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uses a systematic coding framework to convert communication material into data, allowing patterns in words, images or actions to be identified and analysed."},{"text":"Discourse analysis","correct":false,"explanation":"Discourse analysis examines how language constructs meanings and social realities, rather than primarily coding communication into categories. Content analysis is the systematic method described here."},{"text":"Thematic analysis","correct":false,"explanation":"Thematic analysis identifies and interprets themes, usually within qualitative data, but does not necessarily involve systematic coding and counting. Content analysis is the method defined here."},{"text":"Conversation analysis","correct":false,"explanation":"Conversation analysis studies the detailed organisation of naturally occurring talk, such as turn-taking. Content analysis instead systematically codes communication material to identify broader patterns."}]},{"id":"ef247d35-1e45-45fa-8e03-8a4d6d10639e","cardType":"basic","points":1,"front":"In content analysis, what does coding turn raw words, images or actions into?","back":"Organised data that can be counted, compared or interpreted consistently.","mcqOptions":[{"text":"Organised data that can be counted, compared or interpreted consistently.","correct":true,"explanation":"Coding assigns material to defined categories, transforming otherwise unstructured communication into organised data that different observations and coders can analyse consistently."},{"text":"Objective facts that are free from all researcher judgement.","correct":false,"explanation":"Coding does not remove all researcher judgement because decisions are involved in defining and applying categories. It produces organised data that can be counted, compared or interpreted consistently."},{"text":"General conclusions that can be applied to every population automatically.","correct":false,"explanation":"Coding alone cannot make findings generalisable, especially when the sample is unrepresentative. It turns raw material into organised data that can be counted, compared or interpreted consistently."},{"text":"Unstructured material that can be interpreted differently by every coder.","correct":false,"explanation":"Consistent coding is intended to reduce, not preserve, differences between coders. It turns raw material into organised data that can be counted, compared or interpreted consistently."}]},{"id":"a9f852e1-d1c5-4def-a0ae-ab1f6e364c1c","cardType":"basic","points":2,"front":"What is the risk if the sample of communication material is biased?","back":"Conclusions may be misleading despite reliable coding.","mcqOptions":[{"text":"Conclusions may be misleading despite reliable coding.","correct":true,"explanation":"Reliable coding can be applied consistently to a biased sample, but the resulting patterns will not represent the wider body of communication accurately, making conclusions misleading."},{"text":"Conclusions may be unreliable despite accurate coding.","correct":false,"explanation":"A biased sample primarily threatens representativeness and validity, not coding reliability. Even when coding is reliable, conclusions may still be misleading because the sampled material is unrepresentative."},{"text":"Conclusions may be representative despite inconsistent coding.","correct":false,"explanation":"Bias makes a sample less representative, while inconsistent coding creates an additional reliability problem. The key risk is that conclusions may be misleading despite reliable coding."},{"text":"Conclusions may be objective despite subjective coding.","correct":false,"explanation":"Subjective coding does not create objectivity, and this option overlooks the sampling problem. A biased sample can produce misleading conclusions even when its coding is reliable."}]},{"id":"521be5df-aa71-49b1-929f-879214cdf5c4","cardType":"cloze","points":null,"front":"The [ ] is the specific part of the material that will be coded.","back":"The unit of analysis is the specific part of the material that will be coded.","mcqOptions":[{"text":"unit of analysis","correct":true,"explanation":"The unit of analysis specifies exactly what portion of the communication will receive a code, such as each word, sentence, image, scene or interaction."},{"text":"coding category","correct":false,"explanation":"A coding category is the label assigned to a unit, not the part of the material being coded. The specific part selected for coding is the unit of analysis."},{"text":"sampling frame","correct":false,"explanation":"A sampling frame identifies the available material or cases from which a sample can be selected. The particular segment that receives a code is the unit of analysis."},{"text":"operational definition","correct":false,"explanation":"An operational definition states the observable criteria for applying a category, rather than identifying the material being coded. That material is called the unit of analysis."}]},{"id":"c4c67005-a61c-4cbb-a6e4-1ba71ab89133","cardType":"basic","points":3,"front":"What trade-off makes a unit of analysis useful?","back":"

Small enough to code precisely.
Large enough to keep meaning.

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

Small enough to code precisely.
Large enough to keep meaning.

","correct":true,"explanation":"A useful unit must be narrow enough for coders to classify consistently while retaining enough surrounding material for its meaning to be understood accurately."},{"text":"

Large enough to code precisely.
Small enough to keep meaning.

","correct":false,"explanation":"This reverses the trade-off: large units are harder to code precisely, while very small units can lose context. Units should be small enough for precision and large enough for meaning."},{"text":"

Small enough to remove all context.
Large enough to include every theme.

","correct":false,"explanation":"Removing context damages interpretation, and no unit needs to contain every theme. It should instead be small enough for precise coding but large enough to preserve meaning."},{"text":"

Broad enough to prevent interpretation.
Narrow enough to represent the sample.

","correct":false,"explanation":"Unit size cannot eliminate interpretation or make a sample representative. The relevant balance is between being small enough to code precisely and large enough to retain meaning."}]},{"id":"fc6a73ea-7623-4b11-954e-c7d4e12166cd","cardType":"cloze","points":null,"front":"A [ ] is a label used to classify a unit of analysis.","back":"A coding category is a label used to classify a unit of analysis.","mcqOptions":[{"text":"coding category","correct":true,"explanation":"A coding category is a defined label, such as aggressive or non-aggressive, that a coder assigns to a unit according to the coding rules."},{"text":"unit of analysis","correct":false,"explanation":"The unit of analysis is the segment being classified, such as a sentence or action, rather than its label. That label is called a coding category."},{"text":"sampling unit","correct":false,"explanation":"A sampling unit is an element considered when selecting material for study, not a label assigned during coding. The classification label is a coding category."},{"text":"operational definition","correct":false,"explanation":"An operational definition specifies the observable criteria for deciding when a label applies. The label used to classify the unit itself is a coding category."}]},{"id":"99a6f9b5-af0c-411e-8df6-8199ac1dece5","cardType":"basic","points":2,"front":"Why operationalise a coding category such as aggression?","back":"So coders know exactly what counts as an example.","mcqOptions":[{"text":"So coders know exactly what counts as an example.","correct":true,"explanation":"Operationalisation provides observable, specific criteria for a category, helping different coders decide consistently whether a behaviour or communication unit qualifies as an example."},{"text":"So coders can use their own judgement for each example.","correct":false,"explanation":"Relying on unrestricted personal judgement would increase inconsistency between coders. Operationalisation instead ensures that coders know exactly what counts as an example of aggression."},{"text":"So every aggressive act is placed in several categories.","correct":false,"explanation":"Placing one act in several categories may violate mutual exclusivity and is not the purpose of operationalisation. Categories are operationalised so coders know exactly what counts."},{"text":"So the sample becomes representative of the target population.","correct":false,"explanation":"Operationalisation improves clarity and coding consistency, but it cannot correct a biased sample. Its purpose is to tell coders exactly what counts as an example."}]},{"id":"da5b5ab1-343a-4f5e-ba94-5eca7ac1dc12","cardType":"cloze","points":null,"front":"Categories are [ ] when one unit cannot fit more than one category at the same time.","back":"Categories are mutually exclusive when one unit cannot fit more than one category at the same time.","mcqOptions":[{"text":"mutually exclusive","correct":true,"explanation":"Mutually exclusive categories do not overlap, so each unit can be assigned to only one category at a given point in the coding process."},{"text":"collectively exhaustive","correct":false,"explanation":"Collectively exhaustive categories provide a suitable category for every possible unit, but they may still overlap. Categories that cannot overlap are described as mutually exclusive."},{"text":"operationally defined","correct":false,"explanation":"Operationally defined categories have clear observable criteria, but two clearly defined categories could still overlap. Categories allowing only one classification are mutually exclusive."},{"text":"internally reliable","correct":false,"explanation":"Reliability concerns the consistency with which categories are applied, not whether categories overlap. Categories are mutually exclusive when a unit cannot fit more than one simultaneously."}]},{"id":"0552d072-9837-421b-9fd7-2d7462b86428","cardType":"basic","points":2,"front":"Why add a neutral or mixed category when coding headline tone?","back":"It makes categories more exhaustive, reducing forced guesses.","mcqOptions":[{"text":"It makes categories more exhaustive, reducing forced guesses.","correct":true,"explanation":"Including neutral or mixed responses ensures that every headline tone can be classified appropriately. This makes the categories exhaustive and prevents coders forcing unsuitable cases into positive or negative categories."},{"text":"It makes categories more objective, eliminating coder bias.","correct":false,"explanation":"Adding categories does not eliminate coder bias, because coders may still interpret headlines differently. The main benefit is greater exhaustiveness, which reduces forced guesses when tone is neither clearly positive nor negative."},{"text":"It makes categories more reliable, removing consistency checks.","correct":false,"explanation":"A neutral or mixed category cannot guarantee reliability or replace consistency checks between coders. It makes the category system more exhaustive, allowing ambiguous headlines to be coded without forced classification."},{"text":"It makes categories more valid, proving accurate measurement.","correct":false,"explanation":"Adding a category does not by itself prove that headline tone is measured validly. Its direct purpose is to make the categories exhaustive and reduce guesses when headlines have neutral or mixed tone."}]},{"id":"a713b80b-a6fb-4fa5-80bf-a733909c492d","cardType":"cloze","points":null,"front":"A [ ] lists categories, definitions, decision rules and examples for coders.","back":"A coding frame lists categories, definitions, decision rules and examples for coders.","mcqOptions":[{"text":"coding frame","correct":true,"explanation":"A coding frame standardises content analysis by specifying the categories, their definitions, decision rules and examples. This guides coders and helps them classify material consistently."},{"text":"coding unit","correct":false,"explanation":"A coding unit is the specific piece of content being classified, such as a word, sentence or image. The coding frame lists the categories, definitions, rules and examples."},{"text":"sampling frame","correct":false,"explanation":"A sampling frame identifies the population members from which a sample may be selected. A coding frame instead sets out the categories, definitions, decision rules and examples used by coders."},{"text":"thematic map","correct":false,"explanation":"A thematic map shows relationships between themes during qualitative analysis, rather than providing standardised coding instructions. The list of categories, definitions, rules and examples is a coding frame."}]},{"id":"2c642d07-0697-4bbd-b7a1-7541080250ad","cardType":"basic","points":2,"front":"How does a coding frame improve content analysis?","back":"It reduces guesswork and improves replicability.","mcqOptions":[{"text":"It reduces guesswork and improves replicability.","correct":true,"explanation":"Clear categories, definitions and decision rules reduce the need for coders to make personal guesses. This makes the coding process easier for other researchers to repeat consistently."},{"text":"It increases sample size and improves generalisability.","correct":false,"explanation":"A coding frame standardises classification but does not increase the number or representativeness of items sampled. It improves content analysis by reducing guesswork and making the procedure more replicable."},{"text":"It removes observer bias and proves validity.","correct":false,"explanation":"A coding frame can limit subjective judgement, but it cannot completely remove bias or prove validity. Its clearer rules mainly reduce guesswork and improve the replicability of coding."},{"text":"It generates qualitative data and improves ecological validity.","correct":false,"explanation":"Coding frames can be used with quantitative or qualitative analysis and do not automatically improve ecological validity. Their purpose is to reduce guesswork and enable more replicable classification."}]},{"id":"5bacbb4f-6e38-4885-b3b4-95078eec900b","cardType":"cloze","points":null,"front":"[ ] is surface-level and directly present; [ ] is underlying meaning requiring interpretation.","back":"Manifest content is surface-level and directly present; latent content is underlying meaning requiring interpretation.","mcqOptions":[{"text":"Manifest content is surface-level and directly present; latent content is underlying meaning requiring interpretation.","correct":true,"explanation":"Manifest content consists of observable, explicit features of material. Latent content concerns implied or underlying meanings, so identifying it requires interpretation by the coder."},{"text":"Latent content is surface-level and directly present; latent content is underlying meaning requiring interpretation.","correct":false,"explanation":"Latent content is not the directly observable surface material; it is the underlying meaning requiring interpretation. Surface-level features are described as manifest content."},{"text":"Manifest content is surface-level and directly present; manifest content is underlying meaning requiring interpretation.","correct":false,"explanation":"Manifest content correctly describes directly present surface features, but it does not describe underlying meaning. Meaning that must be inferred is latent content."},{"text":"Latent content is surface-level and directly present; manifest content is underlying meaning requiring interpretation.","correct":false,"explanation":"This reverses the two concepts. Manifest content is explicit and directly observable, whereas latent content refers to implied meanings that the coder must interpret."}]},{"id":"4e9ae8ac-6d72-49a3-8d6d-52e1b6d663d6","cardType":"basic","points":2,"front":"Why is latent content harder to code reliably than manifest content?","back":"It requires interpretation, so coding is more subjective.","mcqOptions":[{"text":"It requires interpretation, so coding is more subjective.","correct":true,"explanation":"Latent content concerns implied or underlying meanings rather than directly observable features. Coders must interpret these meanings, creating greater scope for subjective disagreement and lower reliability."},{"text":"It uses frequencies, so coding is more quantitative.","correct":false,"explanation":"Both manifest and latent categories can be converted into frequencies, so quantification does not explain the reliability difference. Latent coding is harder because interpreting underlying meaning introduces subjectivity."},{"text":"It is directly observable, so categories overlap more often.","correct":false,"explanation":"Direct observability is a feature of manifest content, which generally makes coding easier and more reliable. Latent content is harder because its underlying meanings require subjective interpretation."},{"text":"It requires larger samples, so coding is more time-consuming.","correct":false,"explanation":"Latent analysis does not inherently require a larger sample than manifest analysis. Its reliability problem arises because coders must interpret underlying meanings and may reach different judgements."}]},{"id":"8fd974dc-862e-4b16-8609-2ad8f030f202","cardType":"cloze","points":null,"front":"Coding can produce [ ] data such as frequencies, or support [ ] analysis of meanings and themes.","back":"Coding can produce quantitative data such as frequencies, or support qualitative analysis of meanings and themes.","mcqOptions":[{"text":"Coding can produce quantitative data such as frequencies, or support qualitative analysis of meanings and themes.","correct":true,"explanation":"Counts and frequencies are numerical, so they produce quantitative data. Examining meanings and themes focuses on non-numerical interpretation, so it supports qualitative analysis."},{"text":"Coding can produce qualitative data such as frequencies, or support qualitative analysis of meanings and themes.","correct":false,"explanation":"Frequencies are numerical measurements and therefore constitute quantitative, not qualitative, data. Interpretation of meanings and themes is correctly described as qualitative analysis."},{"text":"Coding can produce quantitative data such as frequencies, or support quantitative analysis of meanings and themes.","correct":false,"explanation":"Frequencies are correctly identified as quantitative data, but analysis focused on meanings and themes is qualitative. Quantitative analysis instead examines numerical patterns and relationships."},{"text":"Coding can produce qualitative data such as frequencies, or support quantitative analysis of meanings and themes.","correct":false,"explanation":"This reverses the distinction. Frequencies are numerical and therefore quantitative, while interpreting meanings and themes is non-numerical and therefore qualitative."}]},{"id":"2e574735-f24a-43c1-bfc7-f1712277757f","cardType":"basic","points":1,"front":"What measurement level are categories like positive, negative and neutral?","back":"Nominal data.","mcqOptions":[{"text":"Nominal data.","correct":true,"explanation":"Positive, negative and neutral are separate named categories with no inherent rank order. Data classified into unordered categories are measured at the nominal level."},{"text":"Ordinal data.","correct":false,"explanation":"Treating these categories as ordinal assumes a consistent rank order, which simple tone categories do not necessarily establish. As unordered labels, they are nominal data."},{"text":"Interval data.","correct":false,"explanation":"Interval data require numerical values with equal differences between adjacent points. Positive, negative and neutral are category labels rather than equal numerical intervals, so they are nominal data."},{"text":"Ratio data.","correct":false,"explanation":"Ratio data require equal numerical intervals and a meaningful absolute zero. Tone categories have neither property; they are unordered labels and therefore produce nominal data."}]},{"id":"a63f8b4c-9a6c-4558-9e0d-6d036f31eb01","cardType":"basic","points":1,"front":"Which statistical test may suit category frequencies from independent groups?","back":"Chi-square test.","mcqOptions":[{"text":"Chi-square test.","correct":true,"explanation":"A chi-square test examines whether frequencies across nominal categories differ or are associated. It is suitable when the observations come from independent groups."},{"text":"Mann-Whitney U test.","correct":false,"explanation":"The Mann-Whitney U test compares ordinal scores from two independent groups, not frequencies across nominal categories. Category-frequency data from independent groups may be analysed using chi-square."},{"text":"Unrelated t-test.","correct":false,"explanation":"An unrelated t-test compares mean interval-level scores between two independent groups and assumes parametric data. Nominal category frequencies are instead suited to a chi-square test."},{"text":"Wilcoxon signed-rank test.","correct":false,"explanation":"The Wilcoxon signed-rank test compares ordinal scores from related conditions or matched participants. Independent-group frequencies in nominal categories are more appropriately analysed using chi-square."}]},{"id":"c69c98af-83f2-482c-99bf-b9da8f5b980c","cardType":"basic","points":2,"front":"When is the null hypothesis rejected in the Chi-square decision rule?","back":"When the calculated value is equal to or larger than the critical value.","mcqOptions":[{"text":"When the calculated value is equal to or larger than the critical value.","correct":true,"explanation":"A Chi-square result is significant when the calculated value meets or exceeds the critical value, so the observed difference is unlikely to be due to chance."},{"text":"When the calculated value is equal to or smaller than the critical value.","correct":false,"explanation":"This reverses the decision rule. The null hypothesis is rejected when the calculated value is equal to or larger than the critical value, indicating statistical significance."},{"text":"When the significance level is equal to or larger than the critical value.","correct":false,"explanation":"Comparing the significance level directly with the critical value confuses two different quantities. Rejection occurs when the calculated Chi-square value equals or exceeds the critical value."},{"text":"When the calculated value is smaller than the critical value.","correct":false,"explanation":"A calculated value below the critical value is not statistically significant. The null hypothesis is rejected only when the calculated value equals or exceeds the critical value."}]},{"id":"a87bb39c-462e-4e53-a5f2-841b10322562","cardType":"cloze","points":null,"front":"[ ] is agreement between independent coders applying the same coding frame to the same material.","back":"Inter-rater reliability is agreement between independent coders applying the same coding frame to the same material.","mcqOptions":[{"text":"Inter-rater reliability","correct":true,"explanation":"Inter-rater reliability measures the consistency of judgements made by different coders who use the same coding frame to analyse the same material."},{"text":"Test-retest reliability","correct":false,"explanation":"Test-retest reliability concerns consistency when a measure is repeated over time, not agreement between independent coders. Agreement between coders is inter-rater reliability."},{"text":"Internal reliability","correct":false,"explanation":"Internal reliability assesses consistency among items within a measure, rather than agreement between separate coders. Agreement between independent coders is inter-rater reliability."},{"text":"Concurrent validity","correct":false,"explanation":"Concurrent validity examines whether a measure agrees with an established measure taken at the same time. Agreement between independent coders is inter-rater reliability."}]},{"id":"2d4add3b-8fb6-4bff-8194-3cea0f8304dd","cardType":"basic","points":2,"front":"How can piloting improve inter-rater reliability?","back":"It identifies vague categories so the frame can be revised.","mcqOptions":[{"text":"It identifies vague categories so the frame can be revised.","correct":true,"explanation":"Piloting reveals categories that different coders interpret inconsistently. Researchers can then clarify or revise these vague categories before conducting the full content analysis."},{"text":"It creates more categories so the frame can be expanded.","correct":false,"explanation":"Increasing the number of categories can create more ambiguity rather than improve agreement. Piloting strengthens inter-rater reliability by identifying vague categories that require revision."},{"text":"It allows personal judgements so the frame can be applied.","correct":false,"explanation":"Allowing personal interpretations would reduce consistency between coders. Piloting identifies vague categories so definitions and coding rules can be revised and applied consistently."},{"text":"It removes coding rules so the frame can be simplified.","correct":false,"explanation":"Removing coding rules would make decisions less standardised and reduce agreement. Piloting identifies vague categories so the frame can be revised and clarified."}]},{"id":"2f511103-a8ea-42c8-8bfc-8721c0263a04","cardType":"basic","points":2,"front":"Why can a reliable coding frame still be invalid?","back":"Coders may agree but measure the wrong concept.","mcqOptions":[{"text":"Coders may agree but measure the wrong concept.","correct":true,"explanation":"Reliability concerns consistency, whereas validity concerns whether the intended construct is measured. Coders can apply a frame consistently while its categories represent the wrong concept."},{"text":"Coders may disagree but measure the right concept.","correct":false,"explanation":"Disagreement between coders indicates low inter-rater reliability, not a reliable but invalid frame. A frame can be reliable when coders agree yet still measure the wrong concept."},{"text":"Coders may agree and measure the right concept.","correct":false,"explanation":"Agreeing while measuring the intended concept indicates both reliability and validity. A reliable frame becomes invalid when coders agree but its categories measure the wrong concept."},{"text":"Coders may agree but change the coding frame.","correct":false,"explanation":"Repeatedly changing categories would undermine consistency rather than explain invalidity despite reliability. The key problem is that coders may agree while measuring the wrong concept."}]},{"id":"1869028d-33f5-46aa-a413-47937a918cfc","cardType":"basic","points":2,"front":"How can previous research or theory improve validity in coding?","back":"It helps categories capture the concept being studied.","mcqOptions":[{"text":"It helps categories capture the concept being studied.","correct":true,"explanation":"Theory and earlier findings guide researchers towards categories that accurately represent the construct under investigation, increasing the coding frame’s validity."},{"text":"It helps categories include all content being studied.","correct":false,"explanation":"Making categories broader can remove important distinctions and does not guarantee validity. Research and theory improve validity by helping categories capture the concept being studied."},{"text":"It helps categories occur equally often in the material.","correct":false,"explanation":"Producing equal category frequencies is unrelated to whether the intended construct is measured. Theory improves validity by ensuring categories capture the concept under investigation."},{"text":"It helps categories reduce the detail being studied.","correct":false,"explanation":"Reducing the number of categories may simplify coding but does not necessarily improve validity. Previous research helps categories capture the concept being studied."}]},{"id":"99052f3e-659d-4a18-97e2-859748f53b97","cardType":"basic","points":2,"front":"How can naturally occurring communication strengthen content analysis?","back":"It can improve ecological validity.","mcqOptions":[{"text":"It can improve ecological validity.","correct":true,"explanation":"Naturally occurring communication reflects behaviour in real-life settings rather than responses produced for a study, so findings can have greater ecological validity."},{"text":"It can improve inter-rater reliability.","correct":false,"explanation":"Naturally occurring communication does not guarantee that different coders will agree; that depends on a clear coding frame. Its main advantage is improved ecological validity."},{"text":"It can improve cause-and-effect conclusions.","correct":false,"explanation":"Using real-life communication does not establish a causal relationship because researchers do not manipulate an independent variable. Instead, it can improve ecological validity."},{"text":"It can improve control of variables.","correct":false,"explanation":"Natural communication often contains uncontrolled variables rather than removing them. Its strength is that it reflects real-world behaviour and can improve ecological validity."}]},{"id":"19d97dd4-bdb0-48b8-aa29-caeb4e108bba","cardType":"basic","points":2,"front":"How can reducing rich communication to categories weaken validity?","back":"It may lose context, such as sarcasm or tone.","mcqOptions":[{"text":"It may lose context, such as sarcasm or tone.","correct":true,"explanation":"Placing complex communication into fixed categories can strip away contextual cues. Meaning conveyed through sarcasm, tone or situation may therefore be missed or misinterpreted."},{"text":"It may lose accuracy, such as frequencies or totals.","correct":false,"explanation":"Sarcasm and tone are features of context, not numerical precision. Categorisation weakens validity when it loses contextual meaning contained in the original communication."},{"text":"It may lose quantification, such as frequencies or percentages.","correct":false,"explanation":"Frequencies and percentages are produced by categorising data rather than necessarily lost through it. The validity problem is losing context, including sarcasm or tone."},{"text":"It may lose reliability, such as consistency or agreement.","correct":false,"explanation":"Standardised categories generally increase consistency rather than remove it. Reducing communication to categories threatens validity because contextual details such as sarcasm or tone may be lost."}]},{"id":"4bee74b9-b329-4085-a769-0bb5ef54fbbb","cardType":"basic","points":2,"front":"What ethical step is needed when analysing identifiable social media posts?","back":"Protect confidentiality by anonymising identifying details.","mcqOptions":[{"text":"Protect confidentiality by anonymising identifying details.","correct":true,"explanation":"Identifiable posts can reveal personal information about their authors. Removing names, usernames and other identifying details protects confidentiality when the material is reported or stored."},{"text":"Provide debriefing by publishing identifying details.","correct":false,"explanation":"Debriefing explains a study afterwards but does not prevent identifiable information from being disclosed. Researchers should protect confidentiality by anonymising identifying details."},{"text":"Ensure replicability by retaining identifying details.","correct":false,"explanation":"Repeatedly quoting usernames would make individuals easier to identify and breach confidentiality. Identifying details should instead be anonymised when social media posts are analysed."},{"text":"Obtain informed consent by displaying identifying details.","correct":false,"explanation":"Displaying identifying details is not how informed consent is obtained and could expose users. Researchers must protect confidentiality by anonymising those details."}]}]}],"examCountdown":{"examDate":"2027-05-18","paperLabel":"Paper 1"},"heading":"Coding in content analysis (A-level only)","title":"Coding in content analysis (A-level only) Flashcards","topbar":{"backHref":"/a-level/psychology/aqa/flashcards"},"topicSidebar":"$L38"}]}],"topicId":"907a703c-86b1-5e4f-af95-724f3a2a9691","walled":"$L39"}],"$L3a","$L3b"]