Research method is a specialised ways to collect and understand information. Creating a plan for how you will do your research is an important part of your study plan. A research methodology is a description of how you can do a certain piece of research. It tells how to find and analyse data related to a certain study topic. So, the term “research technique” refers to how a researcher plans their study so that they can get valid and reliable data while also meeting their research goals.
When planning your strategy, you have to decide on two big things regarding research method.
First, decide how you’ll gather information. Your methods will depend on the kind of information you need to answer your research question:
Qualitative vs. quantitative: Will your data be in words or numbers?
Primary data vs. secondary data: How will you collect data?
Descriptive vs. experimental: Will you measure something exactly as it is, or will you try something new?
Second, decide how you’re going to look at the data.
Statistical analysis methods you can use to find out if there is a link between two or more quantitative variables.
You can use methods like thematic analysis to figure out patterns and meanings in qualitative data.
How to get information for Research Method?
Data is the term for the information you gather to answer your research question. The type of information you need depends on the goals of your study.
Whether you collect qualitative or quantitative data will depend on what kind of information you want to get. Numbers can explain Collect qualitative data to answer questions about ideas, experiences, and meanings, or to look into anything.
Collect quantitative data if you want to learn more about how something works or if your research involves testing an idea.
What’s good about qualitative data.
Flexible means that you can change your methods often as you learn more.
In this experiment, you can use small amounts of things.
Cons.
Cannot be studied statistically or extrapolated to bigger groups.
It is hard to make research consistent.
The good things about quantitative data.
You can use it in a methodical way to talk about huge groups of things.
Generates reproducible knowledge.
Cons.
Analyzing data necessitates statistical training.
You require larger samples.
Data from first-hand sources is better than data from second-hand sources.
In a mixed-methods strategy, you can also use both qualitative and quantitative ways to do research.
Primary data is any new information you collect to answer 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’re looking into a new research question, you’ll almost certainly need to collect primary data. On the other hand, secondary data may be a better choice if you want to combine what you already know, look at historical trends, or find patterns on a large scale.
Advantages of Primary Data.
Can be gathered to answer a certain research question.
You decide how to pick samples and take measurements.
Cons.
Collecting costs more money and takes more time.
Methods for collecting data need to be taught.
Advantages of Secondary Data.
It would be easier and faster to get to.
You can gather information over longer periods of time and from a wider area.
Cons.
No one could control how the data were made.
More work needs to be done on it before you can use it for your analysis.
Experimental vs. descriptive data.
In descriptive research method, you learn about your study subject without getting involved. Your study’s validity will depend on how well your sample procedure works. In experimental research, you change a process in a planned way and then measure what happens. How valid your results are will depend on how you set up your experiment.
In order to do an experiment, you need to be able to change your independent variable, measure your dependent variable correctly, and account for variables that might affect the experiment. If it is realistic and ethically possible, this method is the best way to answer questions about cause and effect.
What’s good about descriptive data?
You can talk about your study topic without changing it at all.
Accessibility means that you can gather more data on a larger scale.
Cons.
There is no way to control variables that mix things up.
It is impossible to find links between causes and effects.
Advantages of Experimental Data.
You have more control over the things that can confuse things.
Be able to make connections between causes and effects.
Cons.
You might have an effect on your research subject that you didn’t expect.
Most of the time, collecting data requires more knowledge and resources.
Data analysis techniques.
Your data analysis strategies will depend on what kind of data you collect and how you get it ready for analysis. Data can often be looked at both quantitatively and qualitatively. For example, survey responses could be judged qualitatively by looking at what people said or statistically by looking at how many people answered.
How qualitative research is done?
Qualitative analysis is used to understand words, thoughts, and experiences. It can be used to make sense of the following kinds of data:
You can start with literature reviews, case studies, open-ended survey and interview questions, and other text-based sources.
There are also sampling methods that don’t depend on chance. Because qualitative analysis is flexible and depends on the researcher’s judgement, you need to think carefully about your choices and assumptions.
Ways to analyse data quantitatively.
Quantitative analysis uses numbers and statistics to understand frequency, averages, correlations, or cause-and-effect relationships (in descriptive studies) (in experiments).
Quantitative analysis can be used to look at data gathered in one of two ways:
When doing research for a project.
Using methods of sampling that are based on chance.
Because the data is collected and analysed in a statistically correct way, the results of the quantitative analysis can be easily standardised and shared with other academics.
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