Suchergebnisse

Beobachtung, und insbesondere teilnehmende Beobachtung, kommt in vielen Disziplinen zum Einsatz, um Daten über Personen, Prozesse oder Kulturen im Rahmen qualitativer Forschungsprojekte zu erheben. In diesem Beitrag werden unterschiedliche Definitionen teilnehmender Beobachtung zusammengestellt und deren Geschichte und Ziele sowie die Rolle des Beobachters, d.h. das Wann, Was, und Wie von Beobachtung, skizziert. Anschließend folgen Anmerkungen zum Umgang mit und Schreiben von Feldnotizen, um dann einige Übungen für die Lehre von Beobachtungstechniken vorzustellen.

Due to technological advances in data collection equipment, many transportation agencies are considering the use of mobile mapping systems: data collection vehicles equipped with a Global Positioning System receiver, an inertial navigation system, several digital cameras, and a computer. The host system is driven on a roadway, and data are collected on the fly. These raw data are then processed on a computer using photogrammetric software packages and input to an inventory database. Many transportation agencies are faced with the decision on using mobile mapping systems to improve their infrastructure data. This article presents a stepwise test protocol that transportation agencies can use to judge different data collection methods. Based on results of four experiments, the protocol provides guidance on different activities associated with the recommended evaluation process. The protocol is accompanied by information that transportation agencies may use as a benchmark for evaluating different inventory data collection methods.

AbstractThis chapter focuses on ethical issues that can arise in the collection and analysis of data in evaluations. We move beyond common ethical issues in data collection and analysis, such as informed consent and coercion, to address four issues: the application of cost‐benefit thinking to judgments about research ethics, the quality of research design as an ethical issue, the need to explore one's data, and the censoring of data.

The rising penetration of smartphones now gives researchers the chance to collect data from smartphone users through passive mobile data collection via apps. Examples of passively collected data include geolocation, physical movements, online behavior and browser history, and app usage. However, to passively collect data from smartphones, participants need to agree to download a research app to their smartphone. This leads to concerns about nonconsent and nonparticipation. In the current study, we assess the circumstances under which smartphone users are willing to participate in passive mobile data collection. We surveyed 1,947 members of a German nonprobability online panel who own a smartphone using vignettes that described hypothetical studies where data are automatically collected by a research app on a participant's smartphone. The vignettes varied the levels of several dimensions of the hypothetical study, and respondents were asked to rate their willingness to participate in such a study. Willingness to participate in passive mobile data collection is strongly influenced by the incentive promised for study participation but also by other study characteristics (sponsor, duration of data collection period, option to switch off the app) as well as respondent characteristics (privacy and security concerns, smartphone experience).

In an orderly statistical universe, it is always the purpose that governs. Next most fundamental is the money question: under whose payroll does the collection and processing of data, take place? Our lead‐of paper appropriately raises these two basic issues. Its author is the Director of the Division of Research, College of Business Administration, University of Georgia.

For over 65 years, the American National Election Studies (ANES) has produced high quality data on voting, public opinion, and political participation. Most recently, attention has turned to creating an environment that will allow us to not only produce high quality metadata for the research community, but to also better integrate the creation and use of metadata throughout the lifecycle of our research. On our own, we have taken steps to create tools that allow for more efficient questionnaire development using metadata. This presentation will provide a brief history of our efforts to provide the highest quality of data and metadata and how this current collaboration is further advancing these efforts.

Whether computer assisted data collection methods should be used for survey data collection is no longer an issue. Most professional research organizations, commercial, government and academic, are adopting these new methods with enthusiasm. Computer assisted telephone interviewing (CATI) is most prevalent, and computer assisted personal interviewing (CAPI) is rapidly gaining in popularity. Also, new forms of electronic reporting of data using computers, telephones and voice recognition technology are emerging. This paper begins with a taxonomy of current computer assisted data collection methods. It then reviews conceptual and theoretical arguments and empirical evidence on such topics as: (1) respondents and interviewer acceptance of new techniques, (2) effect of computer assisted interviewing on data quality, (3) consequences for survey costs and (4) centralized vs. decentralized deployment of CATI.

"America's farms and farmers are integral to the U.S. economy and, more broadly, to the nation's social and cultural fabric. A healthy agricultural sector helps ensure a safe and reliable food supply, improves energy security, and contributes to employment and economic development, traditionally in small towns and rural areas where farming serves as a nexus for related sectors from farm machinery manufacturing to food processing. The agricultural sector also plays a role in the nation's overall economic growth by providing crucial raw inputs for the production of a wide range of goods and services, including many that generate substantial export value. If the agricultural sector is to be accurately understood and the policies that affect its functioning are to remain well informed, the statistical system's data collection programs must be periodically revisited to ensure they are keeping up with current realities. This report reviews current information and makes recommendations to the U.S. Department of Agriculture's (USDA's) National Agricultural Statistics Service (NASS) and Economic Research Service (ERS) to help identify effective methods for collecting data and reporting information about American agriculture, given increased complexity and other changes in farm business structure in recent decades"--Publisher's description

American Indian and Alaska Native (AI/AN) tribes need access to quality data and information to make informed decisions concerning their communities, economic development, land and resource management, and other sovereign governance decisions. Federal agencies also need access to quality data to ensure that they are delivering effective services to AI/AN tribes to meet tribal needs and deliver on federal responsibilities. However, various statistical and collection issues often negatively affect the quality and availability of federally collected AI/AN data. As a preliminary step to identifying gaps and improving the quality and accuracy of AI/AN data in federal datasets, this article provides an inventory and preliminary analysis of current AI/AN in federal data collections. This inventory identifies 448 unique data collections from twenty-one federal agencies. These datasets were identified in 2016. One hundred and ninety-four (43.3%) of these datasets are publicly available, and seventy-nine of the publicly available datasets include data at the tribal or reservation level. An analysis of tribal/reservation level datasets by agency show that there are data gaps at the reservation/tribal level on businesses, the financial sector, tribal governments, labor markets, and education.