scholarly journals VARIABEL LATEN SEBAGAI MODERATOR DAN MEDIATOR DALAM HUBUNGAN KAUSAL

2013 ◽  
Vol 2 (4) ◽  
pp. 33
Author(s):  
I KOMANG GEDE ANTARA ◽  
I PUTU EKA NILA KENCANA ◽  
KETUT JAYANEGARA
Keyword(s):  
Causal Relationship ◽  
Latent Variables ◽  
Canonical Correlation ◽  
Latent Variable ◽  
Goodness Of Fit ◽  
Mediation Model

Latent variables are variables that can not be measured directly. In analysis of causal relationship involving three latent variables, one latent variable can be a moderator or mediator variables. Goodness of Fit moderation and mediation model of latent variables is affected by the value of the canonical correlation between moderator/mediator latent variables with the independent latent variables and dependent latent variables. If the value of both canonical correlation is well , so the Goodness of Fit models of mediation is getting better, while the opposite Goodness of Fit models will be better moderation.

Mental Health of Preschool Children and their Mothers in a Mixed Urban/Rural Population

1996 ◽  
Vol 168 (1) ◽  
pp. 26-32 ◽  
Author(s):  
Jim Stevenson ◽  
Margaret J. J. Thompson ◽  
Edmund Sonuga-Barke
Keyword(s):  
Mental State ◽  
Latent Variables ◽  
Latent Variable ◽  
Goodness Of Fit ◽  
Common Factor ◽  
Population Sample ◽  
Behaviour Problems ◽  
Latent Variable Analysis ◽  
Child Adaptation ◽  
Children’S Behaviour

BackgroundThere is a lack of clear and explicit models of the way various family and social influences on children's behaviour interact with factors such as temperament to produce behaviour disturbance in young children.MethodThe following measures had been obtained on a total population sample of 1047 families with a 3-year-old child: the child's perceived cuddliness, difficult temperament, mother's unhappy childhood, maternal disturbance, social class, behaviour problems and overactivity. A latent variable analysis using the LISREL 7 program was applied to the data.ResultsA model that allowed the latent variables child ‘temperament’ and ‘mother's mental state’ to have separate additive effects on ‘child adaptation’ proved an excellent fit (goodness of fit index = 0.956). This model suggests that there is a common factor (‘child adaptation’) underlying behaviour problems and overactivity. Using this model 72% of child adaptation in boys could be explained. For girls however temperament and mother's mental state accounted for only 30% of the variance in child adaptation.ConclusionThere is a need to investigate different mechanisms for the origins of behaviour problems in preschool boys and girls.

scholarly journals HUBUNGAN AKREDITASI DAN UJIAN NASIONAL PADA SEKOLAH NEGERI DENGAN GENERALIZED STRUCTURED COMPONENT ANALYSIS

2019 ◽  
Vol 3 (3) ◽  
pp. 260-271
Author(s):  
Rezi Wahyuni ◽  
Budi Susetyo ◽  
Anwar Fitrianto
Keyword(s):  
Public Schools ◽  
Education System ◽  
Latent Variables ◽  
Latent Variable ◽  
Goodness Of Fit ◽  
Educational Institution ◽  
Component Analysis ◽  
Equation Modeling ◽  
Path Coefficient ◽  
Generalized Structured Component Analysis

There are several views and tendencies that distinguish between schools and madrasas in several aspects, one of them is the curriculum. Madrasah as islamic educational institution contains more religious lessons compared to public schools. As a result, madrasah are considered less able to provide good result in educational achievement. Overall, the education system which is based on National Education Standards (SNP) is used for assessing the educational accreditation. SNP is the minimum criterion of education system in Indonesia can be evaluated from the National Examination (UN). As latent variable, SNP is measured through 124 items as variable indicators. One of methods which is used to measure the relationship among latent variables, and latent variables with their indicator variables is structural equation modeling (SEM). A component-based SEM is called Generalized Structured Component Analysis (GSCA). GSCA analysis based on measurement model, there were 9 indicators were not significant, in which 1 indicator of standard of education and staff (SPT), 5 indicators on standard of infrastructure (SSP), and 3 indicators on standard of cost (SB). Evaluation of the structural model, it was found that the path coefficient of standard of content (SI) to UN was not significant and standard of competency (SKL) given the biggest direct effect to UN. The overall goodness of fit model showed that the total variance that can be explained of all indicators and latent variables in evaluating model of accreditation and national examinations was 63.9%. The difference in the percentage of accreditation status between schools and madrasas shows different UN results. In the 2017-2018 period, MTsN had a higher percentage of accredited schools, in line with that the average MTsN UN obtained was better than that of SMP in all types of subjects.

scholarly journals Mode Choice Model for Public Transport with Categorized Latent Variables

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Jian Chen ◽  
Shoujie Li
Keyword(s):  
Public Transport ◽  
Latent Variables ◽  
Logit Model ◽  
Latent Variable ◽  
Goodness Of Fit ◽  
Choice Model ◽  
Mode Choice ◽  
Integrated Model ◽  
Mode Choice Model ◽  
The Impact

Mode choice model for public transport, which integrates structural equation model (SEM) and discrete choice model (DCM) with categorized latent variables, was presented in this paper. Apart from identifying those important latent variables that affect mode choice for public transport, the objective of this study was also to develop an improved disaggregative model that better explains travel behavior of those decision-makers in choosing public transport. After extensive observations, selective latent variable sets which consist of latent variable components were chosen together with explicit variables in formulating the utility functions. Data collected in Chengdu city, China, were used to calibrate and validate the model. Results showed that the impact of fare on mode choice of public transport escalated in the SEM-DCM integrated model compared with the traditional logit model. The goodness of fit for the integrated model with latent variable sets is 0.201 higher than that of the traditional logit model, which proves that latent variables have an obvious impact on mode choice behavior, and the SEM-DCM integrated model has higher accuracy and stronger explanatory ability. The results are especially helpful for public transport operators to achieve higher mode share split by improving the service quality of public transport in terms of providing more convenience and better service environment for public transport users.

scholarly journals Exploring Psychographic Traits in Tourism Maketing: Causal Relationship from Destination Image to Loyalty

2013 ◽  
Vol 64 (2) ◽  
Author(s):  
Abduljalil Sarli ◽  
Rohaizat Baharun
Keyword(s):  
Causal Relationship ◽  
Latent Variables ◽  
Behavioral Intentions ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Consumption Patterns ◽  
Destination Image ◽  
Equation Modeling ◽  
Academic Researchers ◽  
Travel Satisfaction

Psychographics has been proposed as a valuable topic in the marketing literature. Also, it can be represented as latent variables which are related to the behaviors not only in the product or service discussions but also in the tourism activities. Besides, the tourism as the globally business can be understood in terms of the tourists’ reflections in the different ways. Hence, the survey of original intentions of tourists is very precious subjects by regarding the causal relationship for exploring phenomena from their behavioral intentions. As psychographic constructs, which can impact in the different glances of the affect and cognitive systems, create new consumption patterns for purchasing and repurchasing tourism packages. Therefore, loyalty is significantly considered as a valuable construct for stakeholders and academic researchers. Consequently, the aim of this study is to find out loyalty from psychographic facet. To date, there is a bridge gap through the intervening psychographic constructs like lifestyle, personality, and travel satisfaction to achieve loyalty. Additionally, the current study suggested an integrated model through the contemplated constructs by employing Structural Equation Modeling (SEM) technique to ascertain an appropriate model by Goodness-Of-Fit (GOF) indices.

scholarly journals Structural Causality Between National Examination Score and The School Accreditation Categories

2022 ◽  
Vol 8 (1) ◽  
pp. 73-78
Author(s):  
Anwar Fitrianto ◽  
Budi Susetyo ◽  
Iswan Achlan Setiawan
Keyword(s):  
Academic Achievement ◽  
Causal Relationship ◽  
Latent Variable ◽  
Goodness Of Fit ◽  
Structural Model ◽  
Educational Theory ◽  
Coefficient Of Determination ◽  
Path Coefficient ◽  
Ministry Of Education ◽  
National Education

This study aims to compare and determine the best model to describe the relationship between National Education Standard (NES) and CBNE scores using generalized structured component analysis. Model 1 describes the causal relationship between the NES and CBNE based on the educational theory of the Ministry of National Education and the Ministry of Religion (2010), Model 2 describes the causal relationship between the NES and CBNE based on the educational theory of the Ministry of Education and Culture (2012), and Model 3 describes the causal relationship between the NES and CBNE based on the educational theory of the Ministry of Education and Culture (2017). The results of the structural model evaluation have found that in Model 1, the SI path coefficient to Academic Achievement (PA) is not significant, in Model 2, the SI path coefficient to PA and SPT to SPN is not significant and in Model 3, the SI path coefficient to PA is also not significant. The coefficient of determination of each endogenous latent variable for each model ranges from 0.20 - 0.75. While the resulting Q-square value for all models is more than 0.9 to represent very good predictive relevance. Based on the overall goodness of fit, it is found that Model 3 produces the largest FIT and AFIT values. So it can be said that model 3 is better than other models. This model produces 11 invalid indicator variables, namely points 17, 39, 51, 55, 57, 59, 73, 75, 76, 80, and 108. The study found that National Education Standards that significantly affect academic achievement are graduate competency standards, process standards, and educational assessment standards

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

Methodology ◽  
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.

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

2019 ◽  
Author(s):  
Kevin Constante ◽  
Edward Huntley ◽  
Emma Schillinger ◽  
Christine Wagner ◽  
Daniel Keating
Keyword(s):  
Substance Use ◽  
Measurement Invariance ◽  
Latent Variables ◽  
Latent Variable ◽  
Path Model ◽  
Family Context ◽  
Partial Metric ◽  
Racial Groups ◽  
Protective Methods ◽  
Family Variables

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.

scholarly journals Interpretable Variational Graph Autoencoder with Noninformative Prior

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.

Information Matrices in Latent-Variable Models

1989 ◽  
Vol 14 (4) ◽  
pp. 335-350 ◽  
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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