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"New-Style" Judicial Campaigns and the Legitimacy of State High Courts
Unformatted Document Text:  For the Supreme Court, first two eigenvalues are 2.10 and .56, respectively, and the first extracted 18 factor accounts for 70.1 % of the common variance. For the State Senate, the eigenvalues are 2.10 and.53, with the first factor accounting for 70.0 % of the common variance. See Appendix B for the text of the items used to check the manipulations. 19 -13- judge/senator can be fair and impartial), but for statistical purposes, I employ a continuous indicator of reactions to the vignette. This variable is a factor score derived from a Common Factor Analysis of the three vignette judgments. For both the judicial and legislative analyses, the results reveal strongly unidimensional structures. For the Supreme Court, the factor loadings on the first unrotated factor are: 18 Acceptance of decisions as fair and impartial .89 Believe the judge can be fair and impartial .73 Consider the [State] Supreme Court legitimate .62 This set of items is extremely reliable: alpha = .79. For the State Senate, the factor structure is similar: Acceptance of decisions as fair and impartial .82 Believe the senator can be fair and impartial .75 Consider the [State] State Senate legitimate .65 Cronbach’s alpha for the set of items is .79. Thus, the dependent variables for the statistical analysis of the vignette are quite valid and highly reliable. Manipulation Checks In experimental studies such as this, the manipulations are not always perceived as they are intended (for a classic example, see Gibson and Gouws 2001). Thus, it is necessary to assess empirically how the respondents reacted to the elements of the stories they heard. Table 2 reports data relevant to checking the effectiveness of the manipulations. As is conventional with manipulation checks, the questions asked of the respondents assess the degree to which they heard and understood the attributes of the stories. 19 [PLACE TABLE 2 ABOUT HERE]

Authors: Gibson, James.
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For the Supreme Court, first two eigenvalues are 2.10 and .56, respectively, and the first extracted
18
factor accounts for 70.1 % of the common variance. For the State Senate, the eigenvalues are 2.10 and
.53, with the first factor accounting for 70.0 % of the common variance.
See Appendix B for the text of the items used to check the manipulations.
19
-13-
judge/senator can be fair and impartial), but for statistical purposes, I employ a continuous indicator of
reactions to the vignette. This variable is a factor score derived from a Common Factor Analysis of the
three vignette judgments. For both the judicial and legislative analyses, the results reveal strongly
unidimensional structures. For the Supreme Court, the factor loadings on the first unrotated factor are:
18
Acceptance of decisions as fair and impartial
.89
Believe the judge can be fair and impartial
.73
Consider the [State] Supreme Court legitimate
.62
This set of items is extremely reliable: alpha = .79. For the State Senate, the factor structure is similar:
Acceptance of decisions as fair and impartial
.82
Believe the senator can be fair and impartial
.75
Consider the [State] State Senate legitimate
.65
Cronbach’s alpha for the set of items is .79. Thus, the dependent variables for the statistical analysis of
the vignette are quite valid and highly reliable.
Manipulation Checks
In experimental studies such as this, the manipulations are not always perceived as they are intended (for
a classic example, see Gibson and Gouws 2001). Thus, it is necessary to assess empirically how the
respondents reacted to the elements of the stories they heard. Table 2 reports data relevant to checking
the effectiveness of the manipulations. As is conventional with manipulation checks, the questions asked
of the respondents assess the degree to which they heard and understood the attributes of the stories.
19
[PLACE TABLE 2 ABOUT HERE]


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