The pages below contain examples (often hypothetical) illustrating the
application of different statistical analysis techniques using different
statistical packages. Each page provides a
handful of examples of when the analysis might be used along with sample data,
an example analysis and an explanation of the output, followed by references for more information. These pages merely introduce the
**essence** of the technique and do **not** provide a comprehensive
description of how to use it.

The combination of topics and packages
reflect questions that are often asked in our statistical consulting. As such,
this heavily reflects the demand from our clients at walk in consulting, not
demand of readers from around the world. Many worthy topics will not be covered
because they are not reflected in questions by our clients. Also, not all
analysis techniques will be covered in all packages, again largely determined by
client demand. If an analysis is not shown in a particular package,this does
**not imply that the package cannot do the analysis**, it
may simply mean that the analysis is not commonly done in that package
by our clients.

Stata | SAS | SPSS | Mplus | R | |
---|---|---|---|---|---|

Regression Models | |||||

Robust Regression | Stata | SAS | R | ||

Models for Binary and Categorical Outcomes | |||||

Logistic Regression | Stata | SAS | SPSS | Mplus | R |

Exact Logistic Regression | Stata | SAS | R | ||

Multinomial Logistic Regression | Stata | SAS | SPSS | Mplus | R |

Ordinal Logistic Regression | Stata | SAS | SPSS | Mplus | R |

Probit Regression | Stata | SAS | SPSS | Mplus | R |

Count Models | |||||

Poisson Regression | Stata | SAS | SPSS | Mplus | R |

Negative Binomial Regression | Stata | SAS | SPSS | Mplus | R |

Zero-inflated Poisson Regression | Stata | SAS | Mplus | R | |

Zero-inflated Negative Binomial Regression | Stata | SAS | Mplus | R | |

Zero-truncated Poisson | Stata | SAS | R | ||

Zero-truncated Negative Binomial | Stata | SAS | Mplus | R | |

Censored and Truncated Regression | |||||

Tobit Regression | Stata | SAS | Mplus | R | |

Truncated Regression | Stata | SAS | R | ||

Interval Regression | Stata | SAS | R | ||

Multivariate Analysis | |||||

One-way MANOVA | Stata | SAS | SPSS | ||

Discriminant Function Analysis | Stata | SAS | SPSS | ||

Canonical Correlation Analysis | Stata | SAS | SPSS | R | |

Multivariate Multiple Regression | Stata | SAS | SPSS | Mplus | |

Mixed Effects Models | |||||

Generalized Linear Mixed Models | Introduction to GLMMs | ||||

Mixed Effects Logistic Regression | Stata | R | |||

Other | |||||

Latent Class Analysis | Stata | Mplus |

## Power Analyses

For grants and proposals, it is also useful to have power analyses corresponding to common data analyses. We have examples of some simple power analyses below.

Stata | SAS | SPSS | Mplus | R | G*Power | |
---|---|---|---|---|---|---|

Power Analysis / Sample Size | ||||||

Correlations | Stata | SAS | SPSS | G*Power | ||

Single-sample t-test | Stata | SAS | SPSS | R | G*Power | |

Paired-sample t-test | Stata | SAS | SPSS | R | G*Power | |

Independent-sample t-test | Stata | SAS | SPSS | R | G*Power | |

Two Independent Proportions | Stata | SAS | SPSS | G*Power | ||

One-way ANOVA | Stata | SAS | SPSS | G*Power | ||

Multiple Regression | Stata | SAS | SPSS | G*Power | ||

Accuracy in Parameter Estimation | Stata |