Data

• Data Analysis


  1. Ordering

  2. Categorizing

  3. Manipulating

  4. Summarizing data

  
• Data Analysis in Qualitative

  Process of systematically searching and arranging the interview transcripts, observation notes or other non-textual materials to increase the understanding of the study
• Data Analysis in Quantitative

  Process of analyzing data that is number-based or data that can easily be converted into numbers
• Qualitative Data Analysis Methods


  • Content Analysis
  • Narrative Analysis
  • Discourse Analysis
  • Grounded Theory
  • Thematic Analysis

  
• Quantitative Data Analysis Methods

  • Descriptive - Frequencies and Percentage
  • Mean
  • Standard Deviation

  Inferential - Correlation/Pearson's R

  • T-Test
  • Anova
• Qualitative Data


  Words, observations, pictures, and symbols
• Qualitative Data Analysis


  Processes and procedures used to analyze the data and provide some level of explanation, understanding, or interpretation
• Purpose of Qualitative Data Analysis

  To produce findings. The Data Collection process is not an end in itself. The culminating activities are analysis, interpretation, and presentation of findings
• Challenges in Qualitative Data Analysis

  • To make sense of massive amounts of data
  • Reduce the volume of information
  • Identify significant patterns
  • Construct a framework for communicating the essence of what the data reveal
• Qualitative Data Analysis Steps

  1. Getting familiar with the data

  2. Revisiting research objectives

  3. Developing a framework

  4. Identifying patterns and connections
• Quantitative Data Analysis Steps

  1. Coding

  2. Categorization

  3. Thematic Presentation

  4. Interpretation
• Data Preparation Steps


  1. Data Validation

  2. Data Editing

  3. Data Coding
• Descriptive Statistics


  First level of analysis that helps researchers summarize the data
• Descriptive Statistics


  • Mean
  • Median
  • Mode
  • Percentage
  • Frequency
  • Range
• Inferential Statistics


  Branch of statistics that focuses on conclusions, generalizations, predictions, interpretations, hypotheses, and the like

  
• Types of Inferential Statistics

  • Parametric test
  • Nonparametric test
  • Shapiro-Wilk test
  • Central Limit Theorem
  • Variance
  • Standard Deviation
  • Alpha level
  • P-value

  
• Bivariate Analysis


  Analysis of two variables (independent and dependent variables)
• Multivariate Analysis


  Analysis of multiple relations between multiple variables
• Inferential Statistical Tests


  • T-test
  • ANOVA (One way or Two way)
  • Pearson product-moment correlation (Pearson's r, r or R)

  
• Pearson's r


  Parametric statistical method used for determining whether there is a linear relationship between variables
• Possible Outcomes of Pearson's r


  • Positive correlation
  • Negative correlation
  • No correlation
• Data Interpretation


  Process of reviewing data through predefined processes to assign meaning and arrive at relevant conclusions

  
• Qualitative Data Interpretation


  Used to analyze qualitative/categorical data that is non-numerical in nature
• Types of Qualitative Data

  • Nominal data
  • Ordinal data
• Nominal Data


  Used to label variables without any quantitative value
• Ordinal Data


  Data that follows a natural order
• Quantitative Data Interpretation


  Used to analyze quantitative/numerical data
• Types of Quantitative Data

  • Discrete data
  • Continuous data - Interval data
  • Continuous data - Ratio data

  
• Discrete Data


  Count involving integers with a limited number of possible values
• Continuous Data


  Information that can be divided into finer levels and have almost any numeric value
• Ratio Data


  Quantitative data with an equal and definitive ratio between each data point and an absolute "zero"
• Interval Data


  Numerical data with standardized and meaningful differences between points, but no meaningful zero
• Data Visualization


  Integral to creating a readable and understandable summary of a dataset using graphs and charts

  
• Graph


  Pictorial representation of data in an organized manner, formed from various data points representing relationships
• Chart


  Representation of datasets to make the information more understandable
• Types of Data Visualization

  • Bar Chart/Graph
  • Pie Chart
  • Line Graph or Chart
  • Textual Presentation
• Bar Chart/Graph


  • Summarizes a large amount of data in an understandable form
  • Easily accessible to a wide audience
• Pie Chart


  • Summarizes data into a visually appealing form
  • Quite simple compared to many graph types
• Line Graph or Chart


  • Helps in studying data trends over a period of time
  • Easy to read and plot
• Textual presentations use words, statements or paragraphs with numerals, numbers or measurements to describe data