A multivariate analysis of racial attitudes: Structure, content, and determinants.

Colorblind norms play an important role in shaping how people discuss race. There is reason to believe that these norms also affect the ways respondents interact with social surveys. Specifically, some respondents may be using nonresponse as a tactic to not discuss race in social surveys. If this is the case, very different demographics of respondents would be most prone to nonresponse, and the phenomenon should also vary on the basis of the interviewer’s race. The author conducted bivariate and multivariate analysis of the Chicago Area Study to examine whether colorblindness may be promoting “don’t know” responses and item refusals. The author finds that nonresponse to a perceived race of interviewer item follows a distinct pattern consistent with previous research on colorblind norms. For example, white respondents have nearly five times the rate of nonresponse compared with blacks and Latinos. Bolstering the colorblindness theory, an interracial interview context nearly triples the nonresponse rate compared with same-race interviews. Findings of this research have important implications for both survey researchers using social surveys to examine race and racial attitudes and race scholars who seek to understand the prevalence of colorblind norms across society.

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Statistical analysis of classification

Symposium - International Astronomical Union ◽

10.1017/s0074180900053420 ◽

1966 ◽

Vol 24 ◽

pp. 188-189

Author(s):

T. J. Deeming

Keyword(s):

Multivariate Analysis ◽

Statistical Analysis ◽

Classification Scheme ◽

Analytical Procedure ◽

Narrow Band ◽

Scientific Research ◽

Maximum Amount ◽

Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

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