Search

In Europa il numero di anziani sta rapidamente aumentando. Questo aumento è ancora più consistente se ci si riferisce agli anziani immigrati. Tra il 2010 e il 2015, la presenza di non-nativi di 55 anni e più è aumentata del 50% in Paesi come Finlandia, Portogallo e Lussemburgo. In tale contesto, questioni riguardanti l’inclusione sociale degli anziani immigrati diventano sempre più cruciali. L’approccio del Capitale Sociale (CS) è ampiamente utilizzato negli studi riguardanti l’inclusione sociale. Tuttavia, non è stato altrettanto esplorato nella letteratura sui migranti anziani e futuri anziani. Inoltre, molti studi in questo campo sono condotti a livello nazionale o regionale, piuttosto che a livello europeo. Infine, il CS non possiede un’operativizzazione standard in sociologia o nelle scienze sociali. Queste caratteristiche fanno, della comparazione su questi temi, un’operazione difficile. Colmare queste lacune presenti in letteratura è l’obiettivo di questo studio. Obiettivo generale è quello di mostrare quale tipo di CS (bonding o bridging) consenta al migrante anziano o in fase di invecchiamento di avere una salute migliore ed un più alto livello di benessere. L’analisi è condotta separatamente per migranti provenienti da Paesi ad alto reddito (HIC) e per migranti provenienti da Paesi a medio e basso reddito (LMIC). In questo studio faccio riferimento ad un approccio micro al CS, seguendo la definizione di Van der Gaag and Snijder (2004). Con l’obiettivo di isolare l’effetto del luogo di provenienza, ho incluso nelle analisi anche i nativi anziani e futuri anziani. Lo studio è stato svolto a livello europeo, usando i dati della Survey of Health Ageing and Retirement in Europe (wave 6). Ho creato una misura di CS individuale che copre molte delle dimensioni identificate in letteratura (supporto, partecipazione, social network). Ho inserito termini di interazione all’interno dei miei modelli di regressione, in modo da tenere sotto controllo le diffe, The number of seniors in Europe is rapidly growing. This increase is even more important when focussing on older migrants. Between 2010 and 2015, non-native people over 55 years increased by 50% in countries like Finland, Portugal and Luxemburg. In this context, issues concerning older migrants’ social inclusion are becoming crucial. Social Capital’s (SC) approach is widely used in social inclusion studies of older people; while it has not been used as much in the literature on migrants’ seniors and future seniors. Furthermore, many studies on this field are conducted at national or regional level, rather than at European level. Finally, operationalization of SC is not standard in sociology. These characteristics make it difficult to compare different studies and to have a global view of the phenomenon in Europe. The aim of this study is to fill these gaps in the literature, showing what kind of SC (bonding or bridging) allows the seniors or aging migrant to maximise their health and well-being. The analysis is conducted separately for migrants from High-Income Countries (HIC) and from Low- and Middle-Income Countries (LMIC). Here I refer to a micro-level SC approach, following the definition of Van der Gaag and Snijder (2004). With the aim of isolating the effect given by the place of birth, a comparison with the native populations is performed. I perform this study at the European level using data from the Survey of Health Ageing and Retirement in Europe dataset (wave 6), and I created a measure of individual SC, which covers many of the dimensions identified in the literature (e.g. support, participation, social network). I performed regression models with interaction terms and, in order to check the macro level effects, I clustered countries by the expenditure (Purchasing Power Standard) on social protection of old age function and by Migrant Integration Policies Index (MIPEX). Findings show that those who make up the aging and older population in Europe have so

Questa tesi si colloca nell’ambito della “survey methodology” e indaga il concetto di qualità dei dati nelle indagini campionarie condotte sui membri dei panel online. Nel tempo tali indagini hanno implementato diverse tecniche per superare varie sfide. In particolare, i costi elevati, la necessità di ottenere dati in tempi brevi e i problemi di copertura della popolazione, hanno portato allo sviluppo dei panel online a partire dagli anni ‘90. I panel online sono gruppi di persone che, tramite iscrizione volontaria, hanno acconsentito a partecipare a indagini condotte via web in cambio di una ricompensa. Questi panel hanno il vantaggio di ridurre tempi e costi dedicati alla raccolta dati e di fornire una lista di membri da cui estrarre campioni di rispondenti. L’obiettivo principale di questa tesi è valutare la qualità dei dati raccolti attraverso un panel online non probabilistico italiano, confrontando le stime ottenute da un’indagine web condotta sui membri del panel con quelle di un’indagine ISTAT di tipo probabilistico che adotto come “gold standard”. In particolare mi focalizzo sulle distorsioni generate i) dalla mancata copertura Internet della popolazione italiana, ii) dal meccanismo di auto-selezione dei membri del panel e dei partecipanti all’indagine web, iii) dal fenomeno della non risposta (confronto rispondenti e non rispondenti all’indagine web), e iv) dall’errore di misurazione che può manifestarsi durante la compilazione del questionario (confronto delle stime campionarie con l’indagine di riferimento). Inoltre applico un sistema di pesi per eliminare le distorsioni. Utilizzo tre fonti di dati. La prima sono i dati di profilazione dei membri del panel, solitamente non forniti ai ricercatori, che uso per studiare: i) la rappresentatività del panel rispetto alla popolazione Internet e alla popolazione generale e ii) quella dei rispondenti all’indagine web rispetto ai membri del panel e iii) le differenze tra le caratteristiche di rispondenti e n, This thesis sits in the survey-methodology field and explores the challenging concept of data quality when using online panels as a sample source in the survey industry. Over time, sample surveys implemented different techniques to overcome various challenges. In particular, high costs, need for timely data delivery, and undercoverage (in the case of telephone surveys) are the main drivers of the development of online panels in survey research, from the 1990s on. Online panels are large pools of registered people who agreed to take part in web-based research in exchange for some form of incentive. They have the advantages of i) reducing both costs and time devoted to data collection and delivery, and ii) providing a sampling frame of registered individuals who consented to participate in surveys. The main objective of this thesis is to empirically assess the quality of primary data collected from an Italian non-probability online-panel survey, comparing the estimates obtained from this web survey to those obtained from a probability-based survey conducted by the Italian National Institute of Statistics (ISTAT), used as benchmarks. In particular, I focused on i) undercoverage and self-selection bias in different samples (i.e., the Internet population, panel members, and web survey respondents), ii) nonresponse bias, comparing the characteristics of web survey respondents to those of nonrespondents, iii) data quality, comparing the estimates obtained from the web survey to those obtained from a probability-based reference survey, and iv) a weighting adjustment to remove bias. I used three data sources. The first is data of all the members of the Italian non-probability online panel, recorded in the panel archive. Using this valuable source (because companies usually do not provide panellists’ data to their clients), I was able to study i) the panel members’ representativeness in comparison to both the Internet and the general population, ii) the web survey respondents

In the last 20 years, the Italian Ministry of Education, University and Research (MIUR) has enacted two major reforms and passed a number of parliamentary acts that have radically changed the Italian academic system, e.g., the role of the permanent researcher was abolished and different types of fixed-term researchers were introduced. So far, very little is known on the effects of these reforms on men and women's career progressions. Using a unique dataset that includes information on the whole Italian academic staff, this paper aims to provide an overview of the changes that the reforms introduced in the academic system and compare male and female scholars' conditions within the university. The focus of the paper is on the young cohorts of academics who work as researchers. The main finding is that, in a context in which the selection and recruitment processes are not exclusively based on merit, the "gender neutral" MIUR reforms are contributing to the reproduction of the female disadvantage.

Online panels are increasingly used in social research. The advantages of online panels are undisputed (i.e., fast data collection, lower costs). However, online panels – and in particular the nonprobability ones – have a number of limitations, due to coverage error and self-selection into the panel. The key issues is that online panels may not be representative of the population that they intend to represent and this may have important repercussions on the quality of the estimates produced (AAPOR, 2010; Callegaro et al., 2014). Despite the relevance of these issues, there are few studies in this field (see below); these studies have found strong evidence for bias and have shown that adjustments strategies may not be effective. The overall aim of the work is to assess the representativeness of an Italian nonprobability online panel and investigate the effectiveness of different post-survey adjustment strategies (i.e., post-stratification, propensity score adjustments) in reducing the bias. We compare the demographic and socio-economic composition of the panel and estimates from a set of selected survey items (e. g., consumption) to those of the general population computing percentage differences and appropriate statistical tests. When appropriate, we use regression analysis. We then compare estimates from the online panel and the “gold standard” data, obtained by implementing different post-survey adjustment strategies. We use data from the Italian online panel Opinione.net, and from the Multiscopo survey. The former is a non-probability online panel, established in 2011 (see also http://opinione.net/). The latter (considered as a “gold standard”) is a probability based survey that collects a wealth of information on the socio-economic characteristics and opinions of the Italian population (N=44.974; RR: 78.9%). Preliminary analysis shows evidence for sample selection bias; compared to the general population, the panellists are more educated, are more likely to be y

Monitoring older people’s physical and cognitive conditions is a key issue for sociologists, health researchers and policy makers. Longitudinal surveys of older people are very powerful research resources that can be used to address these topics. However, longitudinal surveys pose specific methodological challenges that may hamper the quality of the data collected and undermine the validity of the research findings. Together with wave one non-response, attrition (i. e., wave on wave non response), is one of the main challanges to data quality in longitudinal surveys. There is an established body of knowledge on attrition in household panel surveys; however, little is known on wave on wave non response in longitudinal surveys of older people. Our starting point is that the processes that lead the general population to continue partecipating in a panel may be different from those of the older people; older people are more socially and psycologically frigile and their propensity to survey response may be more dependent on external circumstances (i. e., the death of a partner). The aim of this paper is to a identify the drivers of wave-on-wave response and identify response patterns; the focus of the analysis, in particular, is to understand the role played by the partner in influencing the decision to continue partecipating. We use data from the Brain Aging in Abbiategrasso study linked to the administrative data from the population registry files provided by the Municipality of Abbiategrasso. The Brain Aging in Abbiategrasso study (also known as InveCe.Ab) is a biannual cohort study of older people born between 1935 and 1939 (aged 70-75 at the first wave) and living in Abbiategrasso, a town near Milan, in northern Italy. InveCe.Ab is a registry-based population study that aims to assess older people’s physical conditions and identify factors associated with the risk of developing dementia and cognitive impairment. We use panel analysis regression models.

In Europe, list frame sampling methods are often adopted in computer-assisted telephone interviewing surveys. When sampling frames (i.e., white pages) are incomplete, the quality of the resulting survey data may be seriously jeopardised. In 2012, only 50% of Italians are included in the sampling frame; the remaining are excluded, as they do not own a landline telephone (32%) or their landline telephone number is unlisted (18%). This paper explores differences in the socioeconomic and demographic characteristics of different sub-samples of respondents, investigates the impact of telephone under-coverage on a set of items belonging to different survey domains, and assesses the effectiveness of current weighting procedures. We find that under-coverage leads to bias and that a “one size fits all” weighting strategy can hardly be considered as an accurate method to correct for noncoverage bias

Il capitolo descrive le caratteristiche del disegno della ricerca adottato per lo svolgimento dello studio “Indagine sugli esiti occupazionali dei laureati in servizio sociale”. Vengono descritte le fasi di progettazione e realizzazione della ricerca; inoltre, sono discusse le scelte metodologiche che sono state adottate nella progettazione di questo studio e la loro finalità di minimizzare l’entità degli errori provenienti dalle diverse fonti di errore non campionario.

La qualità dei dati campionari è funzione di due tipi di errore: l’errore di campionamento e l’errore di non campionamento (Biemer 2010; Groves - Lyberg 2010). In questo saggio ci focalizziamo sull’errore di non-copertura, un tipo particolare di errore di non campionamento. L’errore di non-copertura si verifica quando unità che appartengono alla popolazione di riferimento sono escluse dalla lista di campionamento e tali unità manifestano stati differenti sulle proprietà di interesse rispetto a quelle incluse nel campione (Groves et al. 2009).