Unit 2: Research Approaches and Concept of Theory

Research is the systematic, controlled and critical investigation of a problem in order to discover new facts or verify existing knowledge. Redman and Mory define it as a "systematized effort to gain new knowledge." This unit builds on the idea that any inquiry must move from a felt problem to a defensible conclusion through a repeatable set of steps, guided by an approach and framed by theory.

Defining properties this unit relies on:

• Systematic: follows an ordered sequence of steps rather than guesswork.
• Empirical: grounded in observable, measurable evidence.
• Logical: reasoning proceeds by valid deduction or induction.
• Replicable: procedures are documented so others can repeat and verify them.
• Objective: conclusions rest on data, not the researcher's bias.
• Cyclical: findings feed back into fresh problems and new theory.

II. Research Approaches

How data is treated: numbers, meaning, or a blend of both.

A. Definition and basis of classification

An approach is the overall orientation a researcher takes toward collecting and analysing data. Kothari distinguishes two broad approaches by the nature of the data.

• Quantitative approach: generates data in numerical form for statistical analysis, aiming to measure and quantify a phenomenon.
• Qualitative approach: concerned with subjective assessment of attitudes, opinions and behaviour, producing non-numeric data such as words and images.

B. Quantitative approach

Rests on measurement and statistical inference, testing hypotheses against numeric evidence.

• Inferential: studies a sample to infer characteristics of a population, e.g. estimating average income from a survey of 500 households.
• Experimental: manipulates an independent variable under controlled conditions to observe effect, e.g. testing a fertiliser on crop yield.
• Simulation: builds a mathematical or computer model to study system behaviour under varied inputs, e.g. modelling traffic flow.

C. Qualitative approach

Seeks meaning and interpretation rather than counts, well suited to motives and perceptions.

• Techniques: in-depth interviews, focus groups, projective tests, participant observation.
• Output: thematic descriptions of "why" and "how," e.g. exploring why consumers stay loyal to a brand.
• Mixed approach: many studies combine both, using surveys for scale and interviews for depth, to offset each method's weakness.

III. Significance of Research

Why systematic inquiry matters to knowledge, policy and practice.

A. General significance

Research is the engine of progress; the motto "All progress is born of inquiry" captures its role in economic and social advance.

• Solves problems: provides evidence-based answers to operational and planning problems, e.g. locating a new factory using demand data.
• Builds knowledge: adds verified facts and refines existing theory.
• Aids decision-making: replaces intuition with data in business and government.

B. Significance for specific stakeholders

The value of research differs by who uses it.

• Government and policy: informs economic policy and budgeting, e.g. census and national income studies guiding welfare schemes.
• Business and industry: market research forecasts demand, tests products and reduces investment risk.
• Academics and students: confers method, discipline and a means to a livelihood through consultancy and teaching.
• Society at large: solves social problems and clarifies the causes of poverty, unemployment and inequality.

IV. Research Process

The ordered sequence from problem to report.

A. Nature of the process

The research process is a set of interrelated actions performed in sequence, though steps often overlap and loop back. Kothari lists them as a chain from problem definition to interpretation.

• Ordered but iterative: later steps can force revision of earlier ones.
• Anchored by the problem: every subsequent choice serves the problem statement.

B. Steps in the research process

Each step feeds the next and must be completed before firm conclusions follow.

• Formulating the problem: state the question clearly, e.g. "Does advertising raise sales of product X?" This fixes scope.
• Reviewing the literature: survey existing studies to avoid duplication and locate gaps.
• Developing hypotheses: frame a tentative, testable proposition, e.g. "Higher ad spend increases sales."
• Preparing the research design: plan the blueprint, deciding whether the study is exploratory, descriptive or experimental.
• Determining sample design: choose probability or non-probability sampling and sample size, e.g. random sampling of 300 buyers.
• Collecting data: gather via observation, questionnaire, interview or schedule.
• Analysing data: edit, code, classify and tabulate, then apply statistical tests.
• Testing hypotheses: apply tests such as chi-square or t-test to accept or reject the hypothesis.
• Generalising and interpreting: derive conclusions and, if supported, build or extend theory.
• Preparing the report: document the problem, method, findings and limitations for the reader.

V. Criteria of Good Research

The standards that separate sound inquiry from weak work.

A. Overarching principle

Good research follows the standards of the scientific method so that its conclusions are dependable and its procedure transparent.

• Standard: the whole design and its logic should be open to public scrutiny and re-examination.

B. The main criteria

Each criterion protects one aspect of quality.

• Clearly defined purpose: the aim and problem are stated in unambiguous terms.
• Detailed, reproducible procedure: methods are described fully enough for another researcher to repeat the study.
• Carefully planned design: the design yields results as objective as possible and minimises bias.
• Frank reporting of flaws: the researcher reports procedural flaws and estimates their effect on findings.
• Adequate analysis: analysis is sufficient to reveal significance, and data validity and reliability are checked.
• Justified conclusions: conclusions are confined to those the data support, with no over-generalisation.
• Researcher credibility: greater confidence is warranted when the researcher is experienced, of good reputation and has integrity.

C. Qualities of good research

Beyond procedure, good research shares certain hallmarks.

• Systematic: structured by rules and logical steps, not by intuition alone.
• Logical: guided by valid inductive and deductive reasoning.
• Empirical: conclusions rest on real, verifiable evidence.
• Replicable: repeatable results build confidence and allow generalisation.

VI. Concept of Theory: Deductive and Inductive Theory

What theory is, and the two directions of reasoning that link it to evidence.

A. Meaning of theory

A theory is a set of interrelated concepts, definitions and propositions that presents a systematic view of phenomena by specifying relations among variables in order to explain and predict them.

• Function: organises facts, explains relationships and predicts outcomes.
• Components: concepts (abstract ideas such as "motivation"), variables (measurable forms of concepts) and propositions (statements linking them).
• Link to research: theory both guides which data to gather and is refined by the data collected.

B. Deductive theory

Deductive reasoning moves from the general to the particular, testing a theory against observation; it is the logic of "theory-then-research."

• Direction: starts with an accepted theory, derives a hypothesis, then tests it with data.
• Sequence: theory → hypothesis → observation → confirmation or rejection.
• Nature: if the premises are true and the logic valid, the conclusion must be true; it tests rather than builds theory.
• Example:

Premise 1 (general): All employees given a bonus increase output.
Premise 2 (particular): Team A was given a bonus.
Conclusion: Team A will increase output.

The researcher then collects Team A's output data to confirm the hypothesis.

C. Inductive theory

Inductive reasoning moves from the particular to the general, building theory out of observation; it is the logic of "research-then-theory."

• Direction: starts with specific observations, detects patterns, then formulates a general theory.
• Sequence: observation → pattern → tentative hypothesis → theory.
• Nature: the conclusion is probable, not certain, because it generalises beyond the observed cases.
• Example:

Observation: Firms A, B and C raised output after giving bonuses.
Pattern: Bonuses appear to coincide with higher output.
Theory: Giving bonuses tends to increase employee output.

D. Deductive versus inductive contrasted

The two are complementary directions within the same cycle of inquiry, and many studies alternate between them.

1. Deductive: top-down; begins with theory and narrows to a tested conclusion; conclusions are logically certain given true premises; suited to hypothesis testing and quantitative work.
2. Inductive: bottom-up; begins with data and widens to a general theory; conclusions are probabilistic; suited to theory building and qualitative, exploratory work.

• Shared cycle: induction generates theory that deduction then tests, and the test yields fresh observations that feed further induction, closing the wheel of science.