Data analysis and interpretation

1. Organization and data cleaning:

Ø  Organize data into a logical and manageable structure.

Ø  Clean data to remove errors, inconsistencies, and outliers.

Ø  Code categorical variables for analysis.

2. Descriptive analysis:

Ø  Calculate basic statistical measures such as mean, median, mode, frequency, etc.

Ø  Create graphs and tables to visualize the distribution of data.

Ø  Identify patterns and trends in the data.

3. Inferential analysis:

Ø  Perform statistical tests to determine if there are significant relationships between the variables.

Ø  Carefully interpret test results, considering effect size and statistical significance.

4. Interpretation of results:

Ø   Based on the context and theoretical framework of the research.

Ø  Answer the research questions and draw conclusions about the problem.

Ø  Discuss limitations and implications for practice and future research.

For example, qualitative analysis might identify emerging themes in interviews, while quantitative analysis would calculate descriptive statistics or conduct hypothesis testing to examine differences between groups.


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Research questions and Objectives

Results

Action plan and implementation