Health Research Methods Quiz

GTU 304/3 - RESEARCH METHODOLOGY

• This course covers research methodology, specifically focusing on quantitative research designs.
• The presentation was delivered on November 28th, 2024, for a Semester 1, 2024/2025 academic session.
• The presenter is Dr. Faridah Naim, from the Environmental and Occupational Health department.

Learning Outcomes

• Students will understand various research designs and main types of quantitative research used in Health Sciences.
• Understand the differences between descriptive and analytic quantitative studies.
• Learn about the design features, strengths, and limitations of different types of quantitative studies, including ecological studies, cross-sectional, cohort, case-control, randomized controlled trials, meta-analysis, and systematic reviews.
• Distinguish between prospective, retrospective, observational, and experimental studies.
• Interpret measures of association for different study designs.
• Understand the differences between quantitative and qualitative study approaches.

Quantitative Study

• A positivistic study approach that involves computing numerical values to represent phenomena.
• Based on the scientific method, involving hypothesis formulation, objective experiments, and sound reasoning supported by data.
• Examples in positivistic research include studying the association between cigarette smoking and lung cancer, involving statistical calculations based on predictor and response variables.

Observational Studies

• Quantifies phenomena without intervention to alter outcomes.
• Useful to identify trends and variables of interest.
• Examples include epidemiological studies.

Descriptive Studies

• Describe patterns of disease occurrence in relation to time, person, and place.
• Examples include trends in current cigarette smoking among high school students and adults and UK obesity rates.

Case-Report and Case-Series

• A case-report documents an unusual case of a disease by a physician.
• A case-series documents a set of consecutive cases of a disease.
• Together, they're useful for identifying potential causal hypotheses for analytic studies.

Ecological Studies

• Focuses on correlating groups rather than individuals.
• Data represent aggregate exposures/disease for regions, worksites, schools, etc.
• Examples include meat intake and colon cancer incidence, and chocolate consumption vs. Nobel prize winners.
• Limitations include ecological fallacy, meaning that group-level associations may not hold true for individuals.
• Understanding the strengths and weaknesses of ecological studies is important.

Cross-Sectional Studies

• A snapshot of a population at a specific point in time, assessing exposure and outcome together.
• Also known as prevalence studies or surveys.
• Useful for estimating the prevalence of diseases and exposures.
• Examples include studies on e-cigarette use and second-hand exposure to asthma symptoms.
• Defining point prevalence and cumulative incidence are important.

Retrospective Studies

• Examine an outcome that has already happened using existing data.
• Often used in case-control studies to identify risk factors for a disease.
• Examples may include data from patient charts or existing medical data.
• Common biases include subject selection bias, and patient recall bias.

Prospective Studies

• Start with a hypothesis and collect data over a defined timeframe
• Defining characteristics include a forward orientation in time and defining characteristics.
• These are helpful for assessing disease development in groups over time after an exposure is established.

Cohort Studies

• Involves following a group of people over time to observe the development of disease.
• Useful for assessing the incidence of disease in relation to exposure.
• Examples include the NIH-AARP Diet and Health Study and the EPIC cohort.
• Methods for assessing exposure include self-reported data, and clinic-measured data.
• Understanding defining exposures and types of outcomes is important.
• Bias in cohort study may include selection bias or information bias.

Case-Control Studies

• Comparing individuals with a disease (cases) to individuals without the disease (controls).
• Useful for studying rare diseases or exposures.
• Gathering information on past exposures helps investigate possible risk factors.
• Understanding how cases and controls are defined and selected, as well as possible biases is crucial.

Causal-Comparative/Quasi-Experimental Studies

• Studying a phenomenon where random assignment isn't possible.
• Researcher controls independent variables to study relationships.
• This research design may not be generalizable to the entire population due to limitations in random assignment.

Randomised Controlled Trials

• Patients randomly assigned to treatment or control groups.
• Researcher controls for explanatory variables such as age and gender.
• Commonly used to assess the effectiveness of medical treatments or interventions.
• Often used in situations where a controlled intervention is possible.

Community Trials

• Involves intervening with whole communities rather than individuals to assess the effects of an intervention.
• Often used in public health interventions or preventive studies.

Before-After (Pre-Post) Studies

• Measuring a variable of interest before and after an intervention.
• Used to see if there's a relationship and if the intervention had an impact.
• Can be useful for a quick evaluation of the intervention's effect. However, without a control group it is hard to isolate the cause/effect.

Observational vs Experimental Studies

• Observational studies involve observation of populations/individuals without intervention, whereas experimental studies involve determining and controlling exposures.

Meta-Analysis

• Combining data from several studies to give a more reliable estimate of effectiveness or impact.

Systematic Reviews

• A comprehensive survey of a topic that takes great care to find all relevant studies, both published and unpublished, and provides a balanced summary of the findings.

Quantitative vs Qualitative Studies

• Quantitative research involves numbers and focuses on understanding the causal relationship or statistical differences, whereas Qualitative research involves words, providing insights into lived experiences and phenomenon.