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Diagnosis of Huntington's disease (HD) is with genetic tests and predictive testing for HD has been available for almost two decades. In the age of advancing genetic techniques, the question arises as to how the predictive tests can affect a person, his or her family and relatives, life choices and future. The paper aims to discuss these issues.

A case study is presented demonstrating the complex issues surrounding genetic testing in HD. Relevant literature was then reviewed to further explore ethical issues linked to predictive testing for HD and also looked into findings on resolving this complex issue.

Predictive testing in HD gives rise to ethical issues in social, legal, economical and imperatively personal aspects of an individual and society. Education and dispersion of knowledge to general society, regarding the test, its impact and also the illness would be a starting point in an attempt to resolve these issues. Need for counselling and support for patients in this context is vital and hence the imperative need to ensure provisions for standardised training and supply of professionals in this setting. Universal and enforceable framework along the lines of International Huntington Association recommendation should be adopted nationally.

This paper presents a case study with significant value in demonstrating the challenges faced by genetic testing in HD, and provides insight in to this issue significant for all clinicians.

This chapter develops a predictive approach to Granger causality (GC) testing that utilizes k -fold cross-validation and posterior simulation to perform out-of-sample testing. A Monte Carlo study indicates that the cross-validation predictive procedure has improved power in comparison to previously available out-of-sample testing procedures, matching the performance of the in-sample F-test while retaining the credibility of post- sample inference. An empirical application to the Phillips curve is provided evaluating the evidence on GC between inflation and unemployment rates.

We compare the finite sample power of short- and long-horizon tests in nonlinear predictive regression models of regime switching between bull and bear markets, allowing for time varying transition probabilities. As a point of reference, we also provide a similar comparison in a linear predictive regression model without regime switching. Overall, our results do not support the contention of higher power in longer horizon tests in either the linear or nonlinear regime switching models. Nonetheless, it is possible that other plausible nonlinear models provide stronger justification for long-horizon tests.

The purpose of this paper, a critical reflection, is twofold. First, by comprehensively reviewing scale development procedures in hospitality research, a concerning lack of nomological validity testing is demonstrated. Second, the need for nomological validity testing is discussed and both conceptually and empirically reasoned.

This research systematically reviews scale development studies in three leading hospitality journals, including Cornell Hospitality Quarterly, International Journal of Contemporary Hospitality Management and International Journal of Hospitality Management over ten years (2012–2021) to analyze the completeness of scale development procedures. Specifically, the authors evaluate whether the reviewed studies engage in testing the nomological and predictive validity of the newly developed measures.

The results indicate a concerning gap in the current practices in hospitality research. Specifically, only 33.3% of the examined studies assess nomological validity. These findings collectively underscore the need for improving the comprehensiveness of scale development processes in hospitality research.

The study offers important implications for hospitality researchers. The paper provides an extensive discussion on the importance and benefits of testing for nomological validity in scale development studies, contributing to the completeness and consistency of scale development procedures in the hospitality discipline.

This research critically assesses prevalent, and widely accepted, scale development procedures in hospitality research. This research empirically demonstrates the neglect of nomological validity issues in scale development practices in hospitality research. Scale development is an essential scientific practice used to create a research instrument in a field of study, improving our understanding of a specific phenomenon and contributing to knowledge creation. Considering the significance of scale development in advancing the field of hospitality research, the validation procedures involved in the scale development processes are of utmost importance and should be thoroughly applied.

In this chapter we discuss model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. We begin by summarizing some recent theoretical findings, with particular emphasis on the construction of valid bootstrap procedures for calculating the impact of parameter estimation error. We then discuss the Corradi and Swanson (2002) (CS) test of (non)linear out-of-sample Granger causality. Thereafter, we carry out a series of Monte Carlo experiments examining the properties of the CS and a variety of other related predictive accuracy and model selection type tests. Finally, we present the results of an empirical investigation of the marginal predictive content of money for income, in the spirit of Stock and Watson (1989), Swanson (1998) and Amato and Swanson (2001).

This paper aims to present several Bayesian specification tests for both in- and out-of-sample situations.

The authors focus on the Bayesian equivalents of the frequentist approach for testing heteroskedasticity, autocorrelation and functional form specification. For out-of-sample diagnostics, the authors consider several tests to evaluate the predictive ability of the model.

The authors demonstrate the performance of these tests using an application on the relationship between price and occupancy rate from the hotel industry. For purposes of comparison, the authors also provide evidence from traditional frequentist tests.

There certainly exist other issues and diagnostic tests that are not covered in this paper. The issues that are addressed, however, are critically important and can be applied to most modeling situations.

With the increased use of the Bayesian approach in various modeling contexts, this paper serves as an important guide for diagnostic testing in Bayesian analysis. Diagnostic analysis is essential and should always accompany the estimation of regression models.

This article surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by vector autoregressions. Specific emphasis is placed on highlighting those parts of the existing literature that are applicable to direct multistep forecasts and those parts that are applicable to iterated multistep forecasts. This literature includes advancements in the evaluation of forecasts in population (based on true, unknown model coefficients) and the evaluation of forecasts in the finite sample (based on estimated model coefficients). The article then examines in Monte Carlo experiments the finite-sample properties of some tests of equal forecast accuracy, focusing on the comparison of VAR forecasts to AR forecasts. These experiments show the tests to behave as should be expected given the theory. For example, using critical values obtained by bootstrap methods, tests of equal accuracy in population have empirical size about equal to nominal size.

In this chapter we provide analytical and Monte Carlo evidence that Chow and Predictive tests can be consistent against alternatives that allow structural change to occur at either end of the sample. Attention is restricted to linear regression models that may have a break in the intercept. The results are based on a novel reparameterization of the actual and potential break point locations. Standard methods parameterize both of these locations as fixed fractions of the sample size. We parameterize these locations as more general integer-valued functions. Power at the ends of the sample is evaluated by letting both locations, as a percentage of the sample size, converge to 0 or 1. We find that for a potential break point function, the tests are consistent against alternatives that converge to 0 or 1 at sufficiently slow rates and are inconsistent against alternatives that converge sufficiently quickly. Monte Carlo evidence supports the theory though large samples are sometimes needed for reasonable power.

In the field of public relations and communication management, message evaluation has been one of the starting points for evaluation and measurement research at least since the 1970s. Reliable and valid message evaluation has a central role in message effects research and campaign design in other disciplines as well as communication science. The purpose of this paper is to offer a message testing protocol to efficiently acquire valid and reliable message evaluation data.

A message testing protocol is described in terms of how to conceptualize and evaluate the content and format of messages, in terms of procedures for acquiring and testing messages and in terms of using efficient, reliable and valid measures of perceived message effectiveness (PME) and perceived argument strength (PAS). The evidence supporting the reliability and validity of PME and PAS measures is reviewed.

The message testing protocol developed and reported is an efficient, reliable and valid approach for testing large numbers of messages.

Researchers’ ability to select candidate messages for subsequent deeper testing, for various types of communication campaigns, and for research in theory testing contexts is facilitated. Avoiding the limitations of using a single instance of a message to represent a category (also known as the case-category confound) is reduced.

Communication campaign designers are armed with tools to assess messages and campaign concepts quickly and efficiently, reducing pre-testing time and resources while identifying “best-in-show” examples and prototypes.

Message structures are conceptualized in terms of content and format features using theoretically driven constructs. Measures of PAS and PME are reviewed for their reliability, construct and predictive validity, finding that the measures are acceptable surrogates for actual effectiveness for a wide variety of messages and applications. Coupled with procedures that reduce confounding by randomly nesting messages within respondents and respondents to messages, the measures used and protocol deployed offer an efficient and utilitarian approach to message testing and modeling.

The introductory chapter includes how to design-in good practices in theory, data collection procedures, analysis, and interpretations to avoid these bad practices. Given that bad practices in research are ingrained in the career training of scholars in sub-disciplines of business/management (e.g., through reading articles exhibiting bad practices usually without discussions of the severe weaknesses in these studies and by research courses stressing the use of regression analysis and structural equation modeling), this editorial is likely to have little impact. However, scholars and executives supporting good practices should not lose hope. The relevant literature includes a few brilliant contributions that can serve as beacons for eliminating the current pervasive bad practices and for performing highly competent research.