Richard Williams，is an Associate Professor of the Department of Sociology at the University of Notre Dame, teaching Methods and Statistics, Demography, and Urban Sociology.

homepage：http://www.nd.edu/~rwilliam/ 

有以下教程链接（包括，pdf教程，spss和stata的例子）

Graduate Statistics I

入门级课程，介绍最基础的统计描述量，统计描述方法，假设检验，和OLS回归的基本知识

PART I: Descriptive statistics, probability, distributions, confidence intervals, intro to hypothesis testing.

PART II: Hypothesis testing.

PART III: Bivariate and multivariate regression

Graduate Statistics II

进阶教程，包括OLS回归问题和对策，如何选择合适的model，以及path analysis techniques

PART I:
   In this section, we briefly review the basics of OLS regression. We talk about some of the most common issues (measurement error, missing data, violations of OLS assumptions) encountered in regression analysis.

基础OLS regression

有违OLS regression假设时的问题及对策：multicollinearity（多重共线性）问题、detecting及对策，missing data问题及对策，measurement error，简单的scale construction，outliers（极值）处理，Heteroskedasticity（异方差）问题、detecting及对策，基本的serial correlation（序列自相关）处理

PART II:
  This section shows how regression can be used to properly specify a causal model. We begin by introducing "the logic of causal order," which lets us understand the different kinds of causal relationships that might be present between variables. Common model mis-specifications are then addressed (e.g. omitted variables, extraneous variables, variables with nonlinear effects). We discuss how to choose between alternative causal models. Finally, we introduce path analysis as a method for causal modeling.

因果逻辑关系分析

多因素统计分析：suppressor effects（制约效应？？？），interaction effects（交互作用）；specification error；imposing and testing equality constraints in Models

组间比较方法和模型

path analysis（路径分析？？）介绍

PART III:
   Here, we develop path analysis techniques more fully. We talk about more complicated models that cannot be accurately estimated through conventional OLS regression techniques (e.g. nonrecursive models). We also talk about situations where the nature of the data make OLS regression inappropriate (e.g. dichotomous dependent variables) or less than optimal.

R square，计算及其问题详解

standardization的问题，和recursive model

更复杂的回归模型：Logistic regression；Logit model；Manova 和 LISREL的简介

Categorical Data Analysis

categorical data（分类数据？）分析方法和应用，解释有关非连续变量的统计方法

Overview.
    This course discusses methods and models for the analysis of categorical dependent variables and their applications in social science research. Researchers are often interested in the determinants of categorical outcomes. For example, such outcomes might be binary (lives/dies), ordinal (very likely/ somewhat likely/ not likely), nominal (taking the bus, car, or train to work) or count (the number of times something has happened, such as the number of articles written). When dependent variables are categorical rather than continuous, conventional OLS regression techniques are not appropriate. This course therefore discusses the wide array of methods that are available for examining categorical outcomes.

Contents:

Overview of Generalized Linear Models, Maximum Likelihood Estimation

Brief Review of Models for Continuous Outcomes

Models for Binomial Outcomes

Models for Ordinal Outcomes

Models for Group Comparisons; Heterogeneous Choice Models

Categorical Data Analysis with Complicated Survey Designs   

Models for Multinomial Outcomes

Models for Count Outcomes

[此贴子已经被作者于2008-7-10 13:19:08编辑过]