Methodology of longitudinal studies in social sciences 1600-SZD-ID-MBP
The aim of the course is to familiarize the participants with methodology of longitudinal studies within structural equation (SEM) framework and within other frameworks (such as e.g., GEE analysis). More specifically, the following topics will be discussed during the course, starting with relatively simple ones and progressing towards more advanced ones: I. Cross-sectional studies vs. longitudinal studiesII. Types of longitudinal studies III. The measurement model. The longitudinal CFA model. IV. Longitudinal panel model V. Multigroup longitudinal SEM VI. Longitudinal growth model VII. Moderation and mediation in longitudinal framework VIII. Panel data analysis in R (if enough time) IX. Additional approaches to longitudinal data (GLM, ARIMA, GEE, LMM, GLMM). Additional “small topics” such as e.g., attrition analysis, sensitivity analysis. The course constitutes continuation of “Structural equation modeling”.
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Term 2024Z:
The aim of the course is to familiarize the participants with methodology of longitudinal studies within structural equation (SEM) framework and within other frameworks (such as e.g., GEE analysis). More specifically, the following topics will be discussed during the course, starting with relatively simple ones and progressing towards more advanced ones: I. Cross-sectional studies vs. longitudinal studiesII. Types of longitudinal studies III. The measurement model. The longitudinal CFA model. IV. Longitudinal panel model V. Multigroup longitudinal SEM VI. Longitudinal growth model VII. Moderation and mediation in longitudinal framework VIII. Panel data analysis in R (if enough time) IX. Additional approaches to longitudinal data (GLM, ARIMA, GEE, LMM, GLMM). Additional “small topics” such as e.g., attrition analysis, sensitivity analysis. The course constitutes continuation of “Structural equation modeling”. |
Course coordinators
Learning outcomes
Knowledge | The graduate knows and understands:
WG_02 - the main development trends in the disciplines of the social sciences in which the education is provided
Skills | The graduate is able to:
UW_01 – make use of knowledge from various fields of science, in particular the social sciences in order to creatively identify, formulate and innovatively solve complex problems or perform tasks of a research nature, and in particular to: define the purpose and object of scientific research in the field of the social sciences, formulate a research hypothesis; develop research methods, techniques and tools and apply them creatively; make inferences based on scientific findings
UK_04 - participating in scientific discourse in the field of the social sciences
Social competences | The graduate is ready to
KK_01 - critically evaluating achievements within a given scientific discipline in the field of the social sciences
And others: As a result of attending the proposed course, PhD students will be able to :•understand basic ideas behind methodology of longitudinal studies •distinguish between various types of longitudinal models and different ways of analyzing longitudinal models •being able to apply an adequate technique to analyze longitudinal data within given research problem •analyze longitudinal data within the SEM framework using statistical software (SPSS, Amos, R) •evaluate and describe the results in the impacted alike publication form
Assessment criteria
Description of requirements related to participation in classes, including the
permitted number of explained absences: • requirements at the entrance: knowledge of English, basic idea of statistical software (SPSS, Amos, and R) highly recommended •Permitted number of explained absences: 1 meeting out of 5
Principles for passing the classes and the subject (including resit session): active participation in the course; preparation of one larger project at the end of the course
Methods for the verification of learning outcomes: preparation of a one larger project which will require data analysis using the techniques taught during the course with adequate description of the results in the form of a results chapter (such the ones which are usually prepared for scientific data dissemination in journals with impact factor)
Evaluation criteria: adequacy of choice of structural equation model to the given longitudinal data, correctness of statistical analysis, correctness of results interpretation, clarity and quality of results description
Practical placement
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Bibliography
(1) Little, T. D. (2013). Longitudinal structural equation modeling. Guilford press.o
(2) Selig, J. P., & Little, T. D. (2012). Autoregressive and cross-lagged panel analysis for longitudinal data.
(3) Garson, G. D. (2013). Generalized Linear Models & Generalized Estimating Equations. Statistical Associates Publishers: Asheboro.
(4) Garson, G. D. (2013). Longitudinal Analysis. Statistical Associates Publishers: Asheboro. Dodatkowa literatura może zostać zaproponowana tuż przed rozpoczęciem kursu.
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Term 2024Z:
(1) Little, T. D. (2013). Longitudinal structural equation modeling. Guilford press.o |
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: