Topics and activities covered in the advanced statistics course
Week
Number
Topics
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1
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2
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3
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4
Lectures and Tutorials
Refresher of prerequisites:
types of variables,
descriptive statistics,
sampling distributions and
the ‘bootstrap’, hypothesis
testing, significance and
statistical power
Commentary on core
statistical applications used
across disciplines
compared to applications
that are mostly disciplinespecific
Basics of Linear Algebra
Dealing with Missing Data:
an introduction to multiple
imputation

General Linear Model
Revisited: unifying
framework encompassing
two-sample t-tests, analysis
of variance (ANOVA),
analysis of covariance
(ANCOVA), and simple and
multiple regression.
How the model parameters
are actually estimated using
matrix algebra
Assumptions and strategies
to deal with violations of
these assumptions
Issues of multiple predictors
and model selection
Basic discussion on how
linear models are used in
‘causal’ modeling (i.e.
mediation and moderation)
General Linear Model
Revisited (cont'd)
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Assignments
Orientation lecture:
discussion of course
objectives, syllabus,
textbook, topics covered,
course evaluation, posting
questions in the
discussion boards,
communicating with the
instructor via course
email, and office hours for
consultation in person or
via telephone
Two topic lectures
Two topic lectures
Computer tutorial on
inspecting data,
conducting univariate and
bivariate analyses, and
plotting of histograms,
boxplots, barplots, and
scatterplots
Three topic lectures

Submit a picture and
a small biography to
be shared with the
other students, using
as example a short
biography posted by
the instructor ( 7
days to submit)
Computer tutorial that
illustrated fitting models
with combinations of
continuous and
categorical predictors, as
well as identifying
problems such as
multicollinearity

Homework 1:
Analyze a dataset in
several steps that
include inspecting
the variables,
conducting
univariate and
bivariate analyses,
fitting some linear
models, and writing
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5
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6
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7
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8
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9
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10
Logistic Regression:
proportions, odds, odds
ratios, the binomial
distribution, the likelihood
function. Interpretation of
model parameters as odds
ratios. Extensions to ordinal
and multinomial outcomes
Additional applications:
classification and
propensity scores in 'causal'
modeling
Generalized Linear Models:
Introduction to poisson,
negative binomial, zeroinflated, gamma, and betaregression models
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Three topic lectures
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Linear Models Under NonIndependence: justification,
marginal models (GEE),
‘multilevel’ or ‘hierarchical’
models (Linear Mixed
Models)
Longitudinal or repeated
measure analysis

One topic lecture
Computer tutorials for
fitting logistic models
(both binary and binomial)
and count models using
poisson and negative
binomial models
Two topic lectures

Linear Models Under NonIndependence (cont'd):
Using the Linear Mixed
Model in the presence of
'nesting' in cross-sectional
data, and in situations
classically referred to as
multivariate analysis of
variance (MANOVA)
Principal Components
Analysis (PCA) and
Exploratory Factor Analysis
(EFA)
Commentary on statistical
techniques used in survey
instrument development
PCA and EFA (cont'd)
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
One topic lecture
Computer tutorial for
fitting models with one
and two levels of ‘nesting’
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Midterm discussion
lecture
One topic lecture
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
Computer tutorial
illustrating an example of
Exploratory Factor
Analysis and computation
of Cronbach's alpha
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
results (7 days to
submit)
Group assignment:
peer-review of
homework 1 (7 days
to complete)
Midterm exam:
True/False/It
depends questions;
model interpretation
questions; and
analyses of three
datasets using
general linear, binary
logistic, and binomial
logistic models,
respectively (7 days
to submit)
Group assignment:
peer-review of
midterm data
analyses (7 days to
complete)
Homework 2:
conduct a
longitudinal analysis
using either GEE or
a linear mixed
model. With a
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11
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12
Structural Equation Models
(SEM):
Introduction to the
technique, advantages and
limitations, and comparison
of different software
packages for it
Steps in SEM model fitting:
model specification,
identification, estimation,
evaluation of fit, model
modification, and
interpretation
Structural Equation Models
(cont'd)
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Two topic lectures
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Computer tutorial with
three examples: a
confirmatory factor
analysis; a path model
with indirect effects; and a
complex model with latent
variables as well as
indirect effects

second dataset,
conduct an EFA.
Write up results (7
days to submit)
Group assignment:
peer-review of
homework 2 (7 days
to complete)
Final exam: (7 days
to submit): fitting of a
confirmatory factor
analysis model, and
fitting of a path
model. Write up
results.
Notes:
Group assignments at Weeks 5, 8, and, 11 were implemented in the third iteration of the course (Summer
2012)
GEE (Generalized Estimating Equations): a robust technique to estimate regression-type models for
situations with typically one level of nesting