Multilevel Models in Humanities; Case Study of Nationwide Exam Participants

Document Type : Original Article



In many scientific studies, especially in the humanities, the influence of variables on each other, or prediction of one variable by some other variables is of special interest. In statistical problems, these goals can be achieved by developing proper statistical models. In recent decades, among various statistical models used by humanities scholars, multilevel models have got special attention. In these models, instead of assuming that model parameters are constant, the impacts of interaction between high levels and low levels have been widely seen in human sciences research. In addition, these models in comparison with low-level models, modify the bias of estimated parameters resulted from cluster sampling. This paper presents multilevel models, and addresses their applications, advantages and disadvantages. Moreover, a three-level model was used to analyze the role of constituent variables of socioeconomic status in educational performance (total score) to enter into universities and “higher education institutions” over time. In this example, the effect of each constituent variable, including parents' educational level, father's occupation, level of income in different genders, was investigated considering the status of the city and province from where participants in the experimental science group of the entrance exam of higher education came during the years 2001 to 2009.


امیرکافی، مهدی (1385). اهمیت و منطق مدل‌‌های چند سطحی در تحقیقات اجتماعی. مجلة جامعه شناسی ایران، دوره هفتم، شماره 4 (71-38).
- پورکاظمی، محمدحسین (1379). طبقه‌بندی بخش‌ها و استان‌های کشور براساس شاخص‌های فرهنگی، آموزشی، اقتصادی، بهداشت، سازمان سنجش آموزش کشور، تهران، گزارش پژوهشی شماره 41.
- جمالی، احسان (1391). روند تأثیر موقعیت اقتصادی و اجتماعی بر عملکرد تحصیلی داوطلبان آزمون سراسری طی سال‌های 1380 تا 1388. مجلة آموزش عالی، شماره 16 (56-25).
- کوی، لوتان (1378). آموزش و پرورش «فرهنگ‌ها و جوامع». ترجمة محمد یمنی ‌دوزی سرخابی، تهران، انتشارات دانشگاه شهید بهشتی، چاپ اول.
- نادری، ابوالقاسم (1381). الگوسازی چند سطحی و کاربرد‌های آن در اقتصاد. دانشکده اقتصاد دانشگاه علامه طباطبایی، مجموعه مقاله‌‌های اولین همایش معرفی و کاربرد مدل‌‌های ناخطی.
-     Banks, Kevin (1996). Family Learning Environments and Student's Outcomes: A Review. Journal of Comparative Family Studies, 27 (2), PP. 373-394.
-     Bryk, A., & Raudenbush, S. W (1992). Hierarchical Linear Models for Social and Behavioral Research: Applications and Data Analysis Methods. Newbury Park, CA: Sage.
-     Gelman, A., & Hill, J (2007). Data Analysis Using Regression and Multilevel Hierarchical Models. New York, Cambridge University Press.
-     Harvey Goldstein (1999). Multilevel Statistical Model. Institute of Education, Multilevel Models Project.
-     Jennifer, Barry (2006). The Effect of Socio-Economic Status on Academic Achievement. Wichita State University.
-     Jeynes, William H (2002). Examine the Effects of the Academic Achievement of Adolescents: the Challenge of Controlling for Family Income. Journal of Family and Economic; VO 23, No.2, PP.189-210.
-     Kalmijn, M & Kraaykamp, G (1996). Race, Cultural Capital, and Schooling: An Analysis of Trends in the United States. Sociology of Education 69 (1):22-34.
-     Philip Holmes-Smith (2006). School Socio-Economic Density and Its Effect on School Performance. School Research Evaluation and Measurement Service.
-     Raudenbush, S. W., & Bryk, A. S (2002). Hierarchical Linear Models: Applications and Data Analysis Methods. Second Edition. Newbury Park, CA: Sage.
-     Raudenbush, S. W., & Chan, W.S (1993). Application of Hierarchical Linear Models to Study Adolescent Deviance in an Overlapping Cohort Design. Journal of Clinical and Consulting Psychology, VO 61, NO 6, pp. 941-951.
-     Snijders T. & Bosker R (2000). Multilevel Analysis. Sage.
-     St.Lucia (2008). Factors Influencing the Decision of Students from Low Socio-Economic Backgrounds to Enroll in Higher Education. Research by the Equity Office the University of Queensland.
-     Stephen W. Raudenbush, Anthony Bryk, Richard Congdon (2004). HLM 6: Hierarchical Linear and Nonlinear Modeling. Scientific Software International, Inc.
-     Stephen W. Raudenbush, Anthony S. Bryk (2002). Hierarchical linear models: applications and data analysis methods (Advanced Quantitative Techniques in the Social Sciences). Sage publication, Inc.
-     Tom A. B. Snijders and Roel J. Bosker, (2004). Multilevel Analysis. SAGE, Publications, London.