For example, if one’s theory of intelligence includes creativity as a component (creativity is part of the ‘content’ of intelligence) a test cannot be valid if it does not measure creativity. But given how much flat-Earth beliefs contradict basic science and information from official channels like NASA, we might also include questions that measure trust in science (e.g., The scientific method usually leads to accurate conclusions) and government institutions (e.g., Most of what NASA says about the shape of the Earth is false).Ĭontent validity is one of the most important aspects of validity, and it largely depends on one’s theory about the construct. Obviously, the scale would need to include items measuring people’s beliefs about the shape of the Earth (e.g., do you believe the Earth is flat?). An assessment of content validity would judge how well these questions cover different conceptual components of the flat-Earth conspiracy. Content validity – Content validity is a judgment about whether your survey instrument captures all the relevant components of what you’re trying to measure.įor example, suppose we wrote 10 items to measure flat-Earth beliefs.In the case of a survey instrument to measure beliefs about whether the earth is flat, a researcher may want to show the initial version of the instrument to an expert on the flat earth theory to get their feedback as to whether the items look right. There’s no fancy math, just a judgment about whether things look right on the surface.įace validity is sometimes assessed by experts. Face validity – Do the items used in a study look like they measure what they’re supposed to? That’s the type of judgment researchers make when assessing face validity.Assessments of construct validity can range from a subjective judgment about whether questions look like they measure what they’re supposed to measure to a mathematical assessment of how well different questions or measures are related to each other. Regardless of the particulars of the study, statistical validity is concerned with whether what the research claims is supported by the data.Ĭonstruct validity is an assessment of how well a research team has measured or manipulated the variable(s) in their study. For studies that examine the association between multiple variables or conduct an experiment, judgments of statistical validity may entail examining the study’s effect size or statistical significance. For a survey or poll, judgments of statistical validity may entail looking at the margin of error. There is no one way to evaluate claims of statistical validity. An assessment of statistical validity asks whether that 25% is based on a sample of 12 or 12,000. Suppose a survey says 25% of people believe the Earth is flat. Statistical validity is an assessment of how well the numbers in a study support the claims being made. Within the context of survey research, validity is the answer to the question: does this research show what it claims to show? There are four types of validity within survey research. Validity refers to how reasonable, accurate, and justifiable a claim, conclusion, or decision is. Thus, we lay out the details of both constructs in this blog. However, understanding validity and reliability is important for both the people who conduct and consume research. Digging into these concepts can get a bit wonky. Nevertheless, behavioral scientists persist, using surveys, experiments, and observations to learn why people do what they do.Īt the heart of good research methods are two concepts known as survey validity and reliability. They may not always want to divulge what they really think or they may not be able to accurately report what they think. Let’s start by agreeing that it isn’t always easy to measure people’s attitudes, thoughts, and feelings.
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