⚠️ Details are subject to change. See Canvas for due dates. Last Updated: Mon Apr 22 9:03:20 AM

Date Topic Read Collaborate Practice Assess
Preparing yourself and your data for analysis
01/24 Introduction to the class Syllabus Welcome slides QFT: Preparing data for analysis HW 00: Getting Started Quiz 00 - Ind
Self regulated learning Q&A Class Logistics Quiz 00 - Grp
Reproducible Workflows PMA6 Ch3
ASCN Ch 1
01/26 Review - Linear Regression PMA6 Ch 8, 10.3
ASCN Ch 7
What we know about LinReg
Regression Model Building
01/29 Review - Linear Regression ASCN Ch 7 QFT: Model Building & Variable Selection HW 01: Regression Modeling Quiz 01 - Ind
01/31 Stratification & Moderation ASCN Ch 8 Q&A Model Buliding Quiz 01 - Grp
02/02 Interaction terms PMA6 Ch 8.8
ASCN Ch 10.1
02/05 Model Building PMA6 Ch9
ASCN Ch 10
Automatic Variable Selection PMA6 9.6-9.7
ASCN Ch 10.4
Modeling Binary Outcomes
02/12 Logistic Regression PMA6 Ch 12
ASCN Ch 11.1-11.3
QFT/LJ: Binary outcomes HW 02: Logistic Regression & Classification Quiz 02 - Ind
02/16 Classification and Prediciton PMA6 11.3-11.4
ASCN Ch 12
Q&A Modeling Binary Outcomes Logistic Regression Worksheet (Google Drive) Quiz 02 - Grp
Missing Data: Much ado about nothing
02/28 Identification and Impact PMA6 10.2 ASCN Ch 18 QFT/LJ: Missing Data HW 03: Missing Data Quiz 03 - Ind
03/04 Imputation Q&A Missing Data Watch the Seminar on Missing Data: https://media.csuchico.edu/media/0_tgnydpgf and write a LJ entry (see canvas) Quiz 03 - Grp
Multivariate Analysis: More than one response variable
03/25 Principal Component Analysis PMA6 Ch 14
ASCN Ch 14
QFT/LJ: Dimension Reduction HW04: Dimension Reduction Quiz 04 - Ind
04/02 Factor Analysis PMA6 Ch 15
ASCN Ch 15
Q&A Dimension Reduction Quiz 04 - Grp
Correlated Outcomes: Borrowing information from your neighbors
04/10 Micro and Macro level variables PMA6 Ch 18.1-18.4 ASCN Ch 17 QFT/LJ: Correlated Outcomes HW05: Multilevel Models Quiz 05 - Ind
04/15 Regression of clustered data PMA6 Ch 18.5-18.7 Q&A Correlated Data Quiz 05 - Grp
Special Topics: Student led activities
04/24 Topic 01: Power Analysis
04/29 Topic 02: Inter rater reliability
05/01 Topic 03: Longitudinal and Spatial Analysis
05/03 Topic 04: Intro to Cluster analysis
05/06 Topic 05: Clustering Algorithms (Part I)
05/08 Topic 06: Clustering Algorithm: NMDS
05/10 Topic 07: Intro to Deep Learning
05/15 Topic 08: Non-random sampling. Process & Estimation