The reliability of constructs derived from attitude-behavior theories: an application of generalizability theory in the health sector

Theories of reasoned action, interpersonal behavior and self-efficacy have been frequently used for research into health-related behavior. The present study investigated two methods for assessing the reliability of their theories' constructs: the test-retest method and generalizability theory. Firstly, it is demonstrated with a numerical example that the traditional test-retest coefficient has an anatomical link with the generalizability coefficient. Secondly, generalizability theory was applied in order to identify the number of occasions each construct must be measured to assure its reliability. This procedure was applied to the data collected in a study investigating the psychosocial factors influencing exercising and smoking behaviors among pregnant women. Measuring the constructs of direct attitude, habit, and intention on only one occasion was sufficient to ensure high levels of reliability. Moreover, reliability of the other constructs would be ensured if they were measured on more than one occasion.     

Nonlinear budget constraints and consumer demand: An application to public programs for residential housing

This study presents a methodology for exploiting the nonlinear hedonic nature of housing prices to estimate the compensated demands of households for particular housing attributes. The methodology is employed to provide Hicksian benefit measures of a particular housing subsidy program typical of those undertaken recently in developing countries.     

Counterfactual distributions with sample selection adjustments: Econometric theory and an application to the Netherlands

Several recent papers use the quantile regression decomposition method of Machado and Mata [Machado, J.A.F. and Mata, J. (2005). Counterfactual decomposition of changes in wage distributions using quantile regression, Journal of Applied Econometrics, 20, 445–65.] to analyze the gender gap across log wage distributions. In this paper, we prove that this procedure yields consistent and asymptotically normal estimates of the quantiles of the counterfactual distribution that it is designed to simulate. Since employment rates often differ substantially by gender, sample selection is potentially a serious issue for such studies. To address this issue, we extend the Machado–Mata technique to account for selection. We illustrate our approach to adjusting for sample selection by analyzing the gender log wage gap for full-time workers in the Netherlands.     

Remaining life estimation of used components in consumer products: Life cycle data analysis by Weibull and artificial neural networks

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. To competitively exploit these products, one option is to incorporate used components in “new” or remanufactured products. However, this option is partly limited by a firm's ability to assess the reliability of used components. A comprehensive two-step approach is proposed. The first stage phase statistically analyzes the behavior of components for reuse. A well-known reliability assessment method, the Weibull analysis, is applied to the time-to-failure data to assess the mean life of components. In the second phase, the degradation and condition monitoring data are analyzed by developing an artificial neural network (ANN) model. The advantages of this approach over traditional approaches employing multiple regression analysis are highlighted with empirical data from a consumer product. Finally, the Weibull analysis and the ANN model are then integrated to assess the remaining useful life of components for reuse. This is a critical advance in sustainable management of supply chains since it allows for a better understanding of not only service requirements of product, but the remaining life in a product and hence its suitability for reuse or remanufacture. Future work should assess: (1) reduction in downtime of process equipment through the implementation of this technique as a means to better manage preventative maintenance; (2) reduce field failure of remanufactured product; (3) selling-service strategy through implementation of the proposed methodology.     

Entrepreneurial intent in a transitional economy: an application of the theory of planned behavior to Romanian students

This study examines entrepreneurial intent in Romanian students using the Theory of Planned Behavior (Ajzen, Adv. Exp. Soc. Psychol., 20:1–63, 1987). Using multiple regression analyses to test for mediation, we analyzed survey data from 324 Romanian students. Some of our findings were consistent with the Theory of Planned Behavior, while others were not. As expected, we found that self-efficacy and the desirabilities associated with creating a venture were positively related to entrepreneurial intent. However, surprisingly, we found that the more supportive the students’ referents were, the less likely the students were to have intentions of starting a business.     

Mokken scale analysis for polychotomous items: theory,a computer program and an empirical application

This paper contains three subjects. First, an extension of Mokken's nonparametric item response models from dichotomous items to items with two or more ordered answer categories is proposed. Second, a computer program to analyze multicategory item scores is presented. This program is called MSP. The analyses by means of MSP are based on the multicategory extension of Mokken's theory. Finally, an application of MSP to empirical multicategory test data is presented in order to illuminate its possibilities.     

On the specification and estimation of macro rural-urban migration functions: with an application to Indian data

The authors suggest several extensions and modifications to the existing specifications of macro migration functions as suggested by Todar through a more careful investigation of the micro theory underlying migration decisions. They use a probabilistic migration model to examine internal migration in India and present evidence that migration tends first to rise and then to fall as rural income rises     

Estimating time variation in measurement error from data revisions: an application to backcasting and forecasting in dynamic models

Over time, economic statistics are refined. This implies that data measuring recent economic events are typically less reliable than older data. Such time variation in measurement error affects optimal forecasts. Measurement error, and its time variation, are of course unobserved. Our contribution is to show how estimates of these can be recovered from the variance of revisions to data using a behavioural model of the statistics agency. We illustrate the gains in forecasting performance from exploiting these estimates using a real‐time dataset on UK aggregate expenditure data. Copyright © 2009 John Wiley & Sons, Ltd.     

Classification and aggregation: An application to industrial classification in cps data

In this paper we offer a method for deciding how to aggregate a set of elementary industries. The method is then applied to the problem of estimating a wage equation that allows for industry-specific effects. Our method explicitly formalizes the trade-off between goodness-of-fit and parsimony implicit in an aggregation problem. By varying the parameter of the assumed loss function, one obtains a whole sequence of aggregation levels. Further, the resulting sequence is consistent; that is, groupings formed at one level of aggregation will never be undone when one aggregates further.     

Studying work in theory and practice: insights for a globalising academia from the IR trajectory in Italy

This article investigates the relevance of context to the study of industrial relations by analysing the trajectory of an under‐researched case outside the Anglo‐Saxon hotspots, Italy. Three phases are put under the spotlight revealing a trajectory anchored to the pluralist fulcrum, but with influence first from radical perspectives and then from unitarist ones.     

On random fuzzy variables of second order and their application to linear statistical inference with fuzzy data

This paper summarizes some results on random fuzzy variables with existing expectation and variance, called random fuzzy variables of second order. Using the Frechét-principle and – via support functions – the embedding of convex fuzzy sets into a Banach space of functions it especially presents a unified view on expectation and variance of random fuzzy variables. These notions are applied in developing linear statistical inference with fuzzy data. Detailed investigations are presented concerning best linear unbiased estimation in linear regression models with fuzzy observations. Received: November 1999     

Unit roots in the presence of abrupt governmental interventions with an application to Brazilian data

This paper considers econometric issues related to time-series data that have been subject to abrupt governmental interventions. The motivating example for this study is the Brazilian monthly inflation rate (1974:1–1993:6) which we use throughout for illustration. This series has been heavily influenced by the effect of so-called shock plans implemented by various governments starting in the mid-1980s. The plans act as ‘inliers’ in the sense that the series is temporarily brought down to low levels before returning to its previous trend path. We analyse the effects on standard unit root tests and measures of persistence caused by the presence of these ‘inliers’. We show a substantial bias in favour of concluding that the series is stationary and that shocks have temporary effects. We then construct appropriately corrected statistics which take into account the presence of the plans. These show, unlike the standard tests, that the stochastic behaviour of the inflation rate was indeed unstable over this period. Simulation results are presented to support the adequacy of our corrected statistics. Copyright © 1999 John Wiley & Sons, Ltd.     

The collapsibility theorem in log-linear analysis of categorical data: an application in program evaluation

The collapsibility theorem describes both the circumstances in which the effects of hierarchical models change when additional variables are introduced, as the circumstances in which the exclusion of certain variables and the analysis of specific marginal tables may lead to different conclusions. The partial association model is here considered as a specific example of three-dimensional log-linear analysis. Collapsibility is examined in an empirical study currently being performed in Catalonia with regard to program evaluation in penitentiary centers.     

A data envelopment analysis game theory approach for constructing composite indicator: An application to find out development degree of cities in West Azarbaijan province of Iran

Data envelopment analysis (DEA) model has been widely applied for constructing composite indicator and finding development degree of areas. With the increasing number of indicators, the distinguish power of DEA model is decreased. In this paper, in order to increase distinguish power in DEA model and find out the fair weights in cross-efficiency DEA context, the game theory approach is applied. The DEA-Game theory approach is used to rank cities in West Azarbaijan province of Iran. First, 68 suitable indicators are determined and then, the indicators are classified in 10 sectors. Finally, the actual data for year 2013 is gathered and DEA-Game theory model is applied. To verify and validate the DEA-Game theory approach, simple additive weighting (SAW) and TOPSIS methods are used and the results are compared. The Spearman correlation between DEA-Game, SAW and TOPSIS models shows that the DEA-Game theory model is suitable for constructing the composite indicators.     

Innovations in job analysis: Development and application of metrics to analyze job data

Job analysis is an integral part of any human resource function. Recent advancements in technology and changing worker environments have drastically altered the means by which job analysis data are collected and stored. These changes have led to an increase in the amount of data that is collected and the potential for the data to inform complex decision making. However, due to a lack of tools available for configuring and analyzing data, human resource professionals are often unable to keep themselves abreast of changes in their workforce, make complex decisions using job data, and facilitate communication across jobs, job families or departments in their organization. As a result, advanced methods for analysis of job data are needed. Metrics are quantitative algorithms applied to job data that aid in decision making in areas such as recruitment, selection, transferability, promotion, training, and development. Metrics are a sophisticated, user-friendly approach to analyzing job data that have the potential to meet the needs of human resource professionals in today's dynamic workplace. The development of metrics, their application and benefit to human resource professionals, and their use of O^⁎NET are discussed.     

Accounting for risk in productivity analysis: an application to Norwegian dairy farming

Empirical studies have often shown wide differences in productivity among firms. Although several studies have sought to identify factors causing such differences, only a few studies have examined the effects of risk and risk aversion on productivity. In this study, using Norwegian dairy farming data for 2009, we examined the effects of different aspects of risk on productivity. We used a range of variables to construct indices of risk taking, risk perception and risk management. These indices were then included as arguments in an input distance function which represents the production technology. Our results show that these risk indices did affect productivity. Regional differences in productivity, though small, were also found to exist, suggesting that unobserved edaphic factors that differ between regions also affected productivity.     

Dating and forecasting turning points by Bayesian clustering with dynamic structure: a suggestion with an application to Austrian data

The information contained in a large panel dataset is used to date historical turning points and to forecast future ones. We estimate groups of series with similar time series dynamics and link the groups with a dynamic structure. The dynamic structure identifies a group of leading and a group of coincident series. Robust results across data vintages are obtained when series‐specific information is incorporated in the design of the prior group probability distribution. The forecast evaluation confirms that the Markov switching panel with dynamic structure performs well when compared to other specifications. Copyright © 2009 John Wiley & Sons, Ltd.