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Quantitative Research Designs

Qualitative research design can be used to test hypothesis, confirm assumptions and theories, and determine cause-and-effect relationships. Quantitative research methods include experiments, surveys etc. and data analysis relies on statistical methods.

  • Presentation: Basic and Advanced Designs in Primary Care Research – Dr. Ric Angeles, Research Knowledge and Skill Builder [Video | Slides]
  • Presentation: Research Study Design 101 for Primary Care by Drs. Michelle Howard and Gina Agarwal, Faculty Spring Retreat

Types of Quantitative Study Designs

Non-experimental

Observations or measurements are made without intervening or manipulating variables

Quasi-experimental

Includes an intervention but lacks random assignment to groups

Experimental

Participants are randomly assigned to intervention and control groups

Non-experimental or Observational Designs

Non-experimental designs are used to observe and analyze relationships between variables without manipulating them. Researchers do not assign interventions or control conditions; instead, they study phenomena as they naturally occur.

Descriptive Design

Descriptive studies describe characteristics of a population or phenomenon—essentially to discover “what is.” It provides a snapshot of current conditions, helping identify trends and patterns, and often serves as a foundation for more analytical research. This design is ideal for measuring variables and exploring associations, but it does not establish causality.

Analytical Designs

Correlational Design: is a non-experimental method used to examine the statistical relationship between two or more variables without manipulating them. It helps researchers determine whether variables are positively, negatively, or not at all related, but it does not establish cause-and-effect relationships.

Causal-comparative Design: is a non-experimental approach used to explore cause-and-effect relationships between variables by comparing groups based on a pre-existing characteristic. Unlike experimental designs, researchers do not manipulate the independent variable but instead examine its impact retrospectively. It is useful when experimental manipulation is impractical or unethical, offering insights into potential causal links through group comparisons.

Quasi-experimental Designs

It aims to establish cause-and-effect relationships between variables, similar to true experiments, but without random assignment of participants. Researchers measure an independent variable (e.g., study time) and observe its effect on a dependent variable (e.g., test scores). Participants are grouped based on existing attributes rather than random selection, which introduces potential bias but allows for practical application in real-world settings. While control groups are not mandatory, they are often used to strengthen the study’s validity. This design is especially useful when ethical or logistical constraints prevent full experimental control.

Experimental Designs

It is used to test hypothesis by manipulating one or more variables and observing the effect on other variables. They are considered one of the most rigorous methods for determining causal relationships. The researchers introduce an intervention and study the effects. Experimental studies are usually randomized, meaning the subjects are grouped by chance.

Randomized controlled trial (RCT): In an RCT, eligible people are randomly assigned to one of two or more groups. One group receives the intervention (such as a new drug) while the control group receives nothing or an inactive placebo. The researchers then study what happens to people in each group. Any difference in outcomes can then be linked to the intervention.