Chapter 3: Threats to Validity- construct and statistical validity

The final section under Threats to Validity for quantitative and mixed method studies asks you to describe any threats to construct or statistical conclusion validity. Let's start with a definition of a construct. A construct is an attribute, proficiency, ability, or skill that happens in the human brain and is defined by established theories. For example, "resilience" is a construct. It exists in theory and has been observed to exist in practice.  

Construct validity has traditionally been defined as the experimental demonstration that a test is measuring the construct it claims to be measuring. Such an experiment could take the form of a differential-groups study, wherein the performances on the test are compared for two groups: one that has the construct and one that does not have the construct. If the group with the construct performs better than the group without the construct, that result is said to provide evidence of the construct validity of the test. An alternative strategy is called an intervention study, wherein a group that is weak in the construct is measured using the test, then taught the construct, and measured again. If a non-trivial difference is found between the pretest and posttest, that difference can be said to support the construct validity of the test.  

There are a large number of threats to construct validity- too many to discuss here, but I do suggest that you take a look at http://www.socialresearchmethods.net/kb/consthre.php when you are ready to write this section. The author does a very nice job laying out the many types of possible threats. 

The last issue is statistical conclusion validity. Statistical conclusion validity is the degree to which our conclusions about the relationship between your variables based on the data are correct or ‘reasonable’. This is getting at the two types of statistical errors that can occur: type I (finding a difference or correlation when none exists) and type II (finding no difference when one exists). Statistical conclusion validity concerns the qualities of the study that make these types of errors more likely. Statistical conclusion validity involves ensuring the use of adequate sampling procedures, appropriate statistical tests, and reliable measurement procedures. If you would like a more in depth discussion of this topic, please see http://www.socialresearchmethods.net/kb/concthre.php 

Next time we will continue our review - Chapter 3: Issues of Trustworthiness in qualitative and mixed method studies. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu