Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
Session Overview
Session
JS_RN01_RN21_10: Using Methods of Quantitative Analysis in Ageing Research
Time:
Friday, 23/Aug/2019:
2:00pm - 3:30pm

Session Chair: Jolanta Perek-Białas, Jagiellonian University, Cracow and Warsaw School of Economics
Location: BS.3.17
Manchester Metropolitan University Building: Business School, Third Floor, North Atrium Oxford Road

Show help for 'Increase or decrease the abstract text size'
Presentations

Challenges Of Measuring Age Discrimination In The Labour Market Via Surveys In Central And Eastern European Countries

Maria Varlamova1,3, Jolanta Perek-Białas1,2

1Institute of Sociology, Jagiellonian University, Poland; 2Warsaw School of Economics, Poland; 3Phd Student at Marie Skłodowska-Curie Actions ITN EuroAgeism

Age discrimination, which is increasingly revealed in the ageing labour market, continues to be still an under-studied area of social science mostly due to lack of adequate data. Especially few scientific studies are devoted to the quantitative research in the countries of Central and Eastern Europe.

The paper will present the way in which ageism in the labour market could be currently analysed for this part of Europe (via available secondary data and surveys). It will also outline how the employers surveys could contribute to better understanding of the many facets of ageism in the labour market. And as it is problematic to obtain large scale data, it will be shown, how the concrete techniques such as Bayesian Structural Equation Modelling could be used to overcome the problems of small samples. In this study, the employers surveys from Poland (2009, 2010 and 2017) will be used to show the possibility and the way for such surveys to be helpful in the detection of ageism in the labour market.

The paper is of interest to both the research network of sociology of ageing and of quantitative methods. The conclusion will present a discussion of other available data (besides of surveys) that could be used in such studies.

Also some ethical concerns need to be raised to update the research community on the risks and limitations of using certain quantitative approaches while researching this subject.



Access to Training in Older Age: Using Bayesian Hierarchical Modelling in a Comparative Lifecourse Perspective

Konrad Turek, Kene Henkens

Netherlands Interdisciplinary Demographic Institute, Netherlands, The

Ageing policies give high priority to lifelong learning (LLL) in older age, yet the effects of public investments are unsatisfying in many aspects. One of the most challenging problems is the reproduction or reinforcement of the existing inequalities through unequal access to education. As an indicator of the strength of barriers to enter training, accessibility plays a key role for the efficiency of LLL policies. A lifecourse approach using longitudinal data provides a promising perspective to study determinants of accumulation mechanism. It allows us to see path dependency (i.e. tendency to continue a particular activity or state) in individual trajectories and one of its elements – accessibility. When applied to comparative analysis, longitudinal patterns of behaviours can reveal essential aspects of broader structures or systems.

We analyse path dependency and accessibility of training in a comparative lifecourse perspective. First, we ask whether training participation is path-dependent and analyse how previous attendance affects further attendance. Then we shift to a comparative perspective and ask about the differences in access to training between countries and factors that can explain them. The data come from Survey of Health, Ageing and Retirement in Europe (SHARE). We observe individuals in twelve European countries over five years. As we show, accessibility of training differs between countries and can be related to macro-characteristics such as economic performance, demand for human capital, labour market, welfare regime, active ageing climate, and training systems. The study design requires a complex multilevel modelling with random slopes for lagged dependent variable and cross-level interaction that could be handled only by Hierarchical Bayes Models.



How Does (Old) Age Affect Data Quality? Findings From the German Ageing Survey (DEAS)

Nicole Hameister, Daniela Klaus, Heribert Engstler

German Centre of Gerontology, Germany

One central dimension in exploring the potential and limitations of quantitative ageing research is the quality of existing survey data. Using the German Ageing Survey (DEAS), we will put forward findings for the interrelation of age and several methodological issues.

DEAS is a nationwide representative cross-sectional and longitudinal survey of the German population aged over 40 and had started in 1996. Participants are questioned in detail on their objective and subjective living situation. Also, tests of cognitive capability and physical functioning are applied. Information is gathered via a CAPI and a self-administered PAPI and CAWI (since 2017) questionnaire.

We will concentrate on two main issues: First, we will present findings on whether and how age correlates with attrition, non-response, tendencies for acquiescence or satisficing, as well as cognitive abilities and the factoral structure of several psychological instruments and scales.

Second, we analyse the longitudinal accuracy and plausibility of retrospective biographical information: in each survey year, respondents are asked for their fertility biography (the number of children they have born, in which year they were born, and whether these are still alive). This repeated recording allows to compare deviations in the recollection of major biographic events and to analyse whether intra-personal inconsistency rises with age.

We will conclude with an evaluation of data quality in older (DEAS) respondents and provide some practical suggestions for survey designers and data users in quantitative ageing research.



 
Contact and Legal Notice · Contact Address:
Privacy Statement · Conference: ESA 2019
Conference Software - ConfTool Pro 2.6.132+TC+CC
© 2001 - 2020 by Dr. H. Weinreich, Hamburg, Germany