Research in data analysis: This is the definition of research according to LeCompte & Schensul. It's a process that allows researchers to reduce data into stories and interpret it to gain insights. Data analysis is a process that reduces large amounts of data into smaller pieces. This makes sense.
The first step in data analysis is data organization. The second method for data reduction is summarization and categorization. It helps to identify patterns and themes within the data and allows for linking. Data analysis is the third and final way to analyze data. Researchers can do this in either a top-down or bottom up fashion.
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What's the point of using data analysis in research?
Data is a key component of researchers' work. They need to have a story to tell and problems to solve. Data is only the answer to a question. What if there's no question? Well! You can explore data without any problems - this is what we call 'Data Mining'. It often uncovers interesting patterns in the data that are well worth exploring.
Each type of data has the rare ability to describe things after giving it a value. To make the data useful for analysis, you must organize and present these values in a context. Data can come in many forms. Here are the primary data types.
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Qualitative data is data that has descriptions and words. While you can see this data, it's subjective and difficult to analyze in research, especially when compared with other data. Example: Quality data is anything that describes taste, texture, experience, or opinion. These types of data are usually collected via focus groups, qualitative interviews with individuals, or open-ended questions in surveys.
Quantitative data is data that's expressed in numerical numbers. This data can be grouped, measured and calculated, or ranked. Examples include questions like age, rank and cost. All data falls under this category. These data can be presented in charts or graphical format. You can also apply statistical analysis methods. Surveys can be used to collect numeric data by using the (Outcomes Metric Systems) OMS questionnaires.
Categorical Data: This is data that is presented in groups. An item in the categorical dataset cannot be part of more than one group. Example: When a person answers a survey, he or she tells about his lifestyle, marital status and smoking habits. This data can be analyzed using a standard method called the chi-square test.
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Data analysis methods in qualitative research
There are many ways to analyze qualitative research data. Here are some of the most popular.
Content Analysis is widely used and accepted as the best technique to analyze data in research methodologies. This technique can analyze information in text, images and sometimes physical objects. The research questions will determine when and where you should use it.
Narrative Analysis is a method that analyzes content from various sources, such as field observation, personal interviews, and surveys. Most stories and opinions are focused on answering research questions.
Discourse Analysis: This is a similar method to narrative analysis. It analyzes people's interactions. However, this method also considers the social context in which or within which communication takes place between respondent and researcher. Discourse analysis does not only focus on the person's lifestyle but also their daily environment.
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Grounded Theory: If you need to explain why a phenomenon occurred, then grounded theory is the best option. Grounded theory can be used to analyze data that shows similar cases in different contexts. Researchers may alter or create new explanations when using this method to arrive at a conclusion.