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 into controlled and experimental groups.

Key Takeaways

• Only Experiments Show Cause and Effect: A true experiment needs a manipulated independent variable, a measured dependent variable, and random assignment to conditions. No other research method can establish cause and effect this confidently.
• Four Main Types: Laboratory, field, natural, and quasi experiments trade off control against realism. Lab experiments give the most control; natural and quasi experiments study variables, like age or a real-world event, that cannot be manipulated.
• Control Protects Validity: Randomisation and standardised procedures rule out alternative explanations for the results, protecting the study’s internal validity.
• People Behave Differently When Watched: Participants can pick up on demand characteristics and change their behaviour, and experimenters can unintentionally bias results through their own expectations. Blinding helps guard against both.
• Ethics Limits What Can Be Manipulated: Ethical rules, like informed consent and protection from harm, limit what researchers can manipulate directly. Variables such as trauma or an existing illness can only be studied through natural or quasi experiments.
• Not All Findings Replicate: A large project that re-ran 100 psychology studies found only about a third to a half of the results held up (Open Science Collaboration, 2015). Read any single study’s conclusions with some caution.

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 any factors, other than the IV, that could 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 car crash study.

• 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 is a research method in psychology that takes place in a natural, real-world setting. It is similar to a laboratory experiment in that the experimenter manipulates one or more independent variables and measures the effects on the dependent variable.

However, in a field experiment, the participants are unaware they are being studied, and the experimenter has less control over the extraneous variables.

Field experiments are often used to study social phenomena, such as altruism, obedience, and persuasion. They are also used to test the effectiveness of interventions in real-world settings, such as educational programs and public health campaigns.

A key example is Hofling et al.’s (1966) hospital study on obedience.

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

Method: An unknown caller claiming to be a doctor 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 instruction from an authority figure in their normal workplace, showing that 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 the effects of an event that occurs naturally, without the researcher manipulating anything. The experimenter observes changes in the dependent variable as the event unfolds.

These experiments take place in participants’ everyday environment, and the researcher has no control over the independent variable. Natural experiments often study phenomena that would be difficult or unethical to test in a lab, such as natural disasters, policy changes, or social movements.

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.
For instance, researchers might compare academic achievement among students born before and after a policy change that increased education funding.

Here, the independent variable is the timing of the policy change, and the dependent variable is academic achievement. Researchers could not manipulate the timing, but they could observe its effect on achievement.

• 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

Demand Characteristics and Investigator Effects

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. 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.

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, because the researchers’ expectations changed how they handled the animals.

The same investigator effect works on people. When Rosenthal and Jacobson (1968) told teachers that certain randomly chosen pupils were about to “bloom” academically, those pupils later showed significantly greater IQ gains. 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. In a double-blind study, neither the participants nor the researcher running the session knows, which stops experimenter expectations from influencing the results.