Research Constructs 101
Constructs, Validity & Reliability – Explained Simply
By: Derek Jansen (MBA) | Expert Reviewed By: Eunice Rautenbach (DTech) | March 2023
Navigating the world of academic research can be overwhelming, especially if you’re new to the field. One of the many pieces of terminology that often trips students up is that of the “research construct”. In this post, we’ll explain research constructs, construct validity and reliability in simple terms along with clear examples.
What is a research construct?
Simply put, a research construct is an abstraction that researchers use to represent a phenomenon that’s not directly measurable – for example, intelligence, motivation or agreeableness. Since constructs are not directly measurable, they have to be inferred from other measurable variables, which are gathered through observation. For example, the construct of intelligence can be inferred based on a combination of measurable indicators such as problem-solving skills and language proficiency.
As a researcher, it’s important for you to define your constructs very clearly and to ensure that they can be feasibly operationalised. In other words, you need need to develop ways to measure these abstract concepts with relevant indicators or proxies that accurately reflect the underlying phenomenon you’re studying. In technical terms, this is called construct validity – we’ll unpack this in more detail a little later.
Examples of research constructs
The best way to get a feel for research constructs is to look at some examples. Some common examples of constructs that you might encounter include:
- Self-esteem: a psychological construct measuring an individual’s overall sense of self-worth and confidence.
- Job satisfaction: a social construct reflecting the degree to which employees feel content with their work environment and overall experience in their workplace.
- Personality traits: extraversion, agreeableness, conscientiousness, neuroticism, and openness are commonly studied constructs used to explain individual differences in behaviour, cognition, and emotion.
- Quality of life: a complex multi-dimensional construct encompassing various aspects of an individual’s well-being such as physical health, emotional stability, social relationships, and economic status.
- Stress levels: an often-used psychological construct assessing the mental or emotional strain experienced by individuals in response to various life events or situations.
- Social support: A construct reflecting the perception of having assistance available from family members, friends, colleagues or other networks.
As you can see, all of the above examples reflect phenomena that cannot be directly measured. This is the defining characteristic of a research construct and is what distinguishes a construct from a variable (we’ll look at that next).
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Research construct vs variable
In research, the terms “construct” and “variable” are often used interchangeably, but they’re not the same thing.
A variable refers to a phenomenon that is directly measurable and can take on different values or levels. Examples of variables include age, height, weight, and blood pressure. Notably, these are all directly measurable (using basic equipment or just good old-fashioned logic).
In contrast, a construct refers to an abstract concept, that researchers seek to measure using one or more variables – since it is not directly measurable. Self-esteem, for example, is an abstract concept that cannot be directly measured. Instead, researchers must use self-reported indicators such as feelings of self-worth or pride in oneself to create operational definitions (variables) to measure it.
Another difference between research constructs and variables is their level of abstraction. Constructs tend to be more abstract than variables since they represent broad ideas and concepts, while variables are specific measures within those concepts. If you’d like to learn more about variables, be sure to read this article.
Construct validity and reliability
When it comes to creating and/or using research constructs, there are two important concepts you need to understand – construct validity and reliability.
Construct validity refers to the extent to which a research construct accurately measures what it is intended to measure. In other words, are you actually measuring the thing that you want to measure, as opposed to some other thing that just happens to correlate? For example, if you wanted to measure intelligence using some sort of performance test, you’d need to ask questions that truly reflect the participant’s cognitive abilities and not just their memory recall.
Construct reliability, on the other hand, relates to how consistent the measurement of a construct is over time or across different situations. This focus on consistency serves to ensure that your results are not simply due to random error or inconsistency in data collection. To improve construct reliability, researchers use standardized procedures for collecting data, as well as measures such as test-retest reliability, which involves comparing results from multiple measurements taken at different times. You may have also heard of Cronbach’s alpha, which is a popular statistical test used to assess internal consistency, and in turn, construct reliability.
Both construct validity and reliability play crucial roles in ensuring accurate and meaningful research findings. If the constructs you use in your research are not valid and reliable, your data will be largely meaningless. So, be sure to pay close attention to these when designing your study.
We’ve covered a lot of ground in this post. Let’s do a quick recap of the key takeaways:
- A research construct is an abstraction that researchers use to represent a phenomenon that’s not directly observable.
- Examples of research constructs include self-esteem, motivation, and job satisfaction.
- A research construct differs from a research variable in that it is not directly measurable.
- When working with constructs, you must pay close attention to both construct validity and reliability.
Keep these point front of mind while undertaking your research to ensure your data is sound and meaningful. If you need help with your research, consider our 1:1 coaching service, where we hold your hand through the research journey, step by step.
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