We use quantitative analysis because raw data isn’t always decipherable so quantitative analysts will rearrange data into visual representations that communicate meanings and patterns. Various forms of predictive modeling may have limits, but they can still bring more accuracy to the decision-making process. While quantitative modeling is a valuable financial tool, its use stretches into all data applications.
7 Characteristics of Quantitative Research Methods
- Contains measurable results
- Uses standardized research instruments
- Assumes normal population distribution
- Presents data in tables, graphs or figures
- Uses repeatable methods
- Can predict outcomes
- Uses measuring devices
Why Use Quantitative Modeling?
With its ability to break down data sets, quantitative modeling becomes crucial for forecasting financial trends.
Making accurate predictions is essential within the world of finance, and quantitative modeling serves this very purpose. Methods like data clustering isolate certain variables, which enables researchers to spot specific patterns. For finance, this technique can expose illegal activity and create a safer marketplace.
The forecasting abilities of quantitative modeling are also relevant for tracking securities prices, customer demand and other reactions within financial markets. Within a global context, quantitative modeling can’t predict everything yet it remains a valuable field for calculating probabilities and helping businesses avoid unnecessary risks with the most up-to-date financial data.
What Are the Characteristics of Quantitative Research?
Due to the fact that quantitative research is a systemic approach, it always features the same core traits each time we apply it.
To ensure concrete results, quantitative research involves processes that are reliable and consistent. Each study focuses on measurable variables and wields proven research instruments. These instruments can range from something like a questionnaire to more complex tools like tested machine learning models.
Analysts also give experiments more objectivity by pursuing a normal population distribution, which requires analysts to assemble a large sample size and results in a more randomized data set. By following the characteristics of quantitative research, companies can develop accurate models that better inform their choices with more accurate data.
What Are the Tools of Quantitative Analysis?
Analysts have a range of options when it comes to representing data sets with quantitative models.
Depending on business needs, analysts can customize the modeling process to their needs. Although many types of graphs are excellent at revealing patterns in data, histograms are ideal for teams dealing with ranges. If the study grows to encompass multiple data sets, linear regressions can help determine whether a correlation exists between the sets.
In addition, technology is bringing even more capabilities to the quantitative modeling field. To make data sets more readable for wider audiences, analysts can wield dimensionality reduction to simplify information. More people can also learn how to generate their own models, thanks to Python-based tools like Streamlit. With convenient techniques and advanced tech, there are now numerous ways for researchers to conduct quantitative analysis.