A propensity score is the probability that a participant is assigned to the treatment group based on a set of baseline covariates. Propensity scores provide an excellent basis for equating treatment groups on a large set of covariates when randomization is not possible. This article provides a nontechnical introduction to propensity scores for clinical researchers. If all important covariates are measured, then methods that equate on propensity scores can achieve balance on a large set of covariates that mimics that achieved by a randomized experiment. We present an illustration of the steps in the construction and checking of propensity scores in a study of the effectiveness of a health coach versus treatment as usual on the well-being of seriously ill individuals. We then consider alternative methods of equating groups on propensity scores and estimating treatment effects including matching, stratification, weighting, and analysis of covariance. We illustrate a sensitivity analysis that can probe for the potential effects of omitted covariates on the estimate of the causal effect. Finally, we briefly consider several practical and theoretical issues in the use of propensity scores in applied settings. Propensity score methods have advantages over alternative approaches to equating groups particularly when the treatment and control groups do not fully overlap, and there are nonlinear relationships between covariates and the outcome.

The bifactor model is a widely applied model to analyze general and specific abilities. Extensions of bifactor models additionally include criterion variables. In such extended bifactor models, the general and specific factors can be correlated with criterion variables. Moreover, the influence of general and specific factors on criterion variables can be scrutinized in latent multiple regression models that are built on bifactor measurement models. This study employs an extended bifactor model to predict mathematics and English grades by three facets of intelligence (number series, verbal analogies, and unfolding). We show that, if the observed variables do not differ in their loadings, extended bifactor models are not identified and not applicable. Moreover, we reveal that standard errors of regression weights in extended bifactor models can be very large and, thus, lead to invalid conclusions. A formal proof of the nonidentification is presented. Subsequently, we suggest alternative approaches for predicting criterion variables by general and specific factors. In particular, we illustrate how (1) composite ability factors can be defined in extended first-order factor models and (2) how bifactor(S-1) models can be applied. The differences between first-order factor models and bifactor(S-1) models for predicting criterion variables are discussed in detail and illustrated with the empirical example.

In spite of the increasing demand for virtual cooperation, still relatively little is known about the knowledge, skills, abilities, and other characteristics (KSAOs) individuals need for virtual teamwork. Thus, the current paper aims at synthesizing the existing literature into a comprehensive model of virtual teamwork KSAOs. To this end, we review (a) existing frameworks of KSAO requirements for virtual teamwork, (b) challenges posed by different facets of virtuality, and (c) KSAOs particularly relevant for meeting the identified challenges. The results of this review are integrated into a holistic model of virtual teamwork KSAOs with distal characteristics (personality, experience) and more proximal qualities (knowledge, skills, and motivation). Research gaps as well as avenues for future research will be outlined and applications for virtual team staffing and training will be discussed.

1. Three experiments were conducted to evaluate the effects of the dietary addition of fungal phytase (derived from Aspergillus niger) on the performance and phosphorus utilisation in broiler chickens receiving low phosphorus diets without additional inorganic phosphates. 2. Graded amounts of supplemental phytase (125, 250, or 500 PU/kg diet) resulted in significant increases in both growth rate and food intake. However, only moderate improvements in food conversion were noted. 3. The enhancement of chick performance was related to an improved utilisation of dietary phosphorus, as confirmed by significantly elevated plasma concentrations of inorganic phosphorus and increased tibia ash percentages in birds receiving phytase-treated diets. The apparent availability of phosphorus was markedly improved and its concentration in excreta was reduced (experiment 1, P < 0.05). 4. It was concluded that an inclusion of phytase into practical broiler diet will allow the reduction or omission of additional dietary inorganic phosphorus.

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