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A Multilevel Study of Interpersonal Influence in Academic Influence Networks
Unformatted Document Text:  Influence Networks 10 variable weighting is captured within a regression model. In this model, behavioral intention is regressed onto attitude and subjective norm, where i represents individuals and the slopes, ß 1 and ß 2 , represent the expected change in behavioral intent associated with a unit increase in attitude or subjective norm. These slopes represent the relative contribution, or weights, for attitude (ß 1 ) and subjective norm (ß 2 ). Behavioral Intent i = ß 0 + ß 1 Attitude i + ß 2 Subjective Norm i + r i The original model is based on groups of individuals, but for the purpose of this study it was necessary to derive weights for each individual within their respective academic department. Recent developments in modeling procedures have made it possible to measure individual variation between these behavioral antecedents (Hedeker, Flay, & Petraitis, 1996). Hedeker and his colleagues derived individual level relative weights to empirically verify that the contribution of each component varies across individuals (Hedeker et al., 1996). Individual-level weights for attitude and subjective norm can be estimated, in principle, if multiple observations are used as the random effect variable, either over time or across behavioral observations. This study employed observations for a set of teaching behaviors as the random effect variable. The first level of the multilevel model represents individual level cognitions and behavioral intentions for a set of teaching behaviors. In the following formula, teaching behaviors constitute the levels of the random-effect variable, which is represented by j (Hedeker et al., p. 110): Behavioral Intent ij = ß 0 i + ß 1 i Attitude ij + ß 2 i Subjective Norm ij + r ij Level 2: Adding a Structural Predictor The second level of this model contains structural centrality. Centrality appears at this level because it is based on contextual information rather than individual level perceptions. Two

Authors: Wolski, Stacy.
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Influence Networks 10
variable weighting is captured within a regression model. In this model, behavioral intention is
regressed onto attitude and subjective norm, where i represents individuals and the slopes, ß
1
and
ß
2
, represent the expected change in behavioral intent associated with a unit increase in attitude
or subjective norm. These slopes represent the relative contribution, or weights, for attitude (ß
1
)
and subjective norm (ß
2
).
Behavioral Intent
i
= ß
0
+ ß
1
Attitude
i
+ ß
2
Subjective Norm
i
+ r
i
The original model is based on groups of individuals, but for the purpose of this study it
was necessary to derive weights for each individual within their respective academic department.
Recent developments in modeling procedures have made it possible to measure individual
variation between these behavioral antecedents (Hedeker, Flay, & Petraitis, 1996). Hedeker and
his colleagues derived individual level relative weights to empirically verify that the contribution
of each component varies across individuals (Hedeker et al., 1996).
Individual-level weights for attitude and subjective norm can be estimated, in principle, if
multiple observations are used as the random effect variable, either over time or across
behavioral observations. This study employed observations for a set of teaching behaviors as the
random effect variable. The first level of the multilevel model represents individual level
cognitions and behavioral intentions for a set of teaching behaviors. In the following formula,
teaching behaviors constitute the levels of the random-effect variable, which is represented by j
(Hedeker et al., p. 110):
Behavioral Intent
ij
= ß
0 i
+ ß
1 i
Attitude
ij
+ ß
2 i
Subjective Norm
ij
+ r
ij
Level 2: Adding a Structural Predictor
The second level of this model contains structural centrality. Centrality appears at this
level because it is based on contextual information rather than individual level perceptions. Two


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