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Introduction. Covid 19 is currently a threat and has been generating different reactions, from the most successful to the most erroneous; It is here when our coping styles take a leading role in the face of uncertainty. Objective. The research aims to identify the style of coping with stress that predominates and influences the skills to solve social problems in university students and this influences the skills to solve problems in social isolation due to COVID 19. Materials and method. The approach is quantitative, the hypothetical deductive method, explanatory level and non-experimental design, the sample of 386 university students calculated through simple random probability sampling. The instruments were standardized coping styles and social problem solving questionnaires with validity and reliability of 0.909 and 0.920 by Cronbach's Alpha, respectively. The descriptive results oriented through the crossed tables and the inferential ones with the statistic asymptotic free distribution due to the fact that the data correspond to a normal distribution, also the adjustment indices were carried out to elaborate the model, normed adjustment index 0.977, index of Comparative fit 0.994 and adjusted goodness of fit index 0.917 greater than the acceptable limit, therefore the structural model established in the research is acceptable and shows that the predominant style that influences the resolution of social problems is problemcentered skills. Conclusion. Stress coping styles significantly influence the solution of social problems. [ABSTRACT FROM AUTHOR]

Two k-sample versions of an Anderson-Darling rank statistic are proposed for testing the homogeneity of samples. Their asymptotic null distributions are derived for the continuous as well as the discrete case. In the continuous case the asymptotic distributions coincide with the (k - 1)-fold convolution of the asymptotic distribution for the Anderson-Darling one-sample statistic. The quality of this large sample approximation is investigated for small samples through Monte Carlo simulation. This is done for both versions of the statistic under various degrees of data rounding and sample size imbalances. Tables for carrying out these tests are provided, and their usage in combining independent one- or k-sample Anderson-Darling tests is pointed out. The test statistics are essentially based on a doubly weighted sum of integrated squared differences between the empirical distribution functions of the individual samples and that of the pooled sample. One weighting adjusts for the possibly different sample sizes, and the other is inside the integration placing more weight on tail differences of the compared distributions. The two versions differ mainly in the definition of the empirical distribution function. These tests are consistent against all alternatives. The use of these tests is two-fold: (a) in a one-way analysis of variance to establish differences in the sampled populations without making any restrictive parametric assumptions or (b) to justify the pooling of separate samples for increased sample size and power in further analyses. Exact finite sample mean and variance formulas for one of the two statistics are derived in the continuous case. It appears that the asymptotic standardized percentiles serve well as approximate critical points of the appropriately standardized statistics for individual sample sizes as low as 5. The application of the tests... [ABSTRACT FROM AUTHOR]