Factorial Survey Experiments

2015 ◽  
Author(s):  
Katrin Auspurg ◽  
Thomas Hinz
Keyword(s):  
Factorial Survey ◽  
Survey Experiments

What Can We Learn From Factorial Surveys About Human Behavior?

Methodology ◽  
2019 ◽  
Vol 15 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Knut Petzold ◽  
Tobias Wolbring
Keyword(s):  
Social Sciences ◽  
Behavioral Intentions ◽  
Actual Behavior ◽  
Full Time ◽  
Factorial Survey ◽  
Survey Experiment ◽  
Intended Behavior ◽  
The Social ◽  
Survey Experiments ◽  
High Level

Abstract. Factorial survey experiments are increasingly used in the social sciences to investigate behavioral intentions. The measurement of self-reported behavioral intentions with factorial survey experiments frequently assumes that the determinants of intended behavior affect actual behavior in a similar way. We critically investigate this fundamental assumption using the misdirected email technique. Student participants of a survey were randomly assigned to a field experiment or a survey experiment. The email informs the recipient about the reception of a scholarship with varying stakes (full-time vs. book) and recipient’s names (German vs. Arabic). In the survey experiment, respondents saw an image of the same email. This validation design ensured a high level of correspondence between units, settings, and treatments across both studies. Results reveal that while the frequencies of self-reported intentions and actual behavior deviate, treatments show similar relative effects. Hence, although further research on this topic is needed, this study suggests that determinants of behavior might be inferred from behavioral intentions measured with survey experiments.

scholarly journals Measurement of Fairness Perceptions in Energy Transition Research: A Factorial Survey Approach

2020 ◽  
Vol 12 (19) ◽  
pp. 8084
Author(s):  
Ulf Liebe ◽  
Geesche M. Dobers
Keyword(s):  
Renewable Energy ◽  
Energy Transition ◽  
Factorial Survey ◽  
Perceived Fairness ◽  
Energy Research ◽  
Production And Consumption ◽  
Energy Projects ◽  
Survey Experiments ◽  
Transition Research ◽  
Survey Approach

Justice and fairness are increasingly popular concepts in energy research and comprise several justice dimensions, including distributive and procedural justice, related to energy production and consumption. In this paper, we used factorial survey experiments—a method employed in sociological justice research—for energy transition research. In a factorial survey, respondents evaluated one or more situations described by several attributes, which varied in their levels. The experimental setup of factorial surveys is one of its advantages over simple survey items, as based on this, the relative importance of each attribute for justice evaluations can be determined. We employed the method in a study on the perceived fairness of renewable energy expansion projects related to wind energy, solar energy, and biomass in Germany, and considered aspects of procedural and distributive justice. We show that the effects of these justice dimensions can be separated and the heterogeneity in justice evaluations can be explained. Compared to previous studies applying factorial survey experiments to explain the acceptance of renewable energy projects, we employed the method to directly measure justice concerns and asked respondents to evaluate the vignettes in terms of perceived fairness. This is important because acceptance and fairness as well as inequality and injustice are different phenomena.

scholarly journals Using Factorial Survey Experiments to Measure Attitudes, Social Norms, and Fairness Concerns in Developing Countries

2017 ◽  
Vol 49 (1) ◽  
pp. 161-192 ◽  
Author(s):  
Ulf Liebe ◽  
Ismaïl M. Moumouni ◽  
Christine Bigler ◽  
Chantal Ingabire ◽  
Sabin Bieri
Keyword(s):  
Developing Countries ◽  
Social Norms ◽  
Social Science Research ◽  
Developed Countries ◽  
Global South ◽  
Physical Punishment ◽  
Remote Area ◽  
Factorial Survey ◽  
The Social ◽  
Survey Experiments

Survey-based experimental methods are increasingly used in the social sciences to study, among others, attitudes, norms, and fairness judgments. One of these methods is the factorial survey experiment (FSE or vignette experiment) in which respondents are confronted with various descriptions of situations that differ in a discrete number of attributes (or factors), and they are asked to evaluate those situations according to criteria such as agreement, approval, and fairness. Due to the systematic experimental variation of the presented situations, an FSE can separate effects of single situational attributes, allowing the causal influence of relevant situational attributes to be determined. This is the key advantage over simple survey items. While most studies using FSEs are carried out in developed countries in which respondents are familiar with surveys, we add further evidence that this method can also unfold its power in a developing context. Building on previous applications of FSEs in Africa, we demonstrate the usefulness of this method in four novel studies on social norms regarding the physical punishment of children and the social approval of technology adoption in Benin as well as judgments of just earnings in Rwanda. We also test for the first time the applicability of multiple vignettes per respondents in a Global South/remote area context. The results of these studies are theoretically meaningful and the overwhelming majority of respondents discriminate between vignettes. This supports the validity of FSEs. However, conducting survey experiments in developing countries is different from similar experimental research in developed countries and, therefore, we also discuss some of these differences and corresponding challenges. Last but not least, our article shows, provided a few precautions are heeded, that FSEs could be used as a vehicle to innovate social science research in a Global South/remote area context.

scholarly journals Statistical modeling of vignette data in psychology

2021 ◽  
Author(s):  
Thom Baguley ◽  
Grace Dunham ◽  
Oonagh Steer
Keyword(s):  
Multilevel Models ◽  
Factorial Survey ◽  
Efficient Design ◽  
Data Set ◽  
Random Factor ◽  
Unbalanced Designs ◽  
The Social ◽  
Survey Experiments ◽  
Nested Designs ◽  
Selection Of

Vignette methods are widely used in psychology and the social sciences to obtain responses to multi-dimensional scenarios or situations. Where quantitative data are collected this presents challenges to the selection of an appropriate statistical model. This depends on subtle details of the design and allocation of vignettes to participants. A key distinction is between factorial survey experiments where each participant receives a different allocation of vignettes from the full universe of possible vignettes and experimental vignette studies where this restriction is relaxed. The former leads to nested designs with a single random factor and the latter to designs with two crossed random factors. In addition, the allocation of vignettes to participants may lead to fractional or unbalanced designs and a consequent loss of efficiency or aliasing of the effects of interest. Many vignette studies (including some factorial survey experiments) include unmodeled heterogeneity between vignettes leading to potentially serious problems if traditional regression approaches are adopted. These issues are reviewed and recommendations are made for the efficient design of vignette studies including the allocation of vignettes to participants. Multilevel models are proposed as a general approach to handling nested and crossed designs including unbalanced and fractional designs. This is illustrated with a small vignette data set looking at judgements of online and offline bullying and harassment.

scholarly journals Public attitudes toward pandemic triage: Evidence from conjoint survey experiments in Switzerland

2021 ◽  
pp. 114238
Author(s):  
Carlo Michael Knotz ◽  
Mia Katharina Gandenberger ◽  
Flavia Fossati ◽  
Giuliano Bonoli
Keyword(s):  
Public Attitudes ◽  
Survey Experiments

Increasing Precision without Altering Treatment Effects: Repeated Measures Designs in Survey Experiments

2021 ◽  
pp. 1-18
Author(s):  
SCOTT CLIFFORD ◽  
GEOFFREY SHEAGLEY ◽  
SPENCER PISTON
Keyword(s):  
Treatment Effect ◽  
Repeated Measures ◽  
Treatment Effects ◽  
Repeated Measurements ◽  
Experimental Designs ◽  
Treatment Effect Size ◽  
Standard Design ◽  
Survey Experiments ◽  
Repeated Measures Designs ◽  
Alternative Designs

The use of survey experiments has surged in political science. The most common design is the between-subjects design in which the outcome is only measured posttreatment. This design relies heavily on recruiting a large number of subjects to precisely estimate treatment effects. Alternative designs that involve repeated measurements of the dependent variable promise greater precision, but they are rarely used out of fears that these designs will yield different results than a standard design (e.g., due to consistency pressures). Across six studies, we assess this conventional wisdom by testing experimental designs against each other. Contrary to common fears, repeated measures designs tend to yield the same results as more common designs while substantially increasing precision. These designs also offer new insights into treatment effect size and heterogeneity. We conclude by encouraging researchers to adopt repeated measures designs and providing guidelines for when and how to use them.

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