Component 2: Deciding on a research question (knowledge and understanding of) — Eduqas A-Level Psychology

Deciding on a research question is a fundamental component of psychological research, forming the basis for any investigation. It involves formulating a clear aim and a testable, operationalised hypothesis that specifies the relationship between variables. This topic is crucial because a well-defined research question guides the entire research process, from design to analysis, and ensures ethical integrity.

In the Eduqas A-Level Psychology specification, this topic falls under Component 2: Applications of Psychology, specifically within the research methods section. It connects to other areas such as experimental design, sampling, and data analysis. Mastering this skill is essential for conducting valid and reliable research, and it is frequently assessed in exam questions requiring students to formulate hypotheses or evaluate research questions.

• →Aim: A general statement of what the researcher intends to investigate; the purpose of the study.
• →Hypothesis: A clear, testable prediction about the expected outcome of the study, stating the relationship between the independent variable (IV) and dependent variable (DV).
• →Operationalisation: Defining variables in terms of how they will be measured or manipulated, making them measurable and testable.
• →Directional (one-tailed) hypothesis: Predicts the specific direction of the effect (e.g., 'more than', 'less than').
• →Non-directional (two-tailed) hypothesis: Predicts that there will be an effect, but not the specific direction (e.g., 'there will be a difference').

• States that the aim expresses the purpose of the research, that is, what the researcher intends to find out.
• Distinguishes the aim from a hypothesis, which predicts a specific testable outcome.
• Identifies the aim in a scenario by focusing on the intended purpose rather than the method or sample.
• Rejects options that describe procedures, samples or results instead of the research purpose.
• Identifies a research hypothesis as a precise, testable prediction about the expected relationship or difference between variables.
• Distinguishes a research hypothesis from a research question and from an aim by stating that the hypothesis predicts an outcome.
• States that a well-formed research hypothesis names the variables or conditions and the population under study.
• Explains that operationalising variables makes the prediction measurable and testable.
• Describes how the research hypothesis guides the choice of design and later statistical testing.
• Defines an alternative (experimental) hypothesis as a prediction that a relationship or difference between variables will exist.
• Explains that the alternative hypothesis contrasts with the null hypothesis, which predicts no relationship or difference.
• Distinguishes a directional alternative hypothesis, which predicts the direction of the difference, from a non-directional one, which predicts only that a difference will occur.
• States that an alternative hypothesis names the variables and population and is formulated before data are collected.
• Recognises that the alternative hypothesis is the prediction tested against the null in statistical analysis.
• Identifies a directional hypothesis as one that predicts the direction of the difference or relationship between conditions
• Identifies a non-directional hypothesis as one that predicts a difference or relationship without specifying its direction
• States that both types must operationalise the variables and identify the population being studied
• Explains that prior theory or evidence pointing one way justifies a directional prediction, while conflicting or absent evidence favours a non-directional one
• Distinguishes the two types by applying them to a concrete research scenario with named independent and dependent variables
• Defines a null hypothesis as a statement that there is no difference or no relationship between the variables
• Distinguishes a null hypothesis from a non-directional hypothesis, which still predicts that a difference or relationship exists
• Explains that statistical tests examine whether the null hypothesis can be rejected in favour of an alternative hypothesis
• States that a null hypothesis should operationalise the variables and identify the population under study
• Applies the concept by writing a null hypothesis for a described research scenario with named variables
• Identifies the independent variable as the factor deliberately manipulated or selected by the researcher.
• States at least two operational levels of the independent variable so the manipulation can be repeated.
• Distinguishes the independent variable from the dependent variable by naming which one the researcher changes.
• Recognises that in a quasi-experiment the independent variable is a pre-existing difference selected rather than controlled.
• Identifies the dependent variable as the factor the researcher measures as an outcome.
• Operationalises the dependent variable by stating the measurement method and units used to record it.
• Distinguishes the dependent variable from the independent variable by naming which one is measured rather than changed.
• Links the dependent variable to the hypothesis so the measurement tests the predicted effect.
• Identifies a co-variable as one of two variables that are measured and paired to test a relationship.
• States that the researcher does not manipulate either co-variable, unlike an independent variable.
• Distinguishes a correlational design using co-variables from an experiment using an independent and dependent variable.
• Recognises that a relationship between co-variables may be positive, negative or absent.
• Applies the term to a concrete pair of measures taken from the same participants.
• Defines operationalisation as stating precisely how a variable will be measured or manipulated.
• Gives a concrete example that turns an abstract construct into a specific, recordable measure.
• Explains that operationalisation allows replication and consistent data collection.
• Applies operationalisation to independent variables, dependent variables or co-variables as appropriate.
• Specifies the procedure, measure and units or categories in an operational definition.
• Identifies a confounding variable as one that varies systematically with the independent variable and so cannot be separated from its effect.
• Explains that a confounder threatens internal validity because its influence on the dependent variable is entangled with the effect of the independent variable.
• Applies the concept to a concrete design, such as comparing groups tested at different times of day, and names the confounder.
• Distinguishes a confounding variable from an extraneous variable by noting that a confounder co-varies with the independent variable.
• Suggests a control that removes the confounder, such as random allocation, counterbalancing or holding conditions constant.
• Defines an extraneous variable as an unwanted variable other than the independent variable that could affect the dependent variable.
• Gives a concrete example of an extraneous variable, such as noise, temperature or participant mood, in a described study.
• Explains that an uncontrolled extraneous variable adds unwanted variation and can obscure a genuine effect.
• Distinguishes an extraneous variable from a confounding variable by noting that a confounder varies systematically with the independent variable.
• Proposes a control, such as a standardised procedure or random allocation, to reduce the influence of a named extraneous variable.

• 💡Ask 'what does the researcher want to find out?' to locate the aim.
• 💡Check the chosen option is a purpose, not a prediction, procedure or result.
• 💡Eliminate options that describe how the study was done rather than why it was done.
• 💡Read the stem carefully to check whether it asks about a research hypothesis, an aim or a research question, then select the option that matches the precise definition.
• 💡Look for the option that includes a testable prediction naming variables, since vague statements are not research hypotheses.
• 💡Eliminate options that describe conclusions or results, because a hypothesis is stated before data are collected.
• 💡Check whether the stem asks about directional or non-directional prediction and choose the option that matches the wording.
• 💡Eliminate options describing the null hypothesis, since the alternative hypothesis predicts a relationship or difference.
• 💡Look for the option that names variables and population, because a testable alternative hypothesis must specify both.
• 💡Underline the comparative words such as more, less, higher or lower to check whether a hypothesis is directional
• 💡Practise rewriting one research scenario as both a directional and a non-directional hypothesis to see the difference clearly
• 💡Check every hypothesis names both variables and the population before deciding on its type
• 💡Look for the phrase no difference or no relationship as the signal of a null hypothesis
• 💡Contrast a null hypothesis with the directional and non-directional alternatives for the same scenario to fix the distinction
• 💡Check the null hypothesis names both variables and the population before accepting it
• 💡Underline the words describing what the researcher changes before selecting an option.
• 💡Reject any option that names the measured outcome rather than the manipulated factor.
• 💡Check that the chosen option gives the independent variable identifiable levels rather than a single vague label.
• 💡Look for the word describing what is measured or recorded, not what is changed.
• 💡Prefer options that give a concrete measurement method over vague labels.
• 💡Eliminate options that describe the manipulation, since those name the independent variable.
• 💡Read the stem for words such as 'relationship' or 'association' between two measured scores, which signal co-variables.
• 💡Check each option for whether it says the researcher manipulated a variable; if so, it is unlikely to describe a co-variable.
• 💡Link your chosen answer to a specific example of two measures taken from the same participants.
• 💡Look for the option that names a specific procedure, measure and unit rather than a general construct.
• 💡Check that the operational definition could be repeated by another researcher from the description alone.
• 💡Apply operationalisation to every variable in the stem, not just one.
• 💡Define the term precisely before applying it to a scenario, so the definition earns credit independently of the example.
• 💡Name the specific confounder in the scenario rather than writing 'other variables' generically.
• 💡State the direction of the threat: explain how the confounder could produce the observed difference instead of the independent variable.
• 💡Name the extraneous variable specifically and state how it could affect the dependent variable.
• 💡Explain the control that would reduce its influence, such as standardising instructions or testing conditions.
• 💡Use the distinction from confounding variables to show precise understanding when the question invites comparison.
• 💡Always operationalise both the IV and DV in your hypothesis to gain full marks; vague terms like 'stress' or 'memory' are insufficient.
• 💡Use the correct terminology: 'aim', 'hypothesis', 'directional', 'non-directional', 'operationalise'.
• 💡When asked to write a hypothesis, ensure it is testable and includes a comparison (e.g., 'more than', 'different from') and the word 'significant' to imply statistical testing.

• Confuses the aim with a directional hypothesis; keep the aim as a broad statement of purpose.
• Selects a description of the method or sample as the aim; look for what the researcher wants to find out.
• Treats the results as the aim; state the intended purpose, not the outcome found.
• Confuses a research hypothesis with a research question; state that the hypothesis predicts an outcome rather than merely asking what will be investigated.
• Omits the variables or conditions from the prediction; name both variables and the population so the hypothesis is testable.
• Treats the hypothesis as a conclusion drawn after data collection; state that it is formulated before data are gathered.
• Confuses the alternative hypothesis with the null hypothesis; state that the alternative predicts a relationship or difference while the null predicts none.
• Treats every alternative hypothesis as directional; recognise that a non-directional hypothesis predicts a difference without specifying its direction.
• Omits the variables or population from the prediction; name both so the hypothesis is testable.
• Writes a non-directional hypothesis that secretly predicts a direction; remove the comparative wording so only the existence of a difference is claimed
• Confuses a directional hypothesis with a null hypothesis; keep the prediction of a real difference and reserve the null for predicting no effect
• Omits the population or operationalised variables from the hypothesis; name who is studied and how each variable is measured
• Assumes a directional hypothesis is always the better choice; match the choice to the strength of prior evidence
• Treats a null hypothesis as identical to a non-directional hypothesis; keep the prediction of no effect distinct from a prediction of some effect
• Writes a null hypothesis that predicts a direction; remove any comparative wording so no difference is claimed
• Omits the variables or population from the null hypothesis; specify what is measured and who is studied
• Believes a null hypothesis means the study has failed; recognise that failing to reject it is a legitimate finding
• Confuses the independent variable with the dependent variable; name the manipulated factor as the independent variable and the measured outcome as the dependent variable.
• Describes the independent variable in vague terms such as mood; state its operational levels, for example sad music versus happy music.
• Assumes the independent variable must always be directly manipulated; recognise that a quasi-experiment selects a pre-existing difference as the independent variable.
• Names the manipulated factor as the dependent variable; identify the measured outcome as the dependent variable instead.
• Leaves the dependent variable unoperationalised, for example memory; specify how it is measured, such as number of words recalled.
• Treats any recorded detail as the dependent variable; select the outcome measure that responds to the independent variable.
• Treats one co-variable as an independent variable that the researcher controls; correct this by stating that both co-variables are measured, not manipulated.
• Assumes a correlation between co-variables proves that one causes the other; correct this by noting that correlation shows a relationship, not causation.
• Calls the two measures independent and dependent variables; correct this by naming them co-variables in a correlational design.
• Leaves a variable as an abstract label such as 'memory' without saying how it is measured; correct this by stating the specific task, scoring and units.
• Confuses operationalisation with simply naming the variable; correct this by describing the procedure and measure used to record it.
• Operationalises only the dependent variable and ignores how the independent variable or co-variable is set or measured; correct this by defining every variable in the study.
• Treats any nuisance variable as a confounder; reserve the term for a variable that changes systematically with the independent variable.
• Assumes a confounder only affects one group; check whether it varies alongside the independent variable across conditions.
• Ignores the confounder when writing a research question; identify and control it at the design stage.
• Uses 'extraneous' and 'confounding' interchangeably; keep the distinction that a confounder co-varies with the independent variable.
• Lists a variable without saying how it could affect the dependent variable; explain the mechanism.
• Proposes a control without linking it to the named extraneous variable; match each control to the specific threat.
• Students often think a hypothesis must be proven correct; in reality, it is a testable prediction that may be supported or refuted by data.
• Students may believe that an aim and a hypothesis are interchangeable; an aim is a general statement, while a hypothesis is a specific, operationalised prediction.
• Students sometimes ignore ethical considerations when writing hypotheses, but ethical issues must be considered from the outset to ensure the research is permissible.

1. 1Day 1-2: Learn definitions of aim, hypothesis, directional and non-directional hypotheses, and operationalisation. Create flashcards with examples.
2. 2Day 3-4: Practice writing aims and hypotheses for given scenarios, ensuring operationalisation. Use past exam questions.
3. 3Day 5-6: Study ethical considerations and how they apply to research questions. Practice identifying ethical issues in sample studies.
4. 4Day 7-8: Review examiner reports and mark schemes to understand common pitfalls. Complete a timed exam question on formulating a research question.
5. 5Day 9-10: Consolidate by teaching the topic to a peer or writing a summary sheet, then test yourself with active recall prompts.

• 📋Short answer: 'Write a suitable aim and hypothesis for a study investigating...' (4-6 marks). Advice: Ensure both aim and hypothesis are clear, operationalised, and include IV and DV.
• 📋Application: 'Explain one ethical issue that must be considered when deciding on this research question and how it could be addressed.' (4 marks). Advice: Link the ethical issue specifically to the research question and suggest a practical solution.
• 📋Evaluation: 'Discuss the strengths and weaknesses of the hypothesis you have written.' (6 marks). Advice: Consider testability, operationalisation, ethical implications, and whether it is directional or non-directional.