Data and Metadata Harmonization for the RAND Survey Meta Data Repository

Presentation at the North American Data Documentation Conference (NADDI) 2013The RAND Survey Meta Data Repository aims to help researchers use data and metadata from the HRS-family of surveys on aging, including studies from the US, UK/Europe and Asia. The project consists of 3 major parts: 1) Importing the metadata for each wave of each survey (in various formats such as DDI, Excel, Word/PDF) and linking the various modules/items with a single hierarchy of concepts 2) Creating the RAND Harmonized datasets by combining data across different waves of different studies, to facilitate easier comparison across years and countries 3) The Repository website, which provides researchers a single point of access to browse/search the metadata across all of the different surveys Currently, only one of the studies provides metadata in DDI format to simplify the import process; for the other studies, custom scripts and a great deal of manual effort are required. This presentation will discuss how DDI could be used to improve the process of importing the metadata and creating the RAND Harmonized datasets, as well as benefits for the researchers that access the Repository.Institute for Policy & Social Research, University of Kansas; University of Kansas Libraries; Alfred P. Sloan Foundation; Data Documentation Initiative Alliance, RAND Corporatio

DDI and Relational Databases

Workshop at the North American Data Documentation Conference (NADDI) 2013Although the DDI standard is expressed in XML, many institutions have a requirement or preference to use relational databases (eg. Access, MySQL, Oracle, Postgres) in their applications. This may be because of integration with existing applications, expertise at the institute, or other reasons. This workshop will discuss how to model DDI in a relational database, and the pros/cons of using this approach for application development. Interoperability with other applications, including those based on XML databases, and long term management of applications (including support for multiple versions of DDI) will also be discussed. Prior knowledge of relational databases and SQL is recommended but not required.Institute for Policy & Social Research, University of Kansas; University of Kansas Libraries; Alfred P. Sloan Foundation; Data Documentation Initiative Allianc

Representing and Utilizing DDI in Relational Databases

This document is primarily intended for implementers of DDI-based metadata stores who are considering different technical options for housing and managing their metadata. The Data Documentation Initiative (DDI) metadata specification is expressed in the form of XML schema. With version 3, the DDI specification has become quite complex, including 21 namespaces and 846 elements. Organizations employing DDI, or considering doing so, may want to 1. store and manage the metadata elements in relational databases, for reasons of integration with existing systems, familiarity with the concepts of relational databases (such as Structured Query Language), systems performance, and/or other reasons; 2. select only the subset of the available DDI metadata elements that is of utility to their work, and have the flexibility of capturing metadata they need that would not fit into the DDI model. This paper discusses advantages and disadvantages of the relational database approach to managing DDI. It also describes methods for modeling DDI in relational databases and for formally defining subsets of DDI to employ in this environment.

Representing and Utilizing DDI in Relational Databases

This document is primarily intended for implementers of DDI-based metadata stores who are considering different technical options for housing and managing their metadata. The Data Documentation Initiative (DDI) metadata specification is expressed in the form of XML schema. With version 3, the DDI specification has become quite complex, including 21 namespaces and 846 elements7. Organizations employing DDI, or considering doing so, may want to 1. store and manage the metadata elements in relational databases, for reasons of integration with existing systems, familiarity with the concepts of relational databases (such as Structured Query Language), systems performance, and/or other reasons; 2. select only the subset of the available DDI metadata elements that is of utility to their work, and have the flexibility of capturing metadata they need that would not fit into the DDI model. This paper discusses advantages and disadvantages of the relational database approach to managing DDI. It also describes methods for modeling DDI in relational databases and for formally defining subsets of DDI to employ in this environmen

Mobile-Only Web Survey Respondents

Web surveys are no longer completed on just a desktop or laptop computer. Respondents increasingly use mobile devices, such as tablets and smartphones to complete web surveys. In this article, we study how respondents in the American Life Panel complete surveys using varying devices. We show that about 30 percent of respondents sometimes complete surveys on a mobile device, and about 12 percent always use a mobile device. We study the characteristics of these “mobile-only” web survey respondents and find that they share many characteristics of typically hard-to-recruit survey respondents. They are more likely to be non-white, young, and not have a higher education. In terms of voting behavior, we find no differences between the groups of survey respondents who use different devices. This suggests that biases in political polls conducted through web-surveys are unlikely to occur when mobile-only respondents are underrepresented

Mobile-Only Web Survey Respondents

Web surveys are no longer completed on just a desktop or laptop computer. Respondents increasingly use mobile devices, such as tablets and smartphones to complete web surveys. In this article, we study how respondents in the American Life Panel complete surveys using varying devices. We show that about 30 percent of respondents sometimes complete surveys on a mobile device, and about 12 percent always use a mobile device. We study the characteristics of these “mobile-only” web survey respondents and find that they share many characteristics of typically hard-to-recruit survey respondents. They are more likely to be non-white, young, and not have a higher education. In terms of voting behavior, we find no differences between the groups of survey respondents who use different devices. This suggests that biases in political polls conducted through web-surveys are unlikely to occur when mobile-only respondents are underrepresented