Abstract
Conducting surveys with organizations, like schools, hospitals, businesses and farms, is a real challenge. There are many issues that need to be dealt with to get survey results of good quality. In every step of the design and conducting of the survey issues are involved that affect the quality of the survey outcomes. This includes e.g. defining the target population and drawing a sample, which relates to a sample frame and the sample design. Getting good data also refers to getting the data that you would want to get as a researcher, which refers to designing and developing a measuring instrument, the questionnaire. Another challenge is actually getting response, which relates to communicating the survey to the sampled units, and conducting the fieldwork. In this paper an overview of issues affecting quality in organizational surveys will be presented. In order to do so, the process-quality approach to survey design as discussed by (Snijkers et al. Designing and conducting business surveys. Wiley, Hoboken, 2013) is applied. This approach involves identifying the steps in the survey process, and for each step identifying the resulting survey components and their quality considerations. Apart from this process-quality approach, this paper discusses tailoring and project planning as two other basic survey design approaches in order to get good survey data. With regard to tailoring: three basic tailoring considerations are discussed. All steps and sub-processes, as well as people, resources, and money need to be planned to achieve the targeted survey objectives. These three approaches are integrated in a survey process map, which will be discussed in detail, relating survey components and error sources to each step. Thus an extended Total Survey Error framework is provided, offering a holistic process-quality framework for surveyors who want to conduct an organizational survey. The paper concludes with a brief discussion on survey quality: quality is not achieved by itself; it needs to be planned in and considered at all stages in the survey process!
Statistics Netherlands. The views expressed in this paper are those of the author and do not necessarily reflect the policies of Statistics Netherlands.
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References
Anseel F, Lievens F, Schollaert E, Choragwicka B (2010) Response rates in organizational science, 1995–2008: a meta-analytic review and guidelines for survey researchers. J Bus Psychol 25:335–349
Bavdaz M (2010a) The multidimensional integral business survey response model. Surv Methodol 36(1):81–93
Bavdaz M (2010b) Sources of measurement errors in business surveys. J Off Stat 26(1):25–42
Bethlehem J (2009) Applied survey methods: a statistical perspective. Wiley, Hoboken
Biemer P, Cantor D (2007) Introduction to survey methods for businesses and organizations, short course presented at the 3rd international conference on establishment surveys (ICES-III), Montreal, American Statistical Association, Alexandria, 18 June 2007
Biemer PP, Lyberg LE (2003) Introduction to survey quality. Wiley, Hoboken
Conrad FG, Blair J (2009) Sources of error in cognitive interviews. Public Opin Q 73(1):32–55
Couper MP (2008) Designing effective web surveys. Cambridge University Press, Cambridge
Cox BG, Chinnappa BN (1995) Unique features of business surveys. In: Cox BG, Binder DA, Chinnappa BN, Christianson A, Colledge MJ, Kott PS (eds) Business survey methods. Wiley, New York, pp 1–17
Cox BG, Binder DA, Chinnappa BN, Christianson A, Colledge MJ, Kott PS (eds) (1995) Business survey methods. Wiley, New York
De Leeuw ED, Hox JJ, Dillman DA (2008) The cornerstones of survey research. In: de Leeuw ED, Hox JJ, Dillman DA (eds) International handbook of survey methodology. Lawrence Erlbaum Associates, New York, pp 1–17
De Waal T, Pannekoek J, Scholtus S (2011) Handbook of statistical data editing and imputation. Wiley, Hoboken
Dillman DA, Smyth JD, Christian LM (2009) Internet, mail, and mixed-mode surveys: the tailored design method, 3. edn. Wiley, Hoboken
Erikson J, Haraldsen G, Snijkers G (2012) The future of statistical data collection? Challenges and opportunities, paper presented at the UNECE seminar on new frontiers for statistical data collection, Geneva, 31 Oct.–2 Nov 2012
Eurostat (2011) European statistics code of practice. Eurostat, Luxembourg City
Frame JD (2003) Managing projects in organizations: how to make the best use of time, techniques, and people, 3. edn. Jossey-Bass, San Francisco
Groves RM (1989) Survey errors and survey costs. Wiley, New York
Groves RM (1996) How do we know what we think they think is really what they think? In: Schwarz N, Sudman S (eds) Answering question. Methodology for determining cognitive and communicative processes in survey research. Jossey-Bass, San Francisco, pp 389–402
Groves RM, Fowler FJ Jr, Couper MP, Lepkowski JM, Singer E, Tourangeau R (2004) Survey methodology. Wiley, Hoboken
Jones J (2011) Effects of different modes, especially mixed modes, on response rates, paper presented at the workshop on different modes of data collection, Eurofond, Dublin, 6–7 Apr 2011
Kreuter F (ed) (2013) Improving surveys with paradata: analytic uses of process information. Wiley, Hoboken
Krosnick JA (1991) Response strategies for coping with the cognitive demands of attitude measures in surveys. Applied cognitive psychology, special issue: cognition and survey measurement 5(3):213–236
Lessler JT, Kalsbeek WD (1992) Nonsampling error in surveys. Wiley, New York
Lynn P (ed) (2009) Methodology of longitudinal surveys. Wiley, Hoboken
Schwarz N (1997) Questionnaire design: the rocky road from concepts to answers. In: Lyberg L, Biemer P, Collins M, de Leeuw E, Dippo C, Schwarz N, Trewin D (eds) Survey measurement and process quality. Wiley, New York, pp 29–45
Snijkers G (1992) Computer assisted interviewing: telephone or personal? In: Westlake A, Banks R, Payne C, Orchard T (eds) Survey and statistical computing. North-Holland, Amsterdam, pp 137–146
Snijkers G (2002) Cognitive laboratory experiences: on pre-testing computerized questionnaires and data quality, PhD thesis Utrecht University, Statistics Netherlands, Heerlen
Snijkers G (2003) Cognitive laboratory experiences and beyond: some ideas for future research, In: Prüfer P, Rexroth M, Fowler FJ (eds) Quest 2003: Proceedings of the 4th conference on questionnaire evaluation standards, ZUMA Nachrichten, Spezial Band 9, Mannheim, 21–23 Nov 2003
Snijkers G (2008) Getting data for business statistics: a response model, paper presented at the 4th european conference on quality in Official Statistics, Italian National Institute for Statistics, Rome, 8–11 July
Snijkers G (2009) Getting data for (Business) statistics: what’s new? What’s next?, paper presented at the 2009 european conference for new techniques and technologies for statistics (NTTS), Eurostat, Brussels, 18–20 Febr 2009
Snijkers G, Bavdaz M (2011) Business surveys. In: Lovric M (ed) International encyclopedia of statistical science. Springer, Berlin
Snijkers G, Willimack DK (2011) The missing link: from concepts to questions in economic surveys, paper presented at the 2nd European Establishment Statistics Workshop (EESW11), Swiss Federal Statistical Office, Neuchâtel, Switzerland, 12–14 Sept 2011
Snijkers G, Göttgens R, Hermans H (2011) Data collection and data sharing at statistics Netherlands: yesterday, today, tomorrow, paper presented at the 59th plenary session of the Conference of European Statisticians (CES), United Nations Economic Commission for Europe (UNECE), Geneva, 14–16 June 2011
Snijkers G, Haraldsen G, Jones J, Willimack DK (2013) Designing and conducting business surveys. Wiley, Hoboken
Thompson KJ, Oliver BE (2012) Response rates in business surveys: going beyond the usual performance measure. J Off Stat 28(2):221–237
Tourangeau R (1984) Cognitive science and survey methods. In: Jabine T, Straf M, Tanur JM, Tourangeau R (eds) Cognitive aspects of survey design. Building a bridge between disciplines. National Accademy Press, Washington, pp 73–100
Tourangeau R, Rips LJ, Rasinski K (2000) The psychology of survey response. Cambridge University Press, New York
Tourangeau R, Conrad FG, Couper MP (2013) The science of web surveys. Oxford University Press, Oxford
Vale S (2009) Generic statistical business process model, Paper presented at the joint UNECE/Eurostat/OECD work session on statistical metadata, (Vienna, July 2007), Version 4.0, Apr 2009, UNECE, Geneva
Willimack D, Nichols E (2010) A hybrid response process model for business surveys. J Off Stat 26(1):3–24
Willis GB (2005) Cognitive interviewing. A tool for improving questionnaire design. Sage, London
Wilmot A, Jones J, Dewar A, Betts P, Harper R, Simmons E (2005) Public confidence in official statistics: a qualitative study on behalf of the office for national statistics and the statistics commission. UK Office for National Statistics, London
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Snijkers, G. (2016). Achieving Quality in Organizational Surveys: A Holistic Approach. In: Liebig, S., Matiaske, W. (eds) Methodische Probleme in der empirischen Organisationsforschung. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-08713-5_3
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