Quantitative Data – A Comprehensive Guide For 2021
Introduction
Data is an integral part of organizations today and most of them rely on quantitative data and qualitative data to make key decisions. Quantitative data is used for mathematical calculations and analysis that will be useful for real-life decision. The calculations and analysis will be made on mathematical derivations that will help to solve several problems.
1. Definition
What is the quantitative data? If one has to give the quantitative data definition, it can be defined as a value of data. The value is in the form of counts or numbers within a data set that has a unique numerical value associated with it. This data can be used for mathematical calculations and statistical analysis which can then be used for a real-time decision that needs to be based on mathematical calculative derivations. Quantitative data is any data that can answer questions as ‘How many?”; “How much”; ”What amount?”.
The data can be verified and also evaluated using mathematical techniques. Values and quantities refer to measuring parameters like weight, cost, time. Quantitative data makes measuring different parameters easy and manageable because of mathematical derivations. Most quantitative data is collected for statistical analysis and done via surveys, polls and questionnaires. It is done by collecting data from a specific section of the population and the derived results are established across the population.
A key aspect to note here is the difference between quantitative data and qualitative data. Quantitative data is information about quantities and relates to numbers. Qualitative data is more descriptive and refers to the phenomenon that can be observed and is not tangible.
2. Type
A few common types of quantitative data are:
- Counter- this equates count with objects. For eg: how many people watched a video uploaded on a website
- Physical measurement of objects- physically measuring a tangible object. For eg: the amount of space occupied by each employee on a floor
- Sensory calculation– it is a mechanism to sense the quantifiable parameters that create a source of information. For eg: any digital device that converts digital information to numerical data
- Data projection– Algorithms and mathematical analysis tools can be used to predict future events. For example- a marketing survey to predict how many people want to change their toothpaste
- Quantifying qualitative entities– Assigning numbers to qualitative information. For example, asking customers their experience of a product and rate it on a scale of 1-5.
3. Collection methods
There are two main Quantitative Data Collection Methods:
Surveys have traditionally played a major role in collecting quantitative data. Surveys include close-ended questions, answer options and form an integral part in collecting feedback. Surveys are classified according to the time involved in completing surveys
- Longitudinal surveys
- Cross-sectional studies
- Use of Different types of questions
- Fundamental levels of measurement
Surveys are distributed via email, embedding them into a website, distribution, scanning a QR code, SMS, App and API Integration
Apart from surveys, one to one interviews are also conducted to collect data. This is done in the form of telephonic interviews and online platforms. Some major sections of online interviews are:
- Face to face interviews
- Online or telephonic interviews
- Personal interview assisted by computer interview
4. Analysis methods
Collection of data is a major part of the research which in turn is analysed to make sense of it. There are several methods by which data is collected:
- Cross tabulation is a widely used method to analyse quantitative data. It used basic tabular format to draw inferences between data sets that are mutually exclusive or connected to each other.
- Trend analysis is a method to look at quantitative data gathered over a period of time. It helps to spot trends and understand patterns over a period of time considering different variables.
- MaxDiff analysis is another method to gauge customer preferences for purchase and what kind of parameters rank over the others. It is also called “ best-worst” method and is similar to the conjoint analysis. It is much easier to implement and use interchangeably.
- Conjoint analysis is a similar method to the above that analyses parameters behind a purchasing decision. This is a more advanced method that provides an in-depth insight into buying patterns and decision and the parameters that rank the most important.
- Total Unduplicated Reach Frequency Analysis ( TURF) assesses the complete market reach of a product or service. It helps the organization understand the reach of their products and helps them derive new strategies
- GAP analysis depicts data that explains the difference between the expected and actual performance. This helps to identify gaps and things that need to be done to bridge it.
- SWOT analysis assigns numerical values to strengths, weaknesses, opportunities and threats for a product or service offered by an organization to help identify the holistic picture and create further strategies.
- Text analysis uses intelligent tools as an advanced statistical method to make sense and quantify qualitative and open-ended data. This is useful when data collected is unstructured and has to be given a structure to make sense
5. Examples
Below are a few quantitative data examples that will help you understand how data is collected and analysed.
Example 1: A survey was conducted to see if customers wanted to change their current coffee powder. 150 respondents said that they would not and 200 said they would like to.
Example 2: A survey was conducted to see how many people invited attended a seminar and how many did not to check if it was a success. 300 people attended and 100 did not.
Example 3: A survey was conducted to see how many people preferred to shop online. The results revealed that out of 100 participants, 55% chose to shop online.
A numerical value is assigned to each parameter and is this is what is called quantitative data.
6. Advantages
A few advantages of quantitative data are:
- Research conducted is in-depth and can be statistically analysed.
- There is minimal room for bias as it does not involve incorrect results. As the data is quantified, personal bias is reduced to a large extent.
- Results obtained are accurate and objective in nature
7. Disadvantages
Some disadvantages of quantitative data are:
- It is difficult for researchers to make decisions based on quantitative data as it is not descriptive
- Quantitative data is dependent on the questions. If the questions are not reflective of the answers that are aimed at then the objective of the research will not be achieved.
Conclusion
Quantitative data is useful when specific research needs to be conducted to make key decisions. As the results are a derivation of mathematical calculation and analysis, quantitative data is used by most companies and organizations for all kinds of research.
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