Latent Variable Modeling Using R

Beaujean, A. Alexander

doi:10.4324/9781315869780

Cited by 352 publications

(329 citation statements)

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“…The hypothesis tests for the equivalence of the models through a v 2 test. Therefore, a p value B0.05 rejects the hypothesis of both the models being equivalent; however, a p value greater than 0.05 leads to configural invariance (Beaujean 2014).…”

Section: Measurement and Structural Invariancementioning

confidence: 98%

“…An additional test evaluates the equivalence of unstandardized intercepts or thresholds across groups by constraining intercepts to be equal between groups, and is called scalar or strong invariance. An alternate test evaluates the equivalence of residuals across groups by constraining error variances to be equal between groups and is known as uniqueness or strict invariance (Beaujean 2014). Combined, the configural, metric, scalar and strict invariances evaluate the measurement invariance of the model as these steps are mainly concerned with the indicator-latent variable relationships (Beaujean 2014).…”

Section: Measurement and Structural Invariancementioning

confidence: 99%

“…This follow-up analysis involves invariance testing while leaving the non-invariant indicators in the model and not constraining them to be invariant across the groups. The resulting invariance model is said to have partial invariance, that warrants invariance for most of the parameter estimates with the exception of a few parameters within an invariance model (Beaujean 2014). The non-invariant indicators are identified using their modification indices.…”

Section: Measurement and Structural Invariancementioning

confidence: 99%

“…The SEM methods are a broad class of statistical models that consist of two parts: the measurement model and the structural model (Jackson et al 2009;Beaujean 2014). The measurement model reflects the relationship between the latent factors and the indicators, whereas the structural model relates the relationship of latent factors to each other (Jarvis et al 2003).…”

Section: Measurement and Structural Invariancementioning

confidence: 99%

“…An alternate test evaluates the equivalence of residuals across groups by constraining error variances to be equal between groups and is known as uniqueness or strict invariance (Beaujean 2014). Combined, the configural, metric, scalar and strict invariances evaluate the measurement invariance of the model as these steps are mainly concerned with the indicator-latent variable relationships (Beaujean 2014). Structural invariance testing, on the other hand, evaluates the properties of latent variables, thus involves constraining variances, covariances and means of the latent factors in a stepwise manner.…”

Section: Measurement and Structural Invariancementioning

confidence: 99%

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Latent factor analysis for synthesized speech quality-of-experience assessment

Gupta

Falk

2017

Qual User Exp

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Text-to-speech (TTS) systems are evolving and making way into numerous commercial systems, such as smartphones and assistive technologies. Notwithstanding, their user perceived quality-of-experience (QoE) is still low compared to natural speech, with distortions arising across numerous perceptual dimensions, such as voice pleasantness, comprehension, and appropriateness of intonation, to name a few. Unfortunately, the effects of such perceptual dimensions on overall perceived QoE is still unknown, particularly across listeners of different genders, thus making it difficult for TTS developers to further improve system quality. To overcome this limitation, this study makes use of exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and model invariance tests to shed light on factors responsible for QoE perception across natural and synthesized speech, as well as male and female listeners. Experimental EFA/CFA results on a publicly available database of commercial TTS systems showed the emergence of two key perceptual dimensions responsible for TTS QoE, namely 'listening pleasure' and 'prosody'. Model invariance tests validated the reliability of the model across male and female listeners, as well as across natural and synthetic voices.

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Section: Measurement and Structural Invariancementioning

confidence: 98%

Section: Measurement and Structural Invariancementioning

confidence: 99%

Section: Measurement and Structural Invariancementioning

confidence: 99%

Section: Measurement and Structural Invariancementioning

confidence: 99%

Section: Measurement and Structural Invariancementioning

confidence: 99%

See 3 more Smart Citations

Latent factor analysis for synthesized speech quality-of-experience assessment

Gupta

Falk

2017

Qual User Exp

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Doing SEM Bayesian‐Style

McIntosh¹

2020

Handbook of Sport Psychology

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In‐session interactive dynamics of the psychotherapy process between therapeutic alliance, therapist expertise, therapist technical intervention, patient metacognition and functioning

Locati

Rossi

Lang

et al. 2020

Clin Psychology and Psychoth

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Empirical research has explored different dimensions of the therapy process and their associations, often showing bidirectional links: for example, metacognition may be favoured by a positive alliance with the clinician; on the other hand, metacognitive difficulties may be an obstacle for the alliance. However, little is still known about the overall relationship between multiple dimensions during the psychotherapy process. The aim of this study is to further explore the in-session interaction of therapeutic process variables, focusing on patient metacognition, therapeutic alliance, technical intervention, therapist expertise, and patient functioning. Participants included 45 patients involved in a psychodynamic weekly treatment in two clinical centres. Therapists were both in-training and experienced clinicians. Four instruments were applied on four psychotherapy sessions (178 verbatim transcripts): Metacognition Assessment Scale-Revised (MAS-R) assessing metacognition, Collaborative Interaction Scale (CIS) assessing therapeutic alliance, Psychodynamic Intervention Rating Scale (PIRS) assessing therapist technical interventions, and Shedler-Westen Assessment Procedure (SWAP)-200 assessing patient functioning. Sequential analyses revealed that specific therapist interventions co-occurred with three different levels of therapeutic alliance: a first one characterized by positive collaboration, the second one by neutral collaboration, and the third one by ruptures. Moreover, and critically, the patient metacognition, patient functioning, and therapist expertise were found to exert different effects in the three alliance levels. These findings suggest the existence of a specific interdependence between the variables involved in the research. These results further indicate that the therapist expertise is a key element in the therapeutic process, as it can drastically affect the in-session interactive dynamic.

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Latent Variable Modeling Using R

Cited by 352 publications

References 0 publications

Latent factor analysis for synthesized speech quality-of-experience assessment

Latent factor analysis for synthesized speech quality-of-experience assessment

Doing SEM Bayesian‐Style

In‐session interactive dynamics of the psychotherapy process between therapeutic alliance, therapist expertise, therapist technical intervention, patient metacognition and functioning

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