The repertory grid is a psychological data collection technique that is used to elicit qualitative data in the form of attributes as well as quantitative ratings. A common approach for evaluating multiple repertory grid data is sorting the elicited bipolar attributes (so called constructs) into mutually exclusive categories by means of content analysis. An important question when planning this type of study is determining the sample size needed to a) discover all attribute categories relevant to the field and b) yield a predefined minimal number of attributes per category. For most applied researchers who collect multiple repertory grid data, programming a numeric simulation to answer these questions is not feasible. The gridsampler software facilitates determining the required sample size by providing a GUI for conducting the necessary numerical simulations. Researchers can supply a set of parameters suitable for the specific research situation, determine the required sample size, and easily explore the effects of changes in the parameter set.

IntroductionIntercultural competence (IC) is an important skill to be gained from higher education. However, it remains unclear what IC means to students and what factors might influence their definitions of IC. The aim of the current study was to qualitatively assess how students at one higher education institution in the USA define IC and to quantitatively test for relationships among IC components and various demographic characteristics, including intercultural experience and study context. A further aim was to descriptively compare the IC definitions from the US sample with the definitions obtained from another sample of university students in Germany.Materials and methodsA purposive sample of n = 93 undergraduate, second semester students at Dickinson College, USA, participated in the study by completing an online questionnaire. The qualitative data were content-analyzed to define the dimensions of IC. The quantitative data were cluster-analyzed to assess the multivariate relationships among the IC components and the demographic characteristics of the sample.ResultsThe most important dimensions of IC were Knowledge, External Outcomes (interaction, communication), and Attitudes (respect, tolerance) according to the US sample. The most frequently chosen dimensions of IC differed between both samples: Knowledge was chosen by the sample in the USA while External Outcomes was chosen by the sample in Germany. Relative to the US sample, significantly more students chose Attitudes, External Outcomes, and Intrapersonal Skills in the sample in Germany. The relationships among IC components and demographic characteristics were only weak in the US sample. A person with IC was rated as Open-minded and Respectful by students who lived predominantly in the USA or Tolerant and Curious by those who lived outside the USA for at least six months.DiscussionThe current results suggest that students residing in two countries (USA or Germany) define IC using similar dimensions. However, IC definitions may depend on the intercultural experience and the current global discourse. Longitudinal studies with representative samples are required to assess how IC definitions change over time.

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This paper presents and illustrates Interpretive Clustering, an innovative and original method of qualitative analysis of Repertory Grid data. Repertory Grids are a popular and flexible method of research, but they have primarily been used to gather data that are analysed quantitatively. Although many researchers have used Grids more qualitatively, this is often limited to a content analysis of the elicited constructs across a sample of participants.Interpretive Clustering is a participant-led method which uses the grid data idiographically to explore how a participant's construing may 'cluster' around one or more issues. We show how this is quite different from a thematic analysis, and discuss how Interpretive Clustering can provide insights that are complementary to those gained from methods like thematic analysis. We conclude with suggestions for how this method, which we argue bridges the qualitative/quantitative divide, might be used in future research.

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