FReDA Panel Studyπ’ New data released!
The fourth wave of #FReDA is now available, opening up new opportunities for longitudinal research.
The data is freely accessible via @GESIS https://search.gesis.org/research_data/ZA7777
Python Job SupportBasics of Cross-Sectional Regression Data Analysis in EViews with Diagnostics for Beginners
regression #econometrics #paneldata #crosssectional #regressionanalysis #eviews #dataanalysis Welcome to Our YouTube ... source
GESISπ’ New data released!
The complete third #FReDA wave is now freely available via @GESIS β perfect for longitudinal studies.
π Get access: https://search.gesis.org/research_data/ZA7777
#SocialScience #PanelData #LongitudinalData
The complete third #FReDA wave is now freely available via @GESIS β perfect for longitudinal studies.
π Get access: https://search.gesis.org/research_data/ZA7777
#SocialScience #PanelData #LongitudinalData
#PanelData #FredaAutumnSchool
π’ Join the 3-day #FReDA Autumn School at JGUMainz from September 10β12, 2025!
Enhance your #research skills by learning how to work with the FReDA dataset - a key resource for #family #demography studies.
ποΈ Date: September 10β12, 2025
π Location: Johannes Gutenberg-UniversitΓ€t Mainz
π More information: https://lnkd.in/d7nH7T7D
πΌ Free registration until July 15, 2025: https://lnkd.in/eCCdaX5x
π¬ Questions? Contact us at freda[at]bib.bund.de
π’ New data released!
The complete third #FReDA wave is now freely available via @GESIS β perfect for longitudinal studies.
π Get access: https://search.gesis.org/research_data/ZA7777
Dennis Alexis Valin DittrichOn the (Mis)Use of Machine Learning with Panel Data https://arxiv.org/pdf/2411.09218
"Ignoring the cross-sectional & longitudinal structure can lead to hard-to-detect data leakage, inflated out-of-sample performance, & an inadvertent overestimation of the real-world usefulness and applicability of #ML models
β¦practical guidelines and best practices in #panelData environments, emphasizing the need to define ex ante the primary goal of the analysis"
#statistics
"Ignoring the cross-sectional & longitudinal structure can lead to hard-to-detect data leakage, inflated out-of-sample performance, & an inadvertent overestimation of the real-world usefulness and applicability of #ML models
β¦practical guidelines and best practices in #panelData environments, emphasizing the need to define ex ante the primary goal of the analysis"
#statistics
Dr. Carina CornesseWe have invested a lot of β€οΈ & πͺinto setting up our new collaborative #paneldata #infrastructure, so I'm very happy to announce the release of the 1st @fgz_risc & #SOEP @diw_berlin Social #Cohesion Panel #SUF dataset, available in DE & EN:
Maureen A. EgerHow did academic stress related to the #pandemic affect university studentsβ satisfaction with life and optimism about the future? Andrea Bohman, Mikael Hjerm, Jeffrey Mitchell and I collected #paneldata to find out. Results, years in the making, are #openaccess and available at: https://doi.org/10.1080/21568235.2023.2209707 #highered #highereducation
CorrelAid#Data4GoodMonday
This week: What are panel data?
Read in π§΅!
#data4good #datascience #dataforgood #data4goodmonday #panel #paneldata
NoΓ©mi K. Schuurman(Interested in) Working with the random-intercept Cross-lagged Panel Model and unsure what sample size to use?
Jeroen Mulder fixed it for you:
Mulder, J.D., (2022). Power analysis for the random intercept cross-lagged panel model using the powRICLPM R-package. Structural Equation Modeling: A Multidisciplinary Journal.
https://jeroendmulder.github.io/powRICLPM/
Other questions about the RI-CLPM?
https://jeroendmulder.github.io/RI-CLPM/index.html
#SEM #longitudinal #PanelData #RICLPM #CLPM #Dynamics #power #SampleSize
Power analysis for the RI-CLPM
Perform user-friendly power analyses for the bivariate random intercept cross-lagged panel model (RI-CLPM). The strategy as proposed by Mulder (2022) <doi:10.1080/10705511.2022.2122467> is implemented. Extended power analysis options include the use of bounded estimation, inclusion of measurement error in the data generating model and estimation model (i.e., the stable trait autoregressive trait state, STARTS, model), imposing various constraints over time on the parameters of the estimation model, among others.