# Notes:

(1) The downloadable files contain SAS code for performing various multivariate analyses. The code is documented to illustrate the options for the procedures.

(2) Some of the code was written before the point-and-click routines in SAS were developed (e.g., the ANALYST routine). In some cases, you can accomplish the same task much easier by using the point-and-click interface.

(3) All the files are .sas files. In some cases, the raw data are included in the .sas files. Otherwise, they are available as a SAS data set (.sas7bdat format). When the SAS data set is processed, then the column "SAS Data Set" is annotated. Otherwise, this column is blank. Note that in some cases you must set the appropriate LIBNAME statement for your computer to be able to process the SAS data set.

(4) To download, right click on the SAS code (and, if necessary, on the SAS data set) and select "Save Target As" or "Save Link As."

(5) If a SAS data set is specified, then right-click on that link and select "Save Target As" or "Save Link As" to download. Yout can also download the SAS data sets and related documentation using the Data Sets web page.

SAS Code Example | SAS Data Set | Description |
---|---|---|

ANOVA Logic | Three_Stooges | Computing ANOVA using means and variances |

ANOVA Coding | ANOVA coding of a categorical variable | |

ANOVA: Random effects 1 | Nested, random effects in ANOVA using GLM (Hierarchical Linear Models) | |

ANOVA: Random Effects 2 | Nested, random effects in ANOVA using GLM (Hierarchical Linear Models) | |

Canonical Discriminant Analysis 1 | Wolves | Diccriminant analysis predicting the sex and the location (Arctic vs. Rocky Mountains) of wolf skull measurements |

Canonical Discriminant Analysis 2 | WU Twins | Canonical discriminant analysis predicting a disgonsis of schizophreni from the MMPI |

Cluster Analysis 1 | Wolves | Cluster analysis of wolf skull measurement data |

Cluster Analysis 2 | Crime | Cluster analysis of crime data |

Cluster Analysis 3 | Cluster analysis of mammals teeth data | |

Cluster Analysis 4 | Example from the SAS Manual on PROC CLUSTER (mammals teeth data) | |

Confirmatory Factor Analysis | Nat. Merit Twins | Confirmatory factor analysis of the subtests of the National Merit Scholarship Qualifying Exam |

Confirmatory Factor Analysis | WISC Data | Confirmatory factor analysis of Tabachnick & Fidell's WISC data set. |

Confirmatory Factor Analysis | Nat. Merit Twins | Using CFA to find the number of factors. |

Confirmatory Factor Analysis | WISC Data | Using CFA to find the number of factors. |

Discriminant Analysis 1 | Classic discriminant analysis | |

Discrinimant Analysis 2 | Discriminant analysis of Fisher's Iris data (from SAS Manual) | |

Factor Analysis 1 | Nat. Merit Twins | Introduction to factor analysis |

Factor Analysis 2 | Factor analysis (Hathaway & McKinley MMPI data) | |

Factor Analysis 3 | Illustration how different rotation methods gives same predicted correlation matrix | |

GLIM | Detroit | Example of generalized linear models (GLIM) |

Log Linear Analysis 1 | Demonstration of Log Linear Analysis and Multiple Regression | |

Log Linear Analysis 2 | Log Linear Analysis (Tabachmick & Fidell Reading Preferences data) | |

Log Linear Analysis 3 | Log Linear models and logistic regression (Robins & Regier ASP data) | |

Log Linear Analysis 4 | Log linear analysis (Tabachnick & Fidell Intimacy data) | |

Logic of ANOVA 1 | Three_Stooges | Computing ANOVA using means and variances |

Logic of ANOVA 2 | ANOVA coding of a categorical variable | |

Logistic Regression 1 | WU Twins | Logistic regression for a binary and an ordinal response variable |

Logistic Regression 2 | WU Twins | Comparison of logistic regression, multiple regression, and MANOVA profile analysis |

Logistic Regression 3 | Comparison of logistic regression, classic discriminant analysis, and canonical discrinimant analysis | |

MANOVA 1 | Intro to MANOVA (Example from SAS Manual) | |

MANOVA 2 | Intro to MANOVA (Shorthand training data from Tatsuoka) | |

MANOVA & Profile Analysis | Three_Stooges | Example of a profile analysis |

MANOVA & Profile Analysis | (1) How to interpret MANOVA and Profile Analysis, and (2) Differences between univiariate ANOVAs and MANOVA. (See comments in the program) | |

MANOVA & Profile Analysis | Example of a profile analysis (WAIS data on senile and nonsenile elderly) | |

Manova & Profile Analysis | Genotype | Example of a profile analysis (WAIS data on senile and nonsenile elderly) |

MANOVA & Profile Analysis | Wolves | MANOVA & Profile Analysis with twoway ANOVA |

MANOVA vs. RM | Wolves | Compares MANOVA and Repeated Measures |

Maximum Likelihood 1 | Example of maximum likelihood theory (See Handout on Maximum Likelihood) | |

Maximum Likelihood 2 | Example of maximum likelihood theory (See Handout on Maximum Likelihood) | |

Mixed Models | Random effects and repeated measures using PROC MIXED | |

Mixed Models | Repeated measures using PROC MIXED (noise sensitivity data) | |

Multiple Regression 1 | Introduction to multiple regression. | |

Multiple Regression 2 | Multicollinearity and influence statistics (from SAS Manual) | |

Multiple Regression 3 | Detecting an outlier | |

Multiple Regression 4 | Interest | Multivariate multiple regression (path analysis using PROC REG) |

Multiple Regression 5 | Illustrates the normal equations vis matrix algebra | |

Plot Means | Genotype | Plot the means for the genotype data set |

Plot Means | Koro | Plot the means for the koro data |

Plot Means | Wolves | SAS code that plots the mean values for the different groups in the wolves data. |

Plot Means | SAS code that plots the mean values for the different groups in the Alzheiners data set. | |

Polynomial Regression | Detroit | Effect of different coding schemes on polynomial regression |

Repeated Measures 1 | Intro to repeated measures using PROC GLM | |

Repeated Measures 3 | Into to RM and an old homework assignment | |

Repeated Measures 3 | Another old homework assignment for repeated measures | |

RM: Complicated Design | One between-subjects factor (dose of drug) and three within-subjects factors (recall vs. recognition, names vs. objects, and common vs. rare) in patients with Alzheimer's disease. | |

RM: Sphericity Test | Sphericity tests in repeated measures ANOVA | |

SEM Introduction | Detroit | Introduction to Path Analysis and SEM (Structural Equation Modeling) |

SEM 1 | Interest | Introduction to Path Analysis and SEM using PROC CALIS |

SEM 2 | Nat. Merit Twins | Structural equation model of National Merit test and family demographics |

SEM 2 | Carolyn Abel | Example of imposing an equality constraint |

SEM on Latent Variables | SEM model testing the relationship among trauma, attachment, and three different outcome variables. | |

SEM on Latent Variables | National Merit Twins | SEM 2 but with latent variables. |