Are you trying to understand customers’ perception of your new product? Are you seeking suggestions on how you can improve efficiency in service delivery? Either way, collecting and analyzing qualitative data can help you make informed decisions that translate into business growth.
In this article, we’ll take a deep dive into the definition, types, and examples of quantitative data. We’ll also cover how it’s collected and analyzed.
What Is Quantitative Data?
Quantitative data is any form of data that can be counted, measured and expressed in numbers. In market surveys or research where quantitative data is the primary concern, the researcher is interested in collecting data that has a unique numerical value associated with it. Each quantifiable data has a unique numerical value assigned to it. This can be used in mathematical calculation and statistical analyses to make real-life decisions.
Quantitative data is primarily concerned with variables such as:
So, accurate analysis of quantitative data can help you get answers to questions such as
- “How much?”
- “How often?”
- “To what extent?”
For example, you can gain insight into customer experience by asking them to rate their satisfaction with your customer service on a scale of 1-10.
Quantitative data makes it possible to measure various parameters relating to a particular event or action. For example, an online survey distributed to get customers’ feedback on your new products can help you track metrics such as their:
- Satisfactory level
Quantifiable data is generated when there’s a need to confirm specific hypotheses. It’s more accurate and unbiased as it provides a true example of a large population. Quantitative data is used to capture the underlying variables in a particular event to make informed decisions.
Businesses collect quantitative data for statistical analysis. The most common methods of collecting quantitative data are surveys, polls and questionnaires. You will learn more about collection methods for quantitative data later in this article.
Generally, data can be divided into quantitative and qualitative data.
Qualitative data cannot be counted or measured. That is, the data cannot be expressed in numerical values for statistical analysis. Qualitative data depends on descriptive words, images, and observation. It’s a type of data that approximates or characterizes but doesn’t measure the attributes, values, or parameters of an event or object.
Qualitative data is usually subjective in nature. An unstructured conversation describing the value of a product can qualify as qualitative data. A comment on the efficiency of a company’s customer service can also qualify as qualitative data.
Qualitative data is usually referred to as “categorical data” in statistics. It can take the form of unstructured responses to interviews or surveys. For example, a survey respondent could describe a new feature on your website as “really awesome and helpful.”
Methods of qualitative data analyses include:
- Content analysis
- Narrative analysis
- Discourse analysis
Types of Quantitative Data
There are two main types of quantitative data. They are:
- Discrete data
- Continuous data
Discrete data: Discrete data is a data-set with a limited number of possible values. It usually involves integers. For example, the number of leads that downloaded your free eBook will be discrete data. In this case, you can only count whole, indivisible entities or numbers. It’s impossible to have 96.5 leads or 100.3 customers.
Continuous data: For continuous data, the value can be divided or reduced to finer levels. This data usually takes the form of fractions and decimals. For example, you can measure a distance in its most precise scale. You can have 2.5 miles.
You can use continuous data in different kinds of hypothesis tests. For example, it can be used to access the accuracy of a distance, or weight, and so on.
Quantitative data can also be classified into:
Univariate: This refers to data with a single variable. Single variable data usually comes as a list.
Bivariate/Multivariate: This data has two or more variables. Such data can be expressed with rows and columns. Bivariate data usually contains variables that relate with each other. For example, speed and time in distance.
Different levels of data measurement
Data can be organized into four distinct levels of measurement. They are:
Nominal: At a nominal level of data measurement, each variable has a different value. But there’s no particular order. This involves a random assignment of value. For example, in data that involves genders, male and female could be represented with 0 and 1.
Ordinal: Ordinal data follows a specific progressive order based on their values. Examples may include stages of education such as primary, secondary, tertiary.
Interval: Here, data is continuous and has an order along a scale. Interval data involves values that are equally spaced from the value before and the value after. For example, 5, 10, 15, 20.
Ration: This data is continuous and can go down to absolute zero. Take temperature for example. It’s possible for the temperature to go down to zero degrees.
Methods of Collecting Quantitative Data
There are two main quantitative data collection methods. They are:
- One-on-one interview
Surveys are a common data collection method. Traditionally, a survey is conducted by distributing questions to a sample population. Each question has answer options. The person administering them will consider what’s the most appropriate for the questions.
Surveys have taken different forms with the advent of the internet. Today, individuals and brands can administer closed-ended questions to a sample population online. Surveys are an effective method to collect quantitative data.
Surveys usually include structured questions with answer options. These options are based on what’s most appropriate for the questions asked. It’s an effective tool to get feedback from customers. For example, you can use a survey to ask your audience to rate your company’s performance on the scale of 1-10.
The responses to your survey questions should be reliable and accurate. This is so they can be generalized to the entire population with little or no discrepancies.
A survey can be divided into two types depending on the basis of time involved in completing it. They are:
Longitudinal Studies: Longitudinal surveys are a type of observational research. The survey is designed to analyze a trend or event from a specific time to another. This type of study could be conducted to collect and analyze a pattern in data.
Cross-sectional Studies: Here, the researcher conducts a survey and distributes it across the sample population that represents the target audience. The purpose could be to understand what the customers feel about your company’s new policy. Cross-sectional surveys run at a defined period of time.
Your survey questions are ready but how do you distribute it to your sample population? Here are some possible ways you can get your survey across to your target audience:
Email: This is one of the most common means of distributing surveys today. You’re expected to have an email list of your customers. There are different email management softwares you can use to send your survey via email.
Embed Survey in Website: A survey can be embedded in your website. This is an effective way to understand visitors’ opinions on the subject of your survey. You’re most likely going to have a higher number of responses from this method. This is because respondents are already aware of your brand when the survey pops up.
BuyRespondents: This method involves the distribution of a survey to a selected group of respondents to generate quantitative data. The respondents used are usually knowledgeable on the subject of the research. For example, a group of professional marketers can be targeted in a survey to determine the effectiveness of a marketing software.
Social Distribution: You can leverage social media platforms such as Facebook, Twitter, or Instagram to distribute surveys to your target sample.
SMS Survey: This is another cost-effective method of distributing a survey to your target sample. SMS surveys ensure that you collect a large number of quantifiable data.
One-on-one interviews can take different forms such as face-to-face interviews. These interviews can take place in person or online. This method involves the interviewer and the participant who can be a single person or a group of people. Quantitative interviews are usually structured. They’re also designed to gather relevant quantifiable data from the participants.
There are three main ways to conduct one-on-one interviews:
Face-to-face interviews: Here, an interviewer gets to meet the participant in person. The interview is usually structured and tailored to the specific requirements of the study. The interviewer has to be specific in their questions. They also need to request that an interviewee provides detailed responses that scale as quantitative data. The interviewer can ask the interviewee to shed more light on grey areas in their answers.
Online/Telephone Interviews: This type of interview is conducted either over the phone or on the internet using apps such as Skype or Zoom. Unlike face-to-face interviews, online/telephone interviews are not limited by geographical boundary. The interviewer and the interviewee(s) can be in different locations and still go ahead with the interview as scheduled.
Computer Assisted Personal Interview: This is another computerized form of one-on-one interviews. Here, the interviewer enters all the data they collect into a computer or a similar device. The processing time is very fast as the interviewer doesn’t have to carry physical survey questions.
Methods of Quantitative Data Analysis
It’s not enough for data to be collected. Data has to be analyzed for the researcher to make sense of it. The following are the common methods you can use to analyze quantitative data:
Trend Analysis: This is a method of statistical analysis that makes it possible to access and analyze data that has been collected over a long period of time. Trend analysis can be used to collect feedback about how data has changed over time. It’s aim is to understand the change in variables given that one variable remains constant.
Conjoint analysis: This data analysis method is used to analyze parameters behind a purchasing decision. Conjoint analysis has the ability to collect and analyze advanced metrics. These metrics provide detailed insights into customers’ purchasing decisions. This includes the parameters that rank as the most influential.
TURF analysis: TURF analysis stands for Total Unduplicated Reach and Frequency Analysis. It’s a quantitative data analysis method. It evaluates the total market reach of a product and/or service. This method is used to measure the frequency and avenues through which a company’s messaging reaches both prospective and current customers. This method enables you to track important marketing metrics and tweak your strategy when needed.
SWOT analysis: This method is used to measure and assign numerical values to show an organization’s:
This analysis method can provide a clear picture about completion and market opportunities.
Text Analysis: This is an advanced statistical analysis method. Intelligent tools simplify quantitative and open-ended data into data that’s easy to understand. This method is useful when raw survey data is unstructured and disorganized, but has to be presented in a more structured and logical manner.
Excel: Excel is another magical data analysis method. It has many formulas that can lead to data-driven insights and predictions about your business.
SAS: SAS stands for statistical software suite. It was developed by SAS Institute for data management, advanced analytics, multivariate analysis, business intelligence and predictive analysis. It’s helpful in business as you can use it for forecasting. It can be used to improve efficiency and quality at the workplace as well.
Importance Of Quantitative Data
Unlike qualitative data, quantitative data provides a clearer and more representative picture of a larger population. As a result, you can rely on the data to make informed decisions that will shape your business. Data-driven decision-making is more likely to result in a positive outcome than the one predicated on assumptions or subjective opinion.
So, you should invest in data collection and analysis to have a clear understanding of how your business resonates with your target audience. Taking this step will help you figure out what you’re not doing right and what you can do to improve the performance of your business.