INITIATE is a project that aims to empower higher education institutions to develop R&I through institution transformation. INITIATE, in its widening dimension, seeks to raise excellence in science and knowledge valorisation of Europe's universities through cooperation and knowledge circulation. Through stakeholder inclusion and co-design approach, INITIATE will design an approach for institution transformation that will reflect on the current needs and resources of the institution, external elements such as policy barriers, good practices from other initiatives and identification of possible collaboration areas with other institutions including local ecosystems. Through iterative process and R&I Labs supported by online tools such as Knowledge Hub, INITIATE will generate policy recommendations for helping stimulate R&I development and scientific excellence in Widening countries, in addition to research outputs and creation of joint applications for other funding sources (e.g. Horizon Europe). The approach will be demonstrated in Croatia, Portugal and north Macedonia. This will finally result in a roadmap for long term uptake of R&I in widening countries with identified replication cases and forming of the Alliance for green energy transition that will assure the long-term sustainability of INITIATE results. The action focuses on universities in Widening countries, in which the cases for the implementation of INITIATE approach will be conducted. Additionally, the project aims to achieve several outcomes, including the successful institutional reform and upgrade of higher education institutions in the R&I dimension, empowerment to be actors of change, and the mainstreaming of a culture of excellence in science and value creation amongst higher education institutions, particularly in less research-intensive institutions and countries. To achieve these outcomes, the project will engage universities as well as local ecosystems.

O projeto teve como objetivo a avaliação das opções metodológicas da operação estatística Inquérito de Qualidade dos Censos 2021, relativas a: (a) desenho e dimensionamento da amostra,  (b) processo de emparelhamento automático e manual de dados, e (c) apuramento de resultados e cálculo de indicadores de cobertura e conteúdo.

To date, research about mobile usage to conduct surveys has focused on a comparative analysis with fixed phones (e.g. Keeter et al 2007, Vicente et al 2009, Lynn and Kaminska 2010). Research focused exclusively on mobile phones, specifically in the evaluation of its strengths and weakness as a survey mode is still scarce.  Non-response is one major problem for surveys’ activity. Mobile phone surveys are no exception to this situation. If nonresponse is unaddressed, the resulting damage to data quality may have serious consequences for data analyses underpinning social science research. Mobile phones have specific characteristics that other modes don’t have – they are of personal use, carried at all time to every places. This specificity may affect the likelihood of getting a successful contact when soliciting people to participate. On one side, the time period for contact is large (theoretically, all day) which can improve the likelihood of contact, on the other side, it is easy to reject a call coming from an unknown number. If the proportion of non-responses is high and/or non-respondents are much different from respondents surveys estimates are subject to non-response error.  Paradata are data collected during the survey data collection process (Couper 1998). Examples of paradata are call records, interviewer observations, keystroke data. Paradata can also be obtained by means of questions included on the questionnaire (e.g. the location of the respondent in the time of the interview, whether he/she is alone or accompanied by someone else). Paradata is most used to study unit nonresponse (Steeh et al 2001; De Leeuw and De Heer 2002). Nonresponse research is often limited by the small amount of information available for all sample units. However, call records can guide responsive survey design decisions aimed at reducing nonresponse rates and bias (Groves et al 2009), as they provide important clues on when and how best to contact and to achieve cooperation from...

Using cutting-edge methodology and, when required, developing new methods, this project aims to improve the skills of those involved in longitudinal research, in particular methodologists and researchers in the social sciences. The proposed work addresses both important methodological questions and substantive issues. In particular, data from the Consortium of Household panels for European socio-economic Research (CHER) will be used to illustrate methodological challenges when modelling socio-economic change. Using exemplars from the substantive research this project will present strategies for choosing the most appropriate statistical methods for analysing data with a longitudinal structure, taking into account measurement errors and complex survey designs.Survey data is the main source of information when regarding demographic and social characteristics of the population, economic activity, lifestyle patterns, and public opinion. Longitudinal survey data allow for the periodic measurement of individual’s demographic and socio-economic changes in their conditions. In panel studies the same and (or) different variables are measured on the same units at least at two time points. Panel data is particularly adequate for investigating changes at the individual level. Longitudinal studies also allow us to distinguish the degree of variation in the response variable across time for one person from the variation among subjects and, in principle, also to make stronger causal interpretations mainly regarding inferences about changes, by determining the direction and magnitude of causal relationships. Furthermore, panel data is capable, for example, of providing measures before and after important social and economic policy events. Several statistical approaches have been used to analyse and model panel survey data. These include random effects models, transition models (as is the case of graphical chain models), structural equation models and latent curve growth models. Long...