Maximum likelihood estimation of latent interaction effects with the LMS method

By Andreas G. Klein, Helfried Moosbrugger. 2000

1,334 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Food Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f7b0a9ccac767150631dacb1125bc82e6c280250

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Citations in the corpus

Structural equation modeling: strengths, limitations, and misconceptions. (2005, background): “In fact, a number of SEM procedures have been suggested for modeling interactions (e.g., Algina & Moulder 2001; Bollen & Paxton 1998; Jaccard & Wan 1995; Jöreskog 2000; Jöreskog & Yang 1996; Kenny & Judd 1984; Klein & Moosbrugger 2000; Lee et al. 2004; Ping 1996; Wall & Amemiya 2000, 2001).”
Innovative behaviour: how much transformational leadership do you need? (2018, positive): “To assess the non-linear effects of transformational leadership, we estimated a quadratic term using the latent moderated structural equations method (LMS; Klein and Moosbrugger, 2000).”
The Moderation Role of Transformational Leadership in the Effect of Instructional Leadership on Teacher Professional Learning and Instructional Practice: An Integrated Leadership Perspective (2021, methodology): “…t values, p values under a normal distribution assumption, and the lower and upper limits of the 95% CIs to examine the effects of the moderator variable on TPL and the effects of IL on teacher IP, by including latent interaction in the analysis (Cheung & Lau, 2017; Klein & Moosbrugger, 2000).”
A Socioemotional Wealth Perspective on how Collaboration Intensity, Trust, and International Market Knowledge affect Family Firms' Multinationality (2016, methodology): “We used the advanced LMS method by Klein and Moosbrugger (2000) implemented in Mplus 7.2 to test latent interactions.”
Building a sustainable start: The role of career competencies, career success, and career shocks in young professionals' employability (2019, positive): “…this approach are that LMS estimates multiple latent interaction effects simultaneously in one model and produces unbiased, efficient parameter estimates (paths) between the variables that are robust to nonlinearity and deviations from normality of the latent variables (Klein & Moosbrugger, 2000).”
On the Merits of Orthogonalizing Powered and Product Terms: Implications for Modeling Interactions Among Latent Variables (2006, methodology): “Thus, the orthogonalized approach results in nearly identical parameter estimates as does the unconstrained mean-centered approach of Marsh et al. (2004) and the LMS approach (Klein & Moosbrugger, 2000).”
Testing for Nonuniform Differential Item Functioning With Multiple Indicator Multiple Cause Models (2011, methodology): “In the present MIMIC-interaction models, the grouping variable could be treated as a categorical indi- cator of an underlying continuous normal latent variable, which would presumably improve the performance of the LMS estimator.”
Discovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats (2013, methodology): “Examples of these methods in PLS path modeling can be found in Chin et al. (2003), Goodhue et al. (2007) and Henseler and Chin (2010) and for CB-SEM in Jaccard and Wan (1995), Jöreskog and Yang (1996), and Klein and Moosbrugger (2000).”
Probing for the Multiplicative Term in Modern Expectancy-Value Theory: A Latent Interaction Modeling Study. (2012, positive): “In the final model (M4), we used the latent moderated structural (LMS) equations approach (Klein & Moosbrugger, 2000, see below) to model the latent interaction between expectancy and value in predicting the outcome variables and tested the synergistic effects of the two constructs.”
Learning and Innovation in International Strategic Alliances: An Empirical Test of the Role of Trust and Tacitness (2009, background): “Moreover, Mplus has important efficiency and power advantages for interaction modelling (Klein and Moosbrugger, 2000).”
(Why) Should We Use SEM? Pros and Cons of Structural Equation Modeling (2003, methodology): “For discrete moderators this has been straightforward using a multi group approach, while the problem of continuous moderators has only been recently tackled (Bollen & Paxton, 1998; Klein & Moosbrugger, 2000).”
12 Structural Equation Modeling in Management Research: A Guide for Improved Analysis (2009, methodology): “These methods include the latent moderated structural equations (LMS) approach proposed by Klein and Moosbrugger (2000), the quasi-maximum likelihood method (QML) derived by Klein and Muthén (2007), the two-stage method of moments (2SMM) procedure proposed by Wall and Amemiya (2000, 2003), and…”
A Semiparametric Approach to Modeling Nonlinear Relations Among Latent Variables (2005, methodology): “Third, it would be useful to systematically compare this approach for semiparametrically modeling nonlinear latent variable relations to the flurry of new parametric approaches recently presented in the structural equation modeling literature (e.g., Arminger & Muthen, 1998; Klein & Moosbrugger, 200”