Observational Study

TL;DR

• Researchers record data on subjects without intervening or manipulating variables.
• Commonly used to identify relationships between variables or to study behavior in natural settings.
• Typically less expensive and faster than experiments but cannot establish causation and is prone to bias and data limitations.

Definition

An observational study is a research method in which researchers observe and collect data on subjects without manipulating or intervening in any way.

Explanation

Observational studies are used to examine the relationship between two variables or to understand the behavior of a particular group of people by observing subjects in their natural environment rather than controlling or altering conditions. Because researchers do not manipulate variables, findings describe associations (correlations) rather than causal effects. Observational studies tend to be less expensive and less time-consuming than experimental studies, but they are susceptible to bias from inaccurate self‑reporting and may be limited by the availability of necessary data.

Examples

Longitudinal study

A longitudinal study involves following the same group of people over a period of time. For example, a researcher may conduct a longitudinal study to understand the long-term effects of a particular type of diet on weight loss. In this case, the researcher would recruit a group of participants and collect data on their weight, diet, and other relevant factors at regular intervals. The researcher would then analyze the data to see if there is a relationship between the diet and weight loss.

Cross-sectional study

A cross-sectional study involves collecting data from a group of people at a specific point in time. For example, a researcher may conduct a cross-sectional study to understand the prevalence of a particular health condition within a population. In this case, the researcher would recruit a representative sample of people and collect data on their health status, lifestyle habits, and other relevant factors. The researcher would then analyze the data to see if there is a relationship between the health condition and other variables.

Use cases

• Examining the relationship between two variables.
• Understanding the behavior of a particular group of people in real-world settings.

Notes or pitfalls

• Observational studies cannot establish causality because they do not involve manipulating variables.
• They are prone to bias, for example when subjects do not accurately report their behaviors or characteristics.
• Findings may be limited by the availability of data needed to fully understand a phenomenon.

Related terms

• Longitudinal study
• Cross-sectional study
• Experimental studies