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--- c:ma:2019:factor_analysis_assignment [2019/11/26 07:35] – hkimscil
+++ c:ma:2019:factor_analysis_assignment [2021/11/15 22:32] (current) – hkimscil
@@ Line 1: / Line 1: @@
 ====== Factor analysis assignment ======
+The assignment should be uploaded or [[https://eclass2.ajou.ac.kr/webapps/blackboard/content/listContentEditable.jsp?content_id=_379585_1&course_id=_47711_1|wriitten in the ajou bb technology]].
 data file {{:efa.csv}}: The data set contains what customers consider while purchasing car. The survey questions were framed using 5-point likert scale with 1 being very low and 5 being very high. The variables were the following:
@@ Line 17: / Line 20: @@
 Testimonials
+In order to perform the factor analysis in r, you would need to install the following packages: ''psych'' and ''GPArotation''. Do the following codes.
+<code>
+install.packages('psych')
+install.packages('GPArotation')
+</code>
+__**Assignment**__
 Do the following tasks with your group members
   * Read the data file as "efa" into r
@@ Line 22: / Line 33: @@
     * how many variables are there?
     * how many subjects (participants) are there?
-  *
+  * Use the following code
+    * ''%%?fa.parallel%%''
+    * explain what it is and it does (based on the explanation of help)
+    * do ''%%fa.parallel%%'' with following options (수업에서 다루지 않았읍니다. help 문서를 보고 완성하세요)
+      * ''%%fm = minres%%''
+      * ''%%fa = pc%%''
+    * print out the output and
+    * determine the number of factors
+  * Use the following code
+    * ''%%efa.fa.ini <- fa(efa)%%''
+    * Print out the result, efa.fa.ini
+    * Use the following code, ''%%names(efa.fa.ini)%%'' and answer the below questions
+      * e.values를 출력하시오.
+      * e.values는 무엇을 의미하는가?
+      * communality를 출력하시오
+      * communality는 이 output에서 무엇을 의미하는가?
+  * Analyze the data with fa function.
+    * ''%%efa.fa.3 <- fa(options~) %%''
+    * Use the option
+      * 3 factors
+      * rotate = oblimin
+      * fm = minres
+    * print out the result
+    * examine the communality by comparing efa.fa.ini%%$%%communality
+      * temp <- data.frame(efa.fa.ini%%$%%communality, efa.fa.3%%$%%communality)
+      * Are there any changes from the efa.fa.ini?
+      * 이 값이 커졌다면 무엇을 의미하는가?
+    * print(fa.sort(efa.fa.3%%$%%loadings), cutoff = 0.3)
+      * 3개의 factor에 대한 정의 (무엇에 관한 것인지)를 내리시오
+  * 같은 옵션으로 4개의 factor를 추출하고 그 결과를 출력하시오
+    * 각 factor에 대한 loading의 제곱의합값은?
+    * 이 값의 모든 합은 전체 Y 분산 중 몇 %를 차지하는가?
+    * 4개의 factor들을 설명하는데 기여분이 가장 많은 3개의 변인을 말하시오.
+    * 4개의 factor들은 어떻게 설명되는지 factor에 기여하는 변인들을 중심으로 설명하시오.
+  * 같은 옵션으로 5개의 factor를 추출하고 그 결과를 출력하시오
+    * 위의 4개추출과 같은 내용을 실시하시오.