Web Survey Bibliography
"Dynamic form" is the generic heading for dynamic text fields and dynamic lists, two innovative ways of reactive data collection in self-administered online surveys.
Dynamic forms are considered a
Open-ended questions do not pose limitations on the respondent in choosing an answer. Closed questions often are faster to answer with little mental effort, offer the benefit of ease to standardization, and data gathered from closed-ended questions need little time for coding and lend themselves to statistical analysis. At first glance, _dynamic text fields_ do not differ from ordinary HTML text fields. However, upon beginning with an entry, suggestions for the most probable word are offered in an area below the text field. With each new letter these suggestions are readapted. At http://labs.google.com/suggest Google shows an example for the use of this technique in a search engine.
By using _dynamic lists_, even questions with large numbers of response categories that can be brought into an hierarchical order, can be answered like closed-ended questions. At first, the respondent sees only a single table with very general categories. As soon as one of these categories is selected, more specific choices appear in a second table. Finding the appropriate answer is supported by gradually offering chunks of more detailed descriptions.
Both kinds of dynamic forms are suitable for the measurement of variables more possible values than feasible in tradition al closed-ended questions (e.g. subject of study or classification of occupations).
Dynamic forms provide new ground in online research and have not been examined yet, for example regarding their influence on the quality of data or the cognitive processes underlying the response behavior. We postulate that there is a change from recall to recognition when using dynamic forms instead of open-ended questions. Consequently, number and quality of responses should increase.
ln the experimental panel studies presented, dynamic text fields and lists were compared with radio buttons, drop-down menus and standard text fields. Thereby, the influence of implementing dynamic forms on motivation to participate in a study, response times and efforts needed to code data were examined.
"Dynamische Formulare" ist der Oberbegriff fOr dynamische Textfelder und dynamische Listen, zwei innovative Arten reaktiver Datenerhebung in selbstadministrierten Onlinebefragungen.
Dynamische Formulare werden als eine Web-2.0-Technik angesehen. Wir zeigen hier, dass diese Technik genutzt werden kann, um die Vorteile offener und geschlossener Fragetypen miteinander zu verbinden. Offenen Fragen beschranken die Antwortm6glichkeit des Befragten nicht durch Vorgaben; geschlossenen Fragen lassen sich haufig schneller und mit geringerer kognitiver Beanspruchung beantworten und bieten den Vorteil der einfachen Standardisierung. Zudem k6nnen Daten, die mit geschlossenen Fragen erhoben wurden, schnell fOr statistische Auswertungen vercodet werden.
_Dynamische Textfelder_ sehen auf den ersten Blick genauso aus wie herk6mmliche HTML-Textfelder. Sobaid jedoch mit der Texteingabe begonnen wird, erscheinen in einem Bereich unterhalb des Eingabefeldes Vorschlage, welches Wort gerade wahrscheinlich eingegeben wird. Mit jedem weiteren eingegebenen Zeichen passen sich die Vorschlage an. Auf http://labs.google.com/suggest findet sich ein Beispiel fOr die Anwendung dieser Technik in einer Suchmaschine. _Dynamische Listen_ erm6glichen die geschlossene Erhebung von Items mit einer groflen Zahl von Auspragungen, die sich hierarchisch ordnen lassen. Zunachst sieht der Nutzer nur eine Tabelle mit allgemeinen Kategorien. Sobaid auf der obersten Ebene eine Kategorie gewahlt wird, erscheinen in einer zweiten Tabelle speziellere Auswahlm6glichkeiten. Das Finden der zutreffenden Antwort wird durch das stufenweise Darbieten von Chunks mit detaillierteren Informationen unterstutzt.
Dynamische Formulare sind wissenschaftliches Neuland. Bisher wurde nicht erforscht, ob ihr Einsatz einen Einfluss auf die DatengUte oder die kognitiven Prozesse hat, die dem Antwortverhalten zugrunde liegen. Wir postulieren, dass auf kognitiver Ebene ein Wechsel von Recall zu Recognition stattfindet, wenn dynamische Formulare anstelle von offenen Fragen genutzt werden.
ln den vorgestellten experimentellen Panel-Studien wurden dynamische TextfeIder und Listen mit Radiobuttons, Drop-down-Mens und herk6mmlichen Textfeldern verglichen. Dadurch konnte der Einfluss dynamischer Formulare auf die Teilnahmemotivation, die Datenqualitat, die Responsezeit und den Kodieraufwand analysiert werden .
Web survey bibliography (366)
- Grundzüge des Datenschutzrechts und aktuelle Datenschutzprobleme in der Markt- und Sozialforschung; 2017; Schweizer, A.
- Web- and Phone-based Data Collection using Planned Missing Designs; 2017; Revelle, W.; Condon, M. D.; Wilt, J.; French, A. J.; Brown, A.; Elleman, G. L.
- Finding and Investigating Geographical Data Online; 2017; Martin, D.; Cockings, S.; Leung, S.
- CAQDAS at a Crossroads: Affordances of Technology in an Online Environment; 2017; Silver, C.; Bulloch, L. S.
- Artificial Intelligence/Expert Systems and Online Research; 2017; Brent, E.
- Improving the Effectiveness of Online Data Collection by Mixing Survey Modes; 2017; Dillman, D. A.; Hao, F.; Millar, M. M.
- Online Survey Software; 2017; Kaczmirek, L.
- Online Survey Design; 2017; To, N.
- Sampling Methods for Online Surveys; 2017; Fricker, R. D.
- Research Design and Tools for Online Research; 2017; Hewson, C. M.
- Overview: Online Surveys; 2017; Vehovar, V.; Lozar Manfreda, K.
- Using Visual Analogue Scales in eHealth: Non-Response Effects in a Lifestyle Intervention; 2016; Kuhlmann, T.; Reips, U.-D.; Wienert, J.; Lippke, S.
- A Feasibility Study of Recruiting and Maintaining a Web Panel of People with Disabilities; 2016; Chandler, J.
- Inferences from Internet Panel Studies and Comparisons with Probability Samples; 2016; Lachan, R.; Boyle, J.; Harding, R.
- Exploring the Gig Economy Using a Web-Based Survey: Measuring the Online 'and' Offline Side...; 2016; Robles, B. J.; McGee, M.
- Facebook, Twitter, & Qr codes: An exploratory trial examining the feasibility of social media mechanisms...; 2016; Gu, L. L.; Skierkowski, D.; Florin, P.; Friend, K.; Ye, Y.
- Distractions: The Incidence and Consequences of Interruptions for Survey Respondents ; 2016; Ansolabehere, S.; Schaffner, B. F.
- Mixing modes of data collection in Swiss social surveys: Methodological report of the LIVES-FORS mixed...; 2016; Roberts, C.; Joye, D.; Staehli, M. E.
- Representative web-survey!; 2016; Linde, P.
- The Analysis of Respondent’s Behavior toward Edit Messages in a Web Survey; 2016; Park, Y.
- Refining the Web Response Option in the Multiple Mode Collection of the American Community Survey; 2016; Hughes, T.; Tancreto, J.
- The Utility of an Online Convenience Panel for Reaching Rare and Dispersed Populations; 2016; Sell, R.; Goldberg, S.; Conron, K.
- Comparing online and telephone survey results in the context of a skin cancer prevention campaign evaluation...; 2016; Hollier, L.P.; Pettigrew, S.; Slevin, T.; Strickland, M.; Minto, C.
- Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk; 2016; Berinsky, A.; Huber, G. A.; Lenz, G. S.
- Setting Up an Online Panel Representative of the General Population The German Internet Panel; 2016; Blom, A. G.; Gathmann, C.; Krieger, U.
- Sample Representation and Substantive Outcomes Using Web With and Without Incentives Compared to Telephone...; 2016; Lipps, O.; Pekari, N.
- Effects of Data Collection Mode and Response Entry Device on Survey Response Quality; 2016; Ha, L.; Zhang, Che.; Jiang, W.
- Navigation Buttons in Web-Based Surveys: Respondents’ Preferences Revisited in the Laboratory; 2016; Romano Bergstrom, J. C.; Erdman, C.; Lakhe, S.
- Web-based versus Paper-based Survey Data: An Estimation of Road Users’ Value of Travel Time Savings...; 2016; Kato, H.; Sakashita, A.; Tsuchiya, Tak.
- Reminder Effect and Data Usability on Web Questionnaire Survey for University Students; 2016; Oishi, T.; Mori, M.; Takata, E.
- Reducing Underreports of Behaviors in Retrospective Surveys: The Effects of Three Different Strategies...; 2016; Lugtig, P. J.; Glasner, T.; Boeve, A.
- Dropouts in Longitudinal Surveys; 2016; Lugtig, P. J.; De Leeuw, E. D.
- Participant recruitment and data collection through Facebook: the role of personality factors; 2016; Rife, S. C.; Cate, K. L.; Kosinski, M.; Stillwell, D.
- What drives the participation in a monthly research web panel? The experience of ELIPSS, a French random...; 2016; Legleye, S; Cornilleau, A.; Razakamanana, N.
- Quantifying Under- and Overreporting in Surveys Through a Dual-Questioning-Technique Design. ; 2016; de Jong , M.; Fox, J.-P.; Steenkamp, J. - B. E. M.
- Take the money and run? Redemption of a gift card incentive in a clinician survey. ; 2016; Chen, J. S.; Sprague, B. L.; Klabunde, C. N.; Tosteson, A. N. A.; Bitton, A.; Onega, T.; MacLean, C....
- Electronic and paper based data collection methods in library and information science research: A comparative...; 2016; Tella, A.
- A Technical Guide to Effective and Accessible web Surveys; 2016; Baatard, G.
- The Validity of Surveys: Online and Offline; 2016; Wiersma, W.
- Methods can matter: Where Web surveys produce different results than phone interviews; 2016; Keeter, S.
- Computer-assisted and online data collection in general population surveys; 2016; Skarupova, K.
- Will They Stay or Will They Go? Personality Predictors of Dropout in Online Study; 2016; Nestler, S.; Thielsch, M.; Vasilev, E.; Back, M.
- A Framework of Incorporating Thai Social Networking Data in Online Marketing Survey; 2016; Jiamthapthaksin, R.; Aung, T. H.; Ratanasawadwat, N.
- Development of a scale to measure skepticism toward electronic word-of-mouth; 2016; Zhang, Xia.; Ko, M.; Carpenter, D.
- Improving social media measurement in surveys: Avoiding acquiescence bias in Facebook research; 2016; Kuru, O.; Pasek, J.
- Psychological research in the internet age: The quality of web-based data; 2016; Ramsey, S. R.; Thompson, K. L.; McKenzie, M.; Rosenbaum, A.
- Internet Abusive Use Questionnaire: Psychometric properties; 2016; Calvo-Frances, F.
- Revisiting “yes/no” versus “check all that apply”: Results from a mixed modes...; 2016; Nicolaas, G.; Campanelli, P.; Hope, S.; Jaeckle, A.; Lynn, P.
- A Statistical Approach to Provide Individualized Privacy for Surveys; 2016; Esponda, F.; Huerta, K.; Guerrero, V. M.
- Online and Social Media Data As an Imperfect Continuous Panel Survey; 2016; Diaz, F.; Garmon, F.; Hofman, J. K.; Kiciman, E.; Rothschild, D.