Quantitative data characteristics

By Priya Chetty on February 24, 2020
Photo by Fauxels from Pexels

Quantitative data refers to statistical datasets that express certain quantities, amounts and ranges. Measurement units like meters are used to express quantitative data numerically. Arithmetic operations are also applied to quantitative data for conducting research. Moreover, as essential mandate related to quantitative data is that they are not infinite and limitations are often defined (OECD, 2006). Quantitative data characteristics exhibits:

  • Ability to be analysed statistically for obtaining results
  • Flexibility to meet different study objectives
  • Range of different data collection methods

Since quantitative data is numeric in nature, it can be set in an orderly manner and processed, and the frequency of observation can also be counted. It can be used for developing a broader understanding of a particular feature of a population (Australian Bureau of Statistics, 2013). It uses a variety of scales classified as nominal, ordinal,  interval and ratio (Kabir, 2016).

Quantitative data characteristics
Figure 1: Quantitative data characteristics

Ability to be analysed statistically

One of the most important quantitative data characteristics is that it can be analysed statistically in order to get results (Apuke, 2017). Thus, analysis of quantitative data helps in interpreting variables like:

  • who,
  • how much,
  • what,
  • where, 
  • when, 
  • how many and,
  • how that relates to a specific phenomenon or incident.  

The research of (Kanapur, 2017) was conducted for the identification of the most significant variables that determine employee retention. Twelve variables of employee retention were identified. Quantifiable primary data from 125 employees from four sample organizations were collected. The following arithmetic formula was used for testing the data.

Quantifiable primary data analysis

This research aimed at finding out ‘what’ factors influence employee retention and ‘how much’ significant is one factor over another one through collection and analysis of quantitative data.

Flexibility to meet different objectives

The next quantitative data characteristic is that statistical tools are employed to analyse these data sets at different levels of the study.  In other words, statistical data analysis is used at different stages of the study ranging from planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings when quantitative data are used.  

Example 1

In this context, the study of Elvina and Zhi Chao (2019) can be considered. The research aimed to study the relationship between employee motivation and work performance. The quantitative study was chosen for the purpose and quantitative data was collected through a survey. SPSS software was chosen for the purpose of analysing the survey data. Pearson product-moment correlation coefficient method used for testing the two hypotheses that were developed for the study.  

Example 2

Tremblay et al. (2009) conducted a research on creating an extrinsic and intrinsic motivation scale for employees. 18 variables were selected as work motivators and the individual effect of these variables were tested upon 657 sampled respondents. Quantitative data was collected from these respondents through a questionnaire. Correlation analysis was done for each of the variables and the responses of the individuals towards each of these variables of work motivation were plotted on a Likert scale.

Comparison of the two examples above suggests that in the first example, an attempt was made by the researcher to find out ‘what’ factors of employee motivation have most influence upon employee retention. In the case of the second example, the researcher attempted to find out ‘how much’ does each of the variables affect employee motivation. On one hand, the quest to find ‘what’ and ‘how much’ connected to specific phenomenon establishes that both the researches are quantitative in nature. Moreover, the statistical tests applied indicate the distinctive statistical manner in which the data was analysed. So, the studies exemplify quantitative data characteristics accurately.

Range of data collection methods

Quantitative data is collected through four distinctive methods. They are:

  • Survey: involves the collection of secondary data or use of a questionnaire to collect feedback from a pre-defined set of respondents.
  • Correlational method: statistical testing of numerical data of two variable to test the relationship between them.
  • Experimental research: involves running an experiment on a specific group of people to derive statistical findings.
  • Casual-comparative method: involves testing of secondary or primary data of independent and dependent variables to identify the cause-effect relationship between them.
Types of quantitative data research
Figure 2: Types of quantitative data research

Example 1

Lahey (2013) aimed to understand the age and hiring percentages of women in the workforce. The researchers implemented an experimental method, and a comparative analysis of Value at Risk (VaR) and Expected Shortfall (ES) was made.

Example 2

Jaya Surian and Vezhavendan (2018) implemented a survey method to understand the average number of children employed in child labour before and after implementation of the scheme.  The data that was gathered after conducting a survey on a sampled population of 100 individuals was then put to statistical analysis.  

Example 3

Monica, Dharmmesta and Syahlani (2017) conducted research with the intention of understanding the relationship between service quality, customer satisfaction and customer loyalty in a pharmaceutical company. The researchers decided to implement the method of correlational analysis in order to collect, analyse and interpret the data.

The three examples used in this section suggest that the collection of quantitative data was the preliminary priority of each of the studies. Three different data collection methods were chosen for the purpose of data collection which is a survey, correlational method and survey. This highlights the mentioned quantitative data characteristics discussed above.

Distinguishing quantitative data characteristics

Quantitative data is distinguished by the manner that it can be verified, analysed and interpreted through quantifiable methods. Furthermore, certain quantitative data characteristics that relate to the manner in which such data are collected and analysed. Thus, while survey, correlational method and experimental research are some of the ways in which quantitative data are collected, SPSS, Likert Scale are some tools that are usually used for analysis. The distinctiveness of quantitative data is that they intend to find out ‘who, how much, what, where, when, how many,  and how’ related to any phenomena.


  • Apuke, O. D. (2017) ‘Quantitative Research Methods : A Synopsis Approach’, Kuwait Chapter of Arabian Journal of Business and Management Review. Al Manhal FZ, LLC, 6(11), pp. 40–47. doi: 10.12816/0040336.
  • Australian Bureau of Statistics (2013) Statistical Language – Quantitative and Qualitative Data.
  • Elvina, S. and Zhi Chao, L. (2019) ‘A Study on the relationship between employee motivation and work performance’, IOSR Journal of Business and Management (IOSR-JBM), 21, pp. 59–68. doi: 10.9790/487X-2103025968.
  • Jaya Surian, B. and Vezhavendan, D. (2018) ‘An Empirical Study on the Status of Child Labour before and after the Implementation of National Child Labour Project Scheme in India’, International Journal of Pure and Applied Mathematics, 119(17), pp. 247–267.
  • Kabir, S. M. (2016) ‘METHODS OF DATA COLLECTION’, in BASIC GUIDELINES FOR RESEARCH: An Introductory Approach for All Disciplines. Book Zone Publication, p. 557.
  • Kanapur, M. (2017) ‘A Study on Retention Analysis of Employees Working in Indian Construction Industry’, International Journal for Research in Applied Science and Engineering Technology. International Journal for Research in Applied Science and Engineering Technology (IJRASET), V(VIII), pp. 1290–1295. doi: 10.22214/ijraset.2017.8183.
  • Lahey, J. (2013) ‘Age, Women, and Hiring: An Experimental Study’, SSRN Electronic Journal. Elsevier BV, 43. doi: 10.2139/ssrn.2316964.
  • Monica, E., Dharmmesta, B. S. . and Syahlani, S. P. (2017) ‘Correlation Analysis Between the Service Quality, Customer Satisfaction, and Customer Loyalty of Viva Generik Pharmacy in Semarang’, Journal of Pharmaceutical Sciences and Community, 14(2).
  • OECD (2006) Glossary of Statistical Terms.
  • Tremblay, M. A. et al. (2009) ‘Work Extrinsic and Intrinsic Motivation Scale: Its Value for Organizational Psychology Research’. doi: 10.1037/a0015167.

Priya is the co-founder and Managing Partner of Project Guru, a research and analytics firm based in Gurgaon. She is responsible for the human resource planning and operations functions. Her expertise in analytics has been used in a number of service-based industries like education and financial services.

Her foundational educational is from St. Xaviers High School (Mumbai). She also holds MBA degree in Marketing and Finance from the Indian Institute of Planning and Management, Delhi (2008).

Some of the notable projects she has worked on include:

  • Using systems thinking to improve sustainability in operations: A study carried out in Malaysia in partnership with Universiti Kuala Lumpur.
  • Assessing customer satisfaction with in-house doctors of Jiva Ayurveda (a project executed for the company)
  • Predicting the potential impact of green hydrogen microgirds (A project executed for the Government of South Africa)

She is a key contributor to the in-house research platform Knowledge Tank.

She currently holds over 300 citations from her contributions to the platform.

She has also been a guest speaker at various institutes such as JIMS (Delhi), BPIT (Delhi), and SVU (Tirupati).