4  Variables

A variable is a characteristic or attribute that can vary or take on different values. These values can be measured, observed, or manipulated in a study. For example, we can measure someone’s depressive symptoms, observe a child’s aggresive behavior on the playground, or manipulate the type of learnign intervention a higherschooer gets. Variables are essential in research as they allow us to examine relationships, make comparisons, and draw conclusions about the phenomena they are studying.

4.1 NOIR

Nominal, ordinal, interval, and ratio (NOIR) are four levels of measurement that describe the nature of the values that a variable can take. These levels of measurement are hierarchical, with each level including all the characteristics of the levels below it. Here’s a brief explanation of each:

  1. Nominal Level:

Nominal variables involve categories without any inherent order or ranking. Examples include gender, where categories are mutually exclusive, yet there is no inherent order or ranking among them. In nominal measurement, the focus is on classifying items into discrete categories.Typically nominal variables are analysed using frequencies. However, they have be used in more complex analyses.

For example, individuals may receive a drug versus a placebo, a nominal variable. Research may then determine the impact of severity/degree of psychopathological symptoms [not a nominal variable]. As another example, perhaps we measure Grenfell students’ favourite musician. We sample from 75 students. We can calculate frequencies of this nominal variable.

  1. Ordinal Level:

Ordinal variables possess a meaningful order or ranking, but the intervals between categories are not consistent or meaningful. While relative ranking is meaningful, the differences between these categories are not uniform. For example, the difference between strongly agree and agree is not necessarily the same difference between agree and neither agree not disagree, regardless of the numbers you may assign to them. Or, as another example, consider educational levels of employees at Grenfell (e.g., bachelor’s degree, master’s degree, PhD).

The figure below shows both nominal and ordinal data. There is no inherent order for artists. You could impose some sort of order, such as alphabetical, but it is likely not related to the research question of interest. However, education degree has a typical progress (i.e., order): first comes bachelor’s, second masters, third PhD.

  1. Interval Level:

Interval variables maintain a meaningful order, and there are consistent intervals between values. However, these variables lack a true zero point, where zero does not represent the absence of the measured quantity. Examples include temperature measured in Celsius or Fahrenheit; when it’s zero degrees out, it does not mean there is no temperature. Also, 20 degrees Celcius isn’t twice as much temperature as 10 degrees. Another example would be IQ scores; An IQ score of zero does not exist.

In interval measurement, researchers focus on both the order and the equal intervals between values. The difference between \(n\) values is equal for each ordered pair. Consider four ordered value:

\(a, b, c, d\)

Interval values have the property such that the difference between \(a\) and \(b\) is the same as the difference between \(b\) and \(c\), which is the same as the difference between \(c\) and \(d\):

\(a-b=b-c=c-d\)

  1. Ratio Level:

Ratio variables exhibit a meaningful order, consistent intervals between values, and a true zero point. In this level of measurement, a score of zero represents the absence of the measured quantity. Examples include height, weight, income, and age. Someone 120cm tall is twice as tall as someone who is 60cm tall. Someone who is 50 is twice as old as someone who is 25. Ratio measurement allows for meaningful ratios and absolute distinctions between values.

The following table may be helpful, adapted from Nunnally and Bernstein (1994), who adapted it from Stevens (1951):

Adapted from Nunnally and Bernstein
Scale Operations Transformations Statistics Examples
Nominal \(=\) versus \(\ne\) So many Frequency; mode Gender; political party; employment status
Ordinal \(>\) versus \(<\) Monotonically increasing Median; percentiles SES (low, middle, high); Likert-style items
Interval Equality of intervals General linear Arithmetic mean; variance Temperature
Ratio Equality of ratios Multiplicative Geometric mean Height; weight

4.2 Experimental Variables

There are two main types of variables in experimental psychological research:

  1. Independent Variable (IV): This is the variable that is manipulated or controlled by the researcher. It is the variable that is hypothesized to cause a change in the dependent variable. For example, in an experiment investigating the effects of a new teaching method on student performance, the teaching method would be the independent variable.

Importantly, this is mutually exclusive from our NOIR variables. An independent variable could, technically, be nominal, ordinal, interval, or ratio.

  1. Dependent Variable (DV): This is the variable that is measured or observed in response to changes in the independent variable. It is the outcome variable that researchers are interested in studying. In the example above, student performance would be the dependent variable.

Importantly, this is mutually exclusive from our NOIR variables. A dependent variable could, technically, be nominal, ordinal, interval, or ratio.

So, why care? The type of statistical analyses you use are dependent on your hypotheses and the variable types. If you hypothesize that \(x\) and \(y\) are related and both are interval variables, you could conduct Pearson’s correlation, but not an ANOVA.

4.3 Other Considerations

Researchers also consider and control for extraneous variables, which are variables that are not the focus of the study but could potentially influence the results. Controlling for these variables helps ensure that any observed effects can be attributed to the manipulation of the independent variable.

Extraneous Variables: are any variables other than the independent and dependent variables that may influence the results of an experiment. These variables are unwanted or unplanned factors that can introduce variability into the study, making it difficult to determine the true effect of the independent variable on the dependent variable.

For example, if a researcher is investigating the effect of a new teaching method on student performance, extraneous variables could include the students’ prior knowledge, motivation, or even the time of day the experiment is conducted.

Confounding Variables: are a specific type of extraneous variable that systematically varies with the independent variable and has a causal relationship with the dependent variable. In other words, confounding variables can lead to a false interpretation of the relationship between the independent and dependent variables.

Confounding variables can obscure the true effects of the independent variable, making it challenging to attribute changes in the dependent variable solely to the manipulated independent variable.

For example, if a study examines the impact of a new drug on memory and participants’ age is not controlled for, age becomes a confounding variable. This is because age may independently affect memory performance and could lead to the incorrect conclusion that the drug is influencing memory when age is the actual culprit.

4.4 Concluding Remarks for the Chapter

Variables in psychological research are key elements that researchers manipulate, measure, and analyze to gain a better understanding of psychological phenomena and behavior. How you operationalize and measure your variables will impact how you analyse the data and the conclusions you can draw.

Practice

Identify the type of the following variables (NOIR):

  1. The order of finishing for the participants in a race.

  2. The numerical value representing the income level of individuals in a particular household.

  3. Temperature difference between two consecutive days.

  4. The preferred mode of transportation chosen by respondents.

  5. Number of hours a student spends studying for the exam.

  6. Participant gender,

  7. Customer satisfaction levels on a scale from 1 to 5.

  8. What are the IV and DV in the following experiment?:

A study investigates the impact of sleep duration on memory retention in college students. Participants are randomly assigned to either a group with regular sleep patterns (7-8 hours per night) or a group with restricted sleep (4-5 hours per night). Memory performance is assessed through a standardized memory test administered the following day.

  1. Identify some confounding variables for the previous study.

  1. Ordinal

  2. Ratio

  3. Interval

  4. Nominal

  5. Ratio

  6. Nominal

  7. Ordinal

  8. IV = sleep; DV = standardized memory test