74 research outputs found
Network Structure Explains the Impact of Attitudes on Voting Decisions
Attitudes can have a profound impact on socially relevant behaviours, such as
voting. However, this effect is not uniform across situations or individuals,
and it is at present difficult to predict whether attitudes will predict
behaviour in any given circumstance. Using a network model, we demonstrate that
(a) more strongly connected attitude networks have a stronger impact on
behaviour, and (b) within any given attitude network, the most central attitude
elements have the strongest impact. We test these hypotheses using data on
voting and attitudes toward presidential candidates in the US presidential
elections from 1980 to 2012. These analyses confirm that the predictive value
of attitude networks depends almost entirely on their level of connectivity,
with more central attitude elements having stronger impact. The impact of
attitudes on voting behaviour can thus be reliably determined before elections
take place by using network analyses.Comment: Final version published in Scientific Report
The Gaussian graphical model in cross-sectional and time-series data
We discuss the Gaussian graphical model (GGM; an undirected network of
partial correlation coefficients) and detail its utility as an exploratory data
analysis tool. The GGM shows which variables predict one-another, allows for
sparse modeling of covariance structures, and may highlight potential causal
relationships between observed variables. We describe the utility in 3 kinds of
psychological datasets: datasets in which consecutive cases are assumed
independent (e.g., cross-sectional data), temporally ordered datasets (e.g., n
= 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In
time-series analysis, the GGM can be used to model the residual structure of a
vector-autoregression analysis (VAR), also termed graphical VAR. Two network
models can then be obtained: a temporal network and a contemporaneous network.
When analyzing data from multiple subjects, a GGM can also be formed on the
covariance structure of stationary means---the between-subjects network. We
discuss the interpretation of these models and propose estimation methods to
obtain these networks, which we implement in the R packages graphicalVAR and
mlVAR. The methods are showcased in two empirical examples, and simulation
studies on these methods are included in the supplementary materials.Comment: Accepted pending revision in Multivariate Behavioral Researc
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