Module 1: Research Basics
Research
It is a scientific and systematic search for information on a particular topic or issue. It is also known as the art of scientific investigation. Several social scientists have defined research in different ways. Research in simple terms refers to search for knowledge.
Research comprises defining and redefining problems, formulating hypothesis or suggested solution; collecting, organizing and evaluating data, making deductions and reaching conclusions and carefully testing the conclusions to determine whether they fit the f ormulating hypothesis. The manipulation of things, concepts or symbols for the purpose of generalizing to extend , correct or verify knowledge, whether that knowledge aids in construction of theory or in the practice of an art.
Research is defined as a meticulous and systematic inquiry process designed to explore and unravel specific subjects or issues with precision. This methodical approach encompasses the thorough collection, rigorous analysis, and insightful interpretation of information, aiming to delve deep into the nuances of a chosen field of study. By adhering to established research methodologies, investigators can draw meaningful conclusions, fostering a profound understanding that contributes significantly to the existing knowledge base.
Research methodolgy
The process used to collect information and data for the purpose of making business decisions.
The methodology may include publication research, interviews, surveys and other research
techniques, and could include both present and historical information
Reasearch Method vs Methodology
Relation among tools and technique
The main objective of research is to create new insight or develop a new theory. It is a very important aspect of education and life as a whole. This is why students at all levels of higher education are mandated to conduct research before they can graduate. The research process varies according to fields of knowledge. Research method and research methodology are terms often used interchangeably when carrying out research. But, strictly speaking, both terms are not exactly the same. The latter is a broader concept than the former.
| Research Method | Research Methodology |
| The techniques and procedures used in solving research problems | The study of research methods in other to logically justify why any particular method should be preferred to others |
| The major aim is to solve the research problem | The major aim is to ensure that appropriate methods are used to solve research problems |
| Is a component of research methodology and hence narrower in scope | Encompasses both research methods and the logic behind their adoption and hence broader in scope |
| Involves the use of quantitative and qualitative research methods and utilizing the knowledge and skills learned through research methodology. | Involves the learning of various techniques to conduct research and acquiring methodical knowledge to perform quantitative, qualitative, and mixed research. |
Purpose of research
There are three main purposes:
- Exploratory Research
- Descriptive Research
- Explanatory Research
Objective of Research (Characteristic of Research)
A research objective is defined as a clear and concise statement of the specific goals and aims of a research study. It outlines what the researcher intends to accomplish and what they hope to learn or discover through their research. Research objectives are crucial for guiding the research process and ensuring that the study stays focused and on track.
Key characteristics of research objectives include:
- Clarity: Research objectives should be clearly defined and easy to understand. One should ensure there is no space for ambiguity or misinterpretation.
- Specificity: Objectives should be specific and narrowly focused on the aspects of the research topic that the study intends to investigate. They should answer the question of “what” or “which” rather than “how” or “why.”
- Measurability: Research objectives should be formulated in a way that allows for measurement and evaluation. This means that there should be a way to determine whether the objectives have been achieved or not.
- Relevance: Objectives should be relevant to the research topic and align with the overall research question or hypothesis. They should address important aspects of the subject matter.
- Realistic: Objectives should be attainable within the constraints of the study, including time, resources, and feasibility.
- Time-bound: Research objectives may have associated timelines or deadlines to indicate when the research aims should be accomplished.
Research Types
There are majorly two key types of research: Qualitative and Quantitative:
Qualitative research is concerned with investigating opinions, values, or characteristics that cannot be counted or quantified. Qualitative research is generally holistic in approach and provides more descriptive data.
Eg:
- Observations
- Focus groups
- Questionnaires (with open-ended questions)
- Interviews
- Literature reviews
Quantitative research is concerned with statistics, facts, and values that can be counted or quantified. Quantitative data is often numerical and often used to map correlations and patterns or make predictions.
Eg:
- Surveys
- Polls
- Scientific experimentation
- Questionnaires (with closed-ended questions)
Other Examples of research types:
- Applied Research
- Basic Research
- Correlational Research
- Descriptive Research
- Ethnographic Research
- Experimental Research
- Exploratory Research
- Grounded Theory
- Historical Research
- Phenomenological Research
- Qualitative Research
- Quantitative Research
Research Process Steps (or Research Writing Process)
The research process is a set of ordered steps that can help you to ensure your research is complete.
We know that the research process is about ensuring research is completed effectively and appropriately. But how can you make sure you don't forget any important aspects of the research process?
The key steps to think about are as follows:
- Identify the purpose or research question
- Design a research plan
- Collect the required data
- Interpret the collected data
- Present the research findings
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.
Research problem
A research problem is a gap in existing knowledge, a contradiction in an established theory, or a real-world challenge that a researcher aims to address in their research. It is at the heart of any scientific inquiry, directing the trajectory of an investigation.
The formulation of well-defined research questions is central to addressing a research problem
- A research problem concerns an area of interest, a situation necessitating improvement, an obstacle requiring eradication, or a challenge in theory or practical applications.
- The importance of research problem is that it guides the research and helps advance human understanding and the development of practical solutions.
- Research problem definition begins with identifying a broad problem area, followed by learning more about the problem, identifying the variables and how they are related, considering practical aspects, and finally developing the problem statement.
Characteristics of a Research Problem
: An ideal research problem introduces a fresh perspective, offering something new to the existing body of knowledge. It should contribute original insights and address unresolved matters or essential knowledge.
Significant: A problem should hold significance in terms of its potential impact on theory, practice, policy, or the understanding of a particular phenomenon. It should be relevant to the field of study, addressing a gap in knowledge, a practical concern, or a theoretical dilemma that holds significance.
Feasible: A practical research problem allows for the formulation of hypotheses and the design of research methodologies. A feasible research problem is one that can realistically be investigated given the available resources, time, and expertise. It should not be too broad or too narrow to explore effectively, and should be measurable in terms of its variables and outcomes. It should be amenable to investigation through empirical research methods, such as data collection and analysis, to arrive at meaningful conclusions A practical research problem considers budgetary and time constraints, as well as limitations of the problem. These limitations may arise due to constraints in methodology, resources, or the complexity of the problem.
Clear and specific: A well-defined research problem is clear and specific, leaving no room for ambiguity; it should be easily understandable and precisely articulated. Ensuring specificity in the problem ensures that it is focused, addresses a distinct aspect of the broader topic and is not vague.
Rooted in evidence: A good research problem leans on trustworthy evidence and data, while dismissing unverifiable information. It must also consider ethical guidelines, ensuring the well-being and rights of any individuals or groups involved in the study.
Types of Research Problems
- Theoretical research problems
- Applied research problems
- Action research problems
Module 2: Research Design
Research Design
It refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way. It is a framework or blueprint for conducting the research. In simple words it is the general plan of how you will go about your research.
Research design is the framework of research methods and techniques chosen by a researcher to conduct a study. The design allows researchers to sharpen the research methods suitable for the subject matter and set up their studies for success.
Characteristics of Research Design
Types of research Design
Research Design Methods
- Observation / Participant Observation
- Surveys
- Interviews
- Focus Groups
- Experiments
- Secondary Data Analysis / Archival Study
- Mixed Methods (combination of some of the above)
Literature Survey
In a literary survey, students analyse critically and concisely earlier research and literature related to a particular research problem, and utilize them for their own research purposes. It helps students in understanding the significance of new research and its connections to earlier work. The survey may display an insufficiency in the literature, which a new research can correct. In such case, the survey focuses on what is known about the topic and what is not known.
In the master’s thesis, a literary survey usually forms a theoretical background for the research, in which case it focuses on literature crucial to the research problem. The survey presents earlier viewpoints and research, and how the student’s work relates to these. In examining literature, attention is paid to research methods, main results and conclusions.
The literature survey demonstrates viewpoints, methodological solutions and research results related to the area. The existing information is critically analysed so that contradicting and differing research methods are shown. Only material that is relevant and directly related to the research is selected in the survey. Critical approach is recommended in selecting the literature.
The research problem and personal aims must be kept in mind when compiling the survey. It serves a particular aim and purpose. The intention is to form a dialogue with earlier research knowledge through selection and argumentation. The student’s aim is to create a background for new research and to process information critically.
The survey should be organised so that different viewpoints, schools of thought and interpretations are clearly separated.
A literature review is a survey of scholarly knowledge on a topic.
Module 3 Data Collection
Classification of Data
It is the process of arranging data into homogeneous (similar) groups according to their common characteristics.
Methods of Data Collection
Sampling
Sampling can be defined as the process through which individuals or sampling units are selected from the sample frame
The process of deriving a sample is called a sampling method. Sampling forms an integral part of the
research design as this method derives the quantitative and qualitative data that can be collected as part of a research study.
Sampling Methods
Sampling methods are characterized into two distinct approaches: probability sampling and non-probability sampling.
- Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group. [Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research. If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.]
- Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data [In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included].
Ethical considerations in research
Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.
Scientists and researchers must always adhere to a certain code of conduct when
collecting data from others.
These considerations work to
- protect the rights of research participants
- enhance research validity
- maintain scientific or academic integrity
| Ethical issue | Definition |
|---|
| Voluntary participation | Your participants are free to opt in or out of the study at any point in time. |
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| Informed consent | Participants know the purpose, benefits, risks, and funding behind the study before they agree or decline to join. |
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| Anonymity | You don’t know the identities of the participants. Personally identifiable data is not collected. |
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| Confidentiality | You know who the participants are but you keep that information hidden from everyone else. You anonymize personally identifiable data so that it can’t be linked to other data by anyone else. |
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| Potential for harm | Physical, social, psychological and all other types of harm are kept to an absolute minimum. |
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| Results communication | You ensure your work is free of plagiarism or research misconduct, and you accurately represent your results. |
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Issues
Module 4: Data Analysis and interpretation
Data analysis
Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data.
Data analysis is the process of cleaning, analyzing, interpreting, and visualizing data using various techniques and business intelligence tools. Data analysis tools help you discover relevant insights that lead to smarter and more effective decision-making.
Statistical techniques
Statistical analysis is a powerful tool businesses and organizations use to make sense of data and guide their decision-making. There are different types of statistical analysis techniques that can be applied to a wide range of data, industries and applications. Knowing the different statistical analysis methods and how to use them can help you explore data, find patterns and discover trends in your market.
Types of statistical analysis
- Descriptive statistical analysis
- Inferential statistical analysis
- Associational statistical analysis
- Predictive analysis
- Prescriptive analysis
- Exploratory data analysis
- Causal analysis
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Hypothesis
A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested.
A hypothesis is like a guess or idea that you suggest to check if it’s true. A research hypothesis is a statement that brings up a question and predicts what might happen.
It’s really important in the scientific method and is used in experiments to figure things out. Essentially, it’s an educated guess about how things are connected in the research.
A research hypothesis usually includes pointing out the independent variable (the thing they’re changing or studying) and the dependent variable (the result they’re measuring or watching). It helps plan how to gather and analyze data to see if there’s evidence to support or deny the expected connection between these variables.
Types of hypothesislink
Hypothesis Testing
Hypothesis testing is a tool for making statistical inferences about the population data. It is an analysis tool that tests assumptions and determines how likely something is within a given standard of accuracy. Hypothesis testing provides a way to verify whether the results of an experiment are valid.
A null hypothesis and an alternative hypothesis(research hypothesis) are set up before performing the hypothesis testing. This helps to arrive at a conclusion regarding the sample obtained from the population.
The purpose of hypothesis testing is to test whether the null hypothesis (there is no difference, no effect) can be rejected or approved. If the null hypothesis is rejected, then the research hypothesis can be accepted. If the null hypothesis is accepted, then the research hypothesis is rejected.
In hypothesis testing, a value is set to assess whether the null hypothesis is accepted or rejected and whether the result is statistically significant:
- A critical value is the score the sample would need to decide against the null hypothesis.
- A probability value is used to assess the significance of the statistical test. If the null hypothesis is rejected, then the alternative to the null hypothesis is accepted.
Types of Hypothesis Testing
- Hypothesis Testing Z Test
- Hypothesis Testing T Test
- Hypothesis Testing Chi Square
1. Z-Test:
The z-test is a statistical method primarily employed when comparing means from two datasets, particularly when the population standard deviation is known. Its main objective is to ascertain if the means are statistically equivalent.
A crucial prerequisite for the z-test is that the sample size should be relatively large, typically 30 data points or more. This test aids researchers and analysts in determining the significance of a relationship or discovery, especially in scenarios where the data’s characteristics align with the assumptions of the z-test.
2. T-Test:
The t-test is a versatile statistical tool used extensively in research and various fields to compare means between two groups. It’s particularly valuable when the population standard deviation is unknown or when dealing with smaller sample sizes.
By evaluating the means of two groups, the t-test helps ascertain if a particular treatment, intervention, or variable significantly impacts the population under study. Its flexibility and robustness make it a go-to method in scenarios ranging from medical research to business analytics.
3. Chi-Square Test:
The Chi-Square test stands distinct from the previous tests, primarily focusing on categorical data rather than means. This statistical test is instrumental when analyzing categorical variables to determine if observed data aligns with expected outcomes as posited by the null hypothesis.
By assessing the differences between observed and expected frequencies within categorical data, the Chi-Square test offers insights into whether discrepancies are statistically significant. Whether used in social sciences to evaluate survey responses or in quality control to assess product defects, the Chi-Square test remains pivotal for hypothesis testing in diverse scenarios.
Hypothesis Testing Steps
Hypothesis testing can be easily performed in five simple steps. The most important step is to correctly set up the hypotheses and identify the right method for hypothesis testing. The basic steps to perform hypothesis testing are as follows:
Step 1: Set up the null hypothesis by correctly identifying whether it is the left-tailed, right-tailed, or two-tailed hypothesis testing.
Step 2: Set up the alternative hypothesis.
Step 3: Choose the correct significance level, α�, and find the critical value.
Step 4: Calculate the correct test statistic (z, t or χ�) and p-value.
Step 5: Compare the test statistic with the critical value or compare the p-value with α� to arrive at a conclusion. In other words, decide if the null hypothesis is to be rejected or not.
Statistical Inference
Statistical inference is the process of analysing the result and making conclusions from data subject to random variation. It is also called inferential statistics. Hypothesis testing and confidence intervals are the applications of the statistical inference. Statistical inference is a method of making decisions about the parameters of a population, based on random sampling. It helps to assess the relationship between the dependent and independent variables. The purpose of statistical inference to estimate the uncertainty or sample to sample variation. It allows us to provide a probable range of values for the true values of something in the population.
The components used for making statistical inference are:
- Sample Size
- Variability in the sample
- Size of the observed differences
Types of Statistical Inference
There are different types of statistical inferences that are extensively used for making conclusions. They are:
- Confidence Interval
- Pearson Correlation
- Bi-variate regression
- Multi-variate regression
- Chi-square statistics and contingency table
- ANOVA or T-test
- One sample hypothesis testing
How to Make Statistical Inferences
In its simplest form, the process of making a statistical inference requires you to do the following:
- Draw a sample that adequately represents the population.
- Measure your variables of interest.
- Use appropriate statistical methodology to generalize your sample results to the population while accounting for sampling error.
Interpretation of results
The process of interpreting and making meaning of data produced in a research study is known as research result interpretation. It entails studying the data’s patterns, trends, and correlations in order to develop reliable findings and make meaningful conclusions.
Interpretation is a crucial step in the research process as it helps researchers to determine the relevance of their results, relate them to existing knowledge, and shape subsequent research goals. A thorough interpretation of results in research may assist guarantee that the findings are legitimate and trustworthy and that they contribute to the development of knowledge in an area of study.
The interpretation of results in research requires multiple steps, including checking, cleaning, and editing data to ensure its accuracy, and properly organizing it in order to simplify interpretation. To examine data and derive reliable findings, researchers must employ suitable statistical methods. They must additionally consider the larger ramifications of their results and how they apply to everyday scenarios.
It’s crucial to keep in mind that coming to precise conclusions while generating meaningful inferences is an iterative process that needs thorough investigation.
Process of checking, cleaning, and editing data
The process of data checking, cleaning, and editing may be separated into three stages: screening, diagnostic, and treatment. Each step has a distinct goal and set of tasks to verify the data’s accuracy and reliability.
Screening phase
The screening process consists of a first inspection of the data to find any errors or anomalies. Running basic descriptive statistics, reviewing data distributions, and discovering missing values may all be part of this. This phase’s goal is to discover any concerns with the data that need to be investigated further.
Diagnostic phase
The diagnostic phase entails a more extensive review of the data to identify particular concerns that must be addressed. Identifying outliers, investigating relationships between variables, and spotting abnormalities in the data are all examples of this. This phase’s goal is to identify any problems with the data and propose suitable treatment options.
Treatment phase
The treatment phase entails taking action to resolve any difficulties found during the diagnostic phase. This may involve eliminating outliers, filling in missing values, transforming data, and editing data. This phase’s goal is to guarantee that the data is reliable, precise, and in the appropriate format for analysis.
Researchers may guarantee that their data is high-quality and acceptable for analysis by using a structured approach to data checking, cleaning, and editing.
