Quantitative research is considered one of the most ‘practical’ applications of Data Science fundamentals. Results gleaned from this research method can be used to solve business issues, improve customer satisfaction, increase efficiency and even determine flavour preferences for new concepts. This article discusses the topic in-depth.
What is Quantitative Research?
Quantitative research is the process of answering a question, by quantifying it. The six characteristics of this research are 1;
- Uses numbers to asses information
- It strives to be objective
- Data can be measured and quantified
- Complex problems can be represented using variables
- Data can be statistically analysed
- Findings can be generalised, compared or summarised
Math, Statistics or Computational analysis is applied to gathered or generated numerical data to produce an answer. Non-numerical variables like behaviours, attitudes, opinions etc. become measurable through this process. Comparatively, qualitative research works with observable data, making it impossible to measure numerically.
Findings from this method are considered unbiased and logical. Results can be generalised to the larger population provided correct, relevant sampling procedures have been followed.
What is Quantitative Research used for?
Economics, sociology, psychology, health, political science (voter polls) and multiple other fields use Quantitative research. However, it’s most commonly associated with Market Research; customer satisfaction surveys, Net-promoter score, A/B tests, Product tests etc.
For example, a chocolate brand trying to enter a new market can run the following research;
- Taste-test: Does the existing recipe suit local preferences, or does it need modification? Statistical significance between the competing recipes can guide this decision.
- Pricing models: Data Scientists can build mathematical models to forecast purchase intent versus varying price points.
- Ad testing: Per ad variant, what is the brand or messaging recall after 1 minute, 2 days or 7 days?
Types of Quantitative Research
This method is divided into primary and secondary data collection. Secondary data is information collected by a third-party, while Primary data is obtained first-hand by a Data scientist or market researcher. While secondary data can add valuable context, most businesses prefer to work with primary data. This falls into four main categories;
Survey Research
Surveys remain the most popular tool used in this methodology. A series of structured questions are asked to a target group, quantifying the answers in order to analyse them. Once strictly face-to-face or over the phone, online surveys have became increasingly popular due to its convenience, lower cost and speed of data collection. Survey research is used to quantify abstract concepts such as attitude or behaviour towards a subject, topic, brand etc.
Correlational Research
Correlational research aims to establish the nature and size of the relationship between two intertwined variables, identifying how they change and impact each other. An example of this is understanding the relationship between income and stress.
Experimental Research
Experimental research uses statistical analysis to prove or disprove a theory. Closely adhering to the design of scientific research, it must include a hypothesis and related variables that can be controlled, calculated, measured and compared. An example is proving the following statement as right or wrong: ‘Culturally diverse teams can problem-solve faster than homogeneous ones.
Casual Comparative Research
Casual Comparative research is used to establish the cause-and-effect between two or more interdependent variables. It’s used to analyse if and how variables in a specific group change when under the same influence. A pre-defined or existing relationship between the variables isn’t required. An example would be the impact of a sugar tax in Toronto.
Where can you learn Quantitative Research?
While a specialised degree in Quantitative research is rare, professionals interested in this field are recommended to pursue a degree in Statistics, Mathematics or a Masters in Data Science.
For more information contact a Higher Education Consultant.
References
Goertzen, Melissa J. (2017). “Introduction to Quantitative Research and Data”. Library Technology Reports. 53 (4): 12–18. ISSN 0024-2586.
