Quantitative Data Definition

Quantitative data are any quantifiable information that can be used for mathematical calculations and statistical analysis, such that real-life decisions can be made based on these mathematical derivations. Quantitative data are used to answer questions such as “How many?”, “How often?”, “How much?”. This data can be verified and can also be conveniently evaluated using mathematical techniques.

There are quantities corresponding to various parameters, for instance, “How much did that laptop cost?” is a question which will collect quantitative data. There are values associated with most measuring parameters such as pounds or kilograms for weight, dollars for cost etc. Quantitative data makes measuring various parameters controllable due to the ease of mathematical derivations they come with.

Quantitative data are usually collected for statistical analysis using surveys, polls or questionnaires sent across to a specific section of a population. The retrieved results can be established across a population.  

5 Most Common Types of Quantitative Data with Examples

  • Counter: Count equated with entities. For example, the number of people who download 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 projection of data can be done using algorithms and other mathematical analysis tools. For example, a marketer will predict an increase in the sales after launching a new product with 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- Types of Quantitative Data

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:

  • 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 quantitative data. The survey makes include answer options which they think are the most appropriate for a particular question. Surveys are integral in collecting feedback from an audience which is 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. On the basis of the time involved in completing surveys, they are classified into the following-
    • Longitudinal Survey: A type of observational research in which the market researcher conducts surveys from a specific time period to another, i.e., over a considerable course of time, is called 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 Survey: A type of observational research in which the market research conducts surveys at a particular time period across the target sample is known as cross-sectional survey. This survey type implements a questionnaire to understand a specific subject from the sample at a definite time period.

Learn more: Cross-sectional vs Longitudinal Survey

  • 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 two 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.

Learn about: Quantitative Research

Steps of Quantitative Data Analysis:

For Quantitative Data, raw information has to presented in meaningful manner using analysis methods. Quantitative data should be analyzed in order to find evidential data that would help in the research process.

  • 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:
      1. Mean- An average of values for a specific variable
      2. Median- A midpoint of the value scale for a variable
      3. Mode- For a variable, the most common value
      4. Frequency- Number of times a particular value is observed in the scale
      5. Minimum and Maximum Values- Lowest and highest values for a scale
      6. 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 a descriptive statistics for the variable. For instance, a nominal variable score will never have a mean or median and 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.  

Some Quantitative Data Examples-

  • 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 not 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.
  • There are 1 million orphans in Syria.
  • 68% of parents do not allow their children more than 2 hours a day of screen time.

Advantages of Quantitative Data

  • 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, the personal bias is reduced to a great extent.
  • Accurate results: As the results obtained are objective in nature, they are extremely accurate.

Learn more: Margin of Error

Disadvantages of Quantitative Data

  • 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.

Difference between Quantitative and Qualitative Data

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.

Learn about:

Quantitative Research

Qualitative Research

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