Research Process

Question

Explain different steps of Research Process

The research process consists of a series of actions or steps that are necessary for effectively conducting and completing research.

Various steps of research process are :

1. Formulating the Research Problem

2. Conducting an Extensive Literature Review

3. Developing the Hypotheses

4. Preparing the Research Design

5. Determining the Sample Design

6. Collecting the Data

7. Executing the Project

8. Analyzing the Data

9. Hypothesis Testing

10. Generalizations and Interpretation

11. Preparing the Report or Presenting Results

1. Formulating the Research Problem (Identifying the Problem)

The first and most critical step is identifying a problem or developing a research question. A researcher must decide the general area of interest or aspect of a subject-matter to study. This defines the focus and direction of the entire study.

Key Steps in formulating research problem:

• Understand the problem thoroughly
• Rephrase it into meaningful analytical terms
• Discuss with colleagues or experts for clarity and better understanding

There are two types of research problems,

• Problems related to states of nature
• Problems related to relationships between variables

2. Extensive Literature Review

After identifying the problem, literature review is conducted to:

• Understand previous research in the field

• Discover existing methodologies and findings

• Identify gaps in knowledge

• Help develop a theoretical foundation for the study

3. Development of working hypotheses

Based on the literature review, a clear working hypotheses is formulate to guide the research.

Purpose of Hypotheses:

• Define the research boundaries
• Sharpen focus on important parts of the problem
• Help structure analysis

Methods of Developing Hypotheses:

• Discussions with colleagues or experts
• Review of data, records, and past research
• Exploratory personal investigation involving fieldwork or interviews for better insight

4. Preparing the Research Design

A research design outlines the framework for collecting and analyzing data. It should be tailored to suit the research problem and objectives.

Types of designs include experimental and non-experimental hypothesis testing, and within these, formal and informal designs.

The preparation of the Research Design, appropriate for a particular research problem, involves :

1. Methods of data collection

2. Skills and availability of staff and researcher

3. Explanation of the way in which the collected information will be organized and the reasoning leading to the selection;

4. Time constraints for research

5. Financial resources available

5. Determining sample design

Question

Determine the Sample design with their different types

A sample design is a definite plan determined before any data are actually collected. It is a strategy used for selecting study units from the population.

A brief mention of the important sample designs are:

1. Deliberate sampling
2. Simple Random sampling
3. Systematic sampling
4. Stratified sampling
5. Quota sampling
6. Cluster sampling and area sampling
7. Multi-stage sampling
8. Sequential sampling

1. Deliberate sampling

Non-probability sampling method useful for qualitative research.

Judgement sampling is used quite frequently in qualitative research where the desire happens to be to develop hypotheses rather than to generalize to larger population

2. Simple random sampling

Each unit has an equal chance of selection; e.g., lottery method.

This type of sampling is also known as chance sampling or probability sampling.

Example :

• if we have to select a sample of 300 items from a universe of 15,000 items, then we can put the names or numbers of all the 15,000 items on slips of paper and conduct a lottery.

3. Systematic sampling

Selection at regular intervals; e.g., every 10th household.

In some instances the most practical way of sampling is to select every 15th name on a list, every 10th house on one side of a street and so on.

4. Stratified sampling

Population divided into strata (e.g., urban/rural), and random samples drawn from each.

It is important that the sample includes representative groups of study units with specific characteristics. Random or systematic samples of a predetermined size will then have to be obtained from each group (stratum). This is called stratified sampling.

5. Quota sampling

Non-random; interviewers choose participants based on quotas for various characteristics.

The actual selection of items for the sample is left to the interviewer’s judgement. This is called quota sampling.

6. Cluster sampling

Groups or clusters are selected instead of individuals (e.g., classrooms, neighborhoods).

Cluster sampling involves grouping the population. Then the groups or clusters are selecting rather than individual for inclusion in the sample.

Example:

• Suppose a departmental store wishes to sample its credit card holders. It has issued its cards to 15,000 customers. The sample size is say 450. Among 15,000 customers, 100 clusters are formed. Each cluster consists of 150 card holders. Three clusters are selected for the sample randomly

7. Area Sampling

Type of cluster sampling based on geographic areas.

Under area sampling the total area is to be divided into a number of smaller non-overlapping areas. Then a number of these smaller areas are randomly selected. All units in these small areas are included in the sample.

8. Multi-stage sampling

Sampling in successive stages (e.g., states → districts → towns → households).

Under multi-stage sampling the first stage may be to select large primary sampling units such as states, then districts, then towns and finally certain families within towns.

9. Sequential sampling

Used in quality control; sample size is not fixed in advance.

This design is usually adopted under acceptance sampling plan in the context of statistical quality control.

6. Collecting the data.

Data collection methods vary by cost, time, and required resources.

Data may be:

• Primary data (collected first-hand through experiments or survey)
• Secondary (previously collected by others)

Methods of Primary Data Collection:

1. Observation
2. Personal interviews
3. Telephone interviews
4. Mailed questionnaires
5. Structured schedules (with trained field staff)

Question

Write a Short Note on

• Execution of Project
• Analysis of data
• Hypothesis Testing
• Generalization and Interpretation
• Preparation of Report / Thesis
• Criteria of Good Research

7. Execution of the project.

Successful execution ensures valid and reliable data.

Key Aspects for reliable data collection:

• Systematic and timely implementation
• Proper training for data collectors
• Use of instruction manuals
• Monitor for unanticipated issues
• Ensure statistical control
• Handle non-response issues with sub-sampling and expert intervention

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Execution of the project is a very important step in the research process :

• The data to be collected should be adequate and dependable

• The project should be executed in a systematic manner and in time.

• The data are to be collected through interviewers.

• The training may be given with the help of instruction manuals.

• Manuals explain clearly the job of the interviewers at each step.

• A careful watch should be kept for unanticipated factors in order to keep the survey as much realistic as possible.

• The steps should be taken to ensure that the survey is under statistical control, so that the collected information is in accordance with the pre-defined standard of accuracy.

• If some of the respondents do not cooperate, then some suitable methods should be designed to tackle this problem.

• To deal with the non-response problem, make a list of the non-respondents and take a small sub-sample of them.

• Then with the help of experts vigorous efforts can be made for securing response.

8. Analysis of data

After data collection, the next step is analysis, it involves:

• Classification and coding of raw data

• Tabulation and summarization

• Application of statistical tools (percentages, coefficients, etc.)

• Tests of significance to validate relationships or findings

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After the collection of data the next task is of analysis of the data. The analysis of data requires a number of operations. Such as :

• Establishment of categories, the application of these categories to raw data through coding, tabulation and then drawing.

• The unwieldy data should be converted into a few manageable groups and tables for further analysis.

• The raw data should be classified into some purposeful and usable categories. Coding operation is done at this stage.

• Through this coding the categories of data are transformed into symbols

• Editing is the procedure that improves the quality of the data for coding. With coding the stage is ready for tabulation.

• Tabulation is a part of the technical procedure wherein the classified data are put in the form of tables.

• Analysis work is based on the computation of various percentages, coefficients, etc., by applying various well defined statistical formulae.

• In the process of analysis, relationships or differences supporting or conflicting with original or new hypotheses should be subjected to tests of significance to determine with what validity data can be said to indicate any conclusion(s).

9. Hypothesis testing.

After analyzing the data, test the hypotheses using appropriate statistical tools.

Common Tests:

• Chi-square test
• t-test
• F-test

These tests help confirm or reject the proposed hypotheses based on evidence.

10. Generalizations and interpretation.

With repeated hypothesis testing, researchers can:

• Develop generalizations

• Formulate theories

• Interpret findings in the context of existing knowledge

• Interpretation can lead to new questions and further research.

Interpretation is explaining some finding on the basis of some theory, when there is no hypothesis to start with.

11. Preparation of the report or thesis

The final step is writing the research report or thesis.

Report Structure:

Preliminary Pages

• Title page
• Date
• Acknowledgments
• Foreword
• Table of contents
• List of tables/graphs

Main Text

• Introduction
• Summary of findings
• Detailed report
• Conclusions

End Matter

• References
• Appendices (if any)

Criteria for Good Research

A well-conducted research project should meet the following criteria:

• Clearly defined purpose and use of common concepts

• Detailed methodology: Research procedure described sufficiently to allow replication

• Objective procedural design which yields objective results

• Transparency and frankness about flaw-limitations in design and findings

• Adequate data analysis with proper statistical tools

• Valid and reliable conclusions confined to the justified data of research

• Conducted by an experienced and ethical researcher

Qualities of Good Research:

• Systematic: Follows a structured approach

• Logical: Based on sound reasoning

• Empirical: Relies on observed and measured phenomena

• Replicable: Can be repeated for verification