Suchergebnisse
Suchergebnisse:
Hierarchical linear models (or multilevel regression) organizes the data into a hierarchy of regressions, for example where A is regressed on B, and B is regressed on C. It is often used where the variables of interest have a natural hierarchical structure such as in educational statistics, where students are nested in classrooms, classrooms are nested in schools, and schools are nested in ...
v. t. e. In statistics, the ordered logit model (also ordered logistic regression or proportional odds model) is an ordinal regression model—that is, a regression model for ordinal dependent variables —first considered by Peter McCullagh. [1] For example, if one question on a survey is to be answered by a choice among "poor", "fair", "good ...
t. e. Multilevel regression with poststratification ( MRP) is a statistical technique used for correcting model estimates for known differences between a sample population (the population of the data you have), and a target population (a population you would like to estimate for). The poststratification refers to the process of adjusting the ...
Marginal model. In statistics, marginal models (Heagerty & Zeger, 2000) are a technique for obtaining regression estimates in multilevel modeling, also called hierarchical linear models . People often want to know the effect of a predictor/explanatory variable X, on a response variable Y. One way to get an estimate for such effects is through ...
t. e. Bayesian hierarchical modelling is a statistical model written in multiple levels (hierarchical form) that estimates the parameters of the posterior distribution using the Bayesian method. [1] The sub-models combine to form the hierarchical model, and Bayes' theorem is used to integrate them with the observed data and account for all the ...
Multilevel models have become popular for the analysis of a variety of problems. This chapter gives a summary of the reasons for using multilevel models, and provides examples why these reasons are indeed valid. Next, recent (simulation) research is reviewed on the...
Do multilevel models ever give different results? (PDF, 100kB) by Kelvyn Jones It is sometimes said that the use of multilevel models over OLS regression makes no substantive difference to interpretation and represents something of a fuss over nothing. This short paper demonstrates with a simple example that this is not always the case.
