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Related Concept Videos

Reliability and Validity01:29

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
Measures of Intelligence01:29

Measures of Intelligence

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; it...
Scaling01:26

Scaling

In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
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...
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...

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Related Experiment Video

Updated: May 22, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

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

Published on: August 29, 2025

[Content validity index in scale development].

Jingcheng Shi1, Xiankun Mo, Zhenqiu Sun

  • 1Department of Epidemiology and Statistics, Central South University, Changsha, China. jingzhengs@126.com

Zhong Nan Da Xue Xue Bao. Yi Xue Ban = Journal of Central South University. Medical Sciences
|May 8, 2012
PubMed
Summary
This summary is machine-generated.

Content validity ensures measurement tools accurately reflect constructs. The content validity index (CVI) quantifies this, with specific thresholds recommended for robust scale development and reporting.

Related Experiment Videos

Last Updated: May 22, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

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:

  • Psychometrics
  • Scale Development
  • Quantitative Research Methods

Context:

  • Content validity is crucial for developing reliable measurement instruments.
  • The content validity index (CVI) is a widely adopted metric for quantitative evaluation.
  • Ensuring an instrument adequately samples the intended construct is vital.

Purpose:

  • To define content validity and its importance in scale development.
  • To introduce the content validity index (CVI) and its variations (I-CVI, S-CVI).
  • To present recommended thresholds for excellent content validity.

Summary:

  • Content validity assesses if an instrument's items appropriately represent the construct being measured.
  • The content validity index (CVI) includes individual (I-CVI) and scale-level (S-CVI) metrics.
  • A modified kappa statistic (K*) can adjust I-CVI for chance agreement.
  • Recommended I-CVI values are ≥0.78, and S-CVI/UA and S-CVI/Ave values are ≥0.8 and ≥0.9, respectively.
  • Transparency in reporting expert qualifications, evaluation processes, and results is essential.

Impact:

  • Provides clear guidelines for evaluating and reporting content validity in research.
  • Enhances the rigor and trustworthiness of newly developed measurement scales.
  • Facilitates consistent application of content validity assessment across studies.