The Key Characteristics of Quantitative Design


Before collecting data and performing analyses, researchers of quantitative properties must design an appropriate approach to answering the question of interest. A researcher employs quantitative design to assure that her research methods are valid and will yield scientifically proper results. Thus, a researcher interested in quantitative data or planning to perform quantitative analysis needs to understand the characteristics of quantitative design.

Independent Variable

  • Every quantitative design must specify an independent variable. The independent variable is the one that the researcher believes has an effect on the phenomenon under investigation. For example, if a researcher is planning to investigate how the amount of money a man invests in the stock market can predict his number of past romantic partners, he will set "amount of money invested in the stock market" as the design's independent variable.

Dependent Variable

  • Researchers must state the study's dependent variable in the quantitative design. Quantitative designs make a hypothesis about how one variable (the independent variable) affects another variable. This other variable is the dependent variable; it is the variable in which researchers wish to observe change. The idea of a quantitative design is to construct a project in which the independent variable and dependent variable are related. Thus, a researcher must specify the dependent variable in her design. For the previous example, where the researcher is interested in seeing the effect of investment size on the number of romantic partners, "number of romantic partners" is the dependent variable.


  • The quantitative design also specifies how the researcher will collect the data for study. This data is called a sample. The quantitative design should state what sampling methods will be used (random sampling is often ideal, but not always possible), what population this sample is to represent and how such a sample size will influence the resulting analysis. For example, when researching how investments affect a man's romantic life, a quantitative researcher may sample at random 100 males who invest in the stock market.


  • Quantitative designs must identify the potential confounders, or influences, on the dependent variable that are unrelated to the independent variable. The process of accounting for these influences in the quantitative design is called "control." Researchers must control the confounders in a study so that the analytical inferences hold. In the stock-market-and-romantic-partner example, one potential confounder that researchers must deal with in their designs is age -- older men have had more time for romantic relations, so researchers must be wary of how age influences the dependent variable.


  • "The Reviewer's Guide to Quantitative Methods in the Social Sciences"; Gregory Hancock and Ralph Mueller; 2010
  • "Designing Quantitative Experiments: Prediction Analysis"; John Wolberg; 2010
  • Photo Credit Jupiterimages/BananaStock/Getty Images
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