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The standard arguments for explaining visual experience in terms of intentional content are based on the transparency observation, physicalism about the mind, or on the analysis of statements describing how things look. I believe that the standard arguments fail. In my view, there is no quick and easy argument for the intentional view. Nevertheless I believe that there is an argument to be made for the intentional view of visual experience. It takes the form of an inference to the best explanation. Both veridical and nonveridical visual experiences can ground the capacity to have beliefs about the external world. Visual experiences, like beliefs and other standard intentional states, can be indeterminate and depict impossible scenarios. The best explanation of these and other features of visual experience, I will argue, is that both veridical and nonveridical experiences are themselves intentional states of a kind more basic than belief.

This chapter examines vividness in mathematics and narrative. It first gives two presentations of the mathematical notion of a group before explaining how to calculate highest common factors. It then considers the role of figures of speech, such as metaphor and irony, in mathematics and proceeds by citing a few passages from literature that highlight the use of concrete details to convey abstract thoughts; these include passages from Charles Dickens's Bleak House, Alan Hollinghurst's The Folding Star, and George Eliot's Middlemarch. The chapter argues that when working through a totally analogous process, exactly the same response can be created in certain mathematical texts.

This survey provides a route map, sketching the major issues, models, concepts, and techniques. A taxonomy of model types is presented, each being examined for advantages and drawbacks as a generic form. Exogeneity and the overall model reduction framework are described. Systems are considered, and the problem of testing autoregressive errors against mis‐specification of the lag structure is resolved using the notion of common factors in lag polynomials.

This chapter focuses on important requirements that must be satisfied and decisions that must be made in the actual implementation of an exploratory factor analysis (EFA) in research. More specifically, it outlines three primary decisions that researchers need to address when conducting EFA: selecting from an array of model fitting procedures that estimate the parameters of the common factor model; determining how many common factors should be specified in the model when fitting it to the data; deciding whether the resulting solution should be rotated to aid the interpretation of the results and if so, determining what specific rotation procedure should be used. The challenge of determining the appropriate rotating factor solutions is called rotational indeterminacy.

The ‘time‐series’ approach to econometrics is critically evaluated, and analytical test power response surfaces presented. Non‐stationarity, differencing and ‘error‐correction’ models (though not yet named) are discussed. Residual autocorrelation is reinterpreted using Sargan's common factor approach, and embryonic ideas presented on how to explain competing models’ findings to reduce the proliferation of conflicting results. Finally, the respective roles of criticism and construction are considered.

This book provides a non-mathematical introduction to exploratory factor analysis (EFA) or unrestricted factor analysis and how it is implemented. It also discusses the procedures for conducting confirmatory factor analysis or restricted factor analysis and compares it with principal component analysis. The procedures for determining the appropriate number of factors and methods for rotating factor solutions are described. In addition, the book explains the application of different factor analytic procedures for analyses using common statistical packages, as well as a free package available on the Web. Practical instructions on how to conduct a number of useful factor analytic procedures not included in the statistical packages are presented as well. This introductory chapter looks at the common factor model, a mathematical model that forms the basis of a set of statistical procedures for determining whether large sets of variables can be more parsimoniously represented as measures of one or a few underlying constructs. The model's basic conceptual premises are outlined, along with its pictorial representation and mathematical expression.

Optimal and enhanced treatment attends to parameters that distinguish individual patients beyond the nature of the depression itself. They differ from the basic guidelines described in that they require some special expertise on the part of the clinician and frequently entail the monitoring of actual in-therapy activity in order to ensure that the moment-to-moment interventions are compatible with the needs and reactions of the patient. This chapter summarizes the results of the effort to cross-validate those preliminary principles that relate to the selection of specific treatment models and philosophies or of particular classes and families of techniques. These are the guidelines that seek to apply optimal matches of treatments to patients. Here, the focus is on optimal guidelines, those that may require special clinician training and monitoring of in-session treatment. This chapter also assesses the predictive efficiency of three models from the psychotherapy research and compares them to that of the more elaborate systematic treatment selection model: the common factors model, the psychotherapy procedures model, and the technical-eclectic therapy matching model.

This chapter looks at the key considerations that researchers should take into account in determining when it is appropriate to conduct an exploratory factor analysis (EFA) using the common factor model. It first outlines the requirements for conducting EFA by considering what sorts of research questions are best explored by this type of factor analysis, along with the nature of the data necessary to properly conduct the analysis. It then focuses on the characteristics of the measured variables to be analyzed before turning to the question of when EFA versus confirmatory factor analysis is most appropriate. In case an exploratory approach is selected, the next issue is whether the analysis should be based on the common factor model or the (different but related) principal component analysis model.

This chapter explains how many of the key procedures of an exploratory factor analysis (EFA) can be implemented in practice and how the information it provides can be interpreted. After providing a brief review of key considerations that researchers must take into account before conducting an EFA, the chapter introduces the data set that illustrates how the EFA is implemented and interpreted. It then outlines the procedures for determining the appropriate number of common factors before turning to the program syntax for conducting an EFA with a specified number of common factors. An example is given in which EFA rather than a confirmatory factor analysis is used to determine the latent constructs underlying the pattern of correlations among the measured variables. Finally, the chapter discusses key aspects of the output provided by widely used EFA programs, including SPSS, SAS, and CEFA.

This chapter discusses various assumptions underlying the common factor model and the procedures typically used in its implementation. Ideally, these assumptions should be carefully considered by researchers prior to collecting any data for which an exploratory factor analysis is likely to be used. The chapter first considers the key assumptions underlying the common factor model itself, with particular reference to assumptions about how common factors influence measured variables. It compares effects indicator models and causal indicator models as well as linear effects versus nonlinear effects of common factors. It then explores assumptions underlying various procedures commonly used to fit the common factor model to data. It also explains the nature of each assumption and when it is or is not likely to be plausible, along with methods for evaluating the plausibility of the assumption. Finally, it outlines various courses of action when a given assumption is not met.

This chapter provides a framework for intervention that further builds the language of treatment science by elaborating the concepts of common elements and common factors and broadening the use of evidence beyond manualized evidence supported treatments (MESTs). The authors review the advantages and limitations of MESTs. They conclude that new conceptualizations are necessary and present an expanded view of effective practices that involves two principal concepts--common elements and common factors--shows promise for meeting the prerequisites of being effective and accessible to practitioners and yielding greater opportunity for creating positive outcomes for clients. The chapter presents these concepts and related empirical research and practice supports, such as software and websites. Finally, the authors propose an integrative framework for thinking about the role of common factors, common elements, MESTs as well as other sources of knowledge within the field.

This chapter summarizes the key issues that researchers need to take into consideration when choosing and implementing exploratory factor analysis (EFA) before offering some conclusions and recommendations to help readers who are contemplating the use of EFA in their own research. It reviews the basic assumptions of the common factor model, the general mathematical model on which EFA is based, intended to explain the structure of correlations among a battery of measured variables; the issues that researchers should bear in mind in determining when it is appropriate to conduct an EFA; the decisions to be made in conducting an EFA; and the implementation of EFA as well as the interpretation of data it provides.

The main thesis is that sensory experience is not “predicative” but rather “presentational”, and hence cannot be properly modeled by propositional attitudes to the effect that such and such is the case. Indeed, the propostional attitude model not only (i) mischaracterizes the success conditions of experience, it also (ii) occludes the very thing that makes sensory experience epistemically distinctive. Once we see just how the object-directed and “perceiving-as” states make room for the distinctive epistemic significance of perceptual experience, the newly “discovered” propositional attitude—Exing that p—will be seen to be not only an ill-fitting, but an idle, wheel, both internally to the theory of perception and more broadly within epistemology. Experiences, understood as attentive sensory episodes, provide us with awareness of the truthmakers for what we often go on to immediately judge. We are thereby often in a postion to ratify our immediate perceptual judgments on the basis of what we have experienced. This is the epistemic virtue that has been occluded by the now dominant idea that perceptual experience is a non-factive relation to a propositional content. The factive attitude model, reminiscent of John McDowell’s view, is also shown to be an inadequate account of the kind of entitlement perceptual experience delivers. Facts are pleonastic, and are “etiolated” in a way that the things we perceive are not.