This glossary collects the key concepts, people, and ideas you will meet in this lesson. Use it as a reference while you work through the material, or as a review before assessments. Type in the search box to filter entries.

Introduction and Overview

Welcome to HSCI 410. If you've come from HSCI 230 and 341, you've spent two courses learning how to read epidemiological evidence (230) and how to design and conduct epidemiologic studies (341). HSCI 410 picks up the next link in the chain: how to analyse the data those studies produce. The R skills you've been practicing in the orange boxes throughout 230 and 341 scale up here into the full statistical machinery of modern public-health analysis — linear, logistic, multinomial, ordinal, count, and survival regression; mixed models for clustered and longitudinal data; and increasingly causal-inference tools for observational data. This first lesson sets the foundation: a disciplined workflow for taking raw data from collection through to analysis-ready files. The structured approach has become especially important since the wider scientific community recognised a reproducibility crisis in published research (Ioannidis, 2005; Open Science Collaboration, 2015; Wikipedia: Replication crisis) and called for a manifesto of reproducible practice (Munafò et al., 2017). Across four content sections we walk through this in order: introduction and data collection (Section 1), data coding, entry, and file management (Section 2), program files, editing, and verification (Section 3), and data processing plus the first unconditional associations (Section 4).

Why a Structured Approach?

When starting the analysis of a complex dataset, it is very helpful to have a structured approach in mind. For most people, there is a strong tendency to jump straight into the sophisticated analysis that will provide the ultimate answer. This rarely works out—the results will inevitably be wrong because important preliminary steps were skipped.

Start with a Causal Diagram

Before you start any work with your data, it is essential to construct a plausible causal diagram of the problem you are about to investigate. This will help identify:

• Which variables are important outcomes and predictors
• Which are potential confounders
• Which might be intervening variables between your main predictors and outcomes

From DAG to Regression: A Mediation Example

One of the clearest places to see this bridge is mediation. A DAG of the form X → M → Y with a residual direct path X → Y is the qualitative claim; the Baron & Kenny (1986) procedure introduced in 341 puts numbers on the direct and indirect components. Below we do that fitting in R, on simulated data so you can verify the answer against the truth.

# install.packages(c("mediation", "dagitty"))
library(mediation)

# 1. Simulate data that match the DAG: education -> income -> health,
#    with a smaller direct path education -> health.
set.seed(410)
n         <- 800
education <- rnorm(n)
income    <- 0.6 * education + rnorm(n)              # path a
health    <- 0.3 * education + 0.5 * income + rnorm(n)  # direct + path b
dat       <- data.frame(education, income, health)

# 2. Baron & Kenny by hand --------------------------------------------------
#    Step 1: total effect c  (health on education)
coef(lm(health ~ education, data = dat))["education"]

#    Step 2: a (income on education)
fit_M  <- lm(income ~ education, data = dat)

#    Step 3: direct c' (education) and b (income), from health on both
fit_Y  <- lm(health ~ education + income, data = dat)
coef(fit_Y)                     # c' on education, b on income

# Indirect effect = a * b  (or equivalently c - c')
a <- coef(fit_M)["education"]
b <- coef(fit_Y)["income"]
a * b

# 3. Same answer, with bootstrap CIs, via the mediation package -------------
med <- mediate(fit_M, fit_Y,
                treat    = "education",
                mediator = "income",
                boot     = TRUE, sims = 1000)
summary(med)

# Create directories from R (or by hand). Run once at project start.
dir.create("data/raw",        recursive = TRUE)
dir.create("data/processed",  recursive = TRUE)
dir.create("R");  dir.create("output/figures", recursive = TRUE)

# tidyverse: dplyr (manipulation), ggplot2 (graphics), readr (file IO),
# tidyr (reshape), stringr (text). Install once.
# install.packages("tidyverse")
library(tidyverse)
library(here)                                # robust file paths

# A canonical pipeline: read -> clean -> save -> analyse
raw <- read_csv(here("data/raw/cohort.csv"))
clean <- raw |>
  filter(!is.na(outcome)) |>
  mutate(age_grp = cut(age, c(0, 30, 50, 70, Inf)),
         smoker  = factor(smoker, levels = c("No", "Yes")))
write_csv(clean, here("data/processed/cohort_clean.csv"))

# Sketch a DAG to anchor the analysis (HSCI 341 Lesson 1)
# library(dagitty)
# g <- dagitty("dag { smoker -> outcome ; age -> smoker ; age -> outcome }")

Managing Data-Collection Sheets

It is important to establish a permanent storage system for all original data-collection sheets (survey forms, data-collection forms, etc.) that makes it easy to retrieve individual sheets if they are needed during the analysis.

Introduction and Overview

Section 1 set up the conceptual workflow and the discipline of starting from a causal diagram before you touch any data. Section 2 turns to the practical side: how do you encode raw responses, enter them into a workable file, organise files across a project, and keep track of what every variable in your dataset means? These are unglamorous tasks, but they determine whether the analysis you eventually run is reproducible. Organising data so that each variable is a column and each observation a row — the "tidy data" convention — makes downstream analysis dramatically easier (Wickham, 2014; Wikipedia: Tidy data).

Data Coding

Before entering data into a computer, careful coding is essential. Good coding practices prevent errors that can cascade throughout an entire analysis.

Data Entry

Some important issues to consider when entering your data into a computer file:

Keeping Track of Files

It is important to have a system for keeping track of all your files. Key recommendations:

• Assign a logical name with a 2-digit numerical suffix (e.g., brazil01). A 2-digit suffix allows you to have 99 versions that still sort correctly when listed alphabetically.
• When data manipulations are carried out, save the file with a new name (the next available number). Do not change data and then overwrite the file.
• Keep a simple log of files created with information about the contents (e.g., number of observations and variables).

library(tidyverse)

# Read raw, never modify in place
bp_raw <- read_csv("data/raw/bp01.csv")

# Tidy: drop incomplete rows, build derived variables, lock factors
bp_clean <- bp_raw |>
  drop_na(systolic, diastolic, age) |>
  mutate(
    age_ct    = age - mean(age),                              # centred
    age_ctsq  = age_ct^2,                                       # quadratic term
    age_c3    = cut(age, c(0, 35, 55, Inf),
                    labels = c("young", "middle", "older")),
    htn       = factor(systolic >= 140 | diastolic >= 90,
                       levels = c(FALSE, TRUE),
                       labels = c("normotensive", "hypertensive"))
  )

# Persist as a new versioned file - and a small log line
write_csv(bp_clean, "data/processed/bp02.csv")
cat("bp02.csv", format(Sys.Date()), nrow(bp_clean), "obs",
    "\n", file = "data/file_log.txt", append = TRUE)

## At any time you can rebuild bp02 from bp01 by re-running this script.

Keeping Track of Variables

Even a relatively focused study can give rise to a large number of variables once transformed and recoded variables have been created. Recommendations include:

Introduction and Overview

Section 2 organised your files and variables. Section 3 turns to the analyses themselves: working in program mode rather than interactively (so the work is reproducible), editing data systematically rather than ad hoc, and verifying that the dataset behaves as expected before you draw any inferences from it. The discipline you build here is what separates a defensible analysis from a fragile one, and it has been argued that reproducibility is now the minimum standard for evaluating computational research (Peng, 2011).

Program Mode vs. Interactive Processing

Statistical programs can be used in an interactive mode (selecting items from menus or typing in a command) or in program mode (compiling a series of commands into a program and then running it).

Data Editing

Before beginning any analyses, spend time editing your data. The most important components are:

Data Verification

Before you start any analyses, you must verify that your data are correct. This can be combined with data processing and involves going through all of your variables, one-by-one.

Introduction and Overview

Sections 1–3 produced a verified, well-documented dataset. Section 4 finally takes the step every student is impatient to make: the actual analysis. We start with how to process outcome and predictor variables for analysis, handle multilevel data structure, and run the “unconditional associations” (single-predictor descriptions) that are the necessary first look at any dataset before you touch a multivariable model.

Processing the Outcome Variable(s)

While verifying data, you can also start processing your outcome variable(s). Review the stated goals of the study to determine the format(s) which best suits the goal(s). Consider the following based on outcome type:

Processing Predictor Variables

It is important to go through all predictor variables to determine how they will be handled:

• Missing values: Are there many? If so, you might need to abandon plans to use that predictor, or conduct 2 analyses (one on the subset where the predictor is present and one on the full dataset ignoring the predictor).
• Distribution: For continuous variables, is there a reasonable representation over the whole range of values? If not, it might be necessary to categorise the variable.
• Categorical variables: Are all categories reasonably well represented? If not, you might have to combine categories.

Multilevel Data

If your data are multilevel (e.g., blood pressure measurements within individuals within centres), evaluate the hierarchical structure:

Unconditional Associations

Before proceeding with any multivariable analyses, it is important to evaluate unconditional associations within the data. These serve as the foundation for building more complex models.

When evaluating unconditional associations, pay attention to:

• Associations between predictors and outcome: Determine if there is any association at all; determine the functional form (is it linear?); get a simple picture of the strength and direction.
• Associations between pairs of predictors: Look for potential collinearity problems (highly correlated predictors).
• Confounding variables: Evaluate associations between the confounding variables and the key predictors of interest and the outcome.

Keeping Track of Your Analyses

Before starting the more substantial analysis, set up a system for keeping track of your results:

Bringing It All Together

This lesson laid out a structured workflow for taking a dataset from collection to first analysis. Section 1 insisted that you start with a causal diagram and a clear separation of outcomes, predictors, confounders, and intervening variables — before you touch the data. Section 2 turned that intent into discipline at the level of files: thoughtful coding, a project folder you could hand to a collaborator, and a codebook that captures every variable. Section 3 made the workflow reproducible by moving you out of point-and-click interactive mode into program-mode scripts, with systematic editing and verification. Section 4 closed the loop by processing outcomes and predictors, surfacing multilevel structure, and producing the unconditional associations that should always precede a multivariable model.

The thread running through all four sections is that an analysis is only as trustworthy as the steps that came before it. Every later lesson in HSCI 410 — linear regression, model building, logistic regression, count data, survival, mixed models — assumes you arrive at the modelling step with a clean, documented, and well-understood dataset. The structured approach is what makes the rest of the course possible. It also positions you to interpret p-values, confidence intervals, and effect sizes responsibly when you eventually report them (Wasserstein & Lazar, 2016; Greenland et al., 2016), and to avoid the analytic flexibility that drives false-positive results (Simmons, Nelson, & Simonsohn, 2011; see also Wikipedia: John Tukey on the historical roots of exploratory data analysis).