INTRODUCTION

AND
RESEARCH DESIGN
➢Research: Meaning, Objectives, Types And
Significance, Criteria For Good Research, Problems
Faced By Researchers In India, Steps Involved In
Social Science Research.

➢Research Design: Meaning, Need, Features And

Important Concepts: Dependent And Independent
Variable, Extraneous Variable, Control, Treatments,
Experiment And Research Hypothesis.
 Meaning
➢Search For Knowledge
➢Scientific and Systematic search for pertinent information on a specific
topic.
➢Art of Scientific Investigation.
➢Voyage of Discovery
➢A careful Investigation or Inquiry specially through search for new facts
in any branch of knowledge.
➢Systematised effort to gain new knowledge.
 The term ‘Research’ refers to the systematic
method consisting of enunciating the problem,
formulating a hypothesis, collecting the facts or
data, analysing the facts and reaching certain
conclusions either in the form of solutions
towards the concerned problem or in certain
generalisations for some theoretical
formulation.
 Objectives
The purpose of research -> discover answers to
questions through the application of scientific
procedures.

Aim -> Find out the truth which is hidden and which
has not been discovered yet.
1. To gain familiarity with phenomenon or to achieve new
insights into it (Exploratory or Formulative research
studies)
2. To portray accurately the characteristic of a particular
individual, situation or a group (Descriptive research
studies)
3. To determine the frequency with which something occurs
or with which it is associated with something else
(Diagnostic research studies)

4. To test a hypothesis of a causal relationship between

variables (hypothesis-testing research studies)
• Extends Knowledge of human beings, social life, and
environment. (What, Where, When, How, Why)

• Establishes Generalisations – General Laws – contribute to

theory building in various fields of knowledge.

• Verifies & Tests Existing facts, theories and improve

knowledge.

• Finding Solutions to problems (unemployment, poverty)

• Aids Planning and contributes to National Development.

(schemes and programmes; industry – education – health –
social welfare)
DESCRIPTIVE APPLIED
& &
ANALYTICAL FUNDAMENTAL

Types
QUANTITATIVE CONCEPTUAL
& &
QUALITATIVE EMPIRICAL
DESCRIPTIVE RESEARCH : includes surveys and fact finding
enquiries of different kinds, with adequate interpretation.
• The major purpose of descriptive research is description of the state of
affairs as it exists at present.
• Main characteristic -> the researcher has no control over the variable; only
reports what is happening or what has happened.
• Does not deal with testing of hypothesis.
• Normally used in Social science and business research (Ex post Facto)
• Aim: identifying various characteristics of a community or institution or
problem under study.
• Focuses on particular aspects or dimensions of the problem studied.
• Its designed to gather descriptive information and provides information for
formulating more sophisticated studies.
ANALYTICAL RESEARCH : research has to use facts or information already
available, and analyse these to make a critical evaluation of the material.
• A system of procedures and techniques of analysis applied to quantitative data.
Consist of a system of mathematical models or statistical techniques applicable to
numerical data. Also known as Statistical Method.
• Aim: testing hypothesis and specifying and interpreting relationships.
• Analysing data in depth and examining relationships from various angles by
bringing in as many relevant variables as possible.
• Uses: extensively used in business and other fields in which quantitative numerical
data are generated. Its used for measuring variables, comparing groups and
examining association between factors.
• Data may be collected from either Primary sources or Secondary sources (like data
published by RBI, NABARD, CSO)
 DESCRIPTIVE vs ANALYTICAL
Describe characteristics of a Examines relation between variables,
population, situation, or phenomenon. analyse and interpret information.

Answers “what is” question Answers “why it is” or “how it

happened” question

Used in surveys, case studies, and fact- Used in reports, trend analysis, policy
finding studies, data/statistical analysis

Eg.: A study that shows how many Eg.: A study that analyses how
students in a college own smartphones smartphone usage affects students'
academic performance.
APPLIED RESEARCH : aims at finding a solution for an immediate
problem facing a society or an industrial/business organisation.
Aim: discover a solution for some pressing practical problem.
Its Problem solving - Action directed – Goal Oriented.
Seeks an immediate and practical result, e.g., marketing research for studying
the post purchase experience of customers.
Uses existing theories or knowledge to address specific issues.
Put theory to test – test validity of existing theory.
Seeks practical impact over theoretical advancement.
Uses: Develops innovations & improvements, Enhances organizational
processes & systems, Evaluates programs & policies, Informs policy &
decision making, Connects theory with practice.
Methodology: Case studies, Fieldwork, Surveys
FUNDAMENTAL RESEARCH : is concerned with generalisations and
with the formulation of theory.
“Gathering knowledge for knowledge’s sake.”
Aims at extension of knowledge, also known as Pure or Basic research.
Its original and investigative study.
It may lead to discovery of new theory or refinement of an existing theory.
Undertaken out of intellectual curiosity, not necessarily problem-oriented.
The findings enrich the storehouse of knowledge that can be drawn upon in
future – lays foundation for applied research.
Findings of pure research formed the basis for innumerable scientific and
technological inventions lie steam engine, machines, automobiles,
telecommunication, etc., which have revolutionised and enriched human life.
 FUNDAMENTAL vs APPLIED
To gain new knowledge or Goal is to solve specific, practical
understand basic principles problems

Theoretical and abstract question Real world and specific issues

Develops new theories or concepts Provides immediate solutions or

improvements

Not intended for direct use; Directly used in decision making or

knowledge-building practice
Understanding why something Using that understanding to solve problem
happens
QUANTITATIVE RESEARCH : is a systematic investigation that uses numerical
data and statistical analysis to quantify opinions, attitudes, behaviours, and other
defined variables, with the goal of generalising results to a larger population.
• It’s based on the measurement of quantity or amount.
• Focuses on numbers, facts, statistics and mathematical techniques.
• Data is collected through structured tools like surveys, tests, standardized
questionnaire.
• Uses closed-ended questions
• Results are measurable and comparable
• Findings are often shown using charts and graphs
• Useful for proving or testing a hypothesis
• Objective in nature: focuses on measurable facts, and figure, not opinions or
personal experiences.
Example: Surveying 200 students to measure how many hours they study daily.
QUALITATIVE RESEARCH : focuses on gaining in-depth understanding of
human behaviour, experiences, and perspectives through non-numerical data.
• Its concerned with qualitative phenomenon, i.e., phenomena relating to or
involving quality or kind.
• Focuses on feelings, opinions, and experiences
• Data is collected in words, not numbers
• Uses open-ended methods like interviews or observation
• Helps to understand meaning and context
• Findings are descriptive and subjective
• Useful for exploring new or complex issues.
• Delves into complexities of social life, uncovering meanings, motivations, and
perceptions related to particular topic.
Example: Interviewing students to know how they feel about online classes.
 QUANTITATIVE vs QUALITATIVE
Objective, statistical, measurable Subjective, descriptive, exploratory

To quantify data and generalize results To explore meanings, experiences, and

insights
Data type: numerical (numbers, Data type: textual (words, narratives,
percentages) interviews)
Analysis: statistical and mathematical Analysis: thematic and content analysis

Identifies patterns, relationships, Understands depth, context, meaning

casualties
CONCEPTUAL RESEARCH : focuses on developing and exploring
abstract ideas or theories.
Its generally used by philosophers and thinkers to develop new concepts or to
reinterpret new ones.
Involves Logical Reasoning, Conceptual mapping, Literature analysis,
Theoretical modelling.
Does not involve experiments or data collection.
Provides theoretical groundwork for empirical studies.
Examples:
Philosophical research: use conceptual research to develop new philosophical
systems or interpret existing ones.
Social Science research: use conceptual research to clarify concepts like social
justice, inequality, or power.
EMPIRICAL RESEARCH : relies on experience or observation.
Its data-based research, coming up with conclusions which can be verified by
observations or experiments.
The data collected is analysed often using statistical methods, to identify
patterns, relationships, or trends.
Involves formulating hypotheses and testing hypotheses using collected data.
Its experimental type of research.
Systematic & structured approach to gaining knowledge through observation
and experimentation, allowing researchers to draw conclusions based on real
world evidence.
Examples:
A study on the effects of a new drug,
An experiment on the impact of exercise on cognitive function
 CONCEPTUAL Vs EMPIRICAL

Abstract ideas, theories Real-world data, observations

Analysis, reasoning Data collection, experimentation

Not data – driven Data driven (quantitative - qualitative)

To build or refine theoretical To validate, prove, or disprove through

understanding evidence
E.g., Developing a new model E.g., Testing the effectiveness of a
treatment
➢Cross-sectional Research (One-time): examines data from a population at
a specific point in time.
E.g., Surveys assessing opinions on a particular topic at a single moment.

➢Longitudinal Research (Multiple-time): repeated observations of the same

subjects over an extended period.
E.g., Tracking the development of children's language skills over several
years.

➢Historical Research: To analyze past events to understand the present.

Uses documents, records, archives, journals.
E.g., Studying the causes of World War I, analyzing the evolution of a
specific technology, or examining the social impact of a historical event.
➢Experimental Research: to establish cause-and-effect relationships
between variables by manipulating variables
E.g., Clinical trials testing the effectiveness of a new drug.

➢Action Research: Aims to solve a specific practical problem within a

particular context through a cyclical process of planning, acting,
observing, reflecting.
E.g., A teacher researching the effect of a new teaching method on student
performance.

➢Exploratory Research: to explore a new or poorly understood problem,

focus on gaining insights
E.g., reviewing existing literature, or observing a situation to gain initial
insights.
 Significance of Research
• Significance in Academic Context
- Enhances the understanding of concepts, theories, and frameworks
- Encourages critical thinking and inquiry
- Builds a strong foundation for future empirical studies
- Helps students and scholars develop a research-based approach

• Significance in Business and Management

- Helps in decision-making and strategy formulation
- Provides data for market research, consumer trends, and product development
- Minimizes business risks by forecasting outcomes and testing ideas
- Increases operational efficiency and competitive advantage
 Significance of Research
• Significance in Policy and Governance
- Enables evidence-based policy making
- Assesses the impact of social, economic, and environmental programs
- Supports public planning and welfare schemes
- Offers reliable data for legislative decisions

• Significance in Social Sciences and Humanities

- Addresses social issues like poverty, gender inequality, education gaps
- Gives voice to underrepresented populations
- Assists in designing effective interventions or reforms
- Builds theoretical models for understanding human behavior
 Significance of Research
• Significance in Science and Technology
- Leads to innovation and technological advancement
- Supports experimentation and hypothesis testing
- Contributes to solving real-world scientific problems
- Encourages interdisciplinary research and innovation

• Significance for the Researcher

- Promotes personal growth and intellectual development
- Builds expertise in a specialized domain
- Enhances problem-solving and analytical skills
- Opens career opportunities in academia, research, and industry
 Why is Research Significant?

• Expands knowledge and builds theory

• Solves practical and professional

• Aids in policy, planning, and public welfare

• Enhances academic and intellectual development

• Supports innovation, strategy, and decision-making

• Fosters social change and development

 Criteria For Good Research
1. Systematic
Research must follow a logical and organized procedure. Steps must be structured
and follow a clear methodology
(problem identification → objectives → data collection → analysis → conclusions).
2. Logical
The research process and reasoning must be based on valid principles of logic.
Arguments and conclusions should follow logically from the data and premises.
3. Empirical
Good research is grounded in observable and measurable evidence.
Conclusions should be derived from real-world observations or verifiable data.
4. Replicable (Replicability)
Other researchers should be able to repeat the study and arrive at similar results.
It ensures the reliability and consistency of the findings.
5. Objective
The research should be free from personal bias or subjective interpretation. Facts and evidence
must be prioritized over opinions.

6. Relevant
Research must address a significant problem or question. It should be practical, purposeful, and
applicable to real-world contexts or theoretical advancement.

7. Accuracy and Precision

Definitions, measurements, and procedures must be precise and clearly defined. Ambiguity
reduces the trustworthiness of findings.

8. Credibility and Validity

Conclusions must be credible and logically derived from the data. Research should measure what
it intends to measure (internal validity) and be applicable beyond the study (external validity).

9. Ethical Soundness
Research should follow ethical guidelines including honesty, integrity, and respect for participants.
 Problems Faced By Researchers In India
1. Lack of Scientific Training:
- Insufficient knowledge of research methods.
- “Scissor-and-paste” approach, lacking originality.
- Results often fail to reflect reality.
2. Weak University–Industry Interaction:
- Limited collaboration with industry/government.
- Valuable primary data remains unused.
- Need for university–industry liaison programs.
3. Reluctance to Share Data
- Businesses fear misuse of information.
- Lack of trust is a major barrier.
- Need for confidentiality agreements.
 Problems Faced By Researchers In India
4. Duplication of Research
- Overlapping studies waste resources.
- Lack of centralized research information.
- Need regular updates on ongoing research.
5. Absence of Code of Conduct
- No ethical guidelines for researchers.
- Rivalries between departments/universities.
- Need a common research ethics framework.
6. Inadequate Secretarial & Computer Support
- Delays due to lack of admin/technical help.
- Inefficient typing, data entry, or software use.
- UGC should ensure timely support.
 Problems Faced By Researchers In India
7. Poor Library Management
- Libraries poorly organized or catalogued.
- Time wasted locating books, journals, reports.
8. Delay in Government Publications
- Slow supply of acts, rules, reports.
- Worse in libraries outside major cities.
9. Delay & Inconsistency in Data
- Published data is often delayed.
- Data from agencies vary significantly.
10. Conceptual & Data Collection Issues
- Difficulty defining research problems.
- Problems in designing tools and surveys.
 Problems Faced By Researchers In India
11. Sampling, Coverage & Response Bias
- Designing representative samples is difficult due to migration, informal economies, and poor
demographic databases.
- Social desirability bias (respondents giving “expected” answers) further skew results.
12. Chronic Underfunding and Poor Infrastructure
- India invests only about 0.7% of its GDP in R&D (well below the global average).
- Researchers face outdated labs, limited modern equipment, and delays in procuring essential
materials.
13. Publish-or-Perish Pressure and Predatory Journals
- Academic promotions and funding often depend on the number of publications rather than
quality.
- This pressure leads to submissions in predatory or low-quality journals, undermining research
credibility.
14. Ethical Issues and Plagiarism
- India lacks a centralized ethics oversight body for research misconduct.
- Cases of plagiarism, duplicate publication, and data fabrication are rising, with several retractions
from top institutions.
Steps Involved In Social Science Research
Social sciences research aims to understand human behavior,
relationships, and socio-economic issues using systematic and
scientific methods. While the basic methodology is similar to natural
sciences, social research emphasizes human factors, societal
conditions, and economic activities.
For example, research in Economics deals with human needs,
limited resources, and conflicts between individual and collective
interests. Regardless of the method (logical, statistical, or
mathematical), research follows a series of structured steps that
ensure accuracy, reliability, and meaningful conclusions.
 Steps & Process of a survey

1. Define the problem 7. Execution

2. Literature review
8. Data analysis
3. Hypothesis
9. Hypothesis testing
4. Research design

5. Sampling 10. Interpretation

6. Data collection 11. Report writing

 Step 1 – Conceptualization & Formulating the
Research Problem
• The first step is to clearly define what problem you are studying. A poorly
defined problem leads to irrelevant data and weak conclusions.

• Process: Identify the area of interest. -> Refine it into a specific question. ->
Ensure the problem is researchable (consider data availability, time, and
resources). -> Discuss with experts or academic guides to avoid ambiguity.

Example: A broad topic like “Unemployment in India” is too vague. It is refined

to: “What are the key reasons behind rising youth unemployment in Mumbai’s
service sector post-2020?”
 Explanation of Concepts
• What it means: Social sciences use abstract terms (e.g., poverty,
unemployment) that can have multiple meanings. Clear definitions are
required to ensure precision and avoid confusion.

Example: For the topic, the researcher defines “unemployment” as: “A

youth (aged 18–30) actively seeking employment in the service sector but
unable to secure a job for at least 30 consecutive days.”
 Step 2 – Use of Library & Literature Review
• Conduct a thorough study of existing research, books, academic journals,
government reports, and statistical records to see what is already known about
the topic.
• Two types: Conceptual literature – Theories, models, definitions.
Empirical literature – Past research findings, case studies.
• Importance: *Avoids duplication of work. *Helps identify research gaps.
*Provides methods or tools that can be adapted. *Refining objectives.
Example: Reviewing NSSO reports, labour ministry studies, and articles in
Economic and Political Weekly on urban unemployment.
Step 3 – Development of Working Hypothesis
• A hypothesis is a tentative assumption or explanation that can be tested
with data.

• How to develop it: Based on literature review, discussions with experts,

or preliminary data.

• It should be specific, testable, and related to the research objectives.

Example Hypothesis: “A lack of relevant technical skills is the primary

cause of youth unemployment in Mumbai’s service sector.”
 Step 4 – Preparing the Research Design
• A research design is the blueprint for the entire study.

• It answers how data will be collected, analyzed, and interpreted.

• Components: * Type of research: Exploratory, descriptive, or experimental.

* Tools and methods (e.g., surveys, interviews, statistical analysis).
* Timeframe and budget.

• Ensures systematic data collection and valid results.

Example: The researcher decides on a descriptive research design using

structured questionnaires and interviews with HR managers of service firms.
 Step 5 – Determining the Sample Design

• Define target population and select a representative sample.

• Steps involved:

Identify population (e.g., Local vendors in North Goa)

Choose sampling method: Random, Stratified, Systematic, Convenience

Decide sample size

Example: Out of all unemployed youth in Mumbai, a sample of 300 youth aged 18–
30 is selected using stratified random sampling (classified by education level).
 Step 6 – Collecting the Data
• Choose appropriate tools: Questionnaires, Interviews, Online forms.

• Ensure: Clear and unbiased question Pilot testing before final use

Ethical considerations (informed consent, confidentiality)

• Gathering primary - secondary data relevant to the research objectives.

• Methods: * Primary: Surveys, interviews, observation, experiments.

* Secondary: Government reports, published statistics, research papers.

Example: Primary data: Surveying 300 unemployed youth and interviewing 20 HR

managers. Secondary data: Analyzing city employment records and skill-gap reports.
 Step 7 – Execution of the Project
• Actual implementation of the survey in the field.

• Tasks include:

Training of data collectors

Supervision of fieldwork

Addressing non-response or incomplete data

• Ensure accuracy and consistency in data collection.

 Presentation of Data

• Organize and clean raw data.

• Raw data is meaningless unless organized into tables, charts, and

graphs for easy interpretation.

• Example: A table showing the percentage of youth unemployed by

education level: Graduates: 45%; Diploma holders: 35%; High
school pass: 20%)
 Step 8 – Analysis of Data

• Data is analyzed to identify trends, correlations, and relationships.

• Quantitative: Percentages, Mean, Median, Charts.

• Qualitative: Thematic coding of open responses.

• Use tools like Excel, SPSS, R, etc.

Example: Statistical analysis shows a high (70%) correlation between lack

of digital skills and unemployment rates in the service sector.
 Step 9 – Hypothesis Testing

• Use statistical tools to accept or reject hypotheses.

• Techniques: t-test, Chi-square, ANOVA, Correlation, Regression

(depending on data type).

Example: The t-test confirms that lack of technical skills is a

significant factor for unemployment, supporting the initial
hypothesis.
 Step 10 – Generalizations & Interpretations
• Apply findings to larger population cautiously.

• Explain: Trends Possible causes Unexpected results

• Link back to research objectives and literature.

• Results are interpreted in the context of theory and existing research.

• If patterns hold true in repeated studies, they may lead to theories or

general principles.

Example: The study concludes that digital skill training programs could
significantly reduce youth unemployment in Mumbai.
 Step 11 – Preparing the Report or Thesis
• Documenting the entire research process and findings in a structured format.
• Structure includes:
Introduction Review of Literature
Methodology Analysis & Findings
Conclusion & Recommendations Bibliography and appendices
• Use visuals (graphs, tables), ensure clarity and objectivity.
Example: A report is prepared for policymakers recommending skill-based training
initiatives.
Research Design
Meaning, Need, Features
 What is Research Design?
• Research Design = Blueprint or plan for conducting a research
study.
• It specifies what, where, when, how, and by what means the
research will be conducted.
• It combines relevance to the research purpose with economy of
procedure.
• Provides a conceptual structure for data collection, measurement,
and analysis.
 Meaning of Research Design
A logical and systematic plan for guiding a research study.

Specifies: Acts as a blueprint for:

• Objectives of the study • Data collection
• Methodology and techniques • Measurement
for achieving objectives • Analysis of data

Research design is:

• “The plan, structure and strategy of investigation conceived to obtain
answers to research questions.”
• A program or scheme that guides the researcher in collecting, analyzing,
and interpreting data.
• A systematic plan of procedure for the researcher to follow.
Key Questions in Research Design
1. What is the study about?
2. Why is the study being made?
3. Where will the study be carried out?
4. What type of data is required?
5. Where can the data be found?
6. What time period will the study include?
7. What will be the sample design?
8. What data collection techniques will be used?
9. How will the data be analysed?
10. In what style will the report be prepared?
 Components of Research Design

1. Sampling Design: How items/participants are selected.

2. Observational Design: Conditions for observations.

3. Statistical Design: How data is analyzed.

4. Operational Design: Techniques to implement all procedures.

Key Definitions
• Miller: “Designed research is the planned sequence of the entire process
involved in conducting a research study.”
• Selliz et al.: “Research design is a catalogue of the various phases and facts
relating to the formulation of a research effort… combining relevance with
economy.”
• Philip, Bernard S.,: It ‘constitutes the blueprint for collection, measurement
and analysis of data.’
Must include:
A Good Research Design: a) Clear research problem statement.
• Specifies sources and types of b) Procedures for gathering
information. information.
• Provides a strategy for data collection c) Population to be studied.
and analysis. d) Methods for processing and
• Time and cost budgets are planned. analyzing data.
 Essentials of a Good Research Design
2. Outline
1. Plan
specifies the sources & types of
specifies objectives of the information relevant to the
study & the hypotheses to research questions
be tested

4. the domain of Generalizability

3. Blueprint
whether the obtained information
specifying the methods to be can be generalized to a larger
adopted for gathering and population or to different
analyzing the data situations.
 Need for Research Design
➢Why Do We Need Research Design?
→ Ensures smooth and efficient research operations.
→ Provides maximum information with minimum effort, time, and money.
→ Similar to a blueprint (map) for constructing a house – planning in advance
avoids confusion.

➢Research Design = Advance Planning

• Involves planning methods for data collection and techniques for analysis.
• Considers: – Objectives of the research.
– Time, staff, and financial resources.
• Well-thought-out design = better results.
 Need for Research Design
➢Reliability of Results
• A strong research design builds the foundation for accurate and reliable
findings.
• Errors in design can: – Upset the entire project.
– Produce misleading conclusions.
➢Why Some Research Fails
• Many studies fail because the importance of design is ignored.
• Lack of proper design → irrelevant, incomplete, or misleading outcomes.
• Poor planning may make the entire exercise futile.
➢Benefits of a Good Design
• Helps the researcher organize ideas systematically.
• Reveals flaws and gaps early in the project.
• Can be reviewed and improved with expert feedback.
 Need for Research Design
• Maintains a logical flow of steps from problem to solution.

• Prevents wasteful duplication of work.

• Ensures validity and reliability of research findings.

• Clarifies scope, limitations, and priorities of the study.

Research Design = Roadmap for research

• Saves time, effort, and cost while ensuring quality results.

• Must be prepared carefully before starting the research.

 Nature of Research Design
• A research design is indispensable, but not a rigid or fixed plan.
• It acts as a series of guideposts to keep research moving in the right direction.
• It is a tentative plan, open to modifications as the study progresses.
– New conditions, relationships, or limitations may emerge.
– For example, sampling methods or scope may need adjustment.
• Research design is a practical compromise based on:
– Availability of data and resources.
– Cooperation of informants.
– Researcher’s time, competence, and mental ability to handle complexity.
• It is flexible and adaptable, not as rigid as a building blueprint.
• Must always be kept within manageable limits.
 Features of Research Design
What is a Good Design?
• A design that minimizes bias and maximizes reliability of data.
• It is flexible, appropriate, efficient, and economical.
• A good design ensures maximum information with minimum error.

Factors to Consider in a Good Design

• Means of obtaining information.
• Skills and availability of the researcher and staff.
• Objective and nature of the problem to be studied.
• Availability of time and money.
• Suitability of sampling, experimental, or survey design.
 Features of Research Design
Relevance: Efficiency:
Matches the nature Accuracy: Uses resources
and objective of the Minimizes bias and errors (time, money, staff)
research problem for descriptive or wisely
hypothesis-testing studies.

Flexibility: Reliability:
Allows adjustments Produces consistent and
for exploratory studies dependable results
 Features of a Good Research Design
Different Designs for Different Studies
• Exploratory studies: Need flexible designs for discovering ideas and insights.
• Descriptive studies: Need accurate designs to minimize bias and ensure
precision.
• Causal or hypothesis-testing studies: Require designs that allow causal
inferences while maintaining reliability.

To Summarize -
•No single design fits all research problems.
•A good design balances accuracy, flexibility, and economy.
•Must suit the purpose and primary function of the research.
 Characteristics of a Good Research Design
Objectivity Reliability Generalizability
Validity
The design must be Results must be Findings from the
free from personal consistent if the The design must sample should apply
bias. Data collection research is repeated. measure what it to the larger
tools should give the claims to population. Ensured
Same questions
same results for all measure. by proper sampling
should produce the
observers. and analysis.
same responses over Example: A test
Example: Closed- time. designed for Example: A market
ended questionnaires intelligence survey of 500
Example: Re-
give objective answers, should not people should
administering the measure memory
unlike open-ended reflect the opinions
same test to the same or vocabulary
ones that may vary by of the target
interpretation. group should yield alone. population.
similar results.
 Important Concepts:
Dependent Variable
Independent Variable
Extraneous Variable
Control
Treatments
Experiment
Research Hypothesis
❖V a r i a b l e : a concept which can take on different quantitative values or
characteristics across individuals, groups or time. Its something that varies, unlike a
constant which stays the same.

Examples of Variables:
• Quantitative: weight, height, age, income
• Qualitative: gender, religion, marital status, education level

Why Variables matter in Research?

• Variables are the building blocks of research problems and hypotheses.
• They help researchers measure, compare, & establish relationships.
Qualitative phenomena (or the attributes) are also quantified on the basis of the presence
or absence of the concerning attribute(s).

Quantitative variables → helps in numerical comparison & prediction → used in Surveys,

experiments, statistical testing.
Qualitative variables → helps understand patterns, behaviours, group differences → used
in interviews, thematic analysis, classification
❖C on t i nu ou s va r i ab l es : Phenomena which can take on quantitatively
different values even in decimal points.

❖N on - c o n t i nu ou s va r i ab l es or in statistical language ‘discrete

variables’ : if the variables can only be expressed in integer values.
• Age is an example of continuous variable, but the number of family
members is an example of non-continuous variable.

Types Continuous Variables Non-Continuous Variables

Meaning Can take infinite values (including Can take only whole numbers
decimals) in range
Examples Weight (65.4kg), Age (22), Income No. of children, books read,
visit to doctor
 Dependent Variable & Independent Variable
If one variable depends upon or is a consequence of the other variable, it is
termed as a DEPENDENT VARIABLE
The variable that is antecedent to the dependent variable is termed as an
INDEPENDENT VARIABLE.
• Example: if we say that height depends upon age, then
height → dependent variable & age → independent variable.
• Further, if height also depends upon the individual’s sex,
height → dependent variable and age & sex → independent variables.
• Similarly, readymade films & lectures → independent variables, whereas
behavioural changes, occurring as a result of the environmental manipulations
→ dependent variables
➢Dependent variable is the outcome or the variable being measured or
observed. It depends on changes in other variables.
➢An Independent variable is the variable that is manipulated or used to explain
the dependent variable. It is the assumed cause or influence.
Implication in research:
• The DV is often the main focus of the study.
• Researchers seek to explain or predict changes in the DV.
• Helps to test hypotheses and establish causal relationships.
• IV is often controlled or varied in experiments.
Examples: IV = Cause
• Stress level (DV) affected by number of working hours (IV)
• Blood pressure (DV) in response to medication (IV) DV = Effect
• Teaching method (IV) → Test scores (DV)
• Gender, age, or training program (IVs) → Job performance (DV)
 Extraneous Variable
Independent variables that are not related to the purpose of the study, but may
affect the dependent variable are termed as extraneous variables.
Example: Suppose the researcher wants to test the hypothesis that there is a
relationship between children’s gains in social studies achievement and their self-
concepts.
• Self-concept → IV and social studies achievement → DV
• Intelligence → social studies achievement, but since it is not related to the
purpose of the study undertaken by the researcher, it will be termed as an
extraneous variable.
Effect upon the dependent variable must always be attributed entirely to the
Independent variable(s), and not to some extraneous variable(s).
Experimental error → effect noticed on dependent variable as a result of
Extraneous variables(s).
 Extraneous Variable
• Variables other than the independent variable, that could affect the dependent
variable in a research study.

• These variables are not of primary interest but may interfere with the
relationship being studied.

• They introduce error or bias, making it difficult to determine if the changes in

the dependent variable are due to the independent variable or something else.
• Example: A study on the effect of a new teaching method (IV) on student
performance (DV): student’s prior knowledge, IQ, motivation, or environment
(like noise or light) are Extraneous Variables.
• If not controlled, extraneous variables can lead to invalid or misleading results.
• Researchers must identify and control these variables through design techniques.
 Control
• One important characteristic of a good research design is to minimize the
influence or effect of extraneous variable(s).
• The technical term ‘control’ is used when we design the study minimizing
the impact of extraneous independent variables on the outcome of study.

• In experimental researches, the term ‘control’ is used to refer to restrain

experimental conditions.

Purpose of Control:
• To ensure that the effect on the DV is only due to the IV.
• To increase the internal validity of the study.
Example: in a clinical trial, one group receives a new drug an another gets a
placebo. Keeping both groups under the same conditions ensures control.
 C on f ou n d ed r el a t i on s h i p : When the dependent variable is not free from
the influence of extraneous variable(s), the relationship between the dependent
and independent variables is said to be confounded by an extraneous variable(s).
Confounded relationship → effect of IV + effect of EV.
When it happens:
• When EV are not properly controlled.
• When two or more variables change simultaneously.
Example: A researcher studies if online learning improves test scores, but students in the
online group also get extra tutoring. If scores increase is it due to online learning or the
extra help? This is a confounded relationship, it is unclear which variable caused the
observed effect.
How to avoid confounding:
• Use control techniques
• Maintain clear & concise research deign
• Carefully define all variables and control for others.
 Research Hypothesis
“A hypothesis is a tentative assumption made in order to draw out and test its logical or
empirical consequences.”
• A research hypothesis is a predictive statement about the relationship between two or
more variables that can be tested using scientific methods.
• It specifically links an independent variable (cause) to a dependent variable (effect).
• The hypothesis proposes what the researcher expects to find through the research
process.
Features of research hypothesis
• Predictive: It predicts the relationship between variables
• Testable: It must be possible to verify it using scientific data and methods
• Variables involved: Must include at least one independent and one dependent variable
• Objective: Should be objectively testable, not based on personal opinion or belief
• Directional/Non-directional: May state the direction (positive/negative) of
relationship or not
Examples of Research Hypotheses
1. Directional Hypothesis: “Students who receive online tutoring (IV) will perform better
in mathematics (DV) than those who do not.”
2. Non-directional Hypothesis: “There is a difference in academic performance between
urban and rural students.”

Types of Hypothesis-Testing Research

1 . E x p e r i m e n t a l H y p o t h e s i s - Te s t i n g R e s e a r c h
• Involves manipulation of the IV to observe its effect on the DV.
• Usually conducted in controlled settings (labs, classrooms, etc.).

Example: A researcher divides students into two groups:

o Group A: Taught using traditional method
o Group B: Taught using digital tools → Later tests both groups on performance.
→ The IV (teaching method) is manipulated.
Characteristics:
- Independent variable is actively manipulated.
- Control groups and random assignment are often used.
- Establishes causal relationships.

2. Non-Experimental Hypothesis -Testing Research

• The independent variable is not manipulated.
• The researcher observes and measures variables as they occur naturally.
Example: Studying the relationship between intelligence and reading ability.
→ Intelligence is not manipulated.
→ Researcher simply measures both and checks correlation.
Characteristics:
- Observational in nature.
- No experimental control or manipulation.
- Establishes associational (not causal) relationships.
Why Is Research Hypothesis Important?
• Provides direction to the study.
• Helps in selecting appropriate research design and methods.
• Enables the formulation of testable statements.
• Assists in data interpretation by clarifying what is being tested.
• Makes the research scientific and objective.

Research Hypothesis vs Null Hypothesis

• Research Hypothesis (H₁):
• Predicts a relationship between variables
• Example: Online learning improves performance

• Null Hypothesis (H₀):

• Assumes no relationship between variables
• Example: Online learning has no effect on performance
 Treatments
• A treatment refers to a specific condition or intervention applied to experimental or
control groups in a research study.
• It is the manipulated variable (usually the independent variable) used to study its effect
on the dependent variable.
In hypothesis-testing research, the researcher applies different treatments to different
groups to observe and compare outcomes.
Even the standard or usual condition (used for the control group) is also considered a
type of treatment.

Examples:
• Education Research:
o Group A is taught using a traditional method → Treatment 1 (Control group)
o Group B is taught using a new digital tool → Treatment 2 (Experimental group)
 Treatments
Examples:
• Agricultural Research: Comparing three types of fertilizers on wheat yield →
each fertilizer is a treatment.
• Medical Research: Group 1 receives the real drug; Group 2 receives a placebo →
each is a treatment.

• Treatments may be single or multiple.

• They are directly related to the independent variable in the study.
• The outcome or response is observed in the dependent variable.
• Treatment effects are analyzed to test cause-effect relationships.
 Experiment
• An experiment is a systematic method of testing a hypothesis by applying treatments to
research units and observing the effects.

• It aims to test whether the change in the independent variable causes a change in the
dependent variable.

The process of examining the truth of a statistical hypothesis, relating to some research
problem, is known as an experiment.

Type Description Example

Absolute Tests the effect of a single treatment Applying one fertilizer to a plot and
Experiment on a subject. No comparison involved. observing yield.

Comparative Compares two or more treatments to Comparing Fertilizer A, B, and C on

Experiment determine which is more effective. different plots and analyzing which
one yields better results.
 Experiment
Experimental Units
The individuals, objects, or plots on which treatments are applied in an experiment.

Examples:
• Students in an education experiment
• Plots of land in an agriculture experiment
• Patients in a clinical trial

Important:
• Must be pre-determined and clearly defined.
• Should be homogeneous within each group to reduce bias.
• The proper selection of experimental units ensures valid and reliable results.