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Research Methods

This libguide provides tips on how to choose research methods, whether it be quantitative, qualitative, or mixed methods. It highlights the strengths and limitations of each method, and provides guidance on how to identify the appropriate method for a giv

Mixed Methods

Mixed methods research combines both quantitative and qualitative research methods to collect and analyze data. This method is suitable for research questions that require a comprehensive understanding of a phenomenon. Here are some tips for choosing mixed methods research:

Identify the research question: Determine whether your research question requires a mixed methods approach. Mixed methods research is ideal for research questions that require a combination of numerical and non-numerical data to provide a more comprehensive understanding of the phenomenon being studied.

Choose the appropriate data collection methods: Select data collection methods that allow you to collect both numerical and non-numerical data. Common data collection methods for mixed methods research include surveys, interviews, focus groups, observations, and document analysis. It's important to select methods that are appropriate for the research question and the participants' characteristics, as well as the feasibility, reliability, and validity of the methods.

Determine the sample size: Decide on the sample size that is needed to produce statistically significant and rich data. The sample size should be determined based on the research question and the saturation point for qualitative data, as well as the power analysis for quantitative data.

Analyze data: Analyze the data using both quantitative and qualitative methods. The analysis of quantitative data involves using statistical analysis to identify patterns and relationships between variables. The analysis of qualitative data involves identifying themes and patterns in the data. The integration of the quantitative and qualitative data involves using different techniques to combine the data and identify patterns and relationships that may not have been identified using one method alone.