A Web-based Event History Calendar Approach for Measuring Contraceptive Use Behavior

Event history calendars (EHCs) are frequently used in social measurement to capture important information about the time ordering of events in people’s lives and enable inference about the relationships of the events with other outcomes of interest. To date, EHCs have primarily been designed for face-to-face or telephone survey interviewing, and few calendar tools have been developed for more private, self-administered modes of data collection. Web surveys offer benefits in terms of both self-administration, which can reduce social desirability bias, and timeliness. We developed and tested a web application enabling the calendar-based measurement of contraceptive method use histories. These measures provide valuable information for researchers studying family planning and fertility behaviors. This study describes the development of the web application and presents a comparison of data collected from online panels using the application with data from a benchmark face-to-face survey collecting similar measures (the National Survey of Family Growth).

Doing Web Surveys During Uncertain Times

The chapter will focus on using a web survey administered using social networks as a gathering point to collect data on people's risk perception and their undertaking of protective behaviors during the Italian COVID-19 crisis. This was an unprecedented moment in the digital age when there was no possibility of physical contact due to the limitations imposed on coexistence by the health emergency to stem the spread of the virus. This is when digital connections are the only link among people, and the only tool that can be used for doing social research is trying to satisfy the desire for knowledge without limiting the potential for knowledge production even in times of profound uncertainty and several limitations. Analyzing the participants' feedback on web surveys during times of deep uncertainty allows the authors to show what is clearly happening to social research currently. The discussions will be supported by an auto-ethnography conducted on comments left by the respondents to the survey.

Comparing the Response Burden between Paper and Web Modes in Establishment Surveys

Previous research is inconclusive regarding the effects of paper and web surveys on response burdens. We conducted an establishment survey with random assignment to paper and web modes to examine this issue. We compare how the actual and perceived response burdens differ when respondents complete a survey in the paper mode, in the web mode and when they are allowed to choose between the two modes. Our results show that in the web mode, respondents have a lower estimated time to complete the questionnaire, while we do not find differences between paper and the web on the perceived response time and perceived burden. Even though the response burden in the web mode is lower, our study finds no evidence of an increased response burden when moving an establishment survey from paper to the web.

Web-Based Research in Psychology

The present article reviews web-based research in psychology. It captures principles, learnings, and trends in several types of web-based research that show similar developments related to web technology and its major shifts (e.g., appearance of search engines, browser wars, deep web, commercialization, web services, HTML5…) as well as distinct challenges. The types of web-based research discussed are web surveys and questionnaire research, web-based tests, web experiments, Mobile Experience Sampling, and non-reactive web research, including big data. A number of web-based methods are presented and discussed that turned out to become important in research methodology. These are one-item-one-screen design, seriousness check, instruction manipulation and other attention checks, multiple site entry technique, subsampling technique, warm-up technique, and web-based measurement. Pitfalls and best practices are described then, especially regarding dropout and other non-response, recruitment of participants, and interaction between technology and psychological factors. The review concludes with a discussion of important concepts that have developed over 25 years and an outlook on future developments in web-based research.

Use of E-Health in Dutch General Practice during the COVID-19 Pandemic

The COVID-19 pandemic has forced general practices to search for possibilities to provide healthcare remotely (e.g., e-health). In this study, the impact of the pandemic on the use of e-health in general practices in the Netherlands was investigated. In addition, the intention of practices to continue using e-health more intensively and differences in the use of e-health between practice types were investigated. For this purpose, web surveys were sent to general practices in April and July 2020. Descriptive data analysis was performed and differences in the use of e-health between practice types were tested using one-way ANOVA. Response rates were 34% (n = 1433) in April and 17% (n = 719) in July. The pandemic invoked an increased use of several (new) e-health applications. A minority of practices indicated the intention to maintain this increased use. In addition, small differences in the use of e-health between the different practice types were found. This study showed that although there was an increased uptake of e-health in Dutch general practice during the COVID-19 pandemic, only a minority of practices intends to maintain this increased use in the future. This may point towards a temporary uptake of digital healthcare delivery rather than accelerated implementation of digital processes.

A Meta-Analysis of How Country-Level Factors Affect Web Survey Response Rates

A major challenge in web-based cross-cultural data collection is varying response rates, which can result in low data quality and non-response bias. Country-specific factors such as the political and demographic, economic, and technological factors as well as the socio-cultural environment may have an effect on the response rates to web surveys. This study evaluates web survey response rates using meta-analytical methods based on 110 experimental studies from seven countries. Three dependent variables, so-called effect sizes, are used: the web response rate, the response rate to the comparison survey mode, and the difference between the two response rates. The meta-analysis indicates that four country-specific factors (political and demographic, economic, technological, and socio-cultural) impact the magnitude of web survey response rates. Specifically, web surveys achieve high response rates in countries with high population growth, high internet coverage, and a high survey participation propensity. On the other hand, web surveys are at a disadvantage in countries with a high population age and high cell phone coverage. This study concludes that web surveys can be a reliable alternative to other survey modes due to their consistent response rates and are expected to be used more frequently in national and international settings.

Behavioral Healthcare Providers’ Experiences Related to use of Telehealth as a Result of the COVID-19 Pandemic: An Exploratory Study

Background: Due to the COVID-19 pandemic, healthcare providers were forced to shift many services quickly from in-person to virtual, including substance use disorder (SUD) and mental health (MH) treatment services. This led to a sharp increase in use of telehealth services, with health systems seeing patients virtually at hundreds of times the rate as before the onset of the COVID-19 pandemic.By analyzing qualitative data about SUD and MH care providers’ experiences using telehealth, this study aims to elucidate emergent themes related to telehealth use by the front-line behavioral health workforce.Methods: This study uses qualitative data from large-scale web surveys distributed to SUD and MH providers between May and August 2020. At the end of these surveys, the following question was posed in free-response form: “Is there anything else you would like to say about use of telehealth during or after the COVID-19 pandemic?” The 391 responses to this question were analyzed for emergent themes using a conventional approach to content analysis.Results: Three major themes emerged in the data: COVID-specific experiences with telehealth, general experiences with telehealth, and recommendations to continue telehealth delivery. Convenience, access to new populations, and lack of commute were frequently cited advantages, while perceived ineffectiveness of and limited access to technology were frequently cited disadvantages. Also commonly mentioned was the relaxation of reimbursement regulations. Providers supported continuation of relaxed regulations, increased institutional support, and using a combination of telehealth and in-person care in their practices. Conclusions: This study advanced our knowledge of how the behavioral health workforce experiences telehealth delivery. Further longitudinal research comparing treatment outcomes of those receiving in-person and virtual services will be necessary to undergird organizations’ financial support, and perhaps also legislative support, of virtual SUD and MH services.

On the Use of Gradient Boosting Methods to Improve the Estimation with Data Obtained with Self-Selection Procedures

In the last years, web surveys have established themselves as one of the main methods in empirical research. However, the effect of coverage and selection bias in such surveys has undercut their utility for statistical inference in finite populations. To compensate for these biases, researchers have employed a variety of statistical techniques to adjust nonprobability samples so that they more closely match the population. In this study, we test the potential of the XGBoost algorithm in the most important methods for estimation that integrate data from a probability survey and a nonprobability survey. At the same time, a comparison is made of the effectiveness of these methods for the elimination of biases. The results show that the four proposed estimators based on gradient boosting frameworks can improve survey representativity with respect to other classic prediction methods. The proposed methodology is also used to analyze a real nonprobability survey sample on the social effects of COVID-19.

Evaluation of User Reactions and Verification of the Authenticity of the User’s Identity during a Long Web Survey

Web surveys are very popular in the Internet space. Web surveys are widely incorporated for gathering customer opinion about Internet services, for sociological and psychological research, and as part of the knowledge testing systems in electronic learning. When conducting web surveys, one of the issues to consider is the respondents’ authenticity throughout the entire survey process. We took 20,000 responses to an online questionnaire as experimental data. The survey took about 45 min on average. We did not take into account the given answers; we only considered the response time to the first question on each page of the survey interface, that is, only the users’ reaction time was taken into account. Data analysis showed that respondents get used to the interface elements and want to finish a long survey as soon as possible, which leads to quicker reactions. Based on the data, we built two neural network models that identify the records in which the respondent’s authenticity was violated or the respondent acted as a random clicker. The amount of data allows us to conclude that the identified dependencies are widely applicable.

Isolated Sandbox Environment Architecture for Running Cognitive Psychological Experiments in Web Platforms

Web surveys are an integral part of the feedback of Internet services, a research tool for respondents, including in the field of health and psychology. Web technologies allow conducting research on large samples. For mental health, an important metric is reaction time in cognitive tests and in answering questions. The use of mobile devices such as smartphones and tablets has increased markedly in web surveys, so the impact of device types and operating systems needs to be investigated. This article proposes an architectural solution aimed at reducing the effect of device variability on the results of cognitive psychological experiments. An experiment was carried out to formulate the requirements for software and hardware. Three groups of 1000 respondents were considered, corresponding to three types of computers and operating systems: Mobile Device, Legacy PC, and Modern PC. The results obtained showed a slight bias in the estimates for each group. It is noticed that the error for a group of devices differs both upward and downward for various tasks in a psychological experiment. Thus, for cognitive tests, in which the reaction time is critical, an architectural solution was synthesized for conducting psychological research in a web browser. The proposed architectural solution considers the characteristics of the device used by participants to undergo research in the web platform and allows to restrict access from devices that do not meet the specified criteria.