Bullying at work : a cross cultural perspective.

1. THE 4th INTERNATIONAL CONFERENCE ON BULLYING AND HARASSMENT IN THE WORKPLACE (Bergen, Norway, 2004) Bullying at work : a cross cultural perspective. Assessing measurement equivalence with the bilingual version of the NAQ in Belgium Guy Notelaers1, Jeroen Vermunt,2, Stale Einarsen3 & Hans De Witte1 1: Katholieke Universiteit Leuven, Belgium; 3: Bergen University, Norway; 2:Tilburg Univeristy, Netherlands. Introduction Cross-cultural survey research, whether it involves a comparison of cultures, nations or language groups, usually has to deal with a lot more methodological issues and problems than intracultural survey research. Perhaps the most prominent of these additional concerns is the problem of equivalence, or in other words: the problem of making valid comparisons across groups. ‘In a cross-cultural research one cannot readily assume that the scores of respondents form different cultural or language groups on a certain item or scale, can be compared in a straightforward manner’ (Welkenhuysen-Gybels & van de Vijver, 2001). When adapting the Negative Acts Questionnaire to the Belgian situation, that is a country where two languages are widely used, comparing the Northern flemisch part with the Southern french speaking part, researcher have to asses equivelence. In this poster the equivalence of the French and the Dutch version of the NAQ (Einarsen, et. al, 1994) within the framework of latent class analysis (Vermunt & Magidson, ). Given the multidimensionality of the bullying concept a latent class cluster model modelled in Latent GOLD (Magidson & Vermunt, 2003) will be presented. Equivalence and bias analysis Van de Vijver & Leung (1997) name three types of procedural equivalence that are hierarchically linked to each other. The lowest level of equivalence is construct equivalence and is achieved if the instrument measures the same latent construct or trait in all of the cultural groups in the study. This does not mean that the operationalisation should be the same. Measurement unit equivalence is the next level of equivalence. If the measurement unit of the instrument is identical for each of the cultural groups this level is achieved (the measurements should be on the same interval scale). I f the measurement scales of the cultural groups also have the same origin and hence the instruments have the same ratio scale then the highest level of equivalence, scalar equivalence of full score comparability, is attained (Welkenhysen-Gybels, 2000). Three types of biases can influence the comparability of scores obtained across cultural / language groups (van de Vijver & Leung, 1997). Construct bias will occur when the construct measured is not identical across cultural groups. This type of bias can arise when there is an incomplete overlap of definitions of construct across cultural groups, when there is a difference in the appropriateness of (a part of) the test’s content, when there is poor sampling of relevant behaviors or when there is an incomplete coverage of the construct. The latter type of construct bias is called construct under representation. A second type of bias is method bias. It can either arise from the incomparability of the samples, instruments characteristics to which individuals from different cultures react in a consistently different manner or differences in the administration of the instrument. Method bias usually affects the entire instrument. The last kind of bias Welkenhuysen-Gybels (1998) found in van de Vijver & Leung (1997) is item bias or differential item functioning is caused by anomalies at the item level such as poor translation, incidental differences in response scale, etc. A distinction is made between uniform and non uniform item bias. With a uniform bias, the bias is independent of the score level. A non uniform bias points to influences that are not the same for every score level. In the framework for modeling equivalence in the structural equation modeling approach, the first step is to test construct equivalence. The second step is to analyze item bias to decide which level of equivalence is attained. Equivalence and bias analysis in the framework of latent class analysis In the framework for modeling equivalence in the structural equation modeling approach, the first step is to test construct equivalence. The second step is to analyze item bias to decide which level of equivalence is attained. In LCA this sequence is not strictly followed. Testing construct equivalence and analyzing item bias analysis happens in the same cycle. Furthermore in LCA we do not make a difference between uniform and non uniform bias. This implies that a difference between the three levels of equivalence that van de Vijver en Leung (1997) make is not applicable in LCA. Here we are working with categorical latent traits and not with latent traits of an ordinal, interval or ratio measurement level. In LCA the question is whether there is construct equivalence or not. Nonetheless, analogous to the van de Vijver en Lueng (1997) we distinguish also three levels of equivalence : ‘strong equivalence’, ‘equivalence’ and ‘weak equivalence’. Results NAQ is equivalent in French and Dutch The six cluster solution NAQ with three local dependencies (Hagenaars, 1998) and with 7 direct effects between indicators (information, jokes, insultes, mistakes, silence, no opinion, no reward and funny suprises) has almost the same classification output as the model with only three local dependencies, Kappa, a measure of agreement is 0,95 Sample 6175 observations stem from two kinds of research :research to inventarise wellbeing (14 studies) and research with focus on mobbing (4 studies). 57% of the respondents completed a Dutch and 43% a French questionnaire mean age of the respondents is 41 years (std=10,7). Discussion and conclusion In this contribution the starting point is not formed by likert scales though it can be argued that the negative acts questionnaire consists of litert items (strong formulated items). Using the orgininal responses given by the respondents is given the huge skewness of the data calling for a categorical data approach. This is fundamentally different from treating continous data. Therefore the framework of van de Vijver en Leung (and more technical : Meredith, W, 1993) is translated for categorical data analysis. Hagenaars (1994) framework to investigate equivalence is very helpful. He shows that there are three models to be tested. One of these we labeled as strong equivalence : there is no significant link between stratifier and indicators of the latent trait. This six cluster model for bullying at work shows significant relationships between indicators and stratifier. But the classification of individuals into cluster on the basis of the probability structure of the answering pattern does not differ much from the model were these significant links are not taken into account. Kappa is .95. This situation we call ‘equivalence’. Such equivalent measurement allows us to compare the flemish and french mother tong in Belgium. The results show that french speaking respondents are more approximately two times more bullied at work (comparing the size of both groups across workrelated bullying and severe victim) than flemisch respondents. contact : guy.notelaers@psy.kuleuven.ac.be References Einarsen, Raknes, Matthiesen & Hellesøy, (1994). The negative acts questionnaire Hagenaars, J. (1998) Latent Structure models with direct effects between indicators : local dependence models. Sociological Methods and Research, 1998, 16, 379-405. Hagenaars. J. (1994) Categorical Longitudinal Data. Log-Linear Panel, Trend and Cohort Analysis. Sage Publications Magidson, J. Vermunt, J.K. (2001) Latent Class Factor and Cluster Models, BI Plots and related graphical displays. In: Sociological Methodology, 31, 223-264. Meredith, W. (1993) Measurement invariance, factor analysis and factorial invariance. Psychometrika, 59, 525 – 543. Van de Vijver, F. & Leung, K. (1997) Methods and data analysis for cross-cultural research. Thousand Oaks : Sage. Vermunt, J.K. Magidson, J. (2002) Latent Class Cluster Analysis. In : Hagenaars, J.&McCutcheon (eds), Applied Latent Class Analysis, Cambridge : Cambridge University Press, 89-106. Vermunt, J.K. Magidson, J. (2003) Latent Gold : Users GuideLatent Gold : Users Guide. Statistical Innovations. Welkenhuysen – Gybels, J. & Van de Vijver, F. (2001). A comparison of methods for the evaluation of construct equivalence in a multigroup setting. Proceedings of the Annual Meeting of the American Statistical Association, August, 5 – 9, 2001. Welkenhuysen – Gybels, J. (1998) Cross-cultureel onderzoek : methodologische problemen bij de toepassing van de een verkorte F-schaal in Franstalig en Nederlandstalig België. Tijdschrift voor Sociologie, 19, 449-472. Gedruckt im Rechenzentrum der Universität Leipzig