What affects recognition most – wrong wordstress or wrong word accent?

In an attempt to find out which of the two Swedish prosodic contrasts of 1) wordstress pattern and 2) tonal word accent category has the greatest communicative weight, a lexical decision experiment was conducted: in one part word stress pattern was changed from trochaic to iambic, and in the other part trochaic accentII words were changed to accent I.Native Swedish listeners were asked to decide whether the distorted words werereal words or ‘non-words’. A clear tendency is that listeners preferred to give more‘non-word’ responses when the stress pattern was shifted, compared to when wordaccent category was shifted. This could have implications for priority of phonological features when teaching Swedish as a second language.

Likelihood estimate of treatment effects under selection bias

We consider methods for estimating causal effects of treatment in the situation where the individuals in the treatment and the control group are self selected, i.e., the selection mechanism is not randomized. In this case, simple comparison of treated and control outcomes will not generally yield valid estimates of casual effects. The propensity score method is frequently used for the evaluation of treatment effect. However, this method is based onsome strong assumptions, which are not directly testable. In this paper, we present an alternative modeling approachto draw causal inference by using share random-effect model and the computational algorithm to draw likelihood based inference with such a model. With small numerical studies and a real data analysis, we show that our approach gives not only more efficient estimates but it is also less sensitive to model misspecifications, which we consider, than the existing methods.

Likelihood prediction for generalized linear mixed models under covariate uncertainty

This paper presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction is explained through a series of examples; from a classical one to more complicated ones. The examples show, in simple cases, that the likelihood prediction (LP) coincides with already known best frequentist practice such as the best linear unbiased predictor. The paper outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson error-in-variable generalized linear model, it has been shown that in complicated cases LP produces better results than already know methods.

Exploring self-rated health among adolescents : a think-aloud study

BACKGROUND: Despite extensive use of self-rated health questions in youth studies, little is known about what such questions capture among adolescents. Hence, the aim of this study was to explore how adolescents interpret and reason when answering a question about self-rated health. METHODS: A qualitative study using think-aloud interviews explored the question, "How do you feel most of the time?", using five response options ("Very good", "Rather good", "Neither good, nor bad", "Rather bad", and "Very bad"). The study involved 58 adolescents (29 boys and 29 girls) in lower secondary school (7th grade) and upper secondary school (12th grade) in Sweden. RESULTS: Respondents' interpretations of the question about how they felt included social, mental, and physical aspects. Gender differences were found primarily in that girls emphasized stressors, while age differences were reflected mainly in the older respondents' inclusion of a wider variety of influences on their assessments. The five response options all demonstrated differences in self-rated health, and the respondents' understanding of the middle option, "Neither good, nor bad", varied widely. In the answering of potential sensitive survey questions, rationales for providing honest or biased answers were described. CONCLUSIONS: The use of a self-rated health question including the word 'feel' captured a holistic view of health among adolescents. Differences amongst response options should be acknowledged when analyzing self-rated health questions. If anonymity is not feasible when answering questions on self-rated health, a high level of privacy is recommended to increase the likelihood of reliability.