# 610 Lecture Outlines and Materials

**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

### 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.

**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: **
Complete;

**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: **
Complete;

**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:**