PSY 652: Research Methods in Psychology I
Classification of Data Science Tasks
Three primary tasks
Description
- Summarizes features of the world (e.g., diabetes prevalence, social network visualization).
- Ranges from simple (e.g., mean) to advanced (e.g., cluster analysis).
Prediction
- Uses variables (features) to predict an outcome.
- Ranges from simple (e.g., correlation) to advanced (e.g., complex regression, machine learning).
Causal Inference
- Identifies the causal effect of an intervention or condition on outcomes (e.g., determining how much vaccination reduces infection rates).
- Involves randomized experiments or applying methods like matching and instrumental variables to observational data.
Important Applications
Key applications of Description: COVID-19
Key applications of Description: Biodiversity Loss
Key applications of Prediction: Hurricane Forecasting
Key applications of Prediction: Medical Decision Making
Key applications of Causal Inference: RCT
Key application of Causal Inference: Air Pollution
Leveraging the Three Data Science Tasks
An Example: Physical Activity and Cardiovascular Health
Description
What are you trying to learn?
- What is the average daily physical activity level (in minutes) among adults aged 40-65 in the U.S.?
What are the ideal data to answer this question?
A large national health survey (e.g., NHANES) that includes self-reported or accelerometer-measured physical activity data.
Demographic information (age, sex, education, etc.) to describe the population.
What’s the purpose of this?
- To summarize and understand the baseline levels of physical activity in this population, which could inform public health recommendations and awareness campaigns.
Prediction
What are you trying to learn?
- Can wearable device data (e.g., heart rate, step count, and sleep quality) predict the likelihood of developing cardiovascular disease in the next five years among adults aged 40-65?
What are the ideal data to answer this question?
Longitudinal data from wearable fitness trackers that capture continuous measures like heart rate variability, step count, sleep patterns, and stress levels.
Follow-up clinical data on cardiovascular health outcomes (e.g., diagnosis of hypertension, heart disease).
What’s the purpose of this?
- To explore how real-time, personalized data from wearables can be used to predict cardiovascular disease risk, potentially offering more timely and individualized health interventions and a method for early detection of disease.
Causal inference
What are you trying to learn?
- Does using an Apple Watch with personalized activity tracking and health notifications lead to a reduction in cardiovascular disease incidence compared to standard care in adults aged 40-65?
What are the ideal data to answer this question?
- A randomized controlled trial (RCT) involving adults aged 40-65 at risk for cardiovascular disease but without a prior diagnosis.
- Participants are randomly assigned to either receive an Apple Watch, which tracks their physical activity, heart rate, and sleep while providing personalized goals and health insights, or to a control group receiving standard care with general physical activity advice.
- The primary outcome is incidence of cardiovascular disease 5 years post-intervention, with secondary outcomes including changes in physical activity, heart rate variability, and other health indicators.
What’s the purpose of this?
- To determine whether the use of an Apple Watch, which offers personalized activity monitoring and health alerts, has a causal effect on reducing the risk of cardiovascular disease compared to receiving standard health advice.
Your turn
