Hi,
I am conducting a study where I've asked participants to rate how much they aspire towards 13 different life goals and want to know how upholding these various goals is related to well-being. However, it has been noted in the literature - and my data shows this as well - that essentially all goals are positively related to well-being. Thus, one needs to correct for this response bias. Instead of zero-order correlations, the key question is whether prioritizing certain goals over other goals is related to well-being/ill-being.
Two approaches have been used in the literature to examine this:
1) Regression analysis where the importance of goal in question and average goal importance (calculated by taking the average of all studied goals) are simultaneously regressed on well-being. The overall goal importance thus acts here as a control variable, and one asks whether the individual goal importance has any direct relation to well-being beyond the relation explained by overall goal importance.
2) Mean-centering where each goal is mean-centered to the participant’s mean rating of overall goal importance, by subtracting the mean across all aspirations from each aspiration. This way one gets variables that show how much more (or less) the participant strives for a certain goal in contrast to how much one strives for goals overall. Then one examines these new variables' relation to well-being.
I now have to choose which analytic approach to use to control for response bias. And I have been asked by the editor to justify my choice. Thus I am trying to figure out what are the different theoretical assumptions and implications between these two approaches.
Anyone who could weigh in and tell under what assumptions and in what situations one should prefer one over the other?
I am conducting a study where I've asked participants to rate how much they aspire towards 13 different life goals and want to know how upholding these various goals is related to well-being. However, it has been noted in the literature - and my data shows this as well - that essentially all goals are positively related to well-being. Thus, one needs to correct for this response bias. Instead of zero-order correlations, the key question is whether prioritizing certain goals over other goals is related to well-being/ill-being.
Two approaches have been used in the literature to examine this:
1) Regression analysis where the importance of goal in question and average goal importance (calculated by taking the average of all studied goals) are simultaneously regressed on well-being. The overall goal importance thus acts here as a control variable, and one asks whether the individual goal importance has any direct relation to well-being beyond the relation explained by overall goal importance.
2) Mean-centering where each goal is mean-centered to the participant’s mean rating of overall goal importance, by subtracting the mean across all aspirations from each aspiration. This way one gets variables that show how much more (or less) the participant strives for a certain goal in contrast to how much one strives for goals overall. Then one examines these new variables' relation to well-being.
I now have to choose which analytic approach to use to control for response bias. And I have been asked by the editor to justify my choice. Thus I am trying to figure out what are the different theoretical assumptions and implications between these two approaches.
Anyone who could weigh in and tell under what assumptions and in what situations one should prefer one over the other?