Web Survey Bibliography
Immense reform efforts is going on in . In particular, the so called Hartz reforms aim to improve the efficiency of the Federal Employment Agency (BA), and its local departments. Amongst other things this requires measures for the efficiency of the large variety of labor market policy programs financed by the Agency. In the Hartz reforms many of the evaluation projects are based on the IEB data source. The IEB (Integrierte Erwerbsbiografien) merges data from different data sources. The current version includes process data from the following administrative records:
1. BeH: Employment spells from social security data records,
2. LeH: Unemployment spells for registered unemployed persons receiving unemployment insurance payments (Arbeitslosengeld), stately financed unemployment assistance (Arbeitslosenhilfe),
3. MTG: Spells of participation in labor market programs, and
4. ASU: Search spells for people registered as unemployed and searching for a job.
Since the assignment to any of the possible treatments typically is not based on a random process, complications and corrections have to be considered before drawing causal inference based on these data. The particular question we are confronted with is: Which kind of treatment is the most effective and efficient one for each person registered as unemployed? This requires knowledge about potential outcomes – how would each individual’s employment history had evolved under different programs?
Generally, approaches to predict the outcomes of several labor market programs are known as targeting systems. In this talk, we will first describe the magnitude of the problem and then previous attempts to address it. This includes two systems that have been suggested for a use as Targeting Systems throughout during the last years. According to complications with the existing approaches a new treatment effect and prediction system, called TrEffeR, will be proposed. TrEffeR analyzes, which program should be assigned to a certain unemployed person and what causal effect a program will have for his future employment history.
Web Survey Bibliography - 2005 (418)
- The effects of survey frequency on panelists' responses; 2005; Coen, T., Lorch, J.,
- Questionnaire Length & Fatigue Effects; 2005; La Bruna, A., Rathod, S.
- Non-equivalence of online and paper-and-pencil psychological tests: The case of the Prospective Memory...; 2005; Buchanan, T., Ali, T., Heffernan, T. M., Ling, J., Parrott, A. C., Rodgers, J., Scholey, A. B.
- Using the Internet to Survey College Students About Their Law School Plans; 2005; Meinhold, S. S., Gleiber, D. W.
- Similarity and Helping Behavior on the Web: The Impact of the Convergence of Surnames Between a Solicitor...; 2005; Guéguen, N., Pichot, N., Le Dreff, G.
- Effects of Survey Mode on Self-Reports of Adult Alcohol Consumption: A Comparison of Mail, Web and Telephone...; 2005; Link, M. W., Mokdad, A.
- Toward An Open-Source Methodology: What We Can Learn From The Blogosphere; 2005; M.
- Online surveys for BGLT research: Issues and techniques; 2005; Riggle, E. D. B., Rostosky, S. S., Reedy, C. S.
- Internet data collection; 2005; Hayslett, M. M.
- Web-Based Surveys; 2005; Ellis, B., Zurita, F., Ventura, J.
- Digital Video as Research Practice: Methodology for the Millennium; 2005; Shrum, W., Duque, R., Brown, T.
- Sex Differences in the Acceptability of Discrimination; 2005; Kuran, T., McCaffrey, E. J.
- Effects of survey mode, gender, and perceived sensitivity on the quality of data regarding sensitive...; 2005; Mi Kyung, J.
- Heterosexism in high school among lesbian, gay, bisexual, and questioning students; 2005; Noah, C.-T. D.
- Taking pro-action: A survey of potential users before the availability of wireless access and the implementation...; 2005; Holden, H. A., Deng, M.
- Comparing data from online and face-to-face surveys; 2005; Duffy, C., Smith, K., Terhanian, G., Bremer, J.
- 'Hidden' opportunities and benefits in using web-based business-to-business surveys; 2005; Grant, D. B., Teller, C., Teller, W.
- Dinámica del proceso de recolección y análisis de datos vía web; 2005; Freitas, H., Janissek-Muniz, R., Moscarola, J.
- The Contribution Of Respondent Computer Experience On Primacy Effect And Satisficing in Internet Surveys...; 2005; Cross, F.
- Lessons Learned From Online vs. Paper-based Computer Information Students’ Evaluation System; 2005; Liegle, J., McDonald, D. S.
- Factors to Weigh When Considering Electronic Data Collection; 2005; Courtney, K. L., Craven, C. K.
- Introduction To Survey Research Design; 2005; Owens, L. K.
- Aux Abonnes Absents: Liste Rouge Et Telephone Portable Dans Les Enquetes En Population Generale Sur...; 2005; Beck, F., Legleye, S., Peretti-Watel, P.
- Comparing Responses and Response Rates of Web and Telephone Surveys; 2005; Bedy, Z.
- Using Online Surveys to Evaluate Distance Education Programs; 2005; Strachota, E., Schmidt, S., Conceicao, S.
- Web surveys : Explaining and Reducing Unit Nonresponse, Item Nonresponse and Partial Nonresponse; 2005; Heerwegh, D.
- Methodological issues in the recruitment of ethnic minority subjects to research via the Internet: a...; 2005; Im, E. O., Chee, W.
- How design elements influence web surveys; 2005; Anonymous
- Electronic Surveys – Pros and Cons; 2005; Anonymous
- Web Versus Paper Questionnares: A Design and Functionality - Comparison; 2005; Jones, J., Fraser, C., Dowling, Z.
- Building An Online Panel; 2005; Rathod, S.
- Thoughts on Internet Research in Europe; 2005; Harris Interactive
- Effects of Personal Salutations in E-mail Invitations to Participate in a Web Survey; 2005; Heerwegh, D.
- Web Surveys and the new Disability Discrimination Act; 2005; Macer, T.
- Measuring Perceived and Actual Response Burden in Business Surveys; 2005; Dale, T., Haraldsen, G., Jones, J., Hedlin, D.
- Web survey design for predicting performance using network questions; 2005; Coromina, L.
- How Internet Surveys Are Changing Data Collection Practices: The Case of University Student Surveys...; 2005; Dillman, D. A., Allen, T.
- Online student feedback surveys. Methodological issues in comparison to the traditional classroom survey...; 2005; Fuchs, M.
- Monitoring quality of life in small and medium sized cities –results of online-survey research; 2005; Aehnelt, R., Kuehn, M., Schuette, I.
- Sampling Problems inWeb Surveys; 2005; Steffensen, J. B.
- Web surveys: inference using weighting and imputation in the survey on graduates; 2005; Biffignandi, S., Fabrizi, E., Pratesi, M., Salvati, N.
- Expert Appraisals of BusinessWeb Survey Applications; 2005; Haraldsen, G.
- Nonresponse segments in Internet and mobile phone surveys; 2005; Vehovar, V., Belak, E., Lavtar, D.
- Ten Tangible and Practical Tips to Improve Student Participation in Web Surveys; 2005; Molasso, W. R.
- Use Online Surveys to Get the Feedback You Need; 2005; Toledano, Y.
- The Trouble With Web Surveys; 2005; Cooper, B.
- Behavioral research and data collection via the Internet; 2005; Birnbaum, M. H., Reips, U. -D.
- The Use of Multiple Imputation to Create a Null Data Set from Nonrandomized Job Training Data; 2005; Rubin, D. B.
- Complications When Using Nonrandomized Job Training Data to Draw Causal Inferences; 2005; Raessler, S.
- Inference from non-probability samples in marketing research; 2005; Blyth, B.