The data studied in social sciences in general and in sociology in particular, have a hierarchical structure. During the past two decades researches have made serious efforts to develop appropriate statistical methods for the analysis of such hierarchically structured data. As a result an important set of Models by the name of hierarchical linear Models, along with computer programs, have emerged. This study, considering works already accomplished attempts to demonstrate the significance of Multilevel Models in social research. Moreover, the characteristics of this method will be pointed out. In addition, and more importantly, the logic of linear Models will be discussed. Some of the researches in which Multilevel Models have been used will be referred to. We will also analyze hypotheses which are being tested, and look into structure of hierarchical data and their Multilevel data source. Finally, we will discuss some of the problems with which these Models are involved.

Although the measurement error exists in the most scientific experiments, in order to simplify the modeling, its presence is usually ignored in statistical studying. In this paper, various approaches on estimating the parameters of Multilevel Models in presence of measurement error are studied. In addition, to improve the parameter estimates in this case, a new method is proposed which has high precision and reasonable convergence rate in compare with previous common approaches. Also, the performance of the proposed method as well as usual approaches are evaluated and compared using simulation study and analyzing real data of the income-expenditure of some households in Tehran city in 2008.

This paper analyzes Iran's banking market structure, using unbalanced panel data Models during 1997-2009. To study the market structure, various measures of market concentration are examined theoretically and the U index of concentration is applied to Iran's banking system. The results show that, along with the commencement of privatization in Iran's banking sector (2001), U index of concentration has tended to decrease, indicating that competition in Iran's banking market has intensified. However, this may not happen until the strict regulations on private banks are relaxed.

The Multilevel Models are used in applied sciences including social sciences, sociology, medicine, economic for analysing correlated data. There are various approaches to estimate the model parameters when the responses are normally distributed. To implement the Bayesian approach, a generalized version of the Markov Chain Monte Carlo algorithm, which has a simple structure and removes the correlations among the simulated samples for the fixed parameters and the errors in higher levels, is used in this article. Because the dimension of the covariance matrix for the new error vector is increased, based upon the Cholesky decomposition of the covariance matrix, two methods are proposed to speed the convergence of this approach. Then, the performances of these methods are evaluated in a simulation study and real life data.

Multilevel Models provide suitable framework to study related data that are collected at various levels in many researches. In this paper, we propose a family of scale mixture of multivariate normal distributions for Multilevel Models. This family is more flexible than the normal distribution. The proposed model provides a better fit to the observed data in which their distribution has tails heavier than normal. The statistical inference of model parameters done by the marginal maximum likelihood leads to complex high-dimensional integrals and thus we implement the Markov chain Monte Carlo simulation approach for the Bayesian estimation of related parameters. In is shown that the kurtosis measure of different members in this family is different, therefore we fit Multilevel Models on a set of real data with imposing a variety of distributions in this family. Finally, by using the common model selection criteria we choose the best fitted model.

Introduction: Overcoming under‑, five mortality rate remains a great challenge for Indonesia to meet the national target despite its notable advancements and progress in reducing child mortality rate. Therefore, understanding risk factors of under‑, five mortality is essential to enhance the health and well‑, being of children. This research seeks to investigate associated factors of under‑, five mortality in Indonesia by using the 2017 Indonesia Demographic and Health Survey data. Methods: The Multilevel zero‑, inflated and Multilevel hurdle Models are considered to handle unobserved heterogeneity that may occur at province level, and to model prevalence and risk of child death as a joint process, which are reported in terms of odds ratio (OR) and incidence ratio rate (IRR), respectively. Results: Lower number of household members (IRR = 0. 803, 95% confidence interval [CI]: 0. 784–, 0. 823), older mother’, s age at first birth (IRR = 1. 020, 95% CI: 1. 007–,1. 032), higher number of children ever born (IRR = 1. 491, 95% CI: 1. 450–, 1. 533), lower mother’, s education (IRR = 1. 224, 95% CI: 1. 013–, 1. 479), and lower father’, s education (IRR = 1. 232, 95% CI: 1. 015–, 1. 495) are significantly associated with higher total death numbers in children before the age of 5 years. Furthermore, the odds of no child death are significantly higher among mother who use a contraceptive method (OR = 11. 088, 95% CI: 6. 659–, 18. 462) and among household in higher quantile wealth (OR = 1. 133, 95% CI: 1. 005–, 1. 277). Conclusion: This evidence‑, based empirical highlights priority risk factors that might provide insight for policymakers, health professional, and the community in general to design appropriate intervention to help reduce the burden of under‑, five mortality in the country.

Background: The present study aimed to investigate the prevalence and related factors of under-nutrition among under 5-year-old children in Fars province, Iran. Methods: This cross-sectional survey was conducted during 2017-2018 in Fars province of Iran. The study consisted of 606 children (0-59 months) from 13 regions of Fars province. A structured questionnaire collected the data, and undernutrition indices, including stunting, wasting, and underweight, were investigated by the mixed-effects ordinal logistic regression model. Results: The prevalence of stunting, underweight, and wasting were 2%, 4%, and 4. 1%, respectively. Low birth weight was a risk factor for three undernutrition indices (P < 0. 001,P < 0. 001,and P = 0. 004). Urban children were significantly more underweight and wasted than rural children (P = 0. 001,and P = 0. 002, respectively). Undernutrition rates were impressed by the family size, mother's education, and father's occupation. Conclusion: The prevalence of undernutrition in children can be reduced by identifying and controlling significant factors, including birth weight, maternal education, type of settlement, family size, and father's occupation.

The purpose of this study was to examine graduate studies private rate of return in Iran by using Multilevel Models. Using latest statistics, applying a Multilevel analysis and Mincer earnings function, the effect of university graduate education on employed earnings was investigated. In Multilevel analysis method used, major groups activities are considered as second level units. Meanwhile, by measuring the average years of individuals’ unemployment after completing education and adjusting the years after graduation, the estimates of the years of work experience considered to be closer to reality. The most important results of the study showed that the data have a hierarchical structure. In addition, graduate studies have a positive impact on job earnings and the rate of the impact for each additional year of education is 12. 3%-a rate that exceeds the opportunity cost of education in developing countries (10%). The study also indicated that the effect of work experience on earnings is 2. 6%. This goes beyond the other incentives to enter graduate studies. This result showed the individuals' economic justification. Employees earnings with graduate degrees in urban areas was 21. 4% more than the same group in rural areas, justifying the migration of labor force with graduate degrees to urban areas. Lastly, the return of each additional year of graduate studies is more than the return of each additional year of higher education.