Introduction to Experimental and
Observational Study Design
KNNL – Chapter 16
Experimental and Observational Studies
• Experimental Studies – Units (aka Subjects in human
studies) assigned at random to treatments/conditions
– Experimental Factors – Conditions with 2 or more levels, which
are assigned to units. If there is more than one factor,
treatments are combinations of factor levels assigned to units.
• Observational Studies – Units are sampled from two or
more populations/subpopulations
– Observational Factors – Set of levels of
Populations/subpopulations used in the study
• Causation is directly obtained in Experimental Studies,
not in Observational Studies
• Mixed Designs  Experimental & Observational Factors
Basics of Experimental Studies
• Explanatory Factors – Conditions (with 2 or more levels)
that are assigned to units.
– Crossed Factors – Factors with levels that are the same within
levels of the other factor(s)
– Nested Factors – Factors with levels that are different within
levels of the other factor(s)
• Treatments – Combinations of factor levels given to units
• Experimental Units – Units used in the study, which are
subject to randomization to treatments.
• Randomization Process - Use of random number
generator to assign units to treatments
• Outcome measurement(s) obtained from treated units
Completely Randomized and Blocked Designs
• Experiment with One Factor @ 2 Levels (Treatments)
• Completely Randomized Design – Take all subjects,
and randomize so that half receive Trt A, and other
half receive Trt B
Yi = b0+b1Xi1+ei Xi1 = 1 if subject i received A, 0 if B
• Randomized Block Design – Generate blocks of
subjects that are similar wrt external criteria (gender,
age,…) and randomize treatments to subjects within
blocks. Helps make treatment groups more similar.
Y = Overall Mean + Trt Effect + Block Effect + Error
Standard Experimental Designs - I
• Completely Randomized Design (CRD) – Units
randomized to treatments with no restrictions on
randomization process
• Factorial Experiments – CRD with two or more
crossed factors. Treatment effects are made up
of main factor effects and interaction effects
• Randomized Complete Block Design (RCBD) –
Units are grouped into blocks. Treatments
randomly assigned to units within blocks
Standard Experimental Designs - II
• Nested Designs – Levels of Factor B differ across levels
of Factor A
• Crossed/Nested Designs – Designs with both crossed
and nested factors
• Repeated Measures Designs – Each unit is measured
multiple times
– Each subject receives each treatment once
– Each subject receives only one treatment, but is measured at
multiple time points
• Split-Plot Designs – Two (or more) sizes of experimental
units due to randomization restrictions for factors
Standard Experimental Designs - III
• Incomplete Block Designs – Block Designs with block
sizes smaller than the number of treatments
• 2-Level Factorial Experiments – Several (possibly many)
factors, each at 2 levels (low/high). With k factors, there
will be 2k treatments
• 2-Level Fractional Factorial Designs – Experiments with
only a subset of all 2k treatments to reduce cost, but still
obtain estimates of main effects and lower-order
interactions
• Response Surface Designs – Designs used to fit
polynomial regression models for numeric factors
Observational Study Designs
• Cross-Sectional Studies – Observations made from
populations/subpopulations at a single time point or
interval.
• Prospective Studies – Groups are formed by levels of a
potential causal factor, then observed over time for some
measurable outcome.
• Retrospective Studies – Studies where subjects are
identified based on the outcome of interest, potential
risk factors are identified that previously occurred
• Matching – Subjects from different populations are
matched, based on external factors – like blocking