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工作坊 (二)
工作坊 (二)
講題：心理和教育研究中關於遺漏值的處理方法和應用
Handling Missing Data: Methods and Applications in Psychological and Educational Research
講者：彭昭英 教授 Prof. Chao-Ying Joanne Peng
Professor Emeritus - Department of Counseling and Educational Psychology at Indiana University Bloomington
Adjunct Professor - Department of Psychology at National Taiwan University
語言：中文
日期：2018 / 10 / 26 (五)
時間：13:00 ~ 16:00（12:30 開放簽到，13:00 活動開始）
地點：國立政治大學 藝文中心 2 樓 舜文大講堂
工作坊內容
工作坊內容
1. Taxonomy of missing-data methods
2. Ad Hoc methods
3. Single Imputation
4. EM algorithm
5. Multiple Imputation and its Demo using SAS
6. Statistical Assumptions
7. Implications and Recommendations
本工作坊包含以下主題：
本工作坊包含以下主題：
Taxonomy of missing-data methods
Ad Hoc methods
Single Imputation
EM algorithm
Multiple Imputation and its Demo using SAS
Statistical Assumptions
Implications and Recommendations
若您為政大教職員工生，電腦沒有安裝 SAS，請點擊以上按鈕啟用「政大雲」 服務，以使用該服務所提供之校園授權軟體
心理和教育研究中關於遺漏值的處理方法和應用：簡報檔 1
心理和教育研究中關於遺漏值的處理方法和應用：簡報檔 1
20181026_心理和教育研究中關於遺漏值的處理方法及應用.pdf心理和教育研究中關於遺漏值的處理方法和應用：簡報檔 2
心理和教育研究中關於遺漏值的處理方法和應用：簡報檔 2
20181026_心理和教育研究中關於遺漏值的處理方法及應用_SAS_Demo.pdf/* Slide #9 */
*-------- Data on Physical Fitness ---------------*
These measurements were made on men involved in a physical fitness course at N.C. State University.
Only selected variables of:
*Oxygen (oxygen intake, ml per kg body weight per minute),
*Runtime (time to run 1.5 miles in minutes), and
*RunPulse (heart rate while running) are used.
Certain values were changed to missing for the analysis.
*--------------------------------------------------*;
DATA FitMiss;
INPUT Oxygen RunTime RunPulse @@;
DATALINES;
44.609 11.37 178 45.313 10.07 185
54.297 8.65 156 59.571 . .
49.874 9.22 . 44.811 11.63 176
. 11.95 176 . 10.85 .
39.442 13.08 174 60.055 8.63 170
50.541 . . 37.388 14.03 186
44.754 11.12 176 47.273 . .
51.855 10.33 166 49.156 8.95 180
40.836 10.95 168 46.672 10.00 .
46.774 10.25 . 50.388 10.08 168
39.407 12.63 174 46.080 11.1 156
45.441 9.63 164. 8.92 .
45.118 11.08 . 39.203 12.88 168
45.790 10.47 186 50.545 9.93 148
48.673 9.40 186 47.920 11.50 170
47.467 10.50 170
;
/* ------------------------------Slide #10------------------------------ */
proc mi data=FitMiss seed=501213 mu0=50 10 180 out=outmi nimpute=5;
var Oxygen RunTime RunPulse; run;
/* ------------------------------Slide #14------------------------------ */
proc print data=outmi (obs=10);
title First 10 Observations of the Imputed Data Set;
run;
/* ------------------------------Slide #15A------------------------------ */
proc reg data=outmi outest=outreg covout ;
title 'Step2: analyze imputed data - regression';
model Oxygen= RunTime RunPulse;
by _Imputation_;
run;
/* ------------------------------Slide #15B------------------------------ */
proc print data=outreg(obs=8);
var _Imputation_ _Type_ _Name_
Intercept RunTime RunPulse;
title REG output data(First Two Imputations);
run;
/* ------------------------------Slide #18------------------------------ */
proc mianalyze data=outreg edf=28;
modeleffects Intercept RunTime RunPulse;
run;
References (相關文獻)
References (相關文獻)
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