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Ordinal Level of Measurement

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...

Ranks

Ranks

Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...

Ratio Level of Measurement

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...

Surveys

Surveys

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Friedman Two-way Analysis of Variance by Ranks

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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Related Experiment Video

Updated: Jun 15, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

Improving modified Rankin Scale assessment with a simplified questionnaire.

Askiel Bruno¹, Neel Shah, Chen Lin

¹Department of Neurology, Medical College of Georgia, 1120 15th Street BI 3076, Augusta, GA 30912, USA. abruno@mcg.edu

|March 13, 2010

Summary

A new simplified modified Rankin Scale questionnaire (smRSq) shows very good reliability for stroke outcome assessment. This tool is quick to administer, improving efficiency in clinical practice.

Related Experiment Videos

Last Updated: Jun 15, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

Area of Science:

Neurology
Clinical Research
Medical Statistics

Background:

The modified Rankin Scale (mRS) is widely used for stroke outcome measurement.
However, its reliability, specifically inter-rater agreement, is often suboptimal, limiting its clinical utility.

Purpose of the Study:

To develop and assess the reliability of a simplified mRS questionnaire (smRSq).
To determine if the smRSq offers a more reliable and time-efficient alternative for stroke outcome assessment.

Main Methods:

The smRSq was administered to 50 post-stroke patients in outpatient settings.
Paired, blinded raters independently completed the smRSq within 20 minutes of each other.

Main Results:

Inter-rater agreement was 78%, with a kappa statistic of 0.72 (95% CI, 0.58-0.86).
The weighted kappa statistic was 0.82 (95% CI, 0.72-0.92), indicating substantial agreement.
The average administration time for the smRSq was only 1.67 minutes.

Conclusions:

The simplified mRS questionnaire (smRSq) demonstrates very good reliability.
Its reliability is comparable to traditional structured interview mRS assessments.
The smRSq is significantly more time-efficient, offering a practical advantage in clinical settings.