Improving Data Quality in Face-to-Face Survey Research (original) (raw)

Surveytainment--A possible solution to declining survey data quality

NMSU Business Outlook, 2015

Could the reason for declining survey data quality be ‘widely used data collection methods are outdated’? After all, the most iconic books on survey research methods (e.g., Dillman 1978; Payne 1951) were written in the pre-Internet, pre-social media, and pre-Millenials era.

Data Quality in Mail, Telephone and Face to Face Surveys

1992

Three major methods of survey research, face-to-face interviews, telephone interviews, and mail questionnaires, are compared with respect to the quality of the data. The literature on experimental comparisons of these methods is reviewed, and the effects of the mode of data collection on aspects of data quality are examined. The effects of the data-collection method on research results are also examined with a focus on the consequences for the relations among variables and emerging empirical models. The meta analysis is followed by a field experiment with 762 responses. Meta analysis detected small differences between the modes, suggesting a dichotomy between modes with and without an interviewer. The field experiment found the lowest response 'rates for the face-to-face survey, with more item nonresponse in the mail survey but more self-disclosure through the mail. The mail survey was slightly superior in reliability and scalability. Results suggest that interviewer training should be adapted to the changes in data collection mode. Five figures and 33 tables present meta analysis and survey findings. Three appendixes contain a bibliography, the questionnaire content, and marginal distributions of background variables. A summary in Dutch is included. (Contains 201 references.) (SLD)

Dirty Data: The Effects of Screening Respondents Who Provide Low-Quality Data in Survey Research

Journal of Business and Psychology, 2017

The purpose of this study is to empirically address questions pertaining to the effects of data screening practices in survey research. This study addresses questions about the impact of screening techniques on data and statistical analyses. It also serves an initial attempt to estimate descriptive statistics and graphically display the distributions of popular screening techniques. Data were obtained from an online sample who completed demographic items and measures of character strengths (N = 307). Screening indices demonstrate minimal overlap and differ in the number of participants flagged. Existing cutoff scores for most screening techniques seem appropriate, but cutoff values for consistency-based indices may be too liberal. Screens differ in the extent to which they impact survey results. The use of screening techniques can impact inter-item correlations, inter-scale correlations, reliability estimates, and statistical results. While data screening can improve the quality and trustworthiness of data, screening techniques are not interchangeable. Researchers and practitioners should be aware of the differences between data screening techniques and apply appropriate screens for their survey characteristics and study design. Low-impact direct and unobtrusive screens such as self-report indicators, bogus items, instructed items, longstring, individual response variability, and response time are relatively simple to administer and analyze. The fact that data screening can influence the statistical results of a study demonstrates that low-quality data can distort hypothesis testing in organizational research and practice. We recommend analyzing results both before and after screens have been applied.

A Survey Data Quality Strategy

This discussion constructs a survey data quality strategy for institutional researchers in higher education in light of total survey error theory. It starts with describing the characteristics of institutional research and identifying the gaps in literature regarding survey data quality issues in institutional research and then introduces the quality perspective of a survey process and the major components of total survey error. A proposed strategy for inspecting survey data quality is presented on the basis of five types of survey error and the characteristics of typical institutional research survey projects. The strategy consists of identifying quality measures for each type of survey error, and then identifying quality control and quality assurance procedures for each of the quality measures. The discussion concludes with the implications of the strategy for institutional researchers and some closing thoughts. A checklist for inspecting survey data quality is provided in the app...

Challenges in international survey research: a review with illustrations and suggested solutions for best practice

European J. of International Management, 2013

When conducting international research projects, scholars face a myriad of challenges that reach beyond those encountered in domestic research. In this paper, we explore the specific issues related to international survey research, focusing on the different stages of the research process that include defining the study population and gaining data access, survey development, data collection, data analysis, and finally publication of the results. For each stage, we review the pertinent literature, provide illustrations based on examples from our own research projects, and offer possible solutions to address the inherent challenges by formulating suggestions for improving the quality of international survey research.

Data collection quality assurance in cross-national surveys: the example of the ESS

2009

The significance of cross-national surveys for the social sciences has increased over the past decades and with it the number of cross-national datasets that researchers have access to. Cross-national surveys are typically large enterprises that demand dedicated efforts to coordinate the process of data collection in the participating countries. While cross-national surveys have addressed many important methodological problems, such as translation and the cultural applicability of concepts, the management of the data collection process has yet had little place in cross-national survey methodology. This paper describes the quality standards for data collection and their monitoring in the European Social Survey (ESS). In the ESS data are collected via face-to-face interviewing. In each country a different survey organisation carries out the data collection. Assuring the quality across the large number of survey organisations is a complex but indispensable task to achieve valid and com...

Assessment of Survey Data Quality: A Pragmatic Approach Focused on Interviewer Tasks

International Journal of Market Research, 2004

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A Survey Data Quality Strategy: The Institutional Research Perspective

IR Applications, 2012

This discussion constructs a survey data quality strategy for institutional researchers in higher education in light of total survey error theory. It starts with describing the characteristics of institutional research and identifying the gaps in literature regarding survey data quality issues in institutional research and then introduces the quality perspective of a survey process and the major components of total survey error. A proposed strategy for inspecting survey data quality is presented on the basis of five types of survey error and the characteristics of typical institutional research survey projects. The strategy consists of identifying quality measures for each type of survey error, and then identifying quality control and quality assurance procedures for each of the quality measures. The discussion concludes with the implications of the strategy for institutional researchers and some closing thoughts. A checklist for inspecting survey data quality is provided in the appendix.

Measuring Survey Quality Through Representativeness Indicators Using Sample and Population Based Information

The RISQ (Representativity Indicators for Survey Quality) project, funded by the European 7th Framework Programme, is a joint effort of the NSI's of Norway, The Netherlands and Slovenia, and the Universities of Leuven and Southampton to develop quality indicators for survey response. The response rate alone is insufficient to measure the potential impact of non-response. These Representativity Indicators (R-indicators) are developed to be used as tools at different stages of the data collection process: monitoring field strategies, targeting field resources and comparing strategies for increasing response rates. The auxiliary information for these indicators depend on prior information about respondents and non-respondents and paradata that becomes available during fieldwork. In many countries, prior auxiliary information for non-respondents may be limited. However, marginal distributions at the population level are often available through registers and population estimates. In...

Moving Survey Methodology Forward in Our Rapidly Changing World: A Commentary

2016

Survey methodology now faces an unprecedented challenge for how to collect information from samples of people that will provide scientifically defensible estimates of the characteristics of the population they represent. Many decades of research have shown that in order for such estimates to be made with known precision four major sources of error-coverage, sampling, measurement and nonresponse-must be controlled (Groves, 1989). Subsequent research has produced a great amount of knowledge on how those sample estimates are affected by different survey modes, sample sources, sample sizes, the failure of certain types of people to respond to survey requests, and how questions are structured and worded.Today's challenge stems from many considerations. Response rates for some survey modes, especially voice telephone in national surveys, have fallen precipitously and are not expected to recover (Dillman, Forthcoming). In addition, RDD landline surveys miss nearly half of all household...