Assess Chemical Effects on RBL-2H3 Mast Cell Degranulation: Effects of Triclosan without Use of an Organic Solvent
17:35

A Microplate Assay to Assess Chemical Effects on RBL-2H3 Mast Cell Degranulation: Effects of Triclosan without Use of an Organic Solvent

Published on: November 1, 2013

40.5K

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Linear mixed-effects models (LMMs) utilize random effects to account for individual variability.
  • Testing for random effects is challenging due to boundary parameter space issues, invalidating classical tests.
  • Existing tests often assume independent and identically distributed (i.i.d.) measurement errors, which is frequently violated in practice.

Purpose of the Study:

  • To propose a novel permutation test for random effects in LMMs.
  • To address the limitations of existing tests, particularly in the presence of serially correlated errors.
  • To develop a flexible test applicable to multiple random effects and subsets thereof.

Main Methods:

  • Development of a permutation test specifically designed for LMMs with serially correlated errors.
  • The proposed test circumvents boundary parameter space problems inherent in classical statistical tests.
  • The methodology generalizes existing permutation procedures for i.i.d. errors.

Main Results:

  • The proposed permutation test demonstrates robust performance in simulations and real-world data analysis.
  • The test effectively handles the complexities introduced by serially correlated measurement errors.
  • Findings suggest that random slopes for time effects might be non-significant under serial correlation.

Conclusions:

  • The novel permutation test provides a reliable approach for evaluating random effects in LMMs with correlated errors.
  • This method offers a valuable alternative to standard tests when the i.i.d. assumption is untenable.
  • Careful consideration of error structures is crucial when interpreting the significance of random effects, especially time-related slopes.