RESE201 Research Methods in Psychology Exam Notes and Study Guide

Research Methods in Psychology is the backbone of every serious psychology qualification because it teaches how psychological knowledge is created, tested, and trusted. For RESE201, students are usually expected to move beyond memorising definitions and show that they understand design logic, measurement, ethics, data handling, and how to evaluate evidence critically. These notes bring together the core material in a structured study format suited to Varsity College core psychology modules and comparable South African university research-methods courses.

1. Foundations of Psychological Research

Psychology is both a science and a human-centred discipline. That dual identity is what makes research methods so important: psychologists study thoughts, emotions, and behaviour, but they must do so using systematic procedures that allow findings to be checked, challenged, and improved. RESE201 typically begins with the basic question of what makes psychological research different from opinion, anecdote, or common sense. The answer is not simply that psychology uses “numbers” or “experiments”; rather, it relies on structured inquiry, transparent methods, and evidence that can be evaluated by others.

What counts as psychological research?

Psychological research is the organised investigation of behaviour and mental processes using accepted scientific methods. It may ask questions such as:

Why do some students perform better under timed conditions?
Does sleep deprivation affect memory?
How does stress relate to coping style?
Are attitudes toward mental health shaped by culture or social media exposure?

What makes these questions “research” is not only the topic but the method used to answer them. A good research question is:

Clear — the variables are understandable.
Researchable — it can be investigated ethically and practically.
Specific — it is not too broad.
Testable — it can be answered through observation, measurement, or analysis.

For example, “Does lack of sleep reduce concentration among first-year psychology students at Varsity College?” is more useful than “Why are students tired?” because the first question suggests measurable variables and a defined group.

The scientific attitude in psychology

Psychological science depends on a particular way of thinking. This includes:

Skepticism: do not accept claims without evidence.
Objectivity: minimise personal bias.
Empiricism: base conclusions on observation and data.
Systematic procedure: follow planned steps rather than guesswork.
Replicability: allow others to repeat the study and check the findings.

These principles matter because human beings are naturally prone to error in judgement. People remember striking events more than ordinary ones, see patterns where none exist, and often interpret behaviour through stereotypes or assumptions. A research method is a safeguard against those errors.

A useful example is the belief that “violent games make all young people aggressive.” Such a statement sounds plausible, but without controlled research it remains an assumption. Research methods force the investigator to ask: Which young people? What counts as aggressive? Over what period? Compared to what group? Without those details, a broad claim becomes too vague to evaluate.

Theory, hypothesis, and variables

A theory is an organised explanation of how or why something happens. In psychology, theories provide conceptual frameworks, such as social learning theory, cognitive theory, or attachment theory. A theory is broader than a single study; it connects many studies into a coherent explanation.

A hypothesis is a specific, testable prediction derived from a theory or observation. For example:

Students who sleep fewer than six hours will score lower on a working-memory task than students who sleep seven to eight hours.

This hypothesis identifies:

the independent variable: sleep duration
the dependent variable: working-memory performance
a prediction about direction: less sleep means lower performance

A variable is any characteristic that can vary. Variables may be:

Quantitative: measurable numerically, such as test scores, age, or reaction time.
Qualitative: descriptive categories, such as gender identity, study programme, or coping strategy.

Understanding variables is essential because research depends on how variables are defined and measured. The same concept can be measured in different ways. For example, “stress” could mean self-reported tension, cortisol levels, or frequency of anxious thoughts. The measurement chosen affects the conclusions.

Types of variables

The major variable types often tested in RESE201 include:

Variable type	Meaning	Example
Independent variable (IV)	The variable manipulated or compared	Sleep duration
Dependent variable (DV)	The outcome measured	Memory score
Control variable	Kept constant to reduce confounding	Same test conditions
Confounding variable	A hidden factor that influences results	Caffeine intake
Extraneous variable	Any unwanted variable that may affect outcomes	Noise in the testing room
Moderator	A variable that changes the strength of a relationship	Gender may affect stress-memory link
Mediator	Explains how or why one variable affects another	Anxiety may mediate sleep and performance

A confounding variable is especially important because it threatens the validity of conclusions. If a study finds that students who exercise more also report lower stress, exercise may not be the only reason. Perhaps those students also sleep better or have stronger social support. Good research methods try to isolate the effect of the IV.

The logic of evidence

Research methods are built on logic. A study does not prove a theory in an absolute sense; it supports, weakens, or refines it. Psychological findings are probabilistic, not absolute. This means that a well-designed study may show that one group tends to score higher than another, but there will always be exceptions. Research is about patterns, not certainty.

This is why psychology values falsifiability. A claim is useful only if it can be disproved by evidence. If a theory can explain every possible result after the fact, it is not scientifically strong. For instance, if a theory says “people act this way because they are motivated by hidden forces,” and any behaviour can be explained as evidence of those forces, the theory becomes difficult to test. Good hypotheses must be precise enough to risk being wrong.

Why this foundation matters for exams

Examiners often ask students to:

define key terms accurately,
distinguish theory from hypothesis,
identify variables in scenarios,
explain why research design matters,
evaluate whether a claim is scientific.

A common mistake is memorising terms without understanding relationships. For exam success, it helps to think in a chain:
theory → hypothesis → variables → design → data → conclusion.

If any part of that chain is weak, the research is weakened. A vague theory creates a vague hypothesis. A vague hypothesis creates poor measurement. Poor measurement creates weak data. Weak data leads to weak conclusions.

2. Research Design, Methods, and Sampling

Once a research question has been defined, the next task is choosing a design that can answer it properly. In psychology, design refers to the structure of the study: who is studied, what is measured, how variables are handled, and how data are compared. RESE201 usually requires students to understand the major design families and the practical consequences of choosing one over another.

Experimental research

An experiment is the strongest design for testing cause-and-effect relationships because the researcher manipulates the independent variable and observes its effect on the dependent variable while controlling other factors.

A basic experiment has:

at least one independent variable
at least one dependent variable
an experimental group and often a control group
random assignment of participants to conditions

For example, suppose a researcher wants to test whether background music affects reading comprehension. One group reads in silence, while another group reads with soft instrumental music. If the groups are randomly assigned and the reading materials are equivalent, differences in comprehension can more confidently be attributed to the music condition.

Strengths of experiments

Best for identifying causality
High control over variables
Clear comparisons
Replicable if procedures are standardised

Weaknesses of experiments

Can be artificial
May lack ecological validity
Ethical limits may prevent certain manipulations
Participant behaviour may change because they know they are being studied

Variables and control in experiments

In experimental design, the main challenge is preventing alternative explanations. Researchers do this using:

random assignment: distributes participant differences evenly
standardisation: keeps procedures identical
control groups: provide a baseline for comparison
blinding: reduces expectancy effects
counterbalancing: controls order effects in repeated-measures studies

There are two main experimental structures often emphasised in psychology:

Between-subjects design

Different participants are in different conditions.

Example: Group A studies with music; Group B studies without music.

Advantages:

no practice effects
each participant experiences one condition only

Disadvantages:

individual differences can influence results
needs larger sample sizes

Within-subjects design

The same participants experience all conditions.

Example: Each participant completes a memory task with and without background music.

Advantages:

fewer participants needed
each person serves as their own control

Disadvantages:

order effects
fatigue or learning effects
requires counterbalancing

Non-experimental methods

Not every psychology question can be answered through experimentation. Sometimes the researcher cannot manipulate the variable ethically or practically. In these cases, non-experimental methods are used.

Surveys

Surveys collect self-reported information through questionnaires or interviews. They are useful for measuring attitudes, beliefs, experiences, and behaviours in large groups.

Strengths:

efficient for many participants
useful for descriptive data
can measure private experiences

Weaknesses:

response bias
social desirability bias
poor question wording can distort results
people may not accurately remember their behaviour

Correlational research

Correlational studies examine the relationship between two or more variables without manipulation. The result is usually expressed as a correlation coefficient ranging from -1 to +1.

Interpretation:

positive correlation: variables increase together
negative correlation: one variable increases as the other decreases
zero correlation: no systematic relationship

A key principle is that correlation does not imply causation. If study time and grades are correlated, it does not automatically mean more studying causes better grades. Better grades may also motivate more studying, or a third variable such as motivation may influence both.

Observational research

Researchers observe behaviour in natural or controlled settings. Observation may be:

naturalistic: in the real-world environment
structured: using predefined categories in a controlled setting
participant observation: the researcher is involved in the group
non-participant observation: the researcher remains detached

Observation is useful when behaviour is best understood in context, such as playground interaction, classroom participation, or family communication patterns.

Case studies

A case study is an in-depth investigation of one person, group, event, or situation. It is especially useful for rare or unusual cases.

Strengths:

rich detail
useful for unusual phenomena
can generate hypotheses

Weaknesses:

limited generalisability
may be subjective
difficult to replicate

Sampling methods

A research finding is only as useful as the sample on which it is based. Sampling determines who participates in the study and whether the results can reasonably be generalised to a wider population.

Population and sample

Population: the entire group of interest
Sample: the smaller group selected from the population

For instance, if a study aims to understand stress in first-year psychology students at Varsity College, the population is all first-year psychology students in that context, while the sample is the subset who actually participate.

Probability sampling

Probability sampling gives each member of the population a known chance of being selected.

Common types include:

Simple random sampling: every member has equal chance
Stratified sampling: population divided into subgroups, then sampled proportionally
Systematic sampling: every nth person is selected
Cluster sampling: whole groups are selected rather than individuals

Advantages:

more representative
supports generalisation

Disadvantages:

often difficult and costly
requires a sampling frame

Non-probability sampling

Participants are selected without known random probabilities.

Common types:

Convenience sampling
Volunteer sampling
Purposive sampling
Snowball sampling

These methods are common in student research because they are practical, but they reduce representativeness. A convenience sample of classmates may be easy to collect, but it may not reflect the broader student population.

Sampling error and representativeness

A sample may differ from the population by chance or because of selection bias. This difference is known as sampling error. Researchers try to reduce it by increasing sample size, improving sampling methods, and being transparent about limitations.

A good exam answer should distinguish:

random selection: how participants are chosen from the population
random assignment: how participants are placed into conditions after selection

These are not the same. Random selection supports generalisability. Random assignment supports internal validity.

Why design choices matter

Design is not an abstract academic issue; it shapes what kind of knowledge can be claimed. An experiment can suggest causation but may sacrifice realism. A survey can capture broad attitudes but may not explain why they occur. A case study can provide depth but not broad generalisation. Strong psychology students learn to match the method to the question rather than forcing every question into one method.

3. Measurement, Reliability, Validity, and Ethics

Psychological research depends on measurement. If the concept is not measured properly, the resulting conclusions may be weak even if the design is otherwise good. RESE201 places strong emphasis on the quality of measurement and on the ethical responsibilities of researchers.

Operational definitions

An operational definition explains exactly how a concept will be measured or manipulated in a study. Many psychological concepts are abstract, such as intelligence, anxiety, resilience, or aggression. Operational definitions turn these ideas into observable indicators.

For example:

Anxiety may be operationalised as a score on a standard anxiety questionnaire.
Aggression may be operationalised as number of hostile responses on a behavioural checklist.
Academic performance may be operationalised as final exam percentage.

Operational definitions are important because they make research concrete and replicable. However, no operational definition captures the whole complexity of a concept. A stress scale may measure emotional tension well, but not physiological stress responses. This is why method choice must be linked to theory.

Levels of measurement

Understanding data types is central to statistical interpretation.

Level of measurement	Description	Example
Nominal	Categories with no order	Gender identity, province, diagnosis
Ordinal	Ranked categories	Class position, satisfaction ratings
Interval	Equal intervals, no true zero	Some attitude scales, IQ-style scores
Ratio	Equal intervals with a true zero	Age, reaction time, number of errors

The level of measurement affects what statistical tests can be used and how results should be interpreted. For example, a rating of 8 out of 10 is not automatically “twice as much” as 4 out of 10 unless the scale has ratio properties, which many psychological scales do not. Students often lose marks by treating all numerical data as if they are equivalent.

Reliability

Reliability refers to the consistency of a measure. A reliable measure gives similar results under similar conditions.

Types of reliability include:

Test-retest reliability: stability over time
Inter-rater reliability: agreement between observers
Internal consistency: items on a scale measure the same construct
Parallel-forms reliability: similar results across equivalent versions

A measure can be reliable without being valid. For example, a bathroom scale that always shows 3 kg too much is reliable because it is consistent, but invalid because it is inaccurate. This distinction is frequently examined because it is conceptually important.

Validity

Validity refers to whether a measure or study actually measures what it intends to measure.

Main forms include:

Face validity: appears appropriate on the surface
Content validity: covers the full domain of the construct
Criterion validity: relates to an external standard
Construct validity: truly measures the theoretical concept
Internal validity: conclusions about cause are justified
External validity: findings generalise beyond the study

A study can have high internal validity but low external validity. For example, a tightly controlled laboratory study may show that sleep deprivation affects attention, but the participants may be a small group of university students under artificial conditions. The result may still be true, but the generalisation is narrower.

Threats to validity

Common threats include:

poor sampling
demand characteristics
experimenter bias
confounding variables
measurement error
testing effects
history effects
maturation
attrition

Understanding these threats is crucial because they show how results can become distorted. If participants guess the purpose of a study and adjust their behaviour, the data may reflect expectation rather than genuine response. Likewise, if some participants drop out before the end, the final sample may be biased.

Ethics in psychological research

Psychological research deals with human beings, personal experiences, and sometimes vulnerable groups. Ethics is therefore not an optional extra; it is a core requirement.

Main ethical principles include:

Informed consent

Participants should know what the study involves, what will happen, and what their rights are before agreeing to take part.

Voluntary participation

No one should be forced or manipulated into participating.

Right to withdraw

Participants should be able to leave the study at any time without penalty.

Protection from harm

Researchers must avoid physical, psychological, social, or legal harm.

Confidentiality and anonymity

Personal information should be protected. Anonymity means identities are unknown; confidentiality means identities are known to the researcher but not disclosed.

Deception

Deception may sometimes be used if full disclosure would ruin the study, but it must be justified, minimised, and followed by debriefing.

Debriefing

Participants should be informed about the true purpose of the study after participation, especially if any deception was used.

Ethics in context

Ethics is deeply linked to local context. In South African universities, sensitivity to cultural diversity, language differences, inequality, and historical power relations is important. A questionnaire designed in one cultural setting may not be appropriate in another if it contains assumptions that exclude or misrepresent participants. Ethical research therefore involves respect not only for individuals but also for context.

A good practical example is a study on mental-health stigma among students. If participation is anonymous and the questions are sensitive, the researcher should ensure the survey platform protects confidentiality and that support resources are available should the topic distress participants. Ethics is not only about avoiding harm; it is also about designing research responsibly.

Ethical decision-making

When exam questions ask how to handle an ethical issue, a strong answer usually:

names the principle involved,
explains the risk,
gives the researcher’s response,
states why that response is appropriate.

For example, if a study involves stress-induction tasks, the researcher should consider informed consent, ensure participants can withdraw, avoid excessive stress, and debrief participants fully afterward. It is not enough to say “the study should be ethical.” The specific protections must be named.

4. Data Collection, Descriptive Statistics, and Data Interpretation

After research is designed and carried out, the next challenge is turning raw observations into meaningful results. RESE201 usually expects students to understand how data are collected, organised, summarised, and interpreted. This stage matters because even well-planned research can be misunderstood if the data are poorly handled.

Methods of data collection

Data can be collected in many forms, including:

questionnaires
interviews
behavioural checklists
observational records
physiological measures
tests and task performance
archival records

The best method depends on the research question. For instance, if the topic is student loneliness, a self-report scale may capture internal experience better than observation. If the topic is classroom participation, observation may be more appropriate than self-report because behaviour can be directly recorded.

Qualitative and quantitative data

Quantitative data

Quantitative data are numerical. They are useful when researchers want to compare groups, test relationships, or estimate patterns statistically.

Examples:

test scores
reaction times
frequency counts
rating-scale responses

Qualitative data

Qualitative data are non-numerical and descriptive. They are useful for understanding meaning, lived experience, and context.

Examples:

interview transcripts
open-ended survey responses
field notes
diary entries

Qualitative and quantitative methods are not enemies. They answer different questions. Quantitative data may show that exam anxiety is common; qualitative data may reveal how students describe that anxiety in their own words. Strong research often benefits from combining both.

Data organisation

Before analysis, data are cleaned and organised. This usually includes:

checking for missing values
identifying outliers
coding categories
entering data accurately
verifying consistency

Poor data entry can distort results. If a score of 25 is accidentally entered as 52, a simple typing error can change a mean, standard deviation, or significance test. This is why careful preparation is essential.

Descriptive statistics

Descriptive statistics summarise a dataset so that patterns become easier to understand.

Measures of central tendency

These describe the “typical” score.

Mean: arithmetic average
Median: middle score when ordered
Mode: most frequent score

The mean is sensitive to extreme scores, while the median is more resistant to outliers. If one participant reports an unusually high stress score, the median may represent the group more accurately than the mean.

Measures of variability

These describe spread or dispersion.

Range: difference between highest and lowest score
Variance: average squared deviation from the mean
Standard deviation: typical distance from the mean

Variability matters because two groups may have the same mean but very different spread. For example, one class may have scores clustered tightly around 70, while another has scores ranging from 40 to 100. The mean alone hides this difference.

Frequency distributions

A frequency distribution shows how often each score or category occurs. It can be displayed in tables, histograms, or bar graphs. Visual displays help reveal whether data are symmetrical, skewed, or clustered.

Interpreting graphs and tables

Exam questions often require students to read graphs carefully. Common tasks include:

identifying trends,
comparing groups,
spotting highest and lowest values,
interpreting the shape of the distribution,
explaining what the graph suggests in plain language.

A useful habit is to describe first, interpret second. For example:

“Group A has a higher mean score than Group B.”
“This suggests that Group A performed better on the memory test.”

That two-step approach reduces vague or overconfident answers.

Normal distribution and skewness

Many psychological variables approximate a normal distribution, which is bell-shaped and symmetrical. In a normal distribution, most scores cluster around the mean, with fewer scores at the extremes.

However, many real-world datasets are skewed.

Positive skew: tail extends to the right
Negative skew: tail extends to the left

Skewness matters because it affects which descriptive statistics are most appropriate. In strongly skewed data, the median may be more informative than the mean.

Correlation and interpretation

Correlation is one of the most important concepts in RESE201.

A correlation coefficient indicates:

the direction of the relationship,
the strength of the relationship.

For example:

r = +0.80 indicates a strong positive relationship
r = -0.60 indicates a moderate negative relationship
r = 0.05 indicates virtually no relationship

The closer the coefficient is to +1 or -1, the stronger the relationship. The closer it is to 0, the weaker the relationship.

But correlation must be interpreted carefully:

it does not prove causation,
it may be affected by outliers,
it may hide non-linear relationships,
it may reflect a third variable.

A classic example is the relationship between study time and grades. A positive correlation may exist because more time leads to better performance, but it may also be because better students study more efficiently or because motivated students do both.

Why descriptive analysis matters

Descriptive statistics are not “easy” or secondary. They are the first stage of understanding data and often reveal issues before more advanced analyses are attempted. If a dataset has extreme outliers, a strong skew, or missing values, the researcher must decide whether the data are suitable for further analysis. Students who understand this stage tend to do better in interpretation questions because they can move from raw numbers to meaningful conclusions without overclaiming.

5. Research Reporting, Critical Evaluation, and Exam Strategy

Good research is not complete until it is communicated clearly and evaluated critically. This final section brings together the practical skills needed for RESE201: reading research reports, spotting strengths and weaknesses, and handling exam questions effectively. These skills matter because psychology students are rarely assessed only on definitions; they are often asked to apply concepts to scenarios, critique studies, and explain why certain methodological choices are stronger than others.

Structure of a research report

A standard psychological report usually includes:

Title
Abstract
Introduction
Method
Results
Discussion
References
Appendices if needed

Each part has a distinct purpose.

Title

The title should clearly state the topic and, where appropriate, the key variables.

Abstract

A brief summary of the aim, method, main results, and conclusion.

Introduction

Provides background, relevant theory, literature, and the hypothesis.

Method

Describes participants, design, materials, procedure, and ethics.

Results

Presents the findings objectively, often using tables, graphs, and statistical tests.

Discussion

Interprets the findings, compares them with previous research, explains limitations, and suggests future research.

Reading and evaluating research

Critical evaluation means judging whether a study is well designed, credible, and useful. Strong evaluation goes beyond saying “the sample was small” or “the study was ethical.” It explains how a limitation affects the findings.

Useful evaluation points include:

sample size and representativeness
reliability and validity of measures
control of extraneous variables
appropriateness of the design
quality of the analysis
ethical safeguards
generalisability to other groups or contexts

For example, if a study on stress uses only twenty volunteers from one class, the results may not generalise well to all psychology students. If the stress scale is poorly worded, the findings may reflect misunderstanding rather than actual stress levels. If the researcher did not control caffeine intake, performance differences may be confounded.

Strengths and limitations: a balanced approach

A strong exam answer usually includes both strengths and limitations. That balance shows mature understanding.

Example of a balanced evaluation

A laboratory experiment on memory and sleep may have high internal validity because the researcher controlled the testing environment and randomly assigned participants to conditions. However, it may have lower ecological validity because students in a controlled lab do not behave exactly as they do during ordinary study sessions. The key is not to call the study “good” or “bad” in general, but to explain what type of knowledge it produces and what it cannot prove.

Common exam traps

Students often lose marks because they:

confuse reliability with validity
confuse random sampling with random assignment
claim that correlation proves causation
describe a concept without applying it to the scenario
ignore the question word, such as “evaluate,” “compare,” or “discuss”
give only definitions without explanation

A practical strategy is to underline the command verb:

Define: give a clear meaning
Explain: show how and why
Compare: identify similarities and differences
Evaluate: give strengths, weaknesses, and judgement
Discuss: explore multiple sides in a balanced way
Apply: use the concept in a specific scenario

How to answer scenario questions

Scenario questions are common in research methods because they test understanding, not memory alone.

A strong answer usually follows this structure:

identify the relevant concept,
quote or paraphrase the clue from the scenario,
explain why the concept fits,
connect it to the research outcome.

For example, if a scenario states that participants in a study guessed the purpose and changed their answers to please the researcher, the correct response may be demand characteristics or social desirability bias, depending on the context. The answer should explain that participant behaviour was influenced by awareness of the study, which threatens validity.

Building exam confidence

Effective revision for RESE201 should not rely only on rereading notes. Better methods include:

making concept maps linking design, ethics, and measurement
practising scenario-based questions
writing short comparisons between methods
testing yourself on variable definitions
creating flashcards for terms such as internal validity, external validity, and confounding variable

A useful memory aid is to think in layers:

What is being studied?
How is it being studied?
How well is it being measured?
What can the findings legitimately claim?

This sequence prevents shallow answers.

High-yield comparison points

Topic	Key comparison
Experiment vs correlation	Manipulation and causality vs association only
Reliability vs validity	Consistency vs accuracy
Random sampling vs random assignment	Generalisability vs internal validity
Quantitative vs qualitative data	Numerical patterns vs meaning and context
Internal vs external validity	Causal confidence vs real-world generalisation
Between-subjects vs within-subjects	Different groups vs same participants

Final synthesis

Research methods in psychology is not merely a technical subject; it is the discipline that gives psychology its credibility. A student who understands research methods can read a journal article with a critical eye, design a better assignment project, and distinguish between persuasive claims and supported conclusions. In exam settings, the best responses are accurate, applied, and well reasoned. They show not just what a term means, but why it matters and how it functions inside the larger process of psychological science.

When revising RESE201, the strongest approach is to see every topic as part of one system: questions lead to hypotheses, hypotheses require variables, variables demand measurement, measurement must be reliable and valid, design must control bias, data must be interpreted carefully, and conclusions must stay within the evidence. That is the logic of research methods, and mastering that logic is the real goal of the module.