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The researcher must choose whether the analysis should be of a broad surface structure (a manifest analysis) or of a deep structure (a latent analysis).
Both manifest and latent analysis were used [ 16].
Data quality did not allow for extensive latent analysis, i.e. a coding of the 'underlying' meaning and implied feelings [ 36].
Thematic and latent analysis of the data enabled grouping around issues of risk and responsibility for sexual health.
In the next step the latent analysis was performed by analysing the content in the categories in a more abstract and interpretative way, and thus themes were created.
Latent analysis, which followed, required that coders identify underlying ideas and meaning and use information within and across interviews to arrive at an authentic interpretation of the information.
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In contrast to Latent Class Analysis, discriminant analysis does not permit missing data.
Latent content analysis was used for analysis.
LCCA for categorical indicators assuming multinomial distributions are called latent class analysis, whereas latent profile analysis is designed for continuous variables.
These techniques can be broadly divided into latent class analysis (LCA) and Bayesian analysis [ 6].
Studies have utilized latent class analysis (LCA) and latent profile analysis (LPA) to examine posttraumatic stress disorder (PTSD) symptom profiles in a range of populations.
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