Construct validity: Getting started
Construct validity is important because we want to make sure that the measurement procedure (e.g. a survey, structured interview, structured observation, etc.) that we use to measure the constructs we are interested in (e.g. sexism, obesity, famine, outsourcing, etc.) are valid.

By construct valid, we mean that (a) a clear link between the constructs you are interested in and the measures and interventions that are used to operationalize them (i.e. measure them), and (b) a clear distinction between different constructs.

Construct validity is an overarching term used to refer to the process of assessing the validity of the measurement procedure that you use in your dissertation. It incorporates a number of other forms of validity (i.e. content validity, convergent and divergent validity, and criterion validity: concurrent and predictive validity) that help in the assessment of such construct validity. This article helps you to start thinking about construct validity in your dissertation.
Core ARTICLES
Internal validity: An overview
Internal validity is important because we want to be able to say that the conclusions we made in our dissertation accurately reflect what we were studying. In this article, we not only discuss internal validity in more detail, but also 14 of the main threats to internal validity: history effects, maturation, testing effects, instrumentation, statistical regression, selection biases, experimental mortality, causal time order, diffusion (or imitation) of treatments, compensation, compensatory rivalry, demoralization, experimenter effects, and subject effects.
External validity: An overview
External validity is important because we want to be able to say that the conclusions we made in our dissertation can be generalised. External validity asks the question: To what extent can our conclusions be generalised (a) to a wider population, and/or (b) across populations, treatments, settings/contexts, and time? In this article, we explain what external validity is, as well as discussing the many threats to external validity that you may face.
Reliability in quantitative dissertations: Getting started
Reliability is a way of assessing the quality of the measurement procedure (e.g. a survey, structured interview, structured observation, etc.) used to collect data in a dissertation. In order for the results from a study to be considered valid, the measurement procedure must first be reliable. There are a number of types of reliability test that you need to considered, depending on whether your dissertation involves successive measurements, simultaneous measurements by more than one researcher, or multi-measure procedures. This article gets you started.
Content validity: An overview
Content validity is the extent to which the elements (e.g. questionnaire items, coding criteria, participant instructions, etc.) within a measurement procedure (e.g. a survey, structured observation, structured interviews) are relevant and representative of the construct that they will be used to measure. Establishing content validity is a necessarily initial task in the construction of a new measurement procedure (or revision of an existing one). However, the validity (e.g. construct validity) and reliability (e.g. internal consistency) of the content (i.e. elements) selected should be tested before an assessment of content validity can be made. This article introduces you to content validity.
Face validity: An overview
It would not be a surprise if the majority of dissertations at the undergraduate and master’s level rely heavily on face validity (also known as logical validity), typically because it is the easiest form of validity to apply. Unfortunately, face validity is arguably the weakest form of validity and many would suggest that it is not a form of validity in the strictest sense of the word. Face validity could easily be called surface validity or appearance validity since it is merely a subjective, superficial assessment of whether the measurement procedure (e.g. a survey, structured observation, structured interviews) you use in a study appears to be a valid measure of a given variable or construct (e.g. racial prejudice, balance, anxiety, running speed, emotional intelligence, etc.). However, since face validity is very popular at the undergraduate and master's dissertation level, this article helps you to understand its major characteristics.
Convergent and divergent validity: An overview
Convergent validity and divergent validity are ways to assess the construct validity of a measurement procedure. Convergent validity helps to establish construct validity when you use two different measurement procedures (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. This article will help you understand more about convergent and divergent validity.
Criterion validity (concurrent and predictive validity): An overview
There are many occasions when you might choose to use a well-established measurement procedure (e.g. a 42-item survey on depression) as the basis to create a new measurement procedure (e.g. a 19-item survey on depression) to measure the construct you are interested in (e.g. depression, sleep quality, employee commitment, etc.). This well-established measurement procedure acts as the criterion against which the criterion validity of the new measurement procedure is assessed. To assess criterion validity in your dissertation, you can choose between establishing the concurrent validity or predictive validity of your measurement procedure. This articles explains what criterion validity is, and when you would test for it.