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In continuous test norming, the test score distribution is estimated as a continuous function of predictor(s). A flexible approach for norm estimation is the use of generalized additive models for location, scale, and shape. It is unknown how sensitive their estimates are to model flexibility and sample size. Generally, a flexible model that fits at the population level has smaller bias than its restricted nonfitting version, yet it has larger sampling variability. We investigated how model flexibility relates to bias, variance, and total variability in estimates of normalized z scores under empirically relevant conditions, involving the skew Student t and normal distributions as population distributions. We considered both transversal and longitudinal assumption violations. We found that models with too strict distributional assumptions yield biased estimates, whereas too flexible models yield increased variance. The skew Student t distribution, unlike the Box–Cox Power Exponential distribution, appeared problematic to estimate for normally distributed data. Recommendations for empirical norming practice are provided. [ABSTRACT FROM AUTHOR]

A test score on a psychological test is usually expressed as a normed score, representing its position relative to test scores in a reference population. These typically depend on predictor(s) such as age. The test score distribution conditional on predictors is estimated using regression, which may need large normative samples to estimate the relationships between the predictor(s) and the distribution characteristics properly. In this study, we examine to what extent this burden can be alleviated by using prior information in the estimation of new norms with Bayesian Gaussian distributional regression. In a simulation study, we investigate to what extent this norm estimation is more efficient and how robust it is to prior model deviations. We varied the prior type, prior misspecification and sample size. In our simulated conditions, using a fixed effects prior resulted in more efficient norm estimation than a weakly informative prior as long as the prior misspecification was not age dependent. With the proposed method and reasonable prior information, the same norm precision can be achieved with a smaller normative sample, at least in empirical problems similar to our simulated conditions. This may help test developers to achieve cost‐efficient high‐quality norms. The method is illustrated using empirical normative data from the IDS‐2 intelligence test. [ABSTRACT FROM AUTHOR]

Satisfaction with activity-based work environments (ABW environments) often falls short of expectations, with striking differences among individual workers. A better understanding of these differences may provide clues for optimising satisfaction with ABW environments and associated organisational outcomes. The current study was designed to examine how specific psychological needs, job characteristics, and demographic variables relate to satisfaction with ABW environments. Survey data collected at seven organizations in the Netherlands (N = 551) were examined using correlation and regression analyses. Significant correlates of satisfaction with ABW environments were found: need for relatedness (positive), need for privacy (negative), job autonomy (positive), social interaction (positive), internal mobility (positive), and age (negative). Need for privacy appeared to be a powerful predictor of individual differences in satisfaction with ABW environments. These findings underline the importance of providing work environments that allow for different work styles, in alignment with different psychological need strengths, job characteristics, and demographic variables. Improving privacy, especially for older workers and for workers high in need for privacy, seems key to optimizing satisfaction with ABW environments. [ABSTRACT FROM AUTHOR]

Traffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. The linear multiregression dynamic model (LMDM) has been shown to be promising for forecasting flows, accommodating multivariate flow time series, while being a computationally simple model to use. While statistical flow forecasting models usually base their forecasts on flow data alone, data for other traffic variables are also routinely collected. This paper shows how cubic splines can be used to incorporate extra variables into the LMDM in order to enhance flow forecasts. Cubic splines are also introduced into the LMDM to parsimoniously accommodate the daily cycle exhibited by traffic flows. The proposed methodology allows the LMDM to provide more accurate forecasts when forecasting flows in a real high-dimensional traffic data set. The resulting extended LMDM can deal with some important traffic modelling issues not usually considered in flow forecasting models. Additionally, the model can be implemented in a real-time environment, a crucial requirement for traffic management systems designed to support decisions and actions to alleviate congestion and keep traffic flowing. [ABSTRACT FROM AUTHOR]

. Linear multiregression dynamic models, which combine a graphical representation of a multivariate time series with a state space model, have been shown to be a promising class of models for forecasting traffic flow data. Analysis of flows at a busy motorway intersection near Manchester, UK, highlights two important modelling issues: accommodating different levels of traffic variability depending on the time of day and accommodating measurement errors due to data collection errors. This paper extends linear multiregression dynamic models to address these issues. Additionally, the paper investigates how close the approximate forecast limits that are usually used with the linear multiregression dynamic model are to the true, but not so readily available, forecast limits. [ABSTRACT FROM AUTHOR]

Social functioning is often impaired during the ultra-high risk (UHR) phase for psychosis, but group-level studies regarding the role of social functioning in transition to psychosis are inconsistent. Exploring the inter-individual differences which underlie the association between social functioning and psychotic symptoms in this phase could yield new insights. To examine the idiographic and dynamic association between social activation and suspiciousness in individuals at UHR for psychosis using time-series analysis. Twenty individuals at UHR for psychosis completed a diary application every evening for 90 days. Two items on social activation (quantity: 'time spent alone' and quality: 'feeling supported') and two items on suspiciousness ('feeling suspicious' and 'feeling disliked') were used. Time series (T = 90) of each individual were analyzed using vector auto regression analysis (VAR), to estimate the lagged (over 1 day) effect of social activation on suspiciousness, and vice versa, as well as their contemporaneous associations. Heterogeneous person-specific associations between social activation and suspiciousness were found in terms of strength, direction and temporal aspects. The association between social activation and suspiciousness differs amongst individuals who are at UHR for psychosis. These findings underline the importance of tailoring psychosocial interventions to the individual. Future studies may examine whether using results of single-subject studies in clinical practice to personalize treatment goals leads to better treatment outcomes. [ABSTRACT FROM AUTHOR]