Data Analysis

• Data Analysis


  The process of ordering, categorizing, manipulating, and summarizing data to obtain answers to research questions. It is usually the first step taken towards data interpretation.
• Qualitative Data Analysis


  The process of systematically searching and arranging the interview transcripts, observation notes or other non-textual materials that the researcher accumulates to increase the understanding of the study.
• Quantitative Data Analysis


  The process of analyzing data that is number-based or data that can easily be converted into numbers.
• Steps in Basic Data Analysis

  1. Coding

  2. Categorization

  3. Thematic Presentation

  4. Interpretation
• Qualitative data consist of words, observations, pictures, and symbols.
• The purpose of Qualitative Data Analysis is to produce findings. The data collection process is not an end in itself.
• Challenges in Qualitative Data Analysis include making sense of massive amounts of data, reducing the volume of information, identifying significant patterns, and constructing a framework for communicating the essence of what the data reveal.
• Steps in Data Preparation and Basic Data Analysis

  1. Getting familiar with the data

  2. Revisiting research objectives

  3. Developing a framework

  4. Identifying patterns and connections
• Quantitative data consist of numbers.
• Quantitative Data Analysis refers to the processes and procedures that are used to analyze the data and provide some level or explanation, understanding, or interpretation.
• Descriptive statistics provide absolute numbers but do not explain the rationale or reasoning behind those numbers.
• Mean


  The numerical average of a set of values.
• Median


  The midpoint of a set of numerical values.
• Mode


  The 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.
• Inferential Statistics


  A branch of statistics that focuses on conclusions, generalizations, predictions, interpretations, hypotheses, and the like. There are a lot of hypotheses testing in this method of statistics that require complex and advanced mathematical operations.
• Types of Inferential Statistics

  • Parametric test
  • Nonparametric test
  • Shapiro-Wilk
  • Central Limit Theorem
• Variance


  Refers to how spread out the values are across the data set you are studying. It helps you find how close or not close the data to the mean.
• Standard Deviation


  The square root of the variance.
• Alpha level (Significance level)


  The probability value that must be reached before claiming that findings obtained are statistically significant (0.05/0.01/0.001).
• P-value

  The calculated probability that is compared to the alpha level.
• Types of Inferential Statistical Analysis

  • Bivariate Analysis
  • Multivariate Analysis
• Common Inferential Statistics Used In Research


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


  The hypothesis that states there is no significant difference or relationship between the variables being studied.
• Alternative Hypothesis


  The hypothesis that states there is a significant difference or relationship between the variables being studied.
• Hypothesis Testing Statistical Tests

  • Parametric Test
  • Non-parametric Test
  • Pearson's R
  • T-test (<30)
  • Z-test (>30)
  • ANOVA
  • Spearman's Rho
  • Mann-Whitney
  • Kruskal-Wallis
  • Chi-Square
• Researchers and data analysts may use available software to interpret data, such as Microsoft Excel.
• Pearson's r


  A parametric statistical method used for determining whether there is a linear relationship between variables.
• Possible outcomes after analyzing data using Pearson's r test


  • Positive correlation
  • Negative correlation
  • No correlation