Experimental Method In Psychology

The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants, assigning them to groups purely by chance, into controlled and experimental groups.

What is an Experiment?

An experiment is an investigation in which a hypothesis is scientifically tested. An independent variable (the cause) is manipulated in an experiment, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

A study only counts as a true experiment if it has three key ingredients:

• Manipulation of the IV: the researcher creates at least two conditions to compare, so a difference can be demonstrated.
• Measurement of the DV: the outcome is given a clear, measurable definition.
• Random assignment: participants are randomly allocated to conditions, so the groups differ only in the way the researcher intended.

If any of these three ingredients is missing, the study is not a true experiment. For example, if the IV occurs naturally rather than being manipulated, it becomes a natural or quasi experiment instead.

Extraneous variables are factors other than the IV. They could still affect the results, such as noise or a participant’s mood. Researchers control these so they do not become confounding variables that offer an alternative explanation for the findings.

Experiments aim to be objective, so the researcher’s views and opinions should not affect a study’s results. Objectivity makes the data more valid and less biased.

Types of Experiments

There are four types of experiments you need to know:

1. Lab Experiment

A laboratory experiment is conducted under highly controlled conditions (not necessarily a laboratory) where accurate measurements are possible.

The researcher uses a standardized procedure to determine where the experiment will take place, at what time, with which participants, and in what circumstances.

Participants are randomly allocated to each independent variable group.

Examples are Milgram’s experiment on obedience and Loftus and Palmer’s (1974) car crash study. Loftus and Palmer changed one word in a question, “smashed” versus “hit,” to show how a leading question distorts memory of a car crash’s speed and details.

• Strength: A laboratory experiment is easier to replicate because it uses a standardized procedure.
• Strength: Precise control of extraneous and independent variables lets researchers establish a cause-and-effect relationship.
• Limitation: An artificial setting can produce unnatural behavior, giving the study low ecological validity and findings that may not generalize to real life.
• Limitation: Demand characteristics or experimenter effects may bias the results and become confounding variables.

2. Field Experiment

A field experiment takes place in the participant’s own everyday setting, not a lab. The experimenter still manipulates the independent variable and measures its effect on the dependent variable, just as in a laboratory study.

Control is harder to achieve outside the lab. Participants are often unaware they are being studied, and the experimenter has less control over extraneous variables.

Field experiments are common in social psychology. Researchers have used them to study altruism, obedience, and persuasion, and to test real-world interventions such as educational programs and public health campaigns.

Hofling et al. (1966) is the classic example: an obedience study set in a real hospital.

Aim: To see whether nurses would obey an order from an unfamiliar “doctor” that broke hospital rules.

Method: An unknown caller posed as a doctor. He telephoned nurses on duty and told them to give a patient an overdose of an unauthorised drug.

Results: 21 of the 22 nurses obeyed and started to administer the overdose before being stopped.

Conclusion: Real nurses obeyed an illegitimate order from an authority figure in their normal workplace. Obedience to authority happens outside the laboratory too.

• Strength: A field experiment’s natural setting gives it higher ecological validity than a lab experiment, so behavior is more likely to reflect real life.
• Strength: Demand characteristics are less likely to affect the results, as participants may not know they are being studied. This occurs when the study is covert.
• Limitation: Researchers have less control over extraneous variables that might bias the results, making the study harder to replicate exactly.

3. Natural Experiment

A natural experiment studies an event that occurs naturally, without the researcher manipulating anything. The event does the manipulating, not the experimenter.

The experimenter simply observes changes in the dependent variable as it unfolds, in the participant’s own everyday environment. The researcher has no control over the independent variable itself.

Natural experiments often study phenomena that would be too rare, or too unethical, to stage in a lab: natural disasters, policy changes, social movements. No ethics committee would approve manipulating them directly.

For example, Hodges and Tizard’s attachment research (1989) compared the long-term development of adopted, fostered, and reunited children. A control group had spent their whole lives with their biological families.

Williams (1986) took the same logic to a whole town.

A Canadian community nicknamed “Notel” had just gained television for the first time, giving researchers a rare before-and-after comparison. Nobody switched on Notel’s first set for the study; the event simply happened.

Verbal and physical aggression among 6-11-year-olds rose over the two years after TV arrived, while it stayed flat in nearby towns that already had television.

Charlton and Hannan (2005) later studied St. Helena. This isolated community only gained television when CNN arrived there in 1995. They followed nearly 800 school-age children. Their findings challenged the simple claim that television alone drives antisocial behaviour.

• Strength: Because natural experiments study real-world events, they have very high ecological validity and reflect real life closely.
• Strength: Demand characteristics are less likely to affect the results, as participants may not know they are being studied.
• Strength: It can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g., researching stress.
• Limitation: They may be more expensive and time-consuming than lab experiments.
• Limitation: There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

4. Quasi Experiment

A quasi experiment studies an independent variable that already exists, such as age, gender, or a diagnosed condition. These characteristics cannot be manipulated or randomly assigned, so participants arrive already sorted into their groups.

Because random allocation is not possible, an unknown participant variable could explain the results instead of the IV. This lowers the validity of any cause-and-effect conclusion.

For example, comparing memory scores between younger and older adults is a quasi experiment. Age is the IV, but the researcher cannot assign people to be young or old.

• Strength: It allows psychologists to study variables, like gender or a mental health diagnosis, that could never ethically or practically be manipulated.
• Limitation: Without random allocation, participant variables between the groups may explain the results instead of the IV, weakening any cause-and-effect claim.

The table below compares all four types of experiment.

Type	Who controls the IV?	Setting	Random allocation?	Main strength	Main limitation
Laboratory	Researcher	Controlled	Yes	High control and reliability	Low ecological validity
Field	Researcher	Natural	Sometimes	Higher ecological validity	Less control over extraneous variables
Natural	Nature or events	Natural	No	Studies rare or unethical-to-manipulate events	Hard to replicate; no random allocation
Quasi	Pre-existing characteristic	Any	No	Studies variables that cannot be manipulated	Reduced validity; no random allocation

Key Terminology

• Ecological validity: How far a study’s findings apply to real-life settings beyond its own artificial conditions. Highly controlled lab studies often score low here, even when internal validity is high.
• Experimenter effects: The way a researcher’s own expectations can unintentionally shape a study’s outcome. Small differences in tone or manner toward participants can matter, with no deliberate intent to bias results.
• Demand characteristics: Cues in a study, like the setting or the experimenter’s manner, that let a participant guess what is being tested. Participants may then change their behaviour to fit, or deliberately defy, that guess.
• Independent variable (IV): The variable a researcher deliberately manipulates, on the assumption that it directly affects the outcome being measured.
• Dependent variable (DV): The variable a researcher measures, to see whether its value was affected by the independent variable.
• Extraneous variables (EV): Any variable, other than the IV, that could affect the results, whether a trait of the participants (age, intelligence) or of the situation (noise, lighting).
• Confounding variables: An extraneous variable that has not been controlled and that varies systematically with the IV, not just randomly. Because it moves in step with the IV, its effect on the DV is easily mistaken for the IV’s own effect.
• Random allocation: Assigning participants to conditions purely by chance, so the only systematic difference between groups is the one the researcher intends. This spreads other participant differences evenly, rather than letting them bias one group.
• Order effects: Changes in a participant’s performance from repeating the same or similar test more than once. A practice effect is an improvement from growing familiar with the task; a fatigue effect is a decline from boredom or tiredness. This is why repeated measures designs use counterbalancing: half the participants complete the conditions in one order, half in the other, so practice and fatigue cancel out for everyone.

Demand Characteristics and Investigator Effects

Demand Characteristics

Participants are not passive in an experiment. Orne (1962) showed that people actively pick up on cues in the situation, such as the instructions or the experimenter’s manner. They read the room. From these cues, they work out what the study is testing.

Participants who spot these demand characteristics often try to be a “good participant” and behave in the way they think the researcher wants. This can create the Hawthorne effect, where people perform differently simply because they know they are being watched.

That single insight still shapes how studies are designed today.

Even a classic study can be read this way. Critics have argued that some participants in Milgram’s (1963) obedience experiments worked out the shocks were not real. They kept going only because that seemed to be what the study wanted from them (Orne & Holland, 1968).

Investigator Effects

Experimenters can bias results too, without meaning to. Rosenthal and Fode (1963) told researchers that their laboratory rats had been bred to be either “maze-bright” or “maze-dull”.

In reality, the rats had been randomly allocated. The bright rats still learned the maze faster, simply because the researchers’ expectations changed how they handled the animals.

The same investigator effect works on people, not just rats.

Rosenthal and Jacobson (1968) told teachers that certain randomly chosen pupils were about to “bloom” academically. Those pupils later showed significantly greater IQ gains than their classmates.

The only real difference was the expectation the teachers had been given, a self-fulfilling prophecy that shaped how they treated the children.

Researchers guard against these biases with blinding. In a single-blind study, participants do not know which condition they are in.

A double-blind study goes further: neither the participants nor the researcher running the session knows, which stops experimenter expectations from influencing the results.