WEEK 7 CROSS-SECTIONAL & COHORT STUDY

Observational Studies

• Definition: Researchers observe subjects in their natural environment without intervention.
• Types: Includes cohort studies, case-control studies, and cross-sectional studies.
• Key Point: They can suggest correlations but do not establish definitive cause-and-effect relationships.

Experimental Studies

• Definition: Researchers conduct controlled tests where they manipulate one or more variables.
• Goal: Aim to establish a cause-and-effect relationship between variables.
• Key Point: Variables are controlled to isolate effects and determine causal relationships.

Differences Between Observational and Experimental Studies

• Observational Studies: No manipulation of variables; record occurrences naturally.
• Experimental Studies: Involves manipulation of variables to test outcomes.

Cross-Sectional Studies

• Definition: Captures data at a single point in time among participants.
• Purpose: Often used to assess prevalence of a condition or disease in a population.
• Nature: Descriptive and can identify associations but cannot establish causality.
• Example: Comparing prevalence of stress levels based on seating position in a classroom.
• Limitations: No follow-up or temporality, hence limited in drawing causal conclusions.

Cohort Studies

• Definition: Follows a group of people over time to assess changes and outcomes.
• Nature: Data can be recorded prospectively (future) or retrospectively (past).
• Purpose: Useful for studying incidence and causes of diseases.
• Causality: It can provide evidence for causality as exposure is recorded before outcomes.
• Example: Investigating health outcomes in individuals with different exposure statuses (e.g., comorbid health conditions).

Hierarchy of Evidence in Research

• Clinical Interventions: RCTs (Randomized Controlled Trials) are rated highest.
• Cohort Studies: Represent significant evidence for prognosis and causality, placed just below RCTs.
• Importance: Understanding the evidence hierarchy is essential for program/policy applications.

Conducting a Cross-Sectional Study - Example

• Research Question: Are people at the front of a classroom more stressed than those at the back?
• Null Hypothesis: No significant difference in perceived stress levels based on seating position.
  • H0: No difference between perceived stress of students sitting at the front and back.
• Methodology: Use a stress assessment tool (e.g., Stress-o-meter) to measure stress levels.
• Statistical Analysis: Use t-tests to compare means of two groups (front vs. back).

Potential Biases in Cross-Sectional Studies

• Validity/Reliability: Stress measures may lack established validity.
• Self-Report Limitations: Issues such as recall bias or social desirability factor into results.
• Temporality: Cross-sectional studies represent one point in time; cannot infer causality.