Pre-Conference Workshops 2022

Die PIAAC Workshops richten sich an Forschende aus verschiedenen Disziplinen, die an der Arbeit mit PIAAC-Daten interessiert sind oder bereits damit arbeiten. Es wird erwartet, dass die Teilnehmenden über gute empirische Kenntnisse und Erfahrungen mit der jeweiligen Statistiksoftware verfügen. Die Workshops umfassen Vorträge und praktische Sitzungen und sind wie folgt geplant (a) theoretischer und methodischer Input der Dozenten (siehe Beschreibung der Inhalte weiter unten); (b) Möglichkeit der Präsentation eigener Forschung oder Forschungsideen mit PIAAC-Daten (Optional); (c) Diskussion der im Workshop skizzierten Fragen sowie spezifisches Feedback der Dozenten.

Es werden keine Teilnahmegebühren erhoben. Die Workshops sind auf maximal 15 Teilnehmende beschränkt und werden online durchgeführt. Bitte senden Sie Ihre Bewerbung mit dem jeweiligen Betreff "Workshop 2022 - Analyzing PIAAC data with SEM" ODER "Workshop 2022 - Analyzing PIAAC data with R" bis zum 15.03.2022 an das Forschungsdatenzentrum PIAAC (fdz-piaac(at)gesis(dot)org).

Workshop: Analyzing PIAAC data with structural equation modeling in Mplus
Dozent: Ronny Scherer (Centre for Educational Measurement at the University of Oslo) Datum: March 22-23, 2022 (Time: 4 am – 9 am EST/ Time: 9 am – 2 pm CET) Ort: Virtual (Zoom) Abstract: Structural equation modeling (SEM) represents a statistical approach to disentangle the relationships among latent and/or manifest variables, across groups, over time, and at different analytical levels. The potential of SEM has been recognized in many areas, including educational sciences, sociology, psychology, and business. This workshop provides an introduction to the principles and procedures of basic and more advanced SEM in the context of the international large-scale assessment PIAAC. Specifically, the following topics are covered: (a) Principles of structural equation modeling (model specification, identification, estimation, and evaluation), (b) Measurement models and confirmatory factor analysis, (c) Measurement invariance testing with few and many groups (including multi-group CFA, multilevel CFA, and the alignment method), and (d) Structural regression and indirect effects models (including multi-group and multilevel SEM). Participants can also present their own research or research ideas using PIAAC data and receive feedback on how to improve the analysis (optional). Daten: PIAAC Public Use Files Software: SPSS and Mplus Agenda: Agenda (266 kB) Literaturempfehlung: Maehler, D. & Rammstedt, B. (2020). Large-scale cognitive assessment: Analyzing PIAAC data. Series: Methodology of Educational Measurement and Assessment (MEMA). Springer: Cham. https://link.springer.com/book/10.1007/978-3-030-47515-4

Workshop: Analyzing PIAAC data with structural equation modeling in Mplus

Dozent: Ronny Scherer (Centre for Educational Measurement at the University of Oslo)

Datum: March 22-23, 2022 (Time: 4 am – 9 am EST/ Time: 9 am – 2 pm CET)

Ort: Virtual (Zoom)

Abstract: Structural equation modeling (SEM) represents a statistical approach to disentangle the relationships among latent and/or manifest variables, across groups, over time, and at different analytical levels. The potential of SEM has been recognized in many areas, including educational sciences, sociology, psychology, and business. This workshop provides an introduction to the principles and procedures of basic and more advanced SEM in the context of the international large-scale assessment PIAAC. Specifically, the following topics are covered: (a) Principles of structural equation modeling (model specification, identification, estimation, and evaluation), (b) Measurement models and confirmatory factor analysis, (c) Measurement invariance testing with few and many groups (including multi-group CFA, multilevel CFA, and the alignment method), and (d) Structural regression and indirect effects models (including multi-group and multilevel SEM). Participants can also present their own research or research ideas using PIAAC data and receive feedback on how to improve the analysis (optional).

Daten: PIAAC Public Use Files

Software: SPSS and Mplus

Agenda: Agenda (266 kB)

Literaturempfehlung: Maehler, D. & Rammstedt, B. (2020). Large-scale cognitive assessment: Analyzing PIAAC data. Series: Methodology of Educational Measurement and Assessment (MEMA). Springer: Cham. https://link.springer.com/book/10.1007/978-3-030-47515-4

Workshop: Analyzing PIAAC data using the R EdSurvey package
Dozenten: Paul Bailey¹, Ting Zhang¹, Saida Mamedova¹, Emily Pawlowski¹, Emmanuel Sikali², Michael Lee¹, Eric Buehler¹, & Sinan Yavuz¹ (¹American Institutes for Research; ²National Center for Education Statistics) Datum: March 22, 2022 (Time: 10 am – 2 pm EST/ 3 pm – 7 pm CET) Ort: Virtual (Zoom) Abstract: This course will provide an overview of the PIAAC study and guidance in data analysis strategies, including the selection and use of appropriate plausible values, sampling weights, and variance estimation procedures. The course will train participants in the analysis of PIAAC data files using the R package EdSurvey, which was developed specifically to analyze large-scale assessment data with complex psychometric and sampling designs. Participants will learn how to perform data processing and manipulation, produce descriptive statistics; generate cross-tabulations and plausible value means; and perform linear and logistic regressions. This course is designed for researchers and policy analysts across various sectors and organizations who are interested in learning how to analyze PIAAC data. Participants should have at least a basic knowledge of R software (e.g., have taken entry-level training in R programming), as well as of statistical techniques, including statistical inference and multiple regression. A working knowledge of plausible values and sampling theory would be helpful but is not required. Participants should bring a computer preloaded with the latest version of the R and RStudio software so that they can practice the analytical techniques covered in the lesson plan. Daten: PIAAC Public Use Files Software: R Agenda: Agenda (145 kB) Literaturempfehlung: Maehler, D. & Rammstedt, B. (2020). Large-scale cognitive assessment: Analyzing PIAAC data. Series: Methodology of Educational Measurement and Assessment (MEMA). Springer: Cham. https://link.springer.com/book/10.1007/978-3-030-47515-4

Workshop: Analyzing PIAAC data using the R EdSurvey package

Dozenten: Paul Bailey¹, Ting Zhang¹, Saida Mamedova¹, Emily Pawlowski¹, Emmanuel Sikali², Michael Lee¹, Eric Buehler¹, & Sinan Yavuz¹ (¹American Institutes for Research; ²National Center for Education Statistics)

Datum: March 22, 2022 (Time: 10 am – 2 pm EST/ 3 pm – 7 pm CET)

Ort: Virtual (Zoom)

Abstract: This course will provide an overview of the PIAAC study and guidance in data analysis strategies, including the selection and use of appropriate plausible values, sampling weights, and variance estimation procedures. The course will train participants in the analysis of PIAAC data files using the R package EdSurvey, which was developed specifically to analyze large-scale assessment data with complex psychometric and sampling designs. Participants will learn how to

perform data processing and manipulation,
produce descriptive statistics;
generate cross-tabulations and plausible value means; and
perform linear and logistic regressions.

This course is designed for researchers and policy analysts across various sectors and organizations who are interested in learning how to analyze PIAAC data. Participants should have at least a basic knowledge of R software (e.g., have taken entry-level training in R programming), as well as of statistical techniques, including statistical inference and multiple regression. A working knowledge of plausible values and sampling theory would be helpful but is not required. Participants should bring a computer preloaded with the latest version of the R and RStudio software so that they can practice the analytical techniques covered in the lesson plan.

Daten: PIAAC Public Use Files

Software: R

Agenda: Agenda (145 kB)

For any questions please contact the RDC PIAAC (fdz-piaac@gesis.org).