Related Experiment Video
Updated: Apr 21, 2026

08:13
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
Published on: May 10, 2019
7.0K
Validity Concerns with Multiplying Ordinal Items Defined by Binned Counts: An Application to a Quantity-Frequency
James S McGinley1, Patrick J Curran1
1University of North Carolina at Chapel Hill.
Summary
Multiplying ordinal survey items, common in substance use research, can distort measurements. This method, used for alcohol consumption indices, threatens construct validity and accurate measurement.
Area of Science:
- Social and behavioral sciences
- Quantitative psychology
- Substance use research
Background:
- Social scientists often convert count data into ordinal categories.
- Multiplying these ordinal items to create indices is a common but understudied practice.
- This method is particularly prevalent in substance use research for measuring consumption.
Purpose of the Study:
- To investigate the impact of multiplying ordinal items on construct validity.
- To analyze the consequences for accurately measuring alcohol consumption.
Main Methods:
- Analytical demonstration of the multiplicative procedure's effects.
- Empirical validation of the analytical findings.
Main Results:
- The multiplicative procedure can introduce significant threats to construct validity.
- These threats directly impair the accurate measurement of alcohol consumption.
Conclusions:
- The common practice of multiplying ordinal variables poses a risk to measurement validity.
- Researchers should carefully consider alternative methods for constructing indices, especially in substance use studies.
More Related Videos
Related Concept Videos
Ordinal Level of Measurement
37.9K
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...
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...
37.9K
Friedman Two-way Analysis of Variance by Ranks
629
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...
629
Ranks
593
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...
593
Nominal Level of Measurement
42.8K
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. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal...
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal...
42.8K
Wilcoxon Rank-Sum Test
952
The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a nonparametric test used to determine if there is a significant difference between the distributions of two independent samples. This test is designed specifically for two independent populations and has the following key requirements:
952
Expected Frequencies in Goodness-of-Fit Tests
8.9K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
8.9K

