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observed directly and must be inferred from measurable behaviour or responses."},{"text":"observation","correct":false,"explanation":"An observation is a recorded behaviour or event, not the underlying concept inferred from it. The correct term is construct because constructs are indirectly measured psychological concepts."},{"text":"experiment","correct":false,"explanation":"An experiment is a research method used to investigate cause and effect, not an unobservable psychological concept. The inferred concept is called a construct."},{"text":"procedure","correct":false,"explanation":"A procedure is the sequence of actions used to conduct research, rather than the abstract concept being measured. An indirectly inferred psychological concept is a construct."}]},{"id":"5297f966-9ea5-4c01-8749-7d8bfddbcb1f","cardType":"cloze","points":null,"front":"Reliability means the [ ] of a measure, procedure or set of observations.","back":"Reliability means the consistency of a measure, procedure or set of observations.","mcqOptions":[{"text":"consistency","correct":true,"explanation":"Reliability concerns whether a measure or procedure produces consistent results across items, occasions or observers when the thing being measured has not changed."},{"text":"accuracy","correct":false,"explanation":"Accuracy concerns closeness to a true or intended value, whereas reliability concerns consistency. A measure can repeatedly produce the same result while still being inaccurate."},{"text":"validity","correct":false,"explanation":"Validity concerns whether a measure assesses what it claims to measure. Reliability instead means consistency, so a measure may be reliable without being valid."},{"text":"sensitivity","correct":false,"explanation":"Sensitivity is the ability to detect differences or changes, not the repeatability of results. Reliability specifically refers to the consistency of a measure or procedure."}]},{"id":"d3de0523-6b07-4a24-a7c1-541ac855878a","cardType":"basic","points":1,"front":"If findings can be repeated using the same method, which scientific goal is supported?","back":"Replicability.","mcqOptions":[{"text":"Replicability.","correct":true,"explanation":"Replicability is supported when researchers can repeat a study using the same method and obtain similar findings, showing that the original result was not a one-off."},{"text":"Validity.","correct":false,"explanation":"Validity concerns whether a study or measure captures what it claims to investigate. Repeating the method and obtaining similar findings specifically supports replicability."},{"text":"Objectivity.","correct":false,"explanation":"Objectivity means that findings are not unduly influenced by researchers’ personal judgements. Successful repetition using the same method instead demonstrates replicability."},{"text":"Falsifiability.","correct":false,"explanation":"Falsifiability means a scientific claim can potentially be disproved by evidence. Repeating a method and obtaining similar findings supports replicability rather than falsifiability."}]},{"id":"fbc8a844-dad8-4816-b2f3-0e96e1710c14","cardType":"cloze","points":null,"front":"Validity asks whether a measure assesses what it [ ]; reliability asks whether it is [ ].","back":"Validity asks whether a measure assesses what it claims to measure; reliability asks whether it is consistent.","mcqOptions":[{"text":"Validity asks whether a measure assesses what it claims to measure; reliability asks whether it is consistent.","correct":true,"explanation":"Validity concerns whether the intended construct is actually measured, while reliability concerns consistency. These qualities are distinct, and a measure can be consistent without measuring the correct construct."},{"text":"Validity asks whether a measure assesses what it can measure consistently; reliability asks whether it is consistent.","correct":false,"explanation":"This incorrectly defines validity through consistency, which belongs to reliability. Validity asks whether a measure assesses what it claims to measure, while reliability asks whether results are consistent."},{"text":"Validity asks whether a measure assesses what it claims to measure; reliability asks whether it is accurate.","correct":false,"explanation":"This confuses reliability with accuracy. Reliability asks whether results are consistent, whereas validity asks whether the measure assesses the construct it claims to measure."},{"text":"Validity asks whether a measure assesses what it measures consistently; reliability asks whether it is valid.","correct":false,"explanation":"This reverses and blends the two concepts. Validity asks whether the claimed construct is measured, while reliability asks whether the measurement is consistent."}]},{"id":"14af758a-2ee3-43c6-a12c-3d2fbc585a6b","cardType":"basic","points":2,"front":"Why can a reliable questionnaire still fail as a measure of anxiety?","back":"It may consistently measure the wrong construct.","mcqOptions":[{"text":"It may consistently measure the wrong construct.","correct":true,"explanation":"Consistency alone does not establish validity. A questionnaire may repeatedly produce similar scores while actually measuring stress, social desirability or another construct rather than anxiety."},{"text":"It may produce different scores on each occasion.","correct":false,"explanation":"Different scores across occasions would indicate poor reliability, not explain why a reliable measure lacks validity. A reliable questionnaire can fail because it consistently measures the wrong construct."},{"text":"It may consistently measure the intended construct.","correct":false,"explanation":"Consistently measuring the intended construct would support both reliability and validity, so it would not represent failure. Failure occurs when the wrong construct is measured consistently."},{"text":"It may have strong internal consistency.","correct":false,"explanation":"Strong internal consistency supports reliability because questionnaire items produce related scores. It can still fail as an anxiety measure if those consistent items assess the wrong construct."}]},{"id":"c69fdb58-93be-4166-bc1d-0e4094ed19cb","cardType":"cloze","points":null,"front":"Internal reliability is consistency [ ], especially across items measuring the same [ ].","back":"Internal reliability is consistency within a measure, especially across items measuring the same construct.","mcqOptions":[{"text":"Internal reliability is consistency within a measure, especially across items measuring the same construct.","correct":true,"explanation":"Internal reliability assesses whether components of one measure, such as questionnaire items, produce consistent results because they are intended to assess the same underlying construct."},{"text":"Internal reliability is consistency between occasions, especially across items measuring the same construct.","correct":false,"explanation":"Consistency between occasions describes test-retest reliability rather than internal reliability. Internal reliability concerns consistency within one measure across items assessing the same construct."},{"text":"Internal reliability is consistency within a measure, especially across items measuring the same sample.","correct":false,"explanation":"Questionnaire items are compared because they should measure the same construct, not merely the same sample. Internal reliability is consistency within a measure across construct-related items."},{"text":"Internal reliability is consistency between studies, especially across items measuring the same procedure.","correct":false,"explanation":"Consistency between studies is associated with replication, and procedures are followed rather than measured by items. Internal reliability concerns items within one measure assessing the same construct."}]},{"id":"0593dd4d-b0e6-4dec-baf5-dfcc54f08809","cardType":"basic","points":1,"front":"Which reliability check divides a questionnaire into comparable halves and compares the scores?","back":"Split-half reliability.","mcqOptions":[{"text":"Split-half reliability.","correct":true,"explanation":"Split-half reliability divides the items into two comparable sets and correlates participants’ scores on each half. Strong agreement indicates that the measure has good internal reliability."},{"text":"Test-retest reliability.","correct":false,"explanation":"Test-retest reliability gives the same measure on two separate occasions and compares the scores. Dividing one questionnaire into comparable halves is split-half reliability."},{"text":"Inter-observer reliability.","correct":false,"explanation":"Inter-observer reliability compares records or ratings made by different observers. Comparing scores from two halves of the same questionnaire is split-half reliability."},{"text":"Parallel-forms reliability.","correct":false,"explanation":"Parallel-forms reliability compares scores from two separately constructed equivalent versions of a measure. Dividing a single questionnaire into two halves tests split-half reliability."}]},{"id":"f92a1f2f-127c-460b-b004-e8fca3bbbdd6","cardType":"basic","points":2,"front":"In split-half reliability, what does a strong positive correlation between half-scores show?","back":"Good internal reliability: both halves score participants similarly.","mcqOptions":[{"text":"Good internal reliability: both halves score participants similarly.","correct":true,"explanation":"A strong positive correlation means participants who score highly on one half also tend to score highly on the other, indicating consistent measurement across the questionnaire’s items."},{"text":"Good test-retest reliability: participants obtain similar scores on two occasions.","correct":false,"explanation":"Test-retest reliability requires the measure to be administered on separate occasions. Correlating two halves from one administration instead provides evidence of good internal reliability."},{"text":"Good inter-observer reliability: both observers score participants similarly.","correct":false,"explanation":"Inter-observer reliability concerns agreement between different observers recording the same behaviour. Agreement between questionnaire halves instead shows internal reliability across the measure’s items."},{"text":"Good concurrent validity: both halves agree with an established measure.","correct":false,"explanation":"Concurrent validity requires comparison with an established valid measure administered at a similar time. Correlation between questionnaire halves assesses internal reliability, not validity."}]},{"id":"aa54cd61-34cd-4cee-8fdb-8fae85fa2dc8","cardType":"cloze","points":null,"front":"A correlation coefficient closer to [ ] shows a stronger [ ].","back":"A correlation coefficient closer to +1 shows a stronger positive relationship.","mcqOptions":[{"text":"A correlation coefficient closer to +1 shows a stronger positive relationship.","correct":true,"explanation":"A coefficient approaching +1 indicates that increases in one variable are increasingly consistently associated with increases in the other variable."},{"text":"A correlation coefficient closer to -1 shows a stronger positive relationship.","correct":false,"explanation":"Confusing correlation strength with direction causes this error. A coefficient closer to -1 shows a stronger negative relationship; a stronger positive relationship approaches +1."},{"text":"A correlation coefficient closer to 0 shows a stronger positive relationship.","correct":false,"explanation":"A coefficient near 0 indicates little or no linear relationship. A stronger positive relationship is represented by a coefficient closer to +1."},{"text":"A correlation coefficient closer to +1 shows a stronger negative relationship.","correct":false,"explanation":"The sign has been interpreted in the wrong direction. A coefficient closer to +1 shows a stronger positive relationship, while a strong negative relationship approaches -1."}]},{"id":"88576e53-3e98-40bc-9e13-0c85161b66cd","cardType":"cloze","points":null,"front":"External reliability is consistency beyond one measurement, such as over [ ] or across different [ ].","back":"External reliability is consistency beyond one measurement, such as over time or across different researchers.","mcqOptions":[{"text":"External reliability is consistency beyond one measurement, such as over time or across different researchers.","correct":true,"explanation":"External reliability concerns consistency across separate measurements. Test-retest reliability checks consistency over time, while inter-rater reliability checks agreement across different researchers."},{"text":"External reliability is consistency beyond one measurement, such as over participants or across different researchers.","correct":false,"explanation":"Consistency across participants is not an external reliability check because different people are expected to produce different responses. External reliability is assessed over time or across researchers."},{"text":"External reliability is consistency beyond one measurement, such as over time or across different questionnaire items.","correct":false,"explanation":"Agreement across questionnaire items concerns internal reliability, usually assessed through split-half reliability. External reliability instead examines consistency over time or across different researchers."},{"text":"External reliability is consistency beyond one measurement, such as over samples or across different measures.","correct":false,"explanation":"Changing samples or measures introduces differences rather than directly checking repeatability. External reliability is specifically demonstrated by consistency over time or agreement across different researchers."}]},{"id":"b74ffa2f-85d8-4dbe-9a03-a386d836106d","cardType":"basic","points":1,"front":"Which reliability check repeats the same test with the same participants after a time gap?","back":"Test-retest reliability.","mcqOptions":[{"text":"Test-retest reliability.","correct":true,"explanation":"Test-retest reliability involves administering the same test to the same participants on two occasions and correlating the two sets of scores."},{"text":"Split-half reliability.","correct":false,"explanation":"Split-half reliability compares scores from two halves of one test rather than repeating it later. Repeating the same test after a time gap assesses test-retest reliability."},{"text":"Inter-rater reliability.","correct":false,"explanation":"Inter-rater reliability measures agreement between observers or researchers. Repeating a test with the same participants after a delay assesses test-retest reliability."},{"text":"Parallel-forms reliability.","correct":false,"explanation":"Parallel-forms reliability compares two equivalent versions of a measure, not the same test on two occasions. The procedure described assesses test-retest reliability."}]},{"id":"4ceeba67-5b95-4c5e-b083-18478dcc137e","cardType":"basic","points":1,"front":"When is test-retest reliability most appropriate?","back":"When the construct should be fairly stable.","mcqOptions":[{"text":"When the construct should be fairly stable.","correct":true,"explanation":"Test-retest reliability assumes the underlying characteristic remains reasonably constant, so differences between the two scores are likely to reflect poor measurement consistency."},{"text":"When the construct should be changing rapidly.","correct":false,"explanation":"A rapidly changing construct may produce different scores even when the measure is reliable. Test-retest reliability is most appropriate when the construct should remain fairly stable."},{"text":"When different observers code the same behaviour.","correct":false,"explanation":"Agreement between observers is assessed using inter-rater reliability, not test-retest reliability. Test-retest is appropriate when the measured construct should be fairly stable over time."},{"text":"When questionnaire items assess one construct.","correct":false,"explanation":"Consistency among questionnaire items concerns internal or split-half reliability. Test-retest reliability is most suitable when the underlying construct should remain fairly stable across occasions."}]},{"id":"52d7668d-3da0-4169-9ae7-28a0dcbe9224","cardType":"basic","points":2,"front":"Why can a retest given too soon distort test-retest reliability?","back":"Participants may remember original answers.","mcqOptions":[{"text":"Participants may remember original answers.","correct":true,"explanation":"A short interval can allow participants to recall and repeat their earlier responses, artificially increasing the correlation without proving that the measure is genuinely consistent."},{"text":"Participants may genuinely change over time.","correct":false,"explanation":"Genuine change is mainly a risk when the interval is too long. When a retest occurs too soon, participants may remember their original answers."},{"text":"Participants may misunderstand the construct.","correct":false,"explanation":"Misunderstanding may reduce validity but is not specifically caused by a short retest interval. The immediate reliability problem is remembering and repeating original answers."},{"text":"Participants may become a different sample.","correct":false,"explanation":"Test-retest procedures use the same participants, so they do not become a different sample. A retest given too soon is distorted by memory of original answers."}]},{"id":"3b1b1b8e-b054-4b9c-ae72-9742437750fd","cardType":"basic","points":2,"front":"Why can a retest given too late distort test-retest reliability?","back":"The construct may have genuinely changed.","mcqOptions":[{"text":"The construct may have genuinely changed.","correct":true,"explanation":"A long interval gives the underlying characteristic time to change, so different scores may reflect a real change rather than unreliability in the measure."},{"text":"Participants may have remembered original answers.","correct":false,"explanation":"Remembering earlier responses is primarily a problem when the retest occurs too soon. With a long delay, the construct may instead have genuinely changed."},{"text":"The test may have too many similar items.","correct":false,"explanation":"Similarity among items concerns internal reliability and test construction, not the length of the retest interval. A late retest risks genuine change in the construct."},{"text":"The observers may have disagreed about coding.","correct":false,"explanation":"Observer disagreement indicates low inter-rater reliability and does not explain a delayed retest problem. Over a long interval, the measured construct may genuinely change."}]},{"id":"eac26e8f-d3d6-4522-b2d6-610c1d11a06a","cardType":"basic","points":1,"front":"Which reliability check is needed when two observers code the same behaviour?","back":"Inter-rater reliability.","mcqOptions":[{"text":"Inter-rater reliability.","correct":true,"explanation":"Inter-rater reliability measures the extent to which two observers apply the same behavioural categories consistently and produce similar records of the same behaviour."},{"text":"Test-retest reliability.","correct":false,"explanation":"Test-retest reliability compares the same participants' scores across two occasions. Agreement between two observers coding the same behaviour requires inter-rater reliability."},{"text":"Split-half reliability.","correct":false,"explanation":"Split-half reliability compares two halves of a test to assess internal consistency. Agreement between observers coding the same behaviour is checked using inter-rater reliability."},{"text":"Internal consistency reliability.","correct":false,"explanation":"Internal consistency concerns whether items within one measure assess the same construct. Consistency between two observers is specifically measured through inter-rater reliability."}]},{"id":"8cd0585a-e620-4d65-9d27-938d79a1cc8c","cardType":"basic","points":1,"front":"If one observer counts teasing as aggression and another does not, what reliability problem occurs?","back":"Low inter-rater reliability.","mcqOptions":[{"text":"Low inter-rater reliability.","correct":true,"explanation":"The observers are applying the behavioural category differently, so their coding lacks agreement. This indicates low inter-rater reliability and may require clearer operational definitions."},{"text":"Low test-retest reliability.","correct":false,"explanation":"Test-retest reliability concerns consistency across different times, not disagreement between observers. Different interpretations of teasing indicate low inter-rater reliability."},{"text":"Low split-half reliability.","correct":false,"explanation":"Split-half reliability examines whether two sections of a test produce consistent scores. Disagreement between observers about coding aggression indicates low inter-rater reliability."},{"text":"Low external validity.","correct":false,"explanation":"External validity concerns whether findings generalise to other people or settings. Inconsistent coding by two observers is a problem of low inter-rater reliability."}]},{"id":"074a0281-ba4b-4c70-a4b7-917df3971b26","cardType":"basic","points":1,"front":"How do you calculate inter-rater percentage agreement?","back":"$$\\frac{\\text{agreements}}{\\text{agreements} + \\text{disagreements}} \\times 100$","mcqOptions":[{"text":"$$\\frac{\\text{agreements}}{\\text{agreements} + \\text{disagreements}} \\times 100$","correct":true,"explanation":"Percentage agreement is the number of agreements divided by the total number of observations, including agreements and disagreements, then multiplied by 100."},{"text":"$$\\frac{\\text{disagreements}}{\\text{agreements} + \\text{disagreements}} \\times 100$","correct":false,"explanation":"This calculates percentage disagreement by using disagreements as the numerator. Percentage agreement uses agreements divided by agreements plus disagreements, then multiplies the result by 100."},{"text":"$$\\frac{\\text{agreements}}{\\text{disagreements}} \\times 100$","correct":false,"explanation":"This compares agreements only with disagreements rather than with all observations. The denominator must be agreements plus disagreements before the fraction is multiplied by 100."},{"text":"$$\\frac{\\text{agreements} + \\text{disagreements}}{\\text{agreements}} \\times 100$","correct":false,"explanation":"This reverses the required fraction and can produce a result above 100%. Agreements must be divided by the total of agreements and disagreements, then multiplied by 100."}]},{"id":"371d1509-61f4-4623-bd38-052aac360f27","cardType":"basic","points":2,"front":"Two observers agree on 42 behaviours and disagree on 8. What is percentage agreement?","back":"84%.","mcqOptions":[{"text":"84%.","correct":true,"explanation":"There are 50 observations altogether, so percentage agreement is $\\frac{42}{42+8}\\times100=84\\%$. The observers therefore agreed on 84% of the recorded behaviours."},{"text":"16%.","correct":false,"explanation":"This is the percentage disagreement, calculated from the eight disagreements. Percentage agreement instead uses the 42 agreements out of 50 total observations, giving 84%."},{"text":"81%.","correct":false,"explanation":"This results from dividing 42 by 52 rather than by the actual total. Agreements and disagreements sum to 50, so $\\frac{42}{50}\\times100=84\\%$."},{"text":"80%.","correct":false,"explanation":"This treats the eight disagreements as though they represented 20% of the total. Using 42 agreements out of 50 observations gives an agreement rate of 84%."}]},{"id":"9d5b8383-8aa5-43ea-b191-78ac0674a1dd","cardType":"cloze","points":null,"front":"Operationalisation means defining a variable or behaviour in a precise, [ ] way so researchers know exactly what to [ ].","back":"Operationalisation means defining a variable or behaviour in a precise, measurable way so researchers know exactly what to record.","mcqOptions":[{"text":"Operationalisation means defining a variable or behaviour in a precise, measurable way so researchers know exactly what to record.","correct":true,"explanation":"Operationalisation converts an abstract variable into measurable terms and specifies exactly what researchers should record, making data collection clearer, more consistent and more replicable."},{"text":"Operationalisation means defining a variable or behaviour in a precise, measurable way so researchers know exactly what to interpret.","correct":false,"explanation":"Operational definitions specify what researchers must record, not merely what they should interpret. Clear recording criteria reduce subjective judgement and improve consistency between observers."},{"text":"Operationalisation means defining a variable or behaviour in a precise, theoretical way so researchers know exactly what to record.","correct":false,"explanation":"A theoretical definition explains a concept but does not necessarily make it observable. Operationalisation requires a measurable definition that tells researchers exactly what to record."},{"text":"Operationalisation means defining a variable or behaviour in a precise, subjective way so researchers know exactly what to infer.","correct":false,"explanation":"Subjective judgements and inferred meanings reduce consistency between researchers. Operationalisation instead defines behaviour measurably and states exactly what should be recorded."}]},{"id":"6bc44b3c-2a84-4db1-bd0e-fb9adf83b612","cardType":"basic","points":2,"front":"Why make observation categories mutually exclusive?","back":"So one behaviour cannot fit two categories at once.","mcqOptions":[{"text":"So one behaviour cannot fit two categories at once.","correct":true,"explanation":"Mutually exclusive categories ensure each observed behaviour receives only one code. This prevents overlapping classifications and makes observers more likely to record behaviour consistently."},{"text":"So one behaviour cannot fit any category at once.","correct":false,"explanation":"Observation categories should be exhaustive enough for relevant behaviours to be coded. Mutual exclusivity means a behaviour cannot fit two categories at once, not that it fits none."},{"text":"So one behaviour cannot fit the same category at once.","correct":false,"explanation":"A behaviour is supposed to fit an appropriate single category. Mutual exclusivity prevents it from fitting two categories simultaneously, which would create ambiguous coding."},{"text":"So one behaviour cannot fit several observers at once.","correct":false,"explanation":"Behaviours are coded by observers, not placed into observers. Mutually exclusive categories ensure that one behaviour cannot be assigned to two categories at the same time."}]},{"id":"f8966f6e-d95d-4e54-b031-83189a27afa1","cardType":"basic","points":2,"front":"Why avoid double-barrelled questionnaire items such as “anxious and unhappy”?","back":"They measure two ideas in one item.","mcqOptions":[{"text":"They measure two ideas in one item.","correct":true,"explanation":"A double-barrelled item combines two distinct ideas, so a respondent may agree with one but not the other. Their single answer therefore becomes ambiguous."},{"text":"They measure one idea in two items.","correct":false,"explanation":"Measuring one idea through separate items can improve reliability when done carefully. The problem is combining two ideas in one item, which makes responses difficult to interpret."},{"text":"They measure two samples in one item.","correct":false,"explanation":"Double-barrelled wording concerns the number of ideas in a question, not the number of samples. “Anxious and unhappy” combines two ideas that may receive different answers."},{"text":"They measure one behaviour in two ways.","correct":false,"explanation":"Using multiple measures of one behaviour is not necessarily double-barrelled. The flaw occurs when one questionnaire item asks about two separate ideas but permits only one response."}]},{"id":"0583688f-e856-4c2a-a9b5-d2319671575b","cardType":"basic","points":2,"front":"Why train observers using the same coding manual?","back":"So they apply categories consistently.","mcqOptions":[{"text":"So they apply categories consistently.","correct":true,"explanation":"A shared coding manual gives every observer the same definitions and decision rules. Training with it helps them categorise equivalent behaviours consistently, improving inter-rater reliability."},{"text":"So they apply categories subjectively.","correct":false,"explanation":"Subjective application allows personal judgement to affect coding and reduces agreement. A shared coding manual is used so observers apply categories consistently according to common rules."},{"text":"So they apply categories differently.","correct":false,"explanation":"Different applications of the categories would lower inter-rater reliability. Training observers with one coding manual aims to make their coding consistent rather than different."},{"text":"So they apply categories randomly.","correct":false,"explanation":"Random coding would create disagreement unrelated to the observed behaviour. The same coding manual standardises decisions so observers apply categories consistently."}]},{"id":"3ab8f285-04cb-4fd0-8267-e56f5425d0e0","cardType":"cloze","points":null,"front":"Reliability is improved by reducing [ ] in how data are collected, scored and interpreted.","back":"Reliability is improved by reducing random variation in how data are collected, scored and interpreted.","mcqOptions":[{"text":"random variation","correct":true,"explanation":"Reliability concerns the consistency of measurements. Reducing unpredictable differences in collection, scoring and interpretation makes repeated measurements more likely to produce similar results."},{"text":"systematic validity","correct":false,"explanation":"Validity concerns whether a measure assesses what it intends to assess, whereas reliability concerns consistency. Reliability is improved by reducing random variation across measurements."},{"text":"ecological validity","correct":false,"explanation":"Ecological validity concerns whether findings generalise to real-life settings. Reliability is improved by reducing random variation in the way data are collected, scored and interpreted."},{"text":"standardised procedures","correct":false,"explanation":"Reducing standardised procedures would usually make data collection less consistent. Reliability improves when procedures are standardised because this reduces random variation between measurements."}]},{"id":"0e97db20-0023-440b-af96-6f12059d714e","cardType":"basic","points":1,"front":"Does repeating a test automatically solve poor reliability?","back":"No - it only reveals inconsistency.","mcqOptions":[{"text":"No - it only reveals inconsistency.","correct":true,"explanation":"Repeating a test can show whether scores are stable, but repetition does not remove unclear items or inconsistent procedures. The underlying causes of unreliability must be corrected."},{"text":"Yes - it always removes inconsistency.","correct":false,"explanation":"Repetition can expose inconsistent scores but cannot automatically remove their causes. Poorly worded questions, unclear coding or inconsistent procedures must be identified and improved."},{"text":"Yes - it directly increases validity.","correct":false,"explanation":"Repeated testing assesses consistency rather than whether a test measures the intended construct. It may reveal poor reliability, but it does not directly increase validity."},{"text":"No - it only removes validity.","correct":false,"explanation":"Repeating a test does not automatically remove validity. Its main purpose is to reveal inconsistency between measurements, after which the causes of poor reliability can be addressed."}]}]}],"examCountdown":{"examDate":"2027-05-18","paperLabel":"Component 1"},"heading":"Reliability","title":"Reliability Flashcards","topbar":{"backHref":"/a-level/psychology/eduqas/flashcards"},"topicSidebar":"$L38"}]}],"topicId":"0183f147-a01b-5c3c-ac50-44339eb0ce79","walled":"$L39"}],"$L3a","$L3b"]