One of the most “practical” applications of data science is quantitative research. Findings from this research methodology can be utilized to resolve business problems, boost efficiency, promote customer satisfaction, and even identify flavor preferences for new products. This article goes into great detail on the subject. However, you can visit the Data science course in Delhi, to learn more about the trending tools and techniques used by data scientists.
What is Quantitative research?
In quantitative research, a question is addressed by being quantified. The first of the study’s 6 features are;
- Information is analyzed using numbers.
- It attempts to be impartial.
- Data can be quantified and measured.
- Variables can be used to illustrate complex problems.
- Statistics can be used to analyze data.
- Results can be compared, generalized, or summed up.
To generate an answer, numerical data is collected or generated and then subjected to mathematical, statistical, or computational analysis. This procedure makes non-numerical variables like behaviors, attitudes, opinions, etc., measurable. Comparatively, qualitative research works with observable data, making it impossible to measure mathematically.
Results obtained using this strategy are thought to be unbiased and reasonable. Findings could be extrapolated to the larger population if proper, relevant sampling techniques were used.
The purpose of this study is conclusive since it quantifies the problem under consideration and provides statistical results to measure its impact on society.
What is the purpose of quantitative research?
Quantitative research is used in many disciplines, including economics, sociology, psychology, health, political science (voter surveys), and more. But, it’s most frequently linked to market research, including product testing, A/B testing, Net-promoter scores, and customer satisfaction surveys.
For instance, a chocolate company looking to expand its market reach could conduct the study listed below:
- Taste test: Does the current recipe adhere to regional preferences or require revision? This choice may be guided by statistical significance between the competing recipes.
- Price structures: Data scientists can use mathematical models to predict purchase intent in relation to different price points.
- Ad Status: What is the brand or messaging recall after one minute, two days, or seven days, each ad variant?
Quantitative Research Methods
Primary and secondary data collection comprise this technique. Whereas primary data is acquired directly by a data scientist or market researcher, secondary data is information gathered by a third party. Even though secondary data might provide useful context, most firms favor working with primary data. This can be divided into 4 categories:
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Survey Research
The most common tool employed in this process is still surveys. In order to analyze the responses, a target group is asked a series of structured questions that quantify the responses. Online surveys have grown in popularity as a result of their convenience, reduced cost, and quick data gathering compared to traditional face-to-face or telephone surveys. Survey research is used to measure abstract notions like attitudes or behaviors about a topic, brand, or other things.
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Correlational Research
The goal of correlational research is to establish the type and extent of the relationship between two interconnected variables, as well as how they vary and affect one another. Understanding the link between stress and income is one example of this.
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Experimental Research
Statistical analysis is used in experimental research to support or refute a notion. It must have a hypothesis and relevant variables that can be controlled, calculated, measured, and compared to closely resemble scientific research design. An illustration demonstrates the truth or falsity of the following claim: ‘Culturally diverse teams may solve problems more quickly than homogeneous ones,’ research shows.
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Causal Comparative Research
Establishing cause-and-effect between two or more interdependent variables typically involves causal comparative investigation. It examines whether and how certain variables change when subjected to the same effect. It is not necessary for the variables to already have a known or established relationship. The effects of a sugar tax in Toronto would serve as an illustration.
So this was a quick overview of quantitative research you need to know as a data scientist. If you are an aspiring researcher or data scientist you can visit Learnbay data science courses for more detail.
