How do you decide when to use quantitative or qualitative data?

Quantitative data gives you information about tendencies and behavior in a controlled population. It is good for when you need to give swift and reproducible data. In population research it characterizes your sample, and can offer possible quantifiable predictors.

Quantitative investing reduces emotional and cognitive biases, which are known to lead to suboptimal decisions and portfolio volatility, as highlighted by behavioral finance research. Quantitative models follow predefined rules and data-driven strategies, minimizing emotional bias and susceptibility to cognitive biases like loss aversion. These models can also capitalize on market behavioral biases, identifying overreactions or underreactions for profit. In essence, quantitative investing aligns with rational decision-making principles, delivering consistent and objective outcomes in emotionally charged financial markets.

I use quantitative data to support evidentiary facts and impact the reader; I also use this data to weaken counter arguments helping ignite critical thought/analysis in the reader. The data isn’t always large source because everyday data can also get to the point, i.e. a consumer dispute, where “many” calls were made to resolve an issue is a statement showing diligence whereas showing that “10 daily calls, over a period of two business weeks went unanswered” and providing call logs as further evidence makes a stronger, more impactful statement. It often leads to a better response, sets the pace for quicker resolutions allowing investment in troubleshooting, correction/retraining, with knowledge that it may be a forming negative pattern.

Quantitative data is quick and easy to gather information but unfortunately, it reduces the space to discuss the everyday lived paradoxes, contradictions and ongoing struggles within ourselves. I find it very limiting and reductionist. It aims at reducing reality to a few expressions and words and leaves behind the aspect of thinking and negotiations that we do as thoughtful and versatile personalities.

When facing the need for swift/large-scale data collection and dealing with easily measurable variables, a quantitative approach becomes the go-to choice. Surveys and questionnaires are powerful tools for this, allowing you to efficiently gather large volumes of data. Moreover, suppose you can operationalize (turning abstract conceptual ideas into measurable observations) the concepts, phenomena, or variables under study. In that case, a quantitative approach offers a streamlined path for data analysis and helps eliminate potential biases (inter-rater bias especially) in responses that could skew your findings.

Using Qualitative data can sometimes provide valuable feedback that quantitative data is unable to depict. Sometimes industry trends are not immediately represented in financial statements and factors like quality of management generally cannot be quantified while playing a crucial role in the businesses success. Qualitative tactics such as porters five forces can give you a better idea of how a company is positioned among its competitors and within its industry. Typically, analysts will use qualitative data as a prerequisite to begin quantitative analysis.

Investing with qualitative data involves considering non-measurable information like management quality, industry context, and market sentiment. Qualitative data provides valuable insights that quantitative metrics may miss. It's used for assessing management competence, understanding market sentiment, and gauging factors like culture and ethics. However, it can be subjective and less standardized, so investors often combine qualitative and quantitative data for a more comprehensive investment approach.

Qualitative data can be very rich and informative. However, to collect the most accurate and valuable data, it is important for the researcher to embody empathy, open-mindedness, active listening, analytical skills, and ethical consideration. The researcher needs to connect with the participants and capture nuanced insights, and maintain data integrity. In my experience collecting qualitative data, I had to designate a lot of time after each session to really reflect, learn, and be open to improve how I show up as a researcher. There is always room for improvement in this journey.

I find helpful to use qualitative data as a way to corroborate or build on quantitative aspects of an article or story. Interviews and case studies, for instance, help to illustrate how issues being discussed can materialise and that, in turn, helps the reader to better visualise the topic. It is also a way to make the narrative more interesting and keep the audience engage in presentations and bring a perhaps seemingly abstract topic closer to their reality.

The qualitative research involves detailed and comprehensive parts of the discussions, interviews, information, which need more time to read and analyze the information. This is interesting part of the research which help together with the numeric data to use it for pushing the specific cause and social rights movements.

Mixing quantitative and qualitative data in investing offers a more comprehensive analysis. It helps identify and mitigate various risks, including those related to management and market sentiment. Diversifying across both approaches can enhance portfolio performance and reduce reliance on a single method, leading to better risk adjusted returns over the long-term.

Mixed methods data and analysis are my personal favorite to utilize as a Social Scientist. Having the ability to translate data into understandable language to the public is very important; utilizing both forms of data can help with this. Quantitative statistics help to back qualitative claims, and vice versa.

The mixed method offers the best of both worlds. You have the rigorous quantitative studies offer, and you have the rich information qualitative studies offer. It can be done in phases (e.g., first qualitative to explore the topic, followed by a quantitative study to test hypotheses) or done together (e.g., a mostly quantitative study adding a few qualitative questions to seek additional information). One of my studies (Shum et al., 2019) uses this approach to show the nature, cause, and effects of prosocial rule-breaking. While useful, the mixed method is very time-consuming to conduct and usually makes it more difficult to explain in detail within the word limits of a research article.

Quantitative data is used when - 1. we want to test the hypothesis 2. Understand the correlation and causation between different variables Qualitative data is used when - 1. We want to understand the different experiences of people. 2. Want to have case studies and want to cover exceptions in data .

I am most convinced by quantitative data when making decisions, but it's also important to bear in mind how the data will be used in decision-making. For example, when evaluating market returns, it's important to think about the appropriate time horizon for the relevant investment decision. Moreover, it's helpful to bear in mind behavioral biases—such as loss aversion, which observes that we tend to feel the pain of losses about twice as powerfully as we enjoy the pleasure of gains—that can impact an investor's ability to stick with a certain investment strategy. Applying a mixed methodology, and focusing on language that sets good expectations and addresses possible shortcomings, helps to improve the usefulness of the analysis.

One of the best pieces of advice I received was from a luminary in my field at the very first conference I attended just after starting my Ph.D. program. I had just presented what would end up being the first study of my dissertation and was looking for advice on what sort of follow-up studies might make sense. We were chatting and I told this well-known researcher that I only knew how to run experiments, so the follow-ups would have to be experiments. She replied (paraphrasing here) that you should never let your methodology determine your research question, and that your research question should determine your methodology. This may seem obvious, but, as my dad likes to say, “if you are a hammer, everything is a nail”.

One thing I would add is researcher's ontological position. This would guide to articulate the research methodology, whether to choose qualitative, quantitative or mixed method. Researchers should understand their ontological position before starting writing their research project, in particular those from social science discipline, because it gives the answer to why to choose a particular method not another. Then guides analysis, outcome and perhaps future research.

I want to emphasize how crucial it is to create dummy tables before you begin the study. Many people forget to do this when using either of the research methodologies, which usually leads to either excessive data collection?or poor data collection, both of which prevent you from going back and completing the study again. Therefore, based on your objectives, have a clear understanding of what, why, and how you are actually measuring (qualitative) or using techniques (quantitative).

Successful quantitative investing requires a deep understanding of mathematical modeling, data analysis, and risk management. It's essential to adapt quantitative models to changing market conditions, and continuously refine investment strategies to remain competitive and achieve investment goals.

Qualitative data helps explain the “whys” of your quantitative findings in social science. For example, quantitative data can explain the frequency of a behavior, and qualitative data helps flesh out the reasoning. Often, qualitative data is built into quantitative studies post-hoc to explain an unexpected finding. Consider incorporating qualitative or mixed methods into quant-heavy studies from the design stages to ensure rigor and nuance. It can be more costly to conduct qualitative research (lots of person hours!), but even a small, well-designed qualitative arm can add great depth to your research.

In Weber et al. (2023), we examined firms taking political stances. We ran experimental studies and found there are so many options in the marketplace to switch to, that consumers mostly dissociate from brands they disagree with rather than buy from those they agree with. But, we needed to figure out how to show this effect in real life. So, we recorded 31 firms either taking a political stance or CSR action & the dates of the actions. We then collected all tweets mentioning each firm 7-days before & after (n = ~230,000) & ran textual analysis on the tweets, comparing positivity & negativity before v. after. Results replicated: CSR firms had more positive language on social media after the act while political firms had more negativity.

In research projects, data selection is shaped by the nature of the research objectives. For instance, projects aiming to establish correlations between variables typically employ quantitative data and statistical tests. Those exploring concepts may favor qualitative data and thematic analysis. Assessing population needs may use qualitative data via interviews, complemented by quantitative data for quantifying preferences through surveys. Evaluating program impacts often necessitates mixed methods, combining quantitative data for measuring changes with qualitative data for understanding underlying mechanisms. Tailoring data selection to research goals ensures robust and insightful findings.

A few decades ago, I did (separate) studies on adult migraine sufferers and adolescents with hypertension. All subjects in each case were already confirmed as suffering from migraine headache (in the headache studies) or adolescent hypertensives (who were already in other studies using the same cohort). My innovation was in recruiting subjects for the control groups. All target S's were asked to bring along a friend to the lab who would undergo the same psychophysiological or cognitive testing. As I had expected, the control subjects were of the same ages and SES status as the target S's, where the only difference between groups was the presence or absence of the medical condition under study.

Consider what and where your field is. In my discipline, we traditionally conduct fieldwork, but nowadays, definitions of what the field can be very diverse. It might not be fixed in place or limited to a set group of people. So once you figure out what your "field" is, you can work out, with which methods you can best capture and do justice to that "field" and the individuals who act in it. If they act in different places, you need a multi-sited research design, and if they are excluded from digital media for one reason or another, you need to choose methods that allow them to participate in the research. Even within qualitative research there are many different choices of methods that need to be matched well to the given community.

Choosing between quantitative and qualitative data depends on your research goals. If you aim to measure, quantify, and establish statistical relationships, opt for quantitative data. It's great for surveys and experiments. Qualitative data, on the other hand, is ideal for exploring complex phenomena, understanding context, and capturing people's experiences and perspectives. The choice should align with what you want to learn and the methods that best suit your research objectives. You can also use both for a comprehensive understanding.

Data choices are a fundamental aspect of quantitative investing, and the quality and appropriateness of data can significantly impact the success of the investment strategy. Regularly reviewing and refining the data choices help to gain insights and adapt to changing market dynamics.

It all depends on the topic of your research. However qualitative data should be the main approach and if your topic demands quantitative representation then you give quantitative data by keeping in mind that it is not exceeding your article limit and should be clear and representable

- I have to give the all-purpose researcher answer to every question: “It depends.” Mainly, it depends on (1) the nature of the question you are trying to answer, and (2) what will provide you with the best answer to your question. - The next part of the answer is that it depends on how quantifiable are the causal factors of the answer. Some questions are “physics” while others are “metaphysics.” You have to derive your answers accordingly.

As a researcher you must be open to the changing world of study. You may go in thinking one form of data collecting is better, but start to realize the other form may be best, and that is okay. Adaptability is essential to strong research.

In our rapidly evolving technological landscape, it's imperative to remain agile and seek innovative ways to streamline the entire research process. Embracing technologies like Artificial Intelligence and Machine Learning can revolutionize various aspects of research, from identifying suitable study subjects to optimizing data collection, automating data analysis, etc. These tools can not only enhance efficiency but also offer novel insights and methodologies that were previously unattainable. Therefore, staying attuned to emerging technologies and their potential applications in research can be a transformative factor in making informed choices, ensuring that your research remains at the cutting edge of your field.

I think that the AI written article is better than most of the contributions from community! No offence to any of the contributors. It's interesting that AI performs so well in this task.