INQUIRIES

• The first stage of analyzing data, where the aim is to convert raw data into something meaningful and readable.

  Data preparation
• 3 Steps in Data Preparation

  1. Data Validation
  2. 2. Data Editing
  3. Data Coding
• 2 BRANCHES OF STATISTICS

  Descriptive and Inferential Statistics
• Descriptive statistics (also known as descriptive analysis) is the first level of analysis. It helps researchers summarize the data and find patterns.
• 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.
• Descriptive statistics are most helpful when the research is limited to the sample and does not need to be generalized to a larger population.
• •Inferential statistics is a branch of statistics that focuses on conclusions, generalizations, predictions, interpretations, hypotheses, and the like.
• Bivariate Analysis – analysis of two variables (independent and dependent variables)
•  Multivariate Analysis – analysis of multiple relations between multiple variables
• Parametric test ➢ distribution is known and based on a fixed set of parameters
• Nonparametric test ➢ distribution of population is not known, and parameters are not fixed
• Mean ➢ refers to the average score of a given set of values (sum of all/ divided by the total number of values).
• Variance ➢ refers to how spread out the values are across the data set you are studying. ➢ it helps you to find how close or not close the data to the mean
• Standard Deviation ➢ square root of the variance.
• Alpha level (also known as significance level) ➢ refers to the probability value that must be reached before claiming that findings obtained are statistically significant (0.05/0.01/0.001)
• P-value ➢ calculated probability that is compared to the alpha leve
• T-test? ➢ parametric statistical technique that tests the difference between two means
• T-test for two dependent samples

  ✓ same groups are highly related to each other or in other words SAME subjects (pre-test and post-test)
• T-test for two independent samples

  ✓ it tests the difference between data sets from two different groups such as in the case of control and treatment groups
• ANOVA

  ➢ statistical tool used for testing differences among the means of two or more groups of samples.

  ➢ it considers both the variation within and between the sample groups
• One-way ANOVA ✓ tests differences among groups concerning one variable.
• Two-way ANOVA

  ✓ used for determining the relationship between TWO independent nominal variables and ONE dependent interval or continuous variable.

  ✓ it finds out whether only one or both independent variables cause changes in the dependent variable
• Pearson’s r ➢is a parametric statistical method used for determining whether there is a linear relationship between variables.
• Three possible outcomes after analyzing data using Pearson’s r test: positive, negative and no correlation.
• Positive correlation- when the numerical value of one variable increases or decreases, the other variable increases or decreases as well
• Negative correlation- when the numerical value of one variable increases, the other variable decreases, and vice-versa.
• No correlation- when the two variable have no relationship with each other
• Scatter Plot ➢ Use to present the results of Pearson’s r visually. ➢ Best graph for presenting correlation between two variables
• Data Analysis

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

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

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

  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.
• Qualitative Data Analysis Methods:

  1. Content analysis
  2. Narrative analysis
  3. Grounded theory analysis
  4. Discourse analysis
• Steps in Analyzing Qualitative Data

  1. Getting familiar with the data
  2. Revisiting research objectives
  3. Developing a framework
  4. Identifying patterns and connections
• Analysis Considerations

  1. Words
  2. Context
  3. Frequency and intensity of comments
  4. Iteration
  5. Internal consistency