scholarly journals Algorithms Error in The VisualGSCA Program

Jurnal Varian ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 125-132
Author(s):  
Thesa Adi Purwanto
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Model ◽  
Estimate Parameter

The VisualGSCA program uses an incorrect algorithm, which results in scale inconsistencies between observed and latent variables. The observed variable is standardized, while the latent variable is normalized. This affects the calculation of the wrong estimate parameter value and the goodness-fit value of FIT and AFIT becomes inaccurate. This error occurs because the algorithm used is not a pure GSCA algorithm but a reduced GSCA algorithm that ignores the structural model, resulting in an incorrect FIT value. This study aims to prove that the old version of the GSCA program has problems using its algorithm so that it can affect the results of its statistical calculations. The data used in this study are data from previous studies that have been processed with the old version of the GSCA program, so that the results can be compared with the latest version of the GSCA program. The results obtained prove that there are indeed differences in the value of the Loading Factor and FIT, so that research that has been done previously needs to be reanalyzed using the latest program.

scholarly journals Effect of Hybrid Resources on Management Outcomes Micro-Hydro Power Plants in Remote Areas

2020 ◽  
Vol 15 (6) ◽  
pp. 855-864
Author(s):  
Muhammad Yusan Naim ◽  
Henny Pramoedyo ◽  
Nuddin Harahab ◽  
Syarifuddin Nodjeng ◽  
Sudirman Syam
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Power Plants ◽  
Structural Model ◽  
Electrical Power ◽  
Hydro Power ◽  
Remote Areas ◽  
Manifest Variable ◽  
Hydropower Plants ◽  
Hydro Power Plants

The effect of developing hybrid resources on the management outcomes of micro-hydropower plants in remote areas has been studied and analyzed. The hybrid resource is a combination of two energy sources, such as water and solar energy, that operate together in meeting the needs of electrical power in Ambava Village, Tinondo Sub-district, East-Kolaka Regency, Southeast Sulawesi Province. This study has used a management model describing the relationship and influence of latent variables and their manifestation variables. Here, Confirmatory Factor Analysis (CFA) based Common-Pool-Resources (CPR) is the proper method of testing the structural model used. The results show that the Critics-Ratio (CR) and Standard Loading Factor (SLF) have fulfilled the expected value. The direct influence of the variable exogenous hybrid resources to the endogenous variable outcome of 0.213 has fulfilled the Gold of Fit criteria. Then, the direct impact of the most dominant latent variable is the operating dimension of the resource. At the same time, the indirect effect on the manifest variable is the increase in electricity reserve. Furthermore, the most dominant indirect impact of the hybrid resources latent variable is the benefit and cost dimensions, while the most dominant manifest variable is people's welfare savings.

MODELING OF SEA SURFACE TEMPERATURE BASED ON PARTIAL LEAST SQUARE - STRUCTURAL EQUATION

2021 ◽  
Vol 14 (2) ◽  
pp. 170-182
Author(s):  
Miftahuddin Miftahuddin ◽  
Retno Wahyuni Putri ◽  
Ichsan Setiawan ◽  
Rina Suryani Oktari
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Model ◽  
Partial Least Square ◽  
Least Square ◽  
Sea Surface ◽  
Equation Modeling

Variability of Sea Surface Temperature (SST) is one of the climatic features that influence global and regional climate dynamics. Missing data (gaps) in the SST dataset are worth investigating since they may statistically alter the value of the SST change. The partial least square-structural equation modeling (PLS-SEM) approach is used in this work to estimate the causality relationships between exogenous and endogenous latent variables. The findings of this study, which are significant indicators that have a loading factor value > 0.7 are as follows: i) sea surface temperature (oC) as a measure of the latent variable changes in SST, ii) wind speed (m/s) and relative humidity (%) as a measure of the latent variable of weather, and iii) air temperature (oC), long-wave solar radiation (w/m2) as a measure of climate latent variables. The size of the Rsquare value is influenced by the number of gaps. The results of the boostrapping show that the latent variables of weather and climate have a significant effect on changes in SST which are indicated by the value of tstatistics > ttabel. The structural model obtained Changes in SST (η) = -0.330 weather + 0.793 climate + ζ. The model shows that the weather has a negative coefficient, which means that the better the weather conditions, the lower the SST changes. Climate has a positive coefficient, which means that the better the climate, the SST changes will also increase. Rising sea surface temperatures caused by an increase in climate can lead to global warming, impacting El-Nino and La-Nina events.

scholarly journals ANALISIS KEKAMBUHAN ORANG DENGAN SKIZOFRENIA MENGGUNAKAN METODE PARTIAL LEAST SQUARE STRUCTURAL EQUATION MODEL

2019 ◽  
Vol 8 (3) ◽  
pp. 222
Author(s):  
IRA INDRIYANTI ◽  
G.K. GANDHIADI ◽  
MADE SUSILAWATI
Keyword(s):  
Family Support ◽  
Structural Equation Model ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Model ◽  
Medication Compliance ◽  
Equation Model ◽  
Partial Least Square ◽  
Least Square

Schizophrenia is a psychotic disorder characterized by major disorders in the mind and emotions. People with schizophrenia (ODS) can experience recurrence if they do not receive proper care. The latent variable used in this study was ODS reccurence. One method that can determine the relationship between latent variables and latent variables with the indicator is the partial least square structural equation model (PLS-SEM). This study was conducted to see how the structural model of ODS recurrence data and to know the factors that most influence ODS recurrence. The results of this study concluded that the resulting model was good enough with a large R-square value of 0.8577, but not all variables used in this study had a significant effect on ODS recurrence. ODS recurrence is significantly influenced by family support and community social support variables. While medication compliance and physician control regularity will not have a significant effect without family support. The worse treatment of families and communities around ODS recurrence will occur more often.

scholarly journals Explaining Variance in Breastfeeding Intentions and Behaviors Among a Cohort of Midwest Mothers Using a Theory of Planned Behavior-Based Structural Model

2021 ◽  
Author(s):  
Anita Esquerra-Zw ◽  
Emilie Dykstra Goris ◽  
Aaron Franzen
Keyword(s):  
Theory Of Planned Behavior ◽  
Planned Behavior ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Model ◽  
Statistical Significance ◽  
Significant Finding ◽  
Variable Model ◽  
Convenience Sample ◽  
And Behaviors

Abstract Background The Theory of Planned Behavior (TPB) has guided the investigation of breastfeeding since the 1980’s, incorporating the major constructs of attitudes, subjective norms/normative beliefs, perceived behavioral control, and intentions. The purpose of this research study was to define a TPB-based structural latent variable model so as to explain variance in breastfeeding intentions and behaviors among a cohort of Midwest breastfeeding mothers. Methods The longitudinal descriptive study utilized questionnaire data collected from a convenience sample of 100 women with low-risk pregnancies with the intention to breastfeed at three separate time points (> 30 weeks antepartum, 10 and 60 days postpartum). Data were coded and analyzed using IBM SPSS, SAS and the lavaan package in R. Results Participants were predominantly White (94%, n=94), married (95%, n=95), college-educated (96%, n=96), and had previous breastfeeding experience (75%, n=75). The majority gave birth vaginally (79%, n=75). Varimax analysis revealed a plurality of factors within each domain. Attempts to fit a structural model, including both hierarchical and bi-factor latent variables, failed, revealing a lack of statistical significance and poor fit statistics. Conclusion(s): These findings illustrate the importance of using methods that fit the phenomena explained. Contributors to poor model fit may include outdated tools lacking cultural relevance, a change in social norms, or a failure to capture the possible influence of social media and formula marketing on breastfeeding behaviors. The null finding is a significant finding, indicating the need to revisit and refine the operationalization and conceptual underpinnings of the TPB through qualitative methods such as exploring the lived experiences of breastfeeding women in the Midwest region.

scholarly journals Advertising and Retail Promotion of Washington Apples: A Structural Latent Variable Approach to Promotion Evaluation

1999 ◽  
Vol 31 (1) ◽  
pp. 15-28 ◽  
Author(s):  
Timothy J. Richards ◽  
X.M. Gao ◽  
Paul M. Patterson
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Model ◽  
State Space Model ◽  
Product Knowledge ◽  
Variable Approach ◽  
Latent Effects ◽  
Filter Approach ◽  
Retail Promotion ◽  
Latent Variable Approach

Abstract“Commodity promotion” consists of many activities, each designed to contribute to a consumer's product knowledge or influence tastes. However, both knowledge and tastes are unobservable, or latent, variables influencing demand. This paper specifies a dynamic structural model of fresh fruit demand that treats promotion and other socioeconomic variables as “causal” variables influencing these latent variables. Estimating this state-space model using a Kalman filter approach provides estimates of both the system parameters and a latent variable series. The results show that these latent effects contribute positively to apple and other fruit consumption, while reducing banana consumption.

scholarly journals PLS-based SEM Algorithms: The Good Neighbor Assumption, Collinearity, and Nonlinearity

2015 ◽  
Vol 7 (2) ◽  
pp. 113-130 ◽  
Author(s):  
Ned Kock
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Model ◽  
Measurement Model ◽  
Equation Modeling ◽  
Systems Research ◽  
Inner Model ◽  
Good Neighbor

The partial least squares (PLS) method has been extensively used in information systems research, particularly in the context of PLS-based structural equation modeling (SEM). Nevertheless, our understanding of PLS algorithms and their properties is still progressing. With the goal of improving that understanding, we provide a discussion on the treatment of reflective and formative latent variables in the context of three main algorithms used in PLS-based SEM analyses –PLS regression, PLS Mode A, and PLS Mode B. Two illustrative examples based on actual data are presented. It is shown that the “good neighbor” assumption underlying modes A and B has several consequences, including the following: the inner model influences the outer model in a way that increases inner model coefficients of association and collinearity levels in tandem, and makes measurement model analysis tests dependent on structural model links; instances of Simpson’s paradox tend to occur with Mode B at the latent variable level; and nonlinearity is improperly captured. In spite of these mostly detrimental outcomes, it is argued that modes A and B may have important and yet unexplored roles to play in PLS-based structural equation modeling analyses.

scholarly journals Structural model of sandalwood (Santalum album) regeneration in the forest and community plantation in Timor Island, Indonesia

2018 ◽  
Vol 2 (2) ◽  
pp. 41-47
Author(s):  
YOSEPH NAHAK SERAN ◽  
SUDARTO SUDARTO ◽  
LUCHMAN HAKIM ◽  
ENDANG ARISOESILANINGSIH
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Model ◽  
Diversity Index ◽  
Abiotic Factors ◽  
Partial Least Square ◽  
Index Number ◽  
Santalum Album ◽  
Climate Data ◽  
Host Diversity

Seran YN, Sudarto, Hakim L, Arisoesilaningsih E. 2018. Structural model of sandalwood (Santalum album) regeneration in the forest and community plantation in Timor Island, Indonesia. Trop Drylands 2: 41-47. Sandalwood (Santalum album L.) is a very important forest product in NTT, an endemic species in the world with a high economic value.. This study aimed to identify and produce a structural model of sandalwood regeneration in both the forests and the community plantation in the Regency of Timor Tengah Selatan (TTS) and Timor Tengah Utara (TTU). The method used in this research was vegetation analysis by purposive sampling method on 8 observation stations with 87 plots. The plot size was 20x20 m2 (trees), 10x10 m2 (poles), 5x5 m2 (saplings), and 2x2 m2 (seedlings). Data observed in the field included the mean sandalwood population size in the forms of trees, poles, saplings and seedlings phase, vegetation data in sandalwood habitat which included tree wealth index, diversity index, number of individuals and sandalwood host diversity index data. Geographical factors such as altitude and slope, and abiotic factors such as soil organic matter, soil pH and soil conductivity were also recorded. Climate data included the number of dry months and rainfall. Sandalwood regeneration data included sandalwood vitality, pests and diseases and the number of seeds. Secondary data included climate data (ten years time) obtained from BMKG of NTT Province in Kupang. These data were used as the indicators of the latent variables (six variables) which consisted of geography, soil, climate, population, vegetation, and regeneration. Obtained data were subjected to both descriptive analysis and multivariate statistics with structural modeling of Warp Partial Least Square (WarpPLS 6.0). The results showed that most of the proposed indicators significantly influenced the compiled six latent variables except the host diversity. Some indicators significantly or highly significantly affected the latent variable with 15 indicators that significantly composed the latent variable. The resulting structural model is very relevant and has a relevance value of Q2 prediction of 96,65% so that the structural model proposed in this study has very relevant and high predictive value on factors that influence sandalwood regeneration. Therefore, this model is feasible or appropriate to be used as recommendations in the framework of sandalwood development in the forest and the community plantation in the West part of Timor Island, Nusa Tenggara Timur.

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.

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