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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