Quantitative Data – Types, Analysis, Methods, Pros-Cons & Examples

Understanding & defining qualitative data

Quantitative data can be defined as the data value in the form of numbers or counts. Here each set of data has an exclusive numerical value that is associated with it. This type of data contains quantifiable information that is employed in statistical and mathematical analyses and calculations. These mathematical derivations also help to make decisions in real life. Quantitative data answers various questions like how much? How often? How many? The exact value of the data can also be evaluated and conveniently verified using different mathematical procedures.

Many quantities correspond to different parameters like “How much is the price of this laptop?” This type of question is answered by the collection of qualitative data. The values associated with the measurement of parameters are kilograms or pounds for weight, Yen for cost, Km for distance and many more. Quantitative data is mostly collected through polls, surveys, and questionnaires for statistical analysis. The results that are retrieved can be established across the entire population.

Related: Qualitative Data – Types, Analysis, Methods, Pros-Cons & Examples

Different Types of Quantitative Data

Quantitative data can be classified into five types and they are:

  1. Physical Objects Measurement: This type of quantitative data deals with the measurement of any type of physical object. This may include the measurement of each cubicle assigned to every employee in a company.
  2. Data Projection: Future data projection can be accomplished by implementing many mathematical algorithms and analysis tools. For instance, a market researcher has to predict the sales increase after the introduction of a new service or product through analysis.
  3. Counter: The counter is related to the equities. For instance, Counter means the total number of users that have downloaded a particular application from the Google or Apple store.
  4. Sensory Calculation: Sensory calculation refers to the process of naturally sensing the parameters to generate a continuous source of information. The best example of the sensory calculation is the conversion of electromagnetic information to a constant numerical data string.
  5. Quantification of the entities that is qualitative in nature: In this type of Quantitative data, numbers in the qualitative information are identified. For instance, asking a customer in an online survey for the recommendation of a service or a product on the scale of numbers 0 to 10.

Why Quantitative Data is Important?

Quantitative data talks about precise numbers and figures, unlike qualitative data. Thus the researcher proposes to quantify the attitudes, attributes, behavior and other variables with some motive. The main purpose might be to oppose or back the hypothesis of a particular product or service by representing the data collected through interviewing or surveying the sample. You have the option to go for the traditional collection method or implement the latest technologies to gather the information after appropriate research. The researcher needs statistical, mathematical and computational tools as well to finish the results from the quantitative data.

Quantitative Data

Related: Data Presentation & Analysis, Strategic Analysis, SWOT Analysis

Characteristics of the Quantitative Data

Some of the most important characteristics of the quantitative data are as follows:

  1. Numerical representation: Quantitative data takes up the entire numeric values along with the properties.
  2. Arithmetic Operation: The researcher can perform basic arithmetic operations like subtraction or addition to the quantitative data.
  3. Analysis: It can be examined using inferential and descriptive statistical procedures based on the objectives of the research.
  4. Types: Quantitative data are mainly of two types which are continuous and discrete. Continuous data are further classified into ratio and interval data.
  5. Order: There is an order scale to quantify the data. For instance, one data can be rated as 1,2,3 in an orderly way.
  6. Standardized Scale: Quantitative data includes a measurement scale that includes a standard scale but does not have an order.
  7. Visualization of the Data: Visualization of the data can be accomplished by using some techniques called dot plot, histograms, scatter plot, and stacked dot plot.

Advantages of Quantitative Data

The most noticeable benefits of the quantitative data are:

  • Less Bias: There are many instances in the study of research where personal bias may be involved that lead to an inconclusive result. Due to the numerical value of the data, personal bias is decreased to a higher extent.
  • Conduct in-depth research: All the process of the research is explained in detail because all the data are statistically examined.Exact Results: Since the obtained results are objective, the data is absolutely accurate.

Disadvantages of the Quantitative Data

Apart from the above-mentioned benefits, quantitative data also comes with a number of drawbacks which are:

Dependent on the type of questions: The biases in the questions are directly related to the type of questions that are asked in the collection method of the quantitative data. The knowledge of the question of the researcher and the aim of the research are enormously significant while conducting the analysis of the data.

Classified Information: Before quantitative data was not descriptive. As a result of this, it becomes extremely difficult for the participants to come to the conclusion based entirely on the gathered information.

Thus, Quantitative data is more about divergent thinking than convergent reasoning. It mainly deals with logic, numerical values, and data. Quantitative data is comprehensive in nature and is the only type that could represent the analytics value in terms of graphs and charts. The researchers can conclude precise results due to the integrity and accuracy of the data.

Methods of data collection in Quantitative Data

Since quantitative data is in the number form, full statistical and mathematical analysis of these types of numbers can definitely lead to the establishment of a conclusive result. All the methods for the collection of quantitative data can be completed by using polls and questionnaires. There are generally two types of collection methods for quantitative data. They are:

I) Surveys

Traditionally surveys are conducted on pen and paper. But gradually surveys have evolved to the online mediums. Close-ended questions were more asked in surveys as they are more helpful and efficient in collecting the quantitative data. The surveys also include answers which the correspondent thinks is more appropriate with the question. A survey is the most effective form of collection method of quantitative data as it can reach a larger number of audiences within a short time. The most important factor in the case of surveys is that the responses that are collected should be generalized among the entire population without any discrepancies. Based on the time taken to conduct a survey, they are further divided into two ways and they are as below.

  1. Cross-sectional Studies: Cross-sectional studies are a type of observational research during which the market researcher conducts a survey on any particular time across the sample of the targeted services or products. This collection method of surveys mostly utilizes a questionnaire to understand the nature of a particular subject from the sample of the services or product at a specific time period.
  2. Longitudinal studies: Longitudinal studies are another type of observational research during which a market researcher conducts a survey from one exact time period to another. Longitudinal surveys occur over a considerable time course. This type of collection methods of surveys is mostly employed for the analysis of the trend. It is also implemented in the studies where the main focus is to collect and examine a pattern in the set of data.

To administer a successful survey, the following rules need to be followed and they are:

  • Basic Levels of Measurement: There are a total of 4 scales for measurement which are the most primary elements in the production of MCQs in a survey while collecting the quantitative data. The 4 measurement scales are ordinal, interval, nominal and ratio. Without these scales, no multiple-choice questions can be generated in a survey.
  • Implementation of Different Types of Questions: To create a successful conclusion of a survey, it should include close-ended questions. Multiple choices, rating scales, and semantic differential types of questions are the most popular ones in the surveys. This various type of questions assists in collecting the data that can be examined and made any sense of.

Distribution and Collection of Data in a Survey:

Distribution and collection of data in a survey is another vital characteristic of the process. There are many ways for survey distribution and data collection. But the most common of them are as below.

  1. Purchasing Respondents: The utilization of a sample is the most efficient way of distributing a survey and collection of quantitative data. Most of the respondents have a huge knowledge of the subjects and are also willing to contribute to the studies of research. Thus the responses are much higher in this procedure.
  2. Distribution through Social Media: Distribution of surveys through numerous social media accounts helps to collect huge responses of the people that are already aware of that particular brand.
  3. Surveys through SMS: Short Messaging Services or SMS is another short and time-saving way to collect a higher number of answers from the audiences.
  4. Email: Sending a requesting email along with the survey is another way to distribute and collect the quantitative data. You can also generate an auto thank you response email, once the user has completed the survey.
  5. Embed or Upload the survey on a popular website: Embedding or uploading the survey on a popular website can also get a large number of responses within a glance. The website should have a high organic traffic number.

II) One to One Interviews

This collection method of quantitative data was very popular traditionally. But now it has already shifted to online chatting platforms and telephonic conversations. One to one interview gives the marketer a golden opportunity to collect the necessary data from the audiences directly. Interviews for the collection of quantitative data play a major role in the structured gathering of information. There are mainly three different types of interviews which can be seen in detail as below.

    • Telephonic or Online Interviews: More than telephonic interviews, online platform interviews through Zoom or Skype are has added extra advantage to the interviewers. Communication between the two people living in different time zones, irrespective of the distance between the participants and the interviewer is never a problem with the online interviews. One to one interviews can also be efficiently carried out over the telephone.
    • Computer-Based Personal Interview: In this method of collection of the quantitative data, the researcher enters all the answers of the participants directly into a device like a mobile phone or a laptop. The time for processing is reduced in this system. Also, the interviewer does not have to carry a heavy questionnaire everywhere. All they have to do is enter their laptop and start the process.
    • Face to Face Interviews: In this type of interview, a researcher can add their own list of questions along with those that have already been answered via surveys. In this way, the interviewer can get a detailed insight into the subject in discussion. The researcher can also ask the respondent to explain something in detail that he/she might be not clear of.

III. Probability Sampling

Probability sampling offers the researchers to gather information from some spokesperson of the population who are interested in the research. Thus the probability of the sampling bias is ruled out as the quantitative data is collected randomly from the sample. Probability sampling is classified into three types and they are:

  1. Systematic Random Sampling: In this random sampling, any one of the targeted demographic will be included in a particular sample. But only the first segment for inclusion is randomly selected. All the others are selected in an orderly fashion.
  2. Simple Random Sampling: In this type of random sampling, the targeted demographic is included in the sample.
  3. Stratified Random Sampling: In this random sampling, each section from a specific group of the targeted demographic is selected during the creation of the sample. This is implemented only when the researchers are particular about some particular set of parameters like genders, position in the organization, or people working in that particular industry.

IV) Observation

In this method of collecting quantitative data, the researcher goes through systematic observations by implementing various techniques. For example, counting the total number of people present in a particular event for a specific time is one of them. The researchers require keen observation senses and skills for getting a perfect value of the numerical data about what and not how and why. Observation is implied in the collection of both qualitative and quantitative data. But only structured observation is needed for gathering quantitative information.

Structured Observation

In structured observation, the researchers take careful observations for one or more than one particular behavior in a more structured and comprehensive way. In this type of observation, researchers shift their attention to an only particular set of traits, rather than focusing on everything.

Review of the Documents: Reviewing the document helps to collect data after examining the already existing documents on that specific subject. In this method of data collection, all the documents are practical and reliable sources to obtain the required data from the past. Mainly three types of documents are analyzed in this method and they are as below.

  1. Personal Documents: This type of documents reveals the personal accounts of a particular behavior, actions, physique, health and many more. For instance, distance traveled by the students to attend a school or weight and height of the students.
  2. Public Records: In this type of document review, ongoing and official records of a company are scrutinized for further study in the future. The best example of this kind of documents is manuals for annual report policies, annual sports day results, or annual function in the college.
  3. Physical Evidence: Physical documents represent the achievements of an organization or an individual from the growth and monetary point of view.

Quantitative Data Analysis

Analysis methods of the Quantitative Data

The collection of data is an integral part of the research procedure. The data, however, need to be examined first. There are numerous ways to analyze the quantitative data properly that is gathered through various collection methods. They are:

  1. Analysis of Trend: Analysis of a trend is an analysis of a statistical method that offers to look into the quantitative data that is gathered over a period of time. This method analyzes the feedback about the changes in the data over time.
  2. Conjoint Analysis: Conjoint Analysis analyzes the various parameters that are linked with a purchasing decision. This method collects and examines all the advanced metrics that offer an in-depth detail for the purchasing decisions. It also ranks the parameters according to their importance.
  3. Gap Analysis: Gap analysis implements a side by side matrix to portray the quantitative data that Measures the difference between actual and expected performance of the brand. This analysis method helps to reduce the gap in performance. It also assists in taking the necessary steps to bridge down those gaps.
  4. Text Analysis: Text analysis is one of the most advanced analysis methods where intellectual tools make sense out of the quantitative data. It also quantifies qualitative and open-ended questions into more understandable and simple data. It is mostly implied where unstructured raw data need to be represented in a structured form.
  5. Cross Tabulation: This method is the most widely used one among all the other procedures. It represents the data sets in a tabular form to the interfaces between them in the study of the research. Cross tabulation contains the data sets that have a connection with each other or are mutually exclusive.
  6. MaxDiff Analysis: MaxDiff analysis gauges the preferences of the customers for making a buying decision and what parameters are ranked higher compared to others in this method. This process is also known as the best-worst analysis. MaxDiff is very much identical like conjoint analysis but can be used interchangeably and very simple to use.
  7. TURF Analysis: Total Unduplicated Reach and Frequency or TURF analysis accesses the total reach of a particular product or service in a market. This process is mostly implemented by the companies to understand the avenues or frequencies at which their messages are reaching their customers. In this way, they can also tweak their strategies to go to market with the help of the potential customer’s feedback.
  8. SWOT Analysis: SWOT analysis assigns numerical data to denote the strength, weaknesses, Opportunities, and Threats of an organization. SWOT analysis also offers a holistic depiction of the competition in the market for that particular service or product. It also assists in creating the most effective and efficient business strategies.

Also Read: Competitor Analysis, Cluster Analysis, Information Processing Cycle

Tools Used During Analysis of Quantitative Data

Below are lists of some of the most commonly used tools that are required during the analysis of the quantitative data. They are:

  • R is a language that is open-sourced for the visualization and manipulation of the data
  • SAS assists in improving the quality and efficiency of the data, and measuring the required performance.
  • Statistical Package for the Social Sciences or SPSS allows different types of analysis like SWOT, TURF, MaxDiff, cross-tabulation and many more. It is mostly used to handle all types of complex data in the field of social science and medicine.
  • STATA is mainly used for representing the data sets in econometrics in statistics and graphs. It helps the researcher to apply programming to make the necessary changes.
  • Excel helps to present an insight that is data-driven with formulas already present in it. Specific programming languages like VBA can also be used in Excel.
  • The researchers use different types of tools like Python, Matlab, and R for coding the data.

How to Conduct Analysis for Quantitative Data? 

In the case of quantitative data, the raw information needs to be represented in a meaningful and structured manner using various analysis methods. The data should be properly examined in order to identify the evidential data that would accelerate the procedure. Now let us see how to conduct an analysis for the quantitative data in detail as below.

1. Relation of the variables with the measurement scales

Different measurement scales like Ordinal, Ratio, Nominal and Interval need to be linked with the variables. This is the foundation step for the proper arrangement of the data in order. Data needs to be arranged in a specific format in an excel sheet or other documents.

2. Connection of the data with the descriptive statistics

The data needs to be encapsulated within a descriptive statistics by linking them together. Sometimes it becomes difficult to identify a pattern in the raw data. Some of the most widely implemented descriptive statistics are as below.

  • Median is the middle point of the scale for value of a variable
  • Frequency is the total number of times a specific value is identified in a scale
  • A percentage is a format to express the set of values or scores for the variables
  • Mean is the average numerical value for a particular variable
  • A mode is the most common value for a variable
  • Maximum and minimum values are the highest and lowest values in a scale

3. Deciding a Scale for Measurement

For the conclusion of the descriptive statistics of a variable, it is vital to decide the scale for the measurement. For example, a score of the nominal variable will not have a median or a mean. Thus the descriptive statistics will vary correspondingly. Descriptive statistics meet your requirements in those circumstances where conclusions are not generalized among the population.

4. Selection of appropriate tables for the examination and representation of the gathered data

After making up your mind about which scale for measurement to implement, researchers can represent the collected data in a tabular format. The data can be examined using different analyzing methods like TURF or SWOT.

Examples of the Quantitative Data

In each of the ten examples that are listed below, a numerical value is given to every parameter. This value is known as quantitative data. The examples of the data are:

  • My phone is updated 7 times quarterly
  • 85 people downloaded my mobile application from the Google store
  • 190 respondents have given their opinion that the new product will not be successful
  • Almost 1500 people attended his seminar
  • She has taken 15 holidays this year
  • My son grew by 9 inches every year
  • My mother lost 27 pounds in the year 2019
  • The company will see a rise of 36% due to the inclusion of the new service
  • 87% of people prefer ordering things online than going for shopping to a mall
  • This t-shirt costs $10