What is a research methodology
What is a research methodology
Research Methodology – Overview, Types and Methods
Research methodology revolves around a step-by-step method for garnering, analyzing, and processing the collected data. Everything here is according to the research design.
It is associated with the process that researchers systematically follow to design a study by ensuring validity and reliability while addressing the research goals. In the process, researchers decide what data to collect, from whom this data should be collected via sampling, how to collect it, and how to analyze that data.
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What is Research Methodology?
Research methodology signifies the different procedures, techniques for identification. And it was then processed, followed by analysis.
The methodology section in the research paper has pointed the readers with aspects. Like, a study’s thorough examination. How far the truth reaches, and how is the dependability score. It describes the initiation of the data and its analysis. That all depends on researchers how they can get it through until they catch the aim.
There is a topic of research methods, informal ones. Be it a dissertation, thesis, academic journal article, or any other. To find which method text is a proper one, search for those that have ‘what’ references and ‘why.’
On the best ones, a Practical explanation is what you find. At the same time, none of those poor methodologies have it.
What is Research Design?
Research Design is a structure aiming to provide an ideal framework for the study being conducted.
The most important decision to be made about research is the process or the methodology that the researcher wishes to employ. This design includes the processes that will be used to collect the information and other relevant decisions regarding the study’s conduction.
The research design contains the crux of the study. It has the methodology in the design, as in the questionnaire, survey, etc. Other forms of design to be used are interviews of the employees or participants in the study.
The research design can either be descriptive or objective, depending on the topic or the study’s hypothesis. Thus, the research design being deployed by the researcher becomes very important.
It ensures that the way forward in terms of data collection accuracy and the analysis of data is assured.
An in-detailed explanation of the design being used forms a crux of the researcher’s final paper as it gives the reader an insight into the whole process. It also sets the tone for the researcher to collect data with precision.
Types of Research Methodology
Make sure you check three things- qualitative, quantitative, and mixed-method methodology.
Each of these is a different variation of the method. The only distinctive are either numbers or words or both.
1. Qualitative research
Qualitative research directs on accumulating data, examining them in words or text frames. Also, this research points out the visual facets and body language.
Qualitative research is more often used in experimental objectives. Your best example of this would be examining a person and then understanding them under a certain event or anything needful.
It is in the subjective form and has participants on the go. This is the reason why outcomes of point limit to what study and another context may not exemplify.
In contrast, qualitative research is a lot more in need of time.
2. Quantitative research
As the word ‘Quantity’ suggests, quantitative research tells about ratio, measurement. And an assessment with numerical data.
Quite the opposite of qualitative research, quantitative research is confirmatory. Testing assumptions that are not yet proved true comes under examples of quantitative. Another example is calculating the likes of engaging in crime. Or how stretched is the relationship with it.
Quantitative is easier to collect and needs less time to be over as software is available to carry it.
Because measuring is a part, this research methodology evaluates a better ‘scientific.’ But with some better-visualized answers.
3. Mixed-method methodology
The combination of qualitative and quantitative research serves as a mixed-method methodology. But with a better result.
Types of Sampling Design Approaches used in Research Methodology
Sampling design is about deciding where you shall collect your data from.
There are many sample options, but the two main sampling designs are probability sampling and non-probability sampling.
Probability sampling is one that makes use of a random sample from a group of participants. These participants are called the population in research.
A completely random sample is used to arrive at generalizable conclusions and applied uniformly to the entire population. Putting it simply, the researcher goes ahead with an expectation of getting a similar result from the entire population without indulging in the tiresome practice of collecting data from the entire group.
The non-probability sampling does not practice the usage of a random sample. It involves the researcher choosing a convenient sample instead.
The interviewer here surveys the population he has easy access to rather than picking a random sample due to a resource or mobility constraint. What is different here is that with non-probability sampling, the derived results cannot be generalized and considered applicable to the entire population.
Thus, with either of the ways of sampling, the result varies. The difference lies in the generalization principle of the data gathered. If an assumption can be made or not about the result’s universality depends on the method deployed.
Methods of Data Collection used in Research Methodology
Data collection is yet another essential aspect of research methodology. It depends on the researcher and the nature of the research question that which techniques have to be deployed. Exploratory research uses qualitative data collection techniques, while a few others might use quantitative techniques to gather data.
The choice of which data collection method to use depends on your overall research aims and objectives and practicalities and resource constraints. For example, if your research is exploratory, qualitative methods such as interviews and focus groups would likely be a good fit.
Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.
Thus, the researcher needs to pick the data collection techniques with an intelligible argument defending the choice. This practice goes a long way in ensuring accuracy in data collection.
Main Groups of Research Methods in Social Science
Social Sciences include an array of research methods. All these can be classified systematically under two umbrella methodologies. The first one being the empirical-analytical approach or the one more qualitative. The second one being interpretative, dealing with a more qualitative exploration.
What are the Techniques for Collecting Data?
Collecting data and passing through the analyzing stage vary for qualitative and quantitative. But whichever it is, the factor of objectives must be clear to opt for the effective one.
Qualitative, focused techniques include Qualitative content analysis—also, Narrative analysis, Discourse analysis, and Grounded theory.
While in quantitative, it’s descriptive and inferential statistics. Descriptive statistics, here, refer to means, median modes, etc. And inferential statistics redeem to correlation, structural equation modeling, etc.
How to Choose the Correct Research Methodology?
Again, the aim must be clear and kept on priority, which is why you should first identify the research frame.
Exploratory, confirmatory, or both, distinguish which category it comes under—qualitative, quantitative, or mixed-method methodology.
The correct grab of any piece of research is by:
Taking into account what are the pending aims and objectives meant to meet out at the end.
What is Dissertation Coaching?
Dissertation, written in the past tense, asks for your used methods.
A dissertation enables the reader to judge whether the data is valid and trustworthy. How and what you did to meet whatever attainable is the influencing factor.
The proper ones of this include the type of research undergone. Also, how their collection and how it got analyzed in the way. And materials used with the justification of why only ‘this’ and not others.
How to Describe Methods in Quantitative Methods?
Know that other researchers must be able to repeat the study. As such, you must give adequate details for a good score.
You need to explain the concepts withheld how the measuring of variables took place. Sampling method. And research tools used with nifty.
1. Inputs about survey qualitative research
Do not skip the surveys, too, from when to wear, and how to administer it. Every description must be appealing and polished.
Survey Descriptions must include the following:
2. Focus on your objectives and research questions
How the methods came about as suitability is one question, very important. And also, persuade anyone reading that it was the beat after all.
You need to make sure that the preferences happen to be prominent, though, with the dissertation back again.
3. Write to your audience
Your Details are to be accurate but with limits. You must know how long you should go and stop until when and not contain insignificant details.
Information that does not suit the field you have taken care to be through with a justification. But anything that’s marginalized to the field doesn’t ask for such deep focus.
Well, descriptive, clear are what you should install in the research.
Conclusion
Difficulties are a part. So, you can discuss what obstacles had appeared and how you gave it a pass. And tell about the unpredicted difficulty. Express the difficulty and cut-through of it.
Interviews (in qualitative research) deliver a more accurate report.
Also, they give out only qualitative. Yet, questionnaires help out in quantitative reports. But it also might not be the case in some situations.
Having any doubts about the right research methodology for your research paper? Feel free to ask us in the comment section below.
Research Methods | Definitions, Types, Examples
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make.
First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
Second, decide how you will analyze the data.
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Methods for collecting data
Data is the information that you collect for the purposes of answering your research question. The type of data you need depends on the aims of your research.
Qualitative vs. quantitative data
Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.
You can also take a mixed methods approach, where you use both qualitative and quantitative research methods.
Primary vs. secondary data
Primary data is any original information that you collect for the purposes of answering your research question (e.g. through surveys, observations and experiments). Secondary data is information that has already been collected by other researchers (e.g. in a government census or previous scientific studies).
If you are exploring a novel research question, you’ll probably need to collect primary data. But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.
Descriptive vs. experimental data
To conduct an experiment, you need to be able to vary your independent variable, precisely measure your dependent variable, and control for confounding variables. If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.
What is research methodology?
What is a research methodology?
When you’re working on your first piece of academic research, there are many different things to focus on and it can be overwhelming to stay on top of everything. This is especially true of budding or inexperienced researchers.
If you’ve never put together a research proposal before or find yourself in a position where you need to explain your research methodology decisions, there are a few things you need to be aware of.
Once you understand the in’s and out’s, handling academic research in the future will be less intimidating. We break down the basics below:
The basics of a research methodology
A research methodology encompasses the way in which you intend to carry out your research. This includes how you plan to tackle things like collection methods, statistical analysis, participant observations, and more.
You can think of your research methodology as being a formula. One part will be how you plan on putting your research into practice and another will be why you feel this is the best way to approach it. Your research methodology is ultimately a methodological and systematic plan to resolve your research problem.
In short, you are explaining how you will take your idea and turn it into a study, which in turn will produce valid and reliable results that are in accordance with the aims and objectives of your research. This is true whether your paper plans to make use of qualitative methods or quantitative methods.
Why do you need a research methodology?
Think of it like writing a plan or an outline for you what you intend to do.
When carrying out research, it can be easy to go off-track or depart from your standard methodology.
Having a methodology keepsВ youВ accountableВ andВ on trackВ with your original aims and objectives, andВ gives you a suitable and sound plan to keep your project manageable, smooth, and effective.
What needs to be included?
With all that said, how do you write out your standard approach to a research methodology?
As a general plan, your methodology should include the following information:
Why do you need to document your research method?
In any dissertation, thesis, or academic journal, you will always find a chapter dedicated to explaining the research methodology of the person who carried out the study, also referred to as the methodology sections of the work.
A good research methodology will explain what you are going to do and why, while a poor methodology will lead to a messy or disorganized approach.
You should also be able to justify in this section your reasoning for why you intend on carrying out your research in a particular way, especially if it might be a particularly unique method.
Having a sound methodology in place can also help you in the following scenarios:
What are the different types of research instruments?
A research instrument is a tool you will use to help you collect, measure and analyze the data you use as part of your research.
The choice of research instrument will usually be yours to make as the researcher and will be whichever best suits your methodology.
There are many different research instruments you can use in collecting data for your research.
Generally, they can be grouped as follows:
These are the most common ways of carrying out research, but it is really dependent on your needs as a researcher and what approach you think is best to take.
It is also possible to combine a number of research instruments if this is necessary and appropriate in answering your research problem.
Qualitative / quantitative / mixed research methodologies
There are three different types of methodologies and they are distinguished by whether they focus on words, numbers, or both.
Qualitative vs. Quantitative data.
Data type | What is it? | Methodology | |||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Simple Hypothesis | It shows a relationship between one dependent variable and a single independent variable. For example, – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable. |
Complicated Hypothesis | It illustrates the connection of two or more dependent variables with two or more independent variables. Consuming more fruits and vegetables contributes to weight loss, perfect skin, reducing the risk of many illnesses, such as heart disease, high blood pressure, and certain cancers. |
Directional Hypothesis | This illustrates how an investigator is analytical and dedicated to a specific result. Even the relationship between the variables may forecast their existence. For example, children aged four years who eat proper food over five years have higher IQ levels than children who do not wear appropriate food. It indicates the influence and direction of the impact. |
Non-directional Hypothesis | It has been used when it does not require any explanation. It is a hypothesis that there is an interaction between two variables, without forecasting the relationship’s precise nature (position). |
Null Hypothesis | It offers a declaration contradictory to the Hypothesis. It’s a negative assumption, and the connection between independent and dependent variables is not present. A symbol is labelled with “HO.” |
Associative and Causal Hypothesis | An associative hypothesis arises when one variable shift, contributing to a change in the other variable. The causal theory, however, suggests an association of impact and consequence between two or more variables. |
Hypothesis Examples
Here are the examples of their forms of Hypothesis:
A research hypothesis is a real, simple, and testable idea or predictive assumption on the potential outcome of a population-based scientific research study, such as supposed variations between groups on a specific variable or interactions between variables.
Types of Scaling in Research Methodology
The complete info about the different scales that are utilized in data analysis is given under this section. There are four types of scaling methods. All factors fall in one of these scales. Understanding the numerical properties and doling out the legitimate scale to the elements is significant because they figure out which numerical activities are permitted. That decides measurable tasks we can utilize.
The above mentioned four scales are Nominal, Ordinal, Interval and Ratio scale respectively with Nominal having least mathematical properties, trailed by Ordinal and Interval, though Ratio having most mathematical properties.
Keeping in mind the Statistical perspective, it is the most minimal estimation level. The minor scale is appointed to things that are isolated into classes without having any request or structure, for example, Colors don’t have an allocated order, We can have five hues like Blue, red, green, yellow, and orange.
Here the numbers are assigned to shading only with the end goal of ID and requesting them Ascending or Descending which doesn’t imply that Colors have an Order. The number gives us the character of the classification doled out. The main numerical activity we can perform with nominal information is to check. Another model from research exercises is based on a Yes or No scale, which is insignificant. It has no structure, and there is no separation among NO and Yes.
Ordinal Scale factors have the property of Identity and Magnitude. The numbers speak to a quality being estimated. They can reveal to us whether a case has a more significant amount of the quality calculated or less of the quality assessed than another point. The separation between Scale focuses isn’t equivalent. Positioned inclinations are introduced to act as an illustration of ordinal scales experienced in regular daily existence. Subsequently, an ordinal scale lets the specialist decipher net request and not the general positional separations.
This scale is a stretch scale, for example when requested to rate fulfilment with a preparation on a 5-point scale, in which there are options like Strongly Disagree, Disagree, Neutral, Agree and Strongly Agree. A span scale is being utilized. It is a span scale since it is expected to have an equivalent separation between every one of the scale components, for example, the Magnitude between Strongly Agree and Agree is thought to be identical to Agree and Strongly Agree.
This implies we can decipher contrasts somewhere out there along the scale. We contrast this to an ordinal scale where we can discuss differences all together, not contrasts in the level of request I-e the separation between reactions.
Properties
This one is at a high degree of the scale. The factor which characterizes a proportion scale is that it has a real zero point. The most straightforward case of a proportion scale is the estimation of length, which ignores any philosophical focuses about characterizing how we can distinguish zero-length or cash. Having zero length or zero money implies that there is no length and no cash except for zero temperature isn’t an outright zero, as it indeed has its impact. Proportion sizes of estimation have the entirety of the properties of the theoretical number framework.
Properties of Ratio Scale
Types of Variables in Research Methodology
Variable
A variable in polynomial math truly represents one thing that is an absolute value whose value is unknown. Nonetheless, in measurements, you’ll go over many sorts of factors. Most of the time, the word implies that you’re managing something obscure.
Still, it is dissimilar to variable based mathematics that obscure isn’t generally a number. Some types of variables are utilized more than usual. For instance, you’ll be significantly more prone to go over ceaseless factors than you would dummy factors.
Variables in Research Methodology
In research methodology, A variable is any property, a trademark, a number, or an amount that increments or diminishes after some time or can take on various qualities (rather than constants, for example, n, that don’t change) in multiple circumstances.
For this situation, the variable is the kind of manures. A social researcher may look at the potential impact of early marriage on separate. Here first marriage is the variable. A business specialist may think that it’s helpful to remember the profit for deciding the offer costs. Here profit is the variable.
Types of Variables
Qualitative factors are those that express a personal property, for example, religion, sexual orientation, race, nationality, caste, societal position, strategy for installment, etc. The estimations of a subjective variable don’t infer a significant mathematical requesting
Quantitative factors, likewise called numeric factors, are those factors that are estimated regarding numbers. A primary cause of a quantitative variable is an individual’s age.
Quantitative factors are of two kinds that are continuous and discrete. Factors, for example, a few youngsters in a family unit or several bad things in a container are discrete factors since the potential scores are discrete on the scale. And other than this, if you have something which value may come in decimal is known as continuous variables.
The variable that is utilized to depict or gauge the factor that is accepted to cause or possibly to impact the issue or result is called an independent variable. The variable that is utilized to portray or quantify the problem or result under examination is known as a dependent variable.
In a causal relationship, the reason is the independent, and the impact is the reliant variable. On the off chance that we theorize that smoking causes a cellular breakdown in the lungs, smoking is the free factor and malignancy the needy variable.
In pretty much every investigation, we gather data, for example, gender, age, instructive fulfilment, financial status, conjugal status, religion, the spot of the birth, and so forth. These factors are alluded to as background variable.
In so many experiments, it remains a point to concern the recognizable proof of a solitary autonomous variable and the estimation of its impact on the needy variable. Yet at the same time, a few factors may influence our speculated relationship, accordingly contorting the study. These factors are alluded to as Extraneous factors.
As a rule, we have valid justifications for accepting that the factors of interest encapsulate a relationship, however our information neglect to build up any such relationship. Some shrouded components might be smothering the genuine connection between the two unique factors. Such an element is alluded to as a suppressor variable since it stifles the real relationship between the other two parts.
Types of Data in Research Methodology
Raw and unorganized facts or a set of values of subjects that need to be processed is called Data. Without a proper organization, the Data is of no use and just some random things. After the collection of data, there will be a need to process it, organize it, make the structure of it and then finally present it in a useful way which is termed as information. In short, after the process, data becomes information. Not data but processed data, i.e., the story is essential to conduct Research. That Data can be acquired in various forms and from multiple means. Research papers, journal articles, web sites, books and blogs are used to collect data. A qualitative research methodology is the best Methodology to analyze the data contained in textual form.
The Researcher assigns a specific value to every Data, and each Data describes things of unique quality. Organization, process, and presentation of these values are essential for analysis to get the best result of Research.
The different types of data in research methodology are described below:
Qualitative data:
Qualitative data are those data which contains words and description and are in textual form. This type of Data is not easy to analyze in Research as it is of the subjective kind, especially when it comes to comparing it with other information.
For example, Researcher collects quality data from personal interviews, open-ended questions, and focus groups. This type of data describes taste, experience, texture, or opinion.
Quantitative data:
Quantitative data are those data which are expressed in numbers or numerical figures. This type of data can be measured, ranked, calculated or grouped.
Example: This type of data contains questions like age, scores, rank, cost, length, weight, etc. In short, every Data which is in the form of numbers. Also, such numerical data can be presented in graphical format, charts, or can be applied in statistical analysis methods.
Categorical data:
When Data is available in groups but does not belong to more than the belonged group is called Categorical data. The data grouped into a category is Categorical data.
Example: If there were a survey which asks people to tell their marital status, age, smoking habit, and drinking habit, this information collected from people are categorical data. In simple words, the data of categorical type represents discrete numbers which belong to a specific category or class.
Based on the methods of data collection, data can be divided into four types: observational, experimental, Simulation, and derived.
Observational Data
A researcher observes things or people and their behaviour or activity to collect data which comes under observational data. Methods used to collect observational data are human observation, open-ended surveys, or interviews. The collection of this type of data depends on real-time. The re-creation of observational Data is not accessible if lost.
Experimental Data
The data collected by tests, experiments, measurements, and quasi-experimental designs is called Experimental data. When a researcher intervenes to produce, alter or measure any change in the investigation to collect data, he collects Experimental data. This method of collecting data can be applied based on the need of researchers where it is qualitative or quantitative. Experimental Data is comparatively easier to analyze and interpret.
Simulation Data
To imitate the operation of a system or a process which describes the procedure over time is Simulation. And by using computer test models to imitate the operation of a real-world system or method generates simulation data. Simulation data helps to find what could or what would take place under a specific condition. Experimenting through the computer are often used to collect simulation data.
Example: weather conditions are predicted by simulating data.
Derived / Compiled Data
This type of data use other base data, and it involves the process of creating new data from existing data through some transformation. It is entirely new data constructed from one or more existing data. Derived data are new data or information, and it provides new ways of presenting old or raw attributes.
Example: Population density data can be obtained by a combination of data of area and population. If lost, researchers can replace this type of data. However, it will be expensive and time-consuming.
Types of Sampling in Research Methodology
Sampling
It is a strategy of choosing singular individuals or a subset of the population to build factual inferences from them and gauge attributes of the entire territory. Analysts in statistical surveying broadly utilize diverse inspecting techniques, so they don’t have to investigate the whole population to gather significant experiences. It is additionally a period advantageous and a practical approach and subsequently shapes the premise of any exploration plan. Inspecting strategies can be utilized in an exploration overview programming for the ideal deduction.
Types of sampling
Before we examine the various types of inspecting, let us talk about what the term sampling means. In the research field, this term is considered to be an example for the gathering of individuals, articles, or things that are taken from a vast populace for estimation. Along these lines, to get the precise outcomes, testing is finished.
For instance, if we need to check all the chips in a manufacturing plant made are acceptable or not, it is tough to check each chip, so to check, we will be taking an arbitrary fragment and check for its exact taste, size, and shape.
Probability Sampling | This sampling technique is an inspecting strategy where a specialist sets a determination of a couple of rules and picks individuals from a populace arbitrarily. All the individuals have an equal chance to be an aspect of the example with this choice boundary. |
Non-Probability Sampling | In the non-probability technique for sampling, the scientist picks individuals for Research aimlessly. This inspecting technique is not a fixed or predefined choice cycle. This makes it hard for all components of a populace to have equivalent chances to be remembered for an example. |
Simple random sampling | Outstanding amongst other sampling and testing strategies that help in sparing time and assets, is the Simple Random Sampling strategy. It is a dependable technique for getting data where every individual from a populace is picked haphazardly, just by some coincidence. Every individual has a similar likelihood of being selected to be an aspect of an example. |
Systematic sampling | Scientists generally utilize the systematic sampling strategy to pick the example of individuals from a populace at familiar stretches. It requires more of the choice of a beginning stage for the model and test size that can be rehashed at regular intervals. This kind of testing technique has a predefined range, and subsequently, this examining procedure takes significantly less time than other strategies. |
Cluster sampling | This sampling strategy is where the analysts separate the whole populace into areas or bunches that speak to a crowd. Groups are recognized and remembered for example dependent on segment boundaries like sexual orientation, age, location, and so forth. This makes it necessary for an overview maker to get robust derivation from the responses. |
Stratified random sampling | Stratified random sampling is a technique where scientist separates the populace into littler gatherings to make analyzing more accessible that don’t cover but shows the whole public. While inspecting, these gatherings can be composed and afterward draw an example from each group independently. This sampling technique is majorly used in the real world. |
Conclusion
After a detailed study of what Research is, what research methodology is, and what are types of research methodology, it is clear that for any research there are specific methods to be followed for good or say accurate results. To know the answer of all ‘how’ of any given Research or subject of Research, application Research Methodology and types of Research Methodology is essential.
Through using any of the types of Research Methodology, a researcher can systematically design the study to get reliable results. Also, Research Methodology should justify that the selected type of research methodology is the fittest for the best outcome. A sound research methodology results in scientifically sound effects, but flawed research methodology fails to do so. So, the Researcher should invest in the sound and reliable type of research methodology to apply in Research to get an accurate result.
FAQs
Qualitative Research Methodology and Quantitative Research Methodology are mainly two types of Research Methodology which cover all the aspects of methodologies of any Research.
The way by which researchers can proceed with their Research is Research Methodology while the method by which a research is conducted on a topic or subject is termed as Research Method.
The reason behind designing the methodology for a Research is to solve the difficulties coming in the way to conduct Research.
Problem oriented research is one of the types of Research which targets to determine the the exact problem and then to find the best solution to that problem. The aim to apply this research type is to focus on the problem to find out relevant outcomes. Infact, the word ‘problem’ means not only one problem but the number of problems may vary.
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