The multimodal assessment of emotion dysregulation: how advances in deep multimodal fusion unleash training complex models with small samples (Preprint)

Background: Suicide clusters at Cornell University and the Massachusetts Institute of Technology (MIT) prompted popular and expert speculation of suicide contagion. However, some clustering is to be expected in any random process. Aim: This work tested whether suicide clusters at these two universities differed significantly from those expected under a homogeneous Poisson process, in which suicides occur randomly and independently of one another. Method: Suicide dates were collected for MIT and Cornell for 1990–2012. The Anderson-Darling statistic was used to test the goodness-of-fit of the intervals between suicides to distribution expected under the Poisson process. Results: Suicides at MIT were consistent with the homogeneous Poisson process, while those at Cornell showed clustering inconsistent with such a process (p = .05). Conclusions: The Anderson-Darling test provides a statistically powerful means to identify suicide clustering in small samples. Practitioners can use this method to test for clustering in relevant communities. The difference in clustering behavior between the two institutions suggests that more institutions should be studied to determine the prevalence of suicide clustering in universities and its causes.

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The German Version of the Satisfaction With Life Scale (SWLS)

European Journal of Psychological Assessment ◽

10.1027/1015-5759/a000058 ◽

2011 ◽

Vol 27 (2) ◽

pp. 127-132 ◽

Cited By ~ 180

Author(s):

Heide Glaesmer ◽

Gesine Grande ◽

Elmar Braehler ◽

Marcus Roth

Keyword(s):

Convergent Validity ◽

Satisfaction With Life ◽

Age Groups ◽

Population Based ◽

Factorial Invariance ◽

Satisfaction With Life Scale ◽

Small Samples ◽

Cross Sectional ◽

Confirmatory Factor Analyses ◽

Life Scale

The Satisfaction with Life Scale (SWLS) is the most commonly used measure for life satisfaction. Although there are numerous studies confirming factorial validity, most studies on dimensionality are based on small samples. A controversial debate continues on the factorial invariance across different subgroups. The present study aimed to test psychometric properties, factorial structure, factorial invariance across age and gender, and to deliver population-based norms for the German general population from a large cross-sectional sample of 2519 subjects. Confirmatory factor analyses supported that the scale is one-factorial, even though indications of inhomogeneity of the scale have been detected. Both findings show invariance across the seven age groups and both genders. As indicators of the convergent validity, a positive correlation with social support and negative correlation with depressiveness was shown. Population-based norms are provided to support the application in the context of individual diagnostics.

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Further Insights Into the German Version of the Multidimensional Assessment of Interoceptive Awareness (MAIA)

European Journal of Psychological Assessment ◽

10.1027/1015-5759/a000404 ◽

2019 ◽

Vol 35 (3) ◽

pp. 317-325 ◽

Cited By ~ 6

Author(s):

Dorota Reis

Keyword(s):

Structural Equation Modeling ◽

Structural Equation ◽

Equation Modeling ◽

Interoceptive Awareness ◽

Multidimensional Assessment ◽

New Instrument ◽

Confirmatory Factor ◽

Complex Models ◽

Approximate Zero ◽

Bayesian Structural Equation Modeling

Abstract. Interoception is defined as an iterative process that refers to receiving, accessing, appraising, and responding to body sensations. Recently, following an extensive process of development, Mehling and colleagues (2012) proposed a new instrument, the Multidimensional Assessment of Interoceptive Awareness (MAIA), which captures these different aspects of interoception with eight subscales. The aim of this study was to reexamine the dimensionality of the MAIA by applying maximum likelihood confirmatory factor analysis (ML-CFA), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM). ML-CFA, ESEM, and BSEM were examined in a sample of 320 German adults. ML-CFA showed a poor fit to the data. ESEM yielded a better fit and contained numerous significant cross-loadings, of which one was substantial (≥ .30). The BSEM model with approximate zero informative priors yielded an excellent fit and confirmed the substantial cross-loading found in ESEM. The study demonstrates that ESEM and BSEM are flexible techniques that can be used to improve our understanding of multidimensional constructs. In addition, BSEM can be seen as less exploratory than ESEM and it might also be used to overcome potential limitations of ESEM with regard to more complex models relative to the sample size.

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Assessing Person Fit With the Information Matrix Test

Methodology ◽

10.1027/1614-2241/a000085 ◽

2015 ◽

Vol 11 (1) ◽

pp. 3-12 ◽

Cited By ~ 2

Author(s):

Jochen Ranger ◽

Jörg-Tobias Kuhn

Keyword(s):

Simulation Study ◽

Type I Error ◽

Information Matrix ◽

Small Samples ◽

Type I ◽

Person Fit ◽

Power Of The Test ◽

Order Expansion ◽

Trait Stability ◽

Information Matrix Test

In this manuscript, a new approach to the analysis of person fit is presented that is based on the information matrix test of White (1982) . This test can be interpreted as a test of trait stability during the measurement situation. The test follows approximately a χ2-distribution. In small samples, the approximation can be improved by a higher-order expansion. The performance of the test is explored in a simulation study. This simulation study suggests that the test adheres to the nominal Type-I error rate well, although it tends to be conservative in very short scales. The power of the test is compared to the power of four alternative tests of person fit. This comparison corroborates that the power of the information matrix test is similar to the power of the alternative tests. Advantages and areas of application of the information matrix test are discussed.

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