Class Notes on RCTs and Sampling Techniques

Learning Outcomes for Session:

• Introduction to Randomized Control Trials (RCTs): Definition and design elements.

• Overview of Sampling: Importance in research, with focus on two types of sampling:

• Probability Sampling: Random sampling where each participant has equal chance of selection.

• Non-Probability Sampling: Participants are selected based on non-random criteria, often making it difficult to generalize results.

Power Calculations: Essential for determining the sample size needed for a study to achieve meaningful results. Critical questions include:

• How many participants to recruit? Examples include:

• 10 individuals may not be sufficient.

• Is 100 or 500 needed for certain studies?

Clarification on Random Sampling and Qualitative Studies:

• Probability Sampling: Used to ensure that every individual, say in a class, has an equal chance of being selected.

• Qualitative Research: Focused on understanding human experiences and perspectives, often requiring non-probability sampling methods:

• Example: Studying specific populations like homelessness where random sampling isn’t feasible.

Examples of Sampling Methods:

• Probability Sampling:

• Cluster Sampling: Dividing the population into clusters and then randomly selecting from those clusters.

• Commonly used in research studies where random lists of individuals are not available.

• Non-Probability Sampling:

• Convenience sampling: Selecting individuals that are easily accessible (e.g., students in a college).

• Snowball Sampling: Participants refer other participants; useful in hard-to-reach populations.

• Quota Sampling: Selecting individuals to meet a predefined quota.

Research Design Elements:

• Distinction between experimental and non-experimental designs:

• Experimental Design: Involves manipulation of a variable with a control group.

• Non-Experimental Design: Observational without intervention (e.g., just measuring behavior).

• Emphasis on how experimental designs provide superior evidence of cause and effect.

PICO Framework:

• P - Population: Target group (e.g., nursing students).

• I - Intervention: The treatment being studied (e.g., mindfulness-based stress reduction).

• C - Comparison: Control group receiving usual care.

• O - Outcome: What is being measured (e.g., levels of depression, anxiety).

Flowchart in RCT: Demonstrates recruitment, eligibility, random assignment, and comparison of groups. Vital for understanding participant dropout rates.

Statistical Significance and Power Calculations:

• Importance of having an adequate sample size to detect statistical differences between groups and validate the study findings.

• Software tools, such as G-Power, are used to perform these calculations.

Assessment Overview:

• First student assessment involves creating an infographic summarizing a study’s PICO elements and inclusion/exclusion criteria.

• Importance of proper citation, although minimal for this assignment.

• Ensure understanding of RCT and how it applies to the upcoming group activities and assessments.

Summary of Session Goals:

• Comprehend foundational concepts of RCTs and sampling strategies, engage in group learning activities, and develop skills for critical appraisal of research articles.