Web Survey Bibliography
Reason analysis is a long sidelined method of data collection and analysis. I would like to draw attention to the applicability of the method today for addressing current complex social research problems. In my paper I will recount the roots and principles of the method and how it is applied. In conclusion I will present some examples of it in use and some of the problems connected with its practical application in research.
Reason analysis is an analysis of the individual reasons and motives behind the process of decision
‐making relating to various questions. It can be applied in social research generally, in public opinion research, and in market research. The method and principles of reason analysis were first expounded by Paul Felix Lazarsfeld in his article The Art of Asking “Why?” published in 1935. Over the next thirty years it was employed several times as part of the “Princeton Radio Project” and in research conducted by the Columbia Sociology School. Charles Kadushin wrote the entry on “reason analysis” in the International Encyclopedia of Social Sciences. The method then fell into obscurity for the next forty years.
The principles and approach to reason analysis: The method derives from an attempt to address the variance between a question posed generally and the individual ways in which people answer “why” questions. In the first step we ask respondents a simple question about what reason led them to make the decision they did. This decision may be a serious life decision, such as deciding to marry their chosen partner, moving into a new home, visiting a psychiatrist, or, conversely, smaller individual decisions, such as buying a new car, or a particular cosmetic product. The fact is that people usually give just one reason for their decision. Data obtained this way cannot be processed by simply categorising respondents according to what type of response they gave to this one introductory question. Every respondent naturally has all sorts of reasons for their decision. What is important is that we can hear various types of responses to the introductory question from the same mouth. The reason for buying a new Citroen C3 in light blue may be a personal preference for this brand and the person’s old car has just broken down and the cost of repairs is rising. An important motive may be that the opportunity arises to write an old car off for scrap while there is also a sale on a particular new model. The person’s partner may have heard something complimentary said about a particular model of car. The brand, model, and its accessories may be recommended by the showroom salesperson while a TV commercial aired last Sunday also encouraged a person to buy. A combination of any or all these reasons is the only correct and full answer to the original question. The “reason analysis” method looks for and proposes a concrete “tree” of questions, an “accounting scheme”. The next step is preparation of the structured interview in which variant questions are posed about the “quality of a product”, both the product replaced and the newly bought one, how the product was evaluated in advertising, by the seller, by friends and acquaintances, and the “circumstances of the situation”. In the end, it is necessary to group the responses into classes and types according to which responses are most alike and where significant differences are between them. The method did not catch on mainly owing the demands it puts on researchers. In most survey type research it was replaced by the factorial approach, which examines the effect of individual causes (influential factors) jointly for an entire sample of individuals or other units.
The question for today is whether this method has a place in current social research. The paper presents examples of research situations which directly require individualised models of data collection and whose objective is to reveal and analyse further typologies of actors making certain decisions in a given situation and under the influence of individual factors.
Conference homepage (abstract)
Web survey bibliography (367)
- Displaying Videos in Web Surveys: Implications for Complete Viewing and Survey Responses; 2017; Mendelson, J.; Lee Gibson, J.; Romano Bergstrom, J. C.
- Ideal and maximum length for a web survey; 2017; Revilla, M.; Ochoa, C.
- Handbook of Research Methods in Health Social Sciences; 2017; Liamputtong, P.
- Web Survey Gamification - Increasing Data Quality in Web Surveys by Using Game Design Elements; 2017; Schacht, S.; Keusch, F.; Bergmann, N.; Morana, S.
- Effects of sampling procedure on data quality in a web survey; 2017; Rimac, I.; Ogresta, J.
- Comparability of web and telephone surveys for the measurement of subjective well-being; 2017; Sarracino, F.; Riillo, C. F. A.; Mikucka, M.
- A Meta-Analysis of the Effects of Incentives on Response Rate in Online Survey Studies; 2017; Mohammad Asire, A.
- Interviewer effects on onliner and offliner participation in the German Internet Panel; 2017; Herzing, J. M. E.; Blom, A. G.; Meuleman, B.
- Interviewer Gender and Survey Responses: The Effects of Humanizing Cues Variations; 2017; Jablonski, W.; Krzewinska, A.; Grzeszkiewicz-Radulska, K.
- Comparing the same Questionnaire between five Online Panels: A Study of the Effect of Recruitment Strategy...; 2017; Schnell, R.; Panreck, L.
- Do distractions during web survey completion affect data quality? Findings from a laboratory experiment...; 2017; Wenz, A.
- Predicting Breakoffs in Web Surveys; 2017; Mittereder, F.; West, B. T.
- The 2016 Canadian Census: An Innovative Wave Collection Methodology to Maximize Self-Response and Internet...; 2017; Mathieu, P.
- Push2web or less is more? Experimental evidence from a mixed-mode population survey at the community...; 2017; Neumann, R.; Haeder, M.; Brust, O.; Dittrich, E.; von Hermanni, H.
- In search of best practices; 2017; Kappelhof, J. W. S.; Steijn, S.
- The perils of non-probability sampling; 2017; Bethlehem, J.
- Nonresponse in Organizational Surveying: Attitudinal Distribution Form and Conditional Response Probabilities...; 2017; Kulas, J. T.; Robinson, D. H.; Kellar, D. Z.; Smith, J. A.
- Theory and Practice in Nonprobability Surveys: Parallels between Causal Inference and Survey Inference...; 2017; Mercer, A. W.; Kreuter, F.; Keeter, S.; Stuart, E. A.
- Reducing speeding in web surveys by providing immediate feedback; 2017; Conrad, F.; Tourangeau, R.; Couper, M. P.; Zhang, C.
- A Working Example of How to Use Artificial Intelligence To Automate and Transform Surveys Into Customer...; 2017; Neve, S.
- A Case Study on Evaluating the Relevance of Some Rules for Writing Requirements through an Online Survey...; 2017; Warnier, M.; Condamines, A.
- Estimating the Impact of Measurement Differences Introduced by Efforts to Reach a Balanced Response...; 2017; Kappelhof, J. W. S.; De Leeuw, E. D.
- Targeted letters: Effects on sample composition and item non-response; 2017; Bianchi, A.; Biffignandi, S.
- Analyzing Survey Characteristics, Participation, and Evaluation Across 186 Surveys in an Online Opt-...; 2017; Revilla, M.
- Careless Response and Attrition as Sources of Bias in Online Survey Assessments of Personality Traits...; 2017; Meade, A. W.; Ward, M. K.; Alfred, C. M.; Pappalardo, G.; Stoughton, J. W.
- Do Incentives Increase Response Rates to an Internet Survey of American Evaluation Association Members...; 2017; Wilson, L. N.
- Examining Completion Rates in Web Surveys via Over 25,000 Real-World Surveys; 2017; Liu, M.; Wronski, L.
- Data collection mode differences between national face-to-face and web surveys on gender inequality...; 2017; Liu, M.
- Improving survey response rates: The effect of embedded questions in web survey email Invitations; 2017; Liu, M.; Inchausti, N.
- An experimental comparison of web-push vs. paper-only survey procedures for conducting an in-depth health...; 2017; McMaster, H. S.; LeardMann, C. A.; Speigle, S.; Dillman, D. A.
- Demographic Question Placement: Effect on Item Response Rates and Means of a Veterans Health Administration...; 2017; Teclaw, R.; Price, M.; Osatuke, K.
- Effects of Applying Multimedia and Dialogue Box to Web Survey Design; 2017; Chen, H.
- Role of online survey tools in creating temporally accurate Environmental Product Declarations (EPD)...; 2017; Ganguly, I.; Bowers, T.; Pierobon, F.; Eastin, I.
- A test of sample matching using a pseudo-web sample; 2017; Chatrchi, G., Gambino, J.
- A Partially Successful Attempt to Integrate a Web-Recruited Cohort into an Address-Based Sample; 2017; Kott, P. S., Farrelly, M., Kamyab, K.
- Grundzüge des Datenschutzrechts und aktuelle Datenschutzprobleme in der Markt- und Sozialforschung; 2017; Schweizer, A.
- Data chunking for mobile web: effects on data quality; 2017; Lugtig, P. J.; Toepoel, V.
- Comparing data quality and cost from three modes of on-board transit surveys ; 2017; Agrawal, A. W.; Granger-Bevan, S.; W.; Newmark, G. L.; Nixon, H.
- Finding and Investigating Geographical Data Online; 2017; Martin, D.; Cockings, S.; Leung, S.
- Three Methods for Occupation Coding Based on Statistical Learning; 2017; Geweon, H.; Schonlau, L.; Blohum, M.; Steiner, St.
- Dynamic Question Ordering in Online Surveys; 2016; Early, K.; Mankoff, J.; Fienberg, S. E.
- How to use online surveys to understand human behaviour concerning window opening in terms of building...; 2016; Fabbri, K.
- Impact of satisficing behavior in online surveys on consumer preference and welfare estimates; 2016; Gao, Z.; House, L. A.; Bi, X.
- Targeted Appeals for Participation in Letters to Panel Survey Members; 2016; Lynn, P.
- Can we assess representativeness of cross-national surveys using the education variable?; 2016; Ortmanns, V.; Schneider, S.
- Methodological Aspects of Central Left-Right Scale Placement in a Cross-national Perspective; 2016; Scholz, E.; Zuell, C.
- Fieldwork Effort, Response Rate, and the Distribution of Survey Outcomes: A Multilevel Meta-analysis; 2016; Sturgis, P.; Williams, Jo.; Brunton-Smith, I.; Moore, J.
- Comparison of Face-to-Face and Web Surveys on the Topic of Homosexual Rights; 2016; Liu, M.; Wang, Yic.
- Question order sensitivity of subjective well-being measures: focus on life satisfaction, self-rated...; 2016; Lee, S.; McClain, C.; Webster, N.; Han, S.
- Web-Based Statistical Sampling and Analysis; 2016; Quinn, A.; Larson, K.