To obtain reliable measures researchers prefer multiple-item questionnaires rather than single-item tests. Multiple-item questionnaires may be costly however and timeconsuming for participants to complete. They therefore frequently administer twoitem measures, the reliability of which is commonly assessed by computing a reliability coefficient. There is some disagreement, however, what the most appropriate indicator of scale reliability is when a measure is composed of two items.The most frequently reported reliability statistic for multiple-item scales is Cronbach's coefficient alpha and many researchers report this coefficient for their two-item measure 1,2,3,4 . Others however claim that coefficient alpha is inappropriate and meaningless for two-item scales. Instead, they recommend using the Pearson correlation coefficient as a measure of reliability 5,6,7,8 . Still others argue that the interitem correlation equals the split-half reliability estimate for the two-item measure and they advocate the use of the Spearman-Brown formula to estimate the reliability of the total scale 9 . As these recommendations are reported without elaborating, there is considerable confusion among end users as to the best reliability coefficient for twoitem measures. This note aims to clarify the issue.It is important to emphasize at the outset that it is not our intention in this paper to promote the use of two-item scales. Quite the contrary, having only two items to identify an underlying construct has been recognized as problematic for some time and we support the claim that using more items is better 10,11,12 . The use of multiple, heterogeneous indicators enhances construct validity in the sense that it

influence.ME provides tools for detecting influential data in mixed effects models. The application of these models has become common practice, but the development of diagnostic tools has lagged behind. influence.ME calculates standardized measures of influential data for the point estimates of generalized mixed effects models, such as DFBETAS, Cook's distance, as well as percentile change and a test for changing levels of significance. influence.ME calculates these measures of influence while accounting for the nesting structure of the data. The package and measures of influential data are introduced, a practical example is given, and strategies for dealing with influential data are suggested.

influence.ME provides tools for detecting influential data in mixed effects models. The application of these models has become common practice, but the development of diagnostic tools has lagged behind. influence.ME calculates standardized measures of influential data for the point estimates of generalized mixed effects models, such as DFBETAS, Cook's distance, as well as percentile change and a test for changing levels of significance. influence.ME calculates these measures of influence while accounting for the nesting structure of the data. The package and measures of influential data are introduced, a practical example is given, and strategies for dealing with influential data are suggested.

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The Netherlands has become one of the most secular countries in the world. A vast majority of the Dutch people does not attend church regularly and more than half its population is not affiliated with any church at all. In this study we set out to test which individual and contextual characteristics affect religious disaffiliation. We deduced several hypotheses from theories on social integration and rationalization. To test these hypotheses we used retrospective data containing information on events that took place in the lives of our respondents since adolescence. These data were analysed using a discrete-time event history model. We found that the higher the level of rationalization in a certain year, the more likely people were to disaffiliate. This effect was particularly strong for young people. Moreover, by introducing rationalization in the model we found a number of spurious relationships that at first glance seemed to be causal. Not surprisingly, respondents were more likely to disaffiliate in cases where their partners were nonreligious. However, as respondents and their partners presumably are effected equally by rationalization, we cannot but conclude that the process of rationalization is mainly responsible for the process of religious disaffiliation that takes place in The Netherlands. Manfred Te Grotenhuis is engaged in postdoctoral research at

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