A Comprehensive Guide to Research Methodologies and Sampling Techniques

Posted on Jul 25, 2024 in Other subjects

Casual Research

Also known as causal research, this method explores cause-and-effect relationships between variables. Unlike descriptive or exploratory research, which primarily aims to describe or understand phenomena, causal research seeks to determine the extent to which changes in one variable (independent variable) cause changes in another variable (dependent variable) while controlling for other factors. This type of research often involves experimental designs, where researchers manipulate one variable (independent variable) to observe its effect on another variable (dependent variable) while controlling for other factors.

Descriptive Research

This research aims to describe the characteristics or behaviors of a population or phenomenon. Unlike exploratory research, which seeks to generate new insights, or conclusive research, which aims to confirm or refute hypotheses, descriptive research focuses on providing a detailed snapshot of a particular situation or group. This type of research often involves collecting and analyzing quantitative data through surveys, observations, or existing data sources.

Exploratory Research

This serves as the initial step in the research process, designed to explore new ideas, generate hypotheses, and gain deeper insights into a topic. Unlike conclusive research, which aims to provide definitive answers, exploratory research focuses on uncovering patterns, trends, or relationships that may guide further investigation. This type of research is often qualitative in nature, employing techniques such as interviews, focus groups, or observation to gather rich, descriptive data.

Conclusive Research

Often referred to as confirmatory research, this aims to provide definitive answers or conclusions to specific questions or hypotheses. Unlike exploratory research, which seeks to generate new ideas or insights, conclusive research focuses on verifying or refuting existing theories or claims through systematic investigation and analysis. This type of research typically employs well-defined methodologies, rigorous data collection techniques, and statistical analysis to draw firm conclusions.

Difference Between Exploratory and Conclusive Research

Purpose:

• Exploratory Research: Aims to explore new ideas, generate hypotheses, or gain initial insights into a topic. It is often conducted when the researcher has limited prior knowledge about the subject and wants to gather preliminary information.
• Conclusive Research: Aims to provide definitive answers or conclusions to specific questions or hypotheses. It seeks to confirm or refute existing theories, test hypotheses, or make predictions based on collected data.

Nature:

• Exploratory Research: Typically qualitative in nature, focusing on gathering descriptive data through techniques such as interviews, focus groups, or observations. It emphasizes generating rich, nuanced insights rather than testing hypotheses or making generalizations.
• Conclusive Research: Often quantitative in nature, employing structured methodologies and statistical analysis to draw firm conclusions. It aims to provide precise, reliable answers to research questions and often involves hypothesis testing or experimental designs.

Scope:

• Exploratory Research: Broadens the understanding of a topic, identifies variables or relationships for further investigation, and helps in refining research questions or hypotheses.
• Conclusive Research: Narrows down the focus to specific research questions, hypotheses, or objectives, aiming to reach a definitive conclusion based on collected evidence.

Timing:

• Exploratory Research: Typically conducted at the beginning of the research process, especially when the topic is complex or poorly understood. It helps researchers to orient themselves to the subject matter and inform subsequent research activities.
• Conclusive Research: Conducted after exploratory research (if applicable) or when researchers have specific hypotheses to test or research questions to answer. It aims to provide final, conclusive findings to address the research objectives.

Sampling Unit

This refers to one of the units into which an aggregate is divided for the purpose of sampling, each unit being regarded as individual. When the selection is made, the definition of unit may be made on some natural basis, for example, household, persons, unit of product.

Research Design

It is a framework or blueprint for conducting a research project. It specifies the details of the procedure which are necessary for obtaining the information needed to structure and solve the research problem. The process of designing the research starts after the research problem has been defined and the approach towards the problem is identified. A good research design ensures that the required and relevant information is collected which would be useful for the business and also ensures that the required data is obtained

Steps Before Research Design

• Defining the research problem
• Developing the approach towards the problem
• Formulating the research design

What Do You Mean by Biased Sample?

A sample obtained by a biased sampling process is one that incorporates a systematic component of error as distinct from random error, which balances out on average. Non-random sampling is often subjected to bias, particularly when influenced by subjective judgment on the part of the researcher.

Sampling Error

This refers to the part of the difference between a population value and an estimate derived from a random sample. This is due to the fact that only a sample of values is observed, as distinct from the error arising from imperfect selection, bias in response or estimation, errors of observation and recording, etc. The totality of sampling error encompasses all possible samples of the same size generating the sampling distribution of statistics, which is being used to estimate the present value.

Personal Interview

A personal interview is a crucial step in the hiring process where a candidate meets face-to-face with a potential employer to discuss qualifications, skills, and suitability for a position. It allows both parties to assess each other’s fit for the role and company culture. Prepare well, showcase your strengths, and ask insightful questions to leave a positive impression.

Advantages:

• Highest response rate
• Held to overcome language barriers
• More complete answers can be obtained
• Helps to identify body language of the interviewee

Disadvantages:

• It is expensive
• Time-consuming
• Personal bias
• Geographical limitations

Census

The complete enumeration of a population or a group at a point in time with respect to certain well-defined characteristics is known as a census. In some contexts, the term is associated with the data collected rather than the extent of the collection, so that the term”sample censu” has a distinct meaning. In a nutshell, a census is a procedure of systematically acquiring, recording, and calculating population information about the members of a given population.

In-Depth Interview

An in-depth interview is a qualitative research method characterized by detailed, open-ended questioning, aiming to explore a specific topic comprehensively. It offers rich insights but requires skilled interviewers and extensive time for analysis.

Advantages:

1. Allows for comprehensive exploration of topics.
2. Facilitates understanding of complex issues.
3. Builds rapport between interviewer and interviewee.
4. Provides rich qualitative data.
5. Allows for probing and follow-up questions.
6. Can uncover nuanced insights.

Disadvantages:

1. Time-consuming, especially for analysis.
2. Requires skilled interviewers for effective execution.
3. Potential for interviewer bias.
4. Limited generalizability due to small sample sizes.
5. Vulnerable to participant dishonesty or bias.
6. May be resource-intensive in terms of personnel and logistics.

Unstructured Interview

• Also called in-depth interviews, unstructured interviews are usually described as conversations held with a purpose in mind to gather data about the research study.
• These interviews have the least number of questions as they lean more towards a normal conversation, but with an underlying subject.
• The main objective of most researchers using unstructured interviews is to build a bond with the respondent, due to which there are high chances that the respondent will be 100% truthful with their answers.

Advantages of Unstructured Interviews:

• Participants can clarify all doubts.
• It creates a warm and personal experience.
• It provides free-flowing conversation.
• No requirement of conscious effort.

Disadvantages of Unstructured Interviews:

• Difficult to analyze answers.
• Challenging to identify common patterns.
• Interviewer tends to be biased.
• These are less fair and valid.

Telephonic Interview

Telephonic interviews are a convenient way for employers to screen candidates before inviting them for in-person interviews. They allow for initial assessment of qualifications, communication skills, and cultural fit without the need for scheduling conflicts or travel expenses. However, they lack the visual cues of face-to-face interactions and may require extra clarity in communication.

Advantages:

• Saves time and cost.
• Wide geographical access.
• Information can be collected quickly.
• No physical touch is required.

Disadvantages:

• Technical issues can happen.
• Hard to connect or make a connection.
• Loses people’s interest in answering.
• Lack of body language.

Structured Interview

• Structured interviews are defined as research tools that are extremely rigid in their operation and allow very little or no scope for prompting the participants to obtain and analyze results.
• Structured interviews are excessively used in survey research with the intention of maintaining uniformity throughout all the interview sessions.

Advantages of Structured Interviews:

• You can easily compare multiple responses.
• The interview procedure is easy due to the standardization offered.
• It creates less stress for the interviewer to come up with on-the-spot questions.
• It is more reliable and valid.

Disadvantages of Structured Interviews:

• It is rigid.
• It becomes monotonous.
• It limits creativity.
• It is artificial as it does not reflect real-life situations.

Research Problem

A research problem is a statement about an area of concern, a condition to be improved, a difficulty to be eliminated, or a troubling question that exists in scholarly literature, theory, or in practice that points to the need for meaningful understanding and deliberate investigation.

The Purpose of a Problem Statement Is To:

1. Introduce the reader to the importance of the topic being studied. The reader is oriented to the significance of the study and the research question or hypothesis to follow.
2. Place the problem into a particular context that defines the parameters of what is to be investigated.
3. Provide the framework for reporting the results and indicate what is probably necessary to conduct a study and explain how the findings will present this information.

Problem Statements Should Possess the Following Attributes:

1. Clarity and precision.
2. Identification of what would be studied, avoiding the use of value-laden words and terms.
3. Identification of the important questions and the key factors or variables.
4. Identification of key concepts and terms.
5. Avoidance of unnecessary jargon.

Empirical Research Cycle

This cycle clearly outlines the different phases involved in generating the research hypothesis and testing this hypothesis systematically using empirical data:

Observation

1. This is the process of gathering empirical data for the research.
2. At this stage, the researcher gathers relevant empirical data using qualitative or quantitative observation methods and goes ahead to form the research hypothesis.

Induction

1. At this stage, the researcher makes use of inductive reasoning to arrive at general, probable research conclusions based on his or her observations.
2. The researcher generates a general assumption that tends to explain the empirical data, and he or she goes on to observe the empirical data in line with this assumption.

Deduction

1. This is the deductive reasoning stage.
2. This is where the researcher generates hypotheses by applying logic and rationality to his or her observations.

Testing

1. Here, the researcher puts the hypothesis to the test using qualitative or quantitative research methods.
2. In this stage, the researcher combines relevant instruments of systematic investigation with empirical methods to arrive at objective results that support the research hypothesis.

Evaluation

1. This is the final stage in an empirical research study.
2. Here, the researcher outlines the empirical data, the research findings, and the supporting arguments, and puts forth any challenges encountered during the research process.

Semi-Structured Interview

• Semi-structured interviews offer a considerable amount of freedom to the researcher to probe the respondent while maintaining a basic interview structure.
• A researcher can be assured that multiple interview rounds will not be required in the presence of structure in this type of research interview.
• Additional respondent probing is always necessary to garner information for a research study.
• Keeping the structure in mind, the researcher can follow any idea or take creative advantage of the entire interview.

Disadvantages of Semi-Structured Interviews:

• Comparison of answers becomes difficult.
• Reliability may be questioned.
• Time-consuming.
• Bias can happen.

Advantages of Semi-Structured Interviews:

• It is flexible.
• It gives freedom of expression.
• It provides time to prepare questions.
• Quantitative data can be collected.

Difference Between Management Decision Problem and Marketing Research Problem

Management Decision Problem

1. Asks what the decision-maker needs to do.
2. Action-oriented.
3. Focuses on symptoms.
4. Example: Should we launch a new product?

Marketing Research Problem

1. Asks what information is needed and how it should be oriented.
2. Information-oriented.
3. Focuses on the underlying cause.
4. Example: What is the potential market demand for the new product?

Steps Involved in the Marketing Research Process

• A research problem denotes any loopholes or gaps in the existing body of knowledge.
• The process of going through the previous body of knowledge is popularly known as a literature review.

Steps:

1. Literature review.
2. Research gap identification.
3. Objectives of the study.
4. Research methodology (research design, sampling design, data collection, data analysis).
5. Conclusions and findings.

Sources of Research Problems

1. Market Trends and Consumer Behavior: Changes in consumer preferences, buying patterns, and market trends can lead to research problems. For example, understanding the shift towards online shopping and its implications for brick-and-mortar retailers.
2. Industry Challenges and Competition: Research problems may stem from challenges within an industry, such as technological disruptions, regulatory changes, or intensifying competition. Businesses may need to explore strategies to navigate these challenges effectively.
3. Organizational Issues: Problems within the organization, such as inefficiencies in processes, employee morale, or communication breakdowns, can be the focus of research. For instance, investigating the impact of a new leadership style on employee productivity.
4. Emerging Technologies: With rapid advancements in technology, businesses face research problems related to adopting and integrating new technologies into their operations. This could include research on the implementation of artificial intelligence in customer service or the use of blockchain in supply chain management.

Quantitative Research

1. Deals with numbers and statistics.
2. Primarily focuses on testing theories and hypotheses.
3. Requires many respondents.
4. Closed, multiple-choice questions.
5. Data collection methods include surveys and experiments.
6. Data analysis methods include tools such as R, SPSS, Excel, etc.

Qualitative Research

1. Deals with words and meaning.
2. Focuses on exploring ideas and formulating theories.
3. Requires few respondents.
4. Open-ended questions.
5. Data collection methods include interviews, focus groups, case studies, and literature reviews.
6. Data analysis methods include thematic analysis, content analysis, etc.

Definition of Marketing Research

According to the American Marketing Association, marketing research is the systematic gathering, recording, and analyzing of data about problems relating to the marketing of goods and services.

Objectives of Marketing Research:

1. To study the needs, wants, and expectations of consumers.
2. To find out the reactions of consumers to the products of the company.
3. To assess competitive strengths and policies.
4. To know the company’s expected share of the market.
5. To estimate potential buying power in various geographical areas.

Difference Between Market Research and Marketing Research

1. Scope:

• Market Research: Focuses solely on understanding the market environment, including consumers, competitors, and industry trends.
• Marketing Research: Encompasses a broader range of activities, including market analysis, consumer behavior, product development, pricing strategies, distribution channels, and promotional efforts.

2. Objectives:

• Market Research: Aims to gather information about the market size, demographics, preferences, and behaviors of consumers, helping to identify opportunities and threats.
• Marketing Research: Goes beyond market analysis to include understanding consumer behavior, evaluating marketing strategies, and optimizing marketing activities for better performance.

3. Focus Areas:

• Market Research: Primarily focuses on gathering data related to the external market environment.
• Marketing Research: Extends its focus to internal marketing activities, such as product development, pricing strategies, distribution channels, and promotional campaigns.

4. Decision Making:

• Market Research: Provides insights to support strategic decision-making related to market entry, segmentation, and targeting.
• Marketing Research: Helps in making tactical decisions about product positioning, pricing, distribution, and promotion to achieve marketing objectives.

5. Information Utilization:

• Market Research: Findings are often used to understand market dynamics and assess market opportunities.
• Marketing Research: Insights guide the development and implementation of marketing strategies and tactics.

6. Analytical Approach:

• Market Research: Employs analytical techniques to understand market trends, consumer preferences, and competitive landscapes.
• Marketing Research: Utilizes a variety of analytical methods to evaluate marketing performance, assess customer satisfaction, and measure the effectiveness of marketing campaigns.

Types of Applied Research

1. Problem-Solving Research

It involves research oriented towards a particular problem facing the organization, which may be issue-specific. Example: How do we improve the communication skills of our employees?

2. Problem-Oriented Research

The research is oriented towards a particular problem facing the organization. It is undertaken inside the organization or by an external consultant on its behalf. This research is conceptual in nature, and innovative techniques of problem-solving are applied. Example: How to improve the production yield from machine X using modern techniques.

Basic Research

1. Basic research is also called fundamental or pure research.
2. As the name itself refers, basic research is of a basic nature, which is not carried out in response to a problem.
3. It is more educative towards understanding the fundamentals and aims at expanding the knowledge base of an individual or organization.
4. It does not have any commercial potential. Example of a study: Looking at how alcohol consumption impacts the brain.

Applied Research

1. Applied research, on the other hand, is carried out to seek alternative solutions for a problem at hand.
2. Applied research is done to solve specific practical questions. Its primary aim is not to gain knowledge.
3. It specifies possible outcomes of each of the alternatives and their commercial implications.
4. Applied research can be carried out by academic or industrial institutions.
5. Electronics, informatics, computer science, process engineering, and drug design are some common areas of applied research. Example: A study on how to improve illiteracy in teenagers.

Syndicated Research

A research study that is conducted and funded by a market research firm but not for any specific client. The results of such research are often provided in the form of reports, raw data, presentations, etc., and are made available in the open market for anyone to purchase. In syndicated research, the research problem and scope of research are formulated by the market research companies based on their experiences and methodologies. It is less expensive because the cost is distributed among several clients. It covers broad topics of interest. Syndicated research reports provide insights into market trends, industry performance, etc.

Nominal Scale

A nominal scale is a basic measurement scale used for labeling variables into distinct, non-ordered categories. It classifies data without providing any quantitative value or order among the categories.

Characteristics:

1. Labels Only: Categories are only names or labels.
2. No Order: No rank or order is implied.
3. No Arithmetic Operations: Cannot perform any mathematical operations (addition, subtraction, etc.).

Example:

In a survey on preferred modes of transportation:

• 1 = Car
• 2 = Bicycle
• 3 = Bus
• 4 = Train

These numbers are purely identifiers for the transportation modes. There’s no inherent order suggesting that one mode is better or worse than another. The numbers can’t be added, subtracted, or averaged because they don’t represent any quantitative measure, just distinct groups. This is a clear example of data classified on a nominal scale.

Ordinal Scale

An ordinal scale is a type of measurement scale that categorizes variables into distinct groups while also providing a meaningful order among the categories. However, the differences between the categories are not quantified.

Characteristics:

1. Categories with Order: The categories are ranked in a meaningful sequence.
2. Relative Position: Indicates relative position but not the magnitude of difference.
3. Non-Equidistant Intervals: The intervals between ranks are not necessarily equal.

Example:

Consider a customer satisfaction survey with the following responses:

• 1 = Very dissatisfied
• 2 = Dissatisfied
• 3 = Neutral
• 4 = Satisfied
• 5 = Very satisfied

In this example:

• The responses are categorized (Very dissatisfied, Dissatisfied, etc.).
• There is a clear order from Very dissatisfied to Very satisfied.
• The difference between Very dissatisfied and Dissatisfied is not necessarily the same as between Neutral and Satisfied.

Thus, while the order is meaningful, the exact distances between points are not quantified. The ordinal scale provides a way to rank preferences or opinions but does not measure the exact differences between these ranks.

Difference Between Population and Sample

The term”populatio” is applied to any finite or infinite collection of individuals, whereas a”sampl” is a part of a population or a subset from a set of units that is provided by some process or other, usually by deliberate selection with the objective of investigating the properties or characteristics of the parent population or set.

Interval Scale

An interval scale is a type of measurement scale that categorizes, orders, and specifies the exact differences between values with equal intervals. Unlike nominal and ordinal scales, an interval scale has consistent spacing between points, allowing for meaningful comparisons of differences. However, it lacks a true zero point, meaning that zero does not represent the absence of the measured attribute. For instance, temperature measured in Celsius is an interval scale: the difference between 10°C and 20°C is the same as between 20°C and 30°C, but 0°C does not signify no temperature—it is just a point on the scale. This consistency allows for arithmetic operations like addition and subtraction, but not multiplication or division, since the zero is arbitrary. The interval scale thus enables a range of statistical analyses while facilitating meaningful interpretation of the measured differences.

Paired Comparison Scaling

Paired comparison scaling is a market research technique used to evaluate preferences between two options at a time. Respondents are presented with pairs of items and asked to choose their preferred option from each pair. This method helps in ranking multiple items based on the frequency of their selection. It is particularly useful for understanding consumer preferences when comparing a variety of products, features, or attributes. By analyzing the choices, researchers can derive a clear preference order, identify strengths and weaknesses of each item, and make data-driven decisions. Paired comparison scaling is effective in simplifying complex decision-making processes and providing actionable insights for product development and marketing strategies.

Rank Order Scaling

Rank order scaling is a survey technique where respondents are asked to rank a set of items based on their preferences or specific criteria. Each item is assigned a unique rank, with 1 typically being the most preferred or highest-ranked option. This method helps researchers understand relative preferences and prioritize features, products, or attributes. It is useful for identifying the most and least favored items within a group and provides clear, comparative insights that aid in decision-making and strategy development.

Ratio Scale

A ratio scale is the most informative and precise type of measurement scale, offering categorization, order, equal intervals, and a true zero point, which signifies the absence of the measured attribute. This makes it distinct from other scales like nominal, ordinal, and interval scales. For instance, weight is measured on a ratio scale: 0 kilograms truly means no weight, and the differences between each kilogram are equal. Additionally, you can say that 20 kilograms is twice as heavy as 10 kilograms, which is meaningful because of the true zero point. This allows for a wide range of mathematical operations, including addition, subtraction, multiplication, and division, making the ratio scale highly versatile for statistical analysis and interpretation.

Constant Sum Scaling

Constant sum scaling is a survey technique where respondents allocate a fixed number of points (e.g., 100) across a set of items based on their importance or preference. This method requires participants to distribute the points in a way that reflects the relative weight or value they assign to each item. The sum of the points for all items must equal the predetermined total, ensuring that respondents make trade-offs and prioritize their choices. Constant sum scaling provides quantitative data on the relative importance of each item, helping researchers understand consumer preferences, prioritize features, and make informed decisions about product development and marketing strategies.

Likert Scale

The Likert scale is a popular survey tool used to measure attitudes, opinions, or perceptions by asking respondents to indicate their level of agreement or disagreement with a series of statements. Typically, the scale ranges from 1 to 5 or 1 to 7, with options like “Strongly Disagree,” “Disagree,” “Neutral,” “Agree,” and “Strongly Agree.” Each response is assigned a numerical value, allowing researchers to quantify subjective data and perform statistical analysis. The Likert scale is widely used in social sciences, market research, and psychology to assess and compare attitudes, track changes over time, and identify trends within a population.

Semantic Differential Scale

The semantic differential scale is a survey tool used to measure the connotative meaning of objects, events, or concepts. Respondents rate an item on a series of bipolar adjective pairs, such as “happy-sad,” “strong-weak,” or “efficient-inefficient.” Each pair is placed at opposite ends of a scale, usually 7 or 5 points, with a neutral midpoint. Participants select a point on the scale that best represents their perception. This method captures the qualitative nuances of attitudes and feelings, providing a multidimensional profile of the subject being evaluated. It is often used in marketing, psychology, and social sciences to understand emotional and attitudinal responses.

Focus Interview vs. In-Depth Interview

Probability Sampling

Probability sampling is a technique in which each member of a population has a known, non-zero chance of being selected for a sample. This method ensures that the sample represents the population accurately, allowing for generalizations and inferences about the population based on the sample data.

1. Simple Random Sampling: Every member of the population has an equal chance of being selected. Methods include random number generation and lottery systems.
2. Systematic Sampling: Selection of every k-th member from a list after a random starting point. Ensures even coverage but risks periodicity issues.
3. Stratified Sampling: Population divided into strata (subgroups) and random samples taken from each. Ensures representation of all subgroups.
4. Cluster Sampling: Population divided into clusters, some clusters randomly selected, and all members of selected clusters included. Cost-effective for geographically dispersed populations.
5. Multistage Sampling: Combines multiple sampling methods. E.g., selecting clusters first and then applying random sampling within clusters.

Non-Probability Sampling

Non-probability sampling is a sampling technique in which not all members of the population have a known or equal chance of being selected. This method is often used when probability sampling is impractical or impossible.

1. Convenience Sampling: Samples taken from a group that is easy to access. Quick and inexpensive but prone to bias.
2. Judgmental/Purposive Sampling: Selection based on the researcher’s judgment about which subjects are most useful or representative. Suitable for specific, targeted research.
3. Quota Sampling: Population segmented into mutually exclusive sub-groups, and non-random sampling within each segment to fill quotas. Ensures representation but not randomness.
4. Snowball Sampling: Existing subjects recruit future subjects from their acquaintances. Useful for hard-to-reach or rare populations but risks network bias.

Sampling Process

1. Define the Population: This stage involves identifying the entire group of individuals or elements about which you want to draw conclusions. The population should be defined clearly in terms of its characteristics and the scope of your study.
2. Specifying the Sampling Frame: The sampling frame is a list or database from which the sample will be drawn. It should include all elements of the population. This could be a customer database, a list of registered investors, or any other comprehensive list that represents your population accurately.
3. Specifying the Sampling Unit: A sampling unit is the basic unit containing the elements of the population to be sampled. It could be individuals, households, companies, etc., depending on the study’s focus. For instance, in your study, a sampling unit might be an individual investor.
4. Selection of the Sampling Method: This involves choosing a method to select the sample from the population. Sampling methods can be broadly categorized into probability sampling (where every member of the population has a known, non-zero chance of being selected) and non-probability sampling (where some members may have no chance of being selected). Common methods include simple random sampling, stratified sampling, cluster sampling, and systematic sampling.
5. Determination of Sample Size: Deciding on the number of sampling units to be included in the sample is crucial. The sample size should be large enough to provide reliable and valid results but also feasible in terms of resources and time. Statistical techniques or formulas can help determine the appropriate sample size based on the study’s objectives and the desired confidence level and margin of error.
6. Specifying the Sampling Plan: The sampling plan outlines the procedures and rules to be followed for selecting the sample. It includes details about the chosen sampling method, the sample size, and how to implement the sampling process. A well-defined sampling plan ensures consistency and reduces biases in the selection process.
7. Selecting the Sample: This is the actual process of selecting the units from the sampling frame according to the specified sampling method and plan. It involves implementing the procedures and techniques outlined in the sampling plan to ensure the sample is representative of the population.