Answer format effects revisited

The effect of answer formats presented to respondents in written surveys are investigated for two constructs (attitudes and behavioral intentions) and three response scales (binary, ordinal and metric). Results indicate that (1) formats differ in their susceptibility to response styles but lead to the same results with respect to average values and underlying dimensions; (2) binary format is quicker to complete and perceived as quicker while all formats are perceived as equally simple, pleasant, and useful to express feelings; (3) an interaction between the construct measured and the answer format clearly exists which should be investigated more systematically in future research

Cross-cultural differences in survey response patterns

The existence of variable response styles represents a major threat to the correct interpretation of market research findings. In international marketing this threat is further increased due to samples of respondents from different cultural backgrounds. In this paper we (1) extend the investigation of differences in cross-cultural response styles by studying full response patterns instead of extreme values, (2) quantify the extent of the potential mistake of not accounting for cross-cultural differences in response behaviour, and (3) present a simple way of testing whether or not data sets from various cultural backgrounds can be used without correcting for cross-cultural response styles

Extended beta regression in R : shaken, stirred, mixed, and partitioned

Beta regression – an increasingly popular approach for modeling rates and proportions – is extended in various directions: (a) bias correction/reduction of the maximum likelihood estimator, (b) beta regression tree models by means of recursive partitioning, (c) latent class beta regression by means of finite mixture models. All three extensions may be of importance for enhancing the beta regression toolbox in practice to provide more reliable inference and capture both observed and unobserved/latent heterogeneity in the data. Using the analogy of Smithson and Verkuilen (2006), these extensions make beta regression not only “a better lemon squeezer” (compared to classical least squares regression) but a full-fledged modern juicer offering lemon-based drinks: shaken and stirred (bias correction and reduction), mixed (finite mixture model), or partitioned (tree model). All three extensions are provided in the R package betareg (at least 2.4-0), building on generic algorithms and implementations for bias correction/reduction, model-based recursive partioning, and finite mixture models, respectively. Specifically, the new functions betatree() and betamix() reuse the object-oriented flexible implementation from the R packages party and flexmix, respectively

Cross-cultural comparisons of tourist satisfaction : assessing analytical robustness

Response styles can distort survey findings. Culture-specific response styles (CSRS) are particularly problematic to cross-cultural and empirical tourism researchers using multi-cultural samples because the resulting data contamination can lead to inaccurate conclusions about the research question under study. This is particularly the case when constructs such as satisfaction are measured, which are difficult to operationalise. Nevertheless, possible culture-specific response style effects are typically ignored, thus jeopardizing the validity of reported findings. This chapter raises awareness of the problem, illustrates the problem empirically and presents a method that enables researchers to assess the robustness of empirical findings on cross-cultural differences in satisfaction to CSRS. This approach avoids the disadvantages of ignoring the problem and interpreting spurious results or choosing one single correction technique that potentially introduces new kinds of data contamination

Segmenting the volunteer market: learnings from an Australian study

The volunteering industry in Australia contributes over 42 billion dollars to society each year. It is facing increasing pressures due to reduced funding and growing competition for limited resources. This study provides valuable information to volunteer managers by segmenting what is otherwise an extremely heterogeneous market into homogenous subgroups based on peoples’ motivations to volunteer. This is useful in the development of targeted marketing campaigns to identify, attract, and retain volunteers. Three segments are identified with distinctive motivational patterns – ‘social volunteers’, ‘community volunteers’ and ‘altruistic volunteers’. These segments are then profiled so that managers can identify the most effective way of reaching them and ultimately more efficiently spend their limited marketing dollars

Fitting finite mixtures of linear mixed models with the EM algorithm

Finite mixtures of linear mixed models are increasily applied in differentareas of application. They conveniently allow to account for correlations betweenobservations from the same individual and to model unobserved heterogeneity betweenindividuals at the same time. Different variants of the EM algorithm arepossible for maximum likelihood (ML) estimation. In this paper two different versionsfor fitting this model class are presented. One variant of the EM algorithmrequires weighted ML estimation. As this fitting method might not be readily availablein standard software sufficient conditions which allow to transform a weightedinto an unweighted ML estimation problem are derived

Validly measuring destination image in survey studies

Destination image is among the most frequently measured constructs in empirical survey research. Academic tourism researchers tend to use multi-category scales, often referring to them as Likert scales, while industry typically uses pickany measures. But which leads to results that are more valid? Findings from a large-scale experimental study show that a forced-choice full binary format (where respondents have to tick yes and no for each destination-attribute combination) performs better than both current preferred formats in academic and applied studies

The User-Friendliness of Alternative Answer Formats

Despite the increasing resistance of consumers to participate in market research and the vast amount of literature on the methodological superiority of certain answer formats over others, the issue of user-friendliness of different answer formats has not been investigated extensively in the past. We contribute to this area of research by investigating respondents’ preferences for one of five answer formats. The preference is not measured hypothetically, respondents are invited to choose their preferred format and complete the questionnaire in the respective version. Results indicate that ordinal (polytomous and dichotomous) scales are the respondents’ favourite choices. These favourite answer formats are – after having experienced them in the questionnaire – perceived as equally pleasant and equally capable of capturing the respondents’ opinions

Three good reasons NOT to use factor-cluster segmentation

Market segmentation is very popular both in tourism industry and among tourism researchers. Tourism industry uses it to identify homogenous subsets of tourists and to select the most suitable of them to target over the medium and long term. Tourism researchers use it to gain a deeper understanding of the heterogeneity of consumer behaviour among tourists. There are two basic forms of market segmentation: a priori (Mazanec, 2000) or commonsense segmentation (Dolnicar, 2004) and post-hoc (Myers and Tauber, 1977), a posteriori (Mazanec, 2000), or data-driven segmentation (Dolnicar, 2004). In commonsense segmentation the users determine in advance which tourist characteristic should be used to group tourists. Typically one single characteristic is used (e.g. age, country of origin, gender), tourists are split according to this criterion and then the resulting groups are described. This makes commonsense segmentation a very simple procedure with no major methodological traps that could lead to solutions of questionable validity. The same does not hold for data-driven segmentation. In data-driven segmentation a set of variables is used as the so-called segmentation base. A mathematical algorithm is then required to determine groups of respondents who have responded similarly to the variables included in the segmentation base. This process is not particularly complex, but it does require solid understanding of the foundations of clustering because a number of decisions need to be made by the data analyst throughout the clustering process which – if made wrongly – can lead to segmentation solutions of questionable validity. One of the problems that data-analysts frequently face is that the number of variables in the data set (or the number of questionnaire questions selected to be included in the segmentation base) is too high for the sample size. The recommended ratio is 5*2k or at least 2k (Formann, 1984) which means that a sample size including 1000 respondents does not permit clustering with more than 9 variables in the segmentation base. If the data analyst was not involved in the questionnaire design they are frequently asked to use a set of 20 or 30 variables (e.g. benefits sought, travel motivations, emotions, pre-trip information sources, vacation activities etc.), which typically cannot be accommodated with the available data sets. The typical way of dealing with this problem of having too many variables for a given sample size is to conduct something referred to as “factor-cluster segmentation”. This term appears to have been introduced by Smith (1989) as it is not used outside of the tourism discipline. It involves first factor analysing the full set of variables included in the segmentation base and then using the resulting factor scores in the cluster analysis. There are (at least) three good reasons why this approach should not be used: Firstly, the segmentation analysis is only based on part of the information collected from respondents. A high percentage of variance explained by the factor analysis in survey data sets is 60%. This still means that 40% of the information contained in the data is thrown away before the segmentation analysis is even conducted. The segmentation solution is therefore based only on slightly more than half of the information that was originally deemed to be important when the data was collected and when the segmentation base was selected. Secondly, the segmentation solution is identified in a transformed space and that means the very nature of the data is altered before the segmentation is undertaken (Arabie and Hubert, 1994; Ketchen and Shook, 1996). It is therefore not legitimate to interpret the solution using the original variables. Instead factors have to be used to interpret the segmentation solution. But factors are an abstraction of items. As a consequence, it is not easy to derive direct marketing action implications from factors which are composites of a number of items, often including some which are not logically related. Finally, and most importantly, factor-cluster analysis has been shown to perform worse in identifying the correct data structure in experiments with artificial data (Sheppard, 1996; Dolnicar and Grün, 2008) than running cluster analysis directly on the raw, untransformed data. Even if the artificial data sets were constructed using a factor-analytic model, which should give the factor-cluster segmentation approach a competitive advantage, the factor-cluster analysis did not perform substantially better. In contradiction to current practice in tourism research but in line with the recommendations from leading clustering experts (Arabie and Hubert, 1994) as well as researchers who have conducted comparative studies using factor-cluster analysis and clustering without pre processing (Sheppard, 1996; Dolnicar and Grün, 2008), it has to be concluded that factor-cluster analysis is indeed an “outmoded and statistically insupportable practice” (Arabie and Hubert, 1994) and should not be used in data-driven tourism segmentation studies. A number of simple alternatives are available to data analyst to deal with too many variables. Optimally, the data analyst is involved in preparing data the collection and can either ensure that no redundant variables are included or that the sample size chosen is sufficient to allow clustering with the number of variables included. This is the optimal solution as it solves the problem at its origin. If the data analyst is not consulted before data collection, another alternative approach is to eliminate redundant variables from the segmentation base before segmenting. If users are still interested in segment differences with respect to variables that were eliminated, these can be computed after the segmentation task is completed. Whichever option is chosen, using raw data is preferable to transformed data when looking for groups of individuals in a space defined by carefully selected pieces of information (survey questions)

FlexMix version 2: finite mixtures with concomitant variables and varying and constant parameters

flexmix provides infrastructure for flexible fitting of finite mixture models in R using the expectation-maximization (EM) algorithm or one of its variants. The functionality of the package was enhanced. Now concomitant variable models as well as varying and constant parameters for the component specific generalized linear regression models can be fitted. The application of the package is demonstrated on several examples, the implementation described and examples given to illustrate how new drivers for the component specific models and the concomitant variable models can be defined