Paving the Way for A Normal Life: A Qualitative Study of Coping Strategies Among Iranian Divorced Women

Regarding the devastating aftermath of divorce among Iranian divorced women, which is mainly affected by sociocultural factors, this qualitative study was conducted to explore their applied strategies in reorganizing their lives. Data collection started through deep unstructured interviews followed by semistructured interviews with 18 divorced women who were chosen by purposive sampling from mental health clinics, social work centers, or available cases. Gathered data were analyzed using the qualitative content analysis method. Two main themes—behaviors of self-preservation and abstinence and struggling for balance—emerged. Subcategories were concealing the divorce, feminine self-restriction, avoidance behaviors, replacement of losses, seeking support, role development, handling the tensions, and defending oneself against the divorce failure. This study provides comprehensive knowledge of how Iranian divorced women reregulate their lives and also highlights their unique and culture-based coping strategies. Therefore, it provides specialists with a context-specific foundation for mental health care and enables them to intervene more effectively.     

Perception of Identity Threat as the Main Disturbance of Iranian Divorced Women: A Qualitative Study

Postdivorce identity challenges can be experienced differently in societies that underline the lack of normalcy of divorce. Therefore, this study was conducted to explore Iranian divorced women’s perceptions of their identities. A qualitative content analysis method was applied. Participants included 18 divorced women recruited based on purposive sampling. Data were gathered using unstructured interviews, and analyzed via content analysis method. Three categories—stigmatization, separation shock, and remarriage paradox—were extracted, and the main theme was identity threat. Findings illustrate that Iranian divorced women can be considered at risk, and hence, understanding their views is vital for developing context-specific preventive and intervention programs.     

A Parameter-Tuned Genetic Algorithm for Economic-Statistical Design of Variable Sampling Interval X-Bar Control Charts for Non-Normal Correlated Samples

Among innovations and improvements that occurred in the past two decades on the techniques and tools used for statistical process control (SPC), adaptive control charts have shown to substantially improve the statistical and/or economical performances. Variable sampling intervals (VSI) control charts are one of the most applied types of the adaptive control charts and have shown to be faster than traditional Shewhart control charts in identifying small changes of concerned quality characteristics. While in the designing procedure of the VSI control charts the data or measurements are assumed independent normal observations, in real situations the validity of these assumptions is under question in many processes. This article develops an economic-statistical design of a VSI X-bar control chart under non-normality and correlation. Since the proposed design consists of a complex nonlinear cost model that cannot be solved using a classical optimization method, a genetic algorithm (GA) is employed to solve it. Moreover, to improve the performances, response surface methodology (RSM) is employed to calibrate GA parameters. The solution procedure, efficiency, and sensitivity analysis of the proposed design are demonstrated through a numerical illustration at the end.     

Economic design of VSI X̄ control chart with correlated non-normal data under multiple assignable causes

It has been recently revealed that the Shewhart control charts with variable sampling interval (VSI) perform better than the traditional Shewhart chart with the fixed sampling interval in detecting shifts in the process. In most of these research works, the normality and independency of the process data or measurements are assumed and that the process is subjected to only one assignable cause. While, in practice, these assumptions usually do not hold, some recent studies are focused on working with only one or two of these violations. In this paper, the situation in which the process data are correlated and follow a non-normal distribution and that there is multiplicity of assignable causes in the process is considered. For this case, a cost model for the economic design of the VSI X? control chart is developed, where the Burr distribution is employed to represent the non-normal distribution of the process data. To obtain the optimal values of the design parameters, a genetic algorithm is employed in which the response surface methodology is applied. A numerical example is presented to show the applicability and effectiveness of the proposed methodology. Sensitivity analysis is also carried out to evaluate the effects of cost and input parameters on the performance of the chart.     

Bayesian Unit Root Testing in Unobserved-ARCH Models

This article uses a Bayesian unit-root test in Unobserved-ARCH models. This time series of interest is the volatility that is unobservable. The unit root testing is based on the posterior odds ratio, which is approximated by Markov Chain Monte Carlo methods. Simulations show that the testing procedure is efficient for moderate sample size. The unit-root hypothesis is rejected in the daily exchange rate of the Germany marc (DEM) with respect to the Greek Drachma.