If responses become more consistent across larger numbers of samples, the data becomes more reliable. When asked about the biggest challenges faced in quantitative research, 37% of UX practitioners interviewed by the Norman Nielsen Group claimed that recruiting large samples of participants was the most difficult task of all. When the study permits, deep saturation into the research will also promote validity. If the researcher is able to exclude other scenarios, he is or she is able to strengthen the validity of the findings. This focuses on whether the group taking part in your research is representative of your users and whether you have an adequate number of responses that can provide sound answers to your questions. The respondents should be motivated. Unite For SightInternational Headquarters157 Church Street, 22nd FloorNew Haven, CT 06510United States of AmericaUnite For Sight is a 501(c)(3) nonprofit organization. None of these potential outcomes are ideal, and all severely affect the validity of the overall results. Related to this technique is asking questions in an inverse format. A confounding variable is an extraneous variable that is statistically related to (or correlated with) the independent variable. Steps in Ensuring Validity. Another way to promote validity is to employ a strategy known as triangulation. Ensuring Validity is also not an easy job. Quantitative research is usually done on a large scale and for good reason, or you run the risk of getting narrow results that damage the overall validity of your study.  Avoiding bias and leading the participants, SIGN UP TO THE PEOPLE FOR In fact, it’s an absolute waste of time and budget if this is the case. The research method you select needs to accurately reflect the type, format and depth of data you need to capture in order to suitably answer your questions. (4) Pelham, B. W.; Blanton, H. Conducting Research in Psychology: Measuring the Weight of Smoke, 3rd Edition. (1) Trochim, W. M. K.  “Design” Research Methods Knowledge Base 2nd Edition. (3) For example, if, in the Module 1 example of near-sighted individuals obtaining corrective lenses, the researcher had chosen to operationalize “economically productive” as “the amount of money a person has in his or her savings,” the researcher would have obtained entirely different results. Don't see the date/time you want? Validity within quantitative research is a measure of how accurately the study answers the questions and hypotheses it was commissioned to answer. Test validity gets its name from the field of psychometrics, which got its start over 100 years ago with the measure… This is about approaching your quantitative research from an entirely objective and unassuming standpoint – which can be really challenging, since unintentional bias is often a problem in quantitative studies. Failing to take a confounding variable into account can lead to a false conclusion that the dependent variables are in a causal relationship with the independent variable. The answer is that they conduct research using the measure to confirm that the scores make sense based on their understanding of th… It refers to the extent to which a researcher can claim that accurate inferences can be made from the operationalized measures in a study for the theoretical constructs on which they were based. As a researcher, it is important to keep the concept of validity in mind at all times when designing a study. Getting others who are entirely removed from your research to test the survey is a great workaround – this will also allow you to check their responses do indeed answer or confirm the underlying hypothesis. This can be a bit of a tricky topic, as qualitative research involves humans understanding humans, a necessarily subjective practice from the get-go. He or she is interested in learning as much candid information from the research participants as possible, and respectful neutrality is a must if the goal is valid qualitative research. For more information about how to ensure the validity of research, please review Research Validity. Validity within quantitative research is a measure of how accurately the study answers the questions and hypotheses it was commissioned to answer.