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Can i calculate relation between item of one latent variable with another latent varable? bring sources? 


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Yes, it is possible to calculate the relationship between items of one latent variable with another latent variable. Various latent variable models, such as probit latent state models , full information maximum likelihood estimation models , and latent trait models , allow for the examination of relationships between different latent variables. These models enable the analysis of dependencies among items within and between time points, the inclusion of explanatory variables for latent states and item-effect variables, and the extraction of latent entities from observed variables. By utilizing these models, researchers can investigate differential item functioning, response styles, and the interactions between latent variables in a comprehensive manner.

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Yes, in the study, the authors analyzed sources of differential item functioning (DIF) between latent classes, showing how items relate to different latent variables. (Oliveri et al., 2021)
Yes, Item Response Theory (IRT) allows modeling the relationship between latent variables. Sources: Brzezińska & Stetinensia (Year not provided) discuss IRT analyses in marketing research using R software.
Open accessDissertation
01 Jan 1996
4 Citations
Yes, latent variable models like latent trait and latent class models can calculate relationships between items of different latent variables. Refer to the paper "Latent variable models for mixed manifest variables" for sources.
Yes, in latent variable models for multivariate longitudinal ordinal responses, relationships between items of different latent variables can be calculated using item-specific random effects or a common factor.
Yes, the Probit Latent State IRT model allows for calculating the relationship between items of one latent variable with another latent variable. The paper provides a detailed methodology for this analysis.

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Are the variables independent?5 answersThe variables mentioned in the abstracts are not explicitly stated as independent or dependent. However, the abstracts discuss the influence and interaction of various factors on different phenomena. For example, Dewaele's study explores the complex interaction of independent variables affecting the expression of emotion in multilingual speakers. Fendler's chapter discusses the confluence of factors shaping the chapters in a book on historiography. Delbaen and Majumdar's paper examines the independence of a random variable from a sub sigma algebra. Leonardi's research looks for variables that explain changes in regional development. While the abstracts do not explicitly state the independence of variables, they highlight the importance of considering multiple factors and their influence on different phenomena.
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