Research Methods - Doing Research — OCR GCSE Psychology

Doing Research focuses on the practical mechanics of psychological inquiry, teaching students how to plan, conduct, and refine empirical investigations. It encompasses the formulation of testable aim statements, directional and non-directional hypotheses, experimental designs, and sampling techniques within the OCR GCSE Psychology specification.

This topic forms the foundational backbone of psychological literacy, enabling learners to critique published findings, identify methodological flaws, and uphold British Psychological Society (BPS) ethical standards. It links directly across both exam papers, where students must regularly apply research knowledge to novel novel research scenarios.

• →Formulating aims and operationalised hypotheses (directional/one-tailed and non-directional/two-tailed).
• →Sampling techniques: random, opportunity, stratified, systematic, and volunteer sampling, including their respective trade-offs in representativeness and feasibility.
• →Experimental designs: independent measures, repeated measures, and matched pairs, including controls for order effects and participant variables.
• →Extraneous and confounding variables: identifying, predicting impact, and applying standardisation or counterbalancing controls.
• →BPS ethical guidelines: informed consent, deception, protection from harm, confidentiality, right to withdraw, and debriefing.

• Identifies a laboratory experiment as one conducted in an artificial setting where the researcher manipulates the independent variable and controls extraneous variables
• Identifies a field experiment as one conducted in a natural setting where the researcher still manipulates the independent variable
• Identifies a natural experiment as one where the independent variable occurs naturally and the researcher does not manipulate it
• Distinguishes field from natural experiments by stating that the researcher manipulates the independent variable in a field experiment but not in a natural experiment
• Links each design to a strength or limitation, such as high control in the laboratory or high realism in the field
• Defines a structured interview as one using a fixed set of predetermined questions asked in the same order to each participant
• Defines an unstructured interview as one with no fixed question list where the interviewer guides a conversation around a topic
• Explains that structured interviews allow easier comparison of answers and greater reliability
• Explains that unstructured interviews produce richer, more detailed data but are harder to compare across participants
• Links interview type to a practical consideration such as interviewer training, time or analysis of responses
• Identifies an open question as one that allows a free written response and produces qualitative data.
• Identifies a closed question as one with fixed response options and produces quantitative data.
• Identifies a rating scale as a graded numerical response format that gives a score on a continuum.
• Links each question format to a strength or limitation, such as richness of open data or ease of tallying closed data.
• Identifies a naturalistic observation as one carried out in the participant's normal setting without manipulation by the researcher.
• Identifies a controlled observation as one where the researcher sets up and manages the situation, often in a laboratory.
• Distinguishes overt observation, where participants know they are observed, from covert observation, where they do not.
• Distinguishes participant observation, where the researcher joins the group, from non-participant observation, where the researcher stays separate.
• Links a chosen observation type to a strength or limitation, such as ecological validity, control, demand characteristics or ethics.
• Identifies qualitative data as non-numerical material such as interview transcripts, diary entries or observation notes that record meaning and experience.
• Explains that a case study uses a small sample because it studies one individual, group or event in depth rather than a large representative group.
• States that a small sample limits generalisation of the findings beyond the case studied.
• Recognises that a case study can combine qualitative data with quantitative measures such as test scores.
• Distinguishes qualitative data from quantitative data by noting that qualitative data are words or descriptions rather than numbers.
• States that correlations use quantitative data because both variables are measured as numerical scores.
• Defines a positive correlation as one variable increasing while the other tends to increase.
• Defines a negative correlation as one variable increasing while the other tends to decrease.
• Defines a zero correlation as no consistent relationship between the two variables.
• Classifies a described or plotted relationship as positive, negative or zero from the direction of the pattern.
• Notes that a correlation shows a relationship but does not establish that one variable causes the other.

• 💡Ask two quick questions for each scenario: is the setting artificial or natural, and does the researcher manipulate the independent variable?
• 💡Learn one concrete example of each design so you can match a scenario to the correct label
• 💡When a question asks for a strength or limitation, name the design feature first and then explain its consequence
• 💡Check whether every participant receives identical questions in the same order to decide if an interview is structured
• 💡Pair each interview type with one strength and one limitation so you can answer comparison questions
• 💡Use the phrase 'self-report' to show you understand interviews collect participants' own accounts
• 💡Read the stem carefully to identify which specific format is being described before choosing an answer.
• 💡Eliminate options that mix up data type with question format, such as claiming open questions give numbers.
• 💡Link your chosen answer to a clear reason, such as the type of data produced or the ease of analysis.
• 💡Identify the key feature in the stem, such as setting, awareness or researcher role, before selecting the type.
• 💡Eliminate options that combine two dimensions incorrectly, such as a covert observation where participants know they are watched.
• 💡Justify your choice by naming the defining feature, such as the researcher joining the group for participant observation.
• 💡Read the question stem to check whether it asks about the type of data or the size of the sample, then answer that specific point.
• 💡When asked to identify qualitative data, name a concrete example such as an interview transcript rather than saying only 'words'.
• 💡When weighing strengths and limitations, link the small sample directly to limited generalisability in one clear sentence.
• 💡Check the direction of the pattern before naming the correlation: both rising is positive, one rising while the other falls is negative, no pattern is zero.
• 💡Use the phrase 'as one variable increases, the other tends to...' to make the direction of the correlation explicit.
• 💡Add a short caution that correlation does not show causation when a question asks you to evaluate the method.
• 💡Whenever a question provides a scenario (e.g., 'Dr. Smith studied...'), contextualise every part of your answer. Generic textbook definitions alone will cap your marks at Level 1.
• 💡When explaining how to improve a study, name the exact methodological technique (e.g., 'use counterbalancing (AB/BA)' or 'use a double-blind procedure') rather than writing vague statements like 'make it fairer'.
• 💡Always state the specific unit of measurement when operationalising variables (e.g., seconds, test score out of 20, heartbeats per minute).

• Confuses a field experiment with a natural experiment; remember that the researcher manipulates the independent variable in a field experiment but not in a natural experiment
• Assumes a natural experiment must occur outdoors; recognise that the term refers to a naturally occurring independent variable, not the physical location
• Treats a laboratory experiment as always more valid than a field experiment; note that artificial settings can reduce ecological validity
• Assumes an unstructured interview has no purpose or topic; recognise that the interviewer still guides the conversation around a research aim
• Treats structured interviews as always superior; note that fixed questions limit follow-up and may miss unexpected insights
• Confuses interviews with questionnaires; remember that an interview involves an interviewer asking questions directly, usually face to face or by phone
• Confuses a rating scale with a closed question; note that a rating scale offers a graded numerical continuum rather than simple fixed categories.
• Treats open questions as producing numerical data; remember that open questions produce qualitative, word-based data.
• Assumes questionnaires always produce quantitative data; recognise that the format of each question determines the data type.
• Confuses naturalistic with controlled observation; remember that naturalistic means no manipulation in the normal setting, while controlled means the researcher sets up the situation.
• Treats overt and covert as the same; note that overt means participants are aware, while covert means they are unaware.
• Mixes up participant and non-participant roles; remember that participant means the researcher joins the group, while non-participant means the researcher stays separate.
• Treats a case study as automatically using a large representative sample; correct this by stating that the sample is small and focused on one case.
• Labels numerical test scores as qualitative data; correct this by identifying scores as quantitative and reserving qualitative for words and descriptions.
• Claims a small sample guarantees valid generalisation to the whole population; correct this by noting that generalisability is limited.
• Assumes a case study cannot use any numbers; correct this by recognising that qualitative data may be combined with quantitative measures.
• Describes a negative correlation as no relationship; correct this by stating that a negative correlation is a relationship in which one variable rises as the other falls.
• Confuses zero correlation with a weak negative correlation; correct this by stating that zero correlation means no consistent relationship.
• Claims a correlation proves that one variable causes the other; correct this by noting that correlation does not establish causation.
• Treats correlation data as qualitative; correct this by recognising that paired numerical scores are quantitative data.
• Believing 'random sampling' simply means approaching people at random in public. In psychological research, random sampling strictly requires every member of the target population to have an equal chance of being chosen via a random method.
• Confusing the independent variable (what the researcher manipulates) with the dependent variable (what is measured). A helpful mnemonic is 'I change the IV, the Data is the DV'.
• Assuming repeated measures design always causes demand characteristics, while overlooking the primary threat of order effects (practice and fatigue), which must be resolved by counterbalancing (ABBA design).

1. 1Week 1 (Days 1-3): Master hypothesis writing and variable operationalisation. Practice converting raw aims into clear directional and non-directional hypotheses.
2. 2Week 1 (Days 4-7): Compare the five sampling methods and three experimental designs using flashcards, focusing on distinct strengths and limitations for each.
3. 3Week 2 (Days 1-4): Work through scenario-based exam questions that ask you to identify extraneous variables and write standardisation procedures.
4. 4Week 2 (Days 5-7): Complete timed past paper sections focusing on 4-mark and 6-mark research design questions, self-assessing against the OCR mark scheme.

• 📋Hypothesis formulation (2-3 marks): Writing an operationalised alternative, null, directional, or non-directional hypothesis from a novel prompt.
• 📋Methodological critique (3-4 marks): Identifying an extraneous variable, sampling bias, or design flaw in a given scenario and explaining its impact on validity.
• 📋Investigation design (6 marks): Outlining a complete, viable study to investigate a specific claim, covering sampling, design, controls, and ethics.