Topic Selection, Problem Definition and Research Questions

Introduction

A mass media management dissertation begins long before data collection. Its quality is largely determined by the decisions made when selecting a topic, defining a problem and formulating questions that can be answered with credible evidence. A broad interest such as “digital media growth,” “audience engagement,” or “the future of journalism” is not yet a research topic. It becomes researchable only when it is narrowed to a specific organisational, professional, social or technological issue; located in a defined context; and connected to questions that can be investigated systematically.

For students of mass media management, research problems commonly arise from changes in audience behaviour, platform competition, revenue pressures, newsroom transformation, media regulation, content strategy, workforce management, data analytics, artificial intelligence, ownership, diversity and public trust. The researcher’s task is not merely to describe an interesting trend. It is to identify a significant gap, uncertainty, contradiction or decision problem and convert it into a coherent research design.

This chapter explains how to move from an industry issue to a focused problem statement, research aim, objectives and research questions or hypotheses. It also considers scope, feasibility, originality, practical relevance, ethics and the relationship between research questions and methodology.

Learning Objectives

By the end of this chapter, students should be able to:

1. Distinguish an industry issue, research problem, topic, aim, objective and research question.
2. Select and narrow a suitable mass media management topic.
3. Construct a clear and evidence-based problem statement.
4. Develop aligned research aims, objectives and research questions.
5. Determine when hypotheses are appropriate.
6. Assess scope, feasibility, originality, significance and practical relevance.
7. Identify ethical, legal and governance considerations at the proposal stage.
8. Evaluate the quality of a proposed dissertation topic using a systematic framework.

1. From Industry Issue to Researchable Problem

An industry issue is a condition or development that attracts professional concern. For example, a streaming service may be experiencing subscriber cancellations, a newspaper may be struggling to monetise online readers, or a broadcaster may be uncertain about the effects of artificial intelligence on newsroom work.

A research problem is a precisely expressed issue that requires systematic investigation because existing knowledge is insufficient, contradictory or contextually incomplete. The distinction is important. A manager may say, “Our audience engagement is falling.” A researcher must ask: Which audience? What does engagement mean? Over what period? On which platforms? Compared with what benchmark? What organisational or content factors might explain the change?

A useful progression is:

Industry condition → management concern → knowledge gap → research problem → research questions → evidence and analysis

For example:

• Industry condition: Audiences increasingly consume news through short-form video platforms.
• Management concern: A public broadcaster does not know whether investment in short-form video increases meaningful engagement.
• Knowledge gap: Existing studies may measure views but not repeat use, trust or conversion to the broadcaster’s own platforms.
• Research problem: The relationship between short-form video strategy and meaningful audience engagement among young adults remains insufficiently understood in the selected context.
• Research question: How does the broadcaster’s short-form video strategy influence news engagement among adults aged 18–24?

The final question is narrower, clearer and more researchable than the initial issue.

Topic, Problem and Purpose

These terms are related but not interchangeable.

2. Selecting a Strong Dissertation Topic

Topic selection should combine intellectual interest with academic and professional value. A topic selected only because it is fashionable may lack access to data or a defensible research gap. A topic selected only because data are easy to obtain may have little significance.

2.1 Sources of Topics

Potential topics may emerge from:

• professional placement or employment experience;
• media industry reports and audience data;
• scholarly literature and unresolved findings;
• policy consultations, legislation and regulatory decisions;
• organisational problems identified by media managers;
• technological change, including platformisation and generative AI;
• public debates about representation, trust, misinformation or privacy;
• previous coursework and theoretical interests;
• conversations with practitioners, editors, producers and analysts.

The source of a topic does not determine the research method. An industry-generated topic still requires scholarly framing, while a theory-driven topic should explain its practical relevance.

2.2 Criteria for Topic Quality

A suitable topic normally satisfies six criteria.

Significance. It addresses a meaningful organisational, professional, social or theoretical concern.

Researchability. It can be investigated through accessible evidence, appropriate methods and a realistic timetable.

Specificity. It identifies a population, organisation, platform, period, process or context rather than attempting to study an entire industry.

Originality. It contributes a new case, population, comparison, dataset, interpretation or application. Originality does not necessarily mean discovering an entirely new phenomenon.

Ethical acceptability. It can be conducted without unjustified risks to participants, confidential sources, employees or audiences.

Researcher capability. The student possesses, or can acquire, the necessary methodological, technical and subject knowledge.

A practical screening matrix can help students compare alternatives. Each criterion can be scored from 1 (weak) to 5 (strong), although the total should support rather than replace academic judgement.

2.3 Narrowing the Topic

A broad topic can be narrowed by specifying several dimensions:

• Sector: television, radio, publishing, film, digital news or podcasting;
• Organisation: a named organisation or a defined category of organisations;
• Function: marketing, finance, human resources, content production, distribution or audience analytics;
• Population: managers, journalists, creators, subscribers or a defined audience group;
• Phenomenon: retention, trust, workflow, revenue, discoverability or innovation;
• Location: a city, country or media market;
• Period: a defined year, campaign or transition;
• Outcome: engagement, productivity, willingness to pay, trust or performance.

For instance, “social media and journalism” is excessively broad. It may be narrowed to “the use of Instagram Reels by local news organisations to reach university students in [defined city] during a six-month period.” Even this topic requires further decisions about what “reach” means and whether the study concerns strategy, audience response or organisational capability.

3. Building the Problem Statement

The problem statement is the intellectual centre of a dissertation proposal. It explains what is wrong, unknown or insufficiently understood, why the issue matters, what is already known, what remains unresolved and what the proposed study will investigate.

A strong problem statement usually contains five connected elements:

1. Context: introduce the sector, organisation or development.
2. Evidence of the issue: show that the issue is real using credible sources.
3. Consequences: explain why the issue matters.
4. Gap or uncertainty: identify what existing research or practice has not adequately explained.
5. Study direction: state the population, phenomenon and context the study will examine.

3.1 Evidence and the Research Gap

A problem statement should not rely on unsupported claims such as “audiences no longer trust the media” or “AI is replacing journalists.” Such statements are too general and may be empirically inaccurate. Evidence may include scholarly studies, official statistics, regulator reports, industry data, organisational documents and credible audience research.

Common forms of research gap include:

• Evidence gap: insufficient empirical evidence exists.
• Context gap: a phenomenon has been studied elsewhere but not in the selected media market.
• Population gap: a group such as older audiences, freelancers or minority-language users is under-represented.
• Methodological gap: prior studies use one method and would benefit from another form of evidence.
• Theoretical gap: existing concepts do not adequately explain the phenomenon.
• Contradiction gap: previous studies report inconsistent results.
• Practical gap: industry practice has developed faster than guidance or evaluation.

A gap must be genuine rather than manufactured. A student should not claim that “no research exists” unless a systematic search supports that conclusion. More defensible wording includes “limited research has examined…” or “evidence remains mixed regarding…”.

3.2 Example of a Weak and Strong Problem Statement

Weak version:

“Social media is very important for media organisations, but there are many problems with engagement. This study will investigate social media.”

This version lacks a defined organisation, audience, platform, outcome, gap and feasible direction.

Stronger version:

“Local news organisations increasingly use short-form video to reach younger audiences, yet platform visibility does not necessarily produce sustained use of trusted news services. Existing studies commonly evaluate performance through views and likes, while giving less attention to whether viewers subsequently access the organisation’s own news products. Evidence is also limited concerning small local newsrooms with restricted analytics and production resources. This study therefore examines how short-form video practices influence platform-to-website movement among adults aged 18–24 who encounter local news content in the selected city.”

The stronger version establishes context, identifies a measurement and knowledge gap, defines the population and proposes a manageable study.

4. Aim and Objectives

The research aim is a concise statement of the overall purpose. It should normally contain one central action verb and identify the phenomenon and context.

Examples include:

• “To examine how audience analytics are used in editorial decision-making in independent digital newsrooms.”
• “To assess the factors influencing subscription renewal among users of a regional streaming service.”
• “To explore how freelance media workers experience algorithmic management on creator platforms.”

The aim should not promise more than the design can deliver. A small qualitative study may explore experiences, but it should not claim to measure the entire industry.

4.1 Writing Objectives

Objectives translate the aim into specific tasks. Effective objectives are:

• logically connected to the aim;
• expressed with observable verbs;
• neither too broad nor merely procedural;
• achievable within the study;
• collectively sufficient to answer the research questions.

Useful verbs include identify, examine, compare, assess, analyse, explore, interpret, evaluate, determine and develop. Weak verbs such as “understand” or “look at” may be used in qualitative contexts but should be clarified through more precise wording.

A study might use the following objectives:

1. To identify the audience metrics used by selected digital newsrooms.
2. To examine how editors interpret these metrics in content planning.
3. To compare metric use across organisations with different revenue models.
4. To explore perceived tensions between audience growth and editorial values.
5. To develop practical recommendations for responsible analytics use.

Objectives should not simply list stages such as “to distribute questionnaires” or “to write a literature review.” Those are research activities, not substantive objectives.

5. Formulating Research Questions

Research questions specify what the study seeks to answer. They guide the literature review, data collection, analysis and conclusion. A good question is clear, focused, answerable and aligned with the selected methodology.

5.1 Descriptive, Exploratory, Explanatory and Evaluative Questions

Descriptive questions ask what exists, who is involved, how often something occurs or how a process operates.

• What audience metrics do independent podcasts use to evaluate programme performance?

Exploratory questions investigate experiences, meanings and emerging processes.

• How do podcast producers interpret listener data when deciding programme topics?

Explanatory questions examine relationships or possible causes.

• What factors are associated with subscription renewal among digital news users?

Comparative questions examine similarities and differences.

• How does newsroom analytics use differ between public and commercial broadcasters?

Evaluative questions assess effectiveness, value or consequences against explicit criteria.

• To what extent does a social media campaign increase registrations for a public media service?

A single dissertation may include more than one type, but the combination should remain coherent.

5.2 Characteristics of Strong Questions

A strong question:

• contains a clearly defined phenomenon;
• identifies the relevant population or case;
• specifies context where necessary;
• avoids hidden assumptions;
• can be answered with available evidence;
• does not contain several unrelated questions;
• avoids moral or ideological wording unless normative analysis is intended.

Compare:

• Weak: “Is TikTok good for journalism?”
• Improved: “How do news editors assess the opportunities and risks of TikTok distribution for reaching first-time news users?”

The improved question defines the actors, process and dimensions of inquiry without assuming a predetermined answer.

5.3 Main and Sub-Questions

A dissertation may use one main question supported by three or four sub-questions. For example:

Main question: How do independent digital news organisations use audience analytics in editorial decision-making?

Sub-questions:

1. Which metrics are collected and prioritised?
2. How do editors interpret the meaning of these metrics?
3. How do revenue models influence metric use?
4. What ethical concerns arise from data-informed editorial decisions?

Sub-questions should divide the main question into connected dimensions. They should not introduce a second topic, population or method.

6. Hypotheses and Conceptual Relationships

A hypothesis is a testable, logically derived prediction about a relationship between variables. Hypotheses are most appropriate when the study uses a quantitative design and has a sufficient theoretical or empirical basis for prediction.

For example:

• H₁: Perceived personalisation is positively associated with willingness to renew a digital media subscription.
• H₀: There is no statistically significant association between perceived personalisation and willingness to renew.

Here, perceived personalisation is a possible explanatory variable and willingness to renew is the outcome variable. The hypothesis does not prove causation merely by stating an association. Causal claims require an appropriate design, temporal ordering and control of plausible alternative explanations.

A qualitative study usually uses research questions rather than statistical hypotheses. It may begin with sensitising concepts or propositions, but these should not force participants’ accounts into predetermined categories.

6.1 Variables and Operationalisation

A variable is a measurable characteristic that can take different values. Examples include frequency of platform use, subscription intention, age, perceived credibility and number of weekly posts.

Operationalisation explains how an abstract concept will be observed or measured. For example, “audience engagement” might be operationalised as a composite of average watch time, repeat visits and newsletter registration. Each indicator captures only part of the concept, so the researcher must justify the measure.

The choice of operational definition can change the research question. “Does content increase engagement?” is incomplete until engagement is defined. A study measuring views may reach a different conclusion from one measuring repeat use or conversion.

7. Alignment: The Logic of the Research Design

A high-quality proposal demonstrates alignment among the problem, aim, objectives, questions, literature, method, data and analysis. Misalignment is one of the most common weaknesses in undergraduate dissertations.

For example, a question asking how journalists experience algorithmic management is likely to require interviews, observation or qualitative document analysis. A questionnaire alone may identify perceptions but provide limited insight into lived experience. Conversely, a question about the statistical association between platform use and subscription intention requires measurable variables and an appropriate quantitative sample.

A useful alignment test is to ask:

1. If the problem disappeared, would the study still be necessary?
2. Does every objective help answer the main question?
3. Can the proposed participants or documents answer the questions?
4. Does the method generate the type of evidence required?
5. Does the conclusion promise only what the design can support?

8. Scope, Delimitation and Feasibility

Scope defines what the study covers. Delimitation states what the researcher intentionally excludes. A dissertation becomes stronger when boundaries are explicit rather than implied.

Scope may specify:

• one organisation or a small comparative set;
• one country or media market;
• a defined audience segment;
• a particular platform;
• a period such as one campaign or financial year;
• selected dimensions of a broader concept.

For example, a study may examine audience analytics in editorial planning but exclude advertising-sales analytics, because the latter would require a separate investigation.

Feasibility should be tested before finalising the topic. Consider:

• access to participants and organisational records;
• recruitment and response rates;
• availability and quality of platform data;
• time needed for transcription and analysis;
• ethical approval requirements;
• costs of travel, software and incentives;
• researcher skills;
• risks of organisational withdrawal;
• data protection and secure storage.

A topic can be academically important but infeasible. For example, “the effect of media ownership on national democracy” is unlikely to be manageable as a bachelor’s dissertation. A feasible alternative could examine how ownership concentration is represented in policy debates concerning one national media sector during a defined period.

9. Originality and Practical Relevance

At bachelor level, originality usually means a modest but defensible contribution. It may arise from:

• applying an established framework to a new media organisation;
• studying an under-researched audience;
• comparing two organisational contexts;
• collecting primary evidence where existing work is mainly conceptual;
• updating an earlier study after technological or regulatory change;
• examining a practical issue through a management perspective.

Practical relevance should be specific. A recommendation such as “media organisations should use social media effectively” is too vague. A useful recommendation might identify which metrics should be reported to editorial teams, what training is required, or how a platform strategy can be evaluated without compromising privacy or editorial independence.

The researcher should distinguish evidence-based recommendations from personal preference. Recommendations must follow from findings, acknowledge limitations and identify the stakeholders who may use them.

10. Ethical, Legal and Governance Considerations

Topic selection itself involves ethical judgement. Mass media research may concern employees, vulnerable audiences, political communication, private user data, minors, confidential business information or controversial content.

Important issues include:

• informed and voluntary consent;
• confidentiality and anonymisation;
• secure collection and storage of data;
• lawful processing of personal information;
• avoidance of coercion where managers recruit employees;
• protection of participants from reputational or employment harm;
• responsible use of publicly visible social media data;
• copyright and terms-of-service restrictions;
• transparency concerning funding or organisational sponsorship;
• conflicts of interest;
• responsible use of AI tools in research.

Public availability does not automatically mean ethical unrestricted use. A social media post may be technically public but still involve contextual privacy expectations. Researchers should minimise data collection, remove identifying information where appropriate and follow institutional ethics procedures and applicable data-protection law.

Generative AI may help with brainstorming, transcription support or language editing, but it can produce fabricated citations, misrepresent sources and expose confidential material. Students remain responsible for accuracy, originality, disclosure of assistance and protection of research data. AI should not replace critical reading, participant consent or methodological judgement.

11. An Applied Case: Investigating Digital Subscription Renewal

The following is a fictional illustrative case.

Background

The Metro Herald, a regional digital news organisation, has increased its number of online subscribers but has experienced substantial cancellations after the first three months. Management believes that personalised newsletters may improve retention, but editors are concerned that excessive personalisation could narrow readers’ exposure to public-interest news.

Initial Problem

The initial management statement is: “We need to stop subscribers from cancelling.” This is a legitimate concern but not a researchable problem by itself.

Refinement and Analysis

A review of industry reports and scholarly literature identifies three issues. First, subscription retention is influenced by perceived value, habit, price and user experience. Second, personalisation may improve relevance but can create concerns about privacy or filter bubbles. Third, much available evidence comes from large international platforms rather than regional news organisations.

The research problem can therefore be stated as follows:

“Although digital news organisations increasingly use personalised newsletters to demonstrate subscription value, limited evidence explains how regional news subscribers interpret personalisation during the early period of their subscription. The relationship between perceived relevance, privacy concern and renewal intention remains unclear in the context of a regional news organisation.”

Aim

To examine how personalised newsletters influence perceived value and renewal intention among subscribers to the Metro Herald.

Objectives

1. To measure subscribers’ perceptions of newsletter relevance and personalisation.
2. To examine the relationship between perceived relevance, privacy concern and renewal intention.
3. To explore how subscribers describe the benefits and risks of personalised news delivery.
4. To develop recommendations for retention strategies that respect reader autonomy and privacy.

Research Questions

1. How do subscribers perceive the relevance and value of personalised newsletters?
2. What relationship exists between perceived relevance, privacy concern and renewal intention?
3. How do subscribers describe the effect of personalisation on their news consumption?

A mixed-method design could combine a survey with semi-structured interviews. The survey could measure perceptions across a defined subscriber sample, while interviews could explore meanings and concerns in greater depth. The study should not claim to prove that newsletters cause renewal unless its design supports causal inference. It may instead identify associations and participants’ reported explanations.

Possible Outcome

The study might find that relevance is associated with stronger renewal intention, but that readers with high privacy concerns respond negatively to opaque data practices. The practical recommendation would not simply be “personalise more.” It might propose transparent preference controls, clear explanations of data use, editorial diversity safeguards and evaluation based on renewal rather than open rates alone.

Learning Points

This case demonstrates that a management concern becomes researchable when the researcher identifies a defined population, measurable concepts, a contextual gap and feasible evidence. It also shows that commercial objectives should be analysed alongside editorial and ethical responsibilities.

12. Common Errors and Best Practices

Common Errors

Overly broad topics: Attempting to study an entire platform, industry or social problem.

Problem-free description: Reporting what an organisation does without identifying an uncertainty, gap or evaluative standard.

Leading questions: Wording questions so that the expected answer is built in, such as “Why has harmful clickbait increased?”

Question-method mismatch: Asking causal or population-wide questions with evidence that can support only limited description.

Too many objectives: Listing every possible dimension and creating an unmanageable project.

Unsupported originality claims: Declaring that no previous study exists without a serious literature search.

Confusing recommendations with findings: Presenting preferred solutions before collecting evidence.

Conceptual vagueness: Using terms such as engagement, impact, trust or success without defining them.

Best Practices

• Begin with a real issue but convert it into a knowledge problem.
• Search scholarly and industry literature before finalising the topic.
• Define the population, setting, time period and main concepts.
• Use one clear aim and a limited set of connected objectives.
• Check that every question can be answered with the planned data.
• Pilot instruments and recruitment procedures where possible.
• State exclusions and limitations honestly.
• Build ethical safeguards into the design rather than adding them at the end.
• Discuss the proposal with a supervisor and, where appropriate, an industry stakeholder.
• Revise the topic after initial evidence; refinement is a sign of good research, not failure.

Chapter Summary

A dissertation topic is not simply a broad area of interest. It must be transformed from an industry issue into a focused research problem supported by evidence and framed by a genuine gap or uncertainty. Topic selection should balance significance, specificity, originality, feasibility, ethical acceptability and researcher capability.

The problem statement establishes the context, demonstrates the importance of the issue, identifies what remains unknown and directs the proposed investigation. The research aim expresses the overall purpose, while objectives divide that purpose into achievable tasks. Research questions specify the evidence the study must generate. Hypotheses may be used when theory and a quantitative design support predictions about relationships between variables.

Strong research design depends on alignment. The problem, aim, objectives, questions, method, sample, analysis and conclusion must support one another. Clear scope and delimitations prevent overreach, while operational definitions make abstract concepts such as engagement, trust and value researchable. Ethical and governance issues—including privacy, consent, confidentiality, data protection and responsible AI use—must be considered from the beginning. A well-defined topic produces a dissertation that is academically defensible and practically useful to media organisations and their stakeholders.

Key Terms

Industry issue: A professional or organisational condition that attracts concern.

Research problem: A specific uncertainty, gap or difficulty requiring systematic investigation.

Research topic: The defined subject area selected for investigation.

Problem statement: A structured explanation of the issue, its significance and the knowledge gap.

Research aim: The overall purpose of a study.

Research objective: A specific task that contributes to achieving the aim.

Research question: An answerable question that guides evidence collection and analysis.

Hypothesis: A testable prediction about a relationship between variables.

Variable: A measurable characteristic that can take different values.

Operationalisation: The process of defining how an abstract concept will be observed or measured.

Research gap: An aspect of knowledge that is missing, limited, contradictory or contextually incomplete.

Scope: The boundaries of what a study covers.

Delimitation: A boundary intentionally set by the researcher.

Originality: The distinct contribution made by a study.

Feasibility: The degree to which a study can realistically be completed with available resources.

Population: The broader group to which a study refers.

Sample: The selected portion of a population from which evidence is obtained.

Conceptual framework: A structured representation of key concepts and their proposed relationships.

Research alignment: Consistency among the problem, questions, methods, evidence and conclusions.

Practical relevance: The usefulness of findings for professional, organisational or policy decisions.

Review Questions

Short Answer Questions

1. What is the difference between an industry issue and a research problem?
2. Identify four criteria that should be considered when selecting a dissertation topic.
3. What are the five main elements of a strong problem statement?
4. Distinguish a research aim from a research objective.
5. When is a hypothesis more appropriate than a qualitative research question?

Long Answer Questions

1. Explain how a broad mass media industry issue can be converted into a focused research problem. Use an original example.
2. Discuss the role of evidence and research gaps in constructing a dissertation problem statement.
3. Explain how research questions, methodology and analysis must be aligned in a mass media management study.
4. Examine the importance of scope, feasibility and originality in bachelor-level dissertation planning.
5. Discuss the ethical issues that may arise when researching audience data, employees or social media users.

Analytical / Application Questions

1. A television network reports that its younger audience is declining. Develop a problem statement, aim, three objectives and one main research question for a feasible dissertation.
2. Critically assess the question “Does artificial intelligence improve journalism?” Rewrite it as a focused research question and justify your revisions.
3. A podcast company wants to know whether social media promotion increases paid subscriptions. Identify suitable variables, propose one hypothesis and explain one limitation of making a causal claim.

Multiple Choice Questions with Answers

Question 1

Which statement best distinguishes a research problem from an industry issue?

A. A research problem is always a financial problem faced by a media company.

B. A research problem identifies a specific uncertainty or gap that can be investigated systematically.

C. A research problem is the same as a broad topic such as digital media.

D. A research problem is a list of research methods.

Correct Answer: B. A research problem identifies a specific uncertainty or gap that can be investigated systematically.

Question 2

Which of the following is the strongest research question?

A. Is social media good for journalism?

B. Why is the media changing?

C. How do local news editors assess short-form video for reaching first-time news users?

D. Should all journalists use TikTok?

Correct Answer: C. How do local news editors assess short-form video for reaching first-time news users?

Question 3

Which item belongs in a problem statement?

A. A complete chapter-by-chapter table of contents

B. Evidence of the issue and an explanation of the knowledge gap

C. The final results of the study

D. A list of unrelated personal interests

Correct Answer: B. Evidence of the issue and an explanation of the knowledge gap

Question 4

What is the primary purpose of research objectives?

A. To replace the literature review

B. To list every activity the researcher might perform

C. To translate the research aim into specific achievable tasks

D. To guarantee that the hypothesis is supported

Correct Answer: C. To translate the research aim into specific achievable tasks

Question 5

Which is an example of operationalisation?

A. Choosing a dissertation title

B. Defining audience engagement using watch time, repeat visits and registrations

C. Writing an acknowledgement section

D. Selecting a supervisor

Correct Answer: B. Defining audience engagement using watch time, repeat visits and registrations

Question 6

A hypothesis is most appropriate when a study:

A. Uses a quantitative design and has a defensible basis for predicting relationships

B. Has no measurable concepts

C. Seeks only to collect personal opinions without analysis

D. Has not reviewed previous research

Correct Answer: A. Uses a quantitative design and has a defensible basis for predicting relationships

Question 7

Which criterion concerns whether the required participants or data can realistically be obtained?

A. Originality

B. Access and feasibility

C. Theoretical neutrality

D. Publication impact

Correct Answer: B. Access and feasibility

Question 8

Why should a researcher define the meaning of “engagement”?

A. Because all engagement measures produce identical results

B. Because the concept is abstract and may be measured in different ways

C. Because engagement cannot be studied empirically

D. Because definitions remove the need for data

Correct Answer: B. Because the concept is abstract and may be measured in different ways

Question 9

Which statement about originality at bachelor level is most accurate?

A. It requires discovering an entirely new phenomenon.

B. It may involve applying an existing framework to a new context or population.

C. It means avoiding all previous literature.

D. It is achieved by using the longest questionnaire possible.

Correct Answer: B. It may involve applying an existing framework to a new context or population.

Question 10

Which ethical principle is especially relevant when collecting identifiable audience data?

A. Maximising the number of variables

B. Informed consent, privacy and secure data handling

C. Avoiding all industry sources

D. Guaranteeing a positive result for the organisation

Correct Answer: B. Informed consent, privacy and secure data handling