Web Survey Bibliography
Purpose: Patient-reported outcome (PRO) scores are used to evaluate treatment modalities in orthopaedic surgery. The method of PRO collection may introduce bias to reported surgical outcomes due to the presence of an interviewer. This study evaluates post-operative PROs for variation of outcomes between survey methods—in-person, online, or telephone.
Methods: From 2008 to 2011, 456 patients underwent arthroscopic surgical treatment for acetabular labral tears. All pre-operative surveys were completed in the clinic during pre-operative visit. Two-year follow-up questionnaires were completed by 385 (84 %) patients. The PRO data were prospectively collected pre- and post-operatively using five tools: modified Harris Hip Score (mHHS), Hip Outcome Score Activities of Daily Living (HOS-ADLS), Hip Outcome Score Sports-Specific Subscale (HOS-SSS), Non-Arthritic Hip Score (NAHS), and visual analog scale. Patients were grouped according to method of 2-year follow-up: in-person during follow-up visit (102 patients, 26 %), online by email prompt (138 patients, 36 %), or telephone with an interviewer (145 patients, 38 %).
Results: Pre-operative baseline PRO scores demonstrated no statistically significant difference between groups for mHHS, HOS-ADLS, HOS-SSS, and NAHS. Two-year post-operative PRO scores obtained by telephone were statistically greater than scores obtained in-person or online for mHHS (p < 0.001), HOS-ADLS (p < 0.001), and HOS-SSS (p < 0.01).
Conclusion: This study demonstrates higher patient-reported outcome scores and greater improvement by telephone surveys compared to in-person or online. The variation of results between collection methods is indicative of a confounding variable. Clinically, it is important to understand these confounding variables in order to assess patient responses and guide treatment.
Web survey bibliography (8195)
- Mobile Research im Kontext der digitalen Transformation; 2017; Friedrich-Freksa, M.
- Kognitives Pretesting; 2017; Neuert, C.
- GrundzŁge des Datenschutzrechts und aktuelle Datenschutzprobleme in der Markt- und Sozialforschung; 2017; Schweizer, A.
- Survey mode influence on patient-reported outcome scores in orthopaedic surgery: telephone results may...; 2017; Hammarstedt, J. E.; Redmond, J. M.; Gupta, As.; Dunne, K. F.; Vemula, S. P.; Domb, B. G.
- Marketing survey research best practices: evidence and recommendations from a review of JAMS articles...; 2017; Hulland, J.; Baumgartner, H.; Smith, K. M.
- Comparative analysis of a mobile device and paper as effective survey tools; 2017; Kim, K. J.; Bae, S.; Park, E.
- Enhancing survey participation: Facebook advertisements for recruitment in educational research; 2017; Forgasz, H.; Tan, H.; Leder, G.; McLeod, A.
- Determinants of polling accuracy: the effect of opt-in Internet surveys; 2017; Sohlberg, J.; Gilljam, M.; Martinsson, J.
- Continuity of Web-Survey Completion and Response Behavior; 2017; Karem Hoehne, J.; Schlosser, S.
- Implications of disposition codes for monitoring breakoffs in web surveys; 2017; Cehovin, G.; Vehovar, V.
- Exploring the Influence of Respondents' IT Literacy on Nonresponse in an Online Survey; 2017; Herzing, J. M. E.; Blom, A. G.
- Personalized Feedback in Web Surveys: Does It Affect Respondent Motivation and Data Quality?; 2017; Kuehne, S.; Kroh, M.
- No pay, no gain. The relationship between monetary and non-monetary motivation to participate in web...; 2017; Achimescu, V.; Keusch, F.; Liu, M.
- Day of the week and time of the day for survey dispatch. Two large-scale randomized experiments.; 2017; Andreasson, M.; Martinsson, J.; Markstedt, E.
- Making Conjoint questionnaires more realistic: the effect of random noise and visual presentation on...; 2017; Dobney, S. M.; Ochoa, C.; Revilla, M.
- What do we know about mixed-device online surveys and mobile device use in the UK?; 2017; Maslovskaya, O.; Durrant, G.; Smith, P.
- Understanding mobile respondents and their importance for representative samples: attitudes, behavior...; 2017; Livadic, D.; Badita, M.
- Smartphones Uses Trends 2013-2016: A Digital Divide Perspective; 2017; Ariel, Y.; Levy, E. C.
- Social networking sites as sampling tools – An example from migration research; 2017; Poetzschke, S.; Braun, M.
- Learning from Mouse Movements: Improving Web Questionnaire and Respondents’ User Experience through...; 2017; Keusch, F.; Brockhaus, S.; Henninger, F.; Horwitz, R.; Kreuter, F.; Schierholz, M.
- Automatic versus Manual Forwarding in Web Surveys; 2017; Selkaelae, A.; Couper, M. P.
- Willingness of online panelists to perform additional tasks; 2017; Revilla, M.; Couper, M. P.
- Conversational Survey Frontends: How Can Chatbots Improve Online Surveys?; 2017; Harms, C.; Schmidt, S.
- Evaluation of Agree-Disagree Versus Construct-Specific Scales in a Multi-Device Web Survey; 2017; Kunz, T.
- Is Higher Endorsement in Yes-No Grids Due to Acquiescence Bias vs. Salience in Response?; 2017; Thomas, R. K.; Barlas, F. M.; Buttermore, N. R.; Smyth, J. D.
- Clarification features in close ended questions and their impact on scale effects; 2017; Metzler, A.; Fuchs, M.
- Focus on mobile surveys: Do the number of scale points and scale order affect rating scale results?; 2017; Kraemer, A.
- The Role Played by the Device Screen Size and by the Questionnaire Optimization within the Mobile Survey...; 2017; Toninelli, D.; Revilla, M.
- Device effects on behaviour and participation in mobile-optimised online diaries; 2017; Heeck, A.; Holdt, C.
- Smartphones as digital companions; 2017; Carolus, A.; Schneider, F.; Muench, R.; Schmidt, C.; Binder, J.
- Browsing vs. Searching – Exploring the influence of consumers’ goal directedness on website...; 2017; Dames, H.
- Determinants of Item Nonresponse in the German Internet Panel; 2017; Burgdorf, K.
- Does the Exposure to an Instructed Response Item Attention Check Affect Response Behavior?; 2017; Gummer, T.; Rossmann, J.; Silber, H.
- How Stable is Satisficing in Online Panel Surveys?; 2017; Rossmann, J.
- Is Clean Data Good Data?: Data Cleaning and Bias Reduction; 2017; Thomas, R. K.; Barlas, F. M.; Buttermore, N. R.
- The good, the bad and the ugly data: using indicators to get high quality survey respondents from online...; 2017; Althaus, D.
- Big Insights through Big Data Analytics; 2017; Huyghe, N.
- Virtual reality meets sensory research; 2017; Depoortere, L.
- Artificial Intelligence in Market Research: hype or tomorrow’s business-as-usual?; 2017; De Ruyck, T.
- Searching for Equivalence: An Exploration of the Potential of Online Probing with Examples from National...; 2017; Meitinger, K.
- The Effects on Data Quality of Horizontal and Vertical Question Orientation and Scales of Different...; 2017; Martinsson, J.; Dumitrescu, D.; Markstedt, E.
- Adapting Questionnaires for Smartphones: An Experiment on Grid Format Questions; 2017; Hanson, T.
- Mobile-Friendly Grid Questions: The Accordion Grid as an Alternative to the Traditional Grid; 2017; Barlas, F. M.; Thomas, R. K.; Buttermore, N. R.
- Read It From My Fingertips – Can Typing Behaviour Help Us to Predict Motivation and Answer Quality...; 2017; Hoermann, M.; Bannert, M.
- Balancing Twitter data with survey information to predict electoral outcomes; 2017; Fano, S.; Slanzi, D.
- Effects of additional reminders on survey participation and panel unsubscription; 2017; Andreasson, M.; Martinsson, J.; Markstedt, E.
- Pictures in Online Surveys: To Greet or Avoid?; 2017; Schmid, M.; Batinic, B.
- Asking for Consent to the Collection of Geographical Information; 2017; Felderer, B.; Blom, A. G.
- The influence of Forced Answering on response behavior in Online Surveys: A reactance effect?; 2017; Sischka, P.; Mergener, A.; Neufang, K.; Decieux, J. P.
- Impact of using profiling or passive data to select the sample of web surveys; 2017; Revilla, M.; Ochoa, C.