جستجوی مقالات مرتبط با کلیدواژه "تحلیل چندسطحی" در نشریات گروه "علوم تربیتی"

The main goal of this paper is to present a multi-level model for analyzing the scores of the national entrance exam to universities and higher education institutions.Based on its objective, this research is an applied and a descriptive research for analyzing and explaining the factors influencing the total score of the candidates participating in the university entrance exam. We used data of the 2019 year exam which include information on socio-economic status of the candidates' including educational, health, cultural and economic variables at individual level as well as at the level of counties and provinces of the country and We used the HLM software to stimate the multilevel models.The estimation of the three-level model showed that 94.6% of the changes in the total test score were due to the differences in the individual and family characteristics of the candidates. 3.6% of the changes in the total score were related to the educational, health and cultural factors of the city and 1.8% to the economic participation rate of the province. Cosidering that 5.4% of the changes in the total score are related to the geographical location, it can be acknowledged that the difference in the total score of the candidates is not only rooted in their socio-economic status, but the simultaneous effect of this situation with the level of prosperity of the cities from the perspective of educational, health, economic and cultural factors also contributes to the intensification of the difference in the total score.

The aim of this study was a multilevel analysis of the factors affecting the professional development of faculty members.

The present study population consists of 650 faculty members of Shahid Bahonar University of Kerman in 1398 and the study sample was selected by proportional assignment method consisting of 289 people. The instruments used in this study consist of three researcher-made questionnaires whose validity has been evaluated using structural validity and their reliability has been evaluated using Cronbach's alpha method and composite reliability and for analyzing the collected data using The questionnaires used descriptive statistics (mean, standard deviation, minimum, maximum, skewness and elongation) and inferential statistics (multilevel analysis).

The results of this test show that with each unit change in organizational factors of the second level (faculty), professional development of faculty members 0.52 units, managerial factors 0.01 units, process factors 0.06 units, factors Individual change 0.22 units and social factors change 0.08 units. Also, with each unit of change in management factors, the professional development of faculty member’s changes by 0.06 units; With each change in the process factors of professional development of faculty members, 0.15 changes; With each unit change in individual factors of professional development of faculty member’s changes 0.13 units and finally with each unit change in social factors of professional development of faculty member’s changes 0.15 units. According to the obtained results, it seems that all the mentioned factors have a significant impact on the development of the professions of the faculty members and organizational factors have the most and managerial factors have the least impact on the development of the professions of the faculty members.

The purpose of this study was to determine the impact of students' competencies and organizational climate on their academic achievement using a multilevel approach. In this analysis, students’ competencies and the organizational climate were analyzed at the first and second levels, respectively. Given the purpose of study, this was an applied research that was categorized among descriptive correlational research in terms of data collection method. The research population was consisted of students (N = 13000) and faculty members as Heads of different academic departments (N = 57) at Shahid Bahonar University of Kerman. The research sample included 1140 students and 57 faculty members who were selected and analyzed using stratified random sampling method. The data collection tools included three questionnaires, including Interpersonal Competencies Questionnaire, Systemic and Applied Competencies (Ismaili, Mahani, et al., 2016) and Organizational Climate Questionnaire (Halpin and Kraft, 2000). Students’ total grade point averages were also evaluated for their overall academic achievement. The fluctuation range of Cranbach’s Alpha Coefficient for each of the four scales was between 0.55 and 0.84. The results indicated that academic climate at the second level affected the impact of students' competencies on their academic achievement at the first level.

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 individual's 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

Under certain circumstances, the hierarchical structure of the society necessitates the levels to be of latitude parallel to each other rather than of longitude; therefore, usual nested models cannot be applied. In such cases, it is required to use the cross-classified models as a subclass of the multilevel models. Disregarding the structural classification can significantly affect the direction and magnitude of the obliqueness observed in estimating the parameters. In this paper, by cross-classified modeling the total scores of the students admitted in the Iranian national-wide Masters graduate admission exam in 2013, and by using R software, the cross-classified model is compared to its corresponding multilevel model applying the deviance criterion. Based upon the full conditional distributions of the parameters, the corresponding estimators are derived through the Markov Chain Monte Carlo methods. The deviance statistic was utilized to compare cross-classified model with its corresponding multi-level model. The results showed that modeling the random effects for the crossover populations using the cross-classified models is doing far better than the conventional corresponding multilevel model.

Validity of the multilevel analyses with a focus on differences in learning theories (with both classis approach toward measurement and new approach toward measurement (IRT)) by means of various data has recently been studied. This study is aimed at determining the level of impact of schools on the scores gained by students involved in advanced TIMSS’s 2008 test. In order to achieve the chief goal of the study, the researchers adopted the data relating to administration of advanced TIMSS’s 2008 physics test, which assesses the course of teaching advanced physics to the high school seniors (pre-university students). The population and sample group of this study comprise the pre-university candidates of mathematics and physics taking advanced TIMSS physics test administered in the academic year 2007-2008. The sample size of Iranian students involved in this measurement equaled 2556 subjects. Results of the analyses generally suggest that firstly multilevel IRT (MLIRT) analyses are more powerful than multilevel true scores (MLTS) analyses in clarifying school differences. Secondly, the level of difference in the schools involved in advanced TIMSS math test reflected in intraclass correlation (ICC) is high in MLIRT analyses. Discussion and First, considering measurement error of each item in analyses within Bayesian framework and by means of Gibbs sampling can remarkably improve the power of multilevel analyses and lead to a significant rise in the ratio of the explicated variance. Second, there is too much educational difference and discrimination among schools which is largely due to school-level variables (such as those relating to teacher or school-related variables).