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In political science, the minimal effects hypothesis states that political campaigns only marginally persuade and convert voters. The hypothesis was formulated during early research into voting behavior between the 1940s and the 1960s, and this period formed the initial "minimum effects" era in the United States. [ 1 ]
Unlike the hypodermic needle model, which considers mass media effects to be direct, the two-step flow model stresses human agency. For example, in the field of science communication, Matthew Nisbet describes the use of opinion leaders as intermediaries between scientists and the public as a way to reach the public via trained individuals who ...
Lazarsfeld's debunking of these models of communication provided the way for new ideas regarding the media's effects on the public. Lazarsfeld introduced the idea of the two-step flow of communication [11] in 1944. Elihu Katz contributed to the model in 1955 through studies and publications. [12]
This theory was first introduced by sociologist Paul Lazarsfeld et al. in 1944 and elaborated by Elihu Katz and Lazarsfeld in 1955. [ 1 ] The multi-step flow theory offers a larger range of interaction between opinion leaders, information sources and audiences than the two-step model, which argues that information flows from mass media directly ...
Labeled graphs associate labels to each vertex and/or edge of a graph. Matched with attributed graphs, these labels correspond to attributes comprising only a key, taken from a countable set (typically a character string, or an integer) Colored graphs, as used in classical graph coloring problems, are special cases of labeled graphs, whose ...
Within statistics, Local independence is the underlying assumption of latent variable models (such as factor analysis and item response theory models). The observed items are conditionally independent of each other given an individual score on the latent variable(s). This means that the latent variable(s) in a model fully explain why the ...
Such models assist in controlling for omitted variable bias due to unobserved heterogeneity when this heterogeneity is constant over time. This heterogeneity can be removed from the data through differencing, for example by subtracting the group-level average over time, or by taking a first difference which will remove any time invariant components of the model.
In this model is the school-specific random effect: it measures the difference between the average score at school and the average score in the entire country. The term W i j {\displaystyle W_{ij}} is the individual-specific random effect, i.e., it's the deviation of the j {\displaystyle j} -th pupil's score from the average for the i ...