Related Experiment Video
Updated: Jul 25, 2025
08:12
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
2.6K
Introduction to the special issue on criterion measurement
Deirdre J Knapp1, Michael G Rumsey1
1Human Resources Research Organization, Alexandria, Virginia, USA.
Summary
This research addresses the critical "criterion problem" in military personnel selection. It reviews advances in criterion identification and measurement for improved U.S. military selection and classification systems.
Area of Science:
- Psychometrics
- Military Psychology
- Personnel Selection
Background:
- Criterion identification and measurement are often overlooked in validation research.
- Accurate criteria are essential for selecting appropriate predictor measures in operational settings.
- This special issue focuses on recent advancements in addressing the 'criterion problem' within U.S. military enlisted personnel selection and classification.
Discussion:
- The paper reviews broad issues concerning criterion identification and measurement in military contexts.
- It examines previous research related to the 'criterion problem'.
- Subsequent papers in the special issue delve into specific criterion challenges and introduce a joint-service initiative.
Key Insights:
- Prioritizing criterion development is crucial for effective personnel selection.
- Advances in measurement are key to overcoming the 'criterion problem'.
- A joint-service approach offers a comprehensive solution for military criterion issues.
Outlook:
- Future research should emphasize robust criterion development in validation studies.
- Standardized, high-quality criteria will enhance the predictive validity of selection tools.
- The joint-service project aims to establish unprecedented standards for military criterion measurement.
More Related Videos
09:00Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
Published on: August 16, 2024
826
08:27Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
Published on: September 27, 2019
6.9K
Related Concept Videos
01:29Measures 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
00:54Ratio 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
00:55Ordinal 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
00:56Nominal 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
02:46Uncertainty in Measurement: Reading Instruments
38.4K
Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
38.4K
01:16Critical Region, Critical Values and Significance Level
12.0K
The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...
12.0K