By Albert R. Wildt

ISBN-10: 0585216673

ISBN-13: 9780585216676

ISBN-10: 0803911645

ISBN-13: 9780803911642

This booklet offers a method for reading the results of variables, teams, and coverings in either experimental and observational settings. It considers not just the most results of 1 variable upon one other, but additionally the results of workforce circumstances.

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Extra resources for Analysis of Covariance (Quantitative Applications in the Social Sciences)

Sample text

A Posteriori Nonorthogonal Comparisons In many situations the researcher is not sufficiently knowledgeable concerning the effect of the independent variable to a priori specify the appropriate mean comparisons. However, if the overall F-test leads to the rejection of the null hypothesis of no treatment/group effects, the researcher is usually interested in further "exploring" the data in an effort to uncover the sources of the treatment/gorup effect. In such cases of unplanned mean comparisons it is usually recommended that a procedure be employed which maintains a specified a-level for the entire collection of comparisons.

This particular instance illustrates the use of analysis of covariance in an effort to remove bias which would result because the intact groups were not matched on some relevant variable. Application Four: American Occupational Structure Blau and Duncan (1967), in their study American Occupational Structure, illustrate the use of analysis of covariance in the context of an observational study. They considered the problem of relating present occupational status to occupational status of the first job, where occupational status is measured on an interval scale.

If the interaction is not statistically significant, it supports the assumption of constant slope, and the data for all levels of the categorical independent variable may be analyzed together within the context of the analysis of covariance model. 4. Statistical Procedures for Analysis of Covariance for a One-Way Layout Introduction A one-way layout describes any context involving a single qualitative independent variable, with any number of levels, and one or more covariates (quantitative independent variables).

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Analysis of Covariance (Quantitative Applications in the Social Sciences) by Albert R. Wildt


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