Difference between revisions of "610 Lecture Outlines and Materials"

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===Transparency and reproducibility in science===
 
===Transparency and reproducibility in science===
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'''Lecture Slides: '''
 
'''Lecture Slides: '''

Revision as of 12:46, 27 November 2017

NOTE: PDFs of assigned readings are provided here solely for the academic use of students in Psychology 610. UW students may obtain online access to the required text though the UW library

Introduction to inferential statistics

Dates: 9/7

Lecture Slides: Complete;

Required Readings

Judd, McClelland, & Ryan (2017). Chapter 1. Introduction to Data Analysis. In Data Analysis: A Model Comparison Approach.
Judd, McClelland, & Ryan (2017). Chapter 2. Simple models: Definitions of error and parameter estimates. In Data Analysis: A Model Comparison Approach.

Supplemental Readings
None

R Script:

Data:


Sampling Distributions, Parameters, and Parameter Estimates

Dates: 9/12

Lecture Slides: Complete;

Required Readings

Toothacer, L. (1986) Sampling Distributions. In Introductory Statistics for the Behavioral Sciences.

Supplemental Readings

None

R Script: 2_SamplingDistributions.R

Data: 2_SamplingDistributions_Like.dat


Inferences about a single mean (one-sample t test)

Dates: 9/14, 9/19

Lecture Slides: Complete;

Required Readings

Judd, McClelland, & Ryan (2017). Chapter 3. Simple models: Models of error And sampling distributions. In Data Analysis: A Model Comparison Approach.
Judd, McClelland, & Ryan (2017). Chapter 4. Statistical inferences about parameter values. In Data Analysis: A Model Comparison Approach.

Supplemental Readings

None

R Script: 3_SingleMean.R

Data: 3_SingleMean_FPS.dat


Inferences about a single continuous predictor (simple regression)

Dates: 9/21, 9/26

Lecture Slides: Complete;

Required Readings

Judd, McClelland, & Ryan (2017). Chapter 5. Simple regression: Estimating models with a single continuous predictor. In Data Analysis: A Model Comparison Approach.

Supplemental Readings

Namboodiri, K (38). Matrix algebra: Am introduction (Quantitative Applications in the Social Sciences). Sage Publications.

R Script: 4_SingleQuantitative.R

Data: 4_SingleQuantitative_BAC_FPS.dat


Inferences about a single dichotomous predictor (independent-samples t test)

Dates: 9/28

Lecture Slides: Complete;

Required Readings

None

Supplemental Readings

None

R Script: 5_SingleDichotomousPredictor.R

Data: 5_SingleDichotomous_BG_FPS.dat


Inferences about two+ predictors (multiple regression without interaction)

Dates: 10/3, 10/5, 10/10, 10/12

Lecture Slides: Complete;

Required Readings

Judd, McClelland, & Ryan (2017). Chapter 6. Multiple regression: Models with multiple continuous predictor. In Data Analysis: A Model Comparison Approach.

Supplemental Readings

None

R Script: 6_TwoPredictors.R

Data: 6_TwoPredictors_FPS.dat




Dealing with messy data I – case analysis

Dates: 10/17

Lecture Slides: Complete;

Required Readings

Judd, McClelland, & Ryan (2017). Chapter 13. Outliers and Ill-Mannered Error. In Data Analysis: A Model Comparison Approach. NOTE: Read until the section titled "Systematic Violations of Error Assumptions"

Supplemental Readings

Fox, J. (1991). Regression diagnostics. SAGE Series (#79): Quantitative Applications in the Social Science.

R Script:

Data:


Review Session for Exam 1

Dates: 10/17 @ 7:30 pm in our normal class room

*****In Class Exam 1*****

Dates: 10/19

Dealing with messy data II – model assumptions

Dates: 10/24

Lecture Slides: Complete;

Required Readings

Judd, McClelland, & Ryan (2017). Chapter 13. Outliers and Ill-Mannered Error. In Data Analysis: A Model Comparison Approach. NOTE: Continue from section titled "Systematic Violations of Error Assumptions until end of chapter"


Supplemental Readings

Fox, J. (1991). Regression diagnostics. SAGE Series (#79): Quantitative Applications in the Social Science.


R Script:

Data:


Dealing with messy data III – transformations

Dates: 10/26

Lecture Slides: Complete;

Required Readings

Fox, J. (2008). Transforming Data (Chapter 4).


Supplemental Readings

None

R Script: 9_Transformations.R

Data: 9_Transformations_FPS.dat 9_Transformations_5K.dat


Inferences about two continuous predictors and their interaction

Dates: 10/31, 11/2

Lecture Slides: Complete;

Required Readings

Judd, McClelland, & Ryan (2017). Chapter 7. Moderated and Nonlinear Regression Models. In Data Analysis: A Model Comparison Approach.

Supplemental Readings

None

R Script:

Data:


Inferences about a continuous and dichotomos predictor and their interaction

Dates: 11/7

Lecture Slides: Complete;

Required Readings

Jaccard, J. & Turissi, R. (2003). Interaction effects in multiple regression (2nd Edition). SAGE Series (#72): Quantitative Applications in the Social Science. NOTE: Pages 1-68 assigned

Supplemental Readings

None

R Script:

Data:


Inferences about two dichotomous predictors and their interaction (= 2 x 2 ANOVA)

Dates: 11/9

Lecture Slides: Complete;

Required Readings


Supplemental Readings

None

R Script:

Data:


Catergorical variables w/ > 2 levels

Dates: 11/16, 11/21

Lecture Slides: Abridged;

Required Readings

None

Supplemental Readings

Kirk R. (1995). Multiple Comparison Tests. In Experimental design. Pacific Grove (CA): Brooks/Cole.


R Script:

Monte Carlo simulations of error rates in R

Data:


Repeated measures: Design and analysis options

Dates: 11/28, 11/30, 12/5, 12/7

Lecture Slides: Abridged;

Required Readings

Van Breukelen, G. (2006). ANCOVA versus change from baseline had more power in randomized studies and more bias in nonrandomized studies. Journal of Clinical Epidemiology, 59, 920-925.

Supplemental Readings

None

R Script:

Data:


Power and Power Analysis

Dates: 12/13

Lecture Slides: Abridged;

Required Readings


Supplemental Readings

None

R Script:

Data:


Transparency and reproducibility in science

Dates: 12/15

Lecture Slides: Abridged;

Required Readings


Supplemental Readings

None

R Script:

Data: