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

Variation01:19

Variation

8.3K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
8.3K
Multiple Regression01:25

Multiple Regression

4.3K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.3K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

8.4K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
8.4K
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

5.3K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
5.3K
Coefficient of Correlation01:12

Coefficient of Correlation

9.0K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
9.0K
Ratio Level of Measurement00:54

Ratio Level of Measurement

22.2K
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....
22.2K

You might also read

Related Articles

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

Sort by
Same author

Use of Crossed Random-Effects Models to Assess Multiple-Choice Items: An Experimental Study.

The Spanish journal of psychology·2026
Same author

A qualitative exploration of video-based motor action observation perceptions in patients with chronic low back pain and asymptomatic participants: An interpretative phenomenological analysis.

PloS one·2026
Same author

Dimensionality Assessment in Forced-Choice Questionnaires: First Steps Toward an Exploratory Framework.

Educational and psychological measurement·2025
Same author

A general diagnostic modelling framework for forced-choice assessments.

The British journal of mathematical and statistical psychology·2025
Same author

Cross-validation and predictive metrics in psychological research: Do not leave out the leave-one-out.

Behavior research methods·2025
Same author

The Conscious Side of 'Subliminal' Linguistic Priming: A Systematic Review With Meta-Analysis and Reliability Analysis of Visibility Measures.

Journal of cognition·2025

Related Experiment Video

Updated: Mar 19, 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

3.8K

Evaluating the Performance of R-Squared Measures in Multilevel Models.

Diego Iglesias1, Miguel A Sorrel1, Ricardo Olmos1

  • 1Department of Social Psychology and Methodology, Universidad Autónoma de Madrid, Madrid, Spain.

Multivariate Behavioral Research
|March 18, 2026
PubMed
Summary

This study evaluates multilevel model (MLM) R-squared measures using simulations. Accurate estimation of MLM R-squared requires more clusters with more level-2 predictors and observations per cluster.

Keywords:
R-squaredeffect sizeexplained variancemixed-effects modelsmultilevel models

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.9K

Related Experiment Videos

Last Updated: Mar 19, 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

3.8K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.9K

Area of Science:

  • Behavioral and social sciences research methodology.
  • Statistical modeling and data analysis techniques.

Background:

  • Multilevel Models (MLMs) are essential for analyzing clustered data in social and behavioral sciences.
  • R-squared measures in MLMs are complex due to multi-level variance.
  • Existing MLM R-squared frameworks require empirical evaluation under applied conditions.

Purpose of the Study:

  • To evaluate the performance of MLM R-squared measures as estimators of population values.
  • To investigate the impact of various factors on the accuracy of MLM R-squared estimates.
  • To provide guidance for applied researchers using MLM R-squared measures.

Main Methods:

  • Conducted Monte Carlo simulations to assess MLM R-squared estimator performance.
  • Varied factors including the number of level-1 and level-2 predictors, cross-level interactions, and random slopes.
  • Examined the influence of the number of clusters and observations per cluster on estimation accuracy.

Main Results:

  • Accuracy of MLM R-squared estimates is sensitive to the number of level-2 predictors, requiring more clusters for reliable estimation.
  • Increased number of level-1 predictors, cross-level interactions, and random slopes necessitate larger sample sizes (more clusters or observations per cluster) for accurate R-squared estimates.
  • The study identifies conditions under which MLM R-squared measures perform well and when caution is advised.

Conclusions:

  • The performance of MLM R-squared measures is condition-dependent, impacting their reliability in applied research.
  • Researchers must consider the number of clusters and observations per cluster when interpreting MLM R-squared values.
  • This study offers crucial insights for the accurate application and interpretation of MLM R-squared measures in the behavioral and social sciences.