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Contemporary speech production theory is introduced as the basis for modelling expression and emotive content in speech. The necessity for a computational approach is argued, together with the need for adequate testing of the models. Adding naturalness to synthetic speech, in the form of expressive content, is outlined as well as ideas about the inseparable relationship between production and perception.

Models are tested with data, but also data are tested by agreement with conceptual models. When competing indices exist to measure the same phenomena, one should use the ones that agree with logically supported prediction. These indices need not be philosophically “truer” measures of the underlying concepts, but they are more useful for prediction. The choice between two accepted ways to measure cabinet duration and three ways to measure the number of parties illustrates this advice. Clearest results emerge when symmetric regression is used for testing.

This chapter begins with a discussion of psychometric aspects of neuropsychological tests. It then discusses the classical test theory model, criterion-referenced testing, probabilistic test models, individual case classification, psychometric single-case analysis, and comparison of a single case to a small control or normative sample.

This chapter discusses modern speech synthesis techniques, including the choice of basic building blocks in traditional and more recent systems. The chapter concludes with discussions on unravelling the limitations and complexities of synthesis techniques, as well as the adequacy of synthesis for testing theories of human speech production, particularly in the area of modelling expressive content.

This chapter analyzes the implications of weak contract enforcement for the international organization of production. With that goal in mind, the chapter starts by going back to the benchmark two-country model of global sourcing developed in Chapter 2, highlighting the contracting assumptions underlying the results of that model. It then introduces a model of contractual frictions that can shed light on the effect of contracting institutions on the intensive and extensive margins of intermediate input trade. Next, the chapter develops a series of extensions of the model that can open the door for model-based empirical tests of the effects of contracting considerations on the offshoring decisions of firms.

Model testing and parameter estimation are inextricably linked but often done separately. Chi-squared analysis combines model testing with parameter estimation, improving on both and providing answers to five important questions about the results of an experiment on a system for which a model is available: 1. What are the best fit parameter values? 2. Is the best fit a good fit? 3. What are the uncertainties on the best fit parameters? 4. Even if the fit is good, should the model still be rejected? 5. Is the revised model an improvement?

This chapter generalizes the cointegration analysis of Chapter 22 and provides a brief account of the econometric issues involved in the modelling approach advanced by Pesaran, Shin, and Smith (2000). It begins describing a general VARX model which allows for the possibility of distinguishing between endogenous and weakly exogenous I(1) variables, and considers its efficient estimation. In this framework, the chapter proves that weak exogeneity is sufficient for consistent estimation of the long run parameters of interest that enter the conditional model. It then turns to the analysis of cointegrating VARX model models; presents cointegrating rank tests; derives their asymptotic distribution; and discusses testing the over-identifying restrictions on the cointegrating vectors. It also considers the problem of forecasting using a VARX model, and concludes with an empirical application to the UK economy. Exercises are provided at the end of the chapter.

The one-parameter chi-squared methodology for parameter estimation and model testing is applied to an introductory physics lab experiment involving a falling chain. Data collection details along with sample data are provided and the results of the chi-squared analysis are interpreted. Problems are included (with solutions in an appendix).

The interaction between the Cowles Commission and Milton Friedman was a confrontation between two research programs, a Walrasian versus a Marshallian, each claiming to offer the best scientific approach for empirical economics. This battle was not only characterized by arguments over simultaneous equations versus partitioning and abstract reasoning versus substantive empirical research, but also about whether or not to adopt investigative methods used in the natural sciences. Although the two sides never reached a consensus, their interaction resulted in a particular test for econometric models, the so-called “naive model test,” designed by Friedman and developed by Carl Christ, a member of the Cowles Commission. However, whilst Friedman regarded a negative test result as a failure of the Walrasian program, Cowles saw it as a diagnostic tool for building larger models.

This chapter reports original data from two surveys conducted to find out how scientists view the hypothesis and related concepts. One was an online survey of hundreds of members of biological research societies. The scientists reported on their knowledge of and training in the use of the hypothesis, their views about hypothesis testing as compared with other modes of conducting science, such as Discovery Science, open-ended questioning, and Big Data methods. Respondents estimated how the various scientific modes influenced their work and how much they relied on each one. Most respondents,70% of them, reported having received little or no training in scientific thinking; however, 90% felt confident about their thinking skills. Nevertheless, more than 90% felt that formal training in this area would be helpful. The great majority relied on hypotheses in their research work. The second survey, analyzed all (more than 150) neuroscience research papers that appeared sequentially in top journals during 2015 to determine how the papers were structured, particularly with respect to the hypothesis. Only 33% of the papers had an explicitly stated hypothesis, whereas, in 45%, the hypothesis was “implicit.” A minor, though notable, fraction of the papers misused the term “hypothesis.” The results are germane to several topics covered in the remainder of the book.

The two-parameter chi-squared methodology for parameter estimation and model testing is applied to an introductory physics lab experiment to model air resistance on falling coffee filters. Data collection details along with sample data are provided and the results of a chi-squared analysis are interpreted. Problems are included (with solutions in an appendix).

The chi-squared parameter estimation and model testing methodology is extended to the two-parameter case of a line with a slope and an intercept. Chi-squared is shown to be a paraboloid and is displayed in a contour plot. Techniques for extracting parameter correlations and uncertainties are developed. End-of-chapter problems are included (with solutions in an appendix).

Expectation states theory suggests that status orders emerge in task groups via the activation of exogenous status differences and the development of endogenous behavior interchange patterns. The formal e-state model developed by Skvortez and Fararo postulates three specific mechanisms—status activation, interaction initiation, and bystander effect—that drive the emergence conceptualized as networks of complete asymmetric digraphs, also known as “tournaments.” The author assesses this model with data from three sources: 77 six-person groups in which external status differences are experimentally created to span all possible initial status combinations from one high and five low to five high and one low status actor, 31 six-person and 51 four-person groups in which status combinations leverage existing status differences in the population of subjects (gender and year in college). Overall consistency with model prediction is noted, but challenges with deep evaluation remain, and these are addressed in the final section of the chapter.

The chi-squared method for parameter estimation and model testing is developed for the one-parameter case of a line with a slope but no intercept. Curve fitting is motivated, and several methods for curve fitting are introduced. The chi-squared method is shown to be the optimal curve fitting method whenever Gaussian distributed measurement uncertainties and a model are present. The central limit theorem, which assures Gaussian distributed measurement uncertainties for a wide range of physical experiments, is introduced. End-of-chapter problems are included (with solutions in an appendix).