nach oben

Quality & Quantity

Erschienen in:

01.07.2014

Survey topic and unit nonresponse

Evidence from an online survey on mating

verfasst von: Doreen Zillmann, Andreas Schmitz, Jan Skopek, Hans-Peter Blossfeld

Erschienen in: Quality & Quantity | Ausgabe 4/2014

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Survey topic as a factor influencing participation rates is becoming increasingly important, as there is a growing trend in social science research for surveying specific populations about specific topics. Previous research has shown that respondents with high topic interest (often referred to as salience) are more likely to participate in surveys. However, the identification of mechanisms that affect respondents’ interest in a survey topic has been largely neglected in research literature. We present an explanatory model of participation that conceptualizes topic interest as a function of an actor’s relational position in a particular social setting. To illustrate the relationship between survey topic and participation behavior, we use an online survey on mating conducted on the user population of an online dating site. For our nonresponse analysis we use web-generated process data, consisting of profile and interaction data, which describe all units of the sample frame. Thus, comprehensive information is available for both participants and non-participants of the online survey on an individual level, enabling a particularly accurate analysis of nonresponse. Results show that the probability of participation varies according to a user’s chances of success on the mating market. Users who can be described as less attractive (e.g. older people, less educated men, overweight women) show a higher probability of participation, which we explain with the mechanism of topic salience. We conclude with general implications regarding (1) the relationship between survey topic and survey participation and (2) the potential of web-generated process data for (online) survey research.

Vorheriger Artikel Analysis and construction of stress relief model for healthy indoor environments

Nächster Artikel Current practice in the modelling of age, period and cohort effects with panel data: a commentary on Tawfik et al. (2012), Clarke et al. (2009), and McCulloch (2012)

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

One exception here is an experimental study by van Kenhove et al. (2002), which measured both respondents’ and non-respondents’ interest in and importance of the survey topic directly.

A user was considered to be active when he or she had logged onto the website within the last six months, with the reference point being the start of the survey.

These were user profiles which had been deleted before receiving the invitation, administrator profiles, and profiles whose attributes seemed impossible or implausible (so-called “gimmick accounts”). We define these profiles as sample-neutral drop outs because these users are not part of the survey’s target population, as it seems unlikely that they are looking for a partner.

The website’s terms and conditions informed users of the collaboration with the university. It was explained that the collaboration involved profile and interaction data being passed on in anonymized form under the supervision of the company’s data protection administrator. The data were given to the university for scientific purposes only, namely the analysis of mate selection behavior. Users could opt out of their data being used.

We selected this indicator (RR2, not RR1, based on AAPOR standard definitions) because our analysis of dropout focuses on unit nonresponse, and therefore on the question of self-selection into the survey. The problems of item nonresponse and survey dropout (which we have shown is fairly marginal) are not our focus. However, we also carried out the analyses with survey completion as dependent variable (AAPOR standard RR1). This did not substantially alter our results; in fact, the tendencies we observed were actually stronger in some cases.

We based our descriptions of a user’s educational level on the work of Blossfeld and Timm (1997, 2003). This gave us the following hierarchy: in training: still at school / vocational training; low educational level: (1) no general educational qualifications, (2) general secondary education without vocational training, (3) intermediate secondary education without vocational training; medium educational level: (1) general secondary education as well as vocational training, (2) intermediate secondary education as well as vocational training, (3) higher education entrance qualification (e.g., Abitur) without and (4) with vocational training; high educational level: (1) lower tertiary (e.g., university of applied sciences), (2) higher tertiary (e.g., academically oriented university education), or (3) PhD.

The analyses are primarily based on user self-reports. As with any data source dependent on self-reports, there are questions concerning the validity of the data. We excluded profiles with highly unlikely or implausible information from the nonresponse analysis. More difficult is detecting the less obviously false information in user profiles. Previous analyses have shown that, although there is a certain level of deception in online dating user profiles, the magnitude of deception is quite small, and therefore close to the true value (cf. Toma et al. 2008; Zillmann et al. 2011; Skopek 2011). These small deviations within user profiles should not change our results substantially.

The completion indicator consists of the following 16 variables: gender, desired partner gender, date of birth, school qualifications, vocational qualifications, desired relationship, marital status, height, weight, figure, hair color, eye color, whether the user wants children, whether the user has children, whether the user is a smoker, and whether a partner smoking is undesirable.

Applying the indicator of profile incompleteness has a significant influence on the effects of missing data for each individual profile variable, but not on the effect of the other attributes. Table 5 in the appendix gives the multivariate results without the indicator for missing profile data. Due to suspected multicollinearity within the missing indicators of each variable and between the missing indicators and the general propensity to item nonresponse we also specified a model not reported here. The covariance of all missing indicators was modeled with a common latent factor “propensity to item nonresponse”, which served as a substitute for the additive index as specified in the model reported in Table 2. However, as this alternative structural equation model yielded almost the same regression parameters while being less sparse, we decided to report the regression model in Table 2 only.

One may argue that there is another interpretation possible (separate from the hypotheses on success chances) which may be particularly valid for the effect of education among men, and the effect of age for both genders: we may not be recording an effect of potential success, but an effect of available time resources. That is, there may be an increased likelihood of participation among those users who have more free time, perhaps because they are currently unemployed, or because their children may have moved out of the family home. However, we can exclude a possible time effect because we control for website activity, which indicates time investments in finding a partner. In additional analyses, not reported here, we also controlled for employment (i.e., if a user worked part or full time, or if they were unemployed, retired etc.). Our results remained stable when we controlled for employment; we therefore conclude that our findings can clearly be interpreted as an effect of success chances, rather than as a differential time effect for various population groups.

Adua, L., Sharp, J.S.: Examining survey participation and response quality: the significance of topic salience and incentives. Surv. Methodol. 36(1), 95–109 (2010)

Bethlehem, J.G., Cobben, F., Schouten, B.: Handbook of Nonresponse in Household Surveys. Wiley, Hoboken and NJ (2011)CrossRef

Birkelbach, K.: Befragungsthema und Panelmortalität: Ausfälle in einer Lebenslauferhebung. ZA-Inf. 42, 128–147 (1998)

Blossfeld, H.-P., Timm, A.: Der Einfluß des Bildungssystems auf den Heiratsmarkt: Eine Längsschnittanalyse der Wahl des ersten Ehepartners im Lebenslauf. Kölner Zeitschrift für Soziologie und Sozialpsychologie 49(3), 440–476 (1997)

Blossfeld, H.-P., Timm, A.: Who Marries Whom? Educational Systems as Marriage Markets in Modern Societies, vol. 12. Kluwer, European studies of population, Dordrecht (2003)CrossRef

Cook, C., Heath, F., Thompson, R.L.: A meta-analysis of response rates in web- or internet-based surveys. Educ. Psychol. Meas. 60(6), 821–836 (2000)CrossRef

Couper, M.P.: Survey introductions and data quality. Public Opin. Q. 61(2), 317–338 (1997)CrossRef

DeLeeuw, E.D., Hox, J.J.: Internet surveys as part of a mixed-mode design. In: Das, M., Ester, P., Kaczmirek, L. (eds.) Social and Behavioral Research and the Internet, pp. 45–76. Routledge, New York (2011)

Dillman, D.A.: Mail and Telephone Surveys: The Total Design Method, 1st edn. Wiley, New York (1978)

Dillman, D.A.: Mail and Internet Surveys: The Tailored Design Method, 2nd edn. Wiley, New York (2000)

Esser, H.: Über die Teilnahme an Befragungen. In: ZUMA-Nachrichten 18, pp. 38–47. (1986)

Esser, H.: ’Habits’, ’Frames’ und ’Rational Choice’: Die Reichweite von Theorien der rationalen Wahl (am Beispiel der Erklärung des Befragtenverhaltens). Zeitschrift für Soziologie 19(4), 213–247 (1990)

Fiore, A.T., Donath, J.S.: Homophily in online dating: when do you like someone like yourself? In: van der Veer, G., Gale, C. (eds.) Conference on Human Factors in Computing System, pp. 1371–1374. ACM Press, New York (2005)

Goyder, J.: The Silent Minority: Nonrespondents on Sample Surveys, 1st edn. Polity Pr, Cambridge (1987)

Groves, R.M., Cialdini, R.B., Couper, M.P.: Understanding the decision to participate in a survey. Public Opin. Q. 56(4), 475–495 (1992)CrossRef

Groves, R.M., Couper, M.: Nonresponse in Household Interview Surveys. Wiley, New York (1998)CrossRef

Groves, R.M., Presser, S., Dipko, S.: The role of topic interest in survey participation decisions. Public Opin. Q. 1, 2–31 (2004)CrossRef

Groves, R.M., Singer, E., Corning, A.: Leverage-Saliency theory of survey participation. Public Opin. Q. 64(3), 299–308 (2000)CrossRef

Heberlein, T.A., Baumgartner, R.: Factors affecting response rates to mailed questionnaires: a quantitative analysis of the published literature. Am. Sociol. Rev. 43(4), 447–462 (1978)CrossRef

Hill, D.H.: Adjusting for attrition in event-history analysis. Sociol. Methodol. 21(1), 393–416 (1997)CrossRef

Hitsch, G.J., Hortacsu, A., Ariely, D.: What Makes You Click? Mate Preferences in Online Dating.Quant. Mark. Econ. 8(4), 393–427 (2010)

van Kenhove, P., Wijnen, K., de Wulf, K.: The influence of topic involvement on mail-survey response behavior. Psychol. Mark. 19(3), 293–301 (2002)

Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data, 2nd edn. Wiley, Hoboken and NJ (2002)

Marcus, B., et al.: Compensating for low topic interest and long surveys: a field experiment on nonresponse in web surveys. Soc. Sci. Comput. Rev. 25(3), 372–383 (2007)CrossRef

Martin, C.L.: The impact of topic interest on mail survey response behaviour. J. Mark. Res. Soc. 36(4), 327–338 (1994)

Pawlowski, B., Koziel, S.: The impact of traits offered in personal advertisements on response rates. Evol. Hum. Behav. 23(2), 139–149 (2002)CrossRef

Penke, L., et al.: How self-assessments can guide human mating decisions. In: Geher, G., Miller, G.F. (eds.) Mating Intelligence, pp. 37–75. Lawrence Erlbaum, New York (2008)

Reuband, K.-H.: Möglichkeiten und Probleme des Einsatzes postalischer Befragungen. Kölner Zeitschrift für Soziologie und Sozialpsychologie 53(2), 307–333 (2001)CrossRef

Roose, H., Lievens, J., Waege, H.: The joint effect of topic interest and follow-up procedures on the response in a mail questionnaire: an empirical test of the Leverage-Saliency theory in audience research. Sociol. Methods Res. 35(3), 410–428 (2007)CrossRef

Roose, H., Waege, H., Agneessens, F.: Respondent related correlates of response behaviour in audience research. Qual. Quant. 37(4), 411–434 (2003)

Schmitz, A.: Virtuelle Zwischengeschlechtlichkeit im Kontext relationaler Methodologie. Überlegungen zu einer Soziologie der digitalen Partnerwahl”. In: Soeffner, H.-G. (eds.) Unsichere Zeiten. Herausforderungen gesellschaftlicher Transformationen. Verhandlungen des 34. Kongresses der Deutschen Gesellschaft für Soziologie in Jena 2008. Wiesbaden: VS Verlag. (2010)

Schmitz, A., Skopek, J.: Success in mating markets. A relational indicator of human mate value in E-Dating. Bamberg, University of Bamberg (Working paper of the project ’Processes of Mate Choice in Online Dating’) (2011)

Schnell, R.: Nonresponse in Bevölkerungsumfragen: Ausmaß Entwicklung und Ursachen. Leske + Budrich, Opladen (1997)CrossRef

Sheehan, K.B.: E-mail Survey Response Rates: A Review. In: Journal of Computer-Mediated Communication 6.2 (2001)

Sheehan, K.B., McMillan, S.J.: Response variation in E-Mail surveys: an exploration. J. Advert. Res. 39(4), 45–54 (1999)

Skopek, J.: Partnerwahl im Internet: Eine Quantitative Analyse von Strukturen und Prozessen der Online-Partnersuche, 1st edn. VS Verl. für Sozialwiss, Wiesbaden (2011)

Skopek, J., Schmitz, A., Blossfeld, H.-P.: The gendered dynamics of age preferences-empirical evidence from online dating. Zeitschrift für Familienforschung: ZfF 23(3), 276–290 (2011)

South, S.J.: Sociodemographic differentials in mate selection preferences. J. Marriage Fam. 53(4), 928–940 (1991)CrossRef

The American Association for Public Opinion Research (AAPOR): Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys. 7th edn. AAPOR (2011)

Todd, P.M., Miller, G.F.: From pride and prejudice to persuasion: Satisficing in mate search. In: Gigerenzer, G., Todd, P.M., ABC Research Group (eds.) Simple Heuristics That Make us Smart. Oxford: Oxford University Press, pp. 287–308 (1999)

Toma, C.L., Hancock, N.B., Ellison, J.T.: Separating fact from fiction: an examination of deceptive self-presentation in online dating profiles. Pers. Soc. Psychol. Bull. 34(8), 1023–1036 (2008)CrossRef

Windzio, M., Grotheer, M.: Bleiben die Erfolgreichen übrig? Die Kombination von Sequenzmusteranalyse und log-linearen Pfadmodellen bei der Analyse des Zusammenhangs von Berufserfolg und Panelmortalität. Zeitschrift für Soziologie 31(6), 514–528 (2002)

Zillmann, D., Schmitz, A., Blossfeld, H.-P.: Lügner haben kurze Beine. Zum Zusammenhang unwahrer Selbstdarstellung und partnerschaftlicher Chancen im Online-Dating. Zeitschrift für Familienforschung: ZfF 23(3), 291–318 (2011)

Titel: Survey topic and unit nonresponse
Evidence from an online survey on mating
verfasst von: Doreen Zillmann
Andreas Schmitz
Jan Skopek
Hans-Peter Blossfeld
Publikationsdatum: 01.07.2014
Verlag: Springer Netherlands
Erschienen in: Quality & Quantity / Ausgabe 4/2014
Print ISSN: 0033-5177
Elektronische ISSN: 1573-7845
DOI: https://doi.org/10.1007/s11135-013-9880-y

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 4/2014

Role of health expenditure and ICT in a small island economy: a study of Fiji

A model of state secularism

Extending monitoring methods to textual data: a research agenda

Gender differences in positive perceptions, anxiety, and depression among mothers and fathers of children with intellectual disabilities: a logistic regression analysis

Current practice in the modelling of age, period and cohort effects with panel data: a commentary on Tawfik et al. (2012), Clarke et al. (2009), and McCulloch (2012)

Leaders emergence in artificial populations: the role of networks

Premium Partner