Demographic variables

Demographic variables
Primary topic
Related topics
Methods and techniques

Demographic variables is a term describing variables related to specific and personal statistics of researched person. Great deal of such information as gender, race, ethnicity, age, religion, income level, education level, occupational prestige and so on are important to distinguish in statistics[1]. It helps in appropriate conduct of the research, real interpretation of results and drawing accurate conclusions. Companies using well-prepared research with defined mano of demographic variables can detect dependencies which may result in improvement of realising strategy[2].

Demographic variables as a step of decision making[edit]

In statistics, demographic variables are one of the factor, which appears in almost every research. All over the years great deal of research was conducted and thanks to possibility to analyze their results, people carrying out the tests may see specific correlations and how it changes over time.

Demographic variables most of the time include such variables:

  • gender,
  • race,
  • ethnicity,
  • age,
  • religion,
  • socioeconomic status (income level, education level, occupational prestige),
  • family size.

Political issues, shopping trends, level of happiness and business are only a few areas where research using distinguished demographic variables can provide relevant information[3]. For example, good demographic analysis for retailer is a important factor to formulate target customer profile and make a decision where to locate a new outlet, how high should price be, which promotions may be effective or if the products from the inventory would be attractive enough to earn proper value. Even already located industries can change strategy and find a solution to better match local market conditions. There are many more examples that it is one of the factor which can be used as valuable step during decision making process[4].


  1. J. P.Stevens 2012, 7
  2. K. Kalyanam & D. S. Putler 1997, 129
  3. R. A. Pollak & T. J. Wales 1981, 1543)
  4. K. Kalyanam & D. S. Putler 1997, 132)


  • Achen, C. H. (1992). Social psychology, demographic variables, and linear regression: Breaking the iron triangle in voting research. “Political behavior”, 14(3), 195-211.
  • Frankfort-Nachmias, C., & Leon-Guerrero, A. (2017)., Social statistics for a diverse society, Sage Publications, Thousand Oaks, 269.
  • Gupta, S., & Chintagunta, P. K. (1994). On using demographic variables to determine segment membership in logit mixture models . “Journal of Marketing Research”, 31(1), 128-136.
  • Huber, P. J. (2011). Robust statistics. Springer Berlin Heidelberg, Berlin , 1248-1251.
  • Kalyanam, K., & Putler, D. S. (1997)., Incorporating demographic variables in brand choice models: An indivisible alternatives framework . “Marketing Science”, 16(2), 166-181.
  • Lewbel, A. (1985). A unified approach to incorporating demographic or other effects into demand systems. . “The Review of Economic Studies”, 52(1), 1-18.
  • Pollak, R. A., & Wales, T. J. (1981)., Demographic variables in demand analysis. . “Econometrica: Journal of the Econometric Society, 1533-1551.
  • Stevens, J. P. (2012)., Applied multivariate statistics for the social sciences, Routledge, Abingdon, 7.

Author: Krystian Prorok