Variables and operationalisation

Introduction to Variables and Operationalisation

Imagine you want to prove that drinking coffee makes people "smarter." How would you actually do that in a lab? Would you just give someone a cup of coffee and ask them if they feel clever? Probably not! In Psychology, we need to be much more precise. This chapter is all about identifying the "ingredients" of research—the variables—and defining them so clearly that anyone else in the world could repeat your study exactly. This process is called operationalisation.

Understanding variables is the "skeleton" of your Research Methods knowledge. Once you master this, planning your own studies in Paper 2 becomes much easier!

1. The Independent Variable (IV)

The Independent Variable (IV) is the factor that the researcher manipulates or changes. You can think of this as the "cause" in a cause-and-effect relationship. In a typical experiment, the researcher changes the IV to see if it has any effect on the participants' behavior.

Levels of the IV: To compare results, an IV must have at least two conditions. These are often called "levels."
1. Experimental Group: The group that receives the "treatment" (e.g., the group that drinks the coffee).
2. Control Group: The group that does not receive the treatment, used as a baseline for comparison (e.g., the group that drinks plain water).

Real-World Example from the Core Studies:

In the study by Bandura et al. (aggression), one of the IVs was the type of model the child saw. The "levels" were:
- Level 1: Seeing an aggressive model.
- Level 2: Seeing a non-aggressive model.
- Level 3: Seeing no model at all (control group).

Quick Tip: If you are struggling to find the IV in a scenario, ask yourself: "What did the researcher change between the different groups of people?"

2. The Dependent Variable (DV)

The Dependent Variable (DV) is the factor that the researcher measures. It is the "effect" or the outcome of the study. We call it "dependent" because we expect the results to depend on the changes we made to the IV.

Real-World Example from the Core Studies:

In Milgram (obedience), the researcher wanted to see how far people would go when ordered by an authority figure.
- The DV was the level of obedience, which was measured by the maximum voltage of the shock the participant was willing to give (from \(15V\) to \(450V\)).

Did you know? In the Milgram study described in your syllabus, it is noted that there was no Independent Variable manipulated by Milgram in that specific variation—only the Dependent Variable (obedience) was measured!

Key Takeaway: The \(IV\) is what the researcher starts with (the change), and the \(DV\) is what the researcher ends with (the data/result).

3. Operationalisation: Making it Precise

In everyday life, we use vague words like "happy," "aggressive," or "memory." In Psychology, these are too fuzzy to measure. Operationalisation is the process of defining a variable in terms of the specific procedures or "operations" used to measure or manipulate it.

Think of it like a recipe. If a recipe says "add some flour," you might add too much or too little. If it says "add \(200g\) of plain white flour," you know exactly what to do. Operationalisation is the "\(200g\)" of Psychology.

How to Operationalise the IV:

Instead of saying "the IV is noise," you would say "the IV is listening to heavy metal music at \(70\) decibels vs. sitting in a silent room."

How to Operationalise the DV:

Instead of saying "the DV is helpfulness," you would say "the DV is the number of people who offer to pick up a dropped pen within \(30\) seconds."

Example from Core Studies:

In Andrade (doodling), "memory" was operationalised as the number of names and places correctly recalled from a monitored telephone message.

Why is this important?
- Replicability: It allows other researchers to repeat the study to see if they get the same results.
- Reliability: It ensures that the measurement is consistent.

4. Co-variables in Correlations

When we conduct a correlation, we are not looking for a "cause and effect." Therefore, we don't use the terms IV and DV. Instead, we call them co-variables.

A co-variable is simply one of the two variables being measured to see if there is a relationship between them. Just like IVs and DVs, co-variables must be operationalised.

Example: If you are investigating the relationship between "age" and "reaction time":
- Co-variable 1: Age (measured in years).
- Co-variable 2: Reaction time (measured in milliseconds on a computer task).

Note: For more on the difference between experiments and correlations, you can cross-reference the "Research Methods" chapter on Correlations.

5. Summary Checklist & Common Mistakes

Quick Review:

• Independent Variable (IV): The thing you change/manipulate.
• Dependent Variable (DV): The thing you measure/the data you collect.
• Operationalisation: Making variables specific, measurable, and clear.
• Co-variables: The two variables measured in a correlation (no IV or DV).

Common Mistakes to Avoid:

1. Swapping the IV and DV: Remember, I change the IV. The Data is the DV.

2. Being too vague: On Paper 2, if you are asked to "operationalise a variable," never use one-word answers like "aggression." Always explain how it is measured (e.g., "the number of times a participant punches a wall").

3. Using IV/DV for Correlations: Only use the term "co-variables" when discussing correlations. Using IV/DV implies that one variable is causing the other, which correlations cannot prove!

Key Takeaway for Exam Success: When you are asked to "Identify the IV and DV" in a scenario, always check if you have included the operationalisation in your answer. Instead of just saying "the IV is the light," say "the IV is whether the light is switched on or off." This will help you secure the highest marks!