Survey Research in Tourism: A perspective paper

Survey research and Tourism Review are practically twins; they were ‘born’ almost at the same time – some 75 years ago (Happy Birthday, Tourism Review!). Analysing the history of survey research, Groves (2011) identifies that, initially (1930-1960), the focus was on developing the core components of survey methodology. After that (1960-1990), demand for survey research skyrocketed, fueled by the increasing popularity of quantitative social science. Since 1990, survey research is facing significant challenges: declining response rates, lack of representatively, fraudulent responding, respondent fatigue (GRIT, 2017), and respondents not reading instructions, survey questions, and answer options (Brosnan, Babakhani & Dolnicar, 2019). These challenges undermine the validity of survey research. Yet, tourism researchers have enthusiastically embraced the survey as their preferred mode of data collection. This perspective article discusses where we stand after 75 years of survey research. It argues that we may stand in the wrong spot, and offers a set of questions to contemplate before conducting the next survey study to ensure maximum validity of conclusions.

DieTryin: An R package for data collection, automated data entry, and post-processing of network-structured economic games, social networks, and other roster-based dyadic data

Researchers studying social networks and inter-personal sentiments in bounded or small-scale communities face a trade-off between the use of roster-based and free-recall/name-generator-based survey tools. Roster-based methods scale poorly with sample size, and can more easily lead to respondent fatigue; however, they generally yield higher quality data that are less susceptible to recall bias and that require less post-processing. Name-generator-based methods, in contrast, scale well with sample size and are less likely to lead to respondent fatigue. However, they may be more sensitive to recall bias, and they entail a large amount of highly error-prone post-processing after data collection in order to link elicited names to unique identifiers. Here, we introduce an R package, DieTryin, that allows for roster-based dyadic data to be collected and entered as rapidly as name-generator-based data; DieTryin can be used to run network-structured economic games, as well as collect and process standard social network data and round-robin Likert-scale peer ratings. DieTryin automates photograph standardization, survey tool compilation, and data entry. We present a complete methodological workflow using DieTryin to teach end-users its full functionality.

Planned Missingness: How to and How Much?

Planned missingness (PM) can be implemented for survey studies to reduce study length and respondent fatigue. Based on a large sample of Big Five personality data, the present study simulates how factors including PM design (three-form and random percentage [RP]), amount of missingness, and sample size affect the ability of full-information maximum likelihood (FIML) estimation to treat missing data. Results show that although the effectiveness of FIML for treating missing data decreases as sample size decreases and amount of missing data increases, estimates only deviate substantially from truth in extreme conditions. Furthermore, the specific PM design, whether it be a three-form or RP design, makes little difference although the RP design should be easier to implement for computer-based surveys. The examination of specific boundary conditions for the application of PM as paired with FIML techniques has important implications for both the research methods literature and practitioners regularly conducting survey research

Malignant Pleural Effusions Impact on Fatigue (IMPE-F): A Prospective Observational Cohort Pilot Study

Introduction: Cancer-related fatigue is well described. Fatigue in patients with a malignant pleural effusion (MPE) has not been directly studied. Methods: A prospective observational cohort pilot study ‘Do Interventions for Malignant Pleural Effusions (MPE) impact on patient reported fatigue levels (IMPE-F study)’ is planned to determine whether pleural interventions reduce fatigue in MPE. Fatigue will be assessed with a validated patient reported outcome measure, FACIT-F. Discussion: MPE-F has funding from Rocket Medical Plc, and is part of a Masters in Clinical Research at Newcastle University. Respondent fatigue will be addressed by the investigators going through the questionnaire with the participants. Inclusion criteria are all patients above 18 years of age with a presumed MPE undergoing a procedure and able to consent. The expected number of participants is 50. Trial registration: The IMPE-F study has Research Ethics Committee (REC) [20/YH/0224] and Health Research Authority (HRA) and Health and Care Research Wales (HCRW) approvals [IRAS project ID: 276451]. The study has been adopted on National Institute for Health Research portfolio [CPMS ID 46430].

Data Quality in Web and App Diaries: A Person-Level Comparison

The time-use diary is a complex and burdensome data collection instrument. This can negatively affect data quality, leading to less detailed and/or inaccurate activity reporting as the surveyed time period unfolds. However, it can also be argued that data quality may actually improve over time as respondents become more familiar with the diary instrument format and more interested in the diary task. These competing hypotheses have only been partially tested on data from paper and telephone-administered diaries, which are traditionally used for large-scale data collection. Less is known about self-administered modes that make use of new technologies, despite their increasing popularity among researchers. This research note rectifies this omission by comparing diary quality in self-administered web and app diaries, drawing on data from the Millennium Cohort Study. We construct a person-level data quality typology, using information on missing data, episode changes, and reporting of key daily activity domains. Results show significant mode differences on person-level data quality, after controlling for characteristics known to influence diary mode selection and data quality. App diarists were more likely to return two diaries of inconsistent quality. Both respondent fatigue and improvement of completion over time appear more common among app diarists.

Data Quality in Web and App Diaries: A Person-Level Comparison

The time-use diary is a complex and burdensome data collection instrument. This can negatively affect data quality, leading to less detailed and/or inaccurate activity reporting as the surveyed time period unfolds. However, it can also be argued that data quality may actually improve over time as respondents become more familiar with the diary instrument format and more interested in the diary task. These competing hypotheses have only been partially tested on data from paper and telephone-administered diaries, which are traditionally used for large-scale data collection. Less is known about self-administered modes that make use of new technologies, despite their increasing popularity among researchers. This research note rectifies this omission by comparing diary quality in self-administered web and app diaries, drawing on data from the Millennium Cohort Study. We construct a person-level data quality typology, using information on missing data, episode changes, and reporting of key daily activity domains. Results show significant mode differences on person-level data quality, after controlling for characteristics known to influence diary mode selection and data quality. App diarists were more likely to return two diaries of inconsistent quality. Both respondent fatigue and improvement of completion over time appear more common among app diarists.

Respondent Fatigue in Estimates of the Cost of White-Collar Crime: Implications From Willingness-to-Pay Surveys

Contingent valuation (CV) methods are used in many contexts to estimate non-tangible costs, despite some indications that they may not be reliable. In criminal justice, CV has been used to generate “costs of crime” for street, violent, and white-collar crimes. This article explores respondent fatigue using both quantitative and qualitative indicators from an open-ended CV survey where respondents were asked how much they would be willing to pay to reduce certain crimes. Our findings reveal that willingness to pay (WTP) to reduce crime increases when both problematic response patterns and fatigue effects are accounted for in the calculation, indicating that fatigued respondents who also engage in straight lining are driving the WTP estimates down. We conclude by discussing the implications of our results for policymakers and other consumers of CV studies.

"People come and go but we don’t see anything”: How Might Social Research Contribute to Social Change?

In different fields of study, scholars interested in making a positive difference in the lives of their research communities insist on engaging policy makers and activists in their work. Paulo Freire, one of the most widely known public intellectuals, asserts that praxis enables critical thought, awareness and collaborative action for emancipation for oppressed groups. Within this framework, our contribution aims to provoke thinking on the need for accountability to research subjects in development research through an emphasis on producing policy-focused and change-driven, as opposed to purely theoretically oriented, knowledge. The overarching argument is that research should, in fact, be conscious and proactive about its contribution to positive social change. Drawing on primary data gathered through field research in Ghana between 2010 and 2016, the paper highlights respondent fatigue/distrust, cross-cultural translation, and the peculiarities of the diasporic researcher as some of the methodological challenges faced in the attempt to align one’s research towards the pursuit of positive social change. Some modest suggestions are provided regarding how to enhance the impact of research for social transformation.

Listing at Multiple Stages

Free listing exercises are common and informative ways to explore shared bodies of knowledge and practice, and they can probe widely experienced phenomena in daily life. However, even with a representative sample, this method can suffer under common conditions in anthropological fieldwork: Respondents forget to list items, they fail to provide exhaustive lists, they become fatigued during the interview process, and their responses may not provide representative information. In this article, I suggest an iterative process that combines targeted free listing with key informants, walking probes, and a survey checklist. During an investigation of agricultural biodiversity in Telangana, India, this approach generated more comprehensive lists than free listing exercises alone. This process generates checklist surveys appropriate for larger research populations and can be used to assess widespread knowledge and practices quickly, accurately, and with minimal respondent fatigue.

Impact of degree truncation on the spread of a contagious process on networks

Understanding how person-to-person contagious processes spread through a population requires accurate information on connections between population members. However, such connectivity data, when collected via interview, is often incomplete due to partial recall, respondent fatigue, or study design, e.g. fixed choice designs (FCD) truncate out-degree by limiting the number of contacts each respondent can report. Research has shown how FCD affects network properties, but its implications for predicted speed and size of spreading processes remain largely unexplored. To study the impact of degree truncation on predictions of spreading process outcomes, we generated collections of synthetic networks containing specific properties (degree distribution, degree-assortativity, clustering), and used empirical social network data from 75 villages in Karnataka, India. We simulated FCD using various truncation thresholds and ran a susceptible-infectious-recovered (SIR) process on each network. We found that spreading processes on truncated networks resulted in slower and smaller epidemics, with a sudden decrease in prediction accuracy at a level of truncation that varied by network type. Our results have implications beyond FCD to truncation due to any limited sampling from a larger network. We conclude that knowledge of network structure is important for understanding the accuracy of predictions of process spread on degree truncated networks.