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The correct term is quantitative data because scores, frequencies, ratings and times are numerical measurements."},{"text":"Nominal data","correct":false,"explanation":"Nominal data place cases into named categories and may include non-numerical labels. The broader term required here is quantitative data because the definition concerns measurable numerical values."},{"text":"Secondary data","correct":false,"explanation":"Secondary data are data previously collected by another researcher or organisation. The correct term is quantitative data because the sentence defines data by their numerical form, not their source."}]},{"id":"16740dc0-6488-4461-bc91-3b1f6bca587e","cardType":"cloze","points":null,"front":"[ ] are non-numerical data involving words, meanings, experiences, or interpretations.","back":"Qualitative data are non-numerical data involving words, meanings, experiences, or interpretations.","mcqOptions":[{"text":"Qualitative data","correct":true,"explanation":"Qualitative data are expressed through language and concern meanings, experiences or interpretations, rather than measurements that can be directly represented as numerical values."},{"text":"Quantitative data","correct":false,"explanation":"Quantitative data involve numerical measurements, scores or frequencies. The correct term is qualitative data because words, meanings, experiences and interpretations are non-numerical forms of information."},{"text":"Primary data","correct":false,"explanation":"Primary data are collected first-hand for a particular investigation and can be numerical or non-numerical. The correct term is qualitative data because the definition focuses on non-numerical content."},{"text":"Ordinal data","correct":false,"explanation":"Ordinal data consist of values or categories placed in a meaningful rank order. The correct term is qualitative data because the sentence refers to words, meanings and interpretations."}]},{"id":"2bbac136-48e8-4a0d-b2cf-3683609befd3","cardType":"basic","points":1,"front":"Why is “Participant 17” not quantitative data?","back":"It is a label, not a measurement or amount.","mcqOptions":[{"text":"It is a label, not a measurement or amount.","correct":true,"explanation":"The number identifies a particular participant but does not measure any characteristic or quantity. It therefore functions as an identifying label rather than as quantitative data."},{"text":"It is a score, not a measurement or amount.","correct":false,"explanation":"Treating every number as a score confuses numerical appearance with numerical meaning. “Participant 17” is a label because 17 identifies the person rather than measuring performance."},{"text":"It is a frequency, not a measurement or amount.","correct":false,"explanation":"A frequency states how often something occurred, whereas 17 identifies which participant is being discussed. It is therefore a label, not a frequency or other measured amount."},{"text":"It is a rating, not a measurement or amount.","correct":false,"explanation":"A rating represents a position on a response scale, but 17 does not evaluate the participant. It is simply a label used to identify that participant."}]},{"id":"59c17952-1a17-4e81-9664-e570bc24f37a","cardType":"basic","points":1,"front":"What does operationalisation let psychologists do with an abstract idea?","back":"Make it measurable or recordable consistently.","mcqOptions":[{"text":"Make it measurable or recordable consistently.","correct":true,"explanation":"Operationalisation translates an abstract construct into a specific variable or procedure, allowing psychologists to measure or record it consistently across participants and replications."},{"text":"Make it more subjective and open-ended.","correct":false,"explanation":"Operationalisation reduces ambiguity rather than increasing subjectivity. It makes an abstract concept measurable or consistently recordable by specifying exactly how the variable will be observed."},{"text":"Make it automatically valid and reliable.","correct":false,"explanation":"Operationalisation does not guarantee validity or reliability; a measure can still be poorly designed. Its purpose is to make the abstract idea measurable or recordable consistently."},{"text":"Make it theoretical rather than observable.","correct":false,"explanation":"This reverses the purpose of operationalisation, which moves from an abstract theoretical concept to an observable procedure. It makes the idea measurable or consistently recordable."}]},{"id":"385637ec-f0b9-4d3d-9740-7114e7aa3581","cardType":"basic","points":1,"front":"When identifying data type, what should you check instead of the method name?","back":"What the researcher actually recorded.","mcqOptions":[{"text":"What the researcher actually recorded.","correct":true,"explanation":"The recorded outcome determines the data type. The same method can generate numbers, words or both, so its name alone does not establish whether data are quantitative or qualitative."},{"text":"What the researcher intended to investigate.","correct":false,"explanation":"The research aim identifies the topic, not the resulting data type. Classification depends on what was actually recorded, such as numerical scores or participants’ words."},{"text":"What the researcher called the procedure.","correct":false,"explanation":"A procedure’s name does not reliably indicate whether its output is numerical or verbal. Data type must be identified from what the researcher actually recorded."},{"text":"What the researcher predicted would happen.","correct":false,"explanation":"A prediction states the expected result but does not determine the form of the evidence. Check what the researcher actually recorded to identify the data type."}]},{"id":"ea9f077d-487c-4a17-aa6b-bc083dc04a29","cardType":"basic","points":2,"front":"Why do closed questions and rating scales usually produce quantitative data?","back":"They produce fixed or numerical responses that can be counted.","mcqOptions":[{"text":"They produce fixed or numerical responses that can be counted.","correct":true,"explanation":"Closed questions restrict participants to predetermined alternatives, while rating scales assign numerical or ordered values. These responses can be counted and analysed quantitatively."},{"text":"They produce detailed or personal responses that can be interpreted.","correct":false,"explanation":"Detailed personal responses are more typical of open questions and usually generate qualitative data. Closed questions and rating scales provide fixed or numerical responses that can be counted."},{"text":"They produce unstructured or verbal responses that can be coded.","correct":false,"explanation":"Closed questions are structured rather than unstructured, and rating scales usually generate fixed values. They generally produce quantitative data because their responses can be counted."},{"text":"They produce unique or individual responses that cannot be counted.","correct":false,"explanation":"Predetermined response choices are designed to be comparable across participants, not unique to each person. Their fixed or numerical form allows the responses to be counted."}]},{"id":"9555cc07-47e7-4807-921d-29c5b098bcba","cardType":"basic","points":2,"front":"Why do open questions usually produce qualitative data?","back":"Participants answer in their own words.","mcqOptions":[{"text":"Participants answer in their own words.","correct":true,"explanation":"Open questions do not restrict participants to predetermined choices, so responses are usually expressed in their own words and provide non-numerical, qualitative information."},{"text":"Participants answer in pre-set categories.","correct":false,"explanation":"Pre-set categories are characteristic of closed questions and usually generate countable quantitative data. Open questions instead allow participants to respond in their own words."},{"text":"Participants answer in numerical ratings.","correct":false,"explanation":"Numerical ratings are produced by rating scales and are normally treated as quantitative data. Open questions usually produce qualitative data because participants answer in their own words."},{"text":"Participants answer in ranked alternatives.","correct":false,"explanation":"Ranked alternatives limit participants to predetermined responses and produce ordinal data. Open questions allow unrestricted answers in participants’ own words, making the data usually qualitative."}]},{"id":"5b5c1edb-ab94-42ab-b27a-4d309d24c356","cardType":"basic","points":2,"front":"Why can a structured observation produce quantitative data?","back":"Researchers count behaviours in pre-set categories.","mcqOptions":[{"text":"Researchers count behaviours in pre-set categories.","correct":true,"explanation":"A structured observation uses predetermined behavioural categories, allowing observers to record frequencies. These numerical counts constitute quantitative data that can be compared and analysed."},{"text":"Researchers describe experiences in open-ended accounts.","correct":false,"explanation":"Open-ended descriptions produce qualitative information rather than numerical data. A structured observation can produce quantitative data when researchers count behaviours within pre-set categories."},{"text":"Researchers interpret meanings in unstructured notes.","correct":false,"explanation":"Interpreting meanings from unstructured notes is associated with qualitative observational data. Structured observation becomes quantitative when researchers count behaviours in predetermined categories."},{"text":"Researchers record opinions in participants’ own words.","correct":false,"explanation":"Opinions expressed in participants’ own words are qualitative and are not behavioural frequency counts. Structured observation can generate quantitative data by counting behaviours in pre-set categories."}]},{"id":"7bf6c357-d13e-403b-be88-743b5b8661a5","cardType":"basic","points":2,"front":"Why can an unstructured observation produce qualitative data?","back":"Researchers record detailed behaviour without fixed categories.","mcqOptions":[{"text":"Researchers record detailed behaviour without fixed categories.","correct":true,"explanation":"Unstructured observations do not restrict recording to predetermined categories, so researchers can produce rich, descriptive accounts of behaviour and context rather than only numerical frequencies."},{"text":"Researchers count behaviour frequencies within fixed categories.","correct":false,"explanation":"Counting behaviour in fixed categories characterises a structured observation and usually produces quantitative data. Qualitative data arise when researchers record detailed behaviour without fixed categories."},{"text":"Researchers assign numerical ratings using standardised scales.","correct":false,"explanation":"Standardised rating scales produce numerical measurements rather than detailed qualitative descriptions. An unstructured observation produces qualitative data by recording detailed behaviour without fixed categories."},{"text":"Researchers tick predetermined behaviours on standard checklists.","correct":false,"explanation":"Predetermined behavioural checklists limit researchers to selected categories and generally yield quantitative frequencies. Qualitative data result from recording detailed behaviour without fixed categories."}]},{"id":"87a5db65-abb1-43f5-b159-73bc6282568a","cardType":"basic","points":2,"front":"Why are quantitative data usually easier to compare across participants?","back":"Everyone gives the same kind of response, e.g. a score.","mcqOptions":[{"text":"Everyone gives the same kind of response, e.g. a score.","correct":true,"explanation":"Standardised responses place participants on a common measurement scale, allowing their scores to be compared directly and differences between them to be identified clearly."},{"text":"Everyone gives a different personal account, e.g. a diary.","correct":false,"explanation":"Unique personal accounts are difficult to compare directly because their content and detail vary. Quantitative data are easier to compare because everyone provides the same kind of response, such as a score."},{"text":"Everyone gives an open-ended explanation, e.g. an interview.","correct":false,"explanation":"Open-ended explanations vary in wording and meaning, making consistent comparisons harder. Quantitative methods instead obtain the same kind of response from everyone, such as a score."},{"text":"Everyone completes a different kind of measure, e.g. a task.","correct":false,"explanation":"Different measures would prevent valid direct comparisons because participants would not share a common scale. Comparability comes from everyone giving the same kind of response, such as a score."}]},{"id":"aaf0dc46-c9a6-4869-bead-c5124f4d5cb2","cardType":"basic","points":2,"front":"Why may quantitative data be more objective?","back":"They depend less on researcher interpretation.","mcqOptions":[{"text":"They depend less on researcher interpretation.","correct":true,"explanation":"Numerical data are normally collected and analysed using standardised rules, reducing the extent to which researchers must make subjective judgements about participants’ meanings."},{"text":"They depend more on participant interpretation.","correct":false,"explanation":"Participant interpretation is not what makes quantitative findings more objective. Their objectivity comes from relying less on researcher interpretation through standardised measurement and numerical analysis."},{"text":"They depend more on researcher judgement.","correct":false,"explanation":"Personal researcher judgement increases subjectivity rather than objectivity because expectations may influence conclusions. Quantitative data can be more objective because they depend less on researcher interpretation."},{"text":"They rely mainly on unstructured researcher impressions.","correct":false,"explanation":"Unstructured researcher impressions lack standardised criteria and are vulnerable to bias. Quantitative data may be more objective because they depend less on researcher interpretation."}]},{"id":"138b4b1b-14fb-4f4b-80fe-f0e7334e1fe9","cardType":"basic","points":2,"front":"What is the reductionist weakness of quantitative data?","back":"They may oversimplify experience by reducing it to a number.","mcqOptions":[{"text":"They may oversimplify experience by reducing it to a number.","correct":true,"explanation":"Converting complex experiences into numerical scores can remove context, detail and individual meaning, producing a simplified account that does not represent the whole experience."},{"text":"They may overcomplicate experience by adding detailed personal meanings.","correct":false,"explanation":"Adding detailed personal meanings describes a strength of qualitative data, not a reductionist weakness of quantitative data. Quantitative measures may oversimplify experience by reducing it to a number."},{"text":"They may preserve complex experience by recording it in words.","correct":false,"explanation":"Recording experiences in participants’ own words preserves complexity and context rather than reducing them. The reductionist weakness is that quantitative data may oversimplify experience by reducing it to a number."},{"text":"They may emphasise individual context by examining it in detail.","correct":false,"explanation":"Exploring individual context is associated with holistic qualitative research rather than reductionist quantitative measurement. Quantitative data may oversimplify experience by reducing it to a number."}]},{"id":"69f9b465-3d93-4e85-ac76-fb29d657e316","cardType":"cloze","points":null,"front":"Repeated ideas or patterns of meaning in qualitative data are called [ ].","back":"Repeated ideas or patterns of meaning in qualitative data are called themes.","mcqOptions":[{"text":"themes","correct":true,"explanation":"Themes are recurring ideas or broader patterns of shared meaning identified across qualitative material, often through thematic analysis of interviews, diaries or other textual data."},{"text":"codes","correct":false,"explanation":"Codes are labels attached to particular pieces of qualitative material during analysis. Broader repeated patterns of meaning developed from related codes are called themes."},{"text":"categories","correct":false,"explanation":"Categories group similar items or codes, but the recognised term for repeated ideas or patterns of meaning in qualitative data is themes."},{"text":"frequencies","correct":false,"explanation":"Frequencies are numerical counts showing how often something occurs, not the repeated ideas or patterns of meaning themselves. Those patterns are called themes."}]},{"id":"63885c7e-83c2-4ab1-b185-1c1810196327","cardType":"basic","points":2,"front":"How can content analysis turn qualitative material into quantitative summaries?","back":"By counting coded categories.","mcqOptions":[{"text":"By counting coded categories.","correct":true,"explanation":"Researchers can code qualitative material into defined categories and count how often each category occurs, producing numerical frequencies that can be summarised and compared."},{"text":"By describing uncoded responses.","correct":false,"explanation":"Describing uncoded responses preserves qualitative detail but does not create numerical summaries. Content analysis quantifies material by coding it into categories and counting those categories."},{"text":"By interpreting individual meanings.","correct":false,"explanation":"Interpreting individual meanings may deepen qualitative analysis, but it does not itself generate quantitative results. Quantitative summaries are produced by counting coded categories."},{"text":"By quoting participant accounts.","correct":false,"explanation":"Quoting participants directly illustrates qualitative findings rather than converting them into numbers. Content analysis produces quantitative summaries by counting coded categories."}]},{"id":"a01f887b-1db4-47d5-b908-15b8c89efe48","cardType":"basic","points":2,"front":"What can qualitative data reveal that a rating scale may miss?","back":"Thoughts, feelings and meanings.","mcqOptions":[{"text":"Thoughts, feelings and meanings.","correct":true,"explanation":"Open qualitative responses let participants describe their subjective experiences and interpretations, revealing thoughts, feelings and meanings that fixed numerical ratings may not capture."},{"text":"Scores, rankings and frequencies.","correct":false,"explanation":"Scores, rankings and frequencies are precisely the numerical outputs that rating scales can provide. Qualitative data instead reveal thoughts, feelings and meanings behind responses."},{"text":"Averages, ranges and distributions.","correct":false,"explanation":"Averages, ranges and distributions are statistical summaries derived from quantitative measurements. Qualitative data can reveal participants’ thoughts, feelings and meanings that such summaries may conceal."},{"text":"Percentages, totals and correlations.","correct":false,"explanation":"Percentages, totals and correlations summarise numerical patterns rather than subjective experience. Qualitative responses can uncover thoughts, feelings and meanings that a rating scale may miss."}]},{"id":"54ecf927-4979-4f5b-a3cb-675bdfe0c9af","cardType":"basic","points":2,"front":"Why can qualitative data improve ecological validity?","back":"Participants explain experiences in their own words.","mcqOptions":[{"text":"Participants explain experiences in their own words.","correct":true,"explanation":"Allowing participants to use their own words can capture naturally experienced meanings and context, making findings more representative of real-life experience and therefore more ecologically valid."},{"text":"Participants classify experiences into the researcher’s categories.","correct":false,"explanation":"Using the researcher’s fixed categories may impose artificial assumptions and omit participants’ real meanings. Ecological validity can improve when participants explain experiences in their own words."},{"text":"Participants reduce experiences to standard numerical scores.","correct":false,"explanation":"Restricting participants to numerical scores removes contextual detail and may make responses less representative of everyday experience. Qualitative data allow participants to explain experiences in their own words."},{"text":"Participants repeat experiences using predetermined standard statements.","correct":false,"explanation":"Repeating predetermined statements limits what participants can express and may reduce realism. Ecological validity improves when participants explain experiences freely in their own words."}]},{"id":"ebc1ed63-cc8d-4efb-9c76-4bae6f1e2676","cardType":"basic","points":2,"front":"Why can qualitative analysis be more subjective?","back":"Different researchers may interpret the same data differently.","mcqOptions":[{"text":"Different researchers may interpret the same data differently.","correct":true,"explanation":"Qualitative analysis requires judgement when coding responses and identifying themes, so researchers can apply different interpretations to identical material. This makes the analysis more vulnerable to subjectivity."},{"text":"Different participants may interpret the same questions differently.","correct":false,"explanation":"Differences in how participants understand questions affect the responses collected, not the subjectivity of the later analysis. Subjectivity arises because different researchers may interpret the same data differently."},{"text":"Different researchers may calculate the same mean differently.","correct":false,"explanation":"A correctly calculated mean should not vary between researchers; variation would indicate an arithmetic error. Qualitative analysis is subjective because researchers may interpret and code the same material differently."},{"text":"Larger samples may produce the same findings more consistently.","correct":false,"explanation":"Sample size affects representativeness and confidence, but it does not explain analyst subjectivity. Qualitative analysis can be subjective because different researchers may interpret the same data differently."}]},{"id":"fe69e0b9-e807-44a7-8b10-cbd949a8e5fb","cardType":"cloze","points":null,"front":"[ ] checks agreement when more than one researcher codes the same data.","back":"Inter-rater reliability checks agreement when more than one researcher codes the same data.","mcqOptions":[{"text":"Inter-rater reliability","correct":true,"explanation":"Inter-rater reliability measures the extent to which two or more researchers agree when independently coding or categorising the same qualitative data."},{"text":"Test-retest reliability","correct":false,"explanation":"Test-retest reliability assesses whether a measure produces consistent results on different occasions. Agreement between researchers coding the same data is checked using inter-rater reliability."},{"text":"Internal reliability","correct":false,"explanation":"Internal reliability assesses consistency among items within a test or questionnaire. Inter-rater reliability instead measures agreement between researchers coding the same material."},{"text":"Concurrent validity","correct":false,"explanation":"Concurrent validity is assessed by comparing a measure with an established measure taken at the same time. Agreement between different coders is measured by inter-rater reliability."}]},{"id":"0565ebf6-4666-41e2-bb7a-f1a0963cdced","cardType":"cloze","points":null,"front":"Mixed methods research combines [ ] and [ ] data within the same study or research programme.","back":"Mixed methods research combines quantitative and qualitative data within the same study or research programme.","mcqOptions":[{"text":"Mixed methods research combines quantitative and qualitative data within the same study or research programme.","correct":true,"explanation":"Mixed methods research deliberately integrates numerical quantitative data with detailed qualitative data, allowing researchers to examine patterns while also exploring meanings and experiences."},{"text":"Mixed methods research combines quantitative and secondary data within the same study or research programme.","correct":false,"explanation":"Secondary data describes information previously collected by someone else and can itself be quantitative or qualitative. Mixed methods specifically combines quantitative and qualitative data."},{"text":"Mixed methods research combines primary and qualitative data within the same study or research programme.","correct":false,"explanation":"Primary describes data collected directly by the researcher, not a type of data required for mixed methods. Mixed methods combines quantitative data with qualitative data."},{"text":"Mixed methods research combines objective and subjective data within the same study or research programme.","correct":false,"explanation":"Objective and subjective describe the influence of personal judgement, but they do not define the two components of mixed methods. The required combination is quantitative and qualitative data."}]},{"id":"11799b38-7336-4304-8dd4-7dc3f2a9376a","cardType":"basic","points":2,"front":"How does triangulation make findings more convincing?","back":"Different methods or data sources support each other.","mcqOptions":[{"text":"Different methods or data sources support each other.","correct":true,"explanation":"Triangulation compares evidence obtained through different methods or sources. When these produce consistent conclusions, the findings gain credibility because they do not depend on one approach alone."},{"text":"A single method or data source is repeated several times.","correct":false,"explanation":"Repeating one method may assess reliability, but it does not provide triangulation. Triangulation is convincing because different methods or data sources support the same conclusion."},{"text":"Different researchers or participants always reach identical conclusions.","correct":false,"explanation":"Triangulation does not require everyone to reach identical conclusions, and participants do not usually analyse findings. It uses different methods or data sources to provide converging support."},{"text":"Different hypotheses or variables are combined into one score.","correct":false,"explanation":"Combining hypotheses or variables does not provide independent confirmation of a finding. Triangulation strengthens conclusions when different methods or data sources support each other."}]},{"id":"ee39f830-4f9d-4166-a2b5-5b10695bdab7","cardType":"basic","points":2,"front":"Why is confidentiality especially important for qualitative quotes?","back":"Detailed quotes may make participants identifiable.","mcqOptions":[{"text":"Detailed quotes may make participants identifiable.","correct":true,"explanation":"Quotes can contain distinctive experiences, wording, locations or personal details that reveal a participant’s identity, even when their name has been removed from the report."},{"text":"Detailed quotes may make participants more representative.","correct":false,"explanation":"Quotations illustrate individual experiences but do not make a sample more representative. Confidentiality matters because distinctive details within quotes may allow participants to be identified."},{"text":"Detailed quotes may make participants statistically significant.","correct":false,"explanation":"Statistical significance applies to research results, not individual participants, and is generally derived from quantitative analysis. Qualitative quotes require confidentiality because their details may reveal identities."},{"text":"Detailed quotes may make participants easier to debrief.","correct":false,"explanation":"Debriefing involves explaining the study to participants after their involvement and is not made easier by publishing quotes. Confidentiality is crucial because detailed quotes may identify participants."}]},{"id":"bb6d927e-b9a4-47ed-a9f8-182c7ff7cbe1","cardType":"cloze","points":null,"front":"Use quantitative data when the aim is to measure, compare, count, or test a [ ].","back":"Use quantitative data when the aim is to measure, compare, count, or test a hypothesis.","mcqOptions":[{"text":"hypothesis","correct":true,"explanation":"A hypothesis is a precise, testable prediction about variables. Quantitative measurements can be analysed statistically to determine whether the evidence supports or rejects that prediction."},{"text":"theme","correct":false,"explanation":"A theme is a recurring pattern of meaning identified during qualitative analysis rather than a prediction tested numerically. Quantitative research is commonly used to test a hypothesis."},{"text":"personal account","correct":false,"explanation":"A personal account is usually explored qualitatively to understand an individual’s experiences and meanings. Quantitative data are suited to testing a clearly stated hypothesis."},{"text":"case study","correct":false,"explanation":"A case study is a research method or detailed investigation, not a testable prediction. Quantitative data are used to test a hypothesis by analysing measured variables."}]},{"id":"91c9e92c-c702-4fa7-805f-7eb1d10f6b49","cardType":"cloze","points":null,"front":"Use qualitative data when the aim is to explore meaning, experience, or [ ].","back":"Use qualitative data when the aim is to explore meaning, experience, or context.","mcqOptions":[{"text":"context","correct":true,"explanation":"Qualitative data provide rich, detailed information that helps researchers understand how experiences and meanings are shaped by the social, cultural or personal context in which they occur."},{"text":"frequency","correct":false,"explanation":"Frequency is a numerical count of how often something occurs and is therefore usually examined using quantitative data. Qualitative data are better suited to exploring meaning, experience or context."},{"text":"correlation","correct":false,"explanation":"A correlation is a numerical measure of the relationship between co-variables and requires quantitative data. Qualitative research instead explores meaning, experience or context in depth."},{"text":"effect size","correct":false,"explanation":"Effect size is a quantitative measure of the magnitude of a difference or relationship. Qualitative data are used to investigate meaning, experience or context rather than calculate it."}]}]}],"examCountdown":{"examDate":"2027-05-18","paperLabel":"Paper 1"},"heading":"Quantitative and qualitative data","title":"Quantitative and qualitative data Flashcards","topbar":{"backHref":"/a-level/psychology/aqa/flashcards"},"topicSidebar":"$L39"}]}],"topicId":"0f381dab-ac52-5af1-bece-1149dd2ad1e2","walled":"$L3a"}],"$L3b","$L3c"]