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"What's In It For Me?": Why Members of Congress Pursue Oversight
Unformatted Document Text:  pect longitudinal correlation. 61 Therefore, I employ a GEE model. 62 Negative binomial regression is appropriate for event count dependent variables where the distribution isoverdispersed (in this case, µ = 3.54, σ 2 = 23.90). 3.4 Results As previously mentioned, I theorize that institutional features (e.g., the presence of dividedgovernment and/or a subunit oversight mandate), MC electoral security, and MC/subunitlegislative opportunities will influence the number of oversight hearings that a committeeor subcommittee elects to hold. Converting these ideas into testable hypotheses is fairlystraightforward, except for the notion of “legislative opportunities,” which is a more neb-ulous concept. I use bills passed, preference heterogeneity, committee seniority, and RulesCommittee deference to committee legislation as proxies for legislative opportunities. Irun four different regression models (Models 1-4), each of which includes a different oper-ationalization of “legislative opportunities.” 63 Model 5, which is my preferred theoretical model, includes all of these measure of legislative opportunities, as well as the variablesmeasuring institutional and electoral features. Table 3 presents the results of these anal-yses. 61 In fact, model fitting via Akaike’s information criterion (AIC) indicates that a second-order autore- gressive process exists within clusters for these data. 62 Clusters are defined as the parent committees. In other words, every Ways & Means subcommittee in all of the Congresses under study is classified as a member of the same cluster. I assume that theobservations within clusters conform to an “exchangeable” correlation structure, meaning that the y i value for an observation covaries equally with the y −i values of the other observations within its same cluster (cf. Zorn, 2001, p. 473). 63 While I believe that these operationalizations accurately reflect on facets of this legislative oppor- tunities concept, they are still surrogates for an concept that is impossible to measure more directly. Acontrarian case could be made the these different variables do not merely tap into different parts of thisconcept, as I contend, but rather that they measure the exact same thing, and therefore should not beincluded in the same model. 34

Authors: Feinstein, Brian.
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background image
pect longitudinal correlation.
Therefore, I employ a GEE model.
Negative binomial
regression is appropriate for event count dependent variables where the distribution is
overdispersed (in this case, µ = 3.54, σ
= 23.90).
As previously mentioned, I theorize that institutional features (e.g., the presence of divided
government and/or a subunit oversight mandate), MC electoral security, and MC/subunit
legislative opportunities will influence the number of oversight hearings that a committee
or subcommittee elects to hold. Converting these ideas into testable hypotheses is fairly
straightforward, except for the notion of “legislative opportunities,” which is a more neb-
ulous concept. I use bills passed, preference heterogeneity, committee seniority, and Rules
Committee deference to committee legislation as proxies for legislative opportunities. I
run four different regression models (Models 1-4), each of which includes a different oper-
ationalization of “legislative opportunities.”
Model 5, which is my preferred theoretical
model, includes all of these measure of legislative opportunities, as well as the variables
measuring institutional and electoral features. Table 3 presents the results of these anal-
In fact, model fitting via Akaike’s information criterion (AIC) indicates that a second-order autore-
gressive process exists within clusters for these data.
Clusters are defined as the parent committees. In other words, every Ways & Means subcommittee
in all of the Congresses under study is classified as a member of the same cluster. I assume that the
observations within clusters conform to an “exchangeable” correlation structure, meaning that the y
value for an observation covaries equally with the y
values of the other observations within its same
cluster (cf. Zorn, 2001, p. 473).
While I believe that these operationalizations accurately reflect on facets of this legislative oppor-
tunities concept, they are still surrogates for an concept that is impossible to measure more directly. A
contrarian case could be made the these different variables do not merely tap into different parts of this
concept, as I contend, but rather that they measure the exact same thing, and therefore should not be
included in the same model.

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