2021年3月4日 · The distinction, or lack thereof, between a parameter and a latent variable. At the most general level, in Bayesian statistics, there is no difference between a parameter and a latent variable. Both are treated as unobserved random variables which we would like to compute a posterior distribution on given observed random variables (i.e. data).

The problem is to define when a variable might be considered as a latent variable. I am interested in how to describe a latent variable, and what are the properties of latent variables. My twofold question is: When you try to explain what a latent variable is, what do you consider as the main differences between a manifest and a latent variable?

Edit 2016-09-23. There is one sort of trivial sense in which any GLM is a latent variable model, which is that we can arguably always view the parameter of the outcome distribution being estimated as a "latent variable" -- that is, we don't directly observe, say, the rate parameter of the Poisson, we just infer it from data.

2016年6月13日 · The main selling point for the latent variable representation of logistic regression is its link to a theory of (rational) choice. Sometimes that is extremely useful, but sometimes it makes no sense (and often we are somewhere in between).

My questionnaire includes 48 questions (observed variables) that represent 8 different factors (latent variables). All the variables are continuous. I need to compute the latent variables before doing the correlation & regression analyses but the problem is that SPSS doesn't directly create latent variables like SEM softwares.

2019年9月26日 · In many cases the data we observe depends on some hidden variables, that were not observed, or could not be observed. Knowing those variables would simplify our model, and in many cases we can get away from not knowing their values by assuming a latent variable model, that can "recover" the unobserved variables from the data.

2023年3月24日 · A latent variable is just a bunch of observed variables together to represent a construct (usually a theory). I would familiarize with factor analysis first, before you dive into SEM. SEM is used when you want to use a latent variable (a factor) in a path analysis, usually (said with caution here) to attempt to show causality. Yes, it can be a ...

2021年11月24日 · In general, latent variables are some variables we can't directly measure. That is, we cannot directly measure an 8th graders' level of math knowledge, simply because we cant directly connect to their brains and perform this measurement (unlike physical attributes, for example, which we can directly measure).

2015年9月26日 · The relation between one observed independent (s) and one observed dependent variable (v) is mediated through a latent variable (m) that is defined by two observed indicator variables (x1, x2). This is basically a simplified version of the SEM example in the tutorial on the lavaan project website.

2020年3月20日 · However, an alternate view, inspired by a scientific realist philosophy of science, is that the excluded variable is a genuine entity existing independent of data, that the common factor is only a proxy for the latent variable, and that other techniques might also be used to construct different proxies for the latent variable.