Nowadays, practitioners extensively apply quick and reliable scales of user satisfaction as part of their user experience analyses to obtain well-founded measures of user satisfaction within time and budget constraints. However, in the human–computer interaction literature the relationship between the outcomes of standardized satisfaction scales and the amount of product usage has been only marginally explored. The few studies that have investigated this relationship have typically shown that users who have interacted more with a product have higher satisfaction. The purpose of this article was to systematically analyze the variation in outcomes of three standardized user satisfaction scales (SUS, UMUX, UMUX-LITE) when completed by users who had spent different amounts of time with a website. In two studies, the amount of interaction was manipulated to assess its effect on user satisfaction. Measurements of the three scales were strongly correlated and their outcomes were significantly affected by the amount of interaction time. Notably, the SUS acted as a unidimensional scale when administered to people who had less product experience but was bidimensional when administered to users with more experience. Previous findings of similar magnitudes for the SUS and UMUX-LITE (after adjustment) were replicated but did not show the previously reported similarities of magnitude for the SUS and the UMUX. Results strongly encourage further research to analyze the relationships of the three scales with levels of product exposure. Recommendations for practitioners and researchers in the use of the questionnaires are also provided.

Dietary patterns were recently applied to examine the relationship between eating habits and prostate cancer (PC) risk. While the associations between PC risk with the glycemic index and Mediterranean score have been reviewed, no meta-analysis is currently available on dietary patterns defined by “a posteriori” methods. A literature search was carried out (PubMed, Web of Science) to identify studies reporting the relationship between dietary patterns and PC risk. Relevant dietary patterns were selected and the risks estimated were calculated by a random-effect model. Multivariable-adjusted odds ratios (ORs), for a first-percentile increase in dietary pattern score, were combined by a dose-response meta-analysis. Twelve observational studies were included in the meta-analysis which identified a “Healthy pattern” and a “Western pattern”. The Healthy pattern was not related to PC risk (OR = 0.96; 95% confidence interval (CI): 0.88–1.04) while the Western pattern significantly increased it (OR = 1.34; 95% CI: 1.08–1.65). In addition, the “Carbohydrate pattern”, which was analyzed in four articles, was positively associated with a higher PC risk (OR = 1.64; 95% CI: 1.35–2.00). A significant linear trend between the Western (p = 0.011) pattern, the Carbohydrate (p = 0.005) pattern, and the increment of PC risk was observed. The small number of studies included in the meta-analysis suggests that further investigation is necessary to support these findings.

Statistical Analysis of Questionnaires: A Unified Approach Based on R and Stata presents special statistical methods for analyzing data collected by questionnaires. The book takes an applied approach to testing and measurement tasks, mirroring the growing use of statistical methods and software in education, psychology, sociology, and other fields.

We propose a class of multidimensional Item Response Theory models for polytomously-scored items with ordinal response categories. This class extends an existing class of multidimensional models for dichotomously-scored items in which the latent abilities are represented by a random vector assumed to have a discrete distribution, with support points corresponding to different latent classes in the population. In the proposed approach, we allow for different parameterizations for the conditional distribution of the response variables given the latent traits, which depend on the type of link function and the constraints imposed on the item parameters. Moreover, we suggest a strategy for model selection that is based on a series of steps consisting of selecting specific features, such as the dimension of the model (number of latent traits), the number of latent classes, and the specific parameterization. In order to illustrate the proposed approach, we analyze a dataset from a study on anxiety and depression on a sample of oncological patients.

By adopting a dyadic extension of the Theory of Planned Behavior (Ajzen, 1991), this study examined whether perceived economic uncertainty affects fertility intentions. Three-hundred thirty one heterosexual couples living in Italy participated in a randomized between-group experimental study, in which we manipulated perceived economic uncertainty (low vs. high vs. control). The participants subsequently completed a questionnaire measuring their attitudes, subjective norms, perceived behavioral control, and fertility intentions. We employed Structural Equation Modelling in estimating the Actor–Partner Interdependence Model. The model showed a good fit to the data. Women’s attitudes, subjective norms, and perceived behavioral control were influenced by the high economic uncertain scenario, whereas among men these variables were affected only by the positive economic scenario. Attitudes and perceived behavioral control were significant predictors of fertility intentions for both sexes. Significant partner effects were observed as well. These findings suggest that fertility plans should be examined by adopting a dyadic perspective, as individuals’ intentions are affected not only by their own beliefs, but also by those of their partners.