Please let us know if you would like to end these by
selecting-this/page
==4930*North*Eldon*Drive*Colorado*Springs-CO*80916-3378
Updated preferences can happen using:
this-here-6700_Comanche/Road/NE/2D..Albuquerque..NM..87111-3540
EDITORIAL ADVISORY BOARDNancy Allbritton University of North Carolina Richard M. Crooks The University of Texas at Austin Pekka E. Hnninen University of Turku Daniel A. Higgins Kansas State University Neil L. Kelleher Northwestern University Takehiko Kitamori The University of Tokyo James P. Landers University of Virginia John C. Lindon Imperial College London Huwei Liu Peking University James F. Rusling University of Connecticut Garth J. Simpson Purdue University Lloyd Smith University of WisconsinMadison Weihong Tan University of Florida ZhongQun Tian Xiamen University Sabeth Verpoorte University of Groningen Hanfa Zou Chinese Academy of Sciences Roman Zubarev Karolinska Institutet Ex Officio Member Cynthia K. Larive University of CaliforniaRiversideAPAGE FEATURES PANELRyan C. Bailey University of Illinois, UrbanaChampaign Lane Baker Indiana University Carthene R. BazemoreWalker Brown University Philip BritzMcKibben McMaster University Paul Cremer Texas AM University Rob!
ert J. Forster Dublin City University Vladimir Havlicek Academy of Sciences of the Czech Republic Jianhui Jiang Hunan University Rebecca Jockusch University of Toronto Ryan Julian University of California Lingjun Li University of WisconsinMadison Baohong Liu Fudan University Torsten C. Schmidt University of DuisburgEssen Dana Spence Michigan State University Apryll Stalcup Dublin City University Sarah Trimpin Wayne State University Richard Vachet University of Massachusetts Aaron Wheeler University of Toronto Brandy J. White U.S. Naval Research Laboratory Christine C. Wu University of Pittsburgh Lihua Zhang Chinese Academy of SciencesSocial Science Research 42 2013 957970Contents lists available at SciVerse ScienceDirectSocial Science Researchjournal homepage www.elsevier.comlocatessresearchIntraindividual variation of extreme response style in mixedmode panel studiesJulian Aichholzer ,1Department of Methods in the Social Sciences, University of Vienna, Rathausstrae 19I9, 1!
010 Vienna, Austriaa r t i c l ei n f oa b s t r a c tIt is well known that the selfreport survey method suffers from many idiosyncratic biases, such as varying response styles due to different survey modes used. Using latent statetrait theory it is argued that response styles will also vary intraindividually, depending on the particular survey situation. In this study we examine intraindividual variation in extreme response style behavior ERS using mixedmode survey panel data as a quasiexperimental setting. Data from the Irish National Election Study panel are used, which consists of repeated facetoface and mailback surveys. Latent transition analysis is used to detect switches in ERS, distinguishing stable and volatile respondents in terms of their response style. Overall, ERS is inflated in the intermediate mail component of the panel, whereas preliminary analyses suggest that low education and ideological extremity are drivers of that change. Results are discussed with !
regards to measurement errors in mixedmode and longitudinal surveys. 2!
013 Elsevier Inc. All rights reserved.Article history Received 24 August 2012 Revised 4 December 2012 Accepted 7 January 2013 Available online 16 January 2013 Keywords Extreme response style Mixedmode survey Panel survey Latent transition analysis1. Introduction Researchers in the social sciences and related disciplines make extensive use of standardized selfreport surveys as a quantitative data collection tool. Nevertheless, it is well known that this method is susceptible to many flaws or errors that jeopardize the validity of results. The present study focuses on systematic measurement bias coming from the respondent, namely idiosyncratic differences in how individuals make use of response scales in reporting their answers. This phenomenon in surveys is usually defined as response styles in the literature for an overview Van Vaerenbergh and Thomas, in press. More precisely, this type of bias naturally applies to rating scales e.g. Likerttype agreedisagree that are typica!
lly used for nonfactual subjective measures. This paper investigates extreme response style behavior hereafter ERS, a tendency to select endpoints of a response scale. Hence, ERS is assumed to be a systematic component in response patterns which, independent from the true attitude or assessment, biases observed scores. ERS has, for instance, gained growing attention as a source of nuisance in crosscultural comparative survey research e.g. Morren et al., 2012. Any idiosyncratic variation in that tendency is important, because extreme scores are used to make substantial inferences, such as attitude extremity Visser et al., 2006, opinionation Krosnick and Milburn, 1990, and attitude polarization of issues in public opinion research Baldassarri and Bearman, 2007. As a common source of variance ERS also artificially inflates correlations or itemfactor loadings Baumgartner and Steenkamp, 2001 Cheung and Rensvold, 2000. Fax 43 1 4277 9499.Email address julian.aichholzerunivie.ac.a!
t The author is predoc research associate in the Austrian National Elec!
tion Study AUTNES, a National Research Network NFN sponsored by the Austrian Science Fund FWF S10903G11.10049089X see front matter 2013 Elsevier Inc. All rights reserved. httpdx.doi.org10.1016j.ssresearch.2013.01.002958J. Aichholzer Social Science Research 42 2013 957970This study builds on a lively debate about whether response style behavior, in general, is a traitlike and stable individual feature or whether its manifestation is a state which primarily depends on external stimuli of the measurement method or situation see, for example Baumgartner and Steenkamp, 2001 Kieruj and Moors, 2013 Weijters, 2006. It is argued that both positions are valid. ERS is indeed a situationdependent feature, though some individuals can be described as being rather stable traited and others as being volatile in this feature instable, different states. A similar view on a segmented population can be found in latent statetrait LST theory Eid and Langeheine, 2003 as well as in early contro!
versies about the stability of attitudes Converse, 1964. More precisely, we analyze how ERS varies with the particular data collection method or survey mode used, comparing interview facetoface survey and selfadministered mailback survey surveys. A wide literature suggests that response behavior, in general, differs between survey methods, resulting in differential measurement bias Bowling, 2005 de Leeuw, 2005 Revilla, 2010 Schwarz et al., 1991 Weijters et al., 2008. Turning to ERS and survey mode, most studies coming from independent samples find that extreme responses are triggered in interview or aural modes, especially using CATI telephone Dillman et al., 2009 Groves and Kahn, 1979 Jordan et al., 1980 Martin et al., 1993 Ye et al., 2011. Conversely, an indepth study of Weijters and colleagues 2008 reports that the level of ERS is similar in telephone and mail surveys, but lower in internet surveys. Though findings are not unequivocal, we argue that differences in ERS or!
iginate from distinctive situational aspects of the survey mode, wherea!
s social desirability and satisficing strategies Krosnick, 1991 of respondents play a key role for the manifestation of ERS. It stands to reason that we would also find intraindividual variation in ERS for some respondents, conditional on the survey mode. As far as we know, this is the first study to look at this particular phenomenon. For this purpose we use the so called mixedmode panel design as a unique opportunity to study ERS. It is argued that this design allows us to examine ERS variation intraindividually and longitudinally in a quasiexperimental withinsubject design, because we can observe identical individuals. As Vannieuwenhuyze and Loosveldt in press explain, it is usually difficult to disentangle the mode effect, which is comprised of a selection effects due to differences in respondent characteristics in different samples and b measurement effects or bias in different modes. So, the mixedmode panel design represents a different approach towards evaluating mod!
e effects for other approaches Vannieuwenhuyze and Loosveldt, in press, which tries to avoid confounding of mode and sample selection effects. However, some uncertainty regarding mode effects remains as we usually do not have an additional betweensubject control group a group with a different mixedmode setting, with equal sample characteristics alongside the withinsubject design. Therefore we cannot prevent so called learning or carryover effects in the sense that having been surveyed using one mode affects how subjects behave in other modes. It is therefore mandatory that a mixedmode panel design includes repeated survey modes to better deal with these issues. Besides, while mixedmode panels studies have become more and more common in recent decades Couper, 2011 de Leeuw, 2005, we still know very little about the implications of switching modes for potential measurement bias or reliability. In this paper we thus ask do people change their level of ERS with the survey mode !
and, if so, why It is important to note that strong variations of respo!
nse style behavior with the measurement method would be fatal see also Weijters et al., 2010a,b, since substantial change and artificial change in a variable can be confounded. Inconsistency implies that, besides random error inherent to survey questions, a kind of systematic bias in responses is introduced that is transient from occasion to occasion Le et al., 2009. For instance, the deleterious effect of volatile response behavior is supported by the finding that personality markers yield lower retest reliabilities when switching the survey mode in a panel study e.g. Lang et al., 2011. This would imply that repeated measures in mixedmode panels should be used with caution. This study, for the first time, aims at providing insights into intraindividual variation in response style behavior ERS that accompanies a mixedmode panel study. We show how a latent class model, so called latent transition analysis LTA, can be used to detect switches as well as stability in a persons !
latent level of ERS. The contribution is thus twofold. This study will, first, contribute to a better understanding regarding the nature of survey response styles and the cognitive processes underlying the communication of selfreports. This has implications for studying true change in attitudes as opposed to artificial changes. Second, it serves as a resource for applied researchers who work with mixedmode data. For instance, the latent class model enables to identify respondents who are susceptible to vary their response behavior based on situational mode factors. The paper is structured as follows. We, first, outline the theoretical perspective on the manifestation of individual response styles using latent statetrait theory. Taking a comprehensive approach, we argue that it is useful to separate respondents that actually change ERS behavior with the measurement situation from those who are rather stable. Next, we propose expectations regarding situational aspects in surv!
eys and their impact on ERS. These expectations are examined using uniq!
ue data from the Irish National Election Study INES panel, which uses a mixedmode design of repeated facetoface and mailback surveys. For this purpose we present a latent transition model which helps us to detect variation in the overall level of ERS while allowing us to separate stable individuals and variable individuals. In an exploratory fashion, we also examine some individual covariates of ERS volatility. Finally, the results are summarized and discussed regarding implications for mixedmode surveys and inherent measurement bias in longitudinal studies. 2. Perspectives on survey response styles A large body of the applied survey research literature has been devoted to causes which are responsible for response style bias in public opinion surveys or psychometric tests Van Vaerenbergh and Thomas, in press. As already mentioned, it is notJ. Aichholzer Social Science Research 42 2013 957970959clearcut whether this behavior is, at least to a certain extent, a stable indivi!
dual characteristic, even traitlike, or whether it is largely situationdependent and pops up uniquely at each measurement occasion. Therefore panel surveys are very well suited to examine these questions. We will now summarize the aforementioned arguments with regards to ERS to arrive at a research synthesis. 2.1. Stability vs. inconsistency of response styles trait vs. state A first major perspective, the disposition argument, conceives response styles as a traitlike characteristic or an internal feature of the person e.g. Couch and Keniston, 1960. According to common definitions, a basic feature of individual traits is temporal stability Steyer et al., 1999. Supporting the argument, a strand of research finds evidence for considerable crosstime correlations of ERS Bachman and OMalley, 1984 Weijters et al., 2010a,b as well as other response styles Billiet and Davidov, 2008 when using equal measurement conditions. Similarly, research suggests that ERS patterns seem to endur!
e throughout a questionnaire, across different question content Weijter!
s et al., 2010a,b and different response scale formats Kieruj and Moors, 2013. A second perspective, the situation argument, builds on the assumption that striking response patterns result primarily from external influences due to the measurement situation or stimuli e.g. Schuman and Presser, 1981 Schwarz et al., 1991. This can be the survey mode or other formal features, such as the response scale design. In early attitude research this led researchers to assume that response styles are a very instable phenomenon Hui and Triandis, 1985 or even nonexistent as an internal feature Rorer, 1965. However, a dynamic perspective seems more plausible. An individuals level of ERS may take on different states depending on the measurement situation. Essentially, this is what studies coming from static independent samples suggest. 2.2. The latent statetrait perspective This study takes an encompassing approach to response styles. We follow latent statetrait LST theory Steyer et al., 19!
99 to illustrate the dispositionsituation controversy in understanding response style behavior. LST theory helps us to understand two important issues in this respect. On the one hand, an individual characteristic derived from a sample of individuals can be more or less stable across measurement occasions reliability. For instance, the level of stress or test achievement of students might be highly dependent on the situation being measured instable. On the other hand, particular individuals might differ in being stable or being volatile in a characteristic also see Eid and Langeheine, 2003. The latter position is in line with attitude research focusing on differential stability of opinions in a population Converse, 1964. Similarly, Hui and Triandis 1985, p. 260 share this view and state that fluctuation rates of response sets vary across individuals. Indeed, both perspectives are covered in the two main modeling strategies for longitudinal data, i.e. 1 variablecentered appr!
oaches that focus on crosstime correlations and trajectories in continu!
ous variables and 2 personoriented approaches that identify classes of respondents over time Laursen and Hoff, 2006. We use the latter approach in order to identify groups of respondents who differ in their pattern of ERS scale usage. This not only allows us to examine the overall sample change in the level of ERS during the mixedmode panel Step I, but also to identify individuals that can be described as being stable in ERS as opposed to those who are volatile in ERS Step II. In an exploratory fashion we will then examine some correlates of stability or variability Step III. In line with LST theory, these factors will point out interactions between personal factors and situational factors Steyer et al., 1999. 3. Cognitive aspects of different survey modes In what follows, we briefly propose expectations on the impact of the survey situation on the manifestation of ERS. The rationale of this investigation is that, from the respondents point of view, types of survey modes di!
ffer considerably in their situational aspects. These situational aspects would elicit cognitive processes in the survey respondent when handling questions and reporting their answers for this approach Schwarz et al., 1991 Tourangeau et al., 2000. For reasons of simplicity we only refer to differences between interview and selfadministered modes. While more has been said about survey mode in relation to social desirability or response order effects e.g. Schwarz et al., 1991 Tourangeau et al., 2000, little is known about the causes of unequal manifestation of ERS. Based on three key situational aspects we link the survey mode with the manifestation of ERS. Following the work of Tourangeau et al. 2000 these are a impersonality of the situation, b cognitive burden, and c importance or legitimacy of a survey. As we will see, theoretical explanations might also by conflicting in their expectations. Whether an interviewer is present or not refers to stimuli which are evoked by th!
e social relationship or social norms. This aspect has been studied ext!
ensively as a potential measurement bias in sensitive questions Tourangeau et al., 2000. Regarding ERS, we assume that, independent of the direction of the true position, this stimulus results in avoiding extreme answers low ERS on attitude questions as a specific aspect of socially desirable behavior. The rationale is that some persons might want to present themselves in a better light by avoiding extreme opinions or very explicit positions in interview960
9172. H.R. Department
