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

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

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

Impact of Model Specification Decisions on Unit Root Tests

Performance of unit root 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 data generating process 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. Outcomes of unit root tests are very sensitive to both choice and sequencing of these arbitrary specifications. This means that we can obtain results of our choice from unit root tests by varying these specifications.Model Specification, Trend Stationary, Difference Stationary

Data-driven time-frequency analysis of multivariate data

Empirical Mode Decomposition (EMD) is a data-driven method for the decomposition and time-frequency analysis of real world nonstationary signals. Its main advantages over other time-frequency methods are its locality, data-driven nature, multiresolution-based decomposition, higher time-frequency resolution and its ability to capture oscillation of any type (nonharmonic signals). These properties have made EMD a viable tool for real world nonstationary data analysis. Recent advances in sensor and data acquisition technologies have brought to light new classes of signals containing typically several data channels. Currently, such signals are almost invariably processed channel-wise, which is suboptimal. It is, therefore, imperative to design multivariate extensions of the existing nonlinear and nonstationary analysis algorithms as they are expected to give more insight into the dynamics and the interdependence between multiple channels of such signals. To this end, this thesis presents multivariate extensions of the empirical mode de- composition algorithm and illustrates their advantages with regards to multivariate non- stationary data analysis. Some important properties of such extensions are also explored, including their ability to exhibit wavelet-like dyadic filter bank structures for white Gaussian noise (WGN), and their capacity to align similar oscillatory modes from multiple data channels. Owing to the generality of the proposed methods, an improved multi- variate EMD-based algorithm is introduced which solves some inherent problems in the original EMD algorithm. Finally, to demonstrate the potential of the proposed methods, simulations on the fusion of multiple real world signals (wind, images and inertial body motion data) support the analysis

Fixing number of co-noraml product of graphs

An automorphism of a graph $G$ is a bijective mapping from the vertex set of $G$ to itself which preserves the adjacency and the non-adjacency relations of the vertices of $G$. A fixing set $F$ of a graph $G$ is a set of those vertices of $G$ which when assigned distinct labels removes all the automorphisms of $G$, except the trivial one. The fixing number of a graph $G$, denoted by $fix(G)$, is the smallest cardinality of a fixing set of $G$. The co-normal product $G_1\ast G_2$ of two graphs $G_1$ and $G_2$, is a graph having the vertex set $V(G_1)\times V(G_2)$ and two distinct vertices $(g_1, g_2), (\acute{g_1}, \acute{g_2})$ are adjacent if $g_1$ is adjacent to $\acute{g_1}$ in $G_1$ or $g_2$ is adjacent to $\acute{g_2}$ in $G_2$. We define a general co-normal product of $k\geq 2$ graphs which is a natural generalization of the co-normal product of two graphs. In this paper, we discuss automorphisms of the co-normal product of graphs using the automorphisms of its factors and prove results on the cardinality of the automorphism group of the co-normal product of graphs. We prove that $max\{fix(G_1), fix(G_2)\}\leq fix(G_1\ast G_2)$, for any two graphs $G_1$ and $G_2$. We also compute the fixing number of the co-normal product of some families of graphs.Comment: 13 page