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Abstract
Online panels tend to be more affected by nonresponse bias than cross-sectional online and offline surveys due to an accumulating effect of attrition over time. Further, nonprobability panels that do not offer monetary rewards can be more sensitive to nonparticipation, but the existing research on panel maintenance strategies to increase survey completion in nonprobability/volunteer online panels is still scarce. To identify between-wave communications that could serve as cost-effective panel maintenance activities to increase survey completion, a survey experiment was carried out, and two solutions were empirically tested: sending survey results in a report (a long report or an infographic) and sending a prenotification before the following survey (a short or a longer prenotification). The results showed a small effect of sending an infographic, either with or without a short prenotification. Additionally, a positive effect was observed when emailing a short prenotification to those who completed the previous survey questionnaire, indicating a differential impact of between-wave communication. In summary, this study demonstrates how the survey completion rate can be increased in nonprobability online panels with non-monetary incentives, which can be incorporated into panel maintenance strategies for different types of panels.

Online probability-based panels often apply two or more data collection modes to cover both the online and offline populations with the aim of obtaining results that are more representative of the population of interest. This study used such a panel to investigate how necessary it is, from the coverage error standpoint, to include the offline population by mixing modes in online panel survey research. This study evaluated the problem from three different perspectives: undercoverage bias, bias related to survey item topics and vari­able characteristics, and accuracy of online-only samples relative to nationally representa­tive benchmarks. The results indicated that attitudinal, behavioral, and factual differences between the online and offline populations in Australia are, on average, minor. This means that, considering that survey research commonly includes a relatively low proportion of the offline population, survey estimates would not be significantly affected if probability-based panels did not mix modes and instead were online only, for the majority of topics. The benchmarking analysis showed that mixing the online mode with the offline mode did not improve the average accuracy of estimates relative to nationally representative bench­marks. Based on these findings, it is argued that other online panels should study this issue from different perspectives using the approaches proposed in this paper. There might also be an argument for (temporarily) excluding the offline population in probability-based on­line panel research in particular country contexts as this might have practical implications.

There has been a great deal of debate in the survey research community about the accuracy of nonprobability sample surveys. This work aims to provide empirical evidence about the accuracy of nonprobability samples and to investigate the performance of a range of post-survey adjustment approaches (calibration or matching methods) to reduce bias, and lead to enhanced inference. We use data from five nonprobability online panel surveys and com­pare their accuracy (pre- and post-survey adjustment) to four probability surveys, including data from a probability online panel. This article adds value to the existing research by assessing methods for causal inference not previously applied for this purpose and dem­onstrates the value of various types of covariates in mitigation of bias in nonprobability online panels. Investigating different post-survey adjustment scenarios based on the avail­ability of auxiliary data, we demonstrated how carefully designed post-survey adjustment can reduce some bias in survey research using nonprobability samples. The results show that the quality of post-survey adjustments is, first and foremost, dependent on the avail­ability of relevant high-quality covariates which come from a representative large-scale probability-based survey data and match those in nonprobability data. Second, we found little difference in the efficiency of different post-survey adjustment methods, and inconsis­tent evidence on the suitability of 'webographics' and other internet-associated covariates for mitigating bias in nonprobability samples.

Abstract
In this study, we use qualitative research methods to identify, discuss, and investigate the self-reported motivational factors and barriers in all stages of the probability-based online panel lifecycle—recruitment to the panel, wave-by-wave data collection, and voluntary attrition. Our data were gathered with qualitative in-depth interviews (IDIs). Between March 2020 and February 2021, panelists from the Life in Australia™ probability online panel were classified into four groups based on their previous panel response behavior and each was interviewed. The qualitative data were used to link the reported motivation for and barriers against joining and staying active in the panel with theories about survey participation. Evidence from the IDIs shows that motivations and barriers can be directly linked to social-psychological theories that explain panel/longitudinal survey participation, including how theories such as social-exchange theory, leverage-salience theory, and the reasoned action approach, are sufficiently robust to help understand the time dimension of survey participation and behavioral change of panel members. Our findings have practical implications for probability-based online panel management.

AbstractFood insecurity is a prominent social determinant of health. There is evidence of increasing food insecurity in high‐income countries amid inflationary pressures. Yet, we know relatively little about the strategies that people employ to manage food insecurity nor how the severity of food insecurity affects the use of these strategies. Accordingly, this study of a nonprobability convenience online panel sample of 1284 adults in Tasmania assessed food insecurity and the strategies undertaken to manage rising costs of living. Structural equation modelling of 51 financial management actions revealed five strategies: reducing food quantity and quality, reducing housing and transport costs, drawing on assets, savings and credit, reducing insurance costs and reducing discretionary expenditure. Generalised linear regression modelling demonstrated that the use of coping strategies increased significantly with each categorical increase in food insecurity for each strategy except reducing insurance costs, in which increases became significant at moderate food insecurity. Overall, the results suggest that those experiencing food insecurity are also sacrificing in other areas, which is likely to have immediate and longer‐term effects on health, social and financial wellbeing. Opportunities to mitigate these consequences, including strengthening of social safety nets, nutrition programmes and structural investment to ensure universal access to food, are discussed.