Conceptualizing Protective Family Context and Its effect on Substance Use: Comparisons Across Diverse Ethnic-Racial Youth

Background: Although family behaviors are known to be important for buffering youth against substance use, research in this area often evaluates a particular type of family interaction and how it shapes adolescents’ behaviors, when it is likely that youth experience the co-occurrence of multiple types of family behaviors that may be protective. Methods: The current study (N = 1716, 10th and 12th graders, 55% female) examined associations between protective family context, a latent variable comprised of five different measures of family behaviors, and past 12 months substance use: alcohol, cigarettes, marijuana, and e-cigarettes. Results: A multi-group measurement invariance assessment supported protective family context as a coherent latent construct with partial (metric) measurement invariance among Black, Latinx, and White youth. A multi-group path model indicated that protective family context was significantly associated with less substance use for all youth, but of varying magnitudes across ethnic-racial groups. Conclusion: These results emphasize the importance of evaluating psychometric properties of family-relevant latent variables on the basis of group membership in order to draw appropriate inferences on how such family variables relate to substance use among diverse samples.

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Assessment of PLS-SEM Path Model for Coefficient of Determination and Predictive Relevance of Consumer Trust on Organic Cosmetics

Ushus - Journal of Business Management ◽

10.12725/ujbm.37.1 ◽

2016 ◽

Vol 15 (4) ◽

pp. 1-19

Author(s):

Suhan . ◽

Anantha Padmanabha Achar

Keyword(s):

Latent Variables ◽

Latent Variable ◽

Quantitative Research ◽

Direct Relationship ◽

Threshold Value ◽

Path Model ◽

Coefficient Of Determination ◽

Path Coefficient ◽

Threshold Values ◽

Research Survey

In this paper the researchers investigated the coefficient of determination R-Square and predictive relevance (Q2) through Blindfolding. To fulfil the aim of the study, a structured quantitative research survey has been conducted with 640 sample size. The results emerged from the research survey shows that the R-Square hasmoderate strength for the endogenous latent variable trust and substantial strength or effect for the endogenous latent variables integrity, ability and benevolence. After calculating Q2for the endogenous latent variable ability, benevolence, integrity and trust it was found that the model has predictive relevance for these constructs. The path coefficient threshold values for measuring between indicators namely, cause purview and emotional benefits, cause consequential and trust, cause rubric and trust, cause span and emotional benefits, emotional benefits and trust, functional benefits and trust, ability and trust, benevolence and trust and also for integrity and trust are above the threshold value of 1.96 substantiate the hypothesis and exerts direct relationship between two variables. But the path coefficient threshold values between indicators namely, Cause purview and trust, cause consequential and trust, cause rubric and emotional benefits, cause span and trust, and also self-expressive and trust are below the threshold value of 1.96 does not substantiate the hypothesis and also does not exerts direct relationship between two variables. At the end of the paper, the author highlights the results, along with implications and limitations.

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Probability-Based and Measurement-Related Hypotheses With Full Restriction for Investigations by Means of Confirmatory Factor Analysis

Methodology ◽

10.1027/1614-2241/a000033 ◽

2011 ◽

Vol 7 (4) ◽

pp. 157-164

Author(s):

Karl Schweizer

Keyword(s):

Factor Analysis ◽

Confirmatory Factor Analysis ◽

Cognitive Processing ◽

Latent Variables ◽

Repeated Measures ◽

Latent Variable ◽

Model Fit ◽

Repeated Measures Data ◽

Confirmatory Factor ◽

And Performance

Probability-based and measurement-related hypotheses for confirmatory factor analysis of repeated-measures data are investigated. Such hypotheses comprise precise assumptions concerning the relationships among the true components associated with the levels of the design or the items of the measure. Measurement-related hypotheses concentrate on the assumed processes, as, for example, transformation and memory processes, and represent treatment-dependent differences in processing. In contrast, probability-based hypotheses provide the opportunity to consider probabilities as outcome predictions that summarize the effects of various influences. The prediction of performance guided by inexact cues serves as an example. In the empirical part of this paper probability-based and measurement-related hypotheses are applied to working-memory data. Latent variables according to both hypotheses contribute to a good model fit. The best model fit is achieved for the model including latent variables that represented serial cognitive processing and performance according to inexact cues in combination with a latent variable for subsidiary processes.

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Interpretable Variational Graph Autoencoder with Noninformative Prior

Future Internet ◽

10.3390/fi13020051 ◽

2021 ◽

Vol 13 (2) ◽

pp. 51

Author(s):

Lili Sun ◽

Xueyan Liu ◽

Min Zhao ◽

Bo Yang

Keyword(s):

Latent Variables ◽

Latent Variable ◽

Expert Knowledge ◽

Structural Information ◽

Standard Normal Distribution ◽

Noninformative Prior ◽

Latent Space ◽

Distribution Parameters ◽

Standard Normal ◽

Low Dimensional

Variational graph autoencoder, which can encode structural information and attribute information in the graph into low-dimensional representations, has become a powerful method for studying graph-structured data. However, most existing methods based on variational (graph) autoencoder assume that the prior of latent variables obeys the standard normal distribution which encourages all nodes to gather around 0. That leads to the inability to fully utilize the latent space. Therefore, it becomes a challenge on how to choose a suitable prior without incorporating additional expert knowledge. Given this, we propose a novel noninformative prior-based interpretable variational graph autoencoder (NPIVGAE). Specifically, we exploit the noninformative prior as the prior distribution of latent variables. This prior enables the posterior distribution parameters to be almost learned from the sample data. Furthermore, we regard each dimension of a latent variable as the probability that the node belongs to each block, thereby improving the interpretability of the model. The correlation within and between blocks is described by a block–block correlation matrix. We compare our model with state-of-the-art methods on three real datasets, verifying its effectiveness and superiority.

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Determinants of positive mental health: a path model

Mental Health Review Journal ◽

10.1108/mhrj-05-2013-0018 ◽

2014 ◽

Vol 19 (1) ◽

pp. 47-60 ◽

Cited By ~ 4

Author(s):

Nima S. Ganga ◽

V. Raman Kutty ◽

Immanuel Thomas

Keyword(s):

Public Health ◽

Mental Health ◽

Latent Variables ◽

Positive Mental Health ◽

Path Model ◽

Primary Data ◽

Outcome Variable ◽

Public Health Approach ◽

Cross Sectional Survey ◽

Content Type

Purpose – A public health approach for promoting mental health has become a major health policy agenda of many governments. Despite this worldwide attention on research addressing population mental health and general wellbeing, very little is heard on positive mental health from the low-and middle-income countries. This paper aims to present an attempt to develop a model of positive mental health among young people. This could be used for integrating the concept of positive mental health (PMH) into public health interventions. Design/methodology/approach – The study was conducted in the state of Kerala, India. The paper administered the “Achutha Menon Centre Positive Mental health Scale” to a sample of 453 (230 men and 223 women) in the age group 18-24, along with an interview schedule exploring the relationship of PMH with many explanatory variables such as sex, beliefs, religion, education, employment and social capital. The paper developed an input path model through a series of multiple regressions explaining the levels of PMH in the community, which was then tested statistically (using AMOS version 7.0). The input model was created by identifying the determinants and correlates of PMH based on their predictive power on the outcome variable, the PMH score. The input diagram was used to test the model fit of the data. Findings – The path model (Figure 1) clearly specified the determinants of PMH. Among them, the variables that have a direct determinant effect on PMH are: quality of home learning environment, employment status, education status, marital status, self-perception on possession of skills, happiness with life, membership in social organizations and socializing capability. Research limitations/implications – In this study, path model is used to confirm relationships among observed and latent variables. The path diagram assesses the comparative strength of the correlations between the variables and does not test the directionality. Or, the model itself cannot prove causation. Practical implications – Determinants of PMH those are amenable to interventions as well as those which help in recognizing characteristic groups for intervention could help to plan future intervention programs. Originality/value – Original paper based on primary data collected through a cross-sectional survey.

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Latent Transition Analysis: Benefits of a Latent Variable Approach to Modeling Transitions in Substance Use

Journal of Drug Issues ◽

10.1177/002204261004000106 ◽

2010 ◽

Vol 40 (1) ◽

pp. 93-120 ◽

Cited By ~ 92

Author(s):

Stephanie T. Lanza ◽

Megan E. Patrick ◽

Jennifer L. Maggs

Keyword(s):

Substance Use ◽

Latent Variable ◽

Latent Transition Analysis ◽

Transition Analysis ◽

Variable Approach ◽

Latent Transition ◽

Latent Variable Approach

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A Proposed Model of Jazz Theory Knowledge Acquisition

Journal of Research in Music Education ◽

10.1177/0022429414542979 ◽

2014 ◽

Vol 62 (3) ◽

pp. 291-301 ◽

Cited By ~ 1

Author(s):

Charles R. Ciorba ◽

Brian E. Russell

Keyword(s):

Academic Achievement ◽

Knowledge Acquisition ◽

Latent Variables ◽

Path Model ◽

Confirmatory Factor Analyses ◽

Root Mean Square Residual ◽

Proposed Model ◽

Confirmatory Factor ◽

Predicted Model ◽

Jazz Theory

The purpose of this study was to test a hypothesized model that proposes a causal relationship between motivation and academic achievement on the acquisition of jazz theory knowledge. A reliability analysis of the latent variables ranged from .92 to .94. Confirmatory factor analyses of the motivation (standardized root mean square residual [SRMR] = .067) and jazz theory (SRMR = .063) measures indicated a good fit of the predicted model to the observed data. Results of the latent path model indicated good fit (χ2 = 20.08, p = .692, df = 24, N = 102) and large, positive, and statistically significant direct effects of motivation (β = 0.65) and academic achievement (β = 0.56) on jazz theory knowledge acquisition. The successful identification of this proposed model lends enough support for continued investigation into the process surrounding the acquisition of jazz theory knowledge.

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A general class of latent variable models for ordinal manifest variables with covariate effects on the manifest and latent variables

British Journal of Mathematical and Statistical Psychology ◽

10.1348/000711003770480075 ◽

2003 ◽

Vol 56 (2) ◽

pp. 337-357 ◽

Cited By ~ 56

Author(s):

Irini Moustaki

Keyword(s):

Latent Variables ◽

General Class ◽

Latent Variable ◽

Latent Variable Models ◽

Covariate Effects

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

Political Analysis ◽

10.1093/pan/mpt014 ◽

2014 ◽

Vol 22 (1) ◽

pp. 45-60 ◽

Cited By ~ 34

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).

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Information Matrices in Latent-Variable Models

Journal of Educational Statistics ◽

10.3102/10769986014004335 ◽

1989 ◽

Vol 14 (4) ◽

pp. 335-350 ◽

Cited By ~ 12

Author(s):

Robert J. Mislevy ◽

Kathleen M. Sheehan

Keyword(s):

Latent Variables ◽

Latent Variable ◽

Information Matrix ◽

Missing Information ◽

Variable Model ◽

Related Information ◽

Expected Information ◽

The Difference ◽

Information Matrices ◽

Practical Implications

The Fisher, or expected, information matrix for the parameters in a latent-variable model is bounded from above by the information that would be obtained if the values of the latent variables could also be observed. The difference between this upper bound and the information in the observed data is the “missing information.” This paper explicates the structure of the expected information matrix and related information matrices, and characterizes the degree to which missing information can be recovered by exploiting collateral variables for respondents. The results are illustrated in the context of item response theory models, and practical implications are discussed.

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