FREE 10+ Discriminant Validity Samples & Templates in MS Word | PDF

In research, there are different constructs that researchers are interested in measuring. For example, in a study on the relationship between job application satisfaction and turnover report intentions, the researchers would want to ensure that their measures of job satisfaction and turnover intentions are distinct from one another. Discriminant validity helps ensure that the measures of different constructs are not measuring the same underlying factor, which can lead to spurious findings.

FREE 10+ Discriminant Validity Samples & Templates in MS Word | PDF

1. Discriminant Validity Analysis Template

2. Discriminant Validity Template

3. Discriminant Validity and Cross Validity

4. Convergent and Discriminant Validity

5. Sample Discriminant Validity Template

6. Sample Convergent & Discriminant Validity

7. Convergent and Discriminant Validity Evaluation

8. Discriminant Validity of Child Template

9. Simple Convergent and Discriminant Validity

10. Standard Discriminant Validity Template

11. Discriminant Validity Assessment Template

What is Discriminant Validity?

Discriminant validity is a fundamental concept in research methodology that is used to assess the degree to which measures of different constructs are distinct from one another. It is a crucial aspect of action research, especially in social sciences and psychology case study, as it ensures that a measure is not simply measuring something else.

How To Make Discriminant Validity?

One of the most common methods for assessing discriminant validity is confirmatory factor analysis (CFA). CFA is a statistical technique that examines the extent to which measures of different constructs load onto different factors. To ensure that your measures have adequate discriminant validity, there are several steps you can take:

Step 1- Develop a theoretical framework

Before measuring any constructs, it is important to develop a theoretical framework that specifies the relationships among the constructs. This framework should clearly define the constructs of interest and their expected relationships with one another.

Step 2- Choose appropriate measures

Choose measures that are widely accepted and have been validated in previous research. If you are developing new measures, ensure that they are grounded in the theoretical framework and that their items are unambiguously related to the constructs of interest.

Step 3- Collect data

Collect research data collection form  from participants using the chosen measures. Make sure to collect enough data to allow for robust statistical analysis.

Step 4- Analyze the data

Use statistical techniques such as confirmatory factor analysis to assess the discriminant validity of the measures. In CFA, you will specify a model that represents the theoretical framework and test it against the data. The model should show that the measures of each construct are more closely related to their own construct than to any other constructs.