When our research is over, we would like to be able to conclude that we did a credible job of operationalizing our constructs -- we can assess the construct validity of this conclusion.We are likely to make some claims that our research findings have implications for other groups and individuals in other settings and at other times.
Now, instead of it only being an idea in our minds, it becomes a public entity that anyone can look at and examine for themselves.Measures, samples and designs don't 'have' validity -- only propositions can be said to be valid.Technically, we should say that a measure leads to valid conclusions or that a sample enables valid inferences, and so on.In simpler terms, did we implement the program we intended to implement and did we measure the outcome we wanted to measure?In yet other terms, did we operationalize well the ideas of the cause and the effect?Therefore, it is important data validation occurs before doing any analyses or creating tables or reports from the data you plan to use, whether it is new data or data from an outside source. " When we think about validity in research, most of us think about research components.We reach conclusions about the quality of our measures -- conclusions that will play an important role in addressing the broader substantive issues of our study.When we talk about the validity of research, we are often referring to these to the many conclusions we reach about the quality of different parts of our research methodology. Each type addresses a specific methodological question.The figure shows that there are really two realms that are involved in research. It is what goes on inside our heads as researchers.It is where we keep our theories about how the world operates.