Interpretation of regression coefficients under a latent variable regression model

2001 ◽  
Vol 15 (4) ◽  
pp. 265-284 ◽  
Author(s):  
Alison J. Burnham ◽  
John F. MacGregor ◽  
Roman Viveros
Keyword(s):  
Regression Model ◽  
Latent Variable ◽  
Regression Coefficients ◽  
Latent Variable Regression

Probabilistic latent variable regression model for process-quality monitoring

2014 ◽  
Vol 116 ◽  
pp. 296-305 ◽  
Author(s):  
Le Zhou ◽  
Junghui Chen ◽  
Zhihuan Song ◽  
Zhiqiang Ge ◽  
Aimin Miao
Keyword(s):  
Regression Model ◽  
Latent Variable ◽  
Process Quality ◽  
Quality Monitoring ◽  
Latent Variable Regression

scholarly journals A maximum likelihood latent variable regression model for multiple informants

2008 ◽  
Vol 27 (24) ◽  
pp. 4992-5004 ◽  
Author(s):  
Nicholas J. Horton ◽  
Kevin Roberts ◽  
Louise Ryan ◽  
Shakira Franco Suglia ◽  
Rosalind J. Wright
Keyword(s):  
Maximum Likelihood ◽  
Regression Model ◽  
Latent Variable ◽  
Multiple Informants ◽  
Latent Variable Regression

scholarly journals Detecting Instruction Effects—Deciding Between Covariance Analytical and Change-Score Approach

2021 ◽  
Author(s):  
Carmen Köhler ◽  
Johannes Hartig ◽  
Alexander Naumann
Keyword(s):  
Latent Variable ◽  
Outcome Measurement ◽  
Predictor Variable ◽  
Change Score ◽  
Regression Coefficients ◽  
Outcome Variable ◽  
Analysis Model ◽  
Hypothetical Data ◽  
Instructional Research ◽  
Time Point

AbstractThe article focuses on estimating effects in nonrandomized studies with two outcome measurement occasions and one predictor variable. Given such a design, the analysis approach can be to include the measurement at the previous time point as a predictor in the regression model (ANCOVA), or to predict the change-score of the outcome variable (CHANGE). Researchers demonstrated that both approaches can result in different conclusions regarding the reported effect. Current recommendations on when to apply which approach are, in part, contradictory. In addition, they lack direct reference to the educational and instructional research contexts, since they do not consider latent variable models in which variables are measured without measurement error. This contribution assists researchers in making decisions regarding their analysis model. Using an underlying hypothetical data-generating model, we identify for which kind of data-generating scenario (i.e., under which assumptions) the defined true effect equals the estimated regression coefficients of the ANCOVA and the CHANGE approach. We give empirical examples from instructional research and discuss which approach is more appropriate, respectively.

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