1. ARGUMENT 'FUN' IS MISSING, WITH NO DEFAULT: AN R WORKSHOP

2. Outline  The R “sales pitch”  R Basics  Data Management  Descriptive Statistics in R  Inferential Statistics in R  General Linear Model  Generalized Linear Model  Hierarchical Linear Modeling  Latent Variable Modeling

3. Why Should I Use R?  Free 99  It’s as powerful as SAS and as user friendly as SPSS…really…  You ain’t cool unless you use R  It’s free…seriously

4. R Basics Do not write code directly into the R interface! #Comment #StatsAreCool #Rarrrgh Yes the # lets you add comments to your code R is case sensitive A ≠ a <- is the assignment operator A <- 3; a <- 4

5. R Basics Creating objects in R – Creating a scalar X <- 2 – Creating a vector X <- c(2,2,4,5) – Creating a matrix X <- matrix(c(1,1,2,2,3,3),nrow=2, ncol=3) Y <- matrix(c(1,1,1,1,1,1),nrow=3,ncol=2) – Creating a dataframe A <- c(1,2,3,4) B <- c('T','F','T','F') ds <- data.frame(A,B)

6. R Basics  Arithmetic  2 + 2; 2-2; 2*3;2/3  Boolean Operators  2 > 3; 3 < 6; 4 == 4  Matrix Algebra  X%*%Y  t(X)  ginv(X)

7. R Basics  Packages in R  Like SPSS modules, but free…  Upside: Thousands of packages to do just about anything  Downside: Placing your trust in freeware…which I’m fine with, but some aren’t  library(MASS)  ginv(X)

8. I’m an import-exporter: Database Management  Importing from a text file  Dataset <- read.table(‘filelocation.txt’)  Importing from a csv file  Dataset <- read.csv(‘filelocation.csv’)  Foreign package to read SPSS data files  package(foreign)  Dataset <- read.spss(‘filelocation.sps’)

9. Database Management  Exporting R dataframes to csv  write.csv(dataframe, ‘filelocation.csv’)  Exporting R dataframe to text file  write.table(dataframe, ‘filelocation.txt’)  Variables in a dataframe  Adding: ds$C <- c(4,3,2,1)  Deleting: ds <- ds[,-3]  Referencing: ds$A or ds[,1]

10. Database Management  Indexing Dataframes  ds[,2] gives you column 2 of ds  ds[1,] gives you row 1 of ds  ds[2,2] gives you row 2 column 2 of ds

11. Descriptive Statistics  Measures of central tendency  Mean – mean(X)  Median – med(X)  Mode – table(X) (A little round about, but oh well)  Measures of dispersion  var(X)  sd(X)

12. Descriptive Statistics  Measures of Covariation  cov(X,Y) – Covariance  cor(X,Y) – Correlation

13. Caution! I will not be talking about any of the theoretical underpinnings as to when or why you should use one statistical method over another. We’ll just be doing some PnP statistics…

14. General Linear Model  Read Edwards & Lambert, 2007 X M Y Z

15. Generalized Linear Model  Uses the generalized linear modeling function  glm()  Can handle dvs that are binomial, poisson, multinomial, guassian  glm(y ~ x1 + x2, family=binomial, data=LRDS)

16. Hierarchical Linear Model  HLM allows you to look at between and within group variation  Employees nested within organizations  Repeated measures nested within an individual  Variance Components Analysis

17. Latent Variable Modeling LV1 X1 X2X3 X4 LV2 Y1 Y2Y3 Y4 LV3 Y5 Y6Y7 Y8 First we have to setup a measurement model:

18. Latent Variable Modeling LV1 LV2 LV3 Then we have to setup the structural model: