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5.3 Latent and manifest content. ... Content analysis is the study of documents and ... Machine learning classifiers can greatly increase the number of texts that can ...
The manifest variables in factor analysis and latent profile analysis are continuous and in most cases, their conditional distribution given the latent variables is assumed to be normal. In latent trait analysis and latent class analysis, the manifest variables are discrete. These variables could be dichotomous, ordinal or nominal variables.
Latent variables, as created by factor analytic methods, generally represent "shared" variance, or the degree to which variables "move" together. Variables that have no correlation cannot result in a latent construct based on the common factor model. [5] The "Big Five personality traits" have been inferred using factor analysis. extraversion [6]
As soon as he embraced psychoanalysis, Jung began to multiply his theoretical studies on dreams. In 1908, he published the article "The Freudian Theory of Hysteria", [D 15] followed in 1909 by a synthesis in "The Analysis of Dreams", [D 16] in which he used all Freud's concepts, such as censorship and latent and manifest content. The study even ...
Thus the manifest content is a representation of the latent content in a disguised and distorted form. Freud believed that by uncovering the meaning of one's hidden motivations and deeper ideas, an individual could successfully understand his or her internal struggles, and thus in psychoanalysis the manifest content of the dream is analyzed in ...
The use of Latent Semantic Analysis has been prevalent in the study of human memory, especially in areas of free recall and memory search. There is a positive correlation between the semantic similarity of two words (as measured by LSA) and the probability that the words would be recalled one after another in free recall tasks using study lists ...
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Hierarchical latent tree analysis is an alternative to LDA, which models word co-occurrence using a tree of latent variables and the states of the latent variables, which correspond to soft clusters of documents, are interpreted as topics. Animation of the topic detection process in a document-word matrix through biclustering. Every column ...