Source: Psychology 1100 Key Concepts and Terms
Tags: empirical method, functionalism, structuralism, research methods, hypothesis, variables, experimental design, correlational research, descriptive statistics, inferential statistics, internal validity, random sample, Ohio State psychology
Difficulty: Introductory Prerequisites: None. This is foundational material for the entire course.
Psychology as a discipline rests on two pillars: its intellectual roots (the early schools of thought that shaped how we ask questions about the mind) and its research methods (the tools we use to answer those questions rigorously). If you understand how psychologists collect and interpret evidence, every later topic in the course makes more sense. This material tends to appear early in the midterm and again in application questions throughout.
Psychology grew from competing schools (structuralism, functionalism) that disagreed about what to study. Modern psychology settles debates with the empirical method: systematic observation, controlled experiments, and statistical analysis. The key skill here is telling apart correlation from causation, and knowing which research design does what.
Empirical method
Gaining knowledge through the observation of events, the collection of data, and logical reasoning. Think of it as the scientific approach to psychology: you observe, measure, and reason rather than guess.
Structuralism
A school of thought focused on identifying the basic elements, or structures, of mental processes, the building blocks of the human mind. In simple terms, structuralists wanted to break consciousness down into its smallest parts, the way a chemist breaks a compound into elements.
Functionalism
A school emphasising the functions and purposes of the mind and behaviour in the individual's adaptation to the environment. Think of it as asking "what is this mental process for?" rather than "what is it made of?"
Hypothesis
An educated guess that derives logically from a theory; it must be testable. In simple terms, this is a specific, falsifiable prediction you can check with data.
Independent variable (IV)
The variable the experimenter manipulates, the one deliberately changed to see what its effects are. Think of it as the cause you are testing.
Dependent variable (DV)
The outcome variable that may change in response to manipulations of the independent variable. Think of it as the effect you are measuring.
Experimental group
Participants in an experiment who receive the drug or other treatment under study (the group receiving the independent variable). In simple terms, the group that gets the "thing you are testing."
Control group
Participants who are as similar to the experimental group as possible, except they receive no treatment. They serve as the baseline for comparison. Think of it as the "business as usual" group that lets you see whether the treatment made a difference.
Case study
An in-depth look at a single individual. In simple terms, a detailed investigation of one person, useful for rare conditions but difficult to generalise from.
Correlational research
Research that examines the relationship between variables to find out how and whether two variables change together. Think of it as measuring whether two things move in tandem, without proving one causes the other.
Descriptive statistics
Mathematical procedures used to describe and summarise sets of data meaningfully (e.g., means, medians, standard deviations). In simple terms, these numbers tell you what the data look like.
Inferential statistics
Mathematical methods used to indicate whether the data properly support a research hypothesis. Think of it as the statistics that tell you whether your result is likely real or just due to chance.
Internal validity
The degree to which changes in the dependent variable are genuinely due to the manipulation of the independent variable. In simple terms, high internal validity means you can be confident the IV caused the change in the DV, with no confounds muddying the picture.
Random sample
A sample that gives every member of the population an equal chance of being selected. Think of it as drawing names from a hat: everyone has the same odds.
Third variable problem
The circumstance in which an unmeasured variable accounts for the apparent relationship between two other variables. In simple terms, variables A and B look related, but only because hidden variable C is driving both of them.
Structuralism (Wundt, Titchener) tried to catalogue the elements of conscious experience, largely through introspection.
Functionalism (William James) shifted the focus to why we have certain mental processes, how they help us adapt and survive.
Both schools have faded as formal movements, but their core questions persist: psychology still studies both what mental processes are and what they do.
The empirical method is what separates psychology from philosophy: claims must be tested against observable evidence.
Experiments are the gold standard for establishing cause and effect because they allow the researcher to manipulate the IV and measure the DV while controlling other variables.
Correlational research is valuable when experiments are impractical or unethical, but it cannot establish causation.
The IV is what you change; the DV is what you measure.
The experimental group receives the treatment; the control group does not.
Random assignment (distinct from random sampling) helps ensure groups are equivalent at the start.
The third variable problem is the classic trap in correlational research: ice cream sales and drowning rates both rise in summer, but summer heat (the third variable) drives both.
Internal validity asks: can we be sure the IV, and nothing else, produced the change in the DV?
Random sampling helps generalisability; random assignment helps internal validity. These are different things.
Descriptive statistics summarise (mean, median, range).
Inferential statistics test (p-values, significance tests).
Both are tools, not answers. A statistically significant result can still be practically trivial.
Randomised controlled trials in medicine follow exactly this logic: one group gets the drug (experimental), the other gets a placebo (control), and the outcome (DV) is compared. The research methods covered here are the same framework behind vaccine trials, educational interventions, and policy evaluations.
Students often confuse correlation with causation. A correlation tells you two variables move together; it says nothing about which one (if either) causes the other.
Students sometimes mix up random sampling and random assignment. Random sampling is about who gets into the study; random assignment is about who gets which condition.
A case study can generate rich detail, but findings from one person do not reliably generalise to the broader population.
The control group is not "doing nothing." It is providing the baseline that makes the experimental group's results interpretable.
⚠️ Expect questions asking you to identify the IV and DV in a described experiment.
⚠️ Know the difference between correlational and experimental designs, and why only experiments support causal claims.
⚠️ The third variable problem is a favourite exam topic, often presented as a scenario where you must spot the lurking variable.
⚠️ Be ready to distinguish descriptive from inferential statistics and give an example of each.
True or False: Correlational research can establish that one variable causes changes in another.
Fill in the blank: The variable the experimenter manipulates is called the __________ variable.
True or False: A random sample and random assignment are the same thing.
Fill in the blank: __________ statistics tell you whether your results are likely due to chance.
True or False: Structuralism focused on the purposes of mental processes.
(Answers: 1. False; 2. independent; 3. False; 4. Inferential; 5. False, that was functionalism.)
Q: A researcher finds that students who sleep more get higher exam scores. Can the researcher conclude that sleep causes better scores? Why or why not?
A: No. This is correlational research, so a third variable (e.g., better time management) could explain both more sleep and higher scores. Only an experiment with random assignment could support a causal claim.
Q: In an experiment testing whether caffeine improves reaction time, what is the independent variable and what is the dependent variable?
A: The IV is caffeine (manipulated by the researcher). The DV is reaction time (the measured outcome).
Q: What is the purpose of a control group?
A: The control group provides a baseline. Without it, you cannot tell whether changes in the DV are due to the treatment or to something else entirely.
Q: How does internal validity differ from external validity?
A: Internal validity is about whether the IV truly caused the change in the DV (no confounds). External validity is about whether the findings generalise beyond the specific study to other people, settings, or times.
Q: What distinguishes structuralism from functionalism?
A: Structuralism aimed to identify the basic elements of consciousness (the "what"). Functionalism asked what mental processes are for, how they help the individual adapt (the "why").
This material connects directly to the neuroscience unit: brain-imaging studies are a form of empirical research that must still contend with confounds and validity. It also underpins every later unit, because every claim in sensation, perception, consciousness, and stress research rests on the methods covered here.
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