Research is a multi-stepped process to find the hidden facts behind the happening in this world. Each proceeding step helps the researcher to get closer to the main goals. Among so many major and minor steps in a research process, the steps that affect the outcomes measure of a study the most include the research question formulation, detailed literature review, methodology, and statistical or non-statistical analysis. When to apply statistical analysis or non-statistical analysis is a matter of another debate. However, statistical validity is a matter of discussion in this article. So let’s directly move on to the meaning of statistical validity, know about its different types and get aware of its importance in the academic research process.
Statistical validity- A brief introduction and definition:
As a matter of course, statistical validity is the extent to which the outcomes of research can be considered reliable and accurate. To ensure results, researchers have to make many useful decisions that ultimately drive the accuracy of the findings of a research. Among all those vital decisions, the collection of hands full of authentic data and the ability to get your hand on the right method of analysis comes at the top. In the majority of cases, validity and reliability are two terms used interchangeably. However, there is a very fine line of differences between them. Reliability aims to find the consistency of an outcome, and validity is about the accuracy of findings.
As a matter of fact, the validity of research must be kept under consideration during making a research design. That is, deciding on methods to find answers to research questions and writing up the results both need researchers to use honest means. However, statistical validity is a term usually used for measuring the extent to which the extracted results find closer to the proposed outcome without being biased at any point. Moreover, it refers to the results of only quantitative research designs.
Types of Statistical validity:
There are so many types of statistical validity in research. To find which one best suits your area of interest; you must seek help from experts at Dissertation Writers UK. Let’s briefly discuss each in the following section:
- Content Validity: It is the measure of validity by ensuring whether all the variables being studied are a part of the questionnaire or survey used to collect data relevant to them.
- Face Validity: It helps experimental researchers to check the statistical validity of their findings. It finds whether a particular experimental design exactly mimics the claims that are constructed in earlier stages of research.
- Construct validity: It is the type of validity that ensures the conformity of the actual experimentation and data collected in compliance with the theory being studied. However, based on the different types of constructs, construct validity can be further divided into two types, convergent validity, and divergent validity. In the former one, if the required theory ensures that one measure is depended or correlated with the other, then statistics confirm it. In the latter, if the required theories explain that two variables are not correlated with each other, and the statistics confirm this, then it will be divergent validity.
- Internal Validity: Again, in cause-and-effect study designs such as experiments, researchers have to measure internal validity, which is all about measuring the cause-and-effect relationship between different variables involved in a study.
- External Validity: It is the act of finding ways of applying the results of an experiment conducted in the local setup to a more general population.
Importance of statistical validity in academic research:
Statistical validity is important to be measured in academic research due to several reasons. First, it allows researchers to find whether the results of their study are worth accepting with confidence. Second, it increases the chances of research reproducibility. Third, it offers ways for researchers to know whether the decided research design is the most suitable solution to the problem under study or not. Lastly, it allows researchers to optimize their research outcomes to the standard validation criteria of a study. It is equally important for academic and commercial research outcomes. Even when it comes to finding the statistical validity for a dissertation, you must follow a step-by-step protocol. To get access to such protocols, you must feel free to get Ph.D. Dissertation Help from experts.
After all is said and done, we must conclude the article by recalling the key points. Statistical validity is the measure of the extent to which the results of the study are supported by already existing theories and frameworks. It is of five types: concept, construct, internal, external, and face validity. However, it is important so others can easily replicate and trust your findings.