Insights in Doctoral Researchers: Challenges and pitfalls of assessing construct validity in your PhD research

How to define Construct Validity:

 The term Construct Validity can many a times be misleading and make people wonder how an experiment is conducted or designed. The true and precise meaning of construct validity is whether the scale or test measures the construct sufficiently. Som of the most appropriate examples of this can be, the measurement of human brain, elements such as emotions, ability, proficiency, intellect etc. These could be further made more specific, such as language proficiency, artistic ability. These concepts are abstract and theoretical but they have been found to be in use on daily basis.

One Classic example of construct validity could be a doctor testing the effect of painkillers on migraine patients. On each day, he asks his patients to rate the level of pain they have in their migraine on a scale of one to ten. This kind of measurement needs to be done on case-to-case basis, subjectively. The use of construct validity in an example like this would be to ensure that the doctor was truly measuring pain in this case and no other aspects such as palpation, sweating, aura of migraine, sensitivity to light, nausea etc.

Consequently, the definition of construct validity would be how accurately the doctor measures the effect of the painkiller on the migraine of his patients. That is, how well the test measures the construct. It is a tool that allows researchers to perform a systematic analysis on how properly designed the research is.  Construct validity has a lot of relevance in the field of social sciences. This is because of a lot of subjectivity in social sciences concepts and most of the time there isn’t an accepted unit of measurement for constructs and are open for discussion and debate.


Techniques to measure construct validity

For large scale researches and those that are spread over a span of time, especially in the field of academia, language, the researchers tend to measure construct validity before starting the main research.  Some of the techniques of establishing construct validity are

You often focus on assessing construct validity after developing a new measure. It’s best to test out a new measure with a pilot study, but there are other options.

  • Pre-Test or Pilot Study: A pilot study is a test version of your study. The measures that you intend to use for a large sample, you test the same on a small sample to check the feasibility as well as reliability and validity. It helps you to know at the initial stage only if your research measures need a revision or change to measure your construct effectively.


  • Intervention Test:  Here a group with low scores in the construct is tested, then taught the construct and then re measured. When there is a significant difference in the pre and posttest, which is found by applying simple statistical test, the good construct validity is proven.


  • Statistical analysis is often used to test validity from the data. You test convergent and discriminant validity with correlations to identify results from your tests are related positively or negatively to each other.


  • Regression Analysis: as a researcher, you can deploy regression analysis to test construct validity as it helps you assess whether the measure you are using is truly predictive of the outcomes that you intend to predict theoretically. A good regression analysis which will support your expectations strengthens your claim of construct validity.

Efforts were made by researchers to build up statistical methods to test construct validity, but they did not seem to be productive and successful. The job of establishing a string construct validity comes by means of experience and judgement, building up as much supporting evidence as possible. A huge variety off statistical test interventions and coefficient are used to prove a good construct validity and the researchers continue till the time they feel that they have found the balance between proving practicality and validity.

Researchers should know how to measure construct validity as it directly depends upon the construct. Constructs can range from being simple to complex. A simple construct such as hand preference or dominant hand is measured by a simple survey question or observation of the respondents to understand which hand are they predominantly using to pick up objects and to execute various tasks. At the same time, more complex tasks such as social anxiety cannot be assessed by mere observation or survey. They might need more complex measurement techniques such as psychometric analysis or clinical interviews. Simple constructs have a narrower definition and complex constructs have a broader dimension.

In research it is very important for the researcher to operationalize constructs into more concrete and measurable characteristics that are based on the idea of constructs and its various dimensions. You must know very well how to define your constructs properly and what the relation between the dimensions. This clarity must be there before starting the task of data collection or analysis of data. This further helps to ensure that any measurement method thar is being used assesses the construct well so hat biases can be avoided. The most seen biases in construct validity are omitted variable bias or the information bias. It is crucial to differentiate your construct from related constructs and make sure that every part of your measurement technique is solely focused on your specific construct.

Types of construct validity

There are two main types of construct validity.

  • Convergent validity: The extent to which your measure corresponds to measures of related constructs. In the field of research, you expect that the measures of related constructs have a correlation with one another. When there are two related scales, people who would exhibit high on one scale would show high results on the other scale as well.
  • Discriminant validity: Conversely, discriminant validity means that two measures of unrelated constructs that should be unrelated, very weakly related, or negatively related are in practice. Discriminant validity is checked the same way as construct validity is checked. This is done by comparing results for different measures and assessing whether they correlate and if they do so, how do they correlate.

What is the technique to select unrelated constructs? It is always advisable to pick up constructs that are theoretically distinct from each other or have opposite concept within the same theory.

Threats and challenges to construct validity:

Recognizing the threats to construct validity helps you to counter them and build a robust research design. Some of the common threats associated with construct validity are:

Poor definition of the construct: This is one of the biggest and most common threat to construct validity. A well drafted definition of a construct greatly helps to measure it not just effectively but also accurately every time.  Your measurement protocol also needs to be clear and specific an it can be used under different conditions by other people. In the absence of a good operational definition, there are strong chances that you may have random or systematic error. This can result in a compromise in your result and ultimately lead to information bias and ultimately your measure may not be able to accurately assess your construct.

Experimenter Expectancies: Researchers are only human and may give hints that affect the behavior of the subject. Humans extend cues through body language, and unknowingly smiling when the subject gives a right answer, or raising eyebrows at an unwanted response, all have an impact on the authenticity. This effect can bring down construct validity by making the effect of the actual research variable cloudy. To reduce this effect, interaction should be kept to a minimum, and assistants should be unaware of the overall aims of the project. Experimenter expectancies about your studies can sometimes bias your results and it is very important to know about research bias to be able to avoid it. In order to avoid the experimenter expectancies bias, the researcher should target using triangulation and involve those people in your study for measurement who do not know the hypothesis. Without being aware of the expectations the possibility of bias is reduced largely.

Respondent’s Bias: When respondent has expectations from the study, their expression and responses are at times impacted by their own biases. This can be a threat to your construct validity because you may not be able to exactly measure what is interesting you.

You can dilute subject bias by using the masking technique to keep the true purpose of the study discreet from the respondents. You can create a cover story for your study, and this can bring down the effect of subject bias on your results, as well as stop them guessing the point of your research.   Respondent bias can also lead to Hawthorne effect.

Hypothesis Guessing: This threat is when the respondent estimates the purpose of the test and knowingly or unknowingly, modifies their behavior. For instance, many psychology departments ask scholars to volunteer as research subjects for their PhD guides or for the purpose of completing course credits. The threat is that the students may realize the aim of the research and the desired outcome with their knowledge and exposure to the subject. Even if they do not guess the hypothesis correctly, their behavior can be changed only by guessing them broadly.


Apprehension of Evaluation: This specific challenge is based upon nature of humans to behave differently when under observation or surveillance. Individual testing is notorious for bringing on an adrenalin rush, and this can improve or hinder performance. In this context, evaluation apprehension is related to the process of generalization


Weak definition of the construct: Construct validity revolves around good and effective semantics and language. Defining a construct in broad or narrow terms can even make the entire experiment invalid. For instance, a researcher attempts to prove that job satisfaction is the gateway to define overall happiness. This is not holistic, as somebody may like their job but not have a happy life outside the job. At the same time, using general happiness to measure satisfaction or delight at work is too vague and broad. Many people enjoy life but still detest their work or job

Mislabeling is another common threat stating that you plan to measure depression through your research, but you measure anxiety and it compromises on the quality and outcome of the research

The best way to avoid this specific threat is with good planning and seeking expert opinion before you beginning research journey.

Construct Confounding: This threat to construct validity is seen when other constructs cover up or hide the effects of the construct that is being measured. For example, self-esteem is related by self-confidence and faith in oneself. The impact of these constructs needs to be included into the research.

Variation in Scores: Variation in scores is a very easy threat as well as challenge to fall into. For example, an academic researcher creates a test to measure intelligence that provides excellent results in Canada, and exhibits high construct validity.  But, when the test is used upon immigrant children, with English is not the first language, the scores slip down. The test is faulty here as it measures their language ability and not intelligence.

Mono Operation Bias: This threat takes into consideration the Independent Variable, and is a situation where one manipulation is used to influence a construct. For example, a researcher would want to test whether an anti-depression drug is effective or no. He splits patients into two groups, one given the drug and the other group is given the placebo effect. The problem with this is that it is single group and a solid design would use multi-groups being administered different strength of doses.

Mono Method Bias: This threat to construct validity revolves around the dependent and occurs when only a single technique of measurement is used. For instance, in an experiment to measure self-esteem, the researcher creates a single method to identify the level of that construct, but then later finds out that rathe it is measuring self-confidence.

The effects of threat in construct validity can be diluted and reduced to a large extent by using a variety of techniques, such as questionnaire, self-rating and physiological tests, and observation techniques that minimize the chances of threat affecting construct validity.

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