Design for Data Quality in a Multisite Cross-Sectional and Panel Study

Datos Bibliográficos

ID2407360
AutoresEric C Jones (0000-0002-0999-2726, University of North Carolina at Greensboro), Arthur D Murphy (University of North Carolina at Greensboro), Julia L Perilla (Georgia State University), Isabel Pérez-Vargas (University of North Carolina at Greensboro), Fran H Norris (National Center for PTSD)
Año2010
Volumen22
Número3
Páginas250-269
Fecha de publicación2010-08-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaCAM (JOURNAL)
Identificadores de la revistaISSN: 1087-8513 • E-ISSN: 1552-3969
EditorialSAGE Publications Inc (PUBLISHER)
DOI10.1177/1525822x10371116
OpenAlexW2090083004
IdiomaEN
Citas recibidas2
Referencias citadas24

The authors address the issues faced while collecting survey data as part of a large multisite, multidisciplinary long-term project using interviewers rather than self-administered questionnaires in a country in which the researchers are not native. The issues pertain to the collection of high-quality data that accurately measure the variables of interest from which generalizations can be made. Three issues were prominent: potential cross-cultural variation in the validity of measures; how to manage multiple control sites and multiple study sites; and how to control for problems presented by series/panel data (i.e., the influence of prior interviews or subsequent intervening events on later interviews). The authors addressed these issues through five strategies at different points in the study. This discussion concerns the challenges and benefits of using these techniques to address the three main issues

Control (management) · Data collection · Data quality · Data science · Multidisciplinary approach · Operations management · Quality (philosophy) · Sociology · Statistics · Applied Psychology · Artificial Intelligence · Computer Science · Engineering · Mathematics · Psychology · Qualitative Research Methods and Applications · Survey Methodology and Nonresponse