Introductory Semester Unit   1   2   3   4   5   6   7   8   9   10   A   B   Review
Intermediate Semester Unit   11   12   13   14   15   16   17   18   19   20   C   D
Advanced Semester Unit   21   22   23   24   25   26   27   28   29   30   E   F
Measurement Semester Unit   M1   M2   M3   M4   M5   M6   M7   M8   M9   M0   G   H
Programming Module Unit   R0   R1   R2   R3   R4   R5
Presenting Module Unit   P1   P2
Consulting Module Unit   C1   C2   C3   C4
Note about prerequisites: The Measurement Semester can be taken any time after the Introductory Semester, and modules supplement the semester materials.

## Introductory Semester:Simple Modeling and Assumption Checking

Top Unit   1   2   3   4   5   6   7   8   9   10   A   B   Review
You will find complete drafts of each unit in the Introductory Semester. In the next revision: I will break the lectures into smaller chunks with clearly labelled levels of priority. I will make the switch completely to R from SPSS. I will focus on more and varied K-12 educational datasets. Finally, I will work to improve my pedagogy.

### Unit 1: Introduction to Simple Linear Regression

• Post Hole 1--Use exploratory data analytic techniques to investigate the relationship between two variables.
• Technical Memo and Faculty Memo 1--Conduct two bivariate exploratory data analyses (with one continuous outcome and two predictors (one continuous and one dichotomous) of your choice).
• Unit 1 Slides (PDF format)

### Unit 2: Univariate Statistics (Outlier Resistant)

• Supplementary Reading: OnlineStatBook.Com Chapters 1, 2 and 3.
• Post Hole 2--Use exploratory data analytic techniques to describe the distribution of a variable.
• Technical Memo and Faculty Memo 2--Conduct three univariate exploratory data analyses (with your variables from Memo 1).
• Unit 2 Slides (PDF format)

### Unit 3: Univariate Statistics (Outlier Sensitive)

• Post Hole 3--Conduct a z-score transformation by hand from a small data set.
• Technical Memo and Faculty Memo 3--Produce an appropriate table, and discuss the descriptive statistics for four variables (from Memos 1 and 2, plus an additional continuous or dichotomous predictor of your choice).
• Unit 3 Slides (PDF format)

### Unit 4: Pearson Correlations

• Supplementary Reading: OnlineStatBook.Com Chapter 4.
• Post Hole 4--Interpret a correlation matrix.
• Technical Memo and Faculty Memo 4--Produce an appropriate table, and discuss a correlation matrix for five variables (from Memo 3, plus an additional continuous or dichotomous predictor of your choice).
• Unit 4 Slides (PDF format)

### Unit 5: The R2 Statistic

• Supplementary Reading: OnlineStatBook.Com Chapter 12 (Except Part F).
• Post Hole 5--Interpret an R2 statistic verbally and, using Boolean circles, graphically.
• Technical Memo and Faculty Memo 5--Fit and discuss four regression models (with your variables from Memo 4).
• Unit 5 Slides (PDF format)

### Unit 6: Statistical Inference and Statistical Significance (t-tests)

• Supplementary Reading: OnlineStatBook.Com Chapters 5 through 12.
• Post Hole 6--State the null hypothesis of a test for statistical significance; reject (or not) the null hypothesis; draw an inference (or not) from a sample to a population.
• Technical Memo and Faculty Memo 6--From your regression analyses in Memo 5, draw conclusions from your sample to the population when warranted.
• Unit 6 Slides (PDF format)

### Unit 7: Statistical Inference and Confidence Intervals

• Post Hole 7--Interpret a confidence interval from a frequentist perspective and from a Bayesian perspective.
• Technical Memo and Faculty Memo 7/8--Using a new data set (or at least new variables), fit and discuss two regression models, one with a dichtomous predictor and the other with a continuous predictor.
• Unit 7 Slides (PDF format)

### Unit 8: Statistical Inference and Assumption Checking

• Post Hole 8--Evaluate the assumptions underlying a simple linear regression.
• Technical Memo and Faculty Memo 7/8--Check the regression assumptions..
• Unit 8 Slides (PDF format)

### Unit 9: Regression on Polychotomous Variables (F-tests)

• Supplementary Reading: OnlineStatBook.Com Chapter 8.
• Post Hole 9--Interpret the parameter estimates and F-test from regressing a continuous variable on a set of dummy variables.
• Technical Memo and Faculty Memo 9--Regress a continuous variable on a polychotomous variable, fit the equivalent one-way ANOVA model, produce appropriate tables and discuss your results.
• Unit 9 Slides (PDF format)

### Unit 10: Intro to Multiple Regression, Two-Way ANOVA and Interaction

• Post Hole 10--Interpret a two-way analysis of variance using F-tests and graphs.
• Technical Memo and Faculty Memo 10--Conduct a two-way analysis of variance, produce an appropriate table and graph, fit the equivalent regression model, and discuss your results.
• Unit 10 Slides (PDF format)

### Appendix A: Contingency Table Analysis (chi-square tests)

• Supplementary Reading: OnlineStatBook.Com Chapter 14.
• Post Hole A--Interpret a contingency table with chi-square statistic.
• Technical Memo and Faculty Memo A--Conduct a contingency table analysis with two categorical variables (of your choosing).
• Appendix A Slides (PDF format)

### Appendix B: Logistic Regression

• Post Hole B--From a fitted logistic regression model (in terms of log odds, or logits), calculate two prototypical fitted probabilities (in terms of percentages).
• Technical Memo and Faculty Memo B--Conduct a logistic regression analysis with a dichotomous outcome and a continuous predictor. Generate and discuss a plot of prototypical fitted values.
• Appendix B Slides (PDF format)

### Review

• Practice final exams.

## Intermediate Semester:Sophisticated Modeling and Assumption Fixing

Top Unit   11   12   13   14   15   16   17   18   19   20   C   D   Review
Most of the materials are up as early drafts. As with Introductory Semester, there is a lot of revising to do.

### Unit 11: GLM Assumptions about Measurement Error

• Review: Units 1 and 2.
• Post Hole 11--Note the threats to validity posed by measurement error in the outcome and predictor(s) of a model.
• Technical Memo and Faculty Memo 11--Fit a simple linear regression model (with a continuous outcome and a predictor of your choice), interpret your results, and discuss the threats to validity posed by measurement error in the outcome and predictor.
• Unit 11 Slides (PDF format)

### Unit 12: Checking GLM Assumptions with Regression Diagnostics

• Review: Units 6, 7, and 8.
• Post Hole 12--Check your GLM assumptions by interpreting a residual-versus-fitted (RVF) plot, a histogram of residuals, a normal probability plot, residual statistics, leverage statistics, and influence statistics.
• Technical Memo and Faculty Memo 12--Use regression diagnostics to evaluate the assumptions of your simple linear regression (from Memo 11).
• Unit 12 Slides (PDF format)

### Unit 13: Non-Linear Transformations To Meet Normality and Linearity Assumptions

• Review: Unit 3.
• Post Hole 13--Propose a non-linear transformation, if necessary, to meet the normality and linearity assumptions of the general linear model.
• Technical Memo and Faculty Memo 13--Use simple linear regression to describe a non-linear relationship between two variables (from a provided data set), and graph your results using spreadsheet software.
• Unit 13 Slides (PDF format)

### Unit 14: Robust Standard Errors To Meet The Homoskedasticity Assumption

• Post Hole 14--Judge whether robust standard errors are necessary for estimation.
• Technical Memo and Faculty Memo 14--Use Stata and robust standard errors to fit your regression model (from Memos 11 and 12).
• Unit 14 Slides (PDF format)

### Unit 15: Partial Correlation Matrices

• Review: Units 4 and 5.
• Post Hole 15--Interpret a correlation matrix and/or partial correlation matrix and note what they may foreshadow about multiple regression.
• Technical Memo and Faculty Memo 15--Use a correlation matrix and a partial correlation matrix to get a handle on six variables of your choice (one outcome variable, one predictor variable, and four control variables) in preparation for multiple regression.
• Unit 15 Slides (PDF format)

### Unit 16: Multiple Regression

• Review: Unit 9.
• Post Hole 16--Interpret a fitted multiple regression model.
• Technical Memo and Faculty Memo 16--Fit and interpret a multiple regression model with your variables from Memo 15.
• Unit 16 Slides (PDF format)

### Unit 17: Statistical Interactions

• Review: Unit 10.
• Post Hole 17--Interpret a statistical interaction using spreadsheet software.
• Technical Memo and Faculty Memo 17--Check your final model from Memo 16 for interactions; graph and interpret the interaction with the lowest p-value.
• Unit 17 Slides (PDF format)

### Unit 18: Model Building

• Post Hole 18--Sketch two model building strategies: a baseline-control strategy and a question-centered strategy.
• Technical Memo and Faculty Memo 18--Create a table of hierarchical fitted models that tells a logical and coherent story of your final model, and then use words to tell that logical and coherent story.
• Unit 18 Slides (PDF format)

### Unit 19: General Linear Hypothesis Testing

• Post Hole 19--Formulate a general linear hypothesis to answer a specific research question.
• Technical Memo and Faculty Memo 19--.
• Unit 19 Slides (PDF format)

### Appendix C: Missing Data

• Post Hole C--Use correlations to describe missing data.
• Technical Memo and Faculty Memo C--For observations with partly missing data, use correlations to explore the missingness. Address the (potential) problems of missing data. Define the population to which you can reasonably draw conclusions.
• Appendix C Slides (PDF format)

### Appendix D: Power Analysis

• Post Hole D--Interpret the results of a statistical power analysis, noting the implications for reserach design.
• Technical Memo and Faculty Memo D-- Conduct a statistical power analysis.
• Appendix D Slides (PDF format)

### Review

• Review Slides (PDF format)

## Advanced Semester:Modeling Change and Modeling Causation

Top Unit   21   22   23   24   25   26   27   28   29   30   E   F   Review
This syllabus is only a sketch right now.

### Unit 21: Exploring Multilevel Data

• Reading: Singer and Willett (2003, pp. 3-44)
• Post Hole 21--
• Technical Memo and Faculty Memo 21--
• Unit 21 Slides (PDF format)

### Unit 22: Multilevel Growth Modeling

• Reading: Singer and Willett (2003, pp. 45-137)
• Post Hole 22--
• Technical Memo and Faculty Memo 22--
• Unit 22 Slides (PDF format)

### Unit 23: Discrete-Time Hazard Modeling

• Reading: Singer and Willett (2003, pp. 205-408)
• Post Hole 23--
• Technical Memo and Faculty Memo 23--
• Unit 23 Slides (PDF format)

### Unit 24: True Experimental Data

• Post Hole 24--
• Technical Memo and Faculty Memo 24--
• Unit 24 Slides (PDF format)

### Unit 25: Difference In Differences

• Review:
• Post Hole 25--
• Technical Memo and Faculty Memo 25--
• Unit 25 Slides (PDF format)

### Unit 26: Regression Discontinuity

• Review:
• Post Hole 26--
• Technical Memo and Faculty Memo 26--
• Unit 26 Slides (PDF format)

### Unit 27: Propensity Score Matching

• Review:
• Post Hole 27--2
• Technical Memo and Faculty Memo 27--
• Unit 27 Slides (PDF format)

### Unit 28: Path Diagrams of Causal Relationships

• Post Hole 28--
• Technical Memo and Faculty Memo 28--
• Unit 28 Slides (PDF format)

### Unit 29: Structural Equation Modeling of Causal Paths

• Post Hole 29--
• Technical Memo and Faculty Memo 29--.
• Unit 29 Slides (PDF format)

### Unit 30: Confirmatory Factor Analysis

• Post Hole 30--
• Technical Memo and Faculty Memo 30--
• Unit 30 Slides (PDF format)

### Appendix F: Bayesian Statistics

• Post Hole F--
• Technical Memo and Faculty Memo F--
• Appendix E Slides (PDF format)

### Review

• Review Slides (PDF format)

## Measurement Semester:Meaningful Measures for Meaningful Modeling

Top Unit   M2   M3   M4   M5   M6   M7   M8   M9   M0   G   H   Review
This syllabus is only a sketch right now.

### Unit M1: Empirical Item Characteristic Curves

• Post Hole M1--Given two EICCs, compare and contrast item difficulty, item discrimination and item guessing.
• Technical Memo and Faculty Memo M1--Explore the items of a test or survey using EICCs.
• Unit M1 Slides (PDF format)

### Unit M2: Classical Item Analysis

• Post Hole M2--Use means and correlations to explore item difficulty and item discrimination.
• Technical Memo and Faculty Memo M2--Explore the items of a test or survey using classical test theory.
• Unit M2 Slides (PDF format)

### Unit M3: Item Response Theory

• Post Hole M3--Interpret a fitted IRT model (1-parameter, 2-parameter or 3-parameter).
• Technical Memo and Faculty Memo M3--Use IRT to link two tests or surveys.
• Unit M3 Slides (PDF format)

### Unit M4: Detecting Biased Test Items

• Post Hole M4--Interpret the results of a differential item functioning (DIF) analysis.
• Technical Memo and Faculty Memo 4--Conduct a DIF analysis.
• Unit M4 Slides (PDF format)

### Unit M5: Principle Components Analysis

• Post Hole M5--Propose one (or more) reasonable composites (if any) based the eigen values and eigen vectors from a principle components analysis (PCA).
• Technical Memo and Faculty Memo M5--Conduct a principle components analysis.
• Unit M5 Slides (PDF format)

### Unit M6: Reliability

• Supplementary Reading: OnlineStatBook.Com Chapters 5 through 12.
• Post Hole M6--Interpret a reliabiltiy coefficient, and use the Spearman-Brown prophecy formula to determine the additional number of occassions, raters or items needed to attain a reliabiltiy of at least .90.
• Technical Memo and Faculty Memo M6--Conduct an analysis of reliabiltiy, making recommendations using the the Spearman-Brown prophecy formula.
• Unit M6 Slides (PDF format)

### Unit M7: Generalizability

• Post Hole M7--Make reasonable recommendations based on a D-study.
• Technical Memo and Faculty Memo M7--Conduct a G-study and D-study.
• Unit M7 Slides (PDF format)

### Unit M8: Validity

• Post Hole M8--Use an appropriate correlation matrix to conduct a concurrent/discriminant analysis.
• Technical Memo and Faculty Memo M8--Consider the validity of a test or survey. Propose a strategy for checking the validity. Consider how the validity may change over time as the test is used for different purposes in different conditions.
• Unit M8 Slides (PDF format)

### Unit M9: Test Writing and Survey Writing

• Post Hole M9--Suggest a revision for a test or survey item.
• Technical Memo and Faculty Memo M9--Write a short test or survey. Pilot your instrument. Analyze the results.
• Unit M9 Slides (PDF format)

### Unit M10: Standard Setting

• Post Hole M10--Use Angoff's Method on a test item to define "Proficiency" and "Mastery." (Not so serious!)
• Technical Memo and Faculty Memo M10--With a group, use two method to set standards of proficiency and mastery for a test. Write up your results.
• Unit M10 Slides (PDF format)

### Appendix G: Teaching to High Stakes Tests

• Post Hole G--
• Technical Memo and Faculty Memo G--
• Appendix G Slides (PDF format)

### Appendix H: Measuring Teacher Performance

• Post Hole H--
• Technical Memo and Faculty Memo H--
• Appendix H Slides (PDF format)

### Review

• Practice final exams.

## Programming Module:Using R and Rcmdr

Top Unit   R0   R1   R2   R3   R4   R5   R6   R7
This is not up yet, but it is a major priority.

• Unit R0 Slides (PDF format)

### Unit R1: Simple Command-Line Programming in R

• Post Hole R1--Write a script for a function.
• Unit R1 Slides (PDF format)

### Unit R2: Statistical Output for Units 1-8

• Post Hole R2--Write a script for a scatterplot, a histogram, a univariate summary, a correlation matrix and a regression analysis.
• Unit R2 Slides (PDF format)

### Unit R3: Using Rcmdr to Obtain Statistical Output for Units 1-8

• Post Hole R3--Obtain R script through Rcmdr for a scatterplot, a histogram, a univariate summary, a correlation matrix, a regression analysis and a confidence interval.
• Unit R3 Slides (PDF format)

### Unit R4: Indexing Data Frames and Vectors to Slice, Extract and Sort

• Post Hole R4--Write a script for statistics or graphics for a specified subset of your data.
• Unit R4 Slides (PDF format)

### Unit R5: Using Rcmdr to Obtain Statistical Output for Units 9 & 10

• Post Hole R5--Write a script for a regression analysis with categorical predictors by creating dummy variables and interaction variables and/or by creating factor variables and using model notation.
• Unit R5 Slides (PDF format)

### Unit R6: Using Rcmdr to Obtain Statistical Output for Appendix A

• Post Hole R6--Write a script for a contingency table with a chi-square statistic.
• Unit R6 Slides (PDF format)

### Unit R7: Using Rcmdr to Obtain Statistical Output for Appendix B

• Post Hole R7--Write a script for a logistic-regression analysis including a scatterplot with fitted curve.
• Unit R7 Slides (PDF format)

## Presenting Module:PowerPoint Tips and Tricks

Top Unit   P1   P2
This syllabus is only a sketch right now.

### Unit P1: Using Animations for Clarity

• Post Hole P1--.
• Unit P1 Slides (PDF format)

### Unit P2: Building Histograms and Scatterplots from the Ground Up

• Post Hole P2--.
• Unit P2 Slides (PDF format)

## Consulting Module:Essential Skills for Data Team Consulting

Top Unit   C1   C2   C3   C4
This syllabus is only a sketch right now.

### Unit C1: Listening Skills

• Post Hole C1--In the roleplay, demonstrate congruency, empathy and unconditional positive regard.
• Unit C1 Slides (PDF format)

### Unit C2: Explanatory Skills

• Post Hole C2--In the roleplay, use simplified definitions, apt metaphors, thought experiments and backward planning.
• Unit C2 Slides (PDF format)

• Post Hole C3--In the roleplay, determine and play your role: the helping hand, the knowing expert, the catalyzing collaborator.
• Unit C3 Slides (PDF format)

### Unit C4: Ethical Skills

• Post Hole C4--In the roleplay, responsibly navigate morally murky waters with truthfulness, trustworthiness, respectfulness and carefulness, where 'careful' means 'full of care' for all stakeholders, especially the most vulnerable.
• Unit C4 Slides (PDF format)