Related Experiment Video
Updated: Jul 25, 2025

09:36
Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
27.1K
Identification and evaluation of criterion measurement methods.
Matthew Allen1, Teresa Russell2, Laura Ford1
1Human Resources Research Organization, Alexandria, Virginia, USA.
Summary
This study reviews 16 criterion measurement methods for military contexts, finding a trade-off between data quality and feasibility. Experts evaluated methods on psychometric properties and usability.
Area of Science:
- Psychology
- Military Science
- Measurement Science
Background:
- Criterion measures in military settings vary significantly in psychometric quality and practical application.
- Effective personnel assessment relies on robust and appropriate criterion measures.
Purpose of the Study:
- To summarize approaches to criterion measurement in a military context.
- To review 16 specific criterion measurement methods based on psychometric quality and ease of use.
- To identify the quality-feasibility tradeoff in criterion data collection.
Main Methods:
- A comprehensive review of existing research on criterion measurement methods.
- Expert ratings on nine psychometric and ease-of-use factors for 16 distinct methods.
- Calculation of inter-rater reliability using intra-class correlations (ICCs).
Main Results:
- High inter-rater reliability (median ICC of .91) among experts.
- Identification of a quality-feasibility tradeoff: easily obtained data often exhibit technical flaws.
- Detailed evaluation of 16 common criterion measurement techniques.
Conclusions:
- No single criterion measurement method is optimal; selection requires balancing psychometric rigor with practical constraints.
- Recommendations are provided for military services to improve criterion measurement practices.
- Future research directions include exploring machine learning applications for enhanced criterion measurement.
Related Concept Videos
Measures of Intelligence
7.7K
Psychologists measure intelligence by using standardized tests that produce a score known as the intelligence quotient or IQ. To understand IQ tests, it's important to recognize the key principles behind their construction: validity, reliability, and standardization.
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this;...
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this;...
7.7K
Data Validation
191
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Key parameters for method validation include:
191
Nominal Level of Measurement
29.8K
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...
29.8K
Ordinal Level of Measurement
24.9K
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...
24.9K
Statistical Analysis: Overview
6.7K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.7K
Ratio Level of Measurement
18.4K
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....
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....
18.4K

