Warning
: Undefined variable $raw_excerpt in
/home/quantdev/thechangelab.stanford.edu/wp-content/themes/lagunita-theme/Lagunita.php
on line
2123
J: Modeling categorical and count outcomes | The Change Lab
Skip to content
The Change Lab
Menu
Search form
Search term
Home
About Us
Our Team
Lab Mission
Lab Values
Tutorials
Bayesian Methods
Dynamic Dyadic Systems
Growth Modeling
Intensive Longitudinal Analysis
Longit. Design & Data Analysis
Measurement of Change
Power Analysis
Collaborations
The AMIB Data
The Cortisol Data
Data Musik
iSAHIB
Screenomics
Publications
News & Updates
Joining The Change Lab
Student Corner
J: Modeling categorical and count outcomes
Tags:
AMIB
,
Kevin Grimm
,
Video
,
categorical outcomes
,
count outcomes
,
count regression
,
fixed effects
,
generalized linear models
,
ggplot2
,
link function
,
lme4
,
logistic regression
,
multilevel models
,
psych
,
random effects
,
random intercept
,
random slope
,
state
,
trait
,
unconditional multilevel logistic regression
|
Tutorials
|
Intensive Longitudinal Analysis
|
J: Modeling categorical and count outcomes
Files
Session J – All PDFs
J01 Categorical Outcomes.pdf
J02 Logistic Regression.pdf
J03 Logistic Example.pdf
J04 Multilevel Logistic.pdf
J05 Multilevel Logistic Example.pdf
Session J06 Count Outcomes.pdf
J07 Count Regression.pdf
J08 Count Regression Example.pdf
J09 Multilevel Count.pdf
J10 Multilevel Count Example.pdf