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A Structural Equation Model of Support for English-only Policies and Social Limitation of Immigrants and Minorities
Unformatted Document Text:  Social Limitation Structural Equation Model 12 --------------- (in press) found a relatively strong correlation between support for English-only policies and support for social limitation. Therefore a final hypothesis is proposed: H 5 : Support for English-only policies is a form of support for social limitation of immigrants and minorities. The proposed structural equation model (Figure 1) tests the hypotheses described above. Based on theory, structural equation modeling provides the means to test "causal" relations between multiple variables. As a pictorial representation, the resulting model offers a clearer conceptualization of the theory. This process is an improvement on alternative statistical methods (e.g., regression models) because SEM is a confirmatory (hypothesis-testing) process rather than a descriptive one, and also it provides explicit estimates of error variance. Additionally, while other methods are based upon observed variables only, SEM has the power to include unobserved (i.e., latent variables) in the equation (Byrne, 2001). FIGURE 1 ABOUT HERE Method Population Using random digit dialing (RDD) (Lavrakas, 1993), a sample of households with telephones was drawn from nine zip codes in Santa Barbara, California (provided by Scientific Telephone Samples [STS]). The sample was weighted so that the RDD numbers were created from telephone blocks in proportion to the number of estimated household listings in each working block. An estimated 1.8% of households residing in these zip codes were without telephones.

Authors: Barker, Valerie. and Giles, Howard.
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Social Limitation Structural Equation Model 12
--------------- (in press) found a relatively strong correlation between support for
English-only policies and support for social limitation. Therefore a final hypothesis is
proposed:
H
5
:
Support for English-only policies is a form of support for social limitation of
immigrants and minorities.
The proposed structural equation model (Figure 1) tests the hypotheses
described above. Based on theory, structural equation modeling provides the means to
test "causal" relations between multiple variables. As a pictorial representation, the
resulting model offers a clearer conceptualization of the theory. This process is an
improvement on alternative statistical methods (e.g., regression models) because SEM
is a confirmatory (hypothesis-testing) process rather than a descriptive one, and also it
provides explicit estimates of error variance. Additionally, while other methods are
based upon observed variables only, SEM has the power to include unobserved (i.e.,
latent variables) in the equation (Byrne, 2001).
FIGURE 1 ABOUT HERE
Method
Population
Using random digit dialing (RDD) (Lavrakas, 1993), a sample of households with
telephones was drawn from nine zip codes in Santa Barbara, California (provided by
Scientific Telephone Samples [STS]). The sample was weighted so that the RDD
numbers were created from telephone blocks in proportion to the number of estimated
household listings in each working block. An estimated 1.8% of households residing in
these zip codes were without telephones.


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