scholarly journals Evaluating Sensitivity of Parameters of Interest to Measurement Invariance in Latent Variable Models

2014 ◽  
Vol 22 (1) ◽  
pp. 45-60 ◽  
Author(s):  
Daniel L. Oberski
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Measurement Invariance ◽  
Latent Variable ◽  
Measurement Equivalence ◽  
Latent Variable Models ◽  
Parameter Change ◽  
Electronic Appendix ◽  
Invariance Model ◽  
Parameters Of Interest

Latent variable models can only be compared across groups when these groups exhibit measurement equivalence or “invariance,” since otherwise substantive differences may be confounded with measurement differences. This article suggests examining directly whether measurement differences present could confound substantive analyses, by examining the expected parameter change (EPC)-interest. The EPC-interest approximates the change in parameters of interest that can be expected when freeing cross-group invariance restrictions. Monte Carlo simulations suggest that the EPC-interest approximates these changes well. Three empirical applications show that the EPC-interest can help avoid two undesirable situations: first, it can prevent unnecessarily concluding that groups are incomparable, and second, it alerts the user when comparisons of interest may still be invalidated even when the invariance model appears to fit the data. R code and data for the examples discussed in this article are provided in the electronic appendix (http://hdl.handle.net/1902.1/21816).

Evaluating Measurement Invariance in Categorical Data Latent Variable Models with the EPC-Interest

2015 ◽  
Vol 23 (4) ◽  
pp. 550-563 ◽  
Author(s):  
Daniel L. Oberski ◽  
Jeroen K. Vermunt ◽  
Guy B. D. Moors
Keyword(s):  
Social Sciences ◽  
Sensitivity Analysis ◽  
Measurement Invariance ◽  
Categorical Data ◽  
Latent Variable ◽  
Latent Variable Models ◽  
Continuous Data ◽  
External Variable ◽  
The Social ◽  
The World

Many variables crucial to the social sciences are not directly observed but instead are latent and measured indirectly. When an external variable of interest affects this measurement, estimates of its relationship with the latent variable will then be biased. Such violations of “measurement invariance” may, for example, confound true differences across countries in postmaterialism with measurement differences. To deal with this problem, researchers commonly aim at “partial measurement invariance” that is, to account for those differences that may be present and important. To evaluate this importance directly through sensitivity analysis, the “EPC-interest” was recently introduced for continuous data. However, latent variable models in the social sciences often use categorical data. The current paper therefore extends the EPC-interest to latent variable models for categorical data and demonstrates its use in example analyses of U.S. Senate votes as well as respondent rankings of postmaterialism values in the World Values Study.

Assessment of the Quality of Latent Variable Calibrations Based on Monte Carlo Simulations

1994 ◽  
Vol 66 (7) ◽  
pp. 937-943 ◽  
Author(s):  
Hans R. Keller ◽  
Juergen. Roettele ◽  
Hermann. Bartels
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Latent Variable

scholarly journals Aggressiveness as a latent personality trait of domestic dogs: testing local independence and measurement invariance

2017 ◽  
Author(s):  
Conor Goold ◽  
Ruth C. Newberry
Keyword(s):  
Personality Traits ◽  
Measurement Invariance ◽  
Personality Trait ◽  
Latent Variable ◽  
Structural Equation ◽  
Intraclass Correlation ◽  
Animal Personality ◽  
Latent Variable Models ◽  
Equation Model ◽  
Local Independence

AbstractStudies of animal personality attempt to uncover underlying or ‘latent’ personality traits that explain broad patterns of behaviour, often by applying latent variable statistical models (e.g. factor analysis) to multivariate data sets. Two integral, but infrequently confirmed, assumptions of latent variable models in animal personality are: i) behavioural variables are independent (i.e. uncorrelated) conditional on the latent personality traits they reflect (local independence), and ii) personality traits are associated with behavioural variables in the same way across individuals or groups of individuals (measurement invariance). We tested these assumptions using observations of aggression in four age classes (4 - 10 months, 10 months - 3 years, 3 - 6 years, over 6 years) of male and female shelter dogs (N = 4,743) in 11 different contexts. A structural equation model supported the hypothesis of two positively correlated personality traits underlying aggression across contexts: aggressiveness towards people and aggressiveness towards dogs (comparative fit index: 0.96; Tucker-Lewis index: 0.95; root mean square error of approximation: 0.03). Aggression across contexts was moderately repeatable (towards people: intraclass correlation coefficient (ICC) = 0.479; towards dogs: ICC = 0.303).However, certain contexts related to aggressiveness towards people (but not dogs) shared significant residual relationships unaccounted for by latent levels of aggressiveness.Furthermore, aggressiveness towards people and dogs in different contexts interacted with sex and age. Thus, sex and age differences in displays of aggression were not simple functions of underlying aggressiveness. Our results illustrate that the robustness of traits in latent variable models must be critically assessed before making conclusions about the effects of, or factors influencing, animal personality. Our findings are of concern because inaccurate ‘aggressive personality’ trait attributions can be costly to dogs, recipients of aggression and society in general.

Low-voltage EDS of magnesium ferrite Dendrites in a FEG-SEM

1996 ◽  
Vol 54 ◽  
pp. 478-479
Author(s):  
Matthew T. Johnson ◽  
Ian M. Anderson ◽  
Jim Bentley ◽  
C. Barry Carter
Keyword(s):  
Monte Carlo ◽  
Quantitative Analysis ◽  
Monte Carlo Simulations ◽  
Cross Section ◽  
Spatial Resolution ◽  
Low Voltage ◽  
Magnesium Ferrite ◽  
X Ray ◽  
Interaction Volume ◽  
Ferrite Spinel

Energy-dispersive X-ray spectrometry (EDS) performed at low (≤ 5 kV) accelerating voltages in the SEM has the potential for providing quantitative microanalytical information with a spatial resolution of ∼100 nm. In the present work, EDS analyses were performed on magnesium ferrite spinel [(MgxFe1−x)Fe2O4] dendrites embedded in a MgO matrix, as shown in Fig. 1. spatial resolution of X-ray microanalysis at conventional accelerating voltages is insufficient for the quantitative analysis of these dendrites, which have widths of the order of a few hundred nanometers, without deconvolution of contributions from the MgO matrix. However, Monte Carlo simulations indicate that the interaction volume for MgFe2O4 is ∼150 nm at 3 kV accelerating voltage and therefore sufficient to analyze the dendrites without matrix contributions.Single-crystal {001}-oriented MgO was reacted with hematite (Fe2O3) powder for 6 h at 1450°C in air and furnace cooled. The specimen was then cleaved to expose a clean cross-section suitable for microanalysis.

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