The tutorials presented here have been developed by SDS consultants and provide a general introduction to various software packages.

Click on a software name to be taken to its tutorial(s).

#### Mathematical Software

IMSL Libraries, Matlab, Maple, Mathematica

#### Statistical Software

AMOS, HLM, PRELIS and LISREL, M*plus*, SAS, SPSS, Stata

Mathematical Software Tutorials

##### IMSL Libraries

IMSL, which once stood for "International Mathematical and Statistical Libraries," is an extensive collection of mathematical and statistical subroutines and functions in the Fortran and C programming languages. These subroutines and functions can be linked during compilation and called as embedded objects from a program or script written in the parent programming language as needed. The content of the libraries ranges from commonly used mathematical techniques such as eigensystem analysis and optimization algorithms to special functions such as Bessel, Gamma, trignometric, and hyperbolic functions. As libraries, the IMSL packages are not standalone applications. Individual routines must be called from links compiled in external programs.

##### Matlab

An overview of commands available in Matlab can be accessed through this tutorial series. No prior knowledge of Matlab or any mathematical software is assumed. This four-part tutorial covers the basic aspects of Matlab such as statement syntax, mathematical operations and graphics, as well as some system interactions such as saving and printing your files. This tutorial assumes a basic familiarity with matrices. Click on the links below to download parts 1 through 4 of the tutorial and the files necessary for following each section of the tutorial.

- Part I: Matlab Getting Started Tutorial , Files
- Part II: Matlab Computing and Programming Tutorial , Files
- Part III: Matlab Graphics and Data Analysis Tutorial , Files
- Part IV: Matlab Modeling and Simulation Tutorial , Files

##### Maple

Maple is a mathematical software package for symbolic computation. Conventional mathematical software packages usually require numerical values for all variables. In contrast, Maple can evaluate both symbolic and numerical expressions.

This tutorial is designed for beginning Maple users. No prior knowledge of Maple or any symbolic mathematical software is assumed. It will cover the basic aspects of Maple such as statement syntax, mathematical operations and graphics. While this tutorial will deal with some calculus related material, it is designed so that anyone with a basic algebra background will benefit from it.

##### Mathematica

The following tutorials are for the computational software Mathematica. The "Getting Started" tutorial details the procedures for launching Mathematica and loading notebooks. While it will deal with some calculus related material, it is designed so that anyone with a basic algebra background will benefit from it. No prior knowledge of Mathematica or any symbolic mathematical software is assumed.

*Mathematica* is capable of generating a wide range of graphics, from a simple plot of a function, to a three dimensional animation of complex physical systems. The "Graphics Fundamentals" tutorial concentrates on the most fundamental and most widely used of the Mathematica plotting capabilities, the use of the **Plot** function to generate two dimensional graphics.

We also recommend dowloading tut.zip, which contains tut.nb - a Mathematica notebook that has been found to be useful as a mathematica tutorial. The tutorial in it will cover the basic aspects of Mathematica such as statement syntax, mathematical operations and graphics, as well as some system interactions such as saving and printing your files.

Statistical Software Tutorials

##### AMOS

The AMOS (Analysis of Moment Structures) software program features a powerful, yet easy to use graphical interface. It is designed primarily for structural equation modeling and similar analyses (e.g., path analysis, confirmatory factor analysis), though it can also be used to fit MANOVA, MANCOVA, ANOVA, ANCOVA, and regression models.

- AMOS Tutorial
- wheaton-generated.zip - .sav and .xls versions of the input file needed for the tutorial.

##### HLM

HLM (Hierarchical Linear Models) are used for analyzing data in a clustered or "nested" structure, in which lower-level units of analysis are nested within higher-level units of analysis. For example, students are nested within classrooms, which are nested within schools. While experimenters are often not interested in the effects of a particular classroom or school when they are examining the effects of a classroom intervention, these units potentially have an effect on the outcome of the study that should be accounted for in a statistical model. The program can be used to analyze a variety of questions using either categorical or continuous dependent variables.

- HLM Getting Started Tutorial
- Multilevel Modeling Tutorial - Using SAS, Stata,
**HLM**, R, SPSS, and Mplus

##### PRELIS and LISREL

PRELIS and LISREL are designed primarily for structural equation modeling and similar analyses (e.g., confirmatory factor analysis and path analysis), though it can also be used to fit ANOVA, ANCOVA, MANOVA, and MANCOVA models. Also, it be used to perform regression analysis and some multilevel or hierarchical linear modeling (HLMs). Many of the statistical methods are also now available for the analysis of complex sampling designs.

- LISREL and PRELIS Tutorial
- LISRELtutorial.zip for use in the tutorial.

##### Mplus

M*plus* is primarily designed for conducting exploratory factor analysis, confirmatory factor analysis, and structural equation modeling. The program can also handle multiple group analysis and multilevel SEM.

- MPlus Tutorial
- Multilevel Modeling Tutorial - Using SAS, Stata, HLM, R, SPSS, and
**M***plus*

##### SAS

Over the years SAS has developed a reputation of being a powerful and full-featured package for general statistical analysis. The new release of SAS (version 8) has a number of new features that promise to make SAS more user-friendly. In particular, the current version of SAS has a substantially enhanced windows-driven interface which allows you to point and click your way through many tasks that previously required knowledge of SAS programming syntax.

- SAS Analyst Tutorial (Click HERE to download instructions for creating the dataset.)
- Part I - SAS Getting Started Tutorial
- Part II - SAS Inferential Stats Tutorial
- Multilevel Modeling Tutorial - Using
**SAS**, Stata, HLM, R, SPSS, and Mplus

##### SPSS

SPSS is extremely popular in the social sciences and is known for its user-friendly menus for running a large variety of advanced statistical tests and procedures. In the "Getting Started" tutorial, we introduce readers to the SPSS for Windows environment, and discusses how to create or import a dataset, transform variables, manipulate data, and perform descriptive statistics. The next three tutorials focus on running descriptive and inferential statistics, graphically displaying data and exporting tables into other applications, and data manipulation and management.

- SPSS Getting Started Tutorial
- SPSS Descriptive and Inferential Stats Tutorial
- SPSS Displaying Data
- SPSS Data Manipulation and Advanced Topics Tutorial
- Multilevel Modeling Tutorial - Using SAS, Stata, HLM, R,
**SPSS**, and Mplus

##### Stata

Stata is another extremely popular statistical software package, especially in the social sciences. It is know for a relatively easy-to-learn programming language as well as user-friendly drop-down menus. The "Getting Started" tutorial introduces readers to Stata 12 and discusses how to navigate the different windows in Stata, create or import a dataset, transform variables, and manage data. The "Data Analysis" tutorial dives into running statistical tests and inferences, including linear and non-linear regression, parametric and non-parametric tests, and more advanced analysis topics.

- Part I - Stata Getting Started Tutorial , Dataset
- Part II - Stata Data Analysis Tutorial , Dataset
- Multilevel Modeling Tutorial - Using SAS,
**Stata**, HLM, R, SPSS, and Mplus