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: 9 June 2025

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Journal articles on the topic "Structural Equation Modeling"

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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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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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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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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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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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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 enough to fit more complex models; e.g., those with higher-order latent variables and direct effects of observed variables. It can also fit a model to more than one sample simultaneously. Other relevant topics are also discussed, including data transformations, missing data, metric matrices, robust estimation, and efficient estimation. Examples are given to illustrate the proposed method.
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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 research study tests a team conceptual model derived by Turner (2013) addressing attitudes toward knowledge sharing. Structural equation modeling was conducted to test a portion of Turner’s (2013) team conceptual model. The tested model included the independent variables of psychological safety, team conflict, team cohesion, and transactive memory systems. The dependent variable for the dissertation was knowledge sharing.
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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 carried out to investigate alternative fit measures and to confirm whether the traditional measures are practically blind to nonlinearity in the data. The results of the simulation provide conclusive evidence that fit statistics and fit indices based on the chi-square distribution or the residual covariance matrix are entirely insensitive to nonlinearity. The posterior predictive p-value was also insensitive to nonlinearity. Only fit measures based on the structural residuals (i.e., HFI and R-squared) showed any sensitivity to nonlinearity. Of these, the R-squared was the only reliable measure of nonlinear model misspecification. This thesis shows that an effective strategy for determining whether a nonlinear model is preferable to a linear one involves using the R-squared to compare models that have been fit to the same data. An R-squared that is much larger for the nonlinear model than the linear model suggests that the linear model may be less desirable than the nonlinear model. The proposed method is intended to be supplementary to substantive theory. It is argued that any dependence on fit indices or fit statistics that places these measures on a higher pedestal than substantive theory will invariably lead to blindness on the part of the researcher. In other words, unwavering adherence to goodness-of-fit measures limits the researcher<'>s vision to what the measures themselves can detect.
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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 children’s self-control using provided questionnaires. The primary researcher assessed the classroom physical environment and determined the reliability for the Children’s Physical Environments Rating Scale (CPERS). The structural equation modeling (SEM) statistical approach was employed to analyze the data. The results showed that the level of preschoolers’ engagement in classroom learning activities was directly associated with their self-control (β = .37, p < .001). Preschoolers’ engagement was not indirectly associated with children’s self-control through teacher-child interaction. The level of engagement of preschoolers in classroom learning activities did not directly associate with teacher-child relationships but was indirectly associated with the teacher-child relationship through children’s self-control (β = .20 , p < .001 ). When separating the two subscales of teacher-child relationship (closeness and conflict), teacher-child closeness was directly associated with children’s engagement level (β = .22, p = .003). In addition, teacher-child conflict was both directly (β = - .17, p = .022) and indirectly associated with child’s engagement level through children’s self-control (β = .26, p < .001). Classroom physical environment did not directly predict the level of engagement of preschoolers, while indirect relationships were found between the classroom physical environment scores and the level of engagement of preschoolers, and the relationship was mediated by children’s self-control (β = .09, p = .050). The study offers implications for teachers as they work on enhancing children’s engagement level in their learning activities. Future research suggested by this study include further exploration of intervention strategies to increase children’s active engagement. Increasing sample size and obtaining reliability of the measures on children’s behaviors would also improve the rigor of the study.
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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. This purpose is valuable because little is known about the relationship between Can-Do Questionnaire and affective variables investigated in this study. The final purpose of this study was to develop Can-Do Questionnaires as an internal measure for a university writing class. The participants of this study were 204 university students studying in two private universities in Tokyo, Japan. The first instrument was the writing section of the Eiken Can-Do Questionnaire; this questionnaire served as the outside measure in this study. The second, six out of nine essays written by the students were assessed as a measure of their writing ability in English. The Affective Orientation Questionnaire was administered to measure ten Affective Orientations. The questionnaire and essay data were analyzed using the Rasch rating scale. All of the participants completed the Background Questionnaire and Affective Orientation Questionnaire in April 2010 and 2011 and completed the writing section of the Eiken Can-do Questionnaire in April, July, and December 2010 and 2011. six writing assignments were produced by 179 out of the 204 participants wrote during the 2010 and 2011 academic year, and the relationships among the variables were analyzed using Structural Equation Modeling. The results indicated that the use of the Eiken Can-Do Questionnaires as the proficiency level measure was appropriate for this group of university students. The Eiken Can-Do Questionnaires were predictors of Motivation and L2 Self-Confidence. Motivation was a predictor of WTC in L2 Writing. Therefore, it should be noted that the Eiken Can-Do Questionnaires had an indirect effect with WTC in L2 Writing. The result implies that through having Eiken Can-Do questionnaires and Classroom Can-do Questionnaires to achieve their future goals, their English classes and their future learning objectives were connected.　 It is necessary to provide students with adequate practice and guidance in using the Eiken Can-Do Questionnaires in order to promote a deeper understanding of their purposes and uses.<br>Temple University--Theses
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Books on the topic "Structural Equation Modeling"

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

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

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

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Verma, J. P., and Priyam Verma. Understanding Structural Equation Modeling. Cham: 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. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119579038.

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Maruyama, Geoffrey. Basics of structural equation modeling. Thousand Oaks, Calif: Sage Publications, 1997.

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Avkiran, Necmi K., and Christian M. Ringle, eds. Partial Least Squares Structural Equation Modeling. Cham: 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. Cham: 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. New York, NY: 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. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: 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, 2409. New York, NY: 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, 1–2. Cham: 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, 3331–32. Cham: 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, 165–77. Singapore: 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, 495–512. Totowa, NJ: 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, 335–60. Cham: 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, 1452–54. Boston, MA: 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, 63–78. Boca Raton: 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, 162–81. Boca Raton: 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, 183–201. 3rd ed. New York: 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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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), 195–200. 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. New York, New York, USA: 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. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/634164.634172.

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Wu, Xiao, Kathryn Sharpe, Tianyi Zhang, Hongyan Chen, Wei Zhu, Ellen Li, Safiyh Taghavi, and Daniel Van Der Lelie. "Comparative genetic pathway analysis using structural equation Modeling." In 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2011. http://dx.doi.org/10.1109/iccabs.2011.5729878.

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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 Equivalent points for European PhD students.
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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-Analysis (CoTiMA) using results from multiple studies. An official Instats certificate of completion is provided at the conclusion of the seminar. The seminar offers 2 ECTS Equivalent points for European PhD students.
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Chen, Bryant, and Judea Pearl. Graphical Tools for Linear Structural Equation Modeling. Fort Belvoir, VA: Defense Technical Information Center, June 2014. http://dx.doi.org/10.21236/ada609131.

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Pearl, Judea. The Causal Foundations of Structural Equation Modeling. Fort Belvoir, VA: Defense Technical Information Center, February 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. Fort Belvoir, VA: Defense Technical Information Center, September 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-Analysis (CoTiMA) using results from multiple studies. To frame the seminar content, a free background seminar is provided when enrolling: Longitudinal SEM in R. An official Instats certificate of completion is provided at the conclusion of the seminar. The seminar offers 2 ECTS Equivalent points for European PhD students.
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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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