아래에 통계분석 질문을 남겨주세요.
=>안녕하세요. 요인분석과 상관분석에 대한 질문이 생겨 문의드립니다.
9개의 문항으로 이루어진 설문조사를 바탕으로 (주성분분석/베리맥스회전) 요인분석을 수행하였더니 2개의 요인으로 분류되는 것을 확인하였습니다.
각 요인에 할당된 각 문항의 값을 평균하여 두 개의 독립변수 값을 설정하였습니다.
이후 회귀분석을 수행하고자 하였는데, 상관분석 결과 요인분석의 결과로 도출된 두 개의 변수 간 상관관계가 0.7 이상으로 매우 높게 나타났습니다. 이에 다중공선성 문제로 인해 회귀분석을 수행하지 못하는 상황입니다.
해당 문제에 대해 확인한 결과 다음의 방법들에 대해 고려중인데요, 어떠한 방법이 맞는지 잘 몰라서 문의드립니다.
1. 문항 응답 값을 평균하지 않고, SPSS '변수로 저장' 기능을 활용하여 요인점수를 변수로 설정
-> 이 경우 베리맥스 회전은 요인점수 간 상관관계가 존재하지 않음을 가정하기 때문에 상관관계가 0으로 매우 유의하게 나타나게 됩니다. 상관관계가 0이므로 회귀분석을 수행할 때 두 변수를 모두 활용하는 것에 상관이 없는지요?
2. 베리맥스 회전이 아닌, (공통요인분석/직접 오블리민) 요인분석을 수행 (그 외 값은 모두 디폴트 값을 활용)
-> 이 경우 두 개의 요인으로 추출되나, 구조행렬이 아래와 같이 한 쪽의 요인은 모두 음수의 값만을 갖습니다. 왜 두 개의 요인이 추출되지만 이렇게 한쪽의 요인은 모두 음수가 나타나는지 모르겠네요. 수렴을 위한 반복 횟수를 100으로 늘려보아도 같은 결과가 나타납니다. 각 문항이 요인으로 어떻게 묶이는지 어떻게 알 수 있는지요..?
![](data:image/png;base64,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)
첫댓글 안녕하세요. 내용을 보니 독립변수 1개로 파악됩니다. 다중공선성은 독립변수가 여러개일때 고려해야 합니다.
덧붙여 요인분석 값을 변수로 사용하고자 하면 독립변수와 종속변수간에 상관성이 없는 것으로 나올겁니다. 이 역시 독립변수가 여러개일때 다중공선성이 발생할 경우 독립변수들을 요인분석 값으로 변경하여(참고로 이때는 각 요인변수들의 변수명을 지정하기가 곤란한 경우가 있습니다.) 독립변수들끼리의 상관성을 없앤 후 회귀분석을 진행하는 것인데 선생님의 경우는 단순회귀분석으로 보여 그냥 진행해도 무리가 없어보입니다.