It depends on what your underlying design is. Creswell and Plano-Clark (2018) list three basic designs: exploratory sequential (qual --> QUAN), explanatory sequential (QUAN --> qual), and convergent (QUAL + QUAN).

Of these the convergent design is the most and as far as I am concerned, the most problematic. The difficulty with this design is in integrating the qualitative and quantitative results, because too often, they are reported separately as if two separate studies were done.

As Prof. David L Morgan rightly noted, these are the three designs, and they also depend on the purpose of mixing the two types of methods, whether it is triangulation, complementarity(more towards concurrent design) or expansion, integration and development (more towards sequential design) where one method is followed by another(QUAN-->QUAL or QUAL-->QUANT), as shown by the professor above.

The more advanced version is multiphased mixed method research.

I usually advise my students to select either the sequential exploratory or the sequential explanatory mixed methods, depending on the research question. The convergent design is somewhat problematic because there might be qualitative themes that are not at all related to the quantitative findings; hence, the “mixed” methods become vague.

Mixed methodology is basically a time consuming technique or can say that is little complicated. However it works well in today’s time with advent of new technology to get more accurate results which give a more impressive and impactful solutions to the problems.

In such condition there should be an ample time line to collect the data, and once data is collected the researcher can start quantitative analysis..

Generally, as a suggestion when the researcher is using Mixed methodology the data collection frequency should be regular on time and the same should be analysed at those timelines to achieve more accurate results as situations and conditions can change the ideology drawn 1st time to second time.

Further, as a practice the researcher should apply top down and bottom up analysis techniques to analyse the data and results.

For example – Today many ecommerce platforms provide the deals to the customer basis the last search on their portal, but these platforms don’t know weather buyer is a male or female.

The sequential explanatory mixed-method approach involves collecting and analyzing quantitative data first, followed by qualitative data. This approach is typically used to explain or build upon initial quantitative findings with qualitative insights. Here are possible strategies for analyzing data in a sequential explanatory mixed-method study:

1. Quantitative Data Analysis

• Descriptive Statistics: Begin by summarizing the quantitative data using measures such as mean, median, mode, standard deviation, and frequency distributions. This provides a general overview of the data and identifies patterns.
• Inferential Statistics: Use techniques such as regression analysis, ANOVA, t-tests, or chi-square tests to identify relationships, differences, or predictions within the data. This helps in testing hypotheses and understanding the significance of the findings.
• Correlation Analysis: Analyze the strength and direction of relationships between variables using correlation coefficients (e.g., Pearson’s r or Spearman’s rho).
• Factor Analysis: Identify underlying factors or constructs that explain the patterns observed in the data. This is useful for reducing data dimensionality and identifying key variables.

2. Qualitative Data Collection and Analysis

• Purposeful Sampling: Based on the quantitative results, select specific cases or participants for qualitative data collection. This could involve interviews, focus groups, or case studies with individuals who represent different outcomes or experiences.
• Thematic Analysis: Analyze the qualitative data by identifying recurring themes or patterns that explain or expand upon the quantitative findings. Coding is a crucial part of this process.
• Content Analysis: Systematically categorize the content of qualitative data to quantify certain words, themes, or concepts. This is useful when linking qualitative insights back to quantitative data.
• Narrative Analysis: Explore individual stories or accounts in-depth to understand the context and nuances behind the quantitative results.

3. Integration of Quantitative and Qualitative Data

• Connecting Quantitative and Qualitative Phases: Use the results from the quantitative phase to inform the qualitative phase. For instance, if the quantitative data suggests a certain trend, the qualitative phase can explore why this trend exists.
• Sequential Data Analysis: After analyzing the qualitative data, revisit the quantitative results to see if the qualitative insights provide further explanation or require reevaluation of the initial findings.
• Interpretive Analysis: Combine findings from both phases in a narrative form to develop a comprehensive understanding. This involves interpreting how the qualitative data explains or adds depth to the quantitative results.
• Comparative Analysis: Compare and contrast the quantitative and qualitative findings to identify consistencies, contradictions, or new insights that emerged through the mixed-method approach.

4. Meta-Inference

• Integration of Findings: Synthesize the results from both quantitative and qualitative phases to draw overarching conclusions. This involves creating a coherent narrative that reflects how the qualitative data explains, elaborates, or challenges the quantitative findings.
• Developing Models or Frameworks: Use the integrated findings to develop theoretical models or conceptual frameworks that explain the phenomenon under study.
• Validity and Reliability Checks: Evaluate the credibility of the findings by considering how well the qualitative data supports the quantitative results and vice versa. Triangulation can also be used to validate the conclusions drawn from the mixed methods.

5. Reporting

• Sequential Reporting: Present the findings in the sequence they were collected and analyzed, starting with the quantitative results, followed by the qualitative insights, and then the integrated interpretation.
• Joint Display: Use tables, figures, or matrices to display how quantitative and qualitative data relate to each other, making it easier to see the connections between the different data types.

These strategies ensure that the data is thoroughly analyzed, with each phase informing and complementing the other, leading to a richer and more nuanced understanding of the research problem

Marten - Puyo When you cut and paste an answer from ChatGPT or other artificial intelligence, you should cite your source, just as you would in any other professional setting. Otherwise, people may mistake what you post as being your own ideas.