分类变量数据分析1

1、分类变量数据分析（Categorical Data Analysis）应用与实践 2012-02-151一、课程简介课程缘起课程内容及目标课程讲授方法二、课程导论基本概念2主要内容：1）二分变量Logit模型（Binary Logit model）及二分变量模型的应用实例；2）列联表与Loglinear模型；3）定序变量Logit模型（Ordinal Logit model）及 定序变量Logit模型的应用实例讨论；4）多项分类变量Logit模型（Multinomial logit model）及多项分类变量Logit模型的应用实例等。3本课程注重培养学生的实践能力和实际应用。要求学生具备基本

2、统计知识和方法并能够使用统计软件。通过本课程的学习，掌握社会科学中最常用的分类数据分析方法，并可以较熟练地进行实际应用，提高学生定量研究方法的分析技能。 4授课方式：本课程以研究生为对象。讲课形式以教师和学生共讲、讨论为主。要求学生在课前认真准备，课上积极发言和参与讨论。成绩评定：课程成绩将由学生出勤、课堂表现（发言、讨论）和期末成绩而确定。5参考书：1、Scott Long, J. Scott. 1996. The Analysis of Categorical and Limited Dependent Variables. New York: SAGE2、Daniel A. Powers

3、 & Yu Xie 2008. Statistical Methods for Categorical Data Analysis ACADMIC PRESS（中文版）3、王济川, 郭志刚 Logistic回归模型 方法与应用 高教出版社，6二、课程导论2.1 Categorical VariableWe define categorical variables as those variables that can be measured using only a limited number of values or categories.Vs Continuous Variable特征属

4、性、行为选择及结果、态度意愿等72.2 Categorical Dependent VariablesDependent Variable (response, outcome, endogenous)Independent Variable (explanatory, predetermined, exogenous)因变量的测量类型决定了研究者该选何种统计分析模型或方法。82.3 Types of MeasurementQuantitative & Qualitative92.4 Basics of RegressionThree interpretations of Regression

5、Causation Observed=True Mechanism + DisturbancePrediction Observed=Predicted + ErrorDescription Observed=Summary + Residual102.5 准确（accuracy）与简约（parsimony）There is always a tension between accuracy and parsimony.Occams Razor: Principle of parsimony.The principle states that one should not make more

6、assumptions than the minimum needed. 112.6 Types of RegressionCaseD.VInD.VM. of A.Exam.1ContinuousContinuousCorrelation, LRF.Inc.on IQ2ContinuousCategoricalANOVA,LRGender on Inc.3CategoricalContinuousLogit/Probit RF.Inc on Edu. 4CategoricalCategoricalLoglinear , Logit/Probit RRace on Edu.12例1 教育水平与生

7、育意愿 | des edu | 0 1 2 3 | Total-+-+- 1 | 24 257 960 56 | 1,297 2 | 58 987 2,897 73 | 4,015 3 | 52 1,565 3,144 40 | 4,801 4 | 6 306 555 7 | 874 -+-+- Total | 140 3,115 7,556 176 | 10,987 13a. LR Model1. regress des edu fweight=freq Source | SS df MS Number of obs = 10987-+- F( 1, 10985) = 115.66 Mode

8、l | 30.2967799 1 30.2967799 Prob F = 0.0000 Residual | 2877.59218 10985 .261956502 R-squared = 0.0104-+- Adj R-squared = 0.0103 Total | 2907.88896 10986 .264690421 Root MSE = .51182- des | Coef. Std. Err. t P|t| 95% Conf. Interval-+- edu | -.0653997 .0060812 -10.75 0.000 -.0773201 -.0534794 _cons |

9、1.869079 .0158408 117.99 0.000 1.838028 1.90013-14b. LR Model2 Source | SS df MS Number of obs = 10987-+- F( 3, 10983) = 41.15 Model | 32.3179575 3 10.7726525 Prob F = 0.0000 Residual | 2875.571 10983 .261820177 R-squared = 0.0111-+- Adj R-squared = 0.0108 Total | 2907.88896 10986 .264690421 Root MS

10、E = .51168- des | Coef. Std. Err. t P|t| 95% Conf. Interval-+- _Iedu_2 | -.0645565 .0163425 -3.95 0.000 -.0965907 -.0325223 _Iedu_3 | -.1473228 .0160125 -9.20 0.000 -.1787102 -.1159354 _Iedu_4 | -.1638537 .0223927 -7.32 0.000 -.2077474 -.1199601 _cons | 1.808019 .014208 127.25 0.000 1.780168 1.83586

11、9-15c. Logit Model1 edu | 0 1 | Total-+-+- 1 | 281 1,016 | 1,297 -+-+- 2 | 1,045 2,970 | 4,015 -+-+- 3 | 1,617 3,184 | 4,801 -+-+- 4 | 312 562 | 874 -+-+- Total | 3,255 7,732 | 10,987 16Log likelihood = -6620.592749 AIC = 1.205532BIC = -88968.39586-des1 | Coef. Std. Err. z P|z| 95% Conf. Interval-+- edu | -.2780525 .0265706 -10.46 0.000 -.3301299 -.2259751 _cons| 1.564346 .0709542 22.05 0.000 1.425279 1.703414 -17d. Logit Model2Log likelihood = -6616.715701 AIC = 1.205191BIC = -88957.54102-des1 | Coef. Std. Err. z P|z| 95% Conf. Interval-+- edu_2 | -.2407289 .0763977 -3.15 0.002 -.3904