Science for Everybody

Contents

• Introduction
• The Experimental Method
• Aims
• Hypotheses
• Independent and Dependent Variables
• Levels of the IV and Experimental Conditions
• Operationalisation of Variables
• Extraneous Variables and Control
• Types of Experiment
• Writing a Strong Experimental Hypothesis
• Evaluation
• Strengths
• Weaknesses
• Summary
• Quick Questions
• Key Terms

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Introduction

The experimental method investigates cause and effect by examining whether changes in one variable are associated with changes in another. Psychologists use carefully stated aims, hypotheses and operationalised variables so that an investigation is clear, testable and replicable.

A strong experiment separates the independent variable from the dependent variable, controls other relevant factors and compares different levels or conditions. The AQA specification also distinguishes laboratory and field experiments from natural and quasi-experiments.

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The Experimental Method

An experiment is designed to test whether variation in an independent variable (IV) produces a change in a dependent variable (DV). In a well-controlled study, the researcher aims to keep other relevant variables constant so that the IV is the most plausible explanation for any difference in the DV.

For example, a psychologist might investigate whether an energy drink changes how talkative people are. The researcher could compare participants who drink an energy drink with participants who drink water, then measure how many words each person speaks during the next five minutes.

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Aims

An aim is a broad statement of the purpose of an investigation. It identifies what the researcher wants to find out but does not make a specific prediction about the outcome.

A suitable aim for the energy-drink example would be: To investigate whether drinking an energy drink affects how talkative people are.

The key difference between an aim and a hypothesis is precision. The aim describes the purpose of the study, whereas the hypothesis states the predicted difference or relationship in a form that can be tested.

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Hypotheses

A hypothesis is a clear, precise and testable prediction about the expected outcome of a study. A good experimental hypothesis includes the conditions or levels of the IV and a measurable DV.

A directional hypothesis predicts which condition will produce the higher, lower, faster or slower score. For example: Participants who drink 300 ml of an energy drink will speak more words in the next five minutes than participants who drink 300 ml of water.

A non-directional hypothesis predicts that there will be a difference but does not predict which condition will score higher. For example: There will be a difference in the number of words spoken in the next five minutes between participants who drink 300 ml of an energy drink and participants who drink 300 ml of water.

Psychologists usually choose a directional hypothesis when previous research or a strong theory justifies a specific prediction. A non-directional hypothesis is more suitable when previous evidence is limited, mixed or contradictory.

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Independent and Dependent Variables

The independent variable is the factor that the researcher changes, or the basis on which conditions are compared. The dependent variable is the outcome that the researcher records or measures.

In the energy-drink example, the IV is the type of drink: 300 ml of energy drink or 300 ml of water. The DV is the number of words spoken during the next five minutes.

When identifying variables in a scenario, avoid giving vague labels such as ‘drink’ or ‘talkativeness’. State the exact levels of the IV and the exact measurement of the DV whenever the information is available.

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Levels of the IV and Experimental Conditions

The levels of the IV are the different values or conditions that are compared. An experiment normally needs at least two levels so that the researcher has a meaningful comparison.

One level may be an experimental condition, where participants receive the treatment of interest, while another may be a control condition, which provides a baseline. In the drink example, energy drink could be the experimental condition and water the control condition.

The important point is that the conditions should differ in the IV while other relevant aspects of the procedure remain as similar as possible.

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Operationalisation of Variables

Operationalisation means defining variables in concrete, observable and measurable terms. It turns an abstract concept such as talkativeness, anxiety or memory into something that can be manipulated or recorded consistently.

For example, ‘energy drink’ could be operationalised as 300 ml of a specified caffeinated drink, while ‘talkativeness’ could be operationalised as the number of words spoken during the next five minutes.

Operationalisation improves clarity and replication because another researcher can see exactly what was done. However, a very narrow measure may not capture the full psychological concept. If concentration is defined only as the number of correct answers on one short task, the measure may have limited construct validity.

A useful reflection is: if a study claims that ‘music improves concentration’, what exact behaviour would count as concentration, and how would the researcher measure it without changing the meaning of the concept?

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Extraneous Variables and Control

An extraneous variable is any factor other than the IV that could influence the DV. In an energy-drink study, sleep, previous caffeine intake, noise, time of day and researcher behaviour could all affect how much a participant talks.

Researchers therefore use controls such as standardised instructions, consistent timings and the same testing environment. When an independent groups design is used, random allocation can help spread participant differences across conditions. Good control increases internal validity because alternative explanations become less plausible.

However, control should be proportionate. Excessive control can make a task unlike everyday life, which may reduce ecological validity.

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Types of Experiment

A laboratory experiment takes place in a highly controlled setting and the researcher manipulates the IV. This usually gives strong control over extraneous variables and makes procedures easier to replicate, although the situation can feel artificial.

A field experiment takes place in a natural setting, such as a school, workplace or public space, but the researcher still manipulates the IV. Behaviour may be more natural, although controlling extraneous variables is harder.

A natural experiment uses an IV that changes naturally rather than being created by the researcher. For example, a researcher might compare stress before and after an unexpected transport disruption. Natural experiments can study variables that would be impractical or unethical to manipulate, but the researcher has less control over alternative explanations.

A quasi-experiment compares groups that already differ on a pre-existing characteristic, such as age group or a diagnosed condition. The researcher does not randomly allocate participants to these categories, so pre-existing group differences can make causal conclusions less certain.

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Writing a Strong Experimental Hypothesis

A high-quality hypothesis should be a statement rather than a question. It should identify both levels of the IV, state a measurable DV and use wording that matches whether the prediction is directional or non-directional.

A useful sequence is: identify the IV and its levels; identify the DV; operationalise both variables; decide whether previous evidence justifies a direction; then write one clear sentence that can be tested.

For example: Participants who study a 20-word list while listening to instrumental music at 50 dB will recall more words after two minutes than participants who study the same list in silence. This is directional and both variables are operationalised.

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Evaluation

The value of the experimental method depends on how well the researcher controls alternative explanations while still measuring behaviour in a valid and meaningful way.

Strengths

Stronger Cause-and-Effect Conclusions

A major strength of experiments is that they can support causal conclusions, especially when the researcher manipulates the IV and controls relevant extraneous variables. If the experimental and control conditions are treated in the same way apart from the IV, a difference in the DV is more plausibly caused by the IV. Therefore, well-designed laboratory and field experiments can have high internal validity and can move psychology beyond simply describing associations.

Replicability and Scientific Precision

Experiments encourage researchers to operationalise variables and use standardised procedures. Clear instructions, fixed timings and precise measures allow other psychologists to repeat the study and check whether the same pattern occurs. Replication strengthens the scientific credibility of a finding because results that recur under similar conditions are less likely to depend on chance or an unclear procedure.

Flexible Balance Between Control and Realism

The experimental method can be adapted to different settings. Laboratory experiments prioritise control, whereas field experiments can retain manipulation of the IV while observing behaviour in a more natural context. This flexibility allows researchers to choose a method that best matches the research question and can increase the practical usefulness of experimental evidence.

Weaknesses

Artificiality and Demand Characteristics

High control can create an artificial situation, particularly in laboratory experiments. Participants may work out the aim of the study or behave differently because they know they are being studied. As a result, the DV may reflect demand characteristics rather than normal behaviour. This can reduce ecological validity and makes it harder to assume that the same effect will occur in everyday settings.

Ethical and Practical Limits on Manipulation

Some psychologically important variables cannot be manipulated ethically or practically. Researchers could not deliberately expose participants to severe trauma, long-term deprivation or dangerous levels of stress simply to create an IV. Natural and quasi-experiments can investigate such questions, but they offer less control over the IV and participant allocation. Therefore, experimental evidence varies in how confidently it can establish cause and effect.

Operationalisation Can Reduce Construct Validity

Operationalisation makes variables measurable, but a simple measure may oversimplify a complex construct. For example, counting words spoken may capture one aspect of talkativeness while missing conversational quality, willingness to initiate interaction or context. If the operational definition does not represent the intended psychological concept well, the study can be reliable yet still have low construct validity.

Sample, Cultural and Temporal Limits

Experiments are often convenient to run with accessible samples, such as students from one school, university or cultural setting. If the sample is narrow, the findings may not generalise to different ages, cultures or social groups. In addition, behaviour can change as technology, social norms and everyday environments change, so older experimental findings may have reduced temporal validity. These problems are not inevitable features of experiments, but they must be considered when judging generalisability.

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Quick Questions

What is the difference between an aim and a hypothesis?

An aim states the general purpose of the investigation. A hypothesis is a precise, testable prediction about the expected difference or relationship between variables.

What makes a hypothesis directional?

It predicts the direction of the expected difference or relationship, such as one condition producing a higher score than another.

What are the IV and DV in an experiment?

The IV is the variable manipulated or used to define the conditions. The DV is the outcome measured to assess the effect of the IV.

What does operationalisation mean?

It means defining a variable in terms of the exact way it will be manipulated or measured.

Name the four types of experiment specified by AQA.

Laboratory experiment, field experiment, natural experiment and quasi-experiment.

Key Terms

Experimental method – A research method that examines cause and effect by changing or comparing an independent variable and measuring a dependent variable.

Aim – A general statement of what the researcher intends to investigate.

Hypothesis – A precise, testable statement predicting a relationship or difference between variables.

Directional hypothesis – A hypothesis that predicts the direction of a difference or relationship.

Non-directional hypothesis – A hypothesis that predicts a difference or relationship but does not state its direction.

Independent variable (IV) – The variable that is manipulated by the researcher or used to define the conditions being compared.

Dependent variable (DV) – The variable measured to assess the effect of the IV.

Level of the IV – One specific value or condition of the independent variable, such as energy drink or water.

Experimental condition – The condition in which participants receive the treatment or level of the IV of primary interest.

Control condition – A comparison condition that does not receive the experimental treatment, or receives a baseline condition.

Operationalisation – Defining a variable in terms of the exact procedure used to manipulate or measure it.

Extraneous variable – Any variable other than the IV that could influence the DV and should be controlled where possible.

Laboratory experiment – An experiment conducted in a controlled setting where the researcher manipulates the IV.

Field experiment – An experiment conducted in a natural setting where the researcher manipulates the IV.

Natural experiment – An experiment in which the IV changes naturally rather than being directly manipulated by the researcher.

Quasi-experiment – An experiment in which the IV is based on a pre-existing difference between participants, such as age or diagnosis.