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Related Concept Videos

Ordinal Level of Measurement00:55

Ordinal 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. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks in the...
One-Way ANOVA: Unequal Sample Sizes01:15

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:
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Design Example: Maintaining Level of an Embankment01:19

Design Example: Maintaining Level of an Embankment

Constructing a roadway embankment over uneven terrain requires precise leveling to ensure stability and proper drainage. Surveyors use a leveling instrument and staff to calculate ground elevations and determine the required fill material at each point along the embankment alignment.The process begins by positioning a leveling instrument near a benchmark with a known elevation. A backsight reading establishes the instrument height, which serves as a reference for subsequent measurements. A...
Ratio Level of Measurement00:54

Ratio 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. 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. For...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...

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Updated: Jun 26, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Design efficiency for imbalanced multilevel data.

Wilfried Cools1, Wim Van den Noortgate2, Patrick Onghena2

  • 1Katholieke Universiteit Leuven, Vesaliusstraat 2, B-3000, Leuven, Belgium. wilfried.cools@ped.kuleuven.be.

Behavior Research Methods
|February 3, 2009
PubMed
Summary
This summary is machine-generated.

Accurate statistical estimation and power are crucial in social and behavioral sciences, especially for complex multilevel designs. Simulation studies show that ignoring design imbalance is often valid, simplifying analyses, except for data with many small groups.

Related Experiment Videos

Last Updated: Jun 26, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Social and Behavioral Sciences
  • Statistics
  • Multilevel Modeling

Background:

  • Accurate estimation and statistical power are vital but often overlooked in social and behavioral sciences.
  • Multilevel designs present unique challenges for estimation and power analysis due to multiple variance components and levels.
  • Imbalanced designs further complicate these analyses, frequently requiring simulation studies.

Purpose of the Study:

  • To investigate the impact of design imbalance on accuracy and power in multilevel models.
  • To determine conditions under which design imbalance can be safely ignored in statistical analyses.
  • To provide guidance on handling imbalanced data in multilevel research.

Main Methods:

  • Conducted simulation studies to calculate accuracy and power for multilevel designs.
  • Systematically varied the degree of imbalance in simulated datasets.
  • Analyzed the effects of imbalance on variance parameter estimation and statistical test power.

Main Results:

  • In most scenarios, the distortion caused by imbalance can be disregarded, simplifying efficiency studies and validating existing software.
  • An exception exists for imbalanced data originating from a large proportion of small groups.
  • Skewness and kurtosis in variance parameter distributions depend on group numbers and sizes, particularly for random slopes.

Conclusions:

  • Ignoring design imbalance is generally acceptable in multilevel modeling, streamlining statistical practice.
  • Researchers should exercise caution with imbalanced data from numerous small groups due to potential distortions.
  • Understanding the distributional properties of variance parameters is key when dealing with imbalanced multilevel data.