2.2: Research Terminology
- Page ID
- 225738
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- Identify and explain key research terminology, including: variables, scales of measurement, sampling, reliability, and validity.
Research terminology is unique and can be challenging to understand. Let's break down some of the most common vocabulary and concepts used across various research designs.
Research Methods
Variables
Variables are measurable characteristics that can be studied. There are four types of variables that could be used in various quantitative research methods:
- Independent (IV): the variable(s) the research manipulates to observe its effect on the dependent variable.
- Dependent (DV): the variable that is measured to see how it changes in response to the independent variable.
- Control: variable(s) that are held constant throughout the study to minimize the influence on the dependent and/or independent variables.
- Confounding: outside variable(s) that could influence the results. (There are various ways that a researcher could exclude or control, primarily through careful study design and specific techniques or statistical analyses.)
If having difficulty identifying the independent and dependent variable, use this saying: this affects that. For example, if we were to design a study to examine the relationship between regular soda and diet soda on blood sugar and apply "this affects that," we would identify the type of soda as the independent variable (IV) and blood sugar as the dependent variable (DV).
Scale of Measurement
Scale of measurement is a classification system used to categorize variables based on the type of information they provide (Stevens, 1946). There are four main scales, as noted in Table \(\PageIndex{1}\). Each level of measurement scale has specific properties that determine the techniques used to display, summarize, and analyze the data. Therefore, knowing the level of measurement of your data is critically important.
| Order of Complexity | Scale of Measurement | This is.... | This is what it looks like... |
|---|---|---|---|
| LOW | Nominal | Data that can only be organized into groups with no inherent order. | Gender, hair color, uniform numbers, city where you were born, etc. |
|
|
Ordinal | Data that can be ranked or ordered, but the distances between categories is not necessarily equal. | For example, let's say we observed a horse race. The order of finish is Rosebud #1, Sea Biscuit #2, and Kappa Gamma #3. We lack information about the difference in time or distance that separated the horses as they crossed the finish line. |
| Interval | Data that can be ranked, the intervals between categories are equal, but there is no true zero point. | The classic example of the interval scale is temperature measured on the Fahrenheit or Celsius scales. Let's suppose today's high temperature is 60º F and thirty days ago the high temperature was only 30º F. We can say that the difference between the high temperatures on these two days is 30 degrees. But, because our measurement scale lacks a real, non-arbitrary zero, we cannot say the temperature today is twice as warm as the temperature thirty days ago. | |
| HIGH | Ratio | Data that can be ranked, intervals are equal, and there is a true zero point. | An example of the ratio level of measurement is weight. A person who weighs 150 pounds weighs twice as much as a person who weighs only 75 pounds and half as much as a person who weighs 300 pounds. We can calculate ratios like these because the scale for weight in pounds starts at zero pounds. |
Table \(\PageIndex{1}\) is adapted from Measurement & Measurement Scales by Volchok (2015) and licensed CC-BY-SA-SA.
Other Terms to Know
- Sampling: are methods used to select the group of participants in the study
- Reliability: is the consistency of measurements across different occasions. Meaning, if we take a survey today and retake it a month from now, will the survey questions yield similar answers the first time compared to when they are retaken? If the answer is yes, then it is considered a reliable measure.
- Validity: the accuracy of what is being measured.
- Ecological validity: refers to the realism with which a research design matches the real-world context. A great example of this is video games. A standard PS5 game and controller have less ecological validity compared to a Wii.
When studies are conducted, all of these concepts are essential to ensure the data has integrity. If a researcher is not using sampling methods appropriate to the population being studied, how can results be generalized? If an instrument used to collect data is not valid and reliable, how can results be replicated? When reading research, it's important to check if this information is being reported. If it's not, the study's quality should come into question.
References
Stevens, S.S. (1946). On the theory of scales of measurement. Science, 103(2684), 677-680. doi: 10.1126/science.103.2685.677
Volchock, E. (2015). Measurement & measurement scales. CUNY. Retreived: http://media.acc.qcc.cuny.edu/facult..._Volchok5.html