Jump to Syllabus: | Jump to Lecture and Materials: |
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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 |

Spreadsheet Module | Unit S1 S2 |

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

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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 R^{2} Statistic

- Supplementary Reading: OnlineStatBook.Com Chapter 12 (Except Part F).
- Post Hole 5--Interpret an R
^{2}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

- Choose your own data analytic adventure.
- Practice final exams.

## Intermediate Semester:

Sophisticated Modeling and Assumption Fixing

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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)

### Unit 20: Introduction to Multilevel Modeling

- Post Hole 20--Calculate and interpret an intraclass correlation.
- Technical Memo and Faculty Memo 20--.
- Unit 20 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

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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 E: Poisson Regression

- Post Hole E--
- Technical Memo and Faculty Memo E--
- Appendix E 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

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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

- Supplementary Reading:
- 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

- Supplementary Reading:
- 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

- Supplementary Reading:
- 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

- Supplementary Reading:
- 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

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Top | Unit R0 R1 R2 R3 R4 R5 R6 R7 |

This is not up yet, but it is a major priority. |

### Unit R0: Downloading and Installing R and Rcmdr

- Post Hole R0--Download and install R and Rcmdr.
- 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

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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

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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)

### Unit C3: Adaptive Skills

- 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)

## Spreadsheet Module:

Excel Tips and Tricks

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Top | Unit S1 S2 |

I hope to have materials (at least rough material) up soon. |

### Unit S1: Using Excel for Post Hole 3

- Post Hole 3--Conduct a z-transformation by hand from a small data set.
- Unit S1 Slides (PDF format)

### Unit S2: Using Excel for Graphing Interactions (Extended Post Hole 17)

- Post Hole 17--Interpret a statistical interaction using spreadsheet software.
- Unit S2 Slides (PDF format)