Whether, when, and why perceivers are able to accurately infer the personality traits of other individuals is a key topic in psychological science. Studies examining this question typically ask a number of perceivers to judge a number of targets with regard to a specific trait. The resulting data are then analyzed by averaging the judgments across perceivers or by computing the respective statistic for each single perceiver. Here, we discuss the limitations of the average-perceiver and single-perceiver approaches. Furthermore, we argue that and illustrate how cross-classified structural equation models can be used for the flexible analysis of accuracy data.

When the process of publication favors studies with small p-values, and hence large effect estimates, combined estimates from many studies may be biased. This paper describes a model for estimation of effect size when there is selection based on one-tailed p-values. The model employs the method of maximum likelihood in the context of a mixed (fixed and random) effects general linear model for effect sizes. It offers a test for the presence of publication bias, and corrected estimates of the parameters of the linear model for effect magnitude. The model is illustrated using a well-known data set on the benefits of psychotherapy.

The calibration of probability or confidence judgments concerns the association between the judgments and some estimate of the correct probabilities of events. Researchers rely on estimates using relative frequencies computed by aggregating data over observations. We show that this approach creates conceptual problems, and may result in the confounding of explanatory variables or unstable estimates. To circumvent these problems we propose using probability estimates obtained from statistical models—specifically mixed models for binary data—in the analysis of calibration. We illustrate this methodology by re-analyzing data from a published study and comparing the results from this approach to those based on relative frequencies. The model-based estimates avoid problems with confounding variables and provided more precise estimates, resulting in better inferences.

Researchers in the decision making tradition usually analyze multiple decisions within experiments by aggregating choices across individuals and using the individual subject as the unit of analysis. This approach can mask important variations and patterns within the data. Specifically, it ignores variations in decisions across a task or game and possible influences of characteristics of the subject or the experiment on these variations. We demonstrate, by reanalyzing data from two previously published articles, how a mixed model analysis addresses these limitations. Our results, with a modified Iowa gambling task and a prisoner's dilemma game, illustrate the ways in which such an analysis can test hypotheses not possible with other techniques, is more parsimonious, and is more likely to be faithful to theoretical models.

Telematicsdevices installed in insured vehicles provide actuaries with new risk factors, such as the time of the day, average speeds, and other driving habits. This paper extends the multivariate mixed model describing the joint dynamics of telematics data and claim frequencies proposed by Denuit et al. (2019a) by allowing for signals with various formats, not necessarily integer-valued, and by replacing the estimation procedure with the Expected Conditional Maximization algorithm. A numerical study performed on a database related to Pay-How-You-Drive, or PHYD motor insurance illustrates the relevance of the proposed approach for practice.

Although East Africa is home to one of the most advanced dairy industries in Sub-Saharan Africa, regional annual milk production is insufficient to meet the demand. The challenge of increasing milk yields (MYs) among smallholder dairy cattle farmers (SDCFs) has received considerable attention and resulted in the introduction of various dairy management strategies (DMSs). Despite adoption of these DMSs, MYs remain low on-farm and there is a large discrepancy in the efficacy of DMSs across different farms. Therefore, the present study sought to: (1) identify on-farm DMSs employed by East African SDCFs to increase MYs and (2) summarize existing literature to quantify the expected MY changes associated with these identified DMSs. Data were collected through a comprehensive literature review and in-depth semi-structured interviews with 10 experts from the East African dairy sector. Meta-analysis of the literature review data was performed by deriving four multivariate regression models (i.e. models 1 to 4) that related DMSs to expected MYs. Each model differed in the weighting strategy used (e.g. number of observations and inverse of the standard errors) and the preferred model was selected based on the root estimated error variance and concordance correlation coefficient. Nine DMSs were identified, of which only adoption of improved cattle breeds and improved feeding (i.e. increasing diet quality and quantity) consistently and significantly (P < 0.05) increased daily MYs across the available studies. Improved breeds alongside adequate feeding explained ≤50% of the daily MYs observed in the metadata while improved feeding explained ≤30% of the daily MYs observed across the different models. Conversely, calf suckling significantly (P < 0.05) reduced MYs according to model 2. Other variables including days in milk, trial length and maximum ambient temperature (used as a proxy for heat stress) contributed significantly to decreasing MYs. These variables may explain some of the heterogeneity in MY responses to DMSs reported in the literature. Our results suggest that using improved cattle breeds alongside improved feeding is the most reliable strategy to increase MYs on-farm in East Africa. Nevertheless, these DMSs should not be considered as standalone solutions but as a pool of options that should be combined depending on the resources available to the farmer to achieve a balance between using dairy cattle genetics, proper husbandry and feeding to secure higher MYs.

In ruminants, urea recycling is considered an evolutionary advantage. The amount of urea recycled mainly depends of the nitrogen (N) intake and the amount of organic matter (OM) digested in the rumen. Because recycled N contributes to meeting microbial N requirements, accurate estimates of urea recycling can improve the understanding of efficiency of N utilization and N losses to the environment. The objective of this study was to evaluate urea kinetics and microbial usage of recycled urea N in ruminants using a meta-analytical approach. Treatment mean values were compiled from 25 studies with ruminants (beef cattle, dairy cows and sheep) which were published from 2001 to 2016, totalling 107 treatment means. The data set was analyzed according to meta-analysis techniques using linear or non-linear mixed models, taking into account the random variations among experiments. Urea N synthesized in the liver (UER) and urea N recycled to the gut (GER) linearly increased (P<0.001) as N intake (g/BW0.75) increased, with increases corresponding to 71.5% and 35.2% of N intake, respectively. The UER was positively associated (P<0.05) with dietary CP concentration and the ratio of CP to digestible OM (CP:DOM). Maximum curvature analyses identified 17% dietary CP as the point where there was a prominent increase in hepatic synthesis of urea N, likely due to an excess of dietary N leading to greater ammonia absorption. The GER:UER decreased with increasing dietary CP concentration (P<0.05). At dietary CP⩾19%, GER:UER reached near minimal values. The fraction of UER eliminated as urinary urea N and the contribution of urea N to total urinary N were positively associated with dietary CP (P<0.05), both reaching values near the plateau when dietary CP was 17%. The fractions of GER excreted in the feces and utilized for anabolism decreased, whereas the fraction of GER returned to the ornithine cycle increased with dietary CP concentration (P<0.05). Recycled urea N assimilated by ruminal microbes (as a fraction of GER) decreased as dietary CP and CP:DOM increased (P<0.05). The efficiency of microbial assimilation of recycled urea N was near plateau values at 194 g CP/kg DOM. The models obtained in this study contribute to the knowledge on N utilization, and they could be used in feeding models to predict urea recycling and thus to improve formulation of diets to reduce N losses that contribute to air and water pollution.

The emotional intelligence construct is still the focus of substantial controversy. In this paper we contribute to the current debate regarding the factors that comprise the emotional intelligence construct. While some authors argue that emotional intelligence consists of a number of social and emotional competencies including self-motivation (Goleman 1995; 1998), others maintain emotional intelligence abilities are restricted to abilities that directly link emotions to cognition (Mayer & Salovey 1997). This latter view does not include motivation as a factor, but acknowledges it as a separate, related function. We conducted an empirical study to test these two different conceptualisations. One hundred and thirteen individuals completed measures of emotional intelligence and McClelland's (1987) three motivational needs. Structural equation analyses clearly supported Mayer and Salovey's (1997) conceptualisation that motivation is a factor related to emotional intelligence but is not a component part of the emotional intelligence construct. Implications are discussed.

Research in animal sciences, especially nutrition, increasingly requires processing and modeling of databases. In certain areas of research, the number of publications and results per publications is increasing, thus periodically requiring quantitative summarizations of literature data. In such instances, statistical methods dealing with the analysis of summary (literature) data, known as meta-analyses, must be used. The implementation of a meta-analysis is done in several phases. The first phase concerns the definition of the study objectives and the identification of the criteria to be used in the selection of prior publications to be used in the construction of the database. Publications must be scrupulously evaluated before being entered into the database. During this phase, it is important to carefully encode each record with pertinent descriptive attributes (experiments, treatments, etc.) to serve as important reference points for the rest of the analysis. Databases from literature data are inherently unbalanced statistically, leading to considerable analytical and interpretation difficulties; missing data are frequent, and data structures are not the outcomes of a classical experimental system. An initial graphical examination of the data is recommended to enhance a global view as well as to identify specific relationships to be investigated. This phase is followed by a study of the meta-system made up of the database to be interpreted. These steps condition the definition of the applied statistical model. Variance decomposition must account for inter- and intrastudy sources; dependent and independent variables must be identified either as discrete (qualitative) or continuous (quantitative). Effects must be defined as either fixed or random. Often, observations must be weighed to account for differences in the precision of the reported means. Once model parameters are estimated, extensive analyses of residual variations must be performed. The roles of the different treatments and studies in the results obtained must be identified. Often, this requires returning to an earlier step in the process. Thus, meta-analyses have inherent heuristic qualities.

A doubly stochastic switching model previously used by the author to check against doubly stochastic ‘white-noise' models is generalized in the case of Poisson processes to any distribution of fluctuating intensity rates. The resulting Cox process is used in place of a simple Poisson process as the basis of a mixed model, which is then fitted to some data on automobile accidents previously analysed by Seal (1980) in terms of a mixed Poisson process. It is shown that there is just significant evidence in favour of some additional heterogeneity ‘within' each year for individual drivers; the nature of the data analysed (yearly time intervals) does not, however, allow discrimination between heterogeneity which is definitely attributable to a common fluctuating environment, and that which is in effect independent for each driver.