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Abstract
Collecting sensitive data using surveys is one of the most challenging tasks facing survey methodologists as people may choose to answer questions untruthfully to present themselves in a positive light. In 2014, Mneimneh et al. proposed mixed Rasch models to detect socially desirable answering behaviors. This approach combines item response theory models with latent class analysis to differentiate substantive and biased answering patterns. Their results identified two latent classes, one of which was consistent with socially desirable answering. Our aim is to expand their approach to detecting social desirability by using a mixture confirmatory factor analysis (CFA) in round 7 of the European Social Survey. First, we attempt to estimate social desirability in three constructs separately (RQ1): effect of immigration on the country, allowing people to come in the country and social connection, using a mixture CFA. We then extend the analysis by (RQ2) introducing constraints between the latent classes, (RQ3) combining different constructs in one model, and (RQ4) comparing results in Belgium and the United Kingdom. In contrast with the paper published by Mneimneh et al. in 2014, the models with two latent classes do not have the best model fit. In addition, validation with the presence of a third person, the respondent's reluctance to give answers and personality traits are not systematically in line with our expectations. A small simulation shows that the method would work if the data would behave as we expect, with social desirability being the main factor influencing answering patterns. We conclude that a mixture CFA might not be able to identify social desirability in different survey contexts, especially in complex data as originating in cross-national social surveys.

Research has explained interviewer effects in face-to-face surveys: in terms of question, respondent and interviewer characteristics. In this paper, we relate interview speed to interviewer effects. Three interview types are operationalized (slow, moderate and fast) based on the residual speeds from a model controlling for respondent characteristics. Data from the European Social Survey is used. Results show larger interviewer effects among slow and fast interviews than moderate interviews, although with variations between variables and countries. Assuming that moderate interviews are conducted under standardized interviewing, these results support the idea that deviations from standardized interviewing are linked to greater interviewer effects.

Abstract
The proportion of elderly people in general population samples is increasing. Therefore, it is becoming more important to pay special attention to older respondents when assessing the quality of data. The main hypothesis of the current article is that interviewer effects are higher in the older age-group. We use data collected in 13 countries during Round 7 of the European Social Survey. The results support the supposition that older respondents tend to need more clarification, are more prone to misunderstand the questions, and are likely to have longer interviews. In line with the expectations, we also observe that among older respondents, particularly those aged 71 and above, interviewer effects are more common than among younger respondents.

"Mixed mode surveys are presented as a solution to increasing survey costs and decreasing response rates. The disadvantage of such designs is the lack of control over mode effects and the interaction between selection and measurement effects. In a mixed mode survey, measurement effects can put into doubt data comparability between subgroups, or similarly between waves or rounds of a survey conducted using different modes. To understand the extent of measurement effects, selection and measurement effects between modes have to be disentangled. Almost all techniques to separate these effects depend on covariates that are assumed to be mode-insensitive and to fully explain selection effects. Most of the time, these covariates are sociodemographic variables that might be mode-insensitive, but fail to sufficiently explain selection effects. The aim of this research is to assess the performance of mode preference variables as covariates to evaluate selection and measurement effects between modes. In 2012, a mixed mode survey - a web questionnaire followed by face-to-face interviews - was conducted alongside the face-to-face European Social Survey in Estonia (Ainsaar et al., 2013). The questionnaire included mode preference items. In this paper, the effects of the trade-offs between the two assumptions on the precision of estimated selection and measurement effects are compared. The results show that while adding the mode preference to the propensity score model seems to increase the explanatory power of web participation, it decreases the correlation between propensity scores and target variables. In addition, the estimated selection and measurement effects do not always fit the expectation that more selection effects are explained and more measurement effects are detected." (author's abstract)

"The pursuit of high response rates to minimise the threat of nonresponse bias continues to dominate decisions about resource allocation in survey research. Yet a growing body of research has begun to question this practice. In this study, we use previously unavailable data from a new sampling frame based on population registers to assess the value of different methods designed to increase response rates on the European Social Survey in Switzerland. Using sampling data provides information about both respondents and nonrespondents, making it possible to examine how changes in response rates resulting from the use of different fieldwork methods relate to changes in the composition and representativeness of the responding sample. We compute an R-indicator to assess representativity with respect to the sampling register variables, and find little improvement in the sample composition as response rates increase. We then examine the impact of response rate increases on the risk of nonresponse bias based on Maximal Absolute Bias (MAB), and coefficients of variation between subgroup response rates, alongside the associated costs of different types of fieldwork effort. The results show that increases in response rate help to reduce MAB, while only small but important improvements to sample representativity are gained by varying the type of effort. These findings lend further support to research that has called into question the value of extensive investment in procedures aimed at reaching response rate targets and the need for more tailored fieldwork strategies aimed both at reducing survey costs and minimising the risk of bias." (author's abstract)

Longitudinal or panel surveys suffer from panel attrition which may result in biased estimates. Online panels are no exceptions to this phenomenon, but offer great possibilities in monitoring and managing the data-collection phase and response-enhancement features (such as reminders), due to real-time availability of paradata. This paper presents a data-driven approach to monitor the data-collection phase and to inform the adjustment of response-enhancement features during data collection across online panel waves, which takes into account the characteristics of an ongoing panel wave. For this purpose, we study the evolution of the daily response proportion in each wave of a probability-based online panel. Using multilevel models, we predict the data-collection evolution per wave day. In our example, the functional form of the data-collection evolution is quintic. The characteristics affecting the shape of the data-collection evolution are those of the specific wave day and not of the panel wave itself. In addition, we simulate the monitoring of the daily response proportion of one panel wave and find that the timing of sending reminders could be adjusted after 20 consecutive panel waves to keep the data-collection phase efficient. Our results demonstrate the importance of re-evaluating the characteristics of the data-collection phase, such as the timing of reminders, across the lifetime of an online panel to keep the fieldwork efficient.

Response rates are declining increasing the risk of nonresponse error. The reasons for this
decline are multiple: the rise of online surveys, mobile phones, and information requests,
societal changes, greater awareness of privacy issues, etc. To combat this decline, fieldwork
efforts have become increasingly intensive: widespread use of respondent incentives,
advance letters, and an increased number of contact attempts. In addition, complex fieldwork
strategies such as adaptive call scheduling or responsive designs have been implemented.
The additional efforts to counterbalance nonresponse complicate the measurement of the
increased difficulty of contacting potential respondents and convincing them to cooperate.
To observe developments in response rates we use the first seven rounds of the European
Social Survey, a biennial face-to-face survey. Despite some changes to the fieldwork efforts in
some countries (choice of survey agency, available sample frame, incentives, number of
contact attempts), many characteristics have been stable: effective sample size, (contact and)
survey mode, and questionnaire design. To control for the different country composition in
different rounds, we use a multilevel model with countries as level 2 units and response rates
in each country-year combination as level 1 units. The results show a declining trend,
although only round 7 has a significant negative effect.

All social surveys suffer from different types of errors, of which one of the most studied is non-response bias. Non-response bias is a systematic error that occurs because individuals differ in their accessibility and propensity to participate in a survey according to their own characteristics as well as those from the survey itself. The extent of the problem heavily depends on the correlation between response mechanisms and key survey variables. However, non-response bias is difficult to measure or to correct for due to the lack of relevant data about the whole target population or sample. In this paper, non-response follow-up surveys are considered as a possible source of information about non-respondents. Non-response follow-ups, however, suffer from two methodological issues: they themselves operate through a response mechanism that can cause potential non-response bias, and they pose a problem of comparability of measure, mostly because the survey design differs between main survey and non-response follow-up. In order to detect possible bias, the survey variables included in non-response surveys have to be related to the mechanism of participation, but not be sensitive to measurement effects due to the different designs. Based on accumulated experience of four similar non-response follow-ups, we studied the survey variables that fulfill these conditions. We differentiated socio-demographic variables that are measurement-invariant but have a lower correlation with non-response and variables that measure attitudes, such as trust, social participation, or integration in the public sphere, which are more sensitive to measurement effects but potentially more appropriate to account for the non-response mechanism. Our results show that education level, work status, and living alone, as well as political interest, satisfaction with democracy, and trust in institutions are pertinent variables to include in non-response follow-ups of general social surveys.

All social surveys suffer from different types of errors, of which one of the most studied is non-response bias. Non-response bias is a systematic error that occurs because individuals differ in their accessibility and propensity to participate in a survey according to their own characteristics as well as those from the survey itself. The extent of the problem heavily depends on the correlation between response mechanisms and key survey variables. However, non-response bias is difficult to measure or to correct for due to the lack of relevant data about the whole target population or sample. In this paper, non-response follow-up surveys are considered as a possible source of information about non-respondents. Non-response follow-ups, however, suffer from two methodological issues: they themselves operate through a response mechanism that can cause potential non-response bias, and they pose a problem of comparability of measure, mostly because the survey design differs between main survey and non-response follow-up. In order to detect possible bias, the survey variables included in non-response surveys have to be related to the mechanism of participation, but not be sensitive to measurement effects due to the different designs. Based on accumulated experience of four similar non-response follow-ups, we studied the survey variables that fulfill these conditions. We differentiated socio-demographic variables that are measurement-invariant but have a lower correlation with non-response and variables that measure attitudes, such as trust, social participation, or integration in the public sphere, which are more sensitive to measurement effects but potentially more appropriate to account for the non-response mechanism. Our results show that education level, work status, and living alone, as well as political interest, satisfaction with democracy, and trust in institutions are pertinent variables to include in non-response follow-ups of general social surveys. - See more at: ...

"All social surveys suffer from different types of errors, of which one of the most studied is non-response bias. Non-response bias is a systematic error that occurs because individuals differ in their accessibility and propensity to participate in a survey according to their own characteristics as well as those from the survey itself. The extent of the problem heavily depends on the correlation between response mechanisms and key survey variables. However, non-response bias is difficult to measure or to correct for due to the lack of relevant data about the whole target population or sample. In this paper, non-response follow-up surveys are considered as a possible source of information about non-respondents. Non-response follow-ups, however, suffer from two methodological issues: they themselves operate through a response mechanism that can cause potential non-response bias, and they pose a problem of comparability of measure, mostly because the survey design differs between main survey and non-response follow-up. In order to detect possible bias, the survey variables included in non-response surveys have to be related to the mechanism of participation, but not be sensitive to measurement effects due to the different designs. Based on accumulated experience of four similar non-response follow-ups, we studied the survey variables that fulfill these conditions. We differentiated socio-demographic variables that are measurement-invariant but have a lower correlation with non-response and variables that measure attitudes, such as trust, social participation, or integration in the public sphere, which are more sensitive to measurement effects but potentially more appropriate to account for the non-response mechanism. Our results show that education level, work status, and living alone, as well as political interest, satisfaction with democracy, and trust in institutions are pertinent variables to include in non-response follow-ups of general social surveys." (author's abstract)