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Factor analysis, in which both latent (unobserved) and manifest (observed) variables are continuous, is perhaps the best known.

In latent profile analysis the latent variable (e.g. consumer segments) is categorical and the manifest variables (e.g. responses to rating scales) are continuous.

Latent trait models (e.g. item response theory) are characterized by continuous latent variables and categorical manifest variables (e.g. correct or incorrect answers to test items).

In latent class analysis both latent and observed variables are categorical.

There are also hybrid models which include both continuous and categorical latent and manifest variables.

In some models there is a distinction between dependent and independent variables. Censored, truncated and count variables can also be accommodated.

Any of these models can be multilevel (hierarchical) or longitudinal and can incorporate exogenous variables (covariates).

This popular book is focused on latent class analysis and its longitudinal extension, latent transition analysis. It is well written and covers theoretical and technical issues as well as application.

https://www.google.com/search?kgmid=/g/12bmhby6b&hl=en-JP&kgs=a09137cca2d41ecf&q=Latent+Class+and+Latent+Transition+Analysis:+With+Applications+in+the+Social,+Behavioral,+and+Health+Sciences&shndl=0&source=sh/x/kp/osrp&entrypoint=sh/x/kp/osrp

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Factor analysis, in which both latent (unobserved) and manifest (observed) variables are continuous, is perhaps the best known.

In latent profile analysis the latent variable (e.g. consumer segments) is categorical and the manifest variables (e.g. responses to rating scales) are continuous.

Latent trait models (e.g. item response theory) are characterized by continuous latent variables and categorical manifest variables (e.g. correct or incorrect answers to test items).

In latent class analysis both latent and observed variables are categorical.

There are also hybrid models which include both continuous and categorical latent and manifest variables.

In some models there is a distinction between dependent and independent variables. Censored, truncated and count variables can also be accommodated.

Any of these models can be multilevel (hierarchical) or longitudinal and can incorporate exogenous variables (covariates).

This popular book is focused on latent class analysis and its longitudinal extension, latent transition analysis. It is well written and covers theoretical and technical issues as well as application.

https://www.google.com/search?kgmid=/g/12bmhby6b&hl=en-JP&kgs=a09137cca2d41ecf&q=Latent+Class+and+Latent+Transition+Analysis:+With+Applications+in+the+Social,+Behavioral,+and+Health+Sciences&shndl=0&source=sh/x/kp/osrp&entrypoint=sh/x/kp/osrp

❇️ @AI_Python_EN

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That strategy is the acquisition of a value-priced company by a growth company. Using the growth company's higher-priced stock for the acquisition can produce outsized revenue and earnings growth. Even better is the use of cash, particularly in a growth period when financial aggressiveness is accepted and even positively viewed.he key public rationale behind this strategy is synergy - the 1+1=3 view. In many cases, synergy does occur and is valuable. However, in other cases, particularly as the strategy gains popularity, it doesn't. Joining two different organizations, workforces and cultures is a challenge. Simply putting two separate organizations together necessarily creates disruptions and conflicts that can undermine both operations.

Telegram and Signal Havens for Right-Wing Extremists

Since the violent storming of Capitol Hill and subsequent ban of former U.S. President Donald Trump from Facebook and Twitter, the removal of Parler from Amazon’s servers, and the de-platforming of incendiary right-wing content, messaging services Telegram and Signal have seen a deluge of new users. In January alone, Telegram reported 90 million new accounts. Its founder, Pavel Durov, described this as “the largest digital migration in human history.” Signal reportedly doubled its user base to 40 million people and became the most downloaded app in 70 countries. The two services rely on encryption to protect the privacy of user communication, which has made them popular with protesters seeking to conceal their identities against repressive governments in places like Belarus, Hong Kong, and Iran. But the same encryption technology has also made them a favored communication tool for criminals and terrorist groups, including al Qaeda and the Islamic State.

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