Web Survey Bibliography
The present analysis has been made possible by the survey on graduates' condition that is carried out every year by the Inter
‐universities Consortium ALMALAUREA1. The survey makes it possible to analyse the most recent labour market trends through an examination of the career opportunities available for the Italian graduates of the universities taking part in the Consortium during the 5 years on from graduation. All graduates are contacted 1, 3 and 5 years on from graduation. More specifically, the data have been collected during the last survey conducted by ALMALAUREA in 2008 (over 287,000 graduates examined). This survey also involved all first and second level (=cycle of the Bologna Process) graduates from the class of 2007 (about 140,000). The huge number of graduates involved has determined the necessity to use survey methods that allow the reduction of costs and duration. This objective has been achieved through the introduction of two survey methods: CAWI and CATI. More precisely, the graduates having a mailbox (85% of the cohort) have been emailed and asked to answer to a questionnaire on the web site of ALMALAUREA. The survey procedure also included two e‐mail reminders. Afterwards, all graduates who had not answered to the online questionnaire have been contacted by phone. ‐to‐entry into the labour market and so on. These pieces of information are integrated by the huge quantity of data on the sociodemographic characteristics of graduates (e.g. social origins, gender, age), pre‐university studies, academic studies (e.g. degree course, graduation mark) and further experiences made during studies (foreign languages and IT skills, internships, study experiences made abroad and work experiences). It is possible that the survey methods used may have influenced the answer given by graduates. In other words, since the information have been collected through different survey tools (CAWI and CATI), they may have caused distortions that are not casual. For example, the presence/absence of interviewers is an important determinant for the quality of the information collected. On the other hand, because of the cultural level of the cohort involved in the interview, the contribution given by the interviewer may be limited; in some cases it may even be counterproductive, since they may influence the answer of the graduates. In consideration of the complexity of the subject that is dealt with, it has become important to determine if there are significant differences between the answers given by those who filled in the online questionnaire and those who gave their answers during the telephone interview. This need has also been confirmed by the fact that these two groups of graduates have also turned out during some preliminary analysis to be different in terms of their studies and area of residence. The method for evaluating an error deriving from a differentiated treatment (CATI or CAWI) will be developed by following a particular approach that is referred to the typical notions of the so‐called “causal inference”. This problem may be faced by referring to the approach proposed by Rosembaum and Rubin (1983), that is known as propensity score. The authors demonstrate that, having in hand several information which characterise the individuals and which are related to the time that preceded the treatment, it is possible to create groups of individuals having similar characteristics. These groups are, therefore, theoretically deconditioned by the kind of undergone treatment. Within this groups of individuals it is possible to compare the target variable (e.g. the occupational status) among those who have undergone the treatment and those who have not or just have undergone a different treatment. ALMALAUREA has also implemented a monitoring system of selection bias due to different data collection techniques. In this system an innovative approach was used (Camillo and D’Attoma, 2008). It involves a data transformation that allows measuring and testing in an automatic and multivariate way the presence of selection bias. The aim of ALMALAUREA is to measure and eventually to evaluate the effect of the undergone treatment on the answers given by graduates.
The survey enabled us to collect the main information related to academic and work experiences made after graduation: employment condition at the time of the interview, characteristics of the job (contract, branch of activity, earning), time
Conference homepage (abstract)
Web survey bibliography (317)
- Overview: Online Surveys; 2017; Vehovar, V.; Lozar Manfreda, K.
- Respondent mode choice in a smartphone survey ; 2017; Conrad, F. G., Schober, M. F., Antoun, C., Yan, H. Y., Hupp, A., Johnston, M., Ehlen, P., Vickers, L...
- Collecting Data from mHealth Users via SMS Surveys: A Case Study in Kenya; 2016; Johnson, D.
- Electronic and paper based data collection methods in library and information science research: A comparative...; 2016; Tella, A.
- Stable Relationships, Stable Participation? The Effects of Partnership Dissolution and Changes in Relationship...; 2016; Mueller, B.; Castiglioni, L.
- Identifying Pertinent Variables for Nonresponse Follow-Up Surveys. Lessons Learned from 4 Cases in Switzerland...; 2016; Vandenplas, C.; Joye, D.; Staehli, M. E.; Pollien, A.
- The 2013 Census Test: Piloting Methods to Reduce 2020 Census Costs; 2016; Walejko, G. K.; Miller, P. V.
- The Validity of Surveys: Online and Offline; 2016; Wiersma, W.
- Methods can matter: Where Web surveys produce different results than phone interviews; 2016; Keeter, S.
- Do Polls Still Work If People Don't Answer Their Phones?; 2016; Edwards-Levy, A.; Jackson, N. M.
- HUFFPOLLSTER: Why Reaching Latinos Is A Challenge For Pollsters; 2016; Jackson, N. M.; Edwards-Levy, A.; Velencia, J.
- Comprehension and engagement in survey interviews with virtual agents; 2016; Conrad, F. G.; Schober, M. F.; Jans, M.; Orlowski, R. A.; Nielsen, D.; Levenstein, R. M.
- An Overview of Mobile CATI Issues in Europe; 2015; Slavec, A.; Toninelli, D.
- Using Mobile Phones for High-Frequency Data Collection; 2015; Azevedo, J. P.; Ballivian, A.; Durbin, W.
- Mixed mode surveys ; 2015; Burton, J.
- Two Are Better Than One: The Use of a Mixed-Mode Data Collection to Improve the Electoral Forecast; 2014; de Rada, V. D., Pasadas del Amo, S.
- The impact of contact effort on mode-specific selection and measurement bias; 2014; Schouten, B., van der Laan, J., Cobben, F.
- How much is shorter CAWI questionnaire VS CATI questionnaire?; 2014; Bartoli, B.
- Advantages of a global multimodal print & digital readership survey; 2013; Cour, N., Saint-Joanis, G.
- Relative Mode Effects on Data Quality in Mixed-Mode Surveys by an Instrumental Variable; 2013; Vannieuwenhuyze, J. T. A., Revilla, M.
- A report on the Confirmit Market Research Software Survey 2013; 2013; Macer, T., Wilson, S.
- Mode effect analysis and adjustment in a split-sample mixed-mode Web/CATI survey; 2013; Kolenikov, S., Kennedy, C.
- Evaluating the left‐right dimension: Category Selection Probing conducted in an online access...; 2013; Huefken , V.
- Methodological, legal and technical perspectives on the feasibility of web survey paradata in German...; 2013; Sattelberger, S.
- Impact of mode design on reliability in longitudinal data; 2013; Cernat, A.
- Exploring patterns of academic usage: A Google Scholar based study of ESS, EVS, WVS and ISSP academic...; 2013; Malnar, B.
- Web questionnaires in official population surveys: Do's and don'ts First experiments and impacts...; 2013; Blanke, K.
- Mode effects in Labour Force Surveys - do they really matter?; 2013; Koerner, T.
- Measuring the same concepts in several modes in the "BIBB/BAuA-Employee-Survey 2011/12" ; 2013; Gensicke, M., Tschersich, N., Hartmann, J.
- What works? Getting the General Population To Go Online in a Mixed Mode Local Health Survey; 2013; Frigault, L.-R., Azzou, S. A. K., Molloy, E. J. K., Ammarguellat, F., Couture, M., Gratton, J.
- Using Technology to Conduct Questionnaire Evaluations with Hard to Reach Populations ; 2013; Ridolfo, H., Ott, K.
- Mode Effects in a National Establishment Survey; 2013; Daley, K., Phillips, B. T.
- Evaluating the Effect of a Non-Monetary Incentive in a Nationally Representative Mixed-Mode Establishment...; 2013; Sengupta, M., Harris-Kojetin, L., Hobbs, M., Greene, A.
- Survey Reminder Method Experiment: An Examination of Cost Efficiency and Reminder Mode Salience in the...; 2013; Anderson, M., Rogers, B., CyBulski, K., Hall, J. W., Alderks, C. E., Milazzo-Sayre, L.
- Experiences from a probability-based Internet panel: Sample, recruitment and participation; 2013; Scherpenzeel, A.
- An Evaluation of Internet Versus Paper-based Methods for Public Participation Geographic Information...; 2012; Pocewicz, A.; Nielsen-Pincus, M.; Brown, G.; Schnitzer, R.
- Using paradata to explore item-level response times in surveys; 2012; Couper, M. P., Kreuter, F.
- Specialized Tools for Measuring Past Events ; 2012; Belli, R. F.
- Modes of Data Collection; 2012; Tourangeau, R.
- Mode and non-response effects and their treatment; 2012; Chrysanthopoulos, S., Georgostathi, A.
- “I think I know what you did last summer” Improving data quality in panel surveys; 2012; Lugtig, P. J.
- Using Text-to-Speech (TTS) for Audio-CASI; 2012; Couper, M. P., Kirgis, N., Buageila, S., Berglund, P.
- Does Mode Matter? Initial Evidence from the German Longitudinal Election Study (GLES); 2012; Blumenstiel, J. E., Rossmann, J.
- The Representativity of Web Surveys of the General Population compared to Traditional Modes and Mixed...; 2012; Klausch, L. T., Schouten, B., Hox, J.
- Effects of speeding on satisficing in Mixed-Mode Surveys; 2011; Bathelt, S., Bauknecht, J.
- Web based CATI on Amazon Elastic Compute Cloud and VirtualBox using queXS; 2011; Zammit, A.
- Web/Cloud Based CATI Using queXS; 2011; Zammit, A.
- When Referring to Mode, Is Expressed Preference the Same as Reality?; 2011; Denk, K.
- Three Era's of Survey Research; 2011; Groves, R. M.
- Testing a single mode vs a mixed mode design; 2011; Laaksonen, S.