# 07/20 - Data Analysis and Interpretation

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07/20 - Data Analysis and Interpretation

## 1. Qualitative Analysis

### 1.1. Things to do

1.1.1. Finding categories and making connetions

1.1.2. Combining the evidence

1.1.3. Looking for themes and patterns

1.1.4. looking at the type of language used

## 2. Analysing Data

### 2.1. 1. familiarise yourself with data

2.1.1. Read through it and start noting themes

2.1.2. Examples of reoccuring themes

2.1.2.1. Attitudes

2.1.2.2. Behaviours

2.1.2.3. Motivations

2.1.2.4. Views/opinions

2.1.3. Patterns

2.1.3.1. Frequencies

2.1.3.2. Magnitudes

2.1.3.2.1. Major occurences

2.1.3.3. Structures

2.1.3.3.1. Topics outlining

2.1.3.4. Processes

2.1.3.5. Causes

2.1.3.6. Consequences

### 2.2. 2. Categories the data

2.2.1. Devise a conceptual framework/index

2.2.2. List themes

2.2.2.1. Journal

2.2.2.2. Post-it notes

2.2.4. Aim is to create sub-themes of concepts within larger categories

2.2.4.1. It's worth having an "other" category for themes that don't fit.

2.2.5. CODE and DEFINE categories

### 2.3. 3. Code the data

2.3.1. Place an index next to the data

### 2.4. 4. Sort the data

2.4.1. Focus on categories so you can look at the detail of each category

2.4.2. Thematic sets

### 2.5. 5. Summarise and synthesis the data

2.5.1. Reduce the data and highlight relationships

## 4. Data

### 4.1. Types of Data

4.1.1. Ordinal Data

4.1.1.1. Data in order

4.1.1.2. eg. Unsatisfactory - Excellent

4.1.2. Nominal Data

4.1.2.1. eg. Male/Female

4.1.2.2. Used to compare groups/populations

### 4.2. Likert Scales

4.2.1. Needs to have order and equal value

### 4.3. 3 Steps

4.3.1. 1. Present

4.3.2. 2. Describe

4.3.2.1. In describing, explain the most indicative result

4.3.2.2. The mean is usually the most indicative

4.3.3. 3. Analyse

4.3.3.1. Make Meaning of the data

### 4.4. Averages

4.4.1. Mean

4.4.1.1. Parametric

4.4.1.1.1. Uses the bell curve

4.4.2. Median

4.4.2.1. Middle score in the rankings

4.4.3. Mode

4.4.3.1. Most common score