Articoli di riviste: "Structural Equation Modeling"

1

Reisinger, Yvette, and Felix Mavondo. "Structural Equation Modeling." Journal of Travel & Tourism Marketing 21, no. 4 (August 15, 2007): 41–71. http://dx.doi.org/10.1300/j073v21n04_05.

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Jacobucci, Ross, and John J. McArdle. "Regularized Structural Equation Modeling." Multivariate Behavioral Research 50, no. 6 (November 2, 2015): 736. http://dx.doi.org/10.1080/00273171.2015.1121125.

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Asparouhov, Tihomir, and Bengt Muthén. "Exploratory Structural Equation Modeling." Structural Equation Modeling: A Multidisciplinary Journal 16, no. 3 (July 14, 2009): 397–438. http://dx.doi.org/10.1080/10705510903008204.

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Jacobucci, Ross, Kevin J. Grimm, and John J. McArdle. "Regularized Structural Equation Modeling." Structural Equation Modeling: A Multidisciplinary Journal 23, no. 4 (April 12, 2016): 555–66. http://dx.doi.org/10.1080/10705511.2016.1154793.

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5

Wah, Jack Ng Kok. "Decoding Structural Equation Modeling." Journal of Cases on Information Technology 27, no. 1 (February 13, 2025): 1–20. https://doi.org/10.4018/jcit.369092.

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The study utilizes structural equation modeling to examine issues related to normality, missing data, and sampling errors in digital marketing engagement research. The primary focus is on exploring relationships between self-esteem, social comparison, social interactions, perceived social support, and psychological well-being, with perceived social support as a mediating factor. Confirmatory factor analysis is applied to evaluate model fit using data from 400 social media users. Skewness and Kurtosis values are assessed to ensure normality, with scores kept within the acceptable range of -2 to +2. Questionnaires with over 30% missing values are excluded to maintain data quality, and the “10-times rule” is used to ensure adequate sample size and reduce sampling errors. Results confirm a normal distribution and indicate that the model aligns with SEM assumptions, meeting all fit indices. The research offers insights into SEM's application in digital marketing and suggests future studies should investigate advanced modeling techniques for further exploration.

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Dimitruk, Polina, Karin Schermelleh-Engel, Augustin Kelava, and Helfried Moosbrugger. "Challenges in Nonlinear Structural Equation Modeling." Methodology 3, no. 3 (January 2007): 100–114. http://dx.doi.org/10.1027/1614-2241.3.3.100.

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Abstract. Challenges in evaluating nonlinear effects in multiple regression analyses include reliability, validity, multicollinearity, and dichotomization of continuous variables. While reliability and validity issues are solved by employing nonlinear structural equation modeling, multicollinearity remains a problem which may even be aggravated when using latent variable approaches. Further challenges of nonlinear latent analyses comprise the distribution of latent product terms, a problem especially relevant for approaches using maximum likelihood estimation methods based on multivariate normally distributed variables, and unbiased estimates of nonlinear effects under multicollinearity. The only methods that explicitly take the nonnormality of nonlinear latent models into account are latent moderated structural equations (LMS) and quasi-maximum likelihood (QML). In a small simulation study both methods yielded unbiased parameter estimates and correct estimates of standard errors for inferential statistics. The advantages and limitations of nonlinear structural equation modeling are discussed.

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Shin, YoungJu. "Introduction to Structural Equation Modeling." Journal of Research Methodology 1, no. 1 (March 31, 2016): 119. http://dx.doi.org/10.21487/jrm.2016.05.1.1.119.

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8

Johnson, Knowlton W. "Structural Equation Modeling in Practice." Journal of Social Service Research 24, no. 3-4 (August 17, 1998): 131–71. http://dx.doi.org/10.1300/j079v24n03_06.

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9

Okpych, Nathanael J. "Book Review: Structural equation modeling." Research on Social Work Practice 25, no. 2 (November 10, 2014): 292–94. http://dx.doi.org/10.1177/1049731514558145.

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10

Millsap, Roger E. "Structural equation modeling made difficult." Personality and Individual Differences 42, no. 5 (May 2007): 875–81. http://dx.doi.org/10.1016/j.paid.2006.09.021.

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11

Rabe-Hesketh, Sophia, Anders Skrondal, and Andrew Pickles. "Generalized multilevel structural equation modeling." Psychometrika 69, no. 2 (June 2004): 167–90. http://dx.doi.org/10.1007/bf02295939.

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12

Amorim, Leila Denise Alves Ferreira, Rosemeire L. Fiaccone, Carlos Antônio S. T. Santos, Tereza Nadya dos Santos, Lia Terezinha L. P. de Moraes, Nelson F. Oliveira, Silvano O. Barbosa, et al. "Structural equation modeling in epidemiology." Cadernos de Saúde Pública 26, no. 12 (December 2010): 2251–62. http://dx.doi.org/10.1590/s0102-311x2010001200004.

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Structural equation modeling (SEM) is an important statistical tool for evaluating complex relations in several research areas. In epidemiology, the use and discussion of SEM have been limited thus far. This article presents basic principles and concepts in SEM, including an application using epidemiological data analysis from a study on the determinants of cognitive development in young children, considering constructs related to organization of the child's home environment, parenting style, and the child's health status. The relations between the constructs and cognitive development were measured. The results showed a positive association between psychosocial stimulus at home and cognitive development in young children. The article presents the contributions by SEM to epidemiology, highlighting the need for an a priori theoretical model for improving the study of epidemiological questions from a new perspective.

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Lamb, Eric G., Kerrie L. Mengersen, Katherine J. Stewart, Udayanga Attanayake, and Steven D. Siciliano. "Spatially explicit structural equation modeling." Ecology 95, no. 9 (September 2014): 2434–42. http://dx.doi.org/10.1890/13-1997.1.

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14

Howard, Andrea L. "Handbook of Structural Equation Modeling." Structural Equation Modeling: A Multidisciplinary Journal 20, no. 2 (April 2013): 354–60. http://dx.doi.org/10.1080/10705511.2013.769397.

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15

von Oertzen, Timo, Andreas M. Brandmaier та Siny Tsang. "Structural Equation Modeling With Ωnyx". Structural Equation Modeling: A Multidisciplinary Journal 22, № 1 (8 жовтня 2014): 148–61. http://dx.doi.org/10.1080/10705511.2014.935842.

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16

Guo, Jiesi, Herbert W. Marsh, Philip D. Parker, Theresa Dicke, Oliver Lüdtke, and Thierno M. O. Diallo. "A Systematic Evaluation and Comparison Between Exploratory Structural Equation Modeling and Bayesian Structural Equation Modeling." Structural Equation Modeling: A Multidisciplinary Journal 26, no. 4 (January 14, 2019): 529–56. http://dx.doi.org/10.1080/10705511.2018.1554999.

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17

Grace, James B. "Structural Equation Modeling for Observational Studies." Journal of Wildlife Management 72, no. 1 (January 2008): 14–22. http://dx.doi.org/10.2193/2007-307.

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18

Reinecke, Jost. "Special Issue: Mixture Structural Equation Modeling." Methodology 2, no. 3 (January 2006): 83–85. http://dx.doi.org/10.1027/1614-2241.2.3.83.

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19

Kim, Younglan, and Shinwoo Hwang. "Structural Equation Modeling on Antenatal Depression." Journal of Health Informatics and Statistics 43, no. 4 (November 30, 2018): 336–43. http://dx.doi.org/10.21032/jhis.2018.43.4.336.

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20

Chatterjee, Snigdhansu. "Structural Equation Modeling, A Bayesian Approach." Technometrics 50, no. 3 (August 2008): 411–12. http://dx.doi.org/10.1198/tech.2008.s907.

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21

Leth-Steensen, Craig, and Elena Gallitto. "Testing Mediation in Structural Equation Modeling." Educational and Psychological Measurement 76, no. 2 (July 7, 2015): 339–51. http://dx.doi.org/10.1177/0013164415593777.

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22

Olsen, Joseph A., and David A. Kenny. "Structural equation modeling with interchangeable dyads." Psychological Methods 11, no. 2 (June 2006): 127–41. http://dx.doi.org/10.1037/1082-989x.11.2.127.

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23

Doncaster, C. Patrick. "Structural Equation Modeling and Natural Systems." Fish and Fisheries 8, no. 4 (December 2007): 368–69. http://dx.doi.org/10.1111/j.1467-2979.2007.00260.x.

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24

Preacher, Kristopher J. "Quantifying Parsimony in Structural Equation Modeling." Multivariate Behavioral Research 41, no. 3 (September 2006): 227–59. http://dx.doi.org/10.1207/s15327906mbr4103_1.

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25

Peek, M. Kristen. "Structural Equation Modeling and Rehabilitation Research." American Journal of Physical Medicine & Rehabilitation 79, no. 3 (May 2000): 301–9. http://dx.doi.org/10.1097/00002060-200005000-00014.

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26

Berndt, Andrea E., and Priscilla C. Williams. "Hierarchical Regression and Structural Equation Modeling." Family & Community Health 36, no. 1 (2013): 4–18. http://dx.doi.org/10.1097/fch.0b013e31826d74c4.

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27

Huang, Po-Hsien. "Postselection Inference in Structural Equation Modeling." Multivariate Behavioral Research 55, no. 3 (July 13, 2019): 344–60. http://dx.doi.org/10.1080/00273171.2019.1634996.

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28

McQuitty, Shaun, and Marco Wolf. "Structural Equation Modeling: A Practical Introduction." Journal of African Business 14, no. 1 (January 2013): 58–69. http://dx.doi.org/10.1080/15228916.2013.765325.

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29

Cribbie, Robert A. "Multiplicity Control in Structural Equation Modeling." Structural Equation Modeling: A Multidisciplinary Journal 14, no. 1 (January 1, 2007): 98–112. http://dx.doi.org/10.1080/10705510709336738.

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30

Lee, Taehun, and Robert C. MacCallum. "Parameter Influence in Structural Equation Modeling." Structural Equation Modeling: A Multidisciplinary Journal 22, no. 1 (September 4, 2014): 102–14. http://dx.doi.org/10.1080/10705511.2014.935255.

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31

Rigdon, Edward E. "SEMNET: Structural equation modeling discussion network." Structural Equation Modeling: A Multidisciplinary Journal 1, no. 2 (January 1994): 190–92. http://dx.doi.org/10.1080/10705519409539971.

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32

Robles, Jaime. "Confirmation bias in structural equation modeling." Structural Equation Modeling: A Multidisciplinary Journal 3, no. 1 (January 1996): 73–83. http://dx.doi.org/10.1080/10705519609540031.

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33

Mueller, Ralph O. "Structural equation modeling: Back to basics." Structural Equation Modeling: A Multidisciplinary Journal 4, no. 4 (January 1997): 353–69. http://dx.doi.org/10.1080/10705519709540081.

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34

Davis, M., T. Smith, F. Laden, J. Hart, L. Ryan, and E. Garshick. "Structural Equation Modeling in Exposure Assessment." Epidemiology 17, Suppl (November 2006): S466. http://dx.doi.org/10.1097/00001648-200611001-01253.

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35

Bolla, Marianna, and Fatma Abdelkhalek. "Kalman's filtering technique in structural equation modeling." Studia Universitatis Babes-Bolyai Matematica 66, no. 1 (March 20, 2021): 179–96. http://dx.doi.org/10.24193/subbmath.2021.1.15.

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Abstract (sommario):

"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."

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36

Ghani, Suzaini A., and Yahya Mohamad Faizul. "Seam Puckering: Analysis and Modeling with Structural Equation Modeling." Advanced Materials Research 812 (September 2013): 157–62. http://dx.doi.org/10.4028/www.scientific.net/amr.812.157.

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Research on parameters influencing seam pucker has been quite intensive in the past decade. The difficulties associated with accurate predictions of the interaction between sewing parameters and fabrics properties. Traditional approach of matching variety of sewing parameters with unlimited fabric properties through personal experience has been a challenge in the apparel industry which increased the cost of production due to reprocess or rejection. Hence, in the present study, an alternative mathematical modeling known as Structural Equation Modeling (SEM) was proposed to predict the seam puckering grading together with the usage of high end instrumentation for fabric known as Kawabata Evaluation System (KES-F). The KES-F determined 16 parameters related to handle properties of a fabric and SEM produced prediction equation based on a few selected important parameters. The results show that equation by SEM can be used to predict the level of seam puckering of different categories of fabric weights. Good comparisons with the experimental and previous studies demonstrate the ability of the model to be used as a predictive tool for textile materials particularly for seam puckering.

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37

Brandriet, Alexandra R., Rose Marie Ward, and Stacey Lowery Bretz. "Modeling meaningful learning in chemistry using structural equation modeling." Chem. Educ. Res. Pract. 14, no. 4 (2013): 421–30. http://dx.doi.org/10.1039/c3rp00043e.

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38

Price, Larry R., Angela R. Laird, Peter T. Fox, and Roger J. Ingham. "Modeling Dynamic Functional Neuroimaging Data Using Structural Equation Modeling." Structural Equation Modeling: A Multidisciplinary Journal 16, no. 1 (January 20, 2009): 147–62. http://dx.doi.org/10.1080/10705510802561402.

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39

Reise, Steven P., Richard Scheines, Keith F. Widaman, and Mark G. Haviland. "Multidimensionality and Structural Coefficient Bias in Structural Equation Modeling." Educational and Psychological Measurement 73, no. 1 (July 17, 2012): 5–26. http://dx.doi.org/10.1177/0013164412449831.

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40

Oishi, Nobuhiro, Naoki Yamamoto, Akio Ishida, and Jun Murakami. "A Causal Analysis by Structural Equation Modeling of Sleep Monitoring Sensor Data." International Journal of Electronics and Electrical Engineering 8, no. 3 (September 2020): 58–62. http://dx.doi.org/10.18178/ijeee.8.3.58-62.

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In this paper, structural equation modeling (SEM) is used to analyze the causal relationship between the level of sleep and the environmental data. The data used for the analysis was obtained by a care support device used in an elderly care facility. By applying the stepwise selection method to this data, we were able to find four observation variables that affect the level of sleep. And the latent variables are determined by scree plot. We proposed a causal model in which four observed variables and two latent variables affect the level of sleep. Statistical analysis environment R and the lavaan package were used for SEM analysis in this paper. From this model, it was found that the indoor environment and the vital signs affect sleep, and that heart rate should be reduced to obtain deep sleep.

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Miftahuddin, Miftahuddin, Retno Wahyuni Putri, Ichsan Setiawan, and Rina Suryani Oktari. "MODELING OF SEA SURFACE TEMPERATURE BASED ON PARTIAL LEAST SQUARE - STRUCTURAL EQUATION." MEDIA STATISTIKA 14, no. 2 (December 28, 2021): 170–82. http://dx.doi.org/10.14710/medstat.14.2.170-182.

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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.

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42

Moroc, Andrei, and Octavian Bărnuțiu. "Job Quality, Innovation and Employment – a Structural Equation Modeling on Regional Level." INTERNATIONAL JOURNAL OF INNOVATION AND ECONOMIC DEVELOPMENT 5, no. 1 (2019): 54–65. http://dx.doi.org/10.18775/ijied.1849-7551-7020.2015.51.2005.

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Job quality (JQ) covers the aspects that contribute to wellbeing through the impact on material living conditions or quality of life at work. The research aims to evaluate the relationship between the quality of jobs, in combination with the dimensions of innovation and other economic and social indicators, on the performance of the labor market. The analysis is carried out at the level of 193 NUTS 1, 2 and 3 territorial administrative regions. Job quality is estimated based on the results of Eurofound’s sixth Working Conditions Survey 2015 (EWCS), which outlines some defining features of job quality. In the relationship between JQ and the growth of employment, we also introduced influences of innovation activities concerning the human resources involved, the financial support for research or the creation of collaborative networks between innovators, as well as intellectual assets in the form of patents applications, trademarks or design. The results of our structural equations modeling reveal an intense and positive causal relationship between the intellectual output (intellectual assets) and specific attributes of the job quality, especially regarding job prospects, skills and discretion, and the increase in the rate of employment.

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43

Kim, Hyuk Young, and Ki Gwan Park. "Validation of the Organizational Culture Assessment Instrument Using Structural Equation Modeling." Korean Journal of Local Government Studies 26, no. 4 (February 28, 2023): 423–41. http://dx.doi.org/10.20484/klog.26.4.17.

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44

Gonzales, Joseph E. "Structural Equation Modeling with JMP® Pro." Measurement: Interdisciplinary Research and Perspectives 19, no. 1 (January 2, 2021): 80–92. http://dx.doi.org/10.1080/15366367.2020.1809231.

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45

Smith, Murray D., and R. H. Hoyle. "Structural Equation Modeling: Concepts, Issues, and Applications." Statistician 45, no. 2 (1996): 267. http://dx.doi.org/10.2307/2988418.

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46

Kennedy, Otieno Robert. "Supply Chain Flexibility: Structural Equation Modeling Approach." iBusiness 03, no. 04 (2011): 390–99. http://dx.doi.org/10.4236/ib.2011.34052.

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47

Kock, Ned. "Structural Equation Modeling with Factors and Composites." International Journal of e-Collaboration 13, no. 1 (January 2017): 1–9. http://dx.doi.org/10.4018/ijec.2017010101.

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Recent methodological developments building on partial least squares (PLS) techniques and related ideas have significantly contributed to bridging the gap between factor-based and composite-based structural equation modeling (SEM) methods. PLS-SEM is extensively used in the field of e-collaboration, as well as in many other fields where multivariate statistical analyses are employed. The author compares results obtained with four methods: covariance-based SEM with full information maximum likelihood (FIML), factor-based SEM with common factor model assumptions (FSEM1), factor-based SEM building on the PLS Regression algorithm (FSEM2), and PLS-SEM employing the Mode A algorithm (PLSA). The comparison suggests that FSEM1 yields path coefficients and loadings that are very similar to FIML's; and that FSEM2 yields path coefficients that are very similar to FIML's and loadings that are very similar to PLSA's.

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Buncher, Charles Ralph, Paul A. Succop, and Kim N. Dietrich. "Structural Equation Modeling in Environmental Risk Assessment." Environmental Health Perspectives 90 (January 1991): 209. http://dx.doi.org/10.2307/3430870.

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Darwin, Muhammad, and Khoirul Umam. "Analisis Indirect Effect pada Structural Equation Modeling." NUCLEUS 1, no. 2 (November 15, 2020): 50–57. http://dx.doi.org/10.37010/nuc.v1i2.160.

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Tujuan penelitian ini adalah untuk mengetahui perbedaan dan kesamaan analisis Indirect Effect pada Structural Equation Modeling menggunakan software Amos dan SmartPLS. Metode penelitian yang digunakan adalah menggunakan metode penelitian kualitatif –komparatif. Analisis data yang digunakan menggunakan Model Spradley, dengan proses penelitian yang berangkat dari penjelasan yang lebih luas tentang indirect effect pada SEM, kemudian memfokus pada komparasi antar software dan menemukan benang merah penelitian (discovering cultural themes). Pembatasan penelitian ini adalah terbatas pada komparasi yang dilihat dari segi penggunaan dan ketersediaan yang ada pada output software. Ruang lingkup dalam pembahasan penelitian ini adalah terbatas pada penelitian skala nasional. Hasilnya adalah terdapat perbedaan pada analisis indirect effect pada nilai dan hasil evaluasi yang berbeda. Kemudian berbeda pada pengujian hipotesa Indirect Effect, keduanya menggunakan tool yang tidak sama, namun bisa saja menghasilkan evaluasi yang diterima atau ditolak tergantung jenis data dan model penelitiannya. Sedangkan kesamaan yang diperoleh adalah terletak pada hasil evaluasi pada ilustrasi penelitian ini sama-sama menghasilkan hipotesis yang ditolak.

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Rigdon, Edward E., and Rick H. Hoyle. "Structural Equation Modeling: Concepts, Issues, and Applications." Journal of Marketing Research 34, no. 3 (August 1997): 412. http://dx.doi.org/10.2307/3151904.

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Articoli di riviste sul tema "Structural Equation Modeling"

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