Characteristics of a good measurement

What are the characteristics of a good measurement tool? An intuitive answer to this question is that the tool should be an accurate counter or indicator of what we are interested in measuring. In addition,
it should be easy and efficient to use. There are three major criteria for evaluating a measurement tool:
validity, reliability, and practicality.

  • Validity is the extent to which a test measures what we actually wish to measure.
  • Reliability has to do with the accuracy and precision of a measurement procedure.
  • Practicality is concerned with a wide range of factors of economy, convenience, and interpretability
Many forms of validity are mentioned in the research literature, and the number grows as we expand
the concern for more scientific measurement. This text features two major forms: external and internal validity. The external validity of research findings is the data’s ability to be generalized across persons, settings, and times;
Internal validity is further limited in this discussion to the ability of a research instrument to measure what it is purported to measure.
One widely accepted classification of validity consists of three major forms:

(1) content validity

(2) criterion-related validity and

(3) construct validity.
 1. Content Validity
The content validity of a measuring instrument is the extent to which it provides adequate coverage of the investigative questions guiding the study. 
If the instrument contains a representative sample of the universe of subject matter of interest, then content validity is good. 
To evaluate the content validity  of an instrument, one must first agree on what elements constitute adequate coverage.
A second way is to use a panel of persons to judge how well the instrument meets the standards. The panel independently assesses the test items for an instrument as essential, useful but not essential, or not necessary. “Essential” responses on each item from each panelist are evaluated by a content validity ratio, and those meeting a statistical significance value are retained. In both informal judgments and this systematic process, “content validity is primarily concerned with inferences about test construction rather than inferences about test scores.

Content validity, in the realm of research, refers to the degree to which an assessment tool, like a survey, test, or interview, accurately reflects the specific construct it's designed to measure.

Think of it this way: imagine you're trying to measure student engagement in a classroom. Your assessment tool, in this case, could be a questionnaire asking students about their participation in class discussions, their interest in the material, and their feelings of belonging in the classroom environment.

High content validity means that your questionnaire items effectively capture all the different aspects of student engagement. It wouldn't just focus on one specific aspect, like participation, but also consider factors like curiosity, motivation, and sense of community.

On the other hand, low content validity would occur if your questionnaire only asked about participation, neglecting other crucial aspects of engagement. This could lead to inaccurate conclusions about the overall level of engagement in the classroom.

(2) criterion-related validity

Criterion-related validity, sometimes called criterion validity, is an essential concept in research that assesses how well a test or measurement tool relates to a specific, external outcome or criterion. In simpler terms, it's about demonstrating that your test actually measures what it's supposed to measure in a way that has real-world implications.

Here's a deeper dive into how criterion-related validity plays out in research:

Key elements:

  • Predictor: This is the test or measurement tool you're evaluating. It could be anything from a standardized test to a newly developed survey.
  • Criterion: This is the external variable or outcome that you're trying to predict or relate to. This could be something like job performance, academic achievement, or a specific behavior.
  • Correlation: This refers to the statistical relationship between the predictor and criterion scores. A strong correlation indicates that there's a significant association between the two variables.

Types of criterion-related validity:

There are two main types of criterion-related validity, each with its own focus:

  1. Concurrent validity: This compares the scores on your new test (predictor) with the scores on an established measure (criterion) of the same construct at the same time. In simpler terms, you're seeing how well your new test agrees with an already trusted one.

Example: Assessing how well scores on a newly developed anxiety questionnaire correlate with scores on a well-validated clinical anxiety scale administered simultaneously.

  1. Predictive validity: This focuses on how well scores on your test (predictor) predict future performance or behavior related to the criterion. In other words, you're seeing how well your test can forecast what happens later.

Example: Examining how scores on a standardized college entrance exam relate to subsequent academic performance in the first year of college.

Methods for establishing criterion-related validity:

  • Correlation analysis: Calculating the correlation coefficient (e.g., Pearson's r) between the predictor and criterion scores provides a statistical measure of the relationship's strength.
  • Regression analysis: This determines how well the predictor scores can predict the criterion scores, allowing for insights into the direction and strength of the relationship.

Strengths of criterion-related validity:

  • Provides direct evidence of the practical relevance of a test or measure.
  • Helps researchers choose appropriate tools for specific research questions or decision-making purposes.
  • Enhances the credibility and trustworthiness of research findings.


  • Relies on the availability of a suitable and reliable criterion measure.
  • Can be influenced by factors like sample size, measurement error, and external variables.
 Any criterion measure must be judged in terms of four qualities:

(1) relevance

(2) freedom from bias

(3) reliability

(4) availability
 When evaluating a criterion measure in research, considering its relevance, freedom from bias, reliability, and availability is crucial for ensuring the validity and quality of your research findings. Let's break down each of these qualities further:

1. Relevance:

  • Definition: Does the criterion measure directly represent the construct or outcome you're trying to predict or relate to? Is it truly measuring what you intend it to?
  • Importance: A relevant criterion measure ensures your findings are meaningful and insightful regarding the specific research question. Choosing an irrelevant measure would lead to misleading conclusions.
  • Examples:
    • Predicting job performance: Using supervisor ratings as a criterion measure is more relevant than using employee satisfaction.
    • Assessing anxiety levels: Utilizing a validated clinical anxiety scale as a criterion measure is more relevant than measuring general stress levels.

2. Freedom from Bias:

  • Definition: Is the criterion measure free from systematic errors or prejudices that could unfairly influence the results?
  • Importance: Bias-free measures ensure fairness and accuracy in your findings. Biased measures can distort the relationship between the predictor and criterion variables.
  • Examples:
    • Selecting students based on socioeconomic status for a study on academic achievement can introduce bias based on social background.
    • Using a self-reported measure of health that relies heavily on subjective interpretations can be prone to bias due to individual perceptions.

3. Reliability:

  • Definition: Does the criterion measure produce consistent and stable results when repeated under similar conditions? Can it be reliably measured?
  • Importance: Reliable measures ensure confidence in the accuracy and repeatability of your findings. Unreliable measures lead to inconsistent results and hinder generalizability.
  • Examples:
    • A standardized test with high test-retest reliability ensures consistent scores even if administered on different days.
    • Using multiple observers to rate participants' behavior in a study can enhance reliability by minimizing individual biases.

4. Availability:

  • Definition: Is the criterion measure readily available, accessible, and practical to use in your research context? Can you easily collect and utilize the data?
  • Importance: Available measures ensure feasibility and efficiency in conducting your research. Difficult-to-obtain or expensive measures can hinder research progress.
  • Examples:
    • Utilizing public databases for economic indicators might be more readily available than conducting individual surveys with businesses.
    • Choosing a simple and quick self-report measure of well-being might be more feasible than conducting in-depth clinical interviews.

Remember, considering all four qualities of a criterion measure is crucial for making informed decisions in your research design and ensuring the validity and generalizability of your findings. Choose wisely, and your research will have a stronger foundation for accurate and insightful conclusions.

(3) construct validity

Construct validity, a crucial concept in research, goes beyond simply asking what a test or measure assesses. It delves deeper, asking how well it captures the underlying concept itself. In simpler terms, it's about ensuring your measure truly reflects the abstract construct you're interested in.

Imagine you're researching the concept of "student engagement." Surveys asking about participation may offer some insight, but construct validity demands a more multifaceted approach. It asks you to consider:

  • What are the different dimensions of student engagement? Curiosity, motivation, sense of community, and active learning are just a few aspects.
  • Are your survey items capturing each dimension effectively? Generic questions about class attendance might miss the nuances of deeper engagement.
  • Do the items hang together well? Do all questions about participation, for example, truly reflect the same underlying construct?

Addressing these questions helps establish construct validity through various methods:

1. Convergent Validity:

  • Does your measure correlate with other established measures of the same construct? If your engagement survey aligns with validated scales, it strengthens your confidence.

2. Divergent Validity:

  • Does your measure differ from measures of unrelated constructs? If an engagement survey doesn't significantly overlap with, say, a test anxiety scale, it suggests good discriminatory power.

3. Known-Groups Validity:

  • Does your measure differently in known groups expected to differ on the construct? For example, do engaged students score higher than disengaged ones on your survey?

4. Content Validity:

  • Do your items appropriately represent the theoretical definition of the construct? Expert reviews and pilot testing can ensure your content reflects the intended construct.

5. Multitrait-Multimethod (MTMM) Analysis:

  • This statistical technique assesses whether different measures of the same construct (measured in different ways) converge, while measures of different constructs diverge.

Construct validity isn't a single "yes" or "no" answer. It's a continuous process of gathering evidence to build confidence in your measure's ability to capture the elusive essence of the construct you're researching. This, in turn, strengthens the trustworthiness and value of your research findings.

Remember, a solid understanding of construct validity is essential for ensuring your research stands on firm ground. Don't hesitate to ask if you have any further questions about this intriguing concept!

 The following table summaries all types of validities 

 2. Reliability

In the world of research, reliability is about consistency and dependability. It asks the question: "If I repeat this study, would I get the same results?" Reliable research produces findings that are stable and replicable, increasing their trustworthiness and allowing for meaningful conclusions.

Imagine flipping a coin. A reliable coin toss would consistently land on heads or tails approximately 50% of the time. Similarly, a reliable research study consistently produces the same results when conducted under the same conditions. Here are some key aspects of reliability:

1. Types of Reliability:

  • Test-retest reliability: Assesses consistency over time. Would participants score the same on a test if they took it again later?
  • Inter-rater reliability: Assesses consistency between different observers or raters. Would different teachers rate student essays similarly?
  • Internal consistency reliability: Assesses consistency within a measure itself. Do different items on a questionnaire all measure the same construct (e.g., anxiety) consistently?

2. Importance of Reliability:

  • Increases confidence in findings: Reliable results are less likely to be due to chance or random error, bolstering the validity of your conclusions.
  • Facilitates replication: Reliable studies can be replicated by other researchers, allowing for verification and advancement of knowledge.
  • Enhances generalizability: Findings from reliable studies can be applied more confidently to wider populations or contexts.

3. Improving Reliability:

  • Use standardized procedures: Consistent methods of data collection and analysis minimize variability.
  • Train and calibrate raters: Ensure consistent interpretation and application of rating criteria.
  • Pilot test your measures: Identify and address potential flaws before the main study.
  • Report reliability estimates: Share your results on tests of reliability to allow readers to assess the robustness of your findings.
Reliability is a cornerstone of strong research. By prioritizing and demonstrating consistency in your methods and measures, you pave the way for trustworthy findings that contribute meaningfully to your field.

Validity Versus Reliability

Both validity and reliability are crucial concepts in research, but they address different aspects of measurement quality. Understanding their distinction is essential for ensuring rigorous and meaningful research.


  • Focus: Accuracy of a measure. Does it actually assess what it's supposed to?
  • Analogy: A thermometer that accurately reflects ambient temperature is valid.
  • Types:
    • Content validity: Do items capture the intended construct?
    • Criterion validity: Does the measure relate to other relevant outcomes?
    • Construct validity: Does the measure reflect the theoretical definition of the construct?


  • Focus: Consistency of a measure. Does it produce the same results under the same conditions?
  • Analogy: A scale that consistently weighs the same object is reliable.
  • Types:
    • Test-retest reliability: Do scores remain consistent over time?
    • Internal consistency reliability: Do different items within a measure consistently reflect the same construct?
    • Inter-rater reliability: Do different observers agree on their assessments?

The Relationship:

  • Reliability is necessary but not sufficient for validity. A consistent measure is not necessarily accurate. Imagine a faulty thermometer consistently producing incorrect readings.
  • Validity often depends on reliability. If a measure is not consistent, it's difficult to be confident in its accuracy. Think of a scale that fluctuates with every use.

In Practice:

  • Both validity and reliability should be considered in choosing and developing research measures.
  • Methods like pilot testing and expert reviews can help establish validity.
  • Reliability can be improved through standardized procedures and training.

By focusing on both validity and reliability, researchers can ensure their measurements are accurate and consistent, leading to more trustworthy and robust research findings.


The scientific requirements of a project call for the measurement process to be reliable and valid, while the operational requirements call for it to be practical. Practicality has been defined as economy, convenience, and interpretability . Although this definition refers to the development of educational and psychological tests, it is meaningful for business measurements as well.


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