Opportunities for Improving Consumer Research through Latent Variable Structural Equation Modeling

2001 ◽  
Vol 28 (1) ◽  
pp. 159-166 ◽  
Author(s):  
Scott B. Mackenzie
Keyword(s):  
Structural Equation Modeling ◽  
Consumer Research ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling

scholarly journals Structural Equation Modeling for the Effect of Main Factors on Abortion Issue

2019 ◽  
Vol 7 (1) ◽  
pp. 1-13
Author(s):  
Aras Jalal Mhamad ◽  
Renas Abubaker Ahmed
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Close Contact ◽  
Maternity Hospital ◽  
Equation Modeling ◽  
Research Model ◽  
Statistical Tools ◽  
Main Factors

       Based on medical exchange and medical information processing theories with statistical tools, our study proposes and tests a research model that investigates main factors behind abortion issue. Data were collected from the survey of Maternity hospital in Sulaimani, Kurdistan-Iraq. Structural Equation Modelling (SEM) is a powerful technique as it estimates the causal relationship between more than one dependent variable and many independent variables, which is ability to incorporate quantitative and qualitative data, and it shows how all latent variables are related to each other. The dependent latent variable in SEM which have one-way arrows pointing to them is called endogenous variable while others are exogenous variables. The structural equation modeling results reveal is underlying mechanism through which statistical tools, as relationship between factors; previous disease information, food and drug information, patient address, mother’s information, abortion information, which are caused abortion problem. Simply stated, the empirical data support the study hypothesis and the research model we have proposed is viable. The data of the study were obtained from a survey of Maternity hospital in Sulaimani, Kurdistan-Iraq, which is in close contact with patients for long periods, and it is number one area for pregnant women to obtain information about the abortion issue. The results shows arrangement about factors effectiveness as mentioned at section five of the study. This gives the conclusion that abortion problem must be more concern than the other pregnancy problem.

scholarly journals Investigation of influence of growing pigs’ positive affective state on behavioral and physiological parameters using structural equation modeling

2020 ◽  
Vol 98 (2) ◽  
Author(s):  
Katja L Krugmann ◽  
Farina J Mieloch ◽  
Joachim Krieter ◽  
Irena Czycholl
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variable ◽  
Structural Equation ◽  
Affective State ◽  
Body Language ◽  
Physiological Parameters ◽  
Equation Modeling ◽  
Behavioral Tests ◽  
Positive Affective State ◽  
Fattening Pigs

Abstract The aim of the present study was to investigate whether the primarily positive affective state of fattening pigs influences various behavioral and physiological parameters such as the pigs’ playing behavior, way of behaving in behavioral tests, body language signals, or diameter, and astroglia cell numbers of hippocampi, salivary immunoglobulin A (IgA) content, or salivary protein composition. Additionally, the suitability of the variables mentioned was examined to assess the pigs’ positive affective state in practice, which still constitutes a latent variable not itself measurable. For this, a dataset including behavioral and physiological data of 60 fattening pigs from 3 different farms with different housing systems was analyzed by the partial least squares structural equation modeling (PLS-SEM) method. A hierarchical component model (HCM) was used including the pigs’ positive affective state as a higher-order component (HOC) and the behavioral and physiological parameters as lower-order components (LOC). Playing behavior, body language signals, and behavioral tests were revealed, in this order, to be most influenced by the pigs’ positive affective state since these resulted in the corresponding path coefficients (PC) of PC = 0.83, PC = 0.79, and PC = 0.62, respectively. Additionally moderate and weak R2-values occurred for the endogenous latent variables playing behavior (R2 = 69.8%), body language signals (R2 = 62.7%), and behavioral tests (R2 = 39.5%). Furthermore, the indicator of the “locomotor play” showed the highest indicator reliability (IR) (IR = 0.85) to estimate the latent variable of pigs’ positive affective state. The results of the present study supplement the comprehension and assessment of the pigs’ positive affective state in general.

New Conjoint Approaches to Scaling Brand Equity and Optimising share of Preference Prediction

2015 ◽  
Vol 57 (5) ◽  
pp. 701-725 ◽  
Author(s):  
Hervé Guyon ◽  
Jean-François Petiot
Keyword(s):  
Structural Equation Modeling ◽  
Conjoint Analysis ◽  
Brand Equity ◽  
Repeated Measures ◽  
Latent Variable ◽  
Structural Equation ◽  
Probabilistic Models ◽  
Equation Modeling ◽  
New Approach ◽  
First Choice

Ratings-based conjoint analysis suffers two problems: the distortion raised by consumer perceptions of brand equity, and the lack of efficiency of probabilistic models for estimating preference shares. This article proposes two new approaches to scale customer-based brand equity using repeated measures and structural equation modeling and to estimate the share of preferences on the basis of a randomized first choice. The outcome is a new tool to predict accurate preference shares, taking into account product utilities (estimated by rating-based conjoint analysis) and the brand equity related to product attributes (estimated as a latent variable with structural equation modeling). An example with three products illustrates this new approach.

New developments in latent variable panel analyses of longitudinal data

2007 ◽  
Vol 31 (4) ◽  
pp. 357-365 ◽  
Author(s):  
Todd D. Little ◽  
Kristopher J. Preacher ◽  
James P. Selig ◽  
Noel A. Card
Keyword(s):  
Structural Equation Modeling ◽  
Longitudinal Data ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Null Model ◽  
Factorial Invariance ◽  
Equation Modeling ◽  
New Developments ◽  
Mediated Effects

We review fundamental issues in one traditional structural equation modeling (SEM) approach to analyzing longitudinal data — cross-lagged panel designs. We then discuss a number of new developments in SEM that are applicable to analyzing panel designs. These issues include setting appropriate scales for latent variables, specifying an appropriate null model, evaluating factorial invariance in an appropriate manner, and examining both direct and indirect (mediated), effects in ways better suited for panel designs. We supplement each topic with discussion intended to enhance conceptual and statistical understanding.

A New Dimension of Organizational Justice: Procedural Voice

2009 ◽  
Vol 105 (2) ◽  
pp. 411-426 ◽  
Author(s):  
Denise Jepsen ◽  
John Rodwell
Keyword(s):  
Structural Equation Modeling ◽  
Organizational Justice ◽  
Latent Variable ◽  
Structural Equation ◽  
Factor Model ◽  
Model Fit ◽  
Equation Modeling ◽  
Wide Range

Dimensionality of the Colquitt justice measures was investigated across a wide range of service occupations. Structural equation modeling of data from 410 survey respondents found support for the 4-factor model of justice (procedural, distributive, interpersonal, and informational), although significant improvement of model fit was obtained by including a new latent variable, “procedural voice,” which taps employees' desire to express their views and feelings and influence results. The model was confirmed in a second sample ( N = 505) in the same organization six months later.

scholarly journals Kalman's filtering technique in structural equation modeling

2021 ◽  
Vol 66 (1) ◽  
pp. 179-196
Author(s):  
Marianna Bolla ◽  
Fatma Abdelkhalek
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variable ◽  
Structural Equation ◽  
Real Life ◽  
Test Sample ◽  
Training Sample ◽  
Equation Modeling ◽  
Error Covariance ◽  
Filtering Technique ◽  
Model Equations

"Structural equation modeling finds linear relations between exogenous and endogenous latent and observable random vectors. In this paper, the model equations are considered as a linear dynamical system to which the celebrated R.~E.~K\'alm\'an's filtering technique is applicable. An artificial intelligence is developed, where the partial least squares algorithm of H.~Wold and the block Cholesky decomposition of H.~Kiiveri et al. are combined to estimate the parameter matrices from a training sample. Then the filtering technique introduced is capable to predict the latent variable case values along with the prediction error covariance matrices in the test sample. The recursion goes from case to case along the test sample, without having to re-estimate the parameter matrices. The algorithm is illustrated on real life sociological data."

scholarly journals Prediksi Minat Perilaku Menggunakan Jasa Pajak Elektronik: Analisis Faktor Konfirmatori

InFestasi ◽  
2018 ◽  
Vol 13 (2) ◽  
pp. 321
Author(s):  
Jullie J. Sondakh
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Behavioral Intention ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Model ◽  
Measurement Model ◽  
Equation Modeling ◽  
Confirmatory Factor

<p class="Ventura-Abstract">The purpose of this research is to predict the tax payer behavioral intention of using the e-SPT through the application of Technology Acceptance Model (TAM).</p><p class="Ventura-Abstract">This research used survey method to collect primary data from the population of tax payer in the city of Manado and Bitung with 156 respondents while using judgement sampling method.The data analysis is using Structural Equation Modeling (SEM) that consists of two steps; the measurement model and structural model. The focus of this research is on the first step of SEM modeling, which is the measurement model by using the Confirmatory Factor Analysis (CFA). The purpose of this analysis is to test the validity and reliability from the indicator of the construct or latent variable researched, thus, we will obtain the fit construct or latent variable before proceeding to the next step of SEM which is the structural model.Based on the confirmatory factor analysis (CFA), we obtained the validity test result, convergent validity, and reliability test result, construct reliability and variance extracted, from the indicator of construct or latent variable which are perceived usefulness, perceived ease of use, attitude towards e-SPT, and behavioral intention to use e-SPT. The reliabilty and validity test result showed that there is no indicator from all the tested latent variable to be excluded for the next step of Structural Equation Modeling (SEM)  which is the structural model.</p><p class="Ventura-Abstract"> </p><p class="Ventura-Abstract">Tujuan penelitian ini adalah melakukan prediksi minat perilaku wajib pajak menggunakan  e-SPT melalui penerapanTechnology Acceptance Model (TAM). Penelitian ini menggunakan metode survei untuk mengumpulkan data primer dari populasi yaitu wajib pajak di Kota Manado dan Bitung dengan jumlah sampel sebanyak 156 responden serta penentuan sampel berdasarkan metode  judgment sampling. Teknik analisis data menggunakan pemodelan Structural Equation Modeling (SEM) yang terdiri  dari dua tahapan yaitu model pengukuran (measurement model) dan model struktural (structural model). Fokus penelitian ini adalah pada pemodelan SEM tahap pertama yaitu model pengukuran (measurement model)  melalui  analisis faktor konfirmatori (Confirmatory Factor Analysis - CFA). Analisis ini  bertujuan  untuk menguji validitas dan reliabilitas dari indikator-indikator pembentuk konstruk atau variabel laten yang diteliti sehingga diperoleh  konstruk atau variabel laten yang fit  sebelum lanjut ke tahap pemodelan SEM berikutnya  yaitu model struktural. Berdasarkan analisis faktor konfirmatori (Confirmatory Factor Analysis - CFA) diperoleh hasil uji validitas yaitu signifikansi  factor loading (convergent validity) dan reliabilitas (construct reliability dan variance extracted) dari indikator pembentuk konstruk atau variabel laten kegunaan persepsian (Perceived Usefulness), kemudahan penggunaan persepsian (Perceived ease of use), sikap terhadap penggunaan  e-SPT (Attitude towards e-SPT) dan  minat perilaku  menggunakan e-SPT (Behavioral intention to use e-SPT). Hasil uji validitas dan reliabilitas ini menunjukan bahwa tidak ada indikator dari variabel kegunaan persepsian (Perceived Usefulness), kemudahan penggunaan persepsian (Perceived ease of use), sikap terhadap penggunaan  e-SPT (Attitude towards e-SPT) dan  minat perilaku  menggunakan e-SPT (Behavioral intention to use e-SPT) yang di hilangkan pada analisis selanjutnya yaitu pemodelan Structural Equation Modeling (SEM) tahap kedua  sehingga  dapat dilakukan estimasi model persamaan struktural (structural model).</p>

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