Difficulty: Introductory | Prerequisites: None
Tags: research methods, empirical method, functionalism, structuralism, hypothesis, independent variable, dependent variable, correlational research, internal validity, descriptive statistics, inferential statistics, control group, experimental group, random sample, third variable problem, psychology foundations
This material covers the philosophical roots of psychology (structuralism and functionalism) and the research toolkit that makes psychology a science rather than speculation. You will need this for every other unit in the course, because every study you read about uses these methods and terms. If you skipped ahead and are now confused by phrases like "the IV was manipulated" or "correlation does not imply causation," start here.
Psychology relies on the empirical method: observe, hypothesise, test, measure. Experiments use independent and dependent variables, with control groups to isolate cause and effect. Correlational studies show relationships but cannot prove one thing causes another. Descriptive statistics summarise data; inferential statistics tell you whether results are likely real or due to chance.
Empirical method
Gaining knowledge through systematic observation, data collection, and logical reasoning. Hypotheses are tested via experiments or structured observation, and conclusions rest on measurable evidence rather than intuition.
In simple terms, this means: instead of guessing, you watch, measure, and test.
Functionalism
A school of thought emphasising the functions and purposes of the mind and behaviour in helping individuals adapt to their environment. It asks "what does this mental process do for us?" rather than "what is it made of?"
Think of it as: the mind is a toolbox, and functionalists want to know what each tool is for.
Structuralism
A school of thought that tries to identify the basic elements or structures of mental experience through introspection (trained self-observation of one's own thoughts and sensations).
Think of it as: breaking consciousness into its smallest building blocks, the way a chemist breaks matter into elements.
Hypothesis
A testable, educated prediction derived from theory about the relationship between variables. It guides the design of an experiment.
In simple terms: "If I do X, then Y will happen" stated precisely enough that an experiment can prove it right or wrong.
Independent variable (IV)
The factor the researcher deliberately manipulates in an experiment to observe its effect. For example, in a drug trial, the dosage level is the IV.
Think of it as: the thing the experimenter changes on purpose.
Dependent variable (DV)
The outcome that is measured in an experiment. It may change in response to manipulations of the independent variable (e.g. test scores, reaction times, physiological responses).
Think of it as: the thing that gets measured to see if the IV had an effect.
Control group
The group that receives no treatment or a standard condition, serving as a baseline for comparison with the experimental group. It exists to isolate the effect of the independent variable.
Experimental group
The group that receives the treatment or manipulation being studied (the independent variable). Their results are compared to the control group.
Correlational research
A research design that examines relationships between variables to see how and whether they change together. It measures the strength and direction of an association but does not establish causality.
In simple terms: it tells you two things are related, but not that one causes the other.
Third variable problem (confound)
Occurs when an unmeasured variable influences both the independent and dependent variables, creating a spurious (fake) relationship. Classic example: ice cream sales and drowning rates both rise in summer, but the third variable is temperature.
Internal validity
The degree to which changes in the dependent variable are due to the manipulation of the independent variable rather than extraneous factors. High internal validity means the experiment cleanly isolates cause and effect.
Random sample
A sample in which every member of the population has an equal chance of being selected. This minimises selection bias and makes findings more generalisable.
Descriptive statistics
Mathematical procedures used to summarise data. Includes measures of central tendency (mean, median, mode) and measures of variability (range, standard deviation).
Think of it as: the numbers that describe your data set at a glance.
Inferential statistics
Mathematical methods used to determine whether results likely reflect a real effect or just chance. Techniques include t-tests, ANOVA, and chi-square tests. These allow researchers to draw conclusions about populations from sample data.
Think of it as: the statistics that tell you whether your finding is probably real.
Case study
An in-depth examination of a single individual (or small group) to gain detailed insight into specific psychological phenomena. Rich in qualitative data but limited in generalisability.
Think of it as: zooming all the way in on one person's story to learn something rare or unique.
Psychology became a science by adopting the empirical method: observe behaviour, form hypotheses, test them, revise the theory.
Structuralism (Wundt, Titchener) tried to map consciousness by breaking it into its smallest parts, using introspection as the primary method.
Functionalism (William James) asked what mental processes are for, focusing on how they help us adapt, survive, and learn. This perspective led to applied areas such as educational and industrial psychology.
Experiments are the only method that can establish cause and effect.
The researcher manipulates the independent variable (IV) and measures the dependent variable (DV).
A control group provides the baseline (no treatment); the experimental group receives the treatment.
Good experiments have high internal validity, meaning changes in the DV are confidently attributed to the IV.
Correlational research measures the strength and direction of a relationship between variables (ranging from -1.0 to +1.0).
A positive correlation means both variables increase together.
A negative correlation means one increases as the other decreases.
Correlation does not prove causation, largely because of the third variable problem.
Case studies offer deep, qualitative insight into rare or unusual conditions but are difficult to generalise from.
A random sample gives every member of the target population an equal chance of being selected.
This minimises selection bias and makes findings more representative of the broader population.
Without random sampling, results may only apply to a narrow group.
Descriptive statistics summarise what the data look like:
Measures of central tendency: mean (average), median (middle value), mode (most frequent value).
Measures of variability: range (highest minus lowest), standard deviation (average distance from the mean).
Inferential statistics test whether the results are likely due to the IV or just random chance:
Common tests: t-test (compares two group means), ANOVA (compares three or more group means), chi-square (tests categorical data).
A result is typically called "statistically significant" if the probability of it occurring by chance is less than 5% (p < 0.05).
Correlational research is how public health officials first linked smoking to lung cancer before controlled experiments were ethically possible. The experimental method, with its control and experimental groups, is the backbone of every clinical drug trial.
Students often think "correlation" means two variables are unrelated. It does not. A correlation of 0 means no linear relationship, but a strong correlation (positive or negative) absolutely means the variables are related; it just does not prove that one causes the other.
Students often confuse the independent variable with the dependent variable. Remember: the IV is what the researcher changes; the DV is what the researcher measures.
Students sometimes believe a case study can prove a theory. It cannot. Case studies generate hypotheses and rich detail, but their findings do not generalise reliably to broader populations.
Students often assume that a statistically significant result means the effect is large or important. "Significant" in statistics means "unlikely to be due to chance," not "meaningful in everyday life."
⚠️ "Correlation does not imply causation" is one of the most commonly tested principles in introductory psychology. Be ready to identify third variables in examples.
⚠️ Know the difference between descriptive and inferential statistics and be able to pick the right one from a scenario.
⚠️ Be able to identify the IV and DV in any described experiment.
⚠️ Understand why random sampling matters for generalisability and why control groups matter for internal validity.
True or false: A correlation of -0.85 indicates a weak relationship. (False. It is a strong negative correlation.)
Fill in the blank: The variable the experimenter manipulates is called the ______ variable. (Independent)
True or false: A case study can establish causation. (False.)
Fill in the blank: The group that does not receive the treatment is called the ______ group. (Control)
True or false: Inferential statistics summarise data. (False. Descriptive statistics summarise data; inferential statistics test hypotheses.)
Q: A researcher finds that students who sleep more tend to have higher GPAs. Can the researcher conclude that sleep causes better grades? Why or why not?
A: No. This is a correlational finding. A third variable (for example, better time management) could explain both increased sleep and higher GPA. Only an experiment with random assignment and a manipulated IV could establish causation.
Q: In an experiment testing whether caffeine improves reaction time, identify the IV and DV.
A: The IV is caffeine (the factor being manipulated, e.g. caffeine vs. no caffeine). The DV is reaction time (the outcome being measured).
Q: What is the purpose of a control group in an experiment?
A: The control group serves as a baseline for comparison. Without it, you cannot tell whether changes in the DV are due to the IV or to other factors.
Q: A researcher surveys only university students and tries to generalise the results to all adults. What sampling problem does this illustrate?
A: The sample is not random or representative of the general adult population. This limits the generalisability (external validity) of the findings.
Q: Distinguish between structuralism and functionalism.
A: Structuralism aimed to break conscious experience into its simplest elements through introspection. Functionalism focused on the purposes and adaptive functions of mental processes, asking what they do for us rather than what they are made of.
This material connects directly to the neuroscience unit, where experimental methods are used to study brain function. It also underpins the sensation and perception unit, where researchers use signal detection theory and psychophysics experiments. Understanding variables and research design is essential for evaluating every study you encounter in the rest of the course.
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