Waqua - integrating qualitative and quantitative results requires a mixed methods approach. If your study is no mixed methods, and your two data-sets are on the same topic - then you can 'compare and contrast' the findings at 'face value'. In terms of software, there are common tools i.e. SPSS (quant) and NVivo (qual).

In addition to separate data analysis softwares for qualitative (QDAS, Nvivo) and quantitative( SPSS, Stata, R), MAXQDA is used for mixed methods analysis.

There are quite a few approaches to integrating qualitative and quantitative data. I think you need to review some of the methodological literature to these approaches, including their pros and cons, and decide which approach is most appropriate for your research questions. Make sure you have an adequate justification for choosing a specific approach over others.

Broadly, there are the following approaches that I look:

1) Quantize qualitative data and integrate into the quantitative data

2) Keep quantitative and qualitative data separate and look for corroboration

3) Keep quantitative and qualitative data separate and look for conflicts

4) Use quantitative data as an anchor to guide qualitative analysis (i.e., qualitative interpretations focuses on the relationships and variables most statistically significant)

*There may be others but these are the most common I have came across.

Resources to read:

Bazeley, P. (2009). Integrating data analyses in mixed methods research. Journal of Mixed Methods Research

Fetters, M. D., Curry, L. A., & Creswell, J. W. (2013). Achieving integration in mixed methods designs—principles and practices. Health services research, 48(6pt2), 2134-2156.

Bryman, A. (2007). Barriers to integrating quantitative and qualitative research. Journal of mixed methods research, 1(1), 8-22.

Thanksss everyone for your kind contribution.

This is an importanr question and aspect to address to help enhance your work. As mentioned, examining texts that discuss mixed methods - an exploratory design for instance that used a qualitative approach first then was followed by a quantitative or vice versa with an explanatory design. You can look for methodological texts by Patricia Leavy, David Silverman, Elaine Wilson, John Creswell to name a few. Good luck.

Your quantitative and qualitative methodologies answer different research questions, all related to your research "problem." Quantitative data collection and analysis is about things that can be measured on some sort of scale. Qualitative data is about the experiences and perceptions of people.

When you use these together, the quantitative findings tell you the "what" of your study. What happened? What changed? The qualitative findings help you explain WHY it happened or changed.

As Dr. CohenMiller said, it is often a good process to do your quantitative data collection and analysis first, and use the findings to formulate your qualitative questions, so that the answers to the questions can help you explain the quantitative outcomes.

As for the comparison part; I do not think there is a software that helps to "compare" the analysis results because, firstly, quantitative analysis generates statistical measures which help you formulate a position while qualitative analysis generates inferences or judgements made in narrative form about the data. You can, however, use SPSS or Nvivo to analyse quantitative and qualitative data respectively. I recommend what Michael W. Marek and others have said.

Thank you for your question. There should be reasons to use a mixed-method comprising quantitative and qualitative tools; among these, the use of quantitive methods only cannot adequately answer the research question. The authors should show their skills in presenting the results from both methods, and in synthesizing knowledge as they discuss their findings.

Thankss eveyone for contribution

quantitative research for small sample size - SmartPLS 3.0 Software. Depends on ur research design.

In my opinion, u can do the data analysis in qualitative study as smilarly data analysis results as shown in quantitative study especially for discussion. More clear and easy to understand. ✍️