The potential of statistical matching for the analysis of wider benefits of learning in later life

Abstract

It is challenging to investigate wider benefits of adult learning, especially in later life, due to limited data on educational activities and non-monetary returns in large, longitudinal surveys. Statistical matching provides an approach to exploit the potential of existing data by combining data sources with complementary features based on shared information. The paper describes the matching of two data sources (German Ageing Survey and Study of Educational Attainment and Interests of Older People) in order to examine the effects of educational participation on well-being in later life. We emphasize the matching procedure and how to identify the best-matched dataset. Based on matched data, effects of educational activities on life satisfaction are examined in later life. The

Perceived benefits from non-formal digital training sessions in later life: views of older adult learners, peer tutors, and teachers

“…When considering the benefits of a teaching profession in various fields, topics such as social status, employability, and monetary returns may arise (cf. Wiest et al, 2019). Apart from these, many teachers rely on intrinsic work motivation and desirable consequences or benefits to stay in a teaching career and to prevent work attrition or a crossover career.…”

“…According to earlier studies, older adults are motivated in learning to use digital devices to stay up to date in the current society, to enjoy learning in general (Sayago & Blat, 2009), and to increase their life satisfaction by making daily life easier, such as online banking, shopping online, and communicating with others (Sayago & Blat, 2009). Wiest et al (2019) have demonstrated how challenging it is to investigate the wider benefits of adult learning, especially in later life because of limited data on educational activities and nonmonetary returns in large, longitudinal surveys. Background information, such as age, gender, ethnicity, educational background, and current educational activities, affects the intertwined entity of benefits that individuals perceive they gain from a learning session (Schuller, 2017).…”

“…Background information, such as age, gender, ethnicity, educational background, and current educational activities, affects the intertwined entity of benefits that individuals perceive they gain from a learning session (Schuller, 2017). Also, learning formats and learning contexts need to be differentiated when researching the benefits of learning in later life (Wiest et al, 2019).…”

İstatistiksel Eşleme Metodolojisi ve Sağlıkta Kullanımı İle İlgili Ampirik Bir Değerlendirme

“…Eşleme kalitesi büyük ölçüde, -hedef popülasyonun doğru tanımı, -verilerin uyumu, -eşleşen değişkenlerin seçimi, -uygun bir eşleme yönteminin seçimi, gibi faktörlere bağlıdır. Ayrıca alıcı ve donör veri setlerinin her ikisinde ortak olan değişkenlerin miktarı, kalitesi ve bu değişkenlerin ortak olmayan değişkenler ile ne kadar ilişkili olduğu yüksek kaliteli bir eşleşme için çok önemlidir (18). Literatürde, eşlemede kullanılacak değişkenleri seçmek için iki ana kriter vurgulanmaktadır.…”