Research Approaches and Types

Introduction

Business research is not simply the collection of information. It is a disciplined process of defining a managerial problem, selecting evidence appropriate to that problem, interpreting findings, and using them to support decisions. The quality of a research conclusion depends substantially on the fit between the research question, the research approach, the type of research design, the evidence collected, and the claims made from that evidence.

Two distinctions are particularly important. First, researchers must distinguish among qualitative, quantitative, and mixed-method approaches. These approaches differ in their assumptions about knowledge, the form of evidence they privilege, and the ways in which data are analysed. Second, researchers must distinguish among exploratory, descriptive, and causal research. These are types of research purpose or design logic. They indicate what the study is intended to accomplish: investigate an insufficiently understood issue, describe a phenomenon accurately, or assess whether one factor produces a change in another.

These distinctions are related but not identical. A study may be exploratory and qualitative, descriptive and quantitative, or causal and quantitative. A mixed-method study may begin with qualitative exploration and proceed to quantitative testing. The correct choice depends on the decision context, the maturity of existing theory, the nature of the research question, the availability of data, and the strength of evidence required.

Learning objectives

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

1. Explain the assumptions and practical characteristics of qualitative, quantitative, and mixed-method research.
2. Distinguish research approach from research purpose and design.
3. Select exploratory, descriptive, or causal research according to the managerial problem and required evidence.
4. Evaluate the strengths, limitations, and ethical implications of alternative approaches.
5. Design an appropriate multi-stage research strategy for a business problem.

1. Research approach, methodology, and method

A research approach is the broad logic through which a researcher generates and evaluates knowledge. Qualitative research generally seeks to understand meanings, experiences, processes, and context. Quantitative research represents phenomena numerically and examines patterns, associations, differences, or effects. Mixed-method research intentionally integrates qualitative and quantitative evidence within one programme of inquiry.

A methodology is the overall rationale and design for conducting a study. It connects philosophical assumptions, research questions, design choices, sampling, data collection, analysis, and standards of quality. A method is a specific technique, such as an interview, questionnaire, observation, experiment, or statistical test.

These terms should not be treated as interchangeable. For example, a questionnaire is a method, not automatically a quantitative methodology. Open-ended questionnaire responses may be analysed qualitatively, while a structured questionnaire may produce quantitative data. Similarly, interviews may be used in exploratory qualitative research or as one component of a mixed-method design.

Research choices should be driven by the question. Questions such as “How do frontline employees experience algorithmic scheduling?” invite qualitative inquiry. Questions such as “What proportion of employees report scheduling stress?” invite quantitative description. Questions such as “Does algorithmic scheduling reduce labour costs without lowering service quality?” require evidence concerning relationships and possible causation.

2. Qualitative research

Meaning and purpose

Qualitative research investigates how people interpret situations, construct meaning, experience processes, and explain behaviour in particular contexts. It usually works with non-numerical data, including interview transcripts, observations, documents, images, digital interactions, and case materials. Its aim is not merely to replace numbers with words; it is to develop a contextual and theoretically informed understanding of a phenomenon.

Qualitative research is especially useful when:

• the problem is poorly understood;
• relevant concepts or variables have not yet been clearly identified;
• the researcher wants to understand motives, identity, culture, or experience;
• the phenomenon is complex, sensitive, or processual;
• the context is central to interpretation; or
• existing measures do not adequately represent participants’ realities.

Qualitative inquiry is often associated with an interpretivist view, which holds that social reality is experienced and interpreted by people rather than being fully observable as a single objective entity. However, qualitative research is not limited to one philosophical position. Case study, ethnographic, phenomenological, grounded-theory, and realist traditions use qualitative evidence in different ways.

Characteristics

Qualitative studies commonly have the following features:

1. Open or flexible questions. Questions may evolve as the researcher learns from participants and the setting.
2. Context-sensitive data. Behaviour is examined in relation to organisational, cultural, historical, and situational conditions.
3. Purposive sampling. Participants are selected because they possess relevant experience or represent theoretically important variation, rather than because they statistically represent a population.
4. The researcher as an instrument. The researcher interprets data, develops rapport, notices context, and makes analytic decisions. Reflexivity—the explicit examination of how the researcher may influence the study—is therefore important.
5. Inductive or abductive analysis. Researchers may develop concepts from data or move iteratively between evidence and existing theory.
6. Rich, detailed outputs. Findings may include themes, narratives, process models, typologies, or explanations of how and why events occur.

Common qualitative methods include semi-structured interviews, focus groups, participant observation, non-participant observation, document analysis, diary studies, digital ethnography, and qualitative case studies.

Analysis and quality

Qualitative analysis is systematic even when it is not statistical. A typical thematic analysis involves familiarisation with the data, initial coding, comparison of codes, development of broader themes, review of themes, definition of concepts, and interpretation in relation to the research question. Coding may be manual or supported by software, but software does not replace judgement.

Quality is assessed through criteria suited to the study. Credibility concerns whether the interpretation is convincing and adequately grounded in the evidence. Transferability concerns whether readers can judge the relevance of findings to other contexts. Dependability concerns the transparency and consistency of the research process. Confirmability concerns whether conclusions are supported by the data rather than being merely expressions of researcher preference.

Strategies that can strengthen quality include triangulation, prolonged engagement, negative-case analysis, member reflection, peer debriefing, an audit trail, and thick description. Triangulation may involve comparing different participants, methods, researchers, data sources, or theoretical perspectives. It does not mean that all sources must produce identical accounts; disagreement may reveal important complexity.

Strengths and limitations

Qualitative research can reveal unanticipated issues, explain mechanisms and meanings, and identify how organisational practices are enacted in real settings. It is particularly valuable for service design, organisational change, consumer insight, innovation, culture, and stakeholder research.

Its limitations include potentially small and non-representative samples, substantial time required for data collection and analysis, interpretive dependence on the researcher, and limited ability to estimate population prevalence. A compelling quotation does not establish that a pattern is common, nor does a plausible narrative prove causation. Qualitative evidence can suggest mechanisms and generate propositions, but claims must remain proportionate to the design.

3. Quantitative research

Meaning and purpose

Quantitative research represents observations using numerical variables and applies structured procedures to measure, compare, estimate, or test relationships. It is commonly associated with a positivist or post-positivist orientation, in which researchers seek systematic evidence while recognising that measurement is imperfect and conclusions are probabilistic rather than absolutely certain.

Quantitative research is appropriate when the researcher needs to:

• estimate the prevalence or magnitude of a condition;
• compare groups, markets, products, or time periods;
• test theoretically derived hypotheses;
• assess associations among measurable variables;
• evaluate the effects of an intervention; or
• generalise from a probability sample to a defined population.

A quantitative study begins by translating concepts into operational definitions and measurable indicators. For example, “customer loyalty” might be operationalised through repeat-purchase intention, share of wallet, retention, or recommendation likelihood. Operationalisation is never neutral: the selected indicators capture some aspects of a construct and omit others.

Characteristics

Quantitative research commonly uses structured instruments, such as questionnaires, sensor records, transaction databases, experiments, or coded archival data. It relies on explicit procedures for sampling, measurement, data cleaning, analysis, and reporting.

Important concepts include:

• Variable: a characteristic that can take different values, such as price, satisfaction, tenure, or sales.
• Independent variable: a presumed explanatory or manipulated factor.
• Dependent variable: an outcome whose variation is studied.
• Control variable: a factor included to account for alternative explanations.
• Population: the complete set of units to which the study intends to refer.
• Sample: the units actually observed.
• Reliability: the consistency or stability of a measurement procedure.
• Validity: the extent to which a measure or design supports the intended interpretation.

A basic descriptive statistic is the arithmetic mean:

Here, x̄ is the sample mean, xᵢ is observation i, and n is the number of observations. The mean is appropriate for numerical data when its interpretation is meaningful; it can be strongly affected by extreme values. Quantitative research also uses proportions, medians, standard deviations, confidence intervals, correlation, regression, and experimental comparisons. The appropriate technique depends on measurement level, sampling, distributional assumptions, design, and the research question.

A correlation describes the degree to which two variables vary together. It does not by itself establish that changes in one variable cause changes in another. Causal claims require stronger design logic, discussed later in this chapter.

Measurement quality

Reliability and validity are related but distinct. A scale can be reliable yet invalid if it consistently measures the wrong construct. Validity has several dimensions. Content validity asks whether the measure adequately covers the concept. Construct validity asks whether the measure behaves as theory predicts. Criterion-related validity asks whether it relates appropriately to an outcome or benchmark. Internal validity concerns whether an observed effect can reasonably be attributed to the proposed cause rather than confounding factors. External validity concerns the extent to which findings apply beyond the study setting.

Quantitative studies may suffer from coverage error, non-response bias, social desirability bias, poorly worded questions, common-method bias, missing data, measurement error, and inappropriate statistical models. Large samples do not automatically correct biased measurement or biased sampling.

Strengths and limitations

Quantitative research supports estimation, comparison, formal hypothesis testing, transparency of numerical procedures, and, under suitable sampling and design, generalisation. It is valuable for market sizing, performance measurement, financial analysis, policy evaluation, customer analytics, and operational decisions.

Its limitations include dependence on the quality of operational definitions, possible loss of context, sensitivity to model assumptions, and the danger of treating statistically detectable relationships as managerially important or causal. Statistical significance is not equivalent to practical significance. A small effect may be statistically precise but commercially irrelevant; a meaningful effect may be difficult to detect in a small sample.

4. Mixed-method research

Meaning and rationale

Mixed-method research combines qualitative and quantitative approaches in a planned way so that the resulting evidence provides a more complete answer than either approach alone. The defining feature is not simply using two methods. It is integration: the findings are connected, compared, built upon, or merged during design, data collection, analysis, or interpretation.

Mixed methods are valuable when a business problem contains both a measurement question and a meaning or process question. For example, a company may need to know both how many customers abandon an online checkout and why they abandon it. Quantitative analytics can estimate frequency and identify patterns; interviews or usability observation can explain the underlying experience.

Major designs

A mixed-method design should specify why each component is needed, how participants or cases are connected, when integration will occur, and how apparently inconsistent results will be interpreted. Integration may occur through connecting samples, building one instrument from another phase, merging datasets, comparing findings in a joint display, or developing a meta-inference—a conclusion derived from the combined evidence.

Example of integration

Suppose a retailer’s survey shows that satisfaction is high but repeat purchase is declining. An explanatory sequential design could first model repeat purchase using customer and transaction data. Researchers might then interview high-satisfaction, low-repeat customers and discover that delivery reliability, rather than product evaluation, drives switching. The integrated conclusion is more useful than either result alone: satisfaction is not the sole determinant of retention, and an operational intervention may be required.

Strengths and limitations

Mixed methods can provide breadth and depth, test whether different evidence sources converge, explain statistical patterns, and improve the practical usefulness of findings. They are particularly suitable for complex organisational and policy problems.

The approach also creates demands. It may require more time, funding, methodological expertise, and coordination. Integration can be superficial if the researcher merely places qualitative quotations beside statistical tables. Conflicting findings must be investigated rather than averaged away. A mixed-method label does not guarantee quality; each component must be rigorous, and the logic connecting them must be explicit.

5. Research types by purpose

Research approaches describe how evidence is generated. Research types by purpose describe what the study is intended to accomplish. Exploratory, descriptive, and causal research form a useful progression, although real projects may combine them.

Exploratory research

Exploratory research investigates a problem when its important dimensions, concepts, explanations, or boundaries are not yet sufficiently clear. It is often undertaken at the beginning of a research programme or when a new market, technology, customer behaviour, or organisational issue emerges.

Its objectives may include identifying relevant variables, discovering stakeholder perspectives, clarifying a problem, generating hypotheses, developing a conceptual framework, or assessing whether a larger study is feasible. Typical methods include literature review, expert interviews, focus groups, observation, qualitative case study, social listening, pilot studies, and analysis of secondary data.

Exploration is flexible and often iterative. A researcher may begin with a broad question, collect initial evidence, refine the concepts, and revise the research plan. The outcome is usually not a definitive estimate or causal conclusion. It may instead be a typology of customer needs, a process model, a set of hypotheses, or a measurement instrument.

For example, if a bank observes rising use of digital channels but does not know why some older customers avoid them, exploratory interviews may reveal concerns about fraud, lack of confidence, accessibility, or preference for human contact. These insights can inform a later survey that estimates how prevalent each concern is.

Descriptive research

Descriptive research systematically portrays the characteristics, distribution, frequency, or change of a population, event, process, or market. It answers questions such as who, what, where, when, and how much.

Descriptive designs include cross-sectional surveys, longitudinal panels, structured observation, market audits, customer segmentation profiles, and analysis of administrative or transactional records. They may be qualitative or quantitative, although the term is frequently associated with structured quantitative measurement.

A descriptive study might estimate the percentage of customers using mobile payment, describe the demographic profile of a firm’s most profitable segment, or document monthly staff turnover across business units. It can reveal associations and patterns, but description alone does not show that one variable causes another.

A good descriptive study defines the population, unit of analysis, time period, variables, measurement procedures, and sampling method. It should report uncertainty where estimation is involved and distinguish the observed sample from the wider population. For example, a survey result of 62% is meaningful only when readers know who was surveyed, how respondents were selected, how questions were worded, and the relevant period.

Causal research

Causal research examines whether variation in one factor produces a change in another under specified conditions. A causal claim has more structure than a statement of association. It implies that:

1. the presumed cause and outcome vary together;
2. the cause occurs before the outcome; and
3. alternative explanations have been sufficiently controlled or ruled out.

The strongest design for causal inference is often a randomised experiment. Eligible units are assigned to treatment and control conditions using a random mechanism. The treatment group receives an intervention, while the control group does not receive it or receives a comparison condition. Randomisation tends, in expectation, to balance observed and unobserved factors between groups, allowing differences in outcomes to be attributed more credibly to the intervention.

A simple difference in group means can be expressed as:

Here, ȳₜ is the mean outcome for the treatment group and ȳ𝚌 is the mean outcome for the control group. This estimate is interpretable as a causal effect only when the experimental design is properly implemented, treatment assignment is sufficiently random, outcomes are measured comparably, and interference, attrition, and non-compliance are addressed.

In a pre-test/post-test setting, a difference-in-differences estimator may compare changes over time in treatment and comparison groups:

The notation uses treatment and control group means before and after intervention. A central assumption is parallel trends: without the intervention, the treatment and comparison groups would have followed similar average trends. This assumption cannot be proven solely from the post-intervention result and should be assessed using pre-intervention evidence and substantive reasoning.

When randomisation is impractical or unethical, researchers may use quasi-experiments, natural experiments, regression discontinuity, interrupted time series, matching, or statistical controls. These can be informative, but their assumptions must be made explicit. Statistical adjustment cannot automatically remove unmeasured confounding.

Causal research is appropriate when a decision concerns whether to implement, scale, discontinue, or redesign an intervention. Examples include testing a price change, evaluating a training programme, comparing advertising treatments, or assessing whether a new workflow improves productivity. Ethical and practical constraints matter: withholding a beneficial service, exposing customers to material risk, or manipulating sensitive information may be unacceptable.

6. Choosing the appropriate approach and research type

Selection should begin with the decision, not with a favourite method. The following diagnostic sequence is useful.

Step 1: Clarify the decision problem

A decision problem concerns an action, such as entering a market, redesigning a service, or adopting software. A research problem identifies the uncertainty that prevents an informed decision. “Should we launch?” is a decision question; “Which customer segment values the proposed service, and what evidence predicts adoption?” is a research question.

Step 2: Assess knowledge maturity

If key concepts and explanations are unclear, begin with exploration. If the concepts are clear but their prevalence or distribution is unknown, use descriptive research. If a proposed intervention and outcome are specified and the decision requires evidence of effect, use causal research.

Step 3: Identify the required claim

Step 4: Consider feasibility and ethics

Access, budget, time, sample availability, privacy, organisational disruption, and participant vulnerability may constrain the ideal design. Feasibility should influence the design, but it should not be used to justify claims stronger than the evidence permits. A correlational customer database may be useful for prediction while remaining inadequate for a causal claim.

Step 5: Plan integration and decision use

Specify in advance how results will affect action. A study may be technically sophisticated but managerially weak if it does not identify the decision threshold, relevant stakeholders, implementation conditions, or consequences of error.

7. Applied case: declining adoption of a digital claims service

The following is an illustrative case, not a report of an actual company.

Background and problem

A national insurer introduces a mobile claims service intended to reduce processing time and administrative cost. Six months later, only 18% of eligible customers use it. Management does not know whether low adoption reflects lack of awareness, usability problems, distrust, accessibility barriers, or the fact that many customers prefer assistance for emotionally difficult claims.

Analysis of the research need

The problem is initially exploratory because the organisation lacks a satisfactory explanation of non-adoption. A purely descriptive survey could measure awareness and preferences, but it might miss unanticipated concerns. A causal experiment would be premature because management has not yet identified a well-specified intervention.

Approach and design

The research team uses an exploratory sequential mixed-method design:

1. Conduct semi-structured interviews with recent users and non-users, including customers with different ages, claim types, accessibility needs, and levels of digital confidence.
2. Observe usability sessions in which participants attempt to submit a simulated claim.
3. Analyse service logs to identify points at which users abandon the process.
4. Use the qualitative findings to construct a survey measuring awareness, trust, perceived effort, need for human support, and accessibility.
5. Administer the survey to a defined sample of eligible policyholders and estimate the prevalence of each barrier.
6. Develop two interventions: clearer guidance and an option for live assistance during digital submission.
7. Randomly assign eligible claims handlers or customers, where operationally and ethically appropriate, to alternative communication or support conditions and compare completion rates and processing time.

Findings and interpretation

Suppose interviews reveal that customers understand the service but fear uploading documents after a previous data breach reported in the news. Usability observation also identifies confusing instructions for joint policyholders. The survey indicates that trust concerns are common, while the log analysis shows a large abandonment point at identity verification. The subsequent test compares a redesigned identity-verification explanation with the existing process, measuring completion and support requests.

The stages answer different questions. Qualitative research explains meaning and experience. Descriptive quantitative research estimates how widespread barriers are. The experiment evaluates whether a specific redesign changes behaviour. No single stage should be interpreted as answering all three questions.

Learning points

The case demonstrates that research type should evolve with knowledge. Exploration can prevent the organisation from testing the wrong intervention. Description supports prioritisation by showing prevalence. Causal evaluation supports an implementation decision. The mixed-method design is justified because adoption is simultaneously a behavioural outcome, a measurable population pattern, and an experience shaped by trust and context.

8. Ethics, governance, technology, and contemporary developments

Ethical research requires informed consent where appropriate, voluntary participation, confidentiality, secure data handling, fair inclusion, and minimisation of harm. Researchers must be especially careful when collecting data from employees, customers in vulnerable circumstances, or individuals whose participation may affect access to services. Organisational sponsors should not pressure employees to participate or use identifiable responses for retaliation.

Research governance should address data ownership, retention, access controls, anonymisation or pseudonymisation, conflicts of interest, preregistration where suitable, and transparent reporting of exclusions and deviations from the original plan. In causal evaluations, stopping rules and adverse-event monitoring may be necessary.

Digital traces, platform analytics, artificial intelligence, and automated text analysis create new opportunities for all three approaches. Machine-learning models can classify large bodies of text or predict outcomes, but prediction is not the same as explanation or causation. Automated coding may reproduce training-data bias and can obscure interpretive decisions. Researchers should document model choice, validation, error rates, human oversight, and the consequences of misclassification. Generative AI may assist with transcription, coding suggestions, or instrument drafting, but confidential data should not be uploaded to unauthorised systems, and researchers remain responsible for verification and scholarly integrity.

Contemporary research increasingly uses adaptive and sequential designs, real-world evidence, digital experiments, and participatory approaches involving stakeholders in problem definition and interpretation. These developments expand methodological possibilities, but they do not remove the need for clear questions, valid measurement, transparent assumptions, and proportionate claims.

9. Common errors and best practices

Several errors recur in business research:

• treating a large sample as evidence of causation;
• calling a study “mixed methods” merely because it contains quotations and statistics;
• using exploratory convenience samples to make population estimates;
• confusing correlation with causal effect;
• selecting measures because they are easy rather than valid;
• ignoring negative cases or contradictory findings;
• reporting statistical significance without effect size or practical interpretation;
• allowing sponsor preferences to determine conclusions; and
• failing to align the strength of language with the strength of design.

Best practice is to formulate the claim before selecting the method, define the population and unit of analysis, pilot instruments, document sampling and analytic decisions, test alternative explanations, integrate evidence explicitly, report limitations, and distinguish what the data show from what they merely suggest. At MBA level, methodological competence includes not only knowing techniques but also judging whether evidence is fit for a consequential managerial decision.

Chapter Summary

Qualitative, quantitative, and mixed-method research are distinct approaches to producing knowledge. Qualitative research develops contextual understanding of meanings, experiences, processes, and mechanisms. Quantitative research measures variables numerically to estimate, compare, describe, predict, or test relationships. Mixed-method research integrates both forms of evidence when breadth and depth are needed.

Exploratory, descriptive, and causal research describe the purpose and evidential ambition of a study. Exploratory research clarifies poorly understood problems and generates concepts or hypotheses. Descriptive research estimates characteristics, distributions, and patterns. Causal research evaluates whether an intervention or factor produces an outcome and therefore requires temporal ordering and credible control of alternative explanations.

Approach and purpose should be selected together but should not be confused. A research programme may appropriately move from qualitative exploration to quantitative description and then to causal evaluation. The choice should reflect the decision problem, knowledge maturity, required claim, feasibility, ethical constraints, and intended use of findings. Strong business research is not defined by complexity; it is defined by alignment, transparency, methodological rigour, and conclusions proportionate to the evidence.

Key Terms

Research approach: The broad logic used to generate and evaluate knowledge.

Methodology: The rationale and overall design connecting questions, assumptions, methods, and analysis.

Method: A specific data-collection or analysis technique.

Qualitative research: Inquiry into meanings, experiences, processes, and context using mainly non-numerical evidence.

Quantitative research: Inquiry using numerical measurement and structured analysis.

Mixed-method research: Planned integration of qualitative and quantitative approaches.

Exploratory research: Research undertaken to clarify an insufficiently understood problem or generate concepts and hypotheses.

Descriptive research: Research that systematically portrays characteristics, frequencies, distributions, or trends.

Causal research: Research examining whether a factor produces a change in an outcome.

Operationalisation: Translating an abstract concept into observable indicators or variables.

Variable: A characteristic that can take different values.

Reliability: Consistency or stability of a measurement procedure.

Validity: The extent to which evidence supports the intended interpretation of a measure or design.

Internal validity: Credibility that an observed effect is attributable to the proposed cause.

External validity: The extent to which findings apply beyond the study setting.

Triangulation: Comparing multiple evidence sources, methods, researchers, or perspectives.

Randomisation: Assignment by a random mechanism to reduce systematic differences between groups.

Confounding: A situation in which an alternative factor affects both the presumed cause and outcome, distorting their relationship.

Reflexivity: Critical examination of how the researcher influences the research process and interpretation.

Integration: The deliberate connection of qualitative and quantitative components in mixed-method research.

Review Questions

Short Answer Questions

1. Distinguish a research approach from a research method.
2. What is the main purpose of exploratory research?
3. Why does correlation alone not establish causation?
4. Define operationalisation and provide one business example.
5. What makes a study genuinely mixed-method rather than merely multi-method?

Long Answer Questions

1. Compare qualitative and quantitative research in terms of assumptions, sampling, data, analysis, quality criteria, strengths, and limitations.
2. Explain the differences among exploratory, descriptive, and causal research, using a business example for each.
3. Discuss the role of randomisation in causal research and explain why a causal claim may still be difficult in a field experiment.
4. Evaluate the advantages and challenges of explanatory sequential mixed-method research.
5. Develop a research strategy for a company investigating declining customer retention, justifying each stage and the evidence it would produce.

Analytical / Application Questions

1. A retailer finds that customers who receive personalised emails purchase more often. Design a study to determine whether the emails cause increased purchasing, and identify at least three threats to validity.
2. A university wants to understand why international MBA students underuse career services. Recommend an initial approach and research type, then explain how you would progress to descriptive and causal evidence.
3. A technology firm’s survey shows high satisfaction but low renewal rates. Propose a mixed-method design that integrates customer interviews, behavioural data, and an intervention evaluation. Explain how contradictory findings would be handled.

Multiple Choice Questions with Answers

Question 1

Which research approach is most appropriate for understanding how employees interpret and experience a new performance-management system?

A. Qualitative research

B. Quantitative research

C. Causal research only

D. Descriptive sampling only

Correct Answer: A. Qualitative research

Question 2

A study estimates the percentage of customers who use mobile payment and compares usage across age groups. What is its primary purpose?

A. Exploratory

B. Descriptive

C. Causal

D. Experimental manipulation

Correct Answer: B. Descriptive

Question 3

Which feature most directly strengthens a causal claim in an experiment?

A. A very long interview guide

B. Random assignment to treatment and control conditions

C. A convenience sample of experts

D. A large number of open-ended responses

Correct Answer: B. Random assignment to treatment and control conditions

Question 4

What is the defining feature of mixed-method research?

A. Using two independent samples

B. Collecting data only from digital sources

C. Integrating qualitative and quantitative evidence

D. Using statistics to analyse interview transcripts

Correct Answer: C. Integrating qualitative and quantitative evidence

Question 5

A researcher conducts interviews to identify customer concerns and then develops a survey instrument from the findings. Which mixed-method design is this?

A. Convergent

B. Explanatory sequential

C. Exploratory sequential

D. Embedded causal

Correct Answer: C. Exploratory sequential

Question 6

Which statement about correlation is correct?

A. Correlation always proves causation

B. Correlation describes covariation but does not by itself establish causation

C. Correlation can only be used with qualitative data

D. Correlation eliminates confounding

Correct Answer: B. Correlation describes covariation but does not by itself establish causation

Question 7

In the formula Estimated effect = ȳₜ − ȳ𝚌, what do ȳₜ and ȳ𝚌 represent?

A. Treatment and control group mean outcomes

B. Treatment and control group sample sizes

C. Pre-test and post-test means for one group

D. Two standard deviations

Correct Answer: A. Treatment and control group mean outcomes

Question 8

Which quality criterion asks whether a qualitative interpretation is convincing and grounded in the evidence?

A. Credibility

B. External validity

C. Statistical power

D. Reliability coefficient

Correct Answer: A. Credibility

Question 9

A company wants to know whether a redesigned checkout page increases completed purchases. Which research type is most appropriate?

A. Exploratory research only

B. Descriptive research only

C. Causal research

D. Ethnographic research only

Correct Answer: C. Causal research

Question 10

Which is a limitation of using a large convenience sample for population estimation?

A. It necessarily prevents numerical analysis

B. It may remain unrepresentative because selection is biased

C. It cannot contain any missing data

D. It automatically proves causal effects

Correct Answer: B. It may remain unrepresentative because selection is biased