When we’re asking questions like “How many?“, “How often?” or “How much?” we’re talking about the kind of hard-hitting, verifiable data that can be analyzed with mathematical techniques. It’s the kind of stuff that would make a statistician’s heart skip a beat. Let’s discuss quantitative data.
Thankfully, online surveys are the go-to tool for collecting this kind of data in the internet age. With the ability to reach more people in less time and gather honest responses for later analysis, online surveys are the ultimate quantitative data-gathering machine. Plus, let’s be real: who doesn’t love taking a good survey?
What is Quantitative Data?
Quantitative data is the value of data in the form of counts or numbers where each data set has a unique numerical value. This data is any quantifiable information that researchers can use for mathematical calculations and statistical analysis to make real-life decisions based on these mathematical derivations.
For example, there are quantities corresponding to various parameters. For instance, “How much did that laptop cost?” is a question that will collect quantitative data. Values are associated with most measuring parameters, such as pounds or kilograms for weight, dollars for cost, etc.
It makes measuring various parameters controllable due to the ease of mathematical derivations they come with. It is usually collected for statistical analysis plans using surveys, polls, or questionnaires sent across to a specific section of a population. Researches can establish the retrieved results across a population.
Types of Quantitative Data with Examples
Quantitative data is integral to the research process, providing valuable insights into various phenomena. Let’s explore the most common types of quantitative data and their applications in various fields. The most common types are listed below:
- Counter: Count equated with entities—for example, the number of people downloading a particular application from the App Store.
- Measurement of physical objects: Calculating measurement of any physical thing. For example, the HR executive carefully measures the size of each cubicle assigned to the newly joined employees.
- Sensory calculation: Mechanism to naturally “sense” the measured parameters to create a constant source of information. For example, a digital camera converts electromagnetic information to a string of numerical data.
- Projection of data: Future data projections can be made using algorithms and other mathematical analysis tools. For example, a marketer will predict an increase in sales after launching a new product with a thorough analysis.
- Quantification of qualitative entities: Identify numbers to qualitative information. For example, asking respondents of an online survey to share the likelihood of recommendation on a scale of 0-10.
Quantitative Data: Collection Methods
As quantitative data is in the form of numbers, mathematical and statistical analysis of these numbers can lead to establishing some conclusive results.
There are two main Quantitative Data Collection Methods:
01. Surveys
Traditionally, surveys were conducted using paper-based methods and have gradually evolved into online mediums. Closed-ended questions form a major part of these surveys as they are more effective in collecting data.
The survey includes answer options they think are the most appropriate for a particular question. Surveys are integral in collecting feedback from an audience larger than the conventional size. A critical factor about surveys is that the responses collected should be such that they can be generalized to the entire population without significant discrepancies.
Based on the time involved in completing surveys, they are classified into the following:
- Longitudinal Studies: A type of observational research in which the market researcher conducts surveys from one time period to another, i.e., over a considerable course of time, is called a longitudinal survey. This survey is often implemented for trend analysis or studies where the primary objective is to collect and analyze a pattern in data.
- Cross-sectional Studies: A type of observational research in which market research conducts surveys at a particular time period across the target sample is known as a cross-sectional survey. This survey type implements a questionnaire to understand a specific subject from the sample at a definite time period.
To administer a survey to collect quantitative data, the following principles are to be followed.
- Fundamental Levels of Measurement – Nominal, Ordinal, Interval, and Ratio Scales: Four measurement scales are fundamental to creating a multiple-choice question in a survey in collecting quantitative data. They are nominal, ordinal, interval, and ratio measurement scales without the fundamentals of which no multiple-choice questions can be created.
- Use of Different Question Types: To collect quantitative data, close-ended questions have to be used in a survey. They can be a mix of multiple question types, including multiple-choice questions like semantic differential scale questions, rating scale questions, etc., that can help collect data that can be analyzed and made sense of.
- Survey Distribution and Survey Data Collection: In the above, we have seen the process of building a survey along with the survey design to collect quantitative data. Survey distribution to collect data is the other important aspect of the survey process. There are different ways of survey distribution. Some of the most commonly used methods are:
- Email: Sending a survey via email is the most commonly used and most effective survey distribution method. You can use the QuestionPro email management feature to send out and collect survey responses.
- Buy respondents: Another effective way to distribute a survey and collect quantitative data is to use a sample. Since the respondents are knowledgeable and also are open to participating in research studies, the responses are much higher.
- Embed survey in a website: Embedding a survey in a website increases the number of responses as the respondent is already near the brand when the survey pops up.
- Social distribution: Using social media to distribute the survey aids in collecting a higher number of responses from the people who are aware of the brand.
- QR code: QuestionPro QR codes store the URL for the survey. You can print/publish this code in magazines, signs, business cards, or on just about any object/medium.
- SMS survey: A quick and time-effective way of conducting a survey to collect a high number of responses is the SMS survey.
- QuestionPro app: The QuestionPro App allows the quick creation of surveys, and the responses can be collected both online and offline.
- API integration: You can use the API integration of the QuestionPro platform for potential respondents to take your survey.
02. One-on-one Interviews
This quantitative data collection method was also traditionally conducted face-to-face but has shifted to telephonic and online platforms. Interviews offer a marketer the opportunity to gather extensive data from the participants. Quantitative interviews are immensely structured and play a key role in collecting information. There are three major sections of these online interviews:
- Face-to-Face Interviews: An interviewer can prepare a list of important interview questions in addition to the already asked survey questions. This way, interviewees provide exhaustive details about the topic under discussion. An interviewer can manage to bond with the interviewee on a personal level which will help him/her to collect more details about the topic due to which the responses also improve. Interviewers can also ask for an explanation from the interviewees about unclear answers.
- Online/Telephonic Interviews: Telephone-based interviews are no more a novelty but these quantitative interviews have also moved to online mediums such as Skype or Zoom. Irrespective of the distance between the interviewer and the interviewee and their corresponding time zones, communication becomes one-click away with online interviews. In case of telephone interviews, the interview is merely a phone call away.
- Computer Assisted Personal Interview: This is a one-on-one interview technique where the interviewer enters all the collected data directly into a laptop or any other similar device. The processing time is reduced and also the interviewers don’t have to carry physical questionnaires and merely enter the answers in the laptop.
All of the above quantitative data collection methods can be achieved by using surveys, questionnaires and online polls.
Quantitative Data: Analysis Methods
Data collection forms a major part of the research process. This data, however, has to be analyzed to make sense of. There are multiple methods of analyzing quantitative data collected in surveys. They are:
- Cross-tabulation: Cross-tabulation is the most widely used quantitative data analysis methods. It is a preferred method since it uses a basic tabular form to draw inferences between different data-sets in the research study. It contains data that is mutually exclusive or have some connection with each other.
- Trend analysis: Trend analysis is a statistical analysis method that provides the ability to look at quantitative data that has been collected over a long period of time. This data analysis method helps collect feedback about data changes over time and if aims to understand the change in variables considering one variable remains unchanged.
- MaxDiff analysis: The MaxDiff analysis is a quantitative data analysis method that is used to gauge customer preferences for a purchase and what parameters rank higher than the others in this process. In a simplistic form, this method is also called the “best-worst” method. This method is very similar to conjoint analysis but is much easier to implement and can be interchangeably used.
- Conjoint analysis: Like in the above method, conjoint analysis is a similar quantitative data analysis method that analyzes parameters behind a purchasing decision. This method possesses the ability to collect and analyze advanced metrics which provide an in-depth insight into purchasing decisions as well as the parameters that rank the most important.
- TURF analysis: TURF analysis or Total Unduplicated Reach and Basic Frequency Analysis, is a quantitative data analysis methodology that assesses the total market reach of a product or service or a mix of both. This method is used by organizations to understand the frequency and the avenues at which their messaging reaches customers and prospective customers which helps them tweak their go-to-market strategies.
- Gap analysis: Gap analysis uses a side-by-side matrix to depict data that helps measure the difference between expected performance and actual performance. This data gap analysis helps measure gaps in performance and the things that are required to be done to bridge this gap.
- SWOT analysis: SWOT analysis, is a quantitative data analysis methods that assigns numerical values to indicate strength, weaknesses, opportunities and threats of an organization or product or service which in turn provides a holistic picture about competition. This method helps to create effective business strategies.
- Text analysis: Text analysis is an advanced statistical method where intelligent tools make sense of and quantify or fashion qualitative observation and open-ended data into easily understandable data. This method is used when the raw survey data is unstructured but has to be brought into a structure that makes sense.
Steps to conduct Quantitative Data Analysis
For Quantitative Data, raw information has to presented in a meaningful manner using data analysis methods. This data should be analyzed to find evidential data that would help in the research process. Data analytics and data analysis are closely related processes that involve extracting insights from data to make informed decisions.
- Relate measurement scales with variables: Associate measurement scales such as Nominal, Ordinal, Interval and Ratio with the variables. This step is important to arrange the data in proper order. Data can be entered into an excel sheet to organize it in a specific format.
- Connect descriptive statistics with data: Link descriptive statistics to encapsulate available data. It can be difficult to establish a pattern in the raw data. Some widely used descriptive statistics are:
- Mean- An average of values for a specific variable
- Median- A midpoint of the value scale for a variable
- Mode- For a variable, the most common value
- Frequency- Number of times a particular value is observed in the scale
- Minimum and Maximum Values- Lowest and highest values for a scale
- Percentages- Format to express scores and set of values for variables
- Decide a measurement scale: It is important to decide the measurement scale to conclude descriptive statistics for the variable. For instance, a nominal data variable score will never have a mean or median, so the descriptive statistics will correspondingly vary. Descriptive statistics suffice in situations where the results are not to be generalized to the population.
- Select appropriate tables to represent data and analyze collected data: After deciding on a suitable measurement scale, researchers can use a tabular format to represent data. This data can be analyzed using various techniques such as Cross-tabulation or TURF.
Quantitative Data Examples
Listed below are some examples of quantitative data that can help understand exactly what this pertains:
- I updated my phone 6 times in a quarter.
- My teenager grew by 3 inches last year.
- 83 people downloaded the latest mobile application.
- My aunt lost 18 pounds last year.
- 150 respondents were of the opinion that the new product feature will fail to be successful.
- There will be 30% increase in revenue with the inclusion of a new product.
- 500 people attended the seminar.
- 54% people prefer shopping online instead of going to the mall.
- She has 10 holidays in this year.
- Product X costs $1000.
As you can see in the above 10 examples, there is a numerical value assigned to each parameter and this is known as, quantitative data.
Advantages of Quantitative Data
Some of the advantages of quantitative data are:
- Conduct in-depth research: Since quantitative data can be statistically analyzed, it is highly likely that the research will be detailed.
- Minimum bias: There are instances in research, where personal bias is involved which leads to incorrect results. Due to the numerical nature of quantitative data, personal bias is reduced to a great extent.
- Accurate results: As the results obtained are objective in nature, they are extremely accurate.
Disadvantages of Quantitative Data
Some of disadvantages of quantitative data, are:
- Restricted information: Because quantitative data is not descriptive, it becomes difficult for researchers to make decisions based solely on the collected information.
- Depends on question types: Bias in results is dependent on the question types included to collect quantitative data. The researcher’s knowledge of questions and the objective of research are exceedingly important while collecting quantitative data.
Differences between Quantitative and Qualitative Data
There are some stark differences between quantitative data and qualitative data. While quantitative data deals with numbers and measures and quantifies a specific phenomenon, qualitative data focuses on non-numerical information, such as opinions and observations.
The two types of data have different purposes, strengths, and limitations, which are important in understanding a given subject completely. Understanding the differences between these two forms of data is crucial in choosing the right research methods, analyzing the results, and making informed decisions. Let’s explore the differences:
| Quantitative Data | Qualitative Data |
| Associated with numbers | Associated with details |
| Implemented when data is numerical | Implemented when data can be segregated into well-defined groups |
| Collected data can be statistically analyzed | Collected data can just be observed and not evaluated |
| Examples: Height, Weight, Time, Price, Temperature, etc. | Examples: Scents, Appearance, Beauty, Colors, Flavors, etc. |
Conclusion
Using quantitative data in an investigation is one of the best strategies to guarantee reliable results that allow better decisions. In summary, quantitative data is the basis of statistical analysis.
Data that can be measured and verified gives us information about quantities; that is, information that can be measured and written with numbers. Quantitative data defines a number, while qualitative data collection is descriptive. You can also get quantitative data from qualitative by using semantic analysis.
QuestionPro is a software created to collect quantitative data using a powerful platform with preloaded questionnaires. In addition, you will be able to analyze your data with advanced analysis tools such as cross tables, Likert scales, infographics, and much more.
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