
Handouts I use for teaching

This handout illustrates how you can use G*Power (http://www.gpower.hhu.de/en.html) to estimate power. I use this handout in Correlation and Regression (RMS 4911)

This handout presents one of my roadmaps for multiple regression. I use this handout in Correlation and Regression (RMS 4911)

This handout contains a simple decision tree diagram to decide what is the best statistical analysis, based on the number of independent variables (one more than one) number of levels of the independent variable (2, more than 2) and whether the dependent variable is Interval, ordinal or nominal.

This handout illustrates the rationale behind power estimates using the Smartboard. I use this exercise both in Correlation and Regression (RMS 4911) and Empirical Research Methods (RMS 4930)

RMS4930SAMPLING DISTRIBUTION.pdf
This handout contains an exercise intended to illustrate the law of large numbers using a simulation ( http://onlinestatbook.com/stat_sim/sampling_dist/index.html ). We use this exercise in Empirical Research Methods (RMS 4930)

This handout contains an exercise to illustrate the differences between random, systematic and convenience samples using google documents. We use this exercise in Empirical Research Methods (RMS 4930)

RMS4932metaregression example.pdf
This handout illustrates using an annotated example, how to estimate a metaregression using R (statistical software). in particular, using the package Metafor. We use this package in Metaanalysis (RMS 4932)

RMS4939Mathcad  SimpleMatchingAlgorithm.pdf
This handout illustrates step by step, how to estimate a simple matching estimator. The algorithm is based on the paper: by Abadie, Drukker, LeberHerr & Imbens (2004): Implementing matching estimators for average treatment effects in Stata. The Stata journal,4(3), 290311. We use this handout in Propensity Scores (RMS 4939)

RMS4939Mathcad  weightedRegression.pdf
This handout illustrates how to calculate a weighted regression using matrix algebra. This handout is used during our discussion of nonlinear estimators in Propensity scores (RMS 4939), and is inspired in a discussion found in Guo & Fraser, 2010 (Sage Pubs)

This handout illustrates how to estimate different types of estimators for group comparisons in R (statistical software) using the package Matching. The handout replicates an example in Guo & Fraser, 2010 (Sage Pubs), and is used in Propensity scores (RMS4939)

Links to some videos I use for teaching

Basic Statistics/Descriptive Statistics in SPSS
http://screencastomatic.com/watch/cX6eFMZSH

Crosstabs in SPSS
http://screencastomatic.com/watch/cX6fcvTcO

How to use WORDLE to create word clouds based on frequency
of the wordhttp://screencastomatic.com/watch/cX6fViTc4

How to create a SPSSlike output using RStudio and Knitr
http://screencastomatic.com/watch/c2ntFDnDbv

Effect size estimate for Mean difference in EXCEL
http://screencastomatic.com/watch/c2nT3Nnodu

Fixed Effect Model for a standardized Mean difference using
R (package METAFOR)http://screencastomatic.com/watch/c2nT0EnoKf

Random Effect Model for a standardized Mean difference using
R (package METAFOR)http://screencastomatic.com/watch/c2nOcnnoKr