During the POLPAN Seminar on June 11th Professor Zbigniew Sawiński (Institute of Philosophy and Sociology, Polish Academy of Sciences) presented a talk entitled ‘New challenges in processing and disseminating The Polish Panel Survey, POLPAN.’
The longitudinal nature of the POLPAN study results in increasing data complexity. Every five years since 1988, several hundred variables are added to the data file. After including information from the 2018 wave to the extant file spanning 1988-2013, the number of variables will exceed 5,000. Managing the POLPAN data requires dedicated methods, which need systematic updates. In this presentation, I will focus on three problems which, in my opinion, require rethinking and improving solutions we used thus far.
The first problem relates to the complexity of the POLPAN data, which may make it difficult for some users to find what they are interested in. Part of the difficulty in understanding the POLPAN data structure results from the variable arrangement in the current layout of the POLPAN data files. Variables are presented according to the order of questions in the questionnaire, which is in line with common standards for data files containing results of questionnaire surveys. However, in POLPAN this makes it difficult to understand which variables reoccur across different waves because each wave has its own internal order of variables. A promising solution which can make the POLPAN data more user friendly is to create a new ordering of variables by grouping them according to research topics, and inside topics according to waves. The new solution would be an option for those POLPAN users who prefer to search data according to research problems.
The second problem concerns harmonizing the data across different POLPAN waves. Harmonization requires documentation that includes the justification and description of all transformations made when creating target variables. The format of such documentation will be proposed as a meta-data file on conversion of source variables into target variables. In preparing this meta-data file the intention is to follow DDI standards.
The last problem concerns disseminating the data in a career format. Since 2014, the POLPAN project has a unique data processing platform, the CONVERTER application, which allows users to generate data on job histories in three metrics: calendar year, age of the respondent, and the number of years of work career. I will discuss the possibility of (a) extending the CONVERTER application to other aspects of respondents’ life ‘careers’, including educational and family history, and (b) introducing ‘month’ as an additional metric.
Sawiński, Z. (2016). Occupational Careers and Job Interruptions: On Methodological Issues of Constructing Long Trajectories, International Journal of Sociology, 46:4, 244-263, DOI: 10.1080/00207659.2016.1246290