The psychometric impacts of Karasek's demands and control scale on employees' job dissatisfaction

The aim of this study was to provide the reliability and validity of job factors and to analyze its association with Demands-Control Model and job dissatisfaction in two time cross-sectional study of Distribution Companies of Water and Power Development Authority (WAPDA) in Pakistan. Two times self-reported cross-sectional surveys were conducted, the study samples consisting of 420 respondents at T1 and 388 respondents at T2. Appropriate internal consistencies of the four scales: demands, control, job satisfaction and social supports, were obtained. Zero-order correlation and linear and multiple regressions analysis replicated the theoretically assumed structure of the job factors and job satisfaction construct in men and women collectively. Evidence of criterion validity was obtained from cross-correlations of the scales and from their linear and multiple regression analysis. Finally, all four measures were associated with a highly significant ratio of job dissatisfaction (JD), and the effect was strongest for the JD ratio as predicted by fundamental theory of Karasek. The level of work related to their demands and the level of autonomy and control they enjoy in their work place directly results in satisfaction and wellbeing of employees. Based on the results of this study the four quadrant version of the DCM questionnaire is considered a reliable and valid instrument for measuring psychosocial pressure at work environment. These outcomes and measures are applicable to all services and manufacturing industries

A note on a characterization theorem for a certain class of domains

We have introduced and studied in [3] the class of Globalized multiplicatively pinched-Dedekind domains (GMPD domains). This class of domains could be characterized by a certain factorization property of the non-invertible ideals, (see [3, Theorem 4]). In this note a simplification of the characterization theorem [3, Theorem 4] is provided in more general form

A comparative study on face recognition techniques and neural network

In modern times, face recognition has become one of the key aspects of computer vision. There are at least two reasons for this trend; the first is the commercial and law enforcement applications, and the second is the availability of feasible technologies after years of research. Due to the very nature of the problem, computer scientists, neuro-scientists and psychologists all share a keen interest in this field. In plain words, it is a computer application for automatically identifying a person from a still image or video frame. One of the ways to accomplish this is by comparing selected features from the image and a facial database. There are hundreds if not thousand factors associated with this. In this paper some of the most common techniques available including applications of neural network in facial recognition are studied and compared with respect to their performance.Comment: 8 page

Impact of Model Specification Decisions on Unit Root Tests

Performance of unit tests depends on several specification decisions prior to their application e.g., whether or not to include a deterministic trend. Since there is no standard procedure for making such decisions, therefore the practitioners routinely make several arbitrary specification decisions. In Monte Carlo studies, the design of DGP supports these decisions, but for real data, such specification decisions are often unjustifiable and sometimes incompatible with data. We argue that the problems posed by choice of initial specification are quite complex and the existing voluminous literature on this issue treats only certain superficial aspects of this choice. We also show how these initial specifications affect the performance of unit root tests and argue that Monte Carlo studies should include these preliminary decisions to arrive at a better yardstick for evaluating such tests.model specification, trend stationary, difference stationary

Most Stringent Test for Location Parameter of a Random Number from Cauchy Density

We study the test for location parameter of a random number from Cauchy density, focusing on point optimal tests. We develop analytical technique to compute critical values and power curve of a point optimal test. We study the power properties of various point optimal tests. The problem turned out to be different in its nature, in that, the critical value of a test determines the power properties of test. We found that if for given size  and any point m in alternative space, if the critical value of a point optimal test is 1, the test optimal for that point is the most stringent test.Cauchy density, Power Envelop, Location Parameter, Stringent Test