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Abstract:  This paper explores the possibility of the mixed‐mode survey in Japan in order to ensure a wider range of respondents, focusing on the specific type of mixture that is designed to make contact with a respondent by more than one mode and in which a respondent can choose an alternative mode convenient for him or her. The dataset mainly used comes from the survey Study on the Social Composition of the Urban Local Community conducted in Shinagawa Ward of Tokyo in 1999. Three other studies are introduced for additional information. As an indicator of the effectiveness of a mixed‐mode design, the response rates of four studies are examined and some improvement in the response rates is observed, compared to that of a single‐mode design. Another indicator is the representativeness of a sample and it is evaluated using the data of the Shinagawa survey. When the discrepancy between valid respondents and the population on some demographic characteristics is regarded as representation bias, a mixed‐mode design is found to reduce the bias. An inherent problem in a mixed‐mode design is "mode effect." In order to control for the mode effect in analyses, the inclusion of an additional variable indicating "mode" in the model is recommended. It is further suggested that the interaction effects between the "mode" and the respondents' traits should be checked, on the assumption that the survey mode affects respondents differently. The attempts are made and discussed in this paper using two variables drawn from the Shinagawa survey.

"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)

Recruitment materials for concurrent mixed-mode self-administered web and mail surveys must communicate information about multiple modes simultaneously. Providing the link to the web survey on the cover of the paper questionnaire or including a QR code to access the web survey may increase the visibility of the web mode and thus increase the proportion of people who participate via the web, but whether including either piece of information does so has received surprisingly little empirical attention. In this paper, we examine the results of experiments embedded in two general population probability-based concurrent mixed-mode surveys of Nebraska adults. First, in the Labor Availability Survey of the Greater Omaha Area, respondents were randomly assigned to receive the web link and login information on the cover or the paper questionnaire without this information (all had web information in the cover letter). We then replicated and extended this experiment in the Labor Availability Survey of Northeast Nebraska. The questionnaire cover experiment was fully crossed with the presence or absence of a QR code to access the web survey. Neither of these design features affected response rates or speed of response, but the link on the questionnaire significantly increased the proportion of respondents who participated by web and the QR code significantly increased the proportion of respondents who participated by smartphone. Sample composition was largely unaffected on most characteristics, although the respondent pool was less similar to the population on education when the link was on the questionnaire. About 20% of respondents used a smartphone when typing in a survey link, but virtually all respondents used a smartphone when scanning the QR code. Survey researchers can include a link on the cover of the questionnaire to increase web participation rates in mixed-mode surveys. QR codes can be used when smartphone participation is desired.

Abstract
Mixed-mode designs have become increasingly common in survey data collection. Although different modes often have different measurement properties, the standard practice is to treat mixed-mode data as if they had been collected with a single mode, neglecting the potential impact of mode effects. To account for potential mode effects when making inferences for mixed-mode samples, we propose (i) a Testimator approach, (ii) a Bayesian approach, and (iii) a model averaging approach. In the Testimator approach, we test whether the means and the variances of mixed-mode samples are the same. If the means are the same, we take the average of mode-specific estimates. If the means are different, we take the average when we have no prior information about preferred modes and take the smaller (or larger) estimate when we have prior information about preferred modes (e.g., a smaller estimate is better). In the Bayesian approach, we assume some prior information. We use a data-driven method to determine whether there are mode effects. If there are no mode effects, we draw inferences using a common mean model. If there are mode effects, we draw inferences using the data collected with the mode that produces smaller estimates. In the model averaging approach, we combine estimates of different models (characterized by whether assume same means and variances across modes) using marginal posteriors as weights. We evaluate the approaches in simulation studies and find that they achieve robust inferences compared to the standard approach. We apply the methods to the Arab Barometer study, which employs a randomized mixed-mode design.

Mixing survey modes for data collection can have positive effects on response rates, sample balance, and survey costs. However, data collected in multiple modes may also suffer from mode measurement effects. In this Survey Guideline, we give an overview of empirical evidence related to the benefits and drawbacks of using multiple modes for data collection and outline some recommendations for the im­plementation of mixed-mode surveys. Finally, we provide a brief outlook on the perspectives of mixed­mode surveys in the survey landscape.

One of the main concerns is the nature of the missing values. Let's consider extremes for simplicity. If missing at random we have not to care about. But if missing shows structures that covariate with substantive variables we have to make decisions. There are, in fact, several options to take. We are speaking about one country, one mode. But if you go cross-cultural (or more precisely, cross-state nations) and mixed modes many questions raise. For example, the simple one. What are we comparing? Reports and books usually go straight into variables distributions and coefficient comparisons. This is possible because the annalist presume "tabula rasa" effect from data collections procedures. But this is not, frequently, the real situation. This paper will expose the mixed missing mode imprint in international surveys. This will help to evaluate how deal with this problem. Also, to consider the real meaning of observed cross-national differences.

Mixed-mode surveys have been around since the late 1980s. In the past thirty years, major changes in technology and society influenced and changed data collection and survey methodology. However, in those years, mixed-mode strategies remained part of the daily survey practice, although the type of mix implemented followed the changes in technology and data collection methods. In this paper, I summarize the state of the art in traditional mixed-mode surveys and discuss implications for mixed device surveys.

"It is recommended that researchers who use mixed modal methods for data collection compare their impact on outcome measures. The purpose of this study was to examine the physical activity and fruit/ vegetable consumption behaviors of a multiethnic sample of adults, comparing participants who continued a telephone survey and those who transitioned from a telephone to a web-based survey for a follow-up data collection point. This longitudinal study used a random sample of 700 Hawaii residents (63.3% Female; Mean age=47, SD=17.1). At baseline, participants completed a computer-assisted telephone interview assessing the stage, behavior, and decisional balance of both physical activity and fruit/ vegetable consumption. For the three-month follow-up survey, participants were given the option of completing the survey either on the web or by phone. Repeated measures analysis of variance (ANOVA) was computed for related physical activity scales and fruit/ vegetable consumption variables to compare the change in response across time between a web group and phone group. For both physical activity and fruit/ vegetable consumption, all mode-by-time interactions were significant). The participants who preferred the telephone survey maintained their levels, whereas those who preferred the web survey reported a decrease in each variable. These results suggest that changing the mode of a survey may introduce a systematic bias in data and that researchers should proceed with caution when using mixed modes of data collection." (author's abstract)

Der Band vereinigt Beiträge aus der aktuellen Forschung zu Methoden der gleichzeitigen Anwendung unterschiedlicher Kommunikationsformen in einem Survey (sog. Mixed-Mode-Surveys). Mit Survey-Designs dieser Art können verschiedene Ziele verbunden sein. Dazu gehören etwa eine Reduktion der Erhebungskosten und eine Reduktion von Stichprobenverzerrungen. Der vorliegende Band beschäftigt sich mit den Möglichkeiten und Grenzen des Einsatzes dieser Verfahren sowie mit deren Auswirkungen auf verschiedene Aspekte der Datenqualität. Der Inhalt Mode-Vielfalt, Mode-Komponenten und Datenqualität • Die Rolle von Modes in der Teilnehmerrekrutierung • Mode-Änderungen in Survey-Designs • Mess- und Methodeneffekte Die Herausgeber Prof. Dr. Stefanie Eifler ist Inhaberin des Lehrstuhls für Soziologie und empirische Sozialforschung an der Katholischen Universität Eichstätt-Ingolstadt. Prof. Dr. Frank Faulbaum war bis April 2008 Inhaber des Lehrstuhls für Sozialwissenschaftliche Methoden/Empirische Sozialforschung an der Universität Duisburg-Essen und ist gegenwärtig Vorstandsvorsitzender der Arbeitsgemeinschaft Sozialwissenschaftlicher Institute (ASI) e.V

Survey designs in which data from different groups of respondents are collected by different survey modes have become increasingly popular. However, such mixed-mode (MM) designs lead to a confounding of selection effects and measurement effects (measurement error) caused by mode differences. Consequently, MM data have poor quality. Nevertheless, comparing MM data with data from a comparable single-mode survey allows researchers to measure selection effects and measurement effects separately. The authors develop a method to evaluate mode effects and illustrate this method with data from a Dutch MM experiment within the European Social Survey program. In this experiment, respondents could choose between three modes: a Web survey, a telephone interview, or a face-to-face interview. Mode effects on three political variables are evaluated: interest in politics, perceived complexity of politics, and voter turnout in the last national election.

Reluctance of respondents to participate in surveys has long drawn the attention of survey researchers. Yet, little is known about what drives a respondent's decision to answer the survey invitation early or late during the field period. Moreover, we still lack evidence on response timing in longitudinal surveys. That is, the questions on whether response timing is a rather stable respondent characteristic and what - if anything - affects change in response timing across different interviews remain open. We relied on data from a mixed-mode general population panel survey collected between 2014 and 2016 to study the stability of response timing across 18 panel waves and factors that influence the decision to participate early or late in the field period. Our results suggest that the factors which had effects on response timing are different in the mail and web modes. Moreover, we found that experience with prior panel waves affected the respondent's decision to participate early or late. Overall, the present study advocates understanding response timing as a metric variable and, consequently, the need to reflect this in modeling strategies.

Mixed-mode surveys are popular as they can save costs and maintain (or improve) response rates relative to single-mode surveys. Nevertheless, it is not yet clear how design decisions like survey mode or questionnaire length impact measurement quality. In this study, we compare measurement quality in an experiment of three distinct survey designs implemented in the German sample of the European Values Study: a single-mode face-to-face design, a mixed-mode mail/web design, and a shorter (matrix) questionnaire in the mixed-mode design. We compare measurement quality in different ways, including differences in distributions across several data quality indicators as well as equivalence testing over 140 items in 25 attitudinal scales. We find similar data quality across the survey designs, although the mixed-mode survey shows more item nonresponse compared to the single-mode survey. Using equivalence testing we find that most scales achieve metric equivalence and, to a lesser extent, scalar equivalence across the designs.

"Conducting survey interviews on the internet has become an attractive method for lowering data collection costs and increasing the frequency of interviewing, especially in longitudinal studies. However, the advantages of the web mode for studies with frequent reinterviews can be offset by the serious disadvantage of low response rates and the potential for nonresponse bias to mislead investigators. Important life events, such as changes in employment status, relationship changes, or moving can cause attrition from longitudinal studies, producing the possibility of attrition bias. The potential extent of such bias in longitudinal web surveys is not well understood. We use data from the Relationship Dynamics and Social Life (RDSL) study to examine the potential for a mixed-device approach with
active mode switching to reduce attrition bias. The RDSL design allows panel members to switch modes by integrating telephone interviewing into a longitudinal web survey with the objective of collecting weekly reports. We found that in this design allowing panel members to switch modes kept more participants in the study compared to a web only approach. The characteristics of persons who ever switched modes are different than those who did not - including not only demographic characteristics, but also baseline characteristics related to pregnancy and time-varying characteristics that were collected after the baseline interview. This was true in multivariate models that control for multiple of these dimensions simultaneously. We conclude that mode options and mode switching is important for the success of longitudinal web surveys to maximize participation and minimize attrition." (author's abstract)