<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Good graphical presentation of data is useful during the whole analysis process from the first glimpse into the data to the model fitting and presentation of results. The most popular way of longitudinal data presentation are separate (for each wave, in cross-sectional dimension) comparisons of figures. However, plotting the data over time is useful in suggesting appropriate modeling techniques to deal with the heterogeneity observed in the trajectories. The main aim of this paper is to present the changing perceptions of the financial situation in Poland using different graphical tools for the heterogonous discrete longitudinal data sets and present demographics features for those changes. We will focus on the most important features of the categorical longitudinal data – category sequences and their graphical presentation. We aim to characterize the analyzed sequences on the basis of unidimensional indicators and composite complexity measures, as well as using mainly <monospace>TraMineR</monospace> [<xref ref-type="bibr" rid="j_eada.2019.3.03_ref_006_w2aab3b7b2b1b6b1ab1ab6Aa">Gabadinho et al. 2017</xref>] package of R.</p></abstract>

Good graphical presentation of data is useful during the whole analysis process from the first glimpse into the data to the model fitting and presentation of results. The most popular way of longitudinal data presentation are separate (for each wave, in cross-sectional dimension) comparisons of figures. However, plotting the data over time is useful in suggesting appropriate modeling techniques to deal with the heterogeneity observed in the trajectories. The main aim of this paper is to present the changing perceptions of the financial situation in Poland using different graphical tools for the heterogonous discrete longitudinal data sets and present demographics features for those changes. We will focus on the most important features of the categorical longitudinal data – category sequences and their graphical presentation. We aim to characterize the analyzed sequences on the basis of unidimensional indicators and composite complexity measures, as well as using mainly TraMineR [Gabadinho et al. 2017] package of R.

Graphical Tools of Discrete Longitudinal Data Presentation in R

Econometrics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Graphical Tools of Discrete Longitudinal Data Presentation in R"}

Good graphical presentation of data is useful during the whole analysis process from the first glimpse into the data to the model fitting and presentation of...

Graphical Tools of Discrete Longitudinal Data Presentation in R

Online veröffentlicht: 24. Sept. 2019

Seitenbereich: 26 - 39

DOI: https://doi.org/10.15611/eada.2019.3.03

Schlüsselwörter
longitudinal data, categorical sequences, sequence visualization

© 2019 Ewa Genge, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Graphical Tools of Discrete Longitudinal Data Presentation in R

Online veröffentlicht: 24. Sept. 2019

Seitenbereich: 26 - 39

DOI: https://doi.org/10.15611/eada.2019.3.03

Schlüsselwörterlongitudinal data, categorical sequences, sequence visualization

© 2019 Ewa Genge, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
longitudinal data, categorical sequences, sequence visualization