Convergent and divergent validity
Convergent validity and divergent validity are ways to assess the construct validity of a measurement procedure (Campbell & Fiske, 1959). If you are unsure what construct validity is, we recommend you first read: Construct validity. Convergent validity helps to establish construct validity when you use two different measurement procedures and research methods (e.g., participant observation and a survey) in your dissertation to collect data about a construct (e.g., anger, depression, motivation, task performance). Divergent validity helps to establish construct validity by demonstrating that the construct you are interested in (e.g., anger) is different from other constructs that might be present in your study (e.g., depression). To assess construct validity in your dissertation, you should first establish convergent validity, before testing for divergent validity. In this article, we explain what convergent and divergent validity are, providing some examples.
What is convergent validity?
Convergent validity helps to establish construct validity when you use two different measurement procedures and research methods (e.g., participant observation and a survey) in your dissertation to collect data about a construct (e.g., anger, depression, motivation, task performance). The extent to which convergent validity has been demonstrated is establish by the strength of the relationship between the scores that are obtained from the two different measurement procedures and research methods that you have used to collect data about the construct you are interested in. The idea is that if these scores converge, despite the fact that we use two different measurement procedures and research methods, we must be measuring the same construct.
We use the words, despite the fact, because it can be difficult enough in research to create one reliable operational definition for a construct; that is, a single reliable way of measuring a particular construct. It's one thing to suggest measuring the construct height using centimetres, or a person's weight using kilograms, but these are operational definitions of constructs that are quite obvious, where it is easy to come up with a single operational definition. It is far more challenging to create reliable operational definitions for constructs like anger, depression, motivation, and task performance, let alone multiple operational definitions [see the article on Constructs in quantitative research]. However, in order to establish convergent validity, we must come up with two operational definitions of the construct we are interested in. We have to come up with two operational definitions because we are using two different measurement procedures (e.g., with participant observation and a survey as the research methods). Each of these measurement procedures will require a different operational definition. Let's look at an example:
Construct #1 = Sleep quality
Imagine that we are interested in studying the relationship between fitness level and sleep quality; that is, the impact that exercise has on how well people sleep. For the purpose of this example, let's focus on the scores on the dependent variable, which is sleep quality (i.e., sleep quality is the construct of interest). When participants in the study wake up in the morning, they record their sleep quality using a self-completed survey (i.e., they fill in a questionnaire). This gives us insight into how well the participants felt they slept. However, is this a reliable measurement procedure to measure the construct, sleep quality? Let's imagine that we are simply unsure because sometimes self-completed measurement procedures can be prone to certain biases. Therefore, we also observe the participants whilst they are sleeping using a video camera to monitor their sleeping patterns. When making the observations, we score the participants' sleep quality. We hope that by using two different research methods to assess sleep quality, we will have a more reliable measurement procedure for the construct we are interested in.
This leaves us with two different sets of scores from the two different measurement procedures used under the two research methods (i.e., the scores from the survey and the scores from the participant observation). We will have started to demonstrate convergent validity if there is a strong relationship between the two scores (i.e., the scores from the measurement procedures used under the two different research methods). Such a strong relationship, which helps to demonstrate convergent validity, is an important step in assessing construct validity; that is, we can be more confident that the measurement procedures that we are using to measure sleep quality are a valid measure of the construct, sleep quality.
In order to establish convergent validity, the strength of the relationship between the scores from the two different measurement procedures, from the two different methods, is assessed. This is usually achieved by calculating a correlation between the two scores.
NOTE: Convergent validity is not the same as concurrent validity, which we discuss in more detail in the article: Concurrent validity. However, the distinction is quite straightforward. Both convergent and concurrent validity are ways of assessing construct validity by examining the strength of the relationship between the scores from two different measurement procedures. However, whilst concurrent validity compared a new measurement procedure with a well-established measurement procedure, both measurement procedures are new when testing for convergent validity. Therefore, if one of the measurement procedures you are using to establish construct validity is well-established, you should read the article: Concurrent validity.