What Is Statistical Validity? Understanding Trends in Validating Research Data

What Is Statistical Validity

In this DoNotEdit article, we will review recent trends in the statistical validity of research data. If you want to know how to increase the visibility of your research paper with the help of SEO, click here.

With an aim to understand, analyze, and draw conclusions based on large amounts of data that are often presented in complex formats, the statistical validity of research data is essential. In research, decision modeling and inferential aspects depend on the statistical validity of research data. Therefore, it is necessary for researchers and statisticians to develop new frameworks in the statistical paradigm to evaluate and validate research data.

What Is Statistical Validity?

Statistical validity allows the results of a research study to be considered accurate and reliable using a statistical test. To achieve statistical validity, it is necessary for researchers to have sufficient data and also choose the appropriate statistical approach for data analysis.

In addition, statistical validity also refers to whether the statistics obtained from a research study are in agreement with its scientific laws. Thus, if a given data set lead to drawing a conclusion after experimentation, it is said to be scientifically valid and based on the mathematical and statistical rules of the original study.

What Are the Different Types of Statistical Validities?

Statistical validities relevant to research are generally divided into 6 categories:

1. Construct Validity:

It ensures that the actual experiment and data collection is consistent with the theory being studied.

It is reflected by a questionnaire about public opinion. It provides a clearer picture of what people think about a particular issue.

Construct validity is further divided into 2 types:

A. Convergent Validity– It ensures that if the required theory predicts that one measure is correlated with the other, then the statistics confirm this.

B. Divergent or Discriminant Validity– It ensures that if the required theory predicts that one variable doesn’t correlate with others, then statistics need to confirm this.

2. Content Validity:

This validity ensures that the test or questionnaire that is prepared completely covers all aspects of the variable being studied.

3. Face Validity:

This type of validity estimates whether the given experiment actually mimics the claims that are being verified.

4. Conclusion Validity:

This validity ensures that the conclusion achieved from the data sets obtained from the experiment are actually correct and justified without any violations.

5. Internal Validity:

It is a measure of the relationship between cause and effect being studied in the experiment.

6. External Validity:

This validity is a measure of how to apply the results from a particular experiment to more general populations. Furthermore, it informs the analyst whether or not to generalize the results of a particular experiment to all other populations or to some populations with particular characteristics.

Why Is It Important to Determine Statistical Validity of Research Data?

It is important to determine the statistical validity of research data because;

It allows the analyst to know whether the results of the performed tests can be accepted with confidence or not.

The researcher understands whether a method of analysis is suitable for its intended use to derive conclusive results.

It allows the researcher to ensure the validity of research based on its criteria of method selection.

It increases the probability of research reproducibility.

Furthermore, it also allows the researcher to optimize the number of assays and satisfy the validation criteria of a study.

Understanding Trends in Determining Statistical Validity

1. Robustness

Robustness of data is the measure of how effectively the performance of the research method stands up to not exactly similar implementation of the approach. Exact same results can be replicated following a set procedure; however, to avoid the performance to be severely affected, the procedure must be performed with sufficient precision and in an efficient manner to avoid severely affecting performance. Such influencing aspects should be identified and their impact on method performance should be evaluated using tests for robustness.

2. Specificity and Selectivity

Statistical validity is relevant to specificity—a quantitative indication of the extent to which a method can distinguish between the subject of interest and interfering substances on the basis of signals produced under actual experimental conditions. In case of random interferences, they should be determined using representative blank samples.

3. Precision

While comparing results, they should be analyzed based on their precision of repeatability and reproducibility. In statistics, repeatability can be termed as intra-assay precision.

4. Accuracy

Accuracy is the closeness of agreement between the true value of the subject being analyzed and the mean result obtained by applying experimental procedure to a larger population or sample size.

5. Detection Limit

Detection limit can be determined with several approaches: visual inspection, signal-to-noise, and using the standard deviation of the response and the slope. While presenting, researchers must also ensure that the detection limit and the method used for determining the detection limit is also displayed.

What Are the Challenges in Determining Statistical Validity of Research Data?

Methods are generally developed by the research and development department, while the quality assurance and quality control departments perform data validation. Transferring methods and data from one department to another is important and must be done carefully to ensure proper validation.

If the methods are not built on the strength of the research, the results presented may also be affected. This issue ultimately leads to ineffectiveness of quality testing or long and complicated validation process.

Inadequate knowledge of study design and implementation impairs the statistical validity of research data.

Statistical validation helps to ensure that the developed methods are qualified and able to be used for the intended purposes. What methods do you use to ensure the statistical validity of your research data? Let us know in the comments section.

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