In academic research, especially in quantitative studies, the reliability and validity of the research methods are the major concerns to be addressed to ensure good research practices. It explains how effectively a method, test, or technique measures the results of a study. Reliability and validity have a fine line of difference. The former refers to the consistency of a measure, while the latter explores how accurately results and outcomes are calculated. Considering both reliability and validity while deciding on research design to explore facts relevant to a topic of interest, selecting methods, or writing up results is extremely important. This article will focus on one of these two important aspects for measuring research quality, that is ‘study validity’.
Study Validity in Academic Research- a Brief Introduction:
Study validity explains the accuracy of the measurement of a scientific study. A study will be considered valid if it measures what is expected to be measured and the reported results are very close to the real-world values. However, if a study only satisfies the results it expected to calculate but has no closeness with the real word values, its validity can be challenged. It is the validity that measures whether research is impactful or not. It helps other researchers to gain trust in what has been reported in valid research and leads to the foundation of future research based on the results reported in the previous one. Typically, validly research methods can be divided into seven major types:
In technical terms, face validity is not an authentic way to validate a study’s outcome. It only judges the quality based on whether the research looks valid apparently or not. For example, if the researcher uses a survey method of data collection to collect views of physicians about the knowledge and practice of best waste management practices and the advisor supports the choice only as it is, it apparently seems correct. However, it is the weakest type of validity to measure the quality of research. Therefore, getting PhD dissertation help can be a better choice for this validity type.
Criterion validity is the type of study validity that measures the quality of the measurement method employed to extract the most useful result out of widely scattered data. This is one of the most reliable types of validity, as instead of making a judgment based on the assumption it compares the measurement with the standard or already validated data. For example, if a student has measured the absorptivity of human blood serum by using the spectrophotometer, then the already reported values in other scholarly articles can help in finding the accuracy of the calculated values.
It explores whether the content of the study covers all necessary underlying constructs or not. The underlying construct in this type refers to things necessary to prove a point of interest. It also resembles face validity, but content validity measures whether the provided content is enough to complete a study or if additional points need to be added. For example, if the main goal of a study is to measure introversion, then content validity explores whether all properties of this trait are explained properly or not.
A construct refers to a collection of behaviours that are interconnected with each other in a meaningful way to clear the image of what research intends to explore through research. Thus, construct validity is the measure of the degree to which research measures a construct. For example, depression is a construct that is associated with a disturbed sleep cycle, improper eating behaviour, and less concentration on performing specific tasks. To measure the construct validity, specific indicators and types are used are usually used, such as discriminative validity, nomological network, convergent validity, and multitrait-multimethod matrix.
This is the type of study validity that explores the extent to which the independent variable varies to see its effect on the dependent variable to produce the observed effect. It measures whether the observed effect is the result of the changes in the independent variable or some other factors that influenced the study. If results are solely achieved by manipulating the independent variable then the study will achieve internal validity; otherwise not.
Statistical Conclusion Validity:
Statistical methods include the measure of the relationship between a set of identified variables. In qualitative studies, the results or conclusions are usually reported by referring to the statistical differences between the variables. Thus, statistical conclusion validity explores the accuracy of the relationship or correlation between cause and effect variables. For example, a study reports that the 500 mg/kg of the dose given to the experimental mouse significantly increases the nerve regeneration rate, while the 250mg/kg dose has an insignificant effect on the nerve regeneration rate. Then the statistical conclusion validity aims to measure the accuracy of the statistical analysis method to see the validity. All in all, this type of validity requires three things to be considered:
- Selection of appropriate sample size.
- Selection of appropriate statistical tests.
- Reliability of method used for calculation.
External validity measures the extent of the generalisability of results. It refers to the extent to which the results of a study can be applied beyond the sample. To put it in another way, external validity measures the applicability of research findings to other settings and people. It is extremely important for measuring accuracy in a study, including experiments in a controlled environment. In a controlled environment, specific results can be obtained by using some dishonest means, so measuring external validity will be very useful for measuring the accuracy of the measurements reported in such a study.
All in all, by using different types of study validity procedures, the accuracy of facts and figures provided in research can easily be measured. Different types of validity account for measuring accuracy from different aspects. Content validity measures the degree of underlying constructs, external validity measures the generalizability of outcomes beyond samples, internal validity refers to the effect of manipulation on the final outcomes, construct validity measures the relationship between the associated behaviours, statistical validity measures the accuracy in the use or measurements of the statistical method, face validity measures the subjectivity, and criterion validity measures the effectiveness of the method of measurements.