What is Quantitative Data? + [Types & Examples]
By busayo.longe
| Last updated:
Jul 27
|
12 min read
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Quantitative data is the type of data whose value is measured in the form of numbers or counts, with a unique numerical value associated with each data set. Also known as numerical data, quantitative data further describes numeric variables (e.g. How many? How often? How much?)
This data type can also be defined as a group of quantifiable information that can be used for mathematical computations and statistical analysis which informs real-life decisions. For example, a manufacturing company will need an answer to the question, “How much is the production cost?”.
A quantitative data of the company’s cost of production will be collected through this question and will inform the company’s selling cost (where selling cost = production cost + profit).
Quantitative Data can be divided into two types, namely; Discrete & Continuous Data.
Discrete data is a type of data that consists of counting numbers only, and as such cannot be measured. Measurements like weight, length, height are not classified under discrete data.
Examples of discrete data include; the number of students in a class, the number of days in a year, the age of an individual, etc. When trying to identify discrete data, we ask the following questions; Can it be counted? Can it be divided into smaller parts?
Discrete data can be said to be either countably finite or countably infinite. An example of a countably finite data is an arbitrary set A = {1, 2, 3,…,n; where n is less than infinity} while that of a countably infinite data is an arbitrary set B = {1,2,3,…}.
Also known as attribute data, discrete data can’t be broken down into smaller units. It is typically counted in whole numbers and there is nothing like half a value.
Continuous data is a data type that takes on numeric values that can be meaningfully broken down into smaller units. As opposed to discrete data which can’t be measured, continuous data can be placed on a measurement scale (e.g. weight, length, time, etc.).
Continuous data can be said to be either uncountably finite or uncountably infinite. For example, let us consider the Cumulative Grade Point (CGPA) of students in a class, measured on a 5 point scale.
A student can score any grade between 0 points and 5 points, including figures like 1.573, 4.5, 2.6981, etc. We classify this an uncountably finite continuous data because it has an upper (5) and lower bound (0).
An example of an uncountably infinite data is the set of real numbers, R = {…, -1, 0, 1, …}. In this case, the data has neither an upper nor a lower bound.
Continuous data can also be divided into two types, namely; ratio data and interval data.
Interval data is defined as type of data which is measured along a scale, in which each point is placed at equal distance from one another. It is an extension of ordinal data, with a standardised scale as opposed to the former.
Ratio data on the other hand, is an extension of interval data. It is the ultimate when we talk about data measurement because it tells us about the order, exact distance between units on the scale, and has an absolute zero.
The above characteristics of ratio data allows for the application of a wide range of inferential and descriptive statistical methods.
There are various Quantitative data examples which are applied in both research and statistics. These examples vary and will therefore be separately highlighted below.
Researchers project future data using algorithms and mathematical analysis tools. For instance, a company who is about to launch a new product into the market will analyse quantitative data from previous research to predict an increase or decrease in sales.
The Government carry out census to acquire and record information about the members of a given population. Large government research departments uses census data to predict which sector of the economy needs money and how much they need, how many seats a state will have in the U.S. House of Representatives, etc.
When setting the selling price of a product, businesses use quantitative data of the annual income of a person or household to determine their purchasing power. This exercise is part of business research process and may be conducted before launching a new product or increasing the price of an existing product.
Many online businesses use this to determine the number of website visits they get daily, number of product downloads on the app store, the number of users etc. The numbers are usually automatically generated through pre-programmed codes.
This is a case of quantification of qualitative entities used by businesses to improve their customer service. For example, telling a customer to rate an addition to a menu on a scale of 1-10 will help the restaurant decide whether to remove it, improve on it or leave it as it is.
The mean height of the students in a class will be calculated by recording the height of each student, adding it up and dividing it by the number of students in the class. A school might need to calculate the mean height of students in order to determine how high or low their chairs and tables should be.
It may be used to record the length or width of an object. For example, when assigning a cubicle or office space to a new employee, HR may need to measure its length and breadth, then allocate according to their position or experience level.
The CGPA of a student is calculated by finding the average of the grade points of the student.
A computer program may be written to generate a particular set of numbers with a uniform (0,1) distribution. The accuracy of the number generator can be determined using the Chi-Squared Goodness of fit test. This will compare the counts generated with the expected count and determine whether they are accurate or not.
The probability of an event occurring is calculated using the quantitative data of the ratio between the ways of achieving success and the total number of outcomes. The probability of a certain event is 1, impossible event is 0 and failure is 1 minus success.
Quantitative data is of two types, namely; discrete and continuous data. Continuous data is further divided into interval and ratio data.
Quantitative data takes up numeric values with numeric properties. Unlike categorical data which takes numeric values with descriptive characteristics, quantitative data exhibits numeric features.
There is a scale or order to quantitative data. For example, the numbers 1 to 3 can be written as 1,2,3 and 3,2,1 when arranged in ascending and descending orders respectively.
One can perform arithmetic operations like addition and subtraction on quantitative data. Almost all statistical analysis methods can be carried out using quantitative data.
Quantitative data has a standardised measurement scale. As opposed to ordinal data that has an order, but no standard scale.
Quantitative data can be analysed using descriptive and inferential statistical methods, depending on the aim of the research.
Some of the data visualisation techniques adopted by quantitative data include; scatter plot, dot plot, stacked dot plot, histograms, etc.
The first stage of quantitative data analysis and interpretation is data preparation, where raw data is converted into something meaningful and readable. There are four steps of data preparation:
This is done so as to find out whether the data collection was done without any bias. The process includes:
Researchers do this by picking a random sample from a large population.
Large data sets may inevitably include errors, and that is why they need to be edited. During this process, data is inspected for completeness and consistency.
For example, a respondent may leave a field blank, which is a case of incompleteness. In another case, we may have a respondent who answered that he/she has no children also claim in another part that his(er) first child is in high school — this data is inconsistent.
This is the process of quantifying qualitative data for easy analysis. It involves grouping and assigning values to survey responses. E.g. Male – 1, Female – 2.
This is the process of changing data into new format. For example, reducing a 5 point likert-type scale into 3 categories.
Consider a 5 point likert-type scale with the options very good, good, neutral, bad and very bad. This may be reduced into good, neutral and bad.
After completing the first stage, the data is ready for analysis. There are two main data analysis methods used, namely; descriptive statistics and inferential statistics.
Researchers make use of descriptive statistics to summarise quantitative data. It is often used when analysing a single variable, and as such is sometimes called univariate analysis. Some common descriptive statistical methods include:
This method measures the relationship (similarities and differences) between multiple variables to generate results and infer conclusions. Some examples of inferential statistics include; correlation, regression, ANOVA, etc.
There are also some other approaches to inferential statistics used to analyse real life data or surveys. They include:
Cross tabulation method uses basic tables to draw inferences between different data sets. The data-sets are matched and placed in the same row or column. Inferences are drawn by studying the similarities and differences between the data in each row or column as the case may be.
MaxDiff analysis method is used to gauge respondent’s preference for a particular set of options. It determines the most prefered and the least preferred. This data is compared for various samples to infer a conclusion.
Turf Analysis is mostly used by businesses to determine their go to market strategy. For example, a company may run the same ad across different social media platforms and analyse the number of customers reached for each platform to determine the best place to run ads.
Gap analysis measures gaps in performance and how to breach this gap. It uses a side-by-side matrix to measure the difference between these performances.
Text analysis is used to extract useful information from a text document. Computers use this method to disintegrate unstructured text document and convert them into structured data.
Formplus is the best tool for collecting quantitative data. It has different features for building online surveys for collecting quantitative data and analysis automation for the data collected.
This web based form builder helps businesses create better campaigns, choose the best marketing channels to increase their sales and improve customer satisfaction. As a researcher, you no longer have to share printed forms to respondents and manually input the data for analysis.
Formplus eliminates stress and need for manpower by providing an all in one form builder with a sharing option that makes it easy for you to share the forms to respondents. The data collection process is also automated, while eliminating human error.
To collect quantitative data using formplus builder, follow these steps:
Formplus gives you a 21-day free trial to test all features and start collecting quantitative data from online surveys. Pricing plan starts after trial expiration at $20 monthly, with reasonable discounts for Education and Non-Governmental Organizations.
We will be creating a sample quantitative data collection form that inputs student’s scores (First test score and final test score), then output the total grade in percentage.
(First test score+Final test score) × 100 / 200 = (First test score+Final test score)/2 to calculate the Total Grade in percentage.
You can also customise your forms, share to respondents and view response analytics.
Quantitative data is perhaps the most widely used data type in research. This is partly due to its ease of computation and compatibility with most statistical analysis methods.
In quantitative data analysis, we take a sample population, classify its features, and even construct more complex statistical models in an attempt to explain what is observed. Our findings may be extended to a larger population, and comparisons may be made between two sample populations.
Quantitative data is collected through a standard procedure, making it easy for researchers to replicate past research or build on current ones.
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