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24:[["$","$L33",null,{"feature":"flashcards"}],"$L34",["$","$L35",null,{"href":"/a-level/psychology/eduqas/deciding-on-a-research-question/flashcards","label":"Deciding on a research question","subjectCode":"a_level_psychology__eduqas","surface":"flashcards"}],["$","$L36",null,{"runner":["$","div",null,{"className":"min-h-0 flex-1 overflow-hidden bg-background","children":["$","$L37",null,{"decks":[{"id":"8dd2434e-6a97-4021-90c6-6afbf0873761","title":"Deciding on a research question Flashcards","topicId":"0aa9575a-47fb-5aaf-aedc-a6703561e325","topicSlug":"deciding-on-a-research-question","cards":[{"id":"dfa9c7b3-a91d-4bbb-a1b1-78b956f0d745","cardType":"basic","points":3,"front":"A good research question should have which three qualities?","back":"

Focused
Testable
Ethical

","mcqOptions":[{"text":"

Focused
Testable
Ethical

","correct":true,"explanation":"A strong research question is narrow enough to investigate, capable of being tested using evidence, and compatible with ethical guidelines for research involving humans or animals."},{"text":"

Interesting
Original
Testable

","correct":false,"explanation":"Being interesting or original may add value, but neither is essential for a workable research question. The three required qualities are focused, testable and ethical."},{"text":"

Complicated
Surprising
Ethical

","correct":false,"explanation":"A question need not be complicated or produce surprising findings, and complexity can reduce clarity. A good research question should instead be focused, testable and ethical."},{"text":"

Generalisable
Significant
Objective

","correct":false,"explanation":"Generalisability and statistical significance concern the interpretation of completed research, not the basic quality of its question. The question itself must be focused, testable and ethical."}]},{"id":"d57a0923-7803-4004-a526-3831922b713e","cardType":"basic","points":3,"front":"What details turn a broad topic into a focused aim?","back":"

Behaviour or mental process being studied
People or animals being studied
Variables and difference, effect or relationship

","mcqOptions":[{"text":"

Behaviour or mental process being studied
People or animals being studied
Variables and difference, effect or relationship

","correct":true,"explanation":"A focused aim identifies the behaviour or mental process, the population being investigated, and the variables whose difference, effect or relationship will be examined."},{"text":"

Equipment and materials being used
Laboratory or field being selected
Statistical test and significance level required

","correct":false,"explanation":"Choosing equipment, a location and a statistical test concerns later methodological planning. A focused aim instead specifies the behaviour or process, population and variables or relationship."},{"text":"

Expected results being predicted
Researcher’s beliefs being supported
Conclusions and preferred explanation of findings

","correct":false,"explanation":"Researchers should not build an aim around preferred results or personal expectations. It should identify the behaviour or process, population, and relevant variables or relationship."},{"text":"

Sampling technique being used
Consent and debriefing being arranged
Instructions and standardised procedure for participants

","correct":false,"explanation":"Sampling, consent and debriefing are important procedural and ethical decisions, but they do not define the focused aim. The aim requires a process, population and variables."}]},{"id":"ab0c0809-4ad3-4e6b-98ea-7812cabc63b2","cardType":"cloze","points":null,"front":"The research-planning journey is: broad topic → [ ] → [ ] → operationalised variables.","back":"The research-planning journey is: broad topic → aim → hypothesis → operationalised variables.","mcqOptions":[{"text":"The research-planning journey is: broad topic → aim → hypothesis → operationalised variables.","correct":true,"explanation":"The broad topic is narrowed into an aim, which is then converted into a specific, testable hypothesis before the relevant variables are operationalised."},{"text":"The research-planning journey is: broad topic → hypothesis → aim → operationalised variables.","correct":false,"explanation":"This reverses the logical order: the aim first states what will be investigated, and the hypothesis then predicts the expected difference, effect or relationship."},{"text":"The research-planning journey is: broad topic → aim → conclusion → operationalised variables.","correct":false,"explanation":"A conclusion is produced after data have been collected and analysed, not during initial planning. The aim should lead to a hypothesis and then operationalised variables."},{"text":"The research-planning journey is: broad topic → theory → hypothesis → operationalised variables.","correct":false,"explanation":"A theory can inspire the broad topic, but it does not replace the focused aim. Planning proceeds from the broad topic to an aim, then a hypothesis."}]},{"id":"7718cf4d-d8b1-4057-a116-38a460f8600d","cardType":"cloze","points":null,"front":"An [ ] says what the study is about; a [ ] makes a specific prediction.","back":"An aim says what the study is about; a hypothesis makes a specific prediction.","mcqOptions":[{"text":"An aim says what the study is about; a hypothesis makes a specific prediction.","correct":true,"explanation":"The aim provides the study’s overall purpose, whereas the hypothesis turns that purpose into a precise prediction that can be tested using collected data."},{"text":"An hypothesis says what the study is about; a aim makes a specific prediction.","correct":false,"explanation":"This reverses the two concepts. An aim gives the study’s overall purpose, while a hypothesis makes the specific prediction about an expected outcome or relationship."},{"text":"An conclusion says what the study is about; a hypothesis makes a specific prediction.","correct":false,"explanation":"A conclusion interprets findings after analysis rather than stating the study’s initial purpose. The aim describes the topic, and the hypothesis supplies the specific prediction."},{"text":"An procedure says what the study is about; a hypothesis makes a specific prediction.","correct":false,"explanation":"A procedure explains how the research will be conducted, not what it is broadly about. The aim gives the purpose, while the hypothesis states the prediction."}]},{"id":"e2f5fe03-953d-4f1c-9797-0a2e3ef32224","cardType":"basic","points":2,"front":"Why should a hypothesis be written before data collection?","back":"

Prevents changing the prediction after results
Reduces false finding claims

","mcqOptions":[{"text":"

Prevents changing the prediction after results
Reduces false finding claims

","correct":true,"explanation":"Writing the hypothesis in advance prevents researchers adapting it to fit observed results, reducing the likelihood that chance patterns are presented as predicted findings."},{"text":"

Guarantees significant results after analysis
Removes anomalous scores

","correct":false,"explanation":"A prior hypothesis cannot ensure statistical significance or eliminate anomalous data. It prevents predictions being changed after results and reduces claims based on chance findings."},{"text":"

Eliminates participant differences between conditions
Ensures representative samples

","correct":false,"explanation":"Writing a hypothesis does not control participant differences or guarantee generalisability. Its purpose is to prevent post-result prediction changes and reduce false finding claims."},{"text":"

Operationalises all variables automatically
Makes procedures fully replicable

","correct":false,"explanation":"A hypothesis does not automatically define variables or make the procedure replicable; those require operationalisation and standardisation. Advance writing limits result-driven changes and false claims."}]},{"id":"ce49103c-28dc-44df-86a3-3b9d94e738ee","cardType":"basic","points":1,"front":"If a prediction says there will be a significant difference, effect or relationship, which hypothesis is it?","back":"An alternative hypothesis.","mcqOptions":[{"text":"An alternative hypothesis.","correct":true,"explanation":"An alternative hypothesis predicts that a statistically significant difference, effect or relationship exists, in contrast to the null hypothesis, which predicts no significant result."},{"text":"A null hypothesis.","correct":false,"explanation":"A null hypothesis predicts no statistically significant difference, effect or relationship. Predicting that one will occur therefore describes an alternative hypothesis."},{"text":"A directional hypothesis.","correct":false,"explanation":"A directional hypothesis must also specify the direction of the predicted result, such as higher or lower. Significance alone identifies an alternative hypothesis, not necessarily a directional one."},{"text":"An operational hypothesis.","correct":false,"explanation":"An operational hypothesis must state precisely how variables will be measured or manipulated. Merely predicting a significant result identifies an alternative hypothesis."}]},{"id":"c94105de-1d2b-4e69-be0e-7fd743bb4058","cardType":"basic","points":1,"front":"At $p \\leq 0.05$, what Type I error risk is accepted?","back":"5% risk of a Type I error.","mcqOptions":[{"text":"5% risk of a Type I error.","correct":true,"explanation":"A significance threshold of $p \\leq 0.05$ accepts up to a 5% probability of rejecting a true null hypothesis, which constitutes a Type I error."},{"text":"0.05% risk of a Type I error.","correct":false,"explanation":"Confusing the decimal with its percentage equivalent makes the risk 100 times too small. A probability of $0.05$ corresponds to 5%, not 0.05%."},{"text":"95% risk of a Type I error.","correct":false,"explanation":"This subtracts the significance level from 100% and reports the confidence level instead. At $p \\leq 0.05$, the accepted Type I error risk is 5%."},{"text":"10% risk of a Type I error.","correct":false,"explanation":"This incorrectly doubles the conventional significance threshold. Since $0.05 \\times 100 = 5$, the accepted Type I error risk is 5%, not 10%."}]},{"id":"c63c2fc2-0385-4241-9fa0-f0d1ef520982","cardType":"cloze","points":null,"front":"A Type I error is a false [ ]; a Type II error is a false [ ].","back":"A Type I error is a false positive; a Type II error is a false negative.","mcqOptions":[{"text":"A Type I error is a false positive; a Type II error is a false negative.","correct":true,"explanation":"A Type I error incorrectly concludes that a significant result exists, producing a false positive. A Type II error misses a real result, producing a false negative."},{"text":"A Type I error is a false negative; a Type II error is a false positive.","correct":false,"explanation":"This reverses the definitions. Type I means detecting a result that is not real, a false positive, while Type II means missing a real result, a false negative."},{"text":"A Type I error is a false positive; a Type II error is a false positive.","correct":false,"explanation":"The two errors describe opposite decision failures rather than two false positives. Type I is a false positive, whereas Type II is a false negative."},{"text":"A Type I error is a false negative; a Type II error is a false negative.","correct":false,"explanation":"Only Type II is a false negative because it fails to detect a genuine result. Type I instead reports significance when none exists, making it a false positive."}]},{"id":"d6835f0c-b02f-461c-b6d8-5293146fb1b9","cardType":"cloze","points":null,"front":"A directional hypothesis predicts the [ ]; a non-directional hypothesis does not [ ].","back":"A directional hypothesis predicts the specific direction; a non-directional hypothesis does not state the direction.","mcqOptions":[{"text":"A directional hypothesis predicts the specific direction; a non-directional hypothesis does not state the direction.","correct":true,"explanation":"A directional hypothesis specifies the expected direction of an effect or relationship, whereas a non-directional hypothesis predicts an effect or relationship without stating which way it will go."},{"text":"A directional hypothesis predicts the existence of an effect; a non-directional hypothesis does not state the direction.","correct":false,"explanation":"Predicting only the existence of an effect is non-directional. A directional hypothesis must predict the specific direction, while a non-directional hypothesis does not state that direction."},{"text":"A directional hypothesis predicts the specific direction; a non-directional hypothesis does not predict any effect.","correct":false,"explanation":"A non-directional hypothesis still predicts that an effect, difference or relationship will occur. It differs because it does not state the direction, not because it predicts no effect."},{"text":"A directional hypothesis predicts no difference; a non-directional hypothesis does not state the direction.","correct":false,"explanation":"Predicting no difference describes a null hypothesis rather than a directional hypothesis. A directional hypothesis predicts a specific direction, while a non-directional hypothesis does not state the direction."}]},{"id":"665511fc-a2cc-4778-bd9a-443a89c65bff","cardType":"basic","points":2,"front":"How do you choose between directional and non-directional hypotheses?","back":"

Directional: previous research gives a clear direction
Non-directional: insufficient evidence for direction

","mcqOptions":[{"text":"

Directional: previous research gives a clear direction
Non-directional: insufficient evidence for direction

","correct":true,"explanation":"A directional hypothesis is justified when established evidence supports a particular outcome. Without sufficient evidence about direction, a non-directional hypothesis avoids making an unsupported one-tailed prediction."},{"text":"

Directional: insufficient evidence for direction
Non-directional: previous research gives a clear direction

","correct":false,"explanation":"This reverses the appropriate choices. Clear previous findings justify a directional hypothesis, while insufficient evidence about which way results will go requires a non-directional hypothesis."},{"text":"

Directional: the sample size is sufficiently large
Non-directional: the sample size is relatively small

","correct":false,"explanation":"Sample size affects statistical power, not whether a prediction should specify direction. The choice depends on whether previous research provides clear evidence for a particular direction."},{"text":"

Directional: the study takes place in a laboratory
Non-directional: the study takes place in the field

","correct":false,"explanation":"Research setting does not determine hypothesis directionality. A directional hypothesis needs prior evidence indicating direction; insufficient evidence calls for a non-directional hypothesis."}]},{"id":"c99efee2-7357-4793-804f-ea4b6cf549c9","cardType":"cloze","points":null,"front":"A null hypothesis predicts [ ] and says observed results are due to [ ].","back":"A null hypothesis predicts no significant difference, effect or relationship and says observed results are due to chance.","mcqOptions":[{"text":"A null hypothesis predicts no significant difference, effect or relationship and says observed results are due to chance.","correct":true,"explanation":"The null hypothesis states that no statistically significant outcome exists in the population. Any apparent pattern in the sample is therefore attributed to chance variation."},{"text":"A null hypothesis predicts a significant difference, effect or relationship and says observed results are due to chance.","correct":false,"explanation":"Predicting a significant outcome describes an alternative hypothesis, not the null hypothesis. The null predicts no significant difference, effect or relationship and attributes observed results to chance."},{"text":"A null hypothesis predicts no significant difference, effect or relationship and says observed results are due to researcher bias.","correct":false,"explanation":"The null hypothesis does not automatically attribute results to researcher bias. It predicts no significant difference, effect or relationship and treats observed sample patterns as products of chance."},{"text":"A null hypothesis predicts a specific directional effect and says observed results are due to the independent variable.","correct":false,"explanation":"A specific directional effect belongs to a directional alternative hypothesis, and an independent variable is a manipulated factor rather than an explanation for observed results. The null predicts no significant outcome due to chance."}]},{"id":"0a5716a6-abd2-4e19-9df7-1d2b483ba8b1","cardType":"cloze","points":null,"front":"A [ ] hypothesis leads to a one-tailed test; a [ ] hypothesis leads to a two-tailed test.","back":"A directional hypothesis leads to a one-tailed test; a non-directional hypothesis leads to a two-tailed test.","mcqOptions":[{"text":"A directional hypothesis leads to a one-tailed test; a non-directional hypothesis leads to a two-tailed test.","correct":true,"explanation":"A directional hypothesis predicts which way an outcome will go, so significance is tested in one tail. A non-directional hypothesis allows either direction and requires two tails."},{"text":"A non-directional hypothesis leads to a one-tailed test; a directional hypothesis leads to a two-tailed test.","correct":false,"explanation":"This reverses the relationship between hypotheses and significance tests. Directional predictions use one-tailed tests, while non-directional predictions require two-tailed tests because either direction is possible."},{"text":"A null hypothesis leads to a one-tailed test; a non-directional hypothesis leads to a two-tailed test.","correct":false,"explanation":"A null hypothesis is tested alongside the research hypothesis but does not itself determine one-tailed testing. A directional hypothesis leads to one tail, while a non-directional hypothesis leads to two."},{"text":"A directional hypothesis leads to a two-tailed test; a non-directional hypothesis leads to a one-tailed test.","correct":false,"explanation":"This wrongly assigns a two-tailed test to a directional prediction. Directional hypotheses use one tail because only one direction is predicted; non-directional hypotheses use two tails."}]},{"id":"08022925-aab6-4901-b827-b370dc3f1be6","cardType":"basic","points":1,"front":"In an experiment, which variable is changed to create conditions?","back":"The independent variable (IV).","mcqOptions":[{"text":"The independent variable (IV).","correct":true,"explanation":"The researcher manipulates the independent variable to produce different experimental conditions, then examines whether this manipulation changes the measured dependent variable."},{"text":"The dependent variable (DV).","correct":false,"explanation":"The dependent variable is the outcome measured after conditions have been created; it is not the variable deliberately changed. The manipulated variable is the independent variable."},{"text":"The co-variable.","correct":false,"explanation":"A co-variable is naturally measured in correlational research rather than manipulated to create experimental conditions. The variable changed by an experimenter is the independent variable."},{"text":"The control variable.","correct":false,"explanation":"A control variable is kept constant to prevent it influencing the results; it does not create the experimental conditions. Conditions are created by changing the independent variable."}]},{"id":"fb85097c-6a00-4280-a44d-f4e19f5d779d","cardType":"basic","points":1,"front":"In an experiment, which variable is measured to see whether the change had an effect?","back":"The dependent variable (DV).","mcqOptions":[{"text":"The dependent variable (DV).","correct":true,"explanation":"The dependent variable is measured as the outcome of the experiment, allowing the researcher to assess whether manipulating the independent variable produced an effect."},{"text":"The independent variable (IV).","correct":false,"explanation":"The independent variable is changed by the researcher to create conditions rather than measured as the outcome. The variable measured for an effect is the dependent variable."},{"text":"The control variable.","correct":false,"explanation":"A control variable is kept constant to prevent confounding and is not the outcome used to assess the manipulation. The measured outcome is the dependent variable."},{"text":"The extraneous variable.","correct":false,"explanation":"An extraneous variable is an unwanted factor that may influence results, not the intended outcome measure. The variable measured to detect an effect is the dependent variable."}]},{"id":"7577e430-27f5-4a90-a2c3-38a78f55dba9","cardType":"basic","points":1,"front":"In a correlation, what are the two naturally occurring measured variables called?","back":"Co-variables.","mcqOptions":[{"text":"Co-variables.","correct":true,"explanation":"Correlational research measures two naturally occurring variables without manipulating either one. These paired measured quantities are therefore called co-variables rather than independent and dependent variables."},{"text":"Independent variables.","correct":false,"explanation":"Independent variables are deliberately manipulated to create conditions in experiments. Because neither naturally occurring variable is manipulated in a correlation, both are called co-variables."},{"text":"Dependent variables.","correct":false,"explanation":"Dependent variables are experimental outcomes measured after manipulation of an independent variable. In a correlation, both naturally occurring measures have equal status and are called co-variables."},{"text":"Control variables.","correct":false,"explanation":"Control variables are factors deliberately kept constant to reduce alternative explanations. The two naturally occurring variables measured in a correlation are instead called co-variables."}]},{"id":"6f08c516-7d83-443b-af05-c0ca537a7443","cardType":"basic","points":2,"front":"Why should correlational variables not be called IV and DV?","back":"No variable is manipulated as a cause.","mcqOptions":[{"text":"No variable is manipulated as a cause.","correct":true,"explanation":"Independent and dependent variable terminology implies that one variable is manipulated to test its causal effect. Correlations only measure naturally occurring co-variables, so neither is manipulated as a cause."},{"text":"No variable is measured as a number.","correct":false,"explanation":"Correlational variables can be measured numerically, such as scores, ages or frequencies. They are not called IV and DV because neither variable is manipulated to test causation."},{"text":"One variable is always held constant.","correct":false,"explanation":"Correlational studies do not define one of their two main measures as a constant. They use co-variable terminology because neither measured variable is manipulated as a cause."},{"text":"Both variables are manipulated by researchers.","correct":false,"explanation":"This confuses correlational research with experimentation. Correlational variables are both naturally occurring and measured, so neither is manipulated as a cause and both are called co-variables."}]},{"id":"c7dcabea-edd5-4415-ae6d-9f249f3ddb5a","cardType":"cloze","points":null,"front":"Operationalisation means defining variables precisely so they can be [ ], manipulated and [ ].","back":"Operationalisation means defining variables precisely so they can be measured, manipulated and replicated.","mcqOptions":[{"text":"Operationalisation means defining variables precisely so they can be measured, manipulated and replicated.","correct":true,"explanation":"Operationalisation gives each variable a precise, observable definition, allowing researchers to measure it consistently, manipulate it where appropriate and repeat the procedure in a replication."},{"text":"Operationalisation means defining variables precisely so they can be controlled, manipulated and generalised.","correct":false,"explanation":"Control and generalisation are important research ideas, but they are not the two outcomes completed by this definition. The correct terms are measured and replicated, because precise definitions support observation and repetition."},{"text":"Operationalisation means defining variables precisely so they can be observed, manipulated and standardised.","correct":false,"explanation":"Observation and standardisation may be involved in research, but they do not complete this standard definition. Operationalised variables must be measured and replicated so findings can be assessed consistently and repeated."},{"text":"Operationalisation means defining variables precisely so they can be identified, manipulated and validated.","correct":false,"explanation":"Identifying and validating variables are not the missing outcomes in this definition. The correct completion is measured and replicated, referring to collecting data and enabling another researcher to repeat the method."}]},{"id":"52f41049-040e-4944-a370-e8974934b137","cardType":"basic","points":3,"front":"How can good operationalisation strengthen a study?","back":"

Reliability: repeated consistently
Validity: measures what it claims
Replicability: another researcher can copy it

","mcqOptions":[{"text":"

Reliability: repeated consistently
Validity: measures what it claims
Replicability: another researcher can copy it

","correct":true,"explanation":"Good operationalisation defines procedures clearly enough to produce consistent results, measure the intended construct and allow another researcher to repeat the study."},{"text":"

Reliability: measures what it claims
Validity: repeated consistently
Replicability: another researcher can copy it

","correct":false,"explanation":"The first two definitions are reversed: reliability concerns consistency, whereas validity concerns measuring what is intended. Replicability correctly means another researcher can copy the procedure."},{"text":"

Reliability: another researcher can copy it
Validity: measures what it claims
Replicability: repeated consistently

","correct":false,"explanation":"Reliability means producing consistent results, not simply allowing another researcher to copy the method. Replicability refers to copying the study, while validity means measuring the intended construct."},{"text":"

Reliability: repeated consistently
Validity: another researcher can copy it
Replicability: measures what it claims

","correct":false,"explanation":"Validity and replicability have been swapped here. Validity asks whether the measure assesses what it claims, whereas replicability means another researcher can copy the procedure."}]},{"id":"d31e303b-2e45-42d6-8f11-61915de02c7c","cardType":"basic","points":3,"front":"Match the examples to levels of measurement: passed/failed, stress 1-10, score out of 50.","back":"

Passed/failed: nominal
Stress 1-10: ordinal
Score out of 50: interval

","mcqOptions":[{"text":"

Passed/failed: nominal
Stress 1-10: ordinal
Score out of 50: interval

","correct":true,"explanation":"Passed or failed categories have no order, making them nominal. Stress ratings are ordered, making them ordinal, while a score out of 50 is treated here as interval data."},{"text":"

Passed/failed: ordinal
Stress 1-10: interval
Score out of 50: nominal

","correct":false,"explanation":"The categories passed and failed are nominal because they have no meaningful ranking. Stress 1-10 is ordinal, and a score out of 50 is interval in this classification."},{"text":"

Passed/failed: interval
Stress 1-10: nominal
Score out of 50: ordinal

","correct":false,"explanation":"This option shifts each level incorrectly. Passed or failed is nominal, stress ratings are ordinal because they are ranked, and the score out of 50 is classified as interval."},{"text":"

Passed/failed: nominal
Stress 1-10: interval
Score out of 50: ordinal

","correct":false,"explanation":"Although passed or failed is correctly identified as nominal, stress 1-10 is treated as ordinal rather than interval, and the score out of 50 is classified as interval here."}]},{"id":"e5529695-80c0-4216-88df-fa99b2dac09c","cardType":"basic","points":1,"front":"If tiredness could affect recall but is not the IV, classify it.","back":"An extraneous variable.","mcqOptions":[{"text":"An extraneous variable.","correct":true,"explanation":"Tiredness is an extraneous variable because it could influence recall but is not the independent variable being deliberately changed by the researcher."},{"text":"A confounding variable.","correct":false,"explanation":"A confounding variable is an extraneous variable that varies systematically with the independent variable and offers an alternative explanation. The question only states that tiredness could affect recall."},{"text":"A dependent variable.","correct":false,"explanation":"The dependent variable is the outcome measured by the researcher, such as recall. Tiredness is a possible unwanted influence, so it is an extraneous variable instead."},{"text":"A participant variable.","correct":false,"explanation":"A participant variable is a characteristic of participants, such as age or personality. Tiredness may be one such influence, but the classification requested is the broader term extraneous variable."}]},{"id":"370b75cb-32f2-4dfc-9db3-91495a1b6c5b","cardType":"cloze","points":null,"front":"An extraneous variable becomes a [ ] when it varies systematically with the [ ] and could explain the DV.","back":"An extraneous variable becomes a confounding variable when it varies systematically with the IV and could explain the DV.","mcqOptions":[{"text":"An extraneous variable becomes a confounding variable when it varies systematically with the IV and could explain the DV.","correct":true,"explanation":"An extraneous variable becomes confounding when it changes systematically between levels of the independent variable and could therefore provide an alternative explanation for the dependent variable."},{"text":"An extraneous variable becomes a participant variable when it varies systematically with the DV and could explain the IV.","correct":false,"explanation":"The relationship is reversed here. A confounding variable varies systematically with the IV and could explain the DV; participant variable is not the required term."},{"text":"An extraneous variable becomes a situational variable when it varies randomly with the IV and could explain the DV.","correct":false,"explanation":"Random variation does not create the systematic alternative explanation that defines confounding. The correct term is confounding variable, and it varies systematically with the IV."},{"text":"An extraneous variable becomes a control variable when it varies systematically with the DV and could explain the IV.","correct":false,"explanation":"A control variable is held constant rather than becoming a systematic alternative explanation. The correct completion is confounding variable and IV, because the confound may explain the DV."}]},{"id":"0a9fcb11-ffa9-4261-8c2b-f1039fae4047","cardType":"basic","points":2,"front":"Why is a confounding variable more serious than a general extraneous variable?","back":"It gives an alternative explanation for the DV effect.","mcqOptions":[{"text":"It gives an alternative explanation for the DV effect.","correct":true,"explanation":"A confounding variable changes systematically with the independent variable, so any effect on the dependent variable could be caused by the confound rather than the intended IV."},{"text":"It makes the DV impossible to measure accurately.","correct":false,"explanation":"A confounding variable does not necessarily prevent measurement of the dependent variable. Its serious problem is that it gives an alternative explanation for the observed DV effect."},{"text":"It proves that the IV has caused the DV effect.","correct":false,"explanation":"A confound weakens the conclusion that the IV caused the DV effect because another variable changes alongside it. It does not prove causation."},{"text":"It shows that the sample cannot represent the target population.","correct":false,"explanation":"Poor representativeness concerns sampling and population validity, not confounding. A confounding variable is serious because it provides an alternative explanation for the DV effect."}]},{"id":"17b9bb8f-0489-4709-a505-cc30a73c462f","cardType":"basic","points":2,"front":"In the silence-morning/music-after-lunch study, why is time of day confounding?","back":"

Varies systematically with music condition
Could affect alertness and recall

","mcqOptions":[{"text":"

Varies systematically with music condition
Could affect alertness and recall

","correct":true,"explanation":"Music condition is tied to a particular time of day, so time of day changes systematically with the IV and could independently influence alertness and recall."},{"text":"

Varies randomly with music condition
Could affect alertness and recall

","correct":false,"explanation":"Time of day is not random in this design: silence always occurs in the morning and music after lunch. Its systematic association with condition makes it confounding."},{"text":"

Varies systematically with music condition
Could not affect alertness and recall

","correct":false,"explanation":"The first point is correct, but time of day could affect alertness and recall through normal changes in tiredness or concentration. Therefore it can provide an alternative explanation."},{"text":"

Is controlled completely across music condition
Could affect alertness and recall

","correct":false,"explanation":"Time of day is not controlled because the two music conditions occur at different times. Since it differs systematically and may affect alertness and recall, it is confounding."}]},{"id":"a082849a-8189-4c6d-9c93-02669f0430dd","cardType":"basic","points":2,"front":"If deception is used in an ethical psychology study, what must happen?","back":"It must be justified and followed by full debrief.","mcqOptions":[{"text":"It must be justified and followed by full debrief.","correct":true,"explanation":"Deception is acceptable only when there is a strong methodological justification and participants receive a full debrief afterwards, including the true purpose and any concealed information."},{"text":"It must be approved and followed by payment.","correct":false,"explanation":"Ethical approval may be required, but payment does not replace the need to explain deception afterwards. The deception must be justified and followed by a full debrief."},{"text":"It must be hidden and followed by informed consent.","correct":false,"explanation":"Participants cannot give fully informed consent to information deliberately withheld, and hiding deception is not the safeguard required after the study. Researchers must justify it and provide a full debrief."},{"text":"It must be brief and followed by participant withdrawal.","correct":false,"explanation":"Keeping deception brief and offering withdrawal may be useful safeguards, but they do not provide the required explanation. Deception must be justified and followed by a full debrief."}]}]}],"examCountdown":{"examDate":"2027-05-18","paperLabel":"Component 1"},"heading":"Deciding on a research question","title":"Deciding on a research question Flashcards","topbar":{"backHref":"/a-level/psychology/eduqas/flashcards"},"topicSidebar":"$L38"}]}],"topicId":"0aa9575a-47fb-5aaf-aedc-a6703561e325","walled":"$L39"}],"$L3a","$L3b"]