ALLAMA IQBAL OPEN UNIVERSITY

Assignment No. 02:

Name…….…………………….Murad Zaman
Registration ID..................…... 0000733103
Class…………………..……….BS Economics
Course………………..……..…Research Methodology
Course Code……….………….9318
Semester…………….………....Spring 2025
Question No.1:
What is the purpose of the theoretical framework? Discuss in detail the
techniques which are used to develop a theoretical framework.

Answer:

Introduction:
The theoretical framework is a crucial component of any research study. It provides a
structured lens through which the research problem is examined and understood. Essentially,
the theoretical framework connects the researcher’s ideas to existing theories, guiding the
entire research process—from formulation of research questions to data collection and
analysis. This framework helps position the study within the broader context of established
knowledge, ensuring that the research is coherent, focused, and scientifically grounded.

This essay explains the purpose of the theoretical framework and explores the various
techniques commonly used to develop it in a research study.

Purpose of the Theoretical Framework

1. Guiding Research Design and Methodology

The theoretical framework acts as a roadmap that influences the choice of research design,
methods, and analytical tools. It helps researchers determine which variables are relevant,
how they relate to each other, and the direction of the study.

2. Providing Conceptual Clarity

By anchoring the study in established theories, the framework defines key concepts and
constructs clearly. This prevents ambiguity and ensures that all stakeholders, including
readers and reviewers, understand the study’s focus in the same way.

3. Explaining Relationships Among Variables

Theoretical frameworks often depict relationships between independent and dependent
variables, mediators, or moderators. This explanation helps hypothesize how different factors
influence each other within the context of the research.
4. Situating Research within Existing Knowledge
By linking to prior theories and studies, the framework situates the research within a broader
scholarly tradition. It shows how the study builds on, tests, or challenges existing knowledge.
 5. Justifying the Study’s Importance
Grounding the research in theory helps justify why the study is significant. It identifies gaps
or contradictions in the current literature that the research intends to address.

6. Enhancing Analytical Rigor

A theoretical framework provides tools and perspectives for interpreting findings, allowing
researchers to draw meaningful and systematic conclusions beyond mere description.

Techniques Used to Develop a Theoretical Framework

Developing a theoretical framework is a thoughtful and systematic process involving several

key techniques:

1. Extensive Literature Review

The first and most fundamental technique is conducting a comprehensive literature review.
This involves:

Identifying and reviewing relevant theories, models, and frameworks related to the research
topic.

Understanding how previous studies have applied these theories.

Recognizing gaps, inconsistencies, or areas needing further exploration.

This process helps researchers select a theoretical base that is relevant and robust for their
study.

2. Conceptualizing Key Variables and Constructs

Researchers define the major variables or concepts involved in the study based on theory.
This includes:

Clearly defining independent, dependent, and control variables.

Understanding the dimensions and attributes of each construct.

Identifying how these variables are operationalized in previous research.

This step ensures precision and consistency in measurement and analysis.

3. Identifying Relationships and Hypotheses

Theoretical frameworks typically depict relationships among variables. Researchers:

Use existing theories to hypothesize expected relationships (cause and effect, correlations).

Develop conceptual models illustrating how variables interact.

Consider mediating or moderating factors that influence these relationships.

Visual models or diagrams often help in clarifying these linkages.

4. Synthesizing Multiple Theories

Sometimes, a single theory does not fully explain a research problem. Researchers may:

Combine elements from different theories to create a hybrid framework.

Integrate complementary perspectives to capture complex phenomena.

Adapt existing theories to fit the specific context or population under study.

This synthesis broadens the explanatory power of the framework.

5. Using Inductive or Deductive Reasoning

Deductive Approach: Starting from a general theory, researchers deduce specific hypotheses
or predictions about the research problem.

Inductive Approach: Researchers develop new theories or frameworks by observing patterns

in empirical data and building conceptual explanations.
Often, a combination of both is used to refine and develop the theoretical framework.

6. Applying Theoretical Frameworks from Related Fields

When direct theories are unavailable, researchers may borrow or adapt frameworks from
related disciplines. This cross-disciplinary borrowing enriches understanding and introduces
novel perspectives.

7. Consulting Experts and Stakeholders

Engaging with experts, practitioners, or community members can provide insights into
relevant theories or conceptual frameworks that align with practical realities.

Conclusion

The theoretical framework is essential for structuring and grounding research. It serves
multiple purposes, including guiding methodology, clarifying concepts, explaining variable
relationships, and situating the study within existing knowledge. Developing a strong
theoretical framework requires careful literature review, conceptualization of variables,
identification of relationships, possible synthesis of multiple theories, and application of
deductive and inductive reasoning. Through these techniques, researchers build a coherent
and rigorous foundation that enhances the focus, validity, and impact of their study.

Question No. 02

Discuss the various types of investigation used in research. How the choice
of investigation influence the conclusions that can be drawn from the
study?

Answer:

Introduction
Investigation in research refers to the systematic process of exploring, studying, and
analyzing phenomena or problems to generate knowledge. Different types of investigations
serve diverse purposes, ranging from describing phenomena to establishing causal
relationships. The choice of investigation method greatly influences the nature, reliability, and
validity of conclusions drawn from the study. This essay explores the major types of
investigation employed in research and examines how selecting one over another affects
research outcomes.

Types of Investigation Used in Research

1. Descriptive Investigation

Descriptive investigation focuses on portraying characteristics of a population, phenomenon,

or situation as they exist naturally without intervention or manipulation.

Purpose: To provide an accurate picture of facts, conditions, or relationships.

Examples: Census data collection, demographic surveys, observational studies.

Characteristics: It answers questions like “what,” “where,” and “how many.”

Data Collection Methods: Questionnaires, observations, case studies.

Role: Descriptive investigations are foundational, offering a snapshot of conditions that can
guide further exploratory or explanatory studies.

2. Analytical Investigation

Analytical investigations involve examining data to explore relationships and test hypotheses
about causes and effects. It interprets existing data or findings to uncover patterns or
associations.
Purpose: To analyze and explain why phenomena occur.
Examples: Correlational studies, regression analysis.
Characteristics: Focus on “why” and “how” questions rather than “what.”
Data Collection Methods: Secondary data analysis, surveys with inferential statistics.
Role: Analytical investigations deepen understanding beyond description by identifying
potential factors influencing outcomes.

3. Experimental Investigation
Experimental investigation is the most rigorous type, involving deliberate manipulation of
independent variables to observe effects on dependent variables, usually in controlled
settings.

Purpose: To establish cause-and-effect relationships.

Examples: Clinical drug trials, laboratory experiments.

Characteristics: Random assignment, control groups, and replication.

Data Collection Methods: Controlled tests, randomized controlled trials.

Role: Experiments provide strong evidence for causality due to control over extraneous
factors.

4. Exploratory Investigation

Exploratory investigation is conducted when a problem is not clearly defined. It aims to

gather preliminary information that will help define problems and suggest hypotheses.

Purpose: To explore new topics or identify variables and relationships.

Examples: Pilot studies, qualitative interviews.

Characteristics: Open-ended, flexible, and informal.

Data Collection Methods: Focus groups, unstructured interviews.

Role: Helps in shaping research questions and refining study design for future research.

5. Historical Investigation

Historical investigation studies past events and trends to understand their causes and
consequences.

Purpose: To reconstruct and interpret historical data.

Examples: Archival research, document analysis.

Characteristics: Retrospective analysis of existing records.

Data Collection Methods: Examination of archives, letters, government documents.

Role: Provides context and understanding of present phenomena through past analysis.

6. Case Study Investigation

A case study involves an in-depth, detailed examination of a single subject, group, or event.

Purpose: To gain comprehensive understanding of complex issues.

Examples: Organizational case studies, clinical case reports.

Characteristics: Context-rich and detailed.

Data Collection Methods: Multiple sources such as interviews, observations, and

documents.

Role: Offers insights into real-life phenomena but often with limited generalizability.

How the Choice of Investigation Influences Conclusions

The selected investigation type shapes the kind of conclusions researchers can confidently
draw, affecting their scope, validity, and reliability.

1. Nature of Conclusions: Descriptive vs. Causal

Descriptive investigations provide information on what exists but do not explain why or how
something happens. Conclusions are limited to observations or frequencies.

Experimental investigations allow researchers to establish cause-and-effect relationships,

enabling stronger, more definitive conclusions about the impact of specific variables.

Analytical studies help in identifying associations and correlations but do not prove causality.
Impact: Selecting a descriptive approach limits conclusions to describing phenomena,
whereas experimental designs enable causal inferences.

2. Depth and Contextual Understanding

Case studies and exploratory investigations offer rich, contextual insights and nuanced
understanding of complex phenomena.

Quantitative and experimental investigations produce generalizable and replicable results but
may miss contextual subtleties.

Impact: The choice affects whether conclusions are deep and contextual or broad and
generalizable.

3. Reliability and Validity

Experimental designs, with their control over extraneous variables, tend to have higher
internal validity, meaning conclusions about cause-effect are more trustworthy.

Descriptive and observational studies may have lower internal validity due to potential
confounders but often higher external validity due to naturalistic settings.

Impact: This influences the confidence and applicability of conclusions drawn from the
study.

4. Scope of Generalization

Large-scale surveys and experiments with random sampling allow conclusions to be

generalized to the wider population
Case studies or exploratory investigations often focus on specific cases, limiting broad
applicability.

Impact: The investigation type determines whether conclusions can be generalized or remain
case-specific.
 5. Susceptibility to Bias

Qualitative and exploratory investigations are more prone to researcher bias due to subjective
interpretation.

Quantitative experimental studies minimize bias through structured protocols

Impact: Affects the objectivity and credibility of conclusions.

Conclusion

The type of investigation chosen in research fundamentally shapes the nature, reliability, and
scope of conclusions. Descriptive investigations map the landscape but do not explain
relationships, whereas experimental investigations provide strong causal evidence.
Exploratory and case study investigations offer deep contextual understanding but limited
generalizability.

Researchers must carefully select the investigation type aligned with their research questions,
resources, and objectives. Recognizing the strengths and limitations of each approach helps in
interpreting findings accurately and drawing valid conclusions, thereby advancing scientific
knowledge.

Question No. 03:

Explore the role of primary and secondary data in research. Differentiate
between these two types of data, and how do researchers ensure the
accuracy and relevance of the data they collect?

Answer:

Introduction
Data is the foundation of any research as it provides the evidence to understand phenomena,
answer questions, and test hypotheses. In research, data is broadly classified into two types:
primary data and secondary data. Both types serve important but distinct purposes.
Understanding their roles, differences, and ensuring the quality of data is critical for
conducting reliable and valid research.

Role of Primary Data in Research

Primary data is data collected directly by the researcher specifically for their current study.
This data is original and customized to address the research objectives.

Specificity: Primary data is gathered to answer specific research questions, making it highly
relevant and targeted.

Control: Researchers have full control over how, when, and from whom the data is collected,
allowing them to ensure consistency and reliability.

Freshness: Since the data is collected firsthand, it reflects the current status or phenomena.

Methods: Common ways to collect primary data include surveys, interviews, experiments,
observations, and focus groups.

Use: Primary data is essential when existing information is outdated, insufficient, or

irrelevant.

Role of Secondary Data in Research

Secondary data is data that has already been collected, processed, and published by others,
often for purposes other than the current research.

Efficiency: Using secondary data saves time and resources since data collection is already
done.

Scope: It often covers large populations, long time periods, or broad geographic areas.

Background: Secondary data helps researchers understand the existing knowledge, frame
research questions, and design their studies.
Sources: Examples include government reports, academic publications, census data,
organizational records, and databases.

Use: It can complement primary data or serve as the main data source when primary data
collection is not feasible.

Differences Between Primary and Secondary Data

1. Origin: Primary data is original and collected by the researcher; secondary data is pre-
existing and collected by others.

2. Purpose: Primary data is gathered for the current study; secondary data was collected for
different purposes.

3. Control: Researchers control primary data collection; they rely on others for secondary
data.

4. Cost and Time: Primary data collection is usually costlier and time-consuming; secondary
data is more economical and quicker to access.

5. Relevance: Primary data is highly relevant and specific; secondary data may not perfectly
match research needs.

6. Quality: The researcher can ensure quality in primary data; secondary data quality depends
on the original source.

Ensuring Accuracy and Relevance of Data

For Primary Data

Clear Research Design: Developing precise and focused data collection tools aligned with
research objectives.

Pilot Testing: Testing instruments on a small sample to identify and fix problems.

Training: Ensuring data collectors understand protocols to reduce errors and biases.
Standard Procedures: Using consistent methods for all participants to improve reliability.

Sampling: Selecting a representative sample to ensure generalizability.

Data Verification: Checking data for inconsistencies and errors during collection and
processing.

For Secondary Data:

Source Evaluation: Using data from credible and reputable sources such as government
agencies or peer-reviewed studies.
Understanding Methodology: Knowing how the original data was collected to assess
suitability and limitations.
Timeliness Check: Ensuring the data is current enough to be relevant.
Cross-Verification: Comparing data across multiple sources for consistency.
Context Awareness: Recognizing the original purpose of data to avoid misinterpretation.

Conclusion

Primary and secondary data each play vital roles in research. Primary data offers precise,
current, and controlled information, while secondary data provides broad, cost-effective
background and context. Their differences guide researchers in choosing the best data for
their study. Ensuring data accuracy and relevance through careful planning, evaluation, and
verification is essential for producing valid, reliable, and meaningful research results.

Question No. 04:

Define the meaning of sampling in research. Which important steps are
involved in exploring or calculating the sample size?

ANSWER:

Introduction
In research, studying an entire population is often impractical, time-consuming, and costly.
Sampling provides an efficient solution by selecting a subset of the population to represent
the whole. Sampling enables researchers to make valid inferences about a larger group
without surveying every individual. This essay explains the meaning of sampling in research
and discusses the critical steps involved in determining an appropriate sample size.

Meaning of Sampling in Research

Sampling is the process of selecting a portion of individuals, events, or observations from a

larger population for the purpose of conducting research. The selected subset, known as the
sample, should accurately reflect the characteristics of the entire population. Sampling helps
researchers collect data that is manageable in size while maintaining the ability to generalize
results to the whole population.

Key Points about Sampling:

The population refers to the complete set of entities the researcher is interested in studying.

The sample is a smaller group chosen from the population.

The sampling unit is the individual element (person, organization, event) selected.

Sampling is essential because it saves time, reduces cost, and makes research feasible without
sacrificing accuracy when done correctly.

Importance of Sampling

Cost and Time Efficiency: Collecting data from a sample is less expensive and faster than
from the whole population.

Practicality: In many cases, the population is too large or inaccessible.

Manageability: Smaller samples are easier to handle and analyze.

Validity: Proper sampling methods allow researchers to generalize findings to the entire
population.

Types of Sampling Methods (Brief Overview)

Sampling methods are broadly divided into:

Probability Sampling: Every member of the population has a known chance of selection.
Examples: simple random sampling, stratified sampling, cluster sampling.

Non-Probability Sampling: Selection based on non-random criteria. Examples: convenience

sampling, purposive sampling, quota sampling.

The sampling method affects how the sample size is calculated and the accuracy of results.

Important Steps in Exploring or Calculating Sample Size

Determining the appropriate sample size is vital to ensure the reliability and validity of the
research findings. A sample that is too small may produce misleading results, while an
excessively large sample wastes resources. Below are the key steps involved in exploring or
calculating sample size:

Step 1: Define the Population

Before determining sample size, researchers must clearly define the population from which
the sample will be drawn. This includes identifying the population size and characteristics
relevant to the study.

Population Size: Knowing how many individuals or units make up the entire population
helps in refining sample size calculations.

Population Characteristics: These may affect variability and homogeneity, impacting

sample size needs.

Step 2: Clarify the Research Objectives and Design

The research purpose and design affect sample size. For instance:

Descriptive studies may require larger samples to estimate population parameters accurately.

Experimental or analytical studies need sample sizes sufficient to detect differences or

relationships with statistical significance.
Researchers should identify whether their study is exploratory, descriptive, analytical, or
experimental, as this informs the sample size requirements.

Step 3: Specify the Desired Level of Precision (Margin of Error)

The margin of error (also called allowable error) defines the range within which the sample
estimate is expected to fall relative to the true population parameter.
Smaller margins of error require larger sample sizes.

Common margins of error are 5% or 3%.

For example, a 5% margin of error means the sample estimate is expected to be within ±5
percentage points of the actual population value.

Step 4: Select the Confidence Level

The confidence level indicates how certain the researcher wants to be that the true population
parameter lies within the margin of error.

Common confidence levels are 90%, 95%, and 99%.

Higher confidence levels require larger sample sizes.

For instance, a 95% confidence level means that in 95 out of 100 samples, the true population
value would lie within the margin of error.

Step 5: Estimate the Population Variability

Variability or standard deviation indicates how much the data points differ from the mean.

Greater variability in the population increases the required sample size.

For categorical data, variability is estimated by the proportion (p), where maximum
variability occurs at p = 0.5.

When no prior information is available, researchers often assume maximum variability to

ensure an adequate sample size.
Step 6: Choose the Sampling Method

The sampling method influences the calculation because some methods, such as cluster
sampling, have higher sampling error and may require larger sample sizes.

Simple Random Sampling: Often requires the smallest sample size for a given level of
precision.

Cluster Sampling: Usually requires a larger sample size due to homogeneity within clusters.

Stratified Sampling: Can reduce sample size if strata are homogeneous.

Researchers should consider the design effect when calculating sample size for complex
sampling methods.

Step 7: Calculate the Initial Sample Size

Using the parameters defined above, researchers use statistical formulas or software to
calculate the initial sample size.

For estimating proportions with simple random sampling, the formula is:

n = (Z² × p × (1-p)) / E²

Where:

n = sample size

Z = Z-score corresponding to confidence level (e.g., 1.96 for 95%)

p = estimated proportion (expressed as decimal)

E = margin of error (expressed as decimal)

For estimating means:

n = (Z² × σ²) / E²

Where σ is the estimated standard deviation.

Statistical software like SPSS, R, or online calculators can simplify these calculations.

Step 8: Adjust for Population Size (Finite Population Correction)

If the population size is small or finite, the initial sample size should be adjusted using finite
population correction:

Adjusted n = (n × N) / (n + N - 1)

Where N is the population size.

This adjustment reduces the sample size needed when studying small populations.

Step 9: Account for Non-Response or Attrition

Some participants may not respond or data may be unusable. To compensate, researchers
increase the sample size based on expected non-response rates.

For example, if a 10% non-response rate is expected, multiply the calculated sample size by
1.11 (i.e., 100/90).

Step 10: Review Practical Constraints

Finally, researchers review the calculated sample size in light of available resources, time, and
feasibility. They may adjust sample size accordingly, while acknowledging possible impacts
on precision or power.

Conclusion

Sampling is a critical process in research that enables studying a manageable portion of a

population while drawing valid conclusions about the whole. Defining sampling clearly and
calculating an appropriate sample size are essential to the success of any study. The sample
size determination involves defining the population, setting research objectives, specifying
precision and confidence, estimating variability, choosing sampling methods, calculating
sample size, and making adjustments for finite populations and non-response.
By carefully following these steps, researchers can ensure their sample size is adequate for
producing reliable, valid, and generalizable findings, thereby enhancing the quality and
credibility of their research.

Question No. 05
Explain the tools and strategies that researchers and institutions use to
prevent and detect plagiarism. Which techniques can be used to avoid
plagiarism?

Answer:
Introduction
Plagiarism, the act of presenting someone else's work or ideas as one’s own without proper
acknowledgment, is a serious ethical violation in academic and research communities. It
undermines the integrity of research, disrespects original creators, and damages reputations.
To maintain trust and uphold quality standards, researchers and institutions have developed
various tools and strategies to prevent and detect plagiarism. Moreover, understanding how to
avoid plagiarism is essential for researchers to ensure originality and ethical compliance.

Tools Used to Detect Plagiarism

1. Plagiarism Detection Software

One of the most common methods for identifying plagiarism is the use of specialized
software that scans texts and compares them against vast databases of published materials,
websites, and student submissions.

Turnitin: Widely used in universities, Turnitin checks student papers against millions of
academic articles, books, and internet sources to identify matched text.

Grammarly’s Plagiarism Checker: Offers real-time plagiarism checks alongside grammar

suggestions by comparing submissions with online content.

Copyscape: Popular for web content, it detects duplicate content on the internet.

iThenticate: Designed for researchers and publishers, it compares manuscripts to a large

database of scholarly publications.
PlagScan: A tool used by academic institutions to check for text similarity in submitted
documents.

These tools generate similarity reports highlighting matched content, allowing instructors or
reviewers to assess potential plagiarism.

2. Institutional Databases and Archives

Many universities maintain internal databases of student submissions to cross-check new

work against previous assignments. This helps detect self-plagiarism or recycling of prior
work without acknowledgment.

Strategies to Prevent Plagiarism

1. Education and Awareness

Training Workshops: Institutions often conduct workshops and seminars to educate students
and researchers about what constitutes plagiarism, its consequences, and how to avoid it.

Guidelines and Policies: Clear policies outlining acceptable academic conduct and
plagiarism penalties are provided to students and staff.

Promoting Ethical Research Practices: Encouraging values such as honesty, integrity, and
respect for intellectual property fosters a culture that naturally discourages plagiarism.

2. Promoting Proper Citation and Referencing

Teaching proper citation styles (APA, MLA, Chicago, etc.) ensures that researchers correctly
acknowledge sources.

Use of citation management tools like EndNote, Zotero, or Mendeley helps organize
references and insert citations accurately.

3. Designing Assignments to Reduce Plagiarism

Assignments that require critical thinking, personal reflections, or unique data collection
make plagiarism more difficult.
Frequent drafts or progress reports encourage original work development.

Personalized or localized topics reduce the temptation and feasibility of copying.

4. Use of Honor Codes and Plagiarism Declarations

Many institutions require students and researchers to sign declarations affirming the
originality of their work.

Honor codes create a sense of personal responsibility and ethical obligation.

Techniques to Avoid Plagiarism

1. Proper Paraphrasing and Summarizing

Instead of copying text verbatim, researchers should express ideas in their own words while
retaining the original meaning.

Even when paraphrased, proper citation is necessary to acknowledge the source.

2. Quoting Accurately

When directly using another’s words, quotation marks must be used.

The source must be cited immediately following the quote.

Overuse of quotations should be avoided to maintain originality.

3. Citing All Sources

Every idea, data, theory, or information derived from another’s work must be cited.

This includes books, articles, websites, interviews, and even unpublished work.

Researchers should follow consistent citation styles appropriate to their discipline.

4. Keeping Detailed Notes and Records

Maintaining comprehensive records of sources during research helps ensure accurate citation.
Using reference management software reduces errors and omissions.

5. Using Plagiarism Checkers Before Submission

Researchers should self-check their work using plagiarism detection tools before submission.

This helps identify accidental similarities and gives an opportunity to correct them.

6. Understanding and Respecting Intellectual Property Rights

Recognizing that original ideas, text, and data belong to creators motivates ethical behavior.

Seeking permission when necessary and giving credit where due is fundamental.

Conclusion

Plagiarism prevention and detection are vital to upholding the credibility and ethical standards
of research. Tools such as Turnitin, iThenticate, and institutional databases assist in detecting
copied content, while strategies like education, clear policies, and assignment design help
prevent plagiarism from occurring. Researchers can avoid plagiarism by practicing proper
paraphrasing, accurate quoting, diligent citation, maintaining thorough records, and using
plagiarism detection software proactively.

Through combined efforts of institutions and researchers, plagiarism can be minimized,

fostering an environment of honesty, originality, and respect for intellectual contributions.