How do you compare quantitative, qualitative, and mixed data?

Quantitative data is excellent to do all the things already discussed by previous contributors. I think its also important to consider who you ask as well as what you ask. Your research is only as good as your methodology.

The main advantage of quantitative data resides in its ease of collection and analysis. It is relatively simple to collect quantitative data using online or electronic forms, and the answers are straightforward and easy to store for analysis. However, you have to be very careful in how you word your questions and what values you provide as options for answers. If you ensure that your data is solid and comprehensive, you will then have many ways to analyze it. Here, the power of SQL, spreadsheets, or more advanced programs such as R, SPSS, can help you develop profound insights and visualizations to make your research truly speak.

Quantitative data, as mentioned in the article, are numerical and objective, making them suitable for statistical analysis. They provide precise measurements and allow researchers to draw conclusions based on mathematical models and statistical tests.

Quantitative data is parhaps easy to understand but also quite easy to get lost into. Output from this kind of data is what most people are familiar with. Headlines with numbers usually seems the most click-baity. However, it must be noted that almost all quantitative data output is built on a number of assumptions, limitations, and caveats. Always keep a pinch of salt handy when you try to summarize quantitative data. It makes the most sense and is the most useful with background knowledge and understanding of the context it aims to describe.

Quantitative data are defined by numbers, which can be useful, specifically in the social sciences, when considering larger populations or making comparisons among groups. Limitations of quantitative data include the fact that numbers can only tell us so much, and removes the "human" elements that qualitative research can capture.

Quantitative data, as mentioned in the article, are numerical and objective, making them suitable for statistical analysis. They provide precise measurements and allow researchers to draw conclusions based on mathematical models and statistical tests.

Qualitative data is rich and meaningful, and can help explain some of the reasons behind the quantitative data which we don't see simply with numbers. It is not as generalizable, and requires a smaller population. Because of this, we can focus in more on whole narratives and lived experiences.

Qualitative data is also extremely relevant to capture how programs/interventions are being perceived by key stakeholders. This can be program beneficiaries but also program implementers, managers, funders, and so on. While regular and open communication are important, analyzing formal qualitative data at key intervals can provide important insights and help inform the future of the program.

Mixed methods research plays a pivotal role in explaining certain areas of quantitative research that require a deeper exploration into the "why" or other complex aspects. When dealing with mixed data, which involves both quantitative and qualitative information, researchers can gain a more comprehensive understanding of their research questions or problems. By combining various data types, they can fill gaps, validate findings, and strengthen the overall credibility of their results. Nonetheless, navigating mixed data requires careful consideration in terms of study design, execution, and analysis to effectively address any challenges that may arise during the research process.

I agree with Jennie B. Qualitative data will provide reasoning for the quantitative data that you have collected. Mixed data provides the bigger picture and substantiate the data that has been collected.

Mixed methods research combines qualitative and quantitative data analyses on the same subject, strengthening the validity of the conclusion of the investigation. They are mostly used in the behavioral, health, and social sciences. Data can be assessed through various mixed method designs based on when you collect and how you analyze the data. These methods are less used than standalone qualitative or quantitative data analyses and when not fully integrated, they may put your data at risk for bias in the interpretation stage.

Mixed data is useful in terms of creating more complete data with numbers, and then focusing in on some of those numbers to explore some of the more social factors within the numbers. It can be very useful to use qualitative data to add context to the quantitative data.

Quantitative data is great for tracking trends and prioritizing needs. Qualitative data helps generate empathy and give a more complete picture than numbers alone. I think a mix of both is usually ideal, but when $ is involved, quantitative data is easier to present in visual formats, and may be a more efficient way to make decisions. Humans (and therefore shareholders) are complex and always changing, so even when focusing on graphs and similar metrics, the "why" needs to be examined thoughtfully.

OK. English:) Quantity- numeric data -Statistics - involves a sample of at least 100 subjects - Ie A Study MEASURING THE SUICIDE RATE OF BIPOLAR PATIENTS DO TO A SPECIFIC DRUG INDUCED SIDE EFFECT - Quantitative data answers the following: - HOW MANY - how much - how often - measures statistical significance -exact NUMBERS= TRUTH ******- can prove or disprove a theory QUALITATIVE DATA - non numerical - descriptive - anecdotal (telling stories) -No specific sample is required - not exact Ie. A Study DESCRIBING the effects the patient experiencing do to drug induced AKATHISIA Qualitative data can answer the following questions - how? - what? - who? - where? - feelings Qualitative data may form A Hypothesis.

Quantitative data analysis had added to the dimension such variables which were primarily not quantifiable e.g Pain is usually ordinal type of data but when it is utilised by way of quantitative method in the form of VAS , a scale it can quantify the pain thresholds too. Similar is the subset of Quality of Life tools. A very subjective variable but still can be quantified through the standardized tool of QoL assessment through quantitative tools. The only focus must be researcher and data analyst must be , What, How and Why of the research objective ....then one can decide upon the study design and finally type of study....to address those concern....

The success of a grant-funded research project is determined by various factors, starting from the design of the project, encompassing both technical and financial aspects. This includes achieving the project goals, meeting milestones with high-quality results, and making a significant impact through effective dissemination to relevant communities. These aspects collectively serve as indicators of success for a grant-funded project.

Qualitative data can be used to add more context to quantitative data and vice versa. Numbers can confirm a qualitative finding.

Qualitative opinions are often based on quantitative measurements aligned to lived experience - EFQM performance reviews treat both quantitative and qualitative data as equals - with both subject to group analysis and democratic decision making.

To add to the already great insights provided, I think it should be considered that rigorous and actionable research, be it quantitative or qualitative, requires fair bit of resources. It can add a lot of value to programmatic goals and objectives, provided it is adequately planned and budgeted for.