Steps of Research - Sampling, Data & Report | Research Aptitude for UGC NET PDF Download

Research Process Steps

The research process consists of a series of systematic procedures that a researcher must go through in order to generate knowledge that will be considered valuable by the project and focus on the relevant topic.

Sampling

Sampling is the act of selecting a small number of individuals, objects, or events from a larger population to draw conclusions about that population.

• This process typically involves four key steps:
  1. Identifying the target population from which the sample will be drawn.
  2. Determining the sampling method to be used (random sampling, stratified sampling, etc.).
  3. Selecting the actual sample from the population according to the chosen method.
  4. Conducting analysis and drawing inferences based on the sample data.
• Sampling is crucial in research as it allows researchers to gather data efficiently and make generalizations about a larger group without having to study every single member of that group.

Types of Sampling

1. Non-probability Sampling

• This sampling technique does not form the basis for calculating the likelihood of each population item being part of the sample.
• Within this method, the selection of sample items is left to the researcher's judgment.
• It proves to be highly efficient in terms of cost and time.

(i) Judgement Sampling: Judgement sampling involves the researcher using their discretion to select individuals from the population who are likely to offer accurate information. This method is suitable for historical or descriptive research.

(ii) Quota Sampling: In quota sampling, interviewers are assigned specific quotas to fill from various population strata, with guidelines on how to fill them. This approach is convenient, relatively cost-effective, and ensures representation from different segments.

(iii) Haphazard Sampling: Method where a researcher chooses items haphazardly, attempting to simulate randomness. Results may not be random and are often influenced by selection bias.

2. Snowball Sampling

This technique is used when the desired sample characteristic is rare. Also known as network, chain referral, or reputation sampling method.

• Starts by collecting data on one or more contacts known to the data collector.
• At the end of the data collection process, the collector asks respondents to provide contact information for other potential respondents.
  Types of Snowball Sampling
• Linear Sampling: In this type, each participant refers to another participant, leading to a linear chain of referrals.
• Exponential Sampling: Participants refer more than one new participant, creating an expanding network of referrals.
• Non-Discriminative Sampling: Participants refer others without any specific criteria or characteristics.
• Exponential Discriminative Sampling: Participants refer new participants based on specific traits or criteria.

3. Probability Sampling

• Probability sampling involves techniques where every member of the population has a known and non-zero chance of being selected.

• Importance of Probability Sample: Probability sampling allows researchers to determine the necessary sample size for a desired level of accuracy, specify the probability of selecting each unit, estimate sampling errors, and establish confidence levels.

• Methods of Probability Sampling:

  • Simple or Restricted Random Sampling: Each unit in the population is equally likely to be chosen.
  • Stratified Random Sampling: The population is divided into more homogeneous sub-populations (strata), and samples are selected from each stratum to form the overall sample.
  • Cluster Sampling: In cluster sampling, the total population is divided into several relatively small subdivisions, each of which comprises clusters of even smaller units. From these clusters, some are randomly chosen for inclusion in the overall sample.
  • Multi-Stage Sampling: Multi-stage sampling is employed in extensive surveys to conduct a more thorough investigation. This approach may necessitate the utilization of two, three, or even four-stage sampling techniques.

Collection of Data

When it comes to gathering data for research, it's essential to choose the right method. Researchers have a variety of options available to them, each with its own set of advantages and considerations. The decision should be based on several factors:

1. Nature of Investigation: Depending on whether the research leans towards qualitative or quantitative analysis, different methods may be more appropriate. For instance, qualitative studies may rely on interviews or observations, while quantitative studies might use surveys or experiments.

2. Scope and Objective of Inquiry: Researchers need to identify the specific information they're seeking to uncover. Whether it's exploring complex behaviors, testing hypotheses, or examining opinions, the chosen data collection method should align closely with the research goals.

3. Financial Considerations: Budget constraints play a significant role in determining the feasibility of different data collection methods. Some approaches may require substantial financial investment, such as conducting large-scale surveys or experiments, while others may be more cost-effective, like utilizing existing datasets.

4. Availability of Time: Time is often a limiting factor in research projects. Researchers need to assess how much time they have available for data collection and choose methods that are realistic within those constraints.

5. Desired Accuracy: The level of precision and accuracy required for the research findings also influences the choice of data collection method. Some methods may yield more reliable results but require more time and resources, while others may be quicker but less precise.

Data Collection Process

1. Primary Data

Primary data is gathered by the researcher to meet the specific needs of the study. Methods for collecting primary data include observations, interviews, questionnaires, and schedules.

The data collected by a researcher is through observation, interview method, questionnaires and schedules, are discussed below:

(i) Observation Method

• It is the most commonly used method, especially in studies related to behavioral sciences.
• Information is gathered through the investigator's direct observation without involving the respondents.

(ii) Interview Method:

• Can be carried out through personal or telephone interviews.
• Structured approach is followed in personal interviews with predetermined questions.
• Face-to-Face Interviews: These involve direct conversations with respondents. The results are influenced by the interviewer's approach, which should be friendly, courteous, conversational, and unbiased.
• Telephone Interviews: Involves contacting respondents by phone. It is used when time is limited.

(iii) Questionnaire Methods

• Mailed Questionnaires: Popular for large inquiries, questionnaires are sent to respondents who fill and return them. Common in business and economic surveys.

(iv) Schedules

• Schedules involve specially appointed and trained enumerators tasked with collecting information using relevant questions.
• Enumerators visit respondents with these schedules, filling them based on the replies provided by the respondents.

(v) Survey Method

• This method is most appropriate for gathering descriptive information.
• It entails visiting respondents and collecting detailed and qualitative data.

2. Secondary Data

Secondary data is information that already exists and is collected from sources like research papers, government reports, or online databases.

The researcher must choose a data collection method based on factors like the research nature, scope, financial considerations, time availability, and required accuracy.

• Utilization: Researchers make use of secondary data by exploring various sources from which they can obtain this pre-existing data.

 Let's delve deeper into these concepts with a few examples:

Structured vs. Unstructured Surveys:

• Structured Survey Example: A customer satisfaction survey sent out by a company with a set list of questions for all respondents to answer.
• Unstructured Survey Example: An in-depth interview where the interviewer tailors questions based on the responses given by the interviewee.

Utilization of Secondary Data:

• Example 1: A sociologist analyzing trends in population growth uses census data collected by government agencies.
• Example 2: A marketing researcher studies consumer behavior using sales figures and market reports published by industry associations.

By understanding the distinctions between structured and unstructured surveys, as well as the significance of secondary data in research, scholars can effectively gather and analyze information to draw meaningful conclusions in their respective fields.

Published data is commonly found in various sources, including:

• Publications of central, state, and local governments.
• Publications of foreign governments or international bodies.
• Technical and Trade Journals.
• Books, magazines, and newspapers.
• Reports created by research scholars, economists, etc.
• Historical documents and other published information resources.

Examples of Secondary Data

• A research paper analyzing demographic trends using census data published by the government.
• An economist referencing published industry reports to study market trends.
• A historian using archived newspapers to investigate past events.

Analysis and Interpretation of Data

1. Process of Analyzing Data: Once the data is collected, the next pivotal step is its analysis. This entails a series of closely interconnected operations, such as sorting raw data into categories through coding, organizing it into tables, and then deriving statistical insights from it.

   (i) Editing: Editing, or cleaning the data, marks the initial phase. Its objective is to identify and rectify any errors, miscalculations, misclassifications, or gaps in the information provided by respondents. For instance, in a survey about consumer preferences, editing would involve ensuring that all responses are correctly recorded and consistent.

   (ii) Coding: Coding involves determining the nature of the data, whether it's numerical or qualitative. For instance, in a study on employee satisfaction, qualitative data might include responses to open-ended questions about workplace culture or job satisfaction levels.

2. Classification of Data: Data classification involves grouping data based on shared characteristics. It can be approached in various ways:

   • Classification According to Attributes: For example, in a survey about purchasing behavior, data might be classified based on attributes like age groups or income levels.

   • Classification According to Class Intervals: In studies involving quantitative data such as income brackets or age ranges, classification based on class intervals is common.

3. Tabulation: Tabulation is a technical process where organized data is presented in concise, summarized forms within tables. This aids in visualizing trends, comparisons, and identifying patterns. For instance, in a market research report, tabulated data might display sales figures categorized by product type and region.

4. Data Analysis: Data analysis involves applying mathematical and statistical techniques to derive meaningful insights from the data. The choice of analysis methods depends on the nature of the data:

   • Analysis of Qualitative Data: Qualitative data, such as interview transcripts or observational notes, can be analyzed through deductive or inductive approaches. For instance, in a study on customer feedback, deductive analysis might involve categorizing responses based on predefined themes like product quality or customer service.

   • Analysis of Quantitative Data: Quantitative data, such as numerical survey responses or sales figures, are analyzed using statistical tools to identify patterns or relationships. For example, in a study on market trends, quantitative data analysis might involve calculating averages or correlations between variables like advertising expenditure and sales revenue.

Concluding the Data and Formal Write-up of the Research Report

• This marks the final phase of the research process. Generalizations should not stem from personal opinions but rather from data analysis.

The research's validity hinges on how accurately the data findings are integrated into the conclusion.

This final step of research involves drawing conclusions based on data interpretation. Any generalizations made at this stage must stem directly from the analysis of the data rather than personal opinions. The validity of the research outcomes hinges on how accurately the findings are integrated into the conclusions.