Relevant bibliographies by topics / Structural Equation Modeling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Structural Equation Modeling'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Structural Equation Modeling"

1

Reisinger, Yvette, and Felix Mavondo. "Structural Equation Modeling." Journal of Travel & Tourism Marketing 21, no. 4 (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 (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 (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 (2016): 555–66. http://dx.doi.org/10.1080/10705511.2016.1154793.

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Wah, Jack Ng Kok. "Decoding Structural Equation Modeling." Journal of Cases on Information Technology 27, no. 1 (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
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Dimitruk, Polina, Karin Schermelleh-Engel, Augustin Kelava, and Helfried Moosbrugger. "Challenges in Nonlinear Structural Equation Modeling." Methodology 3, no. 3 (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 norm
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Shin, YoungJu. "Introduction to Structural Equation Modeling." Journal of Research Methodology 1, no. 1 (2016): 119. http://dx.doi.org/10.21487/jrm.2016.05.1.1.119.

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

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

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

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Dissertations / Theses on the topic "Structural Equation Modeling"

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Hwang, Heungsun 1969. "Structural equation modeling by extended redundancy analysis." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=36954.

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A new approach to structural equation modeling based on so-called extended redundancy analysis (ERA) is proposed. In ERA, latent variables are obtained as exact linear combinations of observed variables, and model parameters are estimated by consistently minimizing a single criterion. As a result, the method can avoid limitations of covariance structure analysis (e.g., stringent distributional assumptions, improper solutions, and factor score indeterminacy) in addition to those of partial least squares (e.g., the lack of a global optimization procedure). The method is simple yet versatile enou
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Joshi, Hemanta. "Determinants of mathematics achievement using structural equation modeling." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq23000.pdf.

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Codd, Casey L. "Nonlinear Structural Equation Models: Estimation and Applications." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1301409131.

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Turner, John R. "Knowledge Sharing: Examining Employee Perceptions Using Structural Equation Modeling." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc804846/.

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During team decision-making practices information is often shared among team members as part of the decision making process. Knowledge sharing involves one team member sharing information so that other team members can encode the knowledge to make their own mental representation of the new information (Huan & Jiang, 2012). Unfortunately, the literature has shown that new information is not always shared between team members during decision making processes (Stasser & Titus, 1985). When teams make decisions without considering all the information available poor decisions can result. This resear
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Lortie, Brendan. "A Structural Equation Modeling Approach to Predicting Applicant Faking." Bowling Green State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1573323760174055.

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Pfleger, Phillip Isaac. "Exploring Fit for Nonlinear Structural Equation Models." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7370.

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Fit indices and fit measures commonly used to determine the accuracy and desirability of structural equation models are expected to be insensitive to nonlinearity in the data. This includes measures as ubiquitous as the CFI, TLI, RMSEA, SRMR, AIC, and BIC. Despite this, some software will report these measures when certain models are used. Consequently, some researchers may be led to use these fit measures without realizing the impropriety of the act. Alternative fit measures have been proposed, but these measures require further testing. As part of this thesis, a large simulation study was
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Miles, Carol A. "Structural equation modeling of the WISC-III, a developmental approach." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0014/NQ31054.pdf.

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Geiser, Christian [Verfasser]. "Structural equation modeling of multitrait-multimethod-multioccasion data / Christian Geiser." Berlin : Freie Universität Berlin, 2008. http://d-nb.info/1023232111/34.

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Zhao, Hongxia. "Examining Contributors to Preschoolers’ Classroom Engagement using Structural Equation Modeling." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etd/3475.

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The purpose of this study is to demonstrate whether and how teacher-child interactions, teacher-child relationships, children’s self-control, parents’ education levels, teachers’ teaching experience and education levels, and classroom physical environment impact children’s engagement levels. Preschoolers from Head Start programs and a university childcare center (N = 135, M = 54.93 months, SD = 7.50) and 15 preschool classroom teachers in East Tennessee participated in the study. Classroom head teachers rated child’s engagement, teacher-child interaction, teacher-child relationships, and child
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Kobayashi, Wakako. "Structural Equation Modeling of Writing Proficiency Using Can-Do Questionnaires." Diss., Temple University Libraries, 2017. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/461909.

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Teaching & Learning<br>Ed.D.<br>The purposes of this study were to validate the writing section of the Eiken Can-Do Questionnaires used in this study and the second purpose was to determine the effects of ten affective orientations (i.e., Desire to Write English, Attitude Toward Learning to Write English, Motivational Intensity, Instrumental Orientation for Writing in English, L2 Writing Anxiety, L2 Writing Self-Confidence, Willingness to Communicate in L2 Writing, Self-Esteem, Cognitive Competence, and General Self-Worth), on the participants’ responses to the Eiken Can-Do Questionnaires. Thi
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Books on the topic "Structural Equation Modeling"

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Bowen, Natasha K. Structural equation modeling. Oxford University Press, 2012.

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Wang, Jichuan, and Xiaoqian Wang. Structural Equation Modeling. John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118356258.

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Shenyang, Guo, ed. Structural equation modeling. Oxford University Press, 2012.

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Verma, J. P., and Priyam Verma. Understanding Structural Equation Modeling. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-32673-8.

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Gana, Kamel, and Guillaume Broc. Structural Equation Modeling with lavaan. John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119579038.

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Maruyama, Geoffrey. Basics of structural equation modeling. Sage Publications, 1997.

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Avkiran, Necmi K., and Christian M. Ringle, eds. Partial Least Squares Structural Equation Modeling. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71691-6.

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Dasgupta, Ratan, ed. Growth Curve and Structural Equation Modeling. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17329-0.

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Mueller, Ralph O. Basic Principles of Structural Equation Modeling. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-3974-1.

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Batra, Sachin. How to Use Structural Equation Modeling. SAGE Publications Ltd, 2023. http://dx.doi.org/10.4135/9781529670325.

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Book chapters on the topic "Structural Equation Modeling"

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Franzen, Michael D. "Structural Equation Modeling." In Encyclopedia of Clinical Neuropsychology. Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-79948-3_1252.

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Page, Matthew J. L. "Structural Equation Modeling." In Encyclopedia of Clinical Neuropsychology. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56782-2_1252-3.

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Page, Matthew J. L. "Structural Equation Modeling." In Encyclopedia of Clinical Neuropsychology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_1252.

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Adachi, Kohei. "Structural Equation Modeling." In Matrix-Based Introduction to Multivariate Data Analysis. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4103-2_11.

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Stein, Catherine M., Nathan J. Morris, and Nora L. Nock. "Structural Equation Modeling." In Methods in Molecular Biology. Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-555-8_27.

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Baumgartner, Hans, and Bert Weijters. "Structural Equation Modeling." In International Series in Quantitative Marketing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53469-5_11.

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Grabill, Kristen M. "Structural Equation Modeling." In Encyclopedia of Child Behavior and Development. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-79061-9_2823.

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Singh, Davinder, Jaimal Singh Khamba, and Tarun Nanda. "Structural equation modeling." In Technology Innovation in Manufacturing. CRC Press, 2022. http://dx.doi.org/10.1201/9781003272977-6.

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Okello, Gabriel Otieno. "Structural Equation Modeling." In Statistical Methods Using SPSS. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003386636-8.

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Harlow, Lisa L. "Structural Equation Modeling." In The Essence of Multivariate Thinking, 3rd ed. Routledge, 2023. http://dx.doi.org/10.4324/9780429269103-14.

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Conference papers on the topic "Structural Equation Modeling"

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Nash, Alfred. "Structural Equation Modeling for Efficient Mission Formulation." In 2025 IEEE Aerospace Conference. IEEE, 2025. https://doi.org/10.1109/aero63441.2025.11068452.

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Ballenas, Evelyn B., and Maris T. Lasco. "Exploring Mathematics Teachers’ Behavioral Intentions to Use Artificial Intelligence Through Structural Equation Modeling." In 2024 International Conference on TVET Excellence & Development (ICTeD). IEEE, 2024. https://doi.org/10.1109/icted62334.2024.10844599.

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Mertens, Kai G., Iris Lorscheid, and Matthias Meyer. "Structural equation modeling for simulation metamodeling." In 2015 Winter Simulation Conference (WSC). IEEE, 2015. http://dx.doi.org/10.1109/wsc.2015.7408208.

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Ghani, Suzaini Abdul, Mohamad Faizul Yahya, and Hugh Gong. "Structural equation modeling of seam failures analysis." In 2012 IEEE Colloquium on Humanities, Science and Engineering (CHUSER). IEEE, 2012. http://dx.doi.org/10.1109/chuser.2012.6504409.

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Zhang, Ying, Xuebo Chen, and Qiubai Sun. "Review of structural equation modeling (SEM) applications." In 2013 2nd International Symposium on Instrumentation & Measurement, Sensor Network and Automation (IMSNA). IEEE, 2013. http://dx.doi.org/10.1109/imsna.2013.6743253.

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Mo, Yikui, Yongning Niu, and Yanbin Fu. "Structural Equation Modeling of Traveler Information Needs." In 2008 International Conference on MultiMedia and Information Technology (MMIT). IEEE, 2008. http://dx.doi.org/10.1109/mmit.2008.38.

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Godwin, Allison, Geoff Potvin, Zahra Hazari, and Robynne Lock. "Understanding engineering identity through structural equation modeling." In 2013 IEEE Frontiers in Education Conference (FIE). IEEE, 2013. http://dx.doi.org/10.1109/fie.2013.6684787.

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Watanabe, Shuhei, and Takahiko Horiuchi. "Layered Perceptual Modeling Using Structural Equation Modeling: Exploring Structure with Genetic Algorithm." In 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2022. http://dx.doi.org/10.1109/smc53654.2022.9945376.

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Kurniawan, Sri Hastuti, and R. Darin Ellis. "A structural equation modeling of internet bookmark organizations." In CHI '01 extended abstracts. ACM Press, 2001. http://dx.doi.org/10.1145/634067.634172.

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Kurniawan, Sri Hastuti, and R. Darin Ellis. "A structural equation modeling of internet bookmark organizations." In CHI '01 extended abstracts. ACM Press, 2001. http://dx.doi.org/10.1145/634164.634172.

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Reports on the topic "Structural Equation Modeling"

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Zyphur, Michael. Dynamic Structural Equation Modeling in Mplus. Instats Inc., 2023. http://dx.doi.org/10.61700/aypvl8azm5nlr469.

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This seminar will show you how to model longitudinal panel data as a multilevel model with contemporaneous and lagged effects. This type of dynamic SEM (DSEM) allows separating the stable and unstable components of observed variables, offering advantages such as including lagged effects to assess predictive forms of causality, as well as random slopes and variances to reflect individual differences in effects and volatility. The seminar covers this with hands-on examples that you can apply in your research. An official Instats certificate of completion is provided and the seminar offers 2 ECTS
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Grimm, Kevin. Structural Equation Modeling 3: Advanced Topics. Instats Inc., 2023. http://dx.doi.org/10.61700/q2yiy4jg3ydhz469.

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The 'Structural Equation Modeling 3: Advanced Applications' seminar, led by Professor Kevin Grimm, offers a comprehensive understanding of SEM, a crucial tool in academic research for examining complex relationships among variables. Participants will gain hands-on experience with SEM software, learn to interpret and report SEM results, and develop robust models for predicting outcomes, with an official Instats certificate of completion provided at the end. An official Instats certificate of completion and 2 ECTS Equivalent points are provided at the conclusion of the seminar
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Dormann, Christian. Introduction to Continuous Time Structural Equation Modeling (CTSEM). Instats Inc., 2023. http://dx.doi.org/10.61700/kwigtxevhohxk469.

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This seminar introduces the use of Continuous Time Structural Equation Modeling (CTSEM) to study phenomena over time in the social and health sciences. Day 1 topics include the required conceptual background, mathematical foundations, as well as examples to illustrate the concepts. On Day 2, the R package [b]ctsem [/b]is introduced, with hands-on coverage of topics including data preparation, model setup, parameter estimation, and interpretation of results. Day 3 topics include random intercept modelling (aka., within-person analysis), moderator analysis, and an outlook to Continuous Time Meta
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Chen, Bryant, and Judea Pearl. Graphical Tools for Linear Structural Equation Modeling. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada609131.

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Pearl, Judea. The Causal Foundations of Structural Equation Modeling. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada557445.

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Grimm, Kevin. Structural Equation Modeling 1: Foundations & Programs. Instats Inc., 2023. http://dx.doi.org/10.61700/8eni7h9r63sa3469.

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'Structural Equation Modeling I: Foundations &amp; Programs' is an on-demand workshop led by professor Kevin Grimm, providing a comprehensive review of covariance analysis, regression, covariance expectations, path diagrams, SEM notation, and SEM programs. Participants will gain a solid understanding of SEM and be prepared to specify, estimate, and interpret SEMs. An official Instats certificate of completion and 1 ECTS Equivalent point are provided at the conclusion of the seminar
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Craiger, J. P., R. J. Weiss, A. Butler, D. Goodman, and Gerry L. Wilcove. Navy Quality of Life Survey: Structural Equation Modeling. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada329867.

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Grimm, Kevin. Structural Equation Modeling 2: Path Analysis & CFA. Instats Inc., 2023. http://dx.doi.org/10.61700/mcwks6h8ypzib469.

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The 'Structural Equation Modeling 2: Path Analysis &amp; CFA' seminar, led by Kevin Grimm, offers a comprehensive understanding of SEM, a crucial tool in academic research for testing complex relationships between variables. Participants will gain proficiency in using Mplus, lavaan, and LISREL, and will leave with the ability to specify and estimated SEMs with their own data, interpret and report SEM results, and handle complex statistical analyses. An official Instats certificate of completion and 2 ECTS Equivalent points are provided at the conclusion of the seminar
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Dormann, Christian. Introduction to Continuous Time Structural Equation Modeling (CTSEM) + 1 Free Seminar. Instats Inc., 2022. http://dx.doi.org/10.61700/am2g78fjl1gx5469.

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This seminar introduces the use of Continuous Time Structural Equation Modeling (CTSEM) to study phenomena over time in the social and health sciences. Day 1 topics include the required conceptual background, mathematical foundations, as well as examples to illustrate the concepts. On Day 2, the R package [b]ctsem [/b]is introduced, with hands-on coverage of topics including data preparation, model setup, parameter estimation, and interpretation of results. Day 3 topics include random intercept modelling (aka., within-person analysis), moderator analysis, and an outlook to Continuous Time Meta
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Weijters, Bert. Analyzing Experimental Data in Structural Equation Models. Instats Inc., 2023. http://dx.doi.org/10.61700/zclk0a8vgkfaa706.

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This two-day workshop, 'Analyzing Experimental Data using Structural Equation Modeling', led by Bert Weijters from Ghent University, provides a comprehensive understanding of SEM and its applications in research, with a focus on using Mplus software for SEM analysis. Ideal for PhD students, professors, and professional researchers in Psychology, Education, Management, and Marketing, the seminar offers practical experience in data analysis and an official Instats certificate of completion, with ECTS Equivalent points for European students.
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