2014-Jun-08, 15:27:37

I am in Leipzig, Germany right now, so am pressed for time and don't have access to academic literature.

Good submission. Very thorough paper.

This kind of compositing has been annoying me for some time. It is very arbitrary. As I see it, one should do either or both of the following two:

1) use the variables for the socialeconomic status and do a factor analysis (whichever type: PCA, PAF, maximum-likelihood, ...) and use the general factor. A lack of a general factor means that there is no such thing as the general socioeconomic status. However, one is universally found so this isn't an issue. :)

2) use multiple regression on the desired dependent variable to find the best way to weigh the variables for prediction.

The above methods may give the same results which makes for easy interpretation. :)

Good submission. Very thorough paper.

Quote:Sackett et al estimated a correlation between SAT scores from 1995 to 1997 and socioeconomic status of 0.42, explaining 18% of variance. They defined socioeconomic status as a composite that equally weighted father’s education, mother’s education, and family annual income. Their meta- analysis of 55 standardized-test studies, less than half of which were for the SAT, gave the significant correlations of 0.255, 0.284, 0.223, and 0.186 for composite socioeconomic status, father’s education, mother’s education, and family income, respectively, without utilizing multiple linear regression. A similar study by Sackett et al (2012) determined that composite socioeconomic status explained 21.2% of 2006 composite-SAT variance.

This kind of compositing has been annoying me for some time. It is very arbitrary. As I see it, one should do either or both of the following two:

1) use the variables for the socialeconomic status and do a factor analysis (whichever type: PCA, PAF, maximum-likelihood, ...) and use the general factor. A lack of a general factor means that there is no such thing as the general socioeconomic status. However, one is universally found so this isn't an issue. :)

2) use multiple regression on the desired dependent variable to find the best way to weigh the variables for prediction.

The above methods may give the same results which makes for easy interpretation. :)