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notes about data analysis methods

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- 1. DATA DATA ANALYSIS METHODS ANALYSIS METHODS Research Project Finding the Answers to the Research Questions-Qualitative
- 2. 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. Analyzing Qualitative Data Analyzing Qualitative Data
- 3. Data Data Preparation Preparation and Basic and Basic Data Analysis Data Analysis
- 4. Content analysis: This is one of the most common methods to analyze qualitative data. It is used to analyze documented information in the form of texts, media, or even physical items. When to use this method depends on the research questions. Content analysis is usually used to analyze responses from interviewees. Qualitative Data Analysis Qualitative Data Analysis Methods Methods
- 5. Narrative analysis: This method is used to analyze content from various sources, such as interviews of respondents, observations from the field, or surveys. It focuses on using the stories and experiences shared by people to answer the research questions. Qualitative Data Analysis Qualitative Data Analysis Methods Methods
- 6. Framework analysis. This is a more advanced method that consists of several stages such as familiarization, identifying a thematic framework, coding, charting, mapping, and interpretation. Qualitative Data Analysis Qualitative Data Analysis Methods Methods
- 7. Discourse analysis: Like narrative analysis, discourse analysis is used to analyze interactions with people. However, it focuses on analyzing the social context in which the communication between the researcher and the respondent occurred. Discourse analysis also looks at the respondent’s day-to-day environment and uses that information during analysis. Qualitative Data Analysis Qualitative Data Analysis Methods Methods
- 8. Grounded theory: This refers to using qualitative data to explain why a certain phenomenon happened. It does this by studying a variety of similar cases in different settings and using the data to derive causal explanations. Researchers may alter the explanations or create new ones as they study more cases until they arrive at an explanation that fits all cases. Qualitative Data Analysis Qualitative Data Analysis Methods Methods
- 9. Coding can be explained as he categorization of data. A ‘code’ can be a word or a short phrase that represents a theme or an idea. Step 1: Developing and Applying Codes. The analytical and critical thinking skills of the researcher plays significant role in data analysis in qualitative studies. Therefore, no qualitative study can be repeated to generate the same results. Step 2: Identifying themes, patterns and relationships. Qualitative data analysis can also be Qualitative data analysis can also be conducted through the following conducted through the following three three steps: steps:
- 10. At this last stage, you need to link research findings to hypotheses or research aims and objectives. When writing the data analysis chapter, you can use noteworthy quotations from the transcript in order to highlight major themes within findings and possible contradictions. Step 3: Summarizing the data. Qualitative data analysis can also be Qualitative data analysis can also be conducted through the following conducted through the following three three steps: steps:
- 11. Step 1: Developing and Applying Step 1: Developing and Applying Codes Codes WHAT IS CODE? Code may be a word or short phrase that symbolically assigns a cumulative prominent and sense-capturing portion of a text or visual data.
- 12. Step 1: Developing and Applying Step 1: Developing and Applying Codes Codes There are three types of coding: 1. Open coding. The initial organization of raw data to try to make sense of it. 2. Axial coding. Interconnecting and linking the categories of codes. 3. Selective coding. Formulating the story through connecting the categories.
- 13. Step 1: Developing and Applying Step 1: Developing and Applying Codes Codes There are three types of coding: Coding can be done manually or using qualitative data analysis software such as NVivo, Atlas ti 6.0, Hyper RESEARCH 2.8, Max QDA and others.
- 14. Step 1: Developing and Applying Step 1: Developing and Applying Codes Codes https://www.youtube.com/watch?v=6_gZuEm3Op0
- 15. Step 2: Identifying themes, patterns Step 2: Identifying themes, patterns and relationships. and relationships. most popular and effective methods of qualitative data interpretation Word and phrase repetitions – scanning primary data for words and phrases most commonly used by respondents, as well as, words and phrases used with unusual emotions;
- 16. Step 2: Identifying themes, patterns Step 2: Identifying themes, patterns and relationships. and relationships. most popular and effective methods of qualitative data interpretation Primary and secondary data comparisons – comparing the findings of interview/focus group/observation/any other qualitative data collection method with the findings of the literature review and discussing differences between them;
- 17. Step 2: Identifying themes, patterns Step 2: Identifying themes, patterns and relationships. and relationships. most popular and effective methods of qualitative data interpretation Search for missing information – discussions about which aspects of the issue was not mentioned by respondents, although you expected them to be mentioned;
- 18. Step 2: Identifying themes, patterns Step 2: Identifying themes, patterns and relationships. and relationships. most popular and effective methods of qualitative data interpretation Metaphors and analogues – comparing primary research findings to phenomena from a different area and discussing similarities and differences.
- 19. Step 3: Summarizing the data Step 3: Summarizing the data
- 20. DATA DATA ANALYSIS METHODS ANALYSIS METHODS Research Project Finding the Answers to the Research Questions-Quantitative
- 21. Quantitative or Qualitative Quantitative or Qualitative data data Identify whether the following statements is a quantitative or qualitative data
- 22. Quantitative or Qualitative Quantitative or Qualitative data data It is warm outside
- 23. Quantitative or Qualitative Quantitative or Qualitative data data The cup had a mass of 454 grams
- 24. Quantitative or Qualitative Quantitative or Qualitative data data The cake recipe requires 3 cups of flour
- 25. Quantitative or Qualitative Quantitative or Qualitative data data The shelf life of the Papaya Pickle is 3 days
- 26. Quantitative or Qualitative Quantitative or Qualitative data data The cloth of our table napkin feels rough
- 27. Quantitative or Qualitative Quantitative or Qualitative data data One of the welding rod measures 9 cm long.
- 28. Quantitative or Qualitative Quantitative or Qualitative data data The temperature of the oven increased by 8°C.
- 29. Quantitative or Qualitative Quantitative or Qualitative data data Opening the wine bottle makes a loud pop sound.
- 30. Quantitative or Qualitative Quantitative or Qualitative data data The pastry in the canteen smells sweet.
- 31. Quantitative or Qualitative Quantitative or Qualitative data data Leonora earned 95% on her Math quiz.
- 32. Types of Data Types of Data
- 33. Types of Data Types of Data
- 34. Types of Data Types of Data Qualitative or Categorical Data is data that can’t be measured or counted in the form of numbers. These types of data are sorted by category, not by number. Qualitative or Categorical Gender ( Male, Female) Hair color ( Black, Brown, Gray, etc) Nationality (Indian, American, Chinese, etc) These data consist of audio, images, symbols, or text. The gender of a person, i.e., male, female, or others, is qualitative data. Example
- 35. Types of Data Types of Data Nominal values represent discrete units and are used to label variables that have no quantitative value. Just think of them as “labels.” Note that nominal data that has no order. Therefore, if you would change the order of its values, the meaning would not change. Nominal Data Example Qualitative or Categorical
- 36. Types of Data Types of Data Ordinal data have natural ordering where a number is present in some kind of order by their position on the scale. These data are used for observation like customer satisfaction, happiness, etc., but we can’t do any arithmetical tasks on them. Ordinal Data Example Qualitative or Categorical
- 37. Types of Data Types of Data Quantitative data is also known as numerical data which represents the numerical value (i.e., how much, how often, how many). Numerical data gives information about the quantities of a specific thing. Quantitative data can be used for statistical manipulation. Quantitative or numerical Example Height or weight of a person or object Room Temperature Scores and Marks (Ex: 59, 80, 60, etc.) Time
- 38. Types of Data Types of Data Discrete data can take only discrete values. Discrete information contains only a finite number of possible values. Those values cannot be subdivided meaningfully. Here, things can be counted in whole numbers. Discrete Example Quantitative or numerical
- 39. Types of Data Types of Data Continuous data represent measurements and therefore their values can’t be counted but they can be measured. An example would be the height of a person, which you can describe by using intervals on the real number line. Continuous Example Quantitative or numerical
- 40. Types of Data Types of Data It represents ordered data that is measured along a numerical scale with equal distances between the adjacent units. These equal distances are also referred to as intervals. So a variable contains interval data if it has ordered numeric values with the exact differences known between them. Interval Example Quantitative or numerical
- 41. Types of Data Types of Data Like Interval data, ratio data are also ordered with the same difference between the individual units. However, they also have a meaningful zero so they cannot take negative values. Ratio Example Quantitative or numerical The temperature on a Kelvin scale (0 degrees represent the total absence of thermal energy) Height ( zero is the starting point) weight, length
- 42. Analyzing Quantitative Data Analyzing Quantitative Data 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.
- 43. 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… Step 1: Data Validation Step 1: Data Validation Step 1: Data Validation
- 44. Typically, large data sets include errors. For example, respondents may fill the 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 Step 2: Data Editing Step 2: Data Editing
- 45. This is one of the most important steps in data preparation. It refers to grouping and assigning values to responses from the survey. Step 3: Data Coding Step 3: Data Coding
- 46. 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 respondents 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.) Step 3: Data Coding Step 3: Data Coding
- 47. Quantitative Data Analysis Quantitative Data Analysis Methods 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.
- 48. Descriptive 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.
- 49. Descriptive Statistics Descriptive Statistics 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.
- 50. Descriptive Vs. Inferential Descriptive Vs. Inferential