Data Presentation & Analysis.pptx

Data Presentation & Analysis Meaning, Stages of data analysis, Quantitative & Qualitative data analysis methods, Descriptive & inferential methods of data analysis

1. Data Presentation & Analysis Dr. D. Heena Cowsar Assistant Professor of Commerce Bon Secours College for Women Thanjavur [email protected]

2. Introduction Data analysis is how researchers go from a mass of data to meaningful insights . Research data analysis is a process used by researchers for reducing data to a story and interpreting it to derive insights. The data analysis process helps in reducing a large chunk of data into smaller fragments, which makes sense.  Three essential things take place during the data analysis process — Data Organisation, Data reduction and Data Analysis. Data organization . Summarization and categorization together contribute to becoming the second known method used for Data reduction . It helps in finding patterns and themes in the data for easy identification and linking. Third and the last way is Data analysis – researchers do it in both top-down or bottom-up fashion.

3. What is Data Analysis? Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making . The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis. There are many different data analysis methods, depending on the type of research. The few methods used to analyze the quantitative and qualitative data are:

4. Stages of analyzing data preparation Data Preparation The first stage of analyzing data is data preparation, where the aim is to convert raw data into something meaningful and readable. It includes four steps: Step 1: Data Validation The purpose of data validation is to find out, as far as possible, whether the data collection was done as per the pre-set standards and without any bias. It is a four-step process, which includes… Fraud, to infer whether each respondent was actually interviewed or not. Screening, to make sure that respondents were chosen as per the research criteria. Procedure, to check whether the data collection procedure was duly followed. Completeness, to ensure that the interviewer asked the respondent all the questions, rather than just a few required ones.

5. Step 2: Data Editing Typically, large data sets include errors. For example, respondents may fill fields incorrectly or skip them accidentally. To make sure that there are no such errors, the researcher should conduct basic data checks, check for outliers, and edit the raw research data to identify and clear out any data points that may hamper the accuracy of the results. For example, an error could be fields that were left empty by respondents. While editing the data, it is important to make sure to remove or fill all the empty fields. There are 4 methods to deal with missing data. List wise deletion method Mean/median/mode imputation Last observation carried forward Resurveying

6. Step 3: Data Coding This is one of the most important steps in data preparation. It refers to grouping and assigning values to responses from the survey. For example, if a researcher has interviewed 1,000 people and now wants to find the average age of the respondents, the researcher will create age buckets and categorize the age of each of the respondent as per these codes. (For example, respondents between 13-15 years old would have their age coded as 0, 16-18 as 1, 18-20 as 2, etc.) Then during analysis, the researcher can deal with simplified age brackets, rather than a massive range of individual ages.

7. Quantitative Data Analysis Methods After these steps, the data is ready for analysis. The two most commonly used quantitative data analysis methods are descriptive statistics and inferential statistics. 1 . Descriptive Statistics Typically descriptive statistics (also known as descriptive analysis) is the first level of analysis. It helps researchers summarize the data and find patterns. A few commonly used descriptive statistics are: Mean : numerical average of a set of values. Median : midpoint of a set of numerical values. Mode : most common value among a set of values. Percentage : used to express how a value or group of respondents within the data relates to a larger group of respondents. Frequency : the number of times a value is found. Range : the highest and lowest value in a set of values.

8. Descriptive Vs Inferential measures 1.  Descriptive Statistics :  Descriptive statistics is a term given to the analysis of data that helps to describe, show and summarize data in a meaningful way. It is a simple way to describe our data. Descriptive statistics is very important to present our raw data in a n effective/meaningful way using numerical calculations or graphs or tables. This type of statistics is applied to already known data.  Types of Descriptive Statistics: Measure of Central Tendency Measure of Variability 2. Inferential Statistics :  In inferential statistics, predictions are made by taking any group of data in which you are interested. It can be defined as a random sample of data taken from a population to describe and make inferences about the population . Any group of data that includes all the data you are interested in is known as population. It basically allows you to make predictions by taking a small sample instead of working on the whole population .

9. Descriptive Vs Inferential measures S.No Descriptive Statistics Inferential Statistics 1. It gives information about raw data which describes the data in some manner. It makes inferences about the population using data drawn from the population. 2. It helps in organizing, analyzing , and to present data in a meaningful manner. It allows us to compare data, and make hypotheses and predictions. 3. It is used to describe a situation. It is used to explain the chance of occurrence of an event. 4. It explains already known data and is limited to a sample or population having a small size. It attempts to reach the conclusion about the population. 5. It can be achieved with the help of charts, graphs, tables, etc. It can be achieved by probability.

10. After these steps, the data is ready for analysis. The two most commonly used quantitative data analysis methods are descriptive statistics and inferential statistics. Descriptive Statistics Typically descriptive statistics (also known as descriptive analysis) is the first level of analysis. It helps researchers summarize the data and find patterns. A few commonly used descriptive statistics are: Mean : numerical average of a set of values. Median : midpoint of a set of numerical values. Mode : most common value among a set of values. Percentage : used to express how a value or group of respondents within the data relates to a larger group of respondents. Frequency : the number of times a value is found. Range : the highest and lowest value in a set of values.

11. 2. Analyzing Qualitative Data Qualitative data analysis works a little differently from quantitative data, primarily because qualitative data is made up of words, observations, images, and even symbols. Deriving absolute meaning from such data is nearly impossible; hence, it is mostly used for exploratory research. While in quantitative research there is a clear distinction between the data preparation and data analysis stage, analysis for qualitative research often begins as soon as the data is available.

12. Steps in Qualitative Data Preparation and Basic Data Analysis Analysis and preparation happen in parallel and include the following steps: Getting familiar with the data : Since most qualitative data is just words, the researcher should start by reading the data several times to get familiar with it and start looking for basic observations or patterns. This also includes transcribing the data. Revisiting research objectives : Here, the researcher revisits the research objective and identifies the questions that can be answered through the collected data.

13. Steps in Qualitative Data Preparation & Basic Data Analysis Developing a framework: Also known as coding or indexing, here the researcher identifies broad ideas, concepts, behaviors, or phrases and assigns codes to them. For example, coding age, gender, socio-economic status, and even concepts such as the positive or negative response to a question. Coding is helpful in structuring and labeling the data. Identifying patterns and connections : Once the data is coded, the research can start identifying themes, looking for the most common responses to questions, identifying data or patterns that can answer research questions, and finding areas that can be explored further.

14. Methods used for data analysis in qualitative research There are several techniques to analyze the data in qualitative research, but here are some commonly used methods, Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method. Narrative Analysis:  This method is used to analyze content gathered from various sources such as personal interviews, field observation, and surveys.  The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.

15. Methods used for data analysis in qualitative research Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people . Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion. Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

16. References https://www.guru99.com/what-is-data-analysis.html