Three factors enter into analyses of performance curves suchas learning curves: the amount of training, the learning algorithm, and performance. Often we want to know whether the algorithm affects performance, whether the effect of training on performance depends on the algorithm, and whether these effects are localized in regions of the curves. Analysis of variance is adapted to answer these questions. The carryover effects of learning violate the assumptions of parametric analysis of variance, but they are rendered harmless by a novel, randomized version of the analysis. After a brief outline of the statistical preliminaries, we present the procedure along with some examples on real learning curves, discuss power and Type I error, and give some examples of how our method can be applied to answer more advanced questions in comparing performance curves.

To minimize drug name confusion errors, regulators, drug companies, and clinicians need tools that help them predict which names are most likely to be involved in confusions.Two experiments, carried out in the United States, examined the effects of stimulus frequency (i.e., how frequently a target name is prescribed), neighborhood frequency (i.e., how frequently prescribed are the "neighbors" of the target name), and neighborhood density (how many names are within a fixed distance of the target name) on the probability of pharmacists making an error in a visual perceptual identification task.In both experiments, the task was to correctly identify a series of blurry drug names after a 3 s presentation on a computer monitor.In the first experiment, 45 pharmacists viewed 160 typewritten names, incorrectly identifying 60.6% of them. Random effects regression revealed a significant beneficial effect of stimulus frequency and a detrimental effect of neighborhood density.Significant two-way interactions were observed between stimulus frequency and neighborhood density and neighborhood frequency and neighborhood density.In the second experiment, 37 pharmacists viewed 156 handwritten drug names, incorrectly identifying 45.7%. Random effects regression revealed significant main effects of stimulus frequency and neighborhood density.These were contained within a significant three-way interaction: The interaction between stimulus frequency and neighborhood density was present at high but not low neighborhood frequency.Objectively measurable frequency and neighborhood characteristics have predictable effects on errors in pharmacists' visual perception.Organizations that coin and evaluate drug names, as well as hospitals, pharmacies, and health systems, should consider these characte...

Neuroscientists traditionally thought that information processing was revealed by changes in firing patterns of “smart ” neurons in a bottom-up fashion (1). Such a conceptualization is relatively hard wired (2) and fails to reflect the flexibility needed to cope with top-down constraints, such as attention, expectations, and context. Constraints on bottom-up processing come from highlevel executive systems, such as conscious focusing of attention, but can also come from automatically invoked, lower-level systems. A forward model system involving transmission of an “efference copy ” of motor commands to the sensory cortex to generate “corollary discharges” that prepare it for impending sensory consequences of self-initiated motor acts can help us unconsciously disregard sensations resulting from our own actions. Helmholtz

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The general linear model is one of the most flexible and commonly applied statistical analyses in psychological research. Regression, discriminant analysis, ANOVA, MANOVA, canonical correlation, and multivariate multiple regression all represent special cases of the general linear model. The purpose of this chapter is to present an overview of the general linear model and to discuss recent advances in modeling data using the general linear model. Before presenting the general linear model, it is important to understand what is meant by the term “model. ” Researchers construct experiments to capture interesting dependencies among measured or manipulated variables. We use the term dependency to refer to any form of relationship or non-independence among variables or observations. For example, in an experimental design, the research question is whether participants' status on an outcome measure depends on the treatment group to which they were assigned. In an observational design, the dependencies of interest are the correlations among the measured variables. To represent the patterns of dependency among the data it is useful to apply a

This paper attempts to establish the applicability of the Miles and Snow typology of strategic orientation to small, entrepreneurial organisations. It posits that congruence between strategic orientation and decision making comprehensiveness of the strategic planning process is a superior determinant of firm performance to planning alone. An empirical study in the Regional Airline industry was conducted to investigate this proposition. Results support the importance of the congruence construct in determining performance in small, entrepreneurial ventures. Keywords:

This study aims to understand how website interactivity (active control and reciprocal communication) can impact purchase intention through website involvement and how the impacts are moderated by the type of products featured on websites. In a laboratory experiment, 186 participants were asked to perform purchasing tasks of non-fictional books or greeting cards on websites of varying levels of interactivity. Results indicate that websites with a high level of active control lead to cognitive involvement and, in some instances, affective involvement. Websites with reciprocal communication lead to affective involvement for functional products but not expressive products. Responses from the participants also reveal that an increase in website involvement leads to higher purchase intention. Implications for research and practice are discussed.

Biometrics can unequivocally verify a person’s identity and, thus, have significant potential to curb the crime of identity theft, which costs individuals, organizations, and society billions of dollars annually. While financial institutions may be highly motivated to examine the feasibility of adopting biometrics for identity verification, research regarding consumer acceptance of this technology is limited. This paper proposes and empirically validates a research model for consumer acceptance of biometric identity verification for financial transactions. Additionally, conditions of voluntariness and user control are explored for their effects on user attitudes within this context. Implications for theory, practice, and future research are outlined.

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