Understanding the Meaning of Validation and Its Types, Also Know Its Benefits in Research

Kapanlagi.com - Validation may be a familiar word to us. Or maybe, some of you have even used the word validation in your work. Although it is already classified as a popular word, in fact, there are still many people who do not understand the true meaning of validation.

Actually, the word validation is not a term commonly used in everyday life. This is because, in general, validation is known as a data testing process. Therefore, the word validation is more often used in certain fields, such as work, research, and education. So, it's natural that many people still don't know the meaning of validation.

But because lately the word validation has been used more often, it is important for us to know its meaning. Compiled from various sources, here is an explanation of the meaning of validation.

1. Meaning of Validation

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If viewed etymologically or based on the origin of the word, validation comes from the English word validation. When translated into Indonesian, the meaning is more or less the extent of accuracy or precision of a measuring instrument in conducting testing.

As mentioned earlier, the general meaning of validation is the act of testing data. This is in line with the meaning of validation in the Indonesian Dictionary or KBBI. In KBBI, validation is defined as verification or testing of the truth of a (data). That is why the word validation is always closely related to research activities.

In research, validation becomes an important stage. Without validation, processed data will not provide accurate and precise results. Validation is broadly done by checking or proving several aspects in research, such as appropriate methods, where each material, process, procedure, activity, system, equipment, and supervision in the production mechanism.

2. Benefits of Validation

In research, data validation is a crucial step that cannot be skipped. This is because validation activities bring several real benefits to the credibility of research results. Therefore, in addition to understanding the meaning of validation, we also need to know its benefits in research. The following is an explanation of the benefits of data validation.

1) With data validation, the entry of research data will be faster. This is because the data validation process is closely related to the sample given to respondents as the object of research.

2) Data validation helps to improve the accuracy of research results. This is because data validation will reduce the risk of errors or mistakes in research data.

3) The data validation process will provide convenience in research. This is because data validation will limit the options that appear during research. Furthermore, data validation only displays important choices related to the research.

3. Types of Data Validation

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In addition to the meaning and benefits of validation in research, you also need to understand that this important stage of research is divided into several types. Each type of data validation certainly has its own role. Here are some types of data validation.

1. Appearance Validation

Validation that is only measured based on the appearance or form of an object with an appropriate measuring instrument. This validation is commonly used in measuring individual abilities.

2. Predictive Validation

Validation used to predict someone's behavior towards events in reality. This validation is done by conducting predictive tests.

3. Construct Validation

This validation is a type of validation that is indirectly visible in human behavior, for example, a person's personality. This is because personality consists of several aspects and testing is done based on one of the measured aspects.

4. Content Validation

Validation carried out on the content or material being tested is relevant to the experience, ability, knowledge, or background of the person being tested.

5. External Validation

This validation is a type of validation obtained by correlating a new measuring instrument with an external benchmark in the form of a valid measuring instrument.

6. Cultural Validation

As the name suggests, this validation is used to measure research conducted in a country with diverse ethnic groups. Therefore, each country may use different measuring instruments.

4. How to Test Data Validation with Ms Excel

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As previously mentioned, validation is an important step in research. This means that the meaning of validation must be fully understood. In addition, the validation process must be carried out carefully and meticulously. Fortunately, for now, the data validation process can be done more quickly using Ms. Excel. Although the method may be slightly more complicated for those who are not familiar. According to liputan6.com, here are the steps for data validation with Ms. Excel.

- Enter the questionnaire instrument data results into a worksheet.

- Pay attention to the rightmost column. Add up the scores of each respondent using the functions available in Excel. You can use the syntax/command feature [=sum(range cell)]. For the range cell, fill in the range of cells starting from the first question item to the last question item on the questionnaire instrument.

- Next, at the bottom row, for each column of question items, we calculate the Pearson correlation value using the Excel function with the syntax [=pearson(array cell1; array cell2)]. Array cell1 contains the range of cells of the question items to be calculated, and array cell2 contains the range of cells of the total scores as previously calculated.

- After that, on the row after the Pearson correlation, find the t-value by defining a function in Excel that interprets the t formula. The syntax can be written as [=SQRT(n-2)*rxy/SQRT(1-rxy^2)]. The value of n is filled with the number of respondents in the questionnaire instrument and the value of rxy is filled with the correlation value that has been calculated in the previous row.

- If done, the validity test using the t-table value can be calculated using the Excel function by writing the syntax [=tinv(probability; degree of freedom)]. Probability is filled with the significance level we want, for example, if we use alpha=0.05 with two directions, and the degree of freedom is filled with the value of n-2.

- Determine the validity significance using the command we wrote on the row below the t-value calculation, which is [=IF(p>q;"valid";"not valid")]. P contains the t-value and q contains the t-table value. Done.

Those are some explanations of the meaning of data validation along with its types and benefits. Hopefully useful and good luck trying!

(kpl/gen/psp)