Research Problems, Objectives and Research Design

Introduction

Market research begins long before a questionnaire is written. It begins when an entrepreneur notices a business situation that requires better evidence. Sales may be declining, customers may be abandoning an online cart, a new product may appear attractive, or a founder may be unsure which customer segment to serve. These situations are not yet research problems. They are managerial problems. The researcher’s task is to translate them into questions that can be investigated systematically.

A well-designed research project connects five elements: the business decision, the research problem, the research objectives, the research questions or hypotheses, and the research design. If these elements are poorly aligned, even technically sophisticated data collection may produce information that is irrelevant to the decision. If they are aligned, a modest study can provide useful and defensible evidence.

For an entrepreneur, research design is also a resource-allocation decision. Time, money, access to respondents and analytical expertise are limited. The design must therefore be rigorous enough to reduce uncertainty while remaining practical for a startup.

Learning Objectives

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

1. Distinguish a managerial problem from a research problem.
2. Convert a broad business concern into a clear research purpose and research questions.
3. Write specific, measurable and feasible research objectives.
4. Formulate hypotheses when testing relationships or differences is appropriate.
5. Select exploratory, descriptive or causal research designs.
6. Prepare a practical research brief, work plan and budget.
7. Recognize ethical, technological and operational issues in startup research.
8. Evaluate the quality and limitations of a proposed research design.

1. From Business Situation to Research Problem

1.1 Managerial problems and research problems

A managerial problem concerns an action or decision. Examples include:

• Should a startup launch a subscription plan?
• Why have repeat purchases fallen?
• Which customer segment should receive the initial marketing budget?
• Should the firm increase its price or improve its service package?

A research problem concerns the information needed to make that decision. It identifies what is unknown, who or what should be studied, and what evidence is required. For example, the managerial problem “Should we launch a subscription plan?” may become the research problem “The firm lacks evidence about customers’ perceived value of subscription benefits, acceptable price levels and likely adoption among its priority segment.”

The distinction matters. A research project should not attempt to solve every aspect of a managerial problem. It should produce evidence relevant to a defined decision.

1.2 Sources of research problems

Research problems can arise from several sources:

• Market symptoms: declining sales, low conversion, complaints or weak retention.
• Environmental change: new regulation, technology, competitor activity or economic conditions.
• Opportunity recognition: an unmet need, emerging customer group or new distribution channel.
• Performance gaps: a difference between expected and actual results.
• Previous research: findings that are incomplete, contradictory or outdated.
• Founder assumptions: beliefs that need validation before resources are committed.

A symptom is not necessarily the underlying cause. For example, declining sales may result from lower awareness, poor product-market fit, distribution problems, price perceptions or stronger competition. Research should avoid assuming the cause before evidence is collected.

1.3 The problem-definition sequence

A disciplined problem-definition sequence is:

1. Describe the decision situation.
2. Identify the decision-maker and the decision to be supported.
3. Separate symptoms from possible underlying causes.
4. State what is known and what remains uncertain.
5. Identify the population, market or process involved.
6. Specify the information required.
7. Set boundaries for the study.
8. Convert the problem into questions and objectives.

A useful problem statement normally contains four components:

1.4 The “five whys” as a diagnostic aid

The five-whys technique asks why a problem exists repeatedly until a more fundamental issue is identified. It is not a substitute for empirical research, but it helps prevent premature conclusions.

For example:

• Why are customers not renewing? They do not perceive enough value.
• Why do they not perceive enough value? They use only one feature.
• Why do they use only one feature? They do not understand the other features.
• Why do they lack understanding? Onboarding is incomplete.
• Why is onboarding incomplete? The current process assumes users already understand the product.

The resulting research problem may concern onboarding comprehension and feature discovery rather than price alone. The technique should generate possible explanations, not establish them as facts.

2. Research Purpose, Questions and Objectives

2.1 Research purpose

The purpose statement gives the overall direction of the study. It is usually one sentence beginning with an action verb such as explore, describe, measure, compare, assess or test.

Example: “The purpose of this study is to assess the factors influencing repeat purchase intention among first-time users of the startup’s meal-delivery service in the metropolitan launch area.”

A purpose statement should identify the subject, population or context and broad analytical intention. It should not promise more than the design can deliver.

2.2 Research questions

Research questions divide the purpose into answerable inquiries. Good questions are:

• clear rather than vague;
• focused on one main issue at a time;
• answerable with available evidence;
• neutral rather than leading;
• relevant to the managerial decision; and
• bounded by population, location and time where necessary.

Weak question: “Do customers love our product and want a subscription?”

Improved questions:

1. What benefits do current customers associate with the product?
2. What concerns reduce interest in subscription purchasing?
3. What subscription price ranges are considered acceptable by the priority segment?
4. Does purchase frequency differ between customers who report high and low perceived value?

Questions may be exploratory, descriptive or causal. Exploratory questions seek meanings, motivations and possible explanations. Descriptive questions measure characteristics, behaviours or opinions. Causal questions examine whether changing one factor produces a change in another under appropriate controls.

2.3 Research objectives

Objectives state what the research will accomplish. They should use observable verbs. Common verbs include:

• identify;
• explore;
• describe;
• measure;
• compare;
• estimate;
• assess;
• examine; and
• test.

Avoid objectives such as “understand the market” or “study customers,” because they are too broad to guide design or assess completion.

A strong objective normally specifies:

• the action;
• the subject or variable;
• the target population;
• the context or location; and
• sometimes the period of interest.

For example: “To measure the importance of delivery speed, price and menu variety in food-ordering decisions among university students in City X during the first quarter.”

Objectives should be logically ordered. A project might first explore customer language, then describe the size of identified attitudes, and finally test whether perceived convenience predicts purchase intention. This sequence reflects increasing structure and should be adopted only if resources permit.

2.4 SMART objectives

The SMART framework is a practical quality check:

• Specific: identifies what will be investigated.
• Measurable: indicates what evidence will show completion.
• Achievable: fits resources, access and skills.
• Relevant: supports the business decision.
• Time-bound: has a defined completion period.

SMART does not mean every objective must contain a numerical target. A qualitative objective can be measurable through a clear deliverable, such as a coded set of themes from interviews.

2.5 Alignment matrix

An alignment matrix exposes gaps between the decision and the method.

If a question has no objective, it may be unnecessary. If an objective has no method, the project is incomplete. If neither connects to the decision, the research may be interesting but not useful.

3. Hypotheses and Conceptual Models

3.1 When hypotheses are appropriate

A hypothesis is a provisional, empirically testable statement about a relationship, difference or expected outcome. Hypotheses are most appropriate in descriptive-analytical or causal research where variables can be defined and measured.

Examples:

• H₁: Perceived convenience is positively associated with purchase intention.
• H₁: Customers exposed to free delivery have a higher order-completion rate than comparable customers who see standard delivery charges.

The corresponding null hypothesis is usually stated as no relationship or no difference:

• H₀: Perceived convenience is not associated with purchase intention.
• H₀: There is no difference in order-completion rates between the two delivery conditions.

A hypothesis should specify the population, variables and expected direction when theory or prior evidence supports a direction. It should not be framed as a value judgment.

3.2 Variables and operational definitions

A construct is an abstract idea, such as trust, satisfaction or perceived value. A variable is a measurable representation of a construct. An operational definition explains how the variable will be observed or measured.

Operational definitions improve consistency. “Customer loyalty” could mean repeat purchase, recommendation, emotional attachment or share of wallet; the researcher must decide which meaning applies.

3.3 Conceptual frameworks

A conceptual framework maps the expected relationships among important concepts. For example:

Perceived convenience → perceived value → purchase intention → repeat purchase

Trust and price fairness may influence both perceived value and purchase intention. The framework helps determine which variables to include and prevents the questionnaire from becoming a collection of unrelated questions.

A framework is not proof of causation. It represents a reasoned expectation based on theory, prior evidence or managerial logic. Alternative explanations must remain possible until tested.

4. Research Design: The Overall Plan

Research design is the overall plan for obtaining and interpreting evidence. It specifies the purpose, data sources, timing, unit of analysis, sampling approach, measurement procedures and analysis strategy.

The three foundational designs are exploratory, descriptive and causal. They are not rigid stages in every project, but they represent different purposes and levels of structure.

4.1 Exploratory research

Exploratory research is used when the problem is poorly understood or when the relevant variables and customer language are not yet clear. It is flexible and open-ended.

Common methods include:

• interviews with customers, experts or channel partners;
• focus groups;
• observation and contextual inquiry;
• review of customer-service records;
• social-media or online-community analysis;
• secondary-data review; and
• small-scale pilot studies.

Exploration is useful for discovering motivations, vocabulary, unmet needs and possible explanations. Its typical outputs are themes, propositions, customer personas, journey maps and hypotheses.

Exploratory research generally does not estimate market proportions with statistical precision. A small group of interviews may reveal important issues but cannot establish that a particular percentage of all customers holds the same view.

4.2 Descriptive research

Descriptive research measures and summarizes characteristics of a market, customer group or process. It answers questions such as:

• Who are the target customers?
• How frequently do they purchase?
• Which attributes are most important?
• What proportion recognizes the brand?
• How satisfied are users with delivery?

Typical methods include structured surveys, customer databases, transaction analysis, observation schedules and secondary market reports. Descriptive research requires clearer definitions and more standardized measurement than exploratory research.

The quality of descriptive findings depends on sampling, question wording, response rates, measurement validity and data completeness. A large sample does not automatically produce accurate results if it systematically excludes important customers.

4.3 Causal research

Causal research examines whether a change in one factor produces a change in an outcome. It is particularly relevant to marketing decisions involving price, advertising, design, promotions, messages or distribution.

A causal claim is stronger when the study establishes:

1. Covariation: the proposed cause and outcome change together.
2. Temporal order: the proposed cause occurs before the outcome.
3. Non-spuriousness: the relationship is not explained by another factor.

Experiments support causal inference by manipulating an independent variable and comparing outcomes, ideally with random assignment and control of competing influences. For example, an online retailer might randomly show half of eligible visitors a standard delivery fee and half a free-delivery offer, then compare completed orders.

Causal research can be conducted through laboratory experiments, field experiments or carefully designed quasi-experiments. In a startup, A/B testing is a common field application, but the experiment must define the target population, success metric, test duration and safeguards against overlapping campaigns.

4.4 Comparing the designs

4.5 Selecting a design

Select a design by asking:

• Is the problem sufficiently understood to define variables?
• Is the decision about discovery, measurement or intervention?
• What evidence is needed: meanings, frequencies, associations or effects?
• Can the organization access the relevant population?
• Are time, budget and skills adequate?
• Would a mixed-method design be more appropriate?

A mixed-method design combines qualitative and quantitative evidence. For example, interviews can identify relevant attributes, followed by a survey that measures their prevalence. Mixed methods are useful when numerical patterns require explanation or when exploratory findings need broader assessment. They also require careful integration; simply collecting two types of data does not automatically create a coherent mixed-method study.

5. Units, Data Sources and Sampling Decisions

5.1 Unit of analysis

The unit of analysis is the entity about which conclusions are drawn. It may be an individual customer, household, transaction, store, advertisement, firm or geographic area. Confusing the unit of analysis can produce invalid conclusions. For example, transaction records describe transactions, not necessarily unique customers; one frequent buyer may appear many times.

5.2 Primary and secondary data

Primary data are collected specifically for the current research, such as interviews, surveys, experiments or observations. Secondary data already exist, such as sales records, government statistics, industry reports, web analytics and customer-service logs.

Secondary data are often cheaper and faster, but the researcher must assess their relevance, accuracy, completeness, definitions, date and possible bias. A report about internet users may not represent the startup’s actual target customers.

5.3 Sampling

A population is the complete set of units relevant to the research. A sample is a subset selected for study. Probability sampling gives units a known, non-zero chance of selection and supports stronger statistical generalization. Non-probability sampling, such as convenience, purposive, quota or snowball sampling, may be practical when a sampling frame is unavailable but requires cautious interpretation.

For a diploma-level startup project, the central principle is not “obtain the largest sample.” It is “obtain a sample that is appropriate to the decision and transparent about its limitations.” Recruitment through the founder’s social network may be fast but can overrepresent friends, loyal supporters or particular demographics.

6. Measurement and Instrument Design

Measurement translates concepts into questions, observations or recorded outcomes. A good instrument is relevant, understandable, unbiased and not unnecessarily long.

Important measurement principles include:

• define each construct before writing items;
• use language familiar to respondents;
• ask one issue per question;
• avoid leading, loaded and double-barrelled wording;
• provide balanced response options;
• specify a recall period where behaviour is requested;
• distinguish “do not know” from “not applicable”; and
• pilot-test the instrument with people similar to the target respondents.

Poor question: “How satisfied are you with our affordable and convenient service?”

This is leading and double-barrelled. Better questions ask about affordability and convenience separately and allow a neutral or not-applicable response.

A pilot can reveal confusing instructions, excessive length, missing response options and technical problems. Pilot findings should lead to revision, not merely be filed as documentation.

7. The Practical Research Brief

A research brief is a concise document that authorizes and guides the project. It creates shared expectations among the founder, researcher, agency and other stakeholders.

A practical brief should contain:

1. Project title and sponsor: who owns the decision.
2. Background: relevant market and business context.
3. Decision problem: the action that may follow.
4. Research problem: the knowledge gap.
5. Purpose and objectives: what the study will accomplish.
6. Research questions or hypotheses: the issues to be answered.
7. Target population and unit of analysis: whom or what will be studied.
8. Design and methods: exploratory, descriptive, causal or mixed.
9. Sample and recruitment: selection, expected size and eligibility.
10. Measures and analysis: main variables, coding and analytical approach.
11. Deliverables: report, dashboard, presentation, dataset or recommendations.
12. Constraints: budget, access, deadline and technology.
13. Ethics and data protection: consent, confidentiality, storage and deletion.
14. Decision rules: how findings will inform action.

Decision rules are especially valuable. For example, management may decide to pilot a subscription only if a defined target segment shows sufficient interest and the expected economics remain viable. A decision rule should not be manipulated after seeing the results merely to justify a preferred action.

8. Timeline and Budget Planning

8.1 Timeline

A realistic timeline includes dependencies. Instrument development should normally precede fieldwork, and pilot testing should precede full data collection.

The durations are illustrative rather than universal. A field study involving hard-to-reach participants, regulatory approval or an experiment may require substantially longer.

A simple critical-path view is useful: delay in problem approval delays instrument design; delay in recruitment delays fieldwork; incomplete data delays analysis. The schedule should include contingency time for low response, technical failure or revisions.

8.2 Budget

A research budget lists expected costs by activity. A basic planning identity is:

Direct costs may include incentives, recruitment, software, transport, transcription, data acquisition, printing, specialist analysis and staff time. The contingency allowance covers foreseeable uncertainty, such as replacement recruitment or additional transcription.

If the contingency is calculated as a percentage of direct costs:

The rate must be expressed as a decimal; for example, 10% is 0.10. The formulas assume all cost categories use the same currency and that costs are not double-counted.

Worked example. Suppose direct costs are:

• participant incentives: $240;
• survey software: $90;
• transport: $120; and
• transcription: $150.

Direct costs = $240 + $90 + $120 + $150 = $600.

With a 10% contingency:

Contingency = $600 × 0.10 = $60.

Total budget = $600 + $60 = $660.

A budget should also identify in-kind contributions, such as founder time or free access to an existing analytics platform. Omitting these resources may make the project appear cheaper than it really is.

9. Ethics, Governance and Data Protection

Market research involves people, personal information and potentially influential decisions. Ethical practice is therefore a business requirement as well as an academic requirement.

Key principles include:

• Informed participation: respondents should know the purpose, procedures, expected time and voluntary nature of participation.
• Right to withdraw: participants should be able to stop without penalty where feasible.
• Privacy and confidentiality: collect only necessary information and protect identities in reporting.
• Data minimization: do not collect sensitive data merely because a platform permits it.
• Secure storage: restrict access, use appropriate passwords and define retention and deletion procedures.
• Non-deception: do not misrepresent a sales promotion as independent research.
• Protection of vulnerable groups: apply additional safeguards where participants may face pressure or harm.
• Honest reporting: do not suppress inconvenient findings, fabricate responses or claim causation from descriptive evidence.

Legal requirements vary by jurisdiction. Applicable privacy, consumer-protection, electronic-communications and research-ethics rules should be checked before collection. Customer data obtained for service delivery may not automatically be authorized for unrelated marketing research.

10. Technology and AI in Research Planning

Digital tools can improve speed and consistency. Online survey platforms, web analytics, customer-relationship systems, transcription software and dashboards can support data collection and monitoring. However, automation does not remove the need for research judgment.

Generative AI may assist with brainstorming interview topics, improving readability, coding preliminary text or identifying duplicate questions. It should not be treated as an unquestionable analyst. Researchers must check hallucinated claims, preserve confidentiality, disclose material use where required, and ensure that sensitive data are not uploaded to an unauthorized service.

Automated sentiment analysis can misinterpret sarcasm, local expressions or multilingual responses. Algorithmic targeting can also reproduce sampling bias. Technology should therefore be evaluated against validity, transparency, security and fairness—not merely convenience.

11. Applied Case: GreenBite Subscription Pilot

The following is a fictional illustrative case.

Background

GreenBite is a small meal-delivery startup serving office workers and students. It sells individual meals through an app. Management is considering a weekly subscription that would provide scheduled delivery and a small price saving.

Problem

The founder believes that customers want convenience, but the evidence is limited to informal comments from loyal users. The managerial problem is whether to launch the subscription. The research problem is the lack of reliable evidence about customer needs, barriers, acceptable subscription conditions and likely adoption among the priority market.

Analysis and design choice

The team first reviews transaction data and finds that many customers make only one purchase. This descriptive evidence does not explain why. The team therefore chooses a sequential mixed-method design:

1. Conduct 12 semi-structured interviews with recent and lapsed customers.
2. Use interview findings to refine a questionnaire.
3. Survey eligible customers about needs, perceived value, barriers and purchase intention.
4. Run a small randomized pilot in which eligible customers receive either the subscription offer or the standard individual-meal offer.

The interviews are exploratory. The survey is descriptive and comparative. The pilot provides preliminary causal evidence about offer response, although it cannot establish long-term retention unless the observation period is long enough.

Objectives

• To explore the meanings of convenience and value among recent and lapsed GreenBite customers.
• To measure the prevalence of subscription barriers in the eligible customer population.
• To compare purchase intention across customers with different usage frequencies.
• To estimate whether the subscription offer increases trial enrolment during the pilot period.

Measures and safeguards

The team defines trial enrolment as activating a subscription within seven days of receiving the offer. It records offer exposure, eligibility, enrolment and cancellation. The survey separates price perception, menu variety, delivery reliability and convenience rather than combining them into one question. Participants receive an explanation of data use, and personally identifying information is separated from survey responses.

Outcome

The interviews reveal that customers value scheduled delivery but fear being locked into meals they may not need. The survey shows that flexibility and pause options are more important than a larger discount. The pilot indicates stronger initial enrolment when the offer includes a pause facility, but the team avoids claiming that the feature caused long-term loyalty because the pilot period is short.

GreenBite therefore launches a limited pilot with flexible pauses, clearly states the cancellation terms and schedules a follow-up retention study. The evidence changes the product from a rigid subscription to a flexible plan.

Learning points

1. The initial business question required several research questions.
2. Exploratory research helped identify customer language and unanticipated barriers.
3. Descriptive research measured how widespread those barriers were.
4. The pilot tested an offer, but its causal and long-term conclusions remained limited.
5. Operational definitions and ethical data practices improved decision quality.

Challenges and Limitations

Research design cannot eliminate uncertainty. Common challenges include:

• founders defining the desired answer before the research begins;
• insufficient access to non-customers or dissatisfied customers;
• low response rates and incomplete surveys;
• convenience samples that do not represent the market;
• respondents giving socially desirable answers;
• measurement scales that do not capture the intended construct;
• confusing correlation with causation;
• changing market conditions during fieldwork; and
• treating early pilot results as proof of permanent demand.

A professional report should state these limitations and explain their likely effect. For example, if respondents were recruited from existing followers, awareness and satisfaction may be overestimated. Limitations do not make research useless; transparent limitations make conclusions more credible.

Best-Practice Checklist

Before approving a startup research project, ask:

• Is the decision clearly stated?
• Is the research problem expressed as a knowledge gap?
• Do every objective and question support the decision?
• Are key constructs operationally defined?
• Does the design match the purpose?
• Is the sample relevant and recruitment feasible?
• Has the instrument been piloted?
• Are the timeline and budget realistic?
• Are consent, privacy and data-security arrangements adequate?
• Are decision rules specified before results are known?
• Does the report distinguish evidence, interpretation and recommendation?

Chapter Summary

Effective market research starts with a clear distinction between a managerial problem and a research problem. The managerial problem concerns what the organization should do; the research problem identifies the evidence needed to make that decision. A strong problem statement defines the context, knowledge gap, decision relevance and scope.

Research purpose statements provide overall direction. Research questions break the purpose into answerable issues, while objectives specify observable achievements. Hypotheses are appropriate when relationships, differences or effects can be operationalized and tested. Conceptual frameworks organize expected relationships but do not prove causation.

Exploratory design discovers meanings, motivations and possible explanations. Descriptive design measures market characteristics and patterns. Causal design tests whether an intervention produces an outcome and requires attention to covariation, temporal order and alternative explanations. Mixed-method designs can combine these purposes when integration is planned carefully.

A practical research brief should align the decision, problem, questions, objectives, population, design, sample, measures, analysis, deliverables, timeline, budget and ethical safeguards. Budget calculations should be transparent, and schedules should include dependencies and contingency. Ethical research protects participants, minimizes data collection and reports findings honestly. Technology and AI can support research work, but human judgment remains essential for validity, privacy, fairness and interpretation.

Key Terms

Business problem: A decision or action challenge faced by an organization.

Research problem: A clearly defined gap in knowledge requiring systematic evidence.

Research purpose: A concise statement of the overall intention of a study.

Research question: An answerable inquiry that directs data collection and analysis.

Research objective: A specific statement of what the study will accomplish.

Hypothesis: A testable provisional statement about a relationship, difference or effect.

Null hypothesis: A statement that no specified relationship or difference exists.

Construct: An abstract concept, such as trust, satisfaction or perceived value.

Variable: A measurable characteristic that can take different values.

Operational definition: The specific procedure used to measure a construct or variable.

Conceptual framework: A structured representation of expected relationships among concepts.

Research design: The overall plan for collecting and interpreting evidence.

Exploratory research: Research that investigates poorly understood issues and generates insights or hypotheses.

Descriptive research: Research that measures and summarizes characteristics, behaviours or opinions.

Causal research: Research that examines whether a change in one factor produces a change in an outcome.

Unit of analysis: The entity about which conclusions are drawn.

Population: The complete set of units relevant to a study.

Sample: A subset of the population selected for research.

Primary data: Data collected specifically for the current research project.

Secondary data: Existing data originally collected for another purpose or an earlier study.

Review Questions

Short Answer Questions

1. What is the difference between a managerial problem and a research problem?
2. List four characteristics of a good research question.
3. When is exploratory research most appropriate?
4. What is an operational definition?
5. Identify two ethical principles relevant to market research.

Long Answer Questions

1. Explain how a broad business concern can be translated into a research problem, purpose statement, questions and objectives.
2. Compare exploratory, descriptive and causal research designs, including their purposes, methods and limitations.
3. Explain the role of hypotheses and conceptual frameworks in startup marketing research.
4. Describe the essential contents of a practical research brief.
5. Discuss how sampling, measurement and pilot testing influence the credibility of research findings.

Analytical / Application Questions

1. A new online clothing store has many website visitors but few completed purchases. Develop a research problem, three research questions, four objectives and a suitable research design.
2. A startup wants to know whether a free-delivery message increases completed orders. Design a small field experiment, identifying the independent variable, outcome measure, comparison condition and two possible threats to validity.
3. Prepare a brief timeline and budget structure for a customer study involving interviews, an online survey and a pilot offer. State the assumptions that would be needed to calculate the final cost.

Multiple Choice Questions with Answers

Question 1

Which statement best distinguishes a research problem from a managerial problem?

A. A research problem concerns the evidence needed to support a decision.

B. A research problem is always larger than a managerial problem.

C. A research problem is a financial target set by management.

D. A research problem eliminates the need for managerial judgment.

Correct Answer: A. A research problem concerns the evidence needed to support a decision.

Question 2

Which objective is most specific and measurable?

A. To understand the market better.

B. To study customer opinions.

C. To measure the importance of delivery speed among university customers in City X during the first quarter.

D. To improve marketing performance.

Correct Answer: C. To measure the importance of delivery speed among university customers in City X during the first quarter.

Question 3

A startup is unsure what motivates customers and wants to discover relevant themes and vocabulary. Which design is most suitable initially?

A. Exploratory research

B. Causal research

C. Financial auditing

D. A fully standardized census

Correct Answer: A. Exploratory research

Question 4

Which research design is primarily intended to measure market characteristics and patterns?

A. Exploratory design

B. Descriptive design

C. Causal design

D. Experimental-only design

Correct Answer: B. Descriptive design

Question 5

Which condition is especially important for a causal claim?

A. The proposed cause occurs after the outcome.

B. The relationship is based only on a small convenience sample.

C. The proposed cause precedes the outcome and alternative explanations are addressed.

D. All respondents provide identical answers.

Correct Answer: C. The proposed cause precedes the outcome and alternative explanations are addressed.

Question 6

What is an operational definition?

A. A broad statement of company strategy

B. A specific procedure for measuring a construct or variable

C. A list of competitors

D. A legal contract with respondents

Correct Answer: B. A specific procedure for measuring a construct or variable

Question 7

Direct research costs total $600 and the contingency rate is 10%. What is the total budget?

A. $606

B. $610

C. $650

D. $660

Correct Answer: D. $660

Question 8

Which item belongs in a practical research brief?

A. The researcher’s unrelated personal opinions

B. The target population and unit of analysis

C. An untested guarantee of business success

D. Only the final recommendation

Correct Answer: B. The target population and unit of analysis

Question 9

Why is a pilot test of a questionnaire useful?

A. It guarantees a representative sample.

B. It removes the need for ethical approval.

C. It can identify confusing wording, missing options and technical problems.

D. It proves the research hypothesis.

Correct Answer: C. It can identify confusing wording, missing options and technical problems.

Question 10

Which practice best reflects ethical market research?

A. Collecting all available personal data regardless of need

B. Presenting a sales promotion as independent research

C. Reporting only findings that support the founder’s preferred decision

D. Explaining data use, protecting confidentiality and allowing voluntary participation

Correct Answer: D. Explaining data use, protecting confidentiality and allowing voluntary participation