What is Quantitative Data?
Quantitative data refers to the value of data as a count or number, where each data set is assigned a unique numerical value. This data includes any quantifiable information that is useful for statistical analysis and mathematical calculations. These derivations can then be used to make real-life decisions. Quantitative data can be used to answer questions like “How many?” “How often?” and “How much?” These data can be easily verified and analyzed using mathematical techniques.
There are numbers that correspond to different parameters. For instance, the question “How much was this laptop worth?” will gather quantitative data. Most measuring parameters have values such as weight in pounds, cost in dollars, etc.
Because of the ease with which mathematical derivations can be used, quantitative data allows the measurement of various parameters to be controlled. For statistical analysis, quantitative data is typically collected using surveys or polls. These results can be compared across a population.
Types and Examples of Quantitative Data
- Counter: Count is equated to entities. The number of people who have downloaded a specific application from the App Store, for example.
- Measurement and calculation of physical objects: Calculating the measurement of any physical object. The HR executive measures, for example, each cubicle that is assigned to newly-joined employees.
- Sensory calculation: A mechanism to “sense” the parameters and create a constant source of information. A digital camera, for example, converts electromagnetic information into a string of numerical numbers.
- Future projection of data: Algorithms and other mathematical analysis tools can be used to project future data. A marketer might predict a rise in sales when a new product is launched. This can be done by performing extensive analysis.
- Quantification of qualitative entities: Identify numbers to qualitative information. Asking respondents to an online survey about the likelihood of recommendation using a scale from 0-to 10.
Collection Methods of Quantitative Data
Quantitative data are represented as numbers. Therefore, mathematical and statistical analysis can be used for conclusive results.
There are two ways to collect quantitative data:
Surveys: Surveys were traditionally conducted on paper. However, online methods have slowly replaced them. These surveys include Closed-ended questions. They are more efficient at collecting quantitative data. Survey respondents will choose the answer options that they believe are most appropriate to a question. Surveys are essential in gathering feedback from an audience that is larger than the usual size. Surveys should have responses that are meaningful and can be applied to all people without significant discrepancies. They are classified according to the amount of time required to complete surveys.
Longitudinal studies: This type of observational research is where the market researcher conducts surveys over a period of time. This survey is used for trend analysis and studies that seek to identify patterns in data.
Cross-Sectional Studies: This type of observational research is where the market research conducts surveys over a specific time period in the target sample. It’s also known as a cross-sectional survey. This type of survey uses a questionnaire to gain insight into a particular subject in a sample over a specified time period.
The Basic Levels of Measurement: There are four fundamental measurement scales that are essential to creating a multiple choice question during a survey collecting quantitative data. These are nominal, ordinal, interval, and ratio measurement levels without which multiple-choice questions cannot be created.
Different question types: In order to collect quantitative data, closed-ended questions must be used in a survey. You can have multiple question types such as multiple-choice questions and rating scheme questions. This can allow you to collect data that can then be analyzed and made sense of.
Survey Data Collection and Survey Distribution: In the previous, we saw the process of building a questionnaire along with the survey design for collecting quantitative data. The survey distribution process is another important part of the survey process. There are many ways to distribute surveys. These are some of the most popular methods:
- Email: The most popular and effective method of survey distribution is via email. To send and collect responses to surveys, you can use QuestionPro’s email management feature.
- Purchase respondents: Another effective way to distribute surveys and collect quantitative data is using a sample. The responses are higher because the respondents are more knowledgeable than the average person and are willing to participate in research studies.
- Embed survey on a website: The response rate to a survey embedded in a website is higher if the respondent is within close proximity of the brand at the time the survey appears.
- Social distribution: The use of social media for the survey helps in obtaining a higher number of responses from people who are familiar with the brand.
- QR code: questionPro QR codes save the survey URL. You can publish this code on business cards, signs, and almost any other object/medium.
- SMS Survey: The SMS Survey is a quick and efficient way to collect high-quality responses.
QuestionPro app This QuestionPro App lets you quickly distribute surveys and collect responses both online and offline.
- API Integration: You can use the integration on the QuestionPro platform to get potential respondents to your survey.
One-on-1 Interviews: This quantitative data collection method used to be conducted face-to-face, but it has now been adapted for telephonic and internet platforms. Interviews give marketers the chance to collect extensive data from participants. Quantitative interviews can be extremely structured and are a crucial part of collecting information. These online interviews can be broken down into three main sections:
Face-to-face interviews: An interviewer may prepare a list of essential interview questions, in addition to the survey question. Interviewees can then provide detailed information about the topic. Interviewers can build a relationship with interviewees on a personal basis. This will allow them to gather more information about the topic and improve their responses. Interviewers may also request clarifications from interviewees regarding unclear answers.
Telephonic/Online Interviews: Telephone-based interviews are not a new concept. However, these quantitative interviews have moved online to use Skype or Zoom. Online interviews allow for communication regardless of distance and time zones. Interviews by telephone are as simple as a phone call.
Computer-Assisted Personal Interview: This one-on-one interview method allows the interviewer to enter all data directly into a laptop. This reduces the processing time and interviewers no longer need to bring any questionnaires. They can simply enter the answers into the laptop.
Analysis Methods of Quantitative Data
Data collection is an important part of the research process. These data must be analyzed to make sense. There are several types of data analysis in quantitative research. These are:
Crosstabulation: The most popular method for quantitative data analysis is. This method is preferable because it uses a simple tabular form to draw inferences between different data sets within a research study. It includes data that are mutually exclusive or have some relationship to each other.
Trend analysis: This quantitative statistical analysis method allows you to analyze quantitative data collected over a long time. This data analysis method collects feedback on data changes and attempts to understand why one variable has changed.
MaxDiff Analysis: This MaxDiff Analyse method uses quantitative data analysis to determine customer preferences and which parameters are more important than others. This method is sometimes called the “best-worst”. This method is similar to conjoint analysis, but it is easier to use and is interchangeable.
Conjoint Analysis: Like the previous method, conjoint analyses are similar to quantitative data analysis methods that analyze parameters behind purchasing decisions. This technique can gather and analyze advanced metrics to provide insight into buying decisions as well as identify the most significant parameters.
TURF Analysis: The TURF Analysis, or Total Unduplicated Reach and Frequency analysis, is a quantitative data analytics methodology that evaluates the market reach of a product/service or a combination of both. This is a method that organizations use to determine the frequency and avenues through which their messages reach customers and potential customers. It helps them improve their go-to-market strategies.
Gap Analysis: To show the gap between expected and actual performance, the gap analysis uses a side-by-side matrix. This data analysis can help identify gaps in performance and what needs to be done to close them.
SWOT Analysis: SWOT Analysis is a quantitative analysis method that assigns a numerical value to indicate the strengths, weaknesses, opportunities, threats, and opportunities of an organization, product, or service. This in turn gives a complete picture of the competition. This helps you to develop effective business strategies.
Text analysis: This advanced statistical method makes it possible to use intelligent tools to transform qualitative and open-ended data into easily understandable data. This is useful when raw survey data is not structured but must be arranged in a way that makes sense.
How to Conduct Quantitative Data Analysis
Quantitative data requires that raw information be presented in a meaningful way using data analysis methods. To find evidence that could aid in research, quantitative data must be analyzed.
- Relate measurement ranges with variables: Associate measuring scales such as Nominal and Ordinal as well as Interval, Ratio, and Interval with the variables. This is essential to ensure that the data is organized in the correct order. To organize data in a particular format, you can use an Excel sheet.
- Connect descriptive statistics to data: To encapsulate data, link descriptive statistics. It is not always easy to identify patterns in raw data. These are some of the most commonly used descriptive statistics:
The average value of a variable.
- Median – The midpoint of a value scale for a variable
- Mode- The most common value for a variable.
- Frequency – The number of times that a particular value has been observed on the scale
- Minimum and Maximum Values – The lowest and highest values on a scale.
- Percentages- Use this format to express scores or a set of variables.
Determine a measurement scale: It is crucial to determine the measurement scale in order to create descriptive statistics for the variable. A nominal variable score, for example, will not have a median or mean and the descriptive statistics will reflect this. In situations where results cannot be generalized to the entire population, descriptive statistics are sufficient.
Choose the appropriate tables to represent data, and then analyze it: After determining a measurement scale, researchers can create a tabular format. These quantitative data analysis techniques, such as Cross-tabulation and TURF.
Advantages of Quantitative Data
There are many benefits to quantitative data.
- Conduct in-depth research: Because quantitative data can statistically be analyzed, it’s highly probable that the research will become detailed.
- Minimal bias: In some instances, bias can lead to inaccurate results in research. The numerical nature of quantitative data reduces personal bias to a large extent.
- Exact results: Because the results are objective, they are very accurate.
Disadvantages of Quantitative Data
There are some drawbacks to quantitative data:
- Restricted Information: Quantitative data is not descriptive so it can be difficult for researchers to base their decisions solely on this information.
- Depends upon question types: Bias is influenced by the type of questions used to collect quantitative data. Quantitative data collection is a process that requires the researcher to be able to answer questions and understand the purpose of the research.