Suchergebnisse

Many sample surveys ask respondents for consent to link their survey information with
administrative sources. There is significant variation in how linkage requests are
administered and little experimental evidence to suggest which approaches are useful for
achieving high consent rates. A common approach is to emphasize the positive benefits of
linkage to respondents. However, some evidence suggests that emphasizing the negative
consequences of not consenting to linkage is a more effective strategy. To further examine
this issue, we conducted a gain-loss framing experiment in which we emphasized the benefit
(gain) of linking or the negative consequence (loss) of not linking one's data as it related to
the usefulness of their survey responses. In addition, we explored a sunk-prospective costs
rationale by varying the emphasis on response usefulness for responses that the respondent
had already provided prior to the linkage request (sunk costs) and responses that would be
provided after the linkage request (prospective costs). We found a significant interaction
between gain-loss framing and the sunk-prospective costs rationale: respondents in the
gain-framing condition consented to linkage at a higher rate than those in the loss-framing
condition when response usefulness was emphasized for responses to subsequent survey
items. Conversely, the opposite pattern was observed when response usefulness was
emphasized for responses that had already been provided: loss-framing resulted in a higher
consent rate than the gain-framing, but this result did not reach statistical significance.

"Record linkage is becoming more important as survey budgets are tightening while at the same time demands for more statistical information are rising. Not all respondents consent to linking their survey answers to administrative records, threatening inferences made from linked data sets. So far, several studies have identified respondent-level attributes that are correlated with the likelihood of providing consent (e.g., age, education), but these factors are outside the control of the survey designer. In the present study three factors that are under the control of the survey designer are evaluated to assess whether they impact respondents' likelihood of linkage consent: 1) the wording of the consent question; 2) the placement of the consent question and; 3) interviewer attributes (e.g., attitudes toward data sharing and consent, experience, expectations). Data from an experiment were used to assess the impact of the first two and data from an interviewer survey that was administered prior to the start of data collection are used to examine the third. The results show that in a telephone setting: 1) indicating time savings in the wording of the consent question had no effect on the consent rate; 2) placement of the consent question at the beginning of the questionnaire achieved a higher consent rate than at the end and; 3) interviewers' who themselves would be willing to consent to data linkage requests were more likely to obtain linkage consent from respondents." (author's abstract)

"Education researchers who study the effect of family social background on student achievement often use students' survey reports of parental education to investigate these effects. However, past research has demonstrated that students misreport their parents' education levels. The authors expand upon this research in two ways. First they use cognitive theories about the response process to develop and test hypotheses about reporting inconsistencies across these variables. Second they evaluate the impact of student misreporting on estimates of the relationship between parental education levels and student math achievement. Using data from the German administration of PISA 2000 (OECD Programme for International Student Assessment) in which both students and parents were asked to report parental variables, the authors show that reporting inconsistencies are a function of student achievement: students with higher math scores tend to provide reports that are more consistent with their parents' reports. This interesting case of differential measurement error has consequences for comparisons of the effects of parental background on student achievement across different subgroups of the population and across countries (a common use of PISA data and other international studies similar to PISA)." (author's abstract)

"Falling response rates and the advancement of technology have shaped the discussion in survey methodology in the last few years. Both led to a notable change in data collection efforts. Survey organizations try to create adaptive recruitment and survey designs and increased the collection of non-survey data for sampled cases. While the first strategy is an attempt to increase response rates and to save cost, the latter is part of efforts to reduce possible bias and response burden of those interviewed. To successfully implement adaptive designs and alternative data collection efforts researchers need to understand error properties of mixedmode
and multiple-frame surveys. Randomized experiments might be needed to gain that knowledge. In addition close collaboration between survey organizations and researchers is needed, including the possibility and willingness to shared data between those organizations. Expanding options for graduate and post-graduate education in survey methodology might help to increase the possibility for high quality surveys." [author's abstract]

Abstract
Statistical profiling of job seekers is an attractive option to guide the activities of public employment services. Many hope that algorithms will improve both efficiency and effectiveness of employment services' activities that are so far often based on human judgment. Against this backdrop, we evaluate regression and machine-learning models for predicting job-seekers' risk of becoming long-term unemployed using German administrative labor market data. While our models achieve competitive predictive performance, we show that training an accurate prediction model is just one element in a series of design and modeling decisions, each having notable effects that span beyond predictive accuracy. We observe considerable variation in the cases flagged as high risk across models, highlighting the need for systematic evaluation and transparency of the full prediction pipeline if statistical profiling techniques are to be implemented by employment agencies.

Survey researchers are increasingly seeking opportunities to link interview data with administrative records. However, obtaining consent from all survey respondents (or certain subgroups) remains a barrier to performing record linkage in many studies. We experimentally investigated whether emphasizing different benefits of record linkage to respondents in a telephone survey of employee working conditions improves respondents' willingness to consent to linkage of employment administrative records relative to a neutral consent request. We found that emphasizing linkage benefits related to "time savings" yielded a small, albeit statistically significant, improvement in the overall linkage consent rate (86.0) relative to the neutral consent request (83.8 percent). The time savings argument was particularly effective among "busy" respondents. A second benefit argument related to "improved study value" did not yield a statistically significant improvement in the linkage consent rate (84.4 percent) relative to the neutral request. This benefit argument was also ineffective among the subgroup of respondents considered to be most likely to have a self-interest in the study outcomes. The article concludes with a brief discussion of the practical implications of these findings and offers suggestions for possible research extensions.

Abstract
This article examines the influence of interviewers on the estimation of regression coefficients from survey data. First, we present theoretical considerations with a focus on measurement errors and nonresponse errors due to interviewers. Then, we show via simulation which of several nonresponse and measurement error scenarios has the biggest impact on the estimate of a slope parameter from a simple linear regression model. When response propensity depends on the dependent variable in a linear regression model, bias in the estimated slope parameter is introduced. We find no evidence that interviewer effects on the response propensity have a large impact on the estimated regression parameters. We do find, however, that interviewer effects on the predictor variable of interest explain a large portion of the bias in the estimated regression parameter. Simulation studies suggest that standard measurement error adjustments using the reliability ratio (i.e., the ratio of the measurement-error-free variance to the observed variance with measurement error) can correct most of the bias introduced by these interviewer effects in a variety of complex settings, suggesting that more routine adjustment for such effects should be considered in regression analysis using survey data.

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Editors -- Contributors -- 1. Introduction -- 1.1 Why this book? -- 1.2 Defining big data and its value -- 1.3 The importance of inference -- 1.3.1 Description -- 1.3.2 Causation -- 1.3.3 Prediction -- 1.4 The importance of understanding how data are generated -- 1.5 New tools for new data -- 1.6 The book's "use case" -- 1.7 The structure of the book -- 1.7.1 Part I: Capture and curation -- 1.7.2 Part II: Modeling and analysis -- 1.7.3 Part III: Inference and ethics -- 1.8 Resources -- Part I: Capture and Curation -- 2. Working with Web Data and APIs -- 2.1 Introduction -- 2.2 Scraping information from the web -- 2.2.1 Obtaining data from websites -- 2.2.1.1 Constructing the URL -- 2.2.1.2 Obtaining the contents of the page from the URL -- 2.2.1.3 Processing the HTML response -- 2.2.2 Programmatically iterating over the search results -- 2.2.3 Limits of scraping -- 2.3 Application programming interfaces -- 2.3.1 Relevant APIs and resources -- 2.3.2 RESTful APIs, returned data, and Python wrappers -- 2.4 Using an API -- 2.5 Another example: Using the ORCID API via a wrapper -- 2.6 Integrating data from multiple sources -- 2.7 Summary -- 3. Record Linkage -- 3.1 Motivation -- 3.2 Introduction to record linkage -- 3.3 Preprocessing data for record linkage -- 3.4 Indexing and blocking -- 3.5 Matching -- 3.5.1 Rule-based approaches -- 3.5.2 Probabilistic record linkage -- 3.5.3 Machine learning approaches to record linkage -- 3.5.4 Disambiguating networks -- 3.6 Classification -- 3.6.1 Thresholds -- 3.6.2 One-to-one links -- 3.7 Record linkage and data protection -- 3.8 Summary -- 3.9 Resources -- 4. Databases -- 4.1 Introduction -- 4.2 The DBMS: When and why -- 4.3 Relational DBMSs -- 4.3.1 Structured Query Language -- 4.3.2 Manipulating and querying data.