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Analysis of data is an important part of research. Data analysis can be used to identify patterns and trends that could not be discovered otherwise. The goal of data analysis is to draw meaningful conclusions from the data collected. Analysis of research data allow researchers to make decisions and generalizations about a population or phenomenon being studied. Data analysis involves the aggregation, organization, exploration, and interpretation of information in order to come up with meaningful conclusions or insights into a given topic.
How might these data be analyzed separately and then triangulated as the researcher presents the results?
Data can be analyzed separately using different methods such as descriptive statistics (e.g., mean, median, mode), correlational analyses (e.g., Pearson’s r correlation coefficient), t-tests/ANOVA tests (for comparing means between groups) as well as other more advanced techniques like structural equation modeling and factor analysis (for detecting latent variables). Each type of analytic technique provides a different way of examining relationships between variables in order to uncover important findings from the dataset at hand.
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