Development and Validation of TQM Implementation Constructs

The contemporary quality management (QM) literature prescribes various quality improvement strategies. However, it lacks scientifically developed and tested constructs that represent an integrative QM philosophy. Moreover, an impact of the prescribed QM strategies on a firm's product quality has not been analyzed. Through a detailed analysis of the literature, this research identifies 12 constructs of integrated QM strategies. Using a survey of 371 manufacturing firms, the constructs are then empirically tested and validated. LISREL 7 is used for this purpose. Finally, a framework to examine the effects of integrated QM strategies on a firm's product quality is suggested. Comparisons between this and two other comprehensive scales of TQM are made.     

Unpacking Self-Rated Health and Quality of Life in Older Adults and Elderly in India: A Structural Equation Modelling Approach

The Study on global AGEing and adult health (SAGE) aims at improving empirical understanding of the health and well-being of older adults in low- and middle-income countries. A total of 321 adults aged 50 years and older were interviewed in rural Pune district, India, in 2007. We used Structural Equation Modelling (SEM) to examine the pathways through which social factors, functional disability, risk behaviours, and chronic disease experience influence self-rated health (SRH) and quality of life (QOL) amongst older adults in India. Both SRH and QOL worsened with increased age (indirect effect) and limitations in functional ability (direct effect). QOL, socio-economic status (SES), and social networking had no significant effect on SRH. Smoking was associated with the presence of at least one chronic illness, but this did not have a statistically significant effect on SRH. Higher social networking was seen amongst the better educated and those with regular income, which in turn positively affected the QOL rating. QOL had a direct, but statistically not significant, effect on SRH. In conclusion, the indirect effects of age on SRH mediated through functional ability, and the effects of SES on QOL mediated through social networking, provide new understanding of how age and socio-economic status affect SRH and QOL. By allowing for measurement errors, solving for collinearity in predictor variables by integrating them into measurement models, and specifying causal dependencies between the underlying latent constructs, SEM provides a strong link between theory and empirics.     

The role of reversals in order‐restricted inference

A statistical model is said to be an order‐restricted statistical model when its parameter takes its values in a closed convex cone C of the Euclidean space. In recent years, order‐restricted likelihood ratio tests and maximum likelihood estimators have been criticized on the grounds that they may violate a cone order monotonicity (COM) property, and hence reverse the cone order induced by C. The authors argue here that these reversals occur only in the case that C is an obtuse cone, and that in this case COM is an inappropriate requirement for likelihood‐based estimates and tests. They conclude that these procedures thus remain perfectly reasonable procedures for order‐restricted inference.     

A comparative study of correlation coefficients in spatially MRSI-observed neurochemicals from multiple sclerosis patients

In measuring the association between magnetic resonance spectroscopic imaging (MRSI)-observed neurochemicals from multiple sclerosis (MS) patients, the classic correlation coefficients such as the Pearson's r, Spearman's ρand Kendall's τdo not take into account the spatial dependence of the observations. This paper reports a comparative study on these classic correlation coefficients (Pearson's r, Spearman's ρand Kendall's τ) and some more recent correlation coefficients (Tjostheim's a, the modified t) that take into account the spatial dependence of the intensities of the concentrations of several neurochemicals in MS patients. Our study indicates that the use of the classic correlation coefficients that ignores the spatial dependence of the observations may overestimate the statistical significance of the results.     

Corporate planning in domestic airlines— An Indian case study

In this article the author considers the problems of planning in the airline industry, drawing specifically on experience in the domestic Indian airline. The article seeks to examine some of the strategic issues relevant to domestic airlines which, while related here to the Indian environment, may be seen to be pertinent in most developing countries.     

Increased Fisher’s information for parameters of association in count regression via extreme ranks

The article details a sampling scheme which can lead to a reduction in sample size and cost in clinical and epidemiological studies of association between a count outcome and risk factor. We show that inference in two common generalized linear models for count data, Poisson and negative binomial regression, is improved by using a ranked auxiliary covariate, which guides the sampling procedure. This type of sampling has typically been used to improve inference on a population mean. The novelty of the current work is its extension to log-linear models and derivations showing that the sampling technique results in an increase in information as compared to simple random sampling. Specifically, we show that under the proposed sampling strategy the maximum likelihood estimate of the risk factor’s coefficient is improved through an increase in the Fisher’s information. A simulation study is performed to compare the mean squared error, bias, variance, and power of the sampling routine with simple random sampling under various data-generating scenarios. We also illustrate the merits of the sampling scheme on a real data set from a clinical setting of males with chronic obstructive pulmonary disease. Empirical results from the simulation study and data analysis coincide with the theoretical derivations, suggesting that a significant reduction in sample size, and hence study cost, can be realized while achieving the same precision as a simple random sample.