Property modelling of lysozyme‐crosslinker‐alginate complexes using latent variable methods

This study represents a factor analytic investigation of the Inventory of Socially Supportive Behaviors (ISSB) and the Social Support Questionnaire (SSQ) when used with South African students. Respondents ( N = 242) were undergraduate students at the University of the Western Cape. The obtained internal-consistency estimates of reliability for the ISSB and the SSQ (alphas above 0,90) compared favourably with previously reported reliabilities for these scales. Exploratory factor analyses of the ISSB yielded three factors similar to previously reported factor structures of the scale, while in the case of the SSQ it appears as if one factor is sufficient to represent the factorial structure of the scale. Confirmatory factor analyses, using latent variable methods confirmed the distinctiveness of the instruments and indicated that two interrelated factors accounted for the variation in the subscales of the ISSB and the SSQ.

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Develops in Latent Variable Methods of Analysis

International Journal of Soft Computing ◽

10.3923/ijscomp.2012.281.282 ◽

2012 ◽

Vol 7 (6) ◽

pp. 281-282 ◽

Cited By ~ 1

Author(s):

Shahryar Sorooshian ◽

Tan Seng Teck ◽

Meysam Salimi ◽

Liau Chee How

Keyword(s):

Latent Variable ◽

Methods Of Analysis ◽

Latent Variable Methods

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Music self-concept and self-esteem formation in adolescence: A comparison between individual and normative models of importance within a latent framework

Psychology of Music ◽

10.1177/0305735616672317 ◽

2016 ◽

Vol 45 (6) ◽

pp. 763-780 ◽

Cited By ~ 3

Author(s):

L. Francesca Scalas ◽

Herbert W. Marsh ◽

Walter Vispoel ◽

Alexandre J. S. Morin ◽

Zhonglin Wen

Keyword(s):

Latent Variable ◽

Self Esteem ◽

Music Instruction ◽

Self Concept ◽

General Level ◽

Group Model ◽

Spiritual Self ◽

The Individual ◽

Normative Models ◽

Latent Variable Methods

We examined the possible effects of six dimensions of music self-concept on determination of self-esteem, through the application of models based on individual and normative-group importance. Previous studies have supported the individual model of importance in narrowly defined self-domains such as spiritual self-concept that might be unimportant for most people, but very important for some people. However, results from more recent studies of spiritual, academic, and physical self-concepts involving latent variable methodologies support the normative-group model. Here, we extended the use of latent variable methods to music self-concept using a sample of 512 junior high students (11–16 years old). Our results for music-reading skills supported the individual importance model rather than the normative-group importance model. Additional results revealed that singing, instrument playing, and the importance of instrument playing had direct rather than interactive linkages with self-esteem. Collectively, these results highlight differential effects of performance (singing, instrument playing) and knowledge (reading) on self-esteem, and imply that strategies to enhance self-esteem may vary within different domains of music instruction and participation. At a more general level, the findings together with those from previous studies indicate that interconnections between specific and global aspects of self-concept vary across domains and are more complex than previously thought.

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Designing multi-responsive polymers using latent variable methods

Polymer ◽

10.1016/j.polymer.2013.12.041 ◽

2014 ◽

Vol 55 (2) ◽

pp. 505-516 ◽

Cited By ~ 12

Author(s):

Jenny Mayra Guicela Tzoc Torres ◽

Emily Nichols ◽

John F. MacGregor ◽

Todd Hoare

Keyword(s):

Latent Variable ◽

Responsive Polymers ◽

Latent Variable Methods

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Hybrid Measurement Models for Technology-Enhanced Assessments Through mIRT-bayes

International Journal of Statistics and Probability ◽

10.5539/ijsp.v6n3p168 ◽

2017 ◽

Vol 6 (3) ◽

pp. 168 ◽

Cited By ~ 2

Author(s):

Kathleen Scalise

Keyword(s):

Bayesian Networks ◽

Latent Variable ◽

Latent Variable Models ◽

Measurement Model ◽

Testing Time ◽

Measurement Models ◽

The Stability ◽

Stability And Accuracy ◽

Network Approaches ◽

Latent Variable Methods

Technology-enhanced assessments (TEAs) are rapidly emerging in educational measurement. In contexts such as simulation and gaming, a common challenge is handling complex streams of information, for which new statistical innovations are needed that can provide high quality proficiency estimates for the psychometrics of complex TEAs. Often in educational assessments with formal measurement models, latent variable models such as item response theory (IRT) are used to generate proficiency estimates from evidence elicited. Such robust techniques have become a foundation of educational assessment, when models fit. Another less common approach to compile evidence is through Bayesian networks, which represent a set of random variables and their conditional dependencies via a directed acyclic graph. Network approaches can be much more flexibly designed for complex assessment tasks and are often preferred by task developers, for technology-enhanced settings. However, the Bayesian network-based statistical models often are difficult to validate and to gauge the stability and accuracy, since the models make assumptions regarding conditional dependencies that are difficult to test. Here a new measurement model family, mIRT-bayes, is proposed to gain advantages of both latent variable models and network techniques combined through hybridization. Specifically, the technique described here embeds small Bayesian networks within an overarching multidimensional IRT model (mIRT), preserving the flexibility for task design while retaining the robust statistical properties of latent variable methods. Applied to simulation-based data from Harvard's Virtual Performance Assessments (VPA), the results of the new model show acceptable fit for the overarching mIRT model, along with reduction of the standard error of measurement through the embedded Bayesian networks, compared to use of mIRT alone. Overall for respondents, a finer grain-size of inference is made possible without additiona testing time or scoring resources, showing potentially promise for this family of new hybrid models.

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