Difference between revisions of "610 Lecture Outlines and Materials"
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'''Required Readings'''<br> | '''Required Readings'''<br> | ||
− | :Button, K. et al., (2013). Power failure: why small sample size undermines the reliability of neuroscience, Nature Neuroscience Reviews, 14, 1-12<br> | + | :[Button, K. et al., (2013)]. Power failure: why small sample size undermines the reliability of neuroscience, Nature Neuroscience Reviews, 14, 1-12<br> |
+ | :[Simmons et al (2011) Simmons, J.P., Nelson, L.D., & Simonsohn, U. (2011)]. False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22, 1359-1366.<br> | ||
'''Supplemental Readings'''<br> | '''Supplemental Readings'''<br> |
Revision as of 15:53, 11 December 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
Contents
- 1 Introduction to inferential statistics
- 2 Sampling Distributions, Parameters, and Parameter Estimates
- 3 Inferences about a single mean (one-sample t test)
- 4 Inferences about a single continuous predictor (simple regression)
- 5 Inferences about a single dichotomous predictor (independent-samples t test)
- 6 Inferences about two+ predictors (multiple regression without interaction)
- 7 Dealing with messy data I – case analysis
- 8 Review Session for Exam 1
- 9 *****In Class Exam 1*****
- 10 Dealing with messy data II – model assumptions
- 11 Dealing with messy data III – transformations
- 12 Inferences about two continuous predictors and their interaction
- 13 Inferences about a continuous and dichotomos predictor and their interaction
- 14 Inferences about two dichotomous predictors and their interaction (= 2 x 2 ANOVA)
- 15 Catergorical variables w/ > 2 levels
- 16 Repeated measures: Design and analysis options
- 17 Power and Power Analysis
- 18 Transparency and reproducibility in science
Introduction to inferential statistics
Dates: 9/7
Lecture Slides: Complete;
Required Readings
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
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
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"
- 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
R Script:
Data:
Dealing with messy data III – transformations
Dates: 10/26
Lecture Slides: Complete;
Required Readings
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: Complete;
Required Readings
- None
Supplemental Readings
R Script:
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: 12/12
Lecture Slides: Abridged;
Required Readings
- [Button, K. et al., (2013)]. Power failure: why small sample size undermines the reliability of neuroscience, Nature Neuroscience Reviews, 14, 1-12
- [Simmons et al (2011) Simmons, J.P., Nelson, L.D., & Simonsohn, U. (2011)]. False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22, 1359-1366.
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: