Why Should I Perform Pearsons Correlation and Multiple Linear Regression Analysis?

Suppose you want to find out if there is a correlation between job satisfaction and the perception of the supervisor’s leadership style among non-supervisory employees. Suppose you use the Multifactor Leadership Style Questionnaire (MLQ) to measure five transformational leadership styles (the MLQ measures other leadership styles too but we don’t need them for this explanation). Let’s call . . .

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5 comments on “Why Should I Perform Pearsons Correlation and Multiple Linear Regression Analysis?
  1. Regarding your question, Corina, I think if a variable X is not statistically significantly associated with another variable Y in a univariate analysis, but it becomes significant when other independent variables are accounted for in a multivariate analysis, it could be a correct finding or it could be a spurious result. I think multicollinearity or other mathematical problems (e.g. 0 cells, outliers, sample size too small etc) could sometimes cause this to happen. However, I think it is possible for a variable to become significant only when other variables are accounted for. For example, suppose there is a positive correlation between X and Y for males and a negative correlation for females. Then, when you look at only X and Y, the correlation cancels out. But, when you control for gender, the correlation shows up.

  2. Ah, excelent! I had this question in my mind for a long time. But what if in the regression appears an item that was non significant in the correlation?!is there a mistake or it can happen?!Actually, it happnes, but can you comment it?

  3. I just stumbled across your blog and it answered a whole handful of questions I needed to investigate in about 5 minutes flat. Thank you!

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