Data Analysis Methods
As the job requirements for data analysts is on a rise, knowledge of data analysis methods is in demand. Read on to know all about them.
What happens in stock market? Is there a direction to the stock market? And does it have a specific pattern? We, humans ask lot of diverse questions like these and sometimes, only sometimes, facts help. The answers to most of such questions depend on data and various methods to analyze it. Data analysis methods help us to understand facts, observe patterns, formulate explanations, and try out hypotheses. They are not only used in all kinds of science and business, but also in administration and policy making.
Data Analysis Explained
Data analysis is defined as a practice in which unorganized or unfinished data is ordered and organized so that utile information can be highlighted from it. It involves processing and working on data, in order to understand what all is present in the data and vice versa.
To understand what is involved in data analysis, take a look at this example: Between 1800 and 2000 United States' population increased from 5 million to 255 million people i.e. growth of 250 million. So, these figures illustrate the facts. But, to conclude that the population rose at an average rate of 1.25 million people per year (250 million divided by 200 years), would be wrong. The information would be correct and so would be the arithmetic, but the interpretation "an average growth rate of 1.25 million people per year" would be dead wrong. The analysis would not correctly interpret facts as population of the US did not grow in that fashion, not even approximately.
Here's where correct data analysis methods and procedures come into picture. Charts, graphs, and write ups in text form, are various methods to analyze data. These methods are designed to polish and refine the data, so that the end users can reap interesting or useful information without any need of going through the entire data themselves.
The Various Data Analysis Methods
Qualitative research analysts define 15 types of data analysis methods. Let's go through each of them:
1. Typology: It's basically a classification system or methodology, taken from patterns, themes or other kinds of groups of data. This type of method implements the thought that, ideally, categories should be mutually exclusive and exhaustive, if possible. Here's a list of categories as example: acts, activities, meanings, participation, relationships, settings, etc.
2. Taxonomy: This method is complex classification containing multiple levels of conceptions or abstractions. Higher levels include lower levels forming superordinate and subordinate categories.
3. Constant Comparison/Grounded Theory: This method was developed in the 60s and has the following steps:
5. Logical Analysis/Matrix Analysis: It is basically an outline of generalized causation, logical reasoning process, etc. It mostly includes use of flow charts, diagrams, etc. to graphically represent these, as well as written descriptions.
6. Quasi-statistics: More often than not, enumeration is used in this method to provide manifest for categories formed or to determine if observations are untrue.
7. Event Analysis/Microanalysis: In this method, importance is on finding a accurate beginnings and endings of events by determining specific boundaries or points that mark boundaries or events. This is the method that is specifically oriented towards film and video making. After end points are determined, repeated viewing can help us find phases in the event.
8. Metaphorical Analysis: Here, it's required to go on with various metaphors while checking how well they correspond with what is being observed. Participant may be asked for metaphors which they should interpret. For example: "Hallway as a highway." Many participants will take highway and its components in different ways like, students as traffic and teachers as police, etc.
9. Domain Analysis: This type of analysis is mostly used to describe social and cultural situations, and patterns within it. Start by emphasizing what is social situation to participants while they can interrelate it with cultural meanings.
10. Hermeneutical Analysis: The word 'hermeneutical' literally means not going for objective meaning of text, but interpreting the text for the people involved in the situation. This is done by never overemphasizing self in an analysis, instead reiterating the people's story. Meaning of any content resides in author intent, context, and the reader - finding themes and relating these three is involved in this method.
11. Discourse analysis: This method usually involves video taping of events so that they can be played over and over again for deeper analysis.
12. Semiotics: Here, we determine how signs and symbols are related to their meanings while they are being constructed. The analysis needs to assume that the meaning is not inherent and it comes from other things related to the symbol.
13. Content Analysis: This method is never used with video and it is only qualitative in development of categories. Standard rules of categorization in content analysis include:
15. Narrative Analysis: Also known as 'Discourse analysis', this method gives more importance to interaction. How the narrator chooses to tell frame wise, that is how he/she will be perceived. Always compare ideas while avoiding revealing negatives about self. This analysis can involve study of literature or journals or folklore.
Thus, it can be observed that data analysis methods have multiple aspects and approaches, along with diverse techniques and variety of names. It comes to use in different domains like business, science, and social science. This field of statistics is a very complex one, and the number of methods for data analysis aren't quite easy to learn without training and practice under expert guidance.
Data Analysis Explained
Data analysis is defined as a practice in which unorganized or unfinished data is ordered and organized so that utile information can be highlighted from it. It involves processing and working on data, in order to understand what all is present in the data and vice versa.
To understand what is involved in data analysis, take a look at this example: Between 1800 and 2000 United States' population increased from 5 million to 255 million people i.e. growth of 250 million. So, these figures illustrate the facts. But, to conclude that the population rose at an average rate of 1.25 million people per year (250 million divided by 200 years), would be wrong. The information would be correct and so would be the arithmetic, but the interpretation "an average growth rate of 1.25 million people per year" would be dead wrong. The analysis would not correctly interpret facts as population of the US did not grow in that fashion, not even approximately.
Here's where correct data analysis methods and procedures come into picture. Charts, graphs, and write ups in text form, are various methods to analyze data. These methods are designed to polish and refine the data, so that the end users can reap interesting or useful information without any need of going through the entire data themselves.
The Various Data Analysis Methods
Qualitative research analysts define 15 types of data analysis methods. Let's go through each of them:
1. Typology: It's basically a classification system or methodology, taken from patterns, themes or other kinds of groups of data. This type of method implements the thought that, ideally, categories should be mutually exclusive and exhaustive, if possible. Here's a list of categories as example: acts, activities, meanings, participation, relationships, settings, etc.
2. Taxonomy: This method is complex classification containing multiple levels of conceptions or abstractions. Higher levels include lower levels forming superordinate and subordinate categories.
3. Constant Comparison/Grounded Theory: This method was developed in the 60s and has the following steps:
- Look at the document to be analyzed, such as a field note.
- Identify parameters to categorize events and behavior, which will be named and coded on document.
- Code comparison will help find consistencies and deviations.This is done till categories saturate and no new codes related to it are formed.
- Finally, certain categories become centrally focused categories more commonly known as core categories. These core categories are made subjects of case study.
5. Logical Analysis/Matrix Analysis: It is basically an outline of generalized causation, logical reasoning process, etc. It mostly includes use of flow charts, diagrams, etc. to graphically represent these, as well as written descriptions.
6. Quasi-statistics: More often than not, enumeration is used in this method to provide manifest for categories formed or to determine if observations are untrue.
7. Event Analysis/Microanalysis: In this method, importance is on finding a accurate beginnings and endings of events by determining specific boundaries or points that mark boundaries or events. This is the method that is specifically oriented towards film and video making. After end points are determined, repeated viewing can help us find phases in the event.
8. Metaphorical Analysis: Here, it's required to go on with various metaphors while checking how well they correspond with what is being observed. Participant may be asked for metaphors which they should interpret. For example: "Hallway as a highway." Many participants will take highway and its components in different ways like, students as traffic and teachers as police, etc.
9. Domain Analysis: This type of analysis is mostly used to describe social and cultural situations, and patterns within it. Start by emphasizing what is social situation to participants while they can interrelate it with cultural meanings.
10. Hermeneutical Analysis: The word 'hermeneutical' literally means not going for objective meaning of text, but interpreting the text for the people involved in the situation. This is done by never overemphasizing self in an analysis, instead reiterating the people's story. Meaning of any content resides in author intent, context, and the reader - finding themes and relating these three is involved in this method.
11. Discourse analysis: This method usually involves video taping of events so that they can be played over and over again for deeper analysis.
12. Semiotics: Here, we determine how signs and symbols are related to their meanings while they are being constructed. The analysis needs to assume that the meaning is not inherent and it comes from other things related to the symbol.
13. Content Analysis: This method is never used with video and it is only qualitative in development of categories. Standard rules of categorization in content analysis include:
- Identifying a chunk of data to be analyzed at a time (whether it is a line, a sentence, a phrase, a paragraph?).
- Categories must be inclusive and mutually exclusive.
- Should have precisely defined properties.
- All data fits some category i.e. exhaustive categorization.
15. Narrative Analysis: Also known as 'Discourse analysis', this method gives more importance to interaction. How the narrator chooses to tell frame wise, that is how he/she will be perceived. Always compare ideas while avoiding revealing negatives about self. This analysis can involve study of literature or journals or folklore.
Thus, it can be observed that data analysis methods have multiple aspects and approaches, along with diverse techniques and variety of names. It comes to use in different domains like business, science, and social science. This field of statistics is a very complex one, and the number of methods for data analysis aren't quite easy to learn without training and practice under expert guidance.
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