PhD in Technology program

Dr. Sema Kalaian


Dr. Sema Kalaian

Sema Kalaian, Ph.D.
Professor of Statistics and Measurement

School of Technology Studies
122 Sill Hall
College of Technology
Ypsilanti, MI 48197

(734) 487-1161

skalaian@emich.edu


Courses Taught:

  • Introductory Research Design and Applied Statistics (COT 710)
  • Advanced Research Design and Applied Statistics (COT 711)
  • Special Topics (STAT REVIEW COT 781)
  • Hierarchical Linear Modeling
  • Meta Analysis
  • Structural Equation Modeling
  • Multivariate Statistics
  • Survey Research Methods
  • Research Design
  • Quantitative Methods I
  • Quantitative Methods II
  • Program Evaluation
 

Short Bio:

Dr. Sema Kalaian received her Ph.D. in Quantitative Methods and Measurement from Michigan State University in 1994. Prior to joining Eastern Michigan faculty in Fall 2004, Dr. Kalaian served as a faculty member of Research and Statistical Methods:

  • In the College of Education at The University of Toledo
  • In the College of Human Medicine at Michigan State University
  • In the College of Health and Human Services at the University of Alabama in Birmingham.

Dr. Kalaian was a recipient of the:

  • "Best Paper" award from the American Educational Research Association (AERA)
  • "Distinguished Paper Award"from the Society for the Advancement of Information Systems (SAIS).

Professor Kalaian's research interests focus on the development of new statistical methods and its applications to technology, management, education, psychology, medicine, business, health, and social and behavioral research. Much of her methodological developments have focused on:

  • The development of the multivariate meta-analytic techniques for combining evidence from multiple primary studies
  • The applications of the meta-analysis methods to multi-site and multi-center studies
  • The developments of the new statistical methods for analyzing Delphi survey data
  • The applications of Hierarchical Linear Modeling and Structural Equation Modeling (SEM) to large-scale multilevel and longitudinal data sets
  • The applications of the multilevel meta-analysis methods to Science, Technology, Engineering, and Mathematics (STEM) teaching and learning research.

Over the years, Dr. Kalaian taught introductory and advanced statistical and measurement courses such as Hierarchical Linear Modeling, Multivariate Statistics, Methods of Survey Research, Research Design, Structural Equation Modeling, Meta-Analysis, and program evaluation.

 

Areas of Expertise:

  • Methodology Development
  • Multilevel Modeling
  • Statistical Modeling of Change and Growth
  • Statistical Modeling of Organizational Effectiveness
  • Meta-Analysis Methods
  • Program Evaluation
 

Book Chapters:

  • Kalaian, S. A. & Kasim, R. M. (2008). Applications of Multilevel Models for Meta- Analysis. In A. A. O'Connell and D. B. McCoach (Eds.). Multilevel Analysis of Educational Data (pp.315-343). Charlotte, NC: Information Age Publishing.
  • Kalaian, S. A. (2008). Multilevel Modeling Methods for E-Collaboration Data. In Ned Kock (Ed.), Encyclopedia of E-Collaboration (pp. 450-456). IGI Global.
 

Professional Awards:

  • Best Paper Award" from the American Educational Research Association (AERA), Annual Meeting in Chicago (April 16, 1998). Paper entitled:
  • "Modeling the Effects of PBL and Traditional Curriculum on NBME I and II Performance: A Hierarchical Linear Model Meta-Analytic perspective."
  • "Distinguished Paper Award" from the Society for the Advancement of Information Systems (SAIS). Quantitative Methods at the SAIS 2003 Midwest Business Administration Association (MBAA) 2003 Conference in Chicago (March 14, 2003). Paper entitled:

"Structural Linear Modeling for Information Systems Strategic Grid."

 

Publications (Selected and The Most Important/Recent):

  • Kalaian, S. A., & Kasim, R. M. (2010). Effectiveness of Response Rates of Online surveys: A Meta-Analytic Review. Proceedings of the 39th Annual Meeting of the Midwest Decision Science Institute, 921-916.
  • Kasim, R. M., & Kalaian, S. A.(2010). Measuring Mean-Change in Organizational Research. Proceedings of the 39th Annual Meeting of the Midwest Decision Science Institute, 711- 716.
  • Shah, H., & Kalaian, S. A. (Acepted, in press). Which Parametric Statistical Method to Use For Analyzing Delphi Data? Journal of Modern Applied Statistical Methods.
  • Kasim, R. M., & Kalaian, S. A. (2008). External Validity. In Paul J. Lavarkas (Ed.), Encyclopedia of Survey Research Methods. Thousand Oaks, CA: Sage Publications.
  • Kalaian, S. A. (2008). Frequency Distribution. In Paul J. Lavarkas (Ed.), Encyclopedia of Survey Research Methods. Thousand Oaks, CA: Sage Publiations.
  • Kalaian, S. A., & Kasim, R. M. (2008). Research Hypothesis. In Paul J. Lavarkas (Ed.), Encyclopedia of Survey Research Methods. Thousand Oaks, CA: Sage Publications.
  • Kalaian, S. A., & Kasim, R. M. (2008). Longitudinal Studies. In Paul J. Lavarkas (Ed.), Encyclopedia of Survey Research Methods. Thousand Oaks, CA: Sage Publications
  • Kalaian, S. A. (2008). Research Design. In Paul J. Lavarkas (Ed.), Encyclopedia of Survey Research Methods. Thousand Oaks, CA: Sage Publications.
  • Kalaian, S. A., & Kasim, R. M. (2007). Hierarchical Linear Modeling. In Neil Salkind (Ed.). Encyclopedia of Measurement and Statistics (pp. 432-435). Thousand Oaks, CA: Sage Publications.
  • Kalaian, S. A., & Kasim, R. M. (2007). Longitudinal//Repeated Measures Analysis. In Neil Salkind (Ed.). Encyclopedia of Measurement and Statistics (pp. 558-561). Thousand Oaks, CA: Sage Publications.
  • Jipaiboon, T., & Kalaian, S. A. (2005). Using Hierarchical Linear Modeling to Examine Moderator Effects: IS Organization-by-Industrial Sector Interactions. Journal of International Technology and Information Management, 14 (2), 131-143.
  • Kalaian, H. A., Mullan, P. B., & Kasim, R. M. (1999). What Can Studies of Problem-Based Learning Tell Us? Synthesizing and Modeling PBL Effects on National Board of Medical Examination Performance: Hierarchical Linear Modeling Meta-Analytic Approach. Advances in Health Sciences Education, 4, 209-221.
  • Kalaian, H. A., & Raudenbush, S. W. (1996). A Multivariate Mixed-Effects Linear Model for Meta-Analysis. Psychological Methods, 1(3). 227-235.
  • Kalaian, H. A., & Mullan, P. B. (1996). Exploratory Factor Analysis of Students' Ratings of a Problem-based Learning Curriculum. Academic Medicine, 71(4), 390-392.
 

Presentations at Professional Conferences:

  • Kalaian, S. A., & Kasim, R. M. (Accepted, May 2010). Effectiveness of Cooperative Learning Compared to Lecture-Based Learning in College STEM Classes. Paper will be presented at the Annual Meeting of the American Educational Research Association (AERA) in May 2010. Denver, Colorado.
  • Kalaian, S. A., & Kasim, R. M. (2010, April). Effectiveness of Response Rates of Online surveys: A Meta-Analytic Review. Paper presented at the Annual Meeting of the Midwest Decision Science Institute (MWDSI). Toledo, Ohio.
  • Kasim, R. M., & Kalaian, S. A. (2010, April). Measuring Mean-Change in Organizational Research. Paper presented at the Annual Meeting of the Midwest Decision Science Institute (MWDSI). Toledo, Ohio.
  • Kasim, R. M., & Kalaian, S. A. (2010, March). Preliminary Results of the Effectiveness of small-group learning in the Mathematics and Statistics College Courses: A Meta-Analysis. Poster presented at the National Science Foundation's (NSF) REESE program Principal Investigator (PI) meeting. Virginia.
  • Kasim, R. M., & Kalaian, S. A. (2009, October). Multilevel Modeling of the Effectiveness of small-group learning in the Mathematics and Statistics College Courses: A Meta-Analysis. Paper presented at the Annual Meeting of the Mid-Western Educational Research Association. St. Louis, Missouri.
  • Kalaian, S. A., & Kasim, R. M., &. (2009, October). Synthesizing the Effectiveness of small-group learning in STEM Classes Using Multilevel Meta-Analysis Methods. Paper presented at the Annual Meeting of the Mid-Western Educational Research Association. St. Louis, Missouri.
  • Kalaian, S. A., & Kasim, R. M. (2009, April). Should We Use the Mixed-or-Fixed-Effects Meta-Analysis Approaches for Analyzing STEM Effectiveness Data? Paper presented at the Annual Meeting of the American Educational Research Association (AERA). San Diego.
  • Kalaian, S. A., & Kasim, R. M. (2009, February). Effectiveness of Active Small-Group Instruction Compared to Lecture-Based Instruction in Science, Technology, Engineering, and Mathematics (STEM) College Classes. Poster presented at the National Science Foundation's (NSF) REESE program Principal Investigator (PI) meeting. Washington, D.C.
  • Kalaian, S. A., & Kasim, R. M. (2008, October). Why Multivariate Meta-Analysis Methods for Studies with Multivariate Outcomes? Paper presented at the Annual Meeting of the Mid-Western Educational Research Association. Columbus, Ohio.
  • Kalaian, S. A., & Kasim, R. M. (2007, October). Fixed-Effects vs. Mixed-Effects Approaches to Meta-Analysis. Paper presented at the Annual Meeting of the Mid-Western Educational Research Association. St. Louis, Missouri.
  • Kalaian, S. A., & Kasim, R. M. (2007, October). Multilevel Model for Meta-Analytic Data with Categorical Outcomes. Paper presented at the Annual Meeting of the Mid-Western Educational Research Association. St. Louis, Missouri.
  • Kalaian, S. A., & Kasim, R. M. (2007, April). Applications of Multilevel Models for Meta- Analysis. Paper presented at the Annual Meeting of the American Educational Research Association (AERA). Chicago, Illinois.
  • Kalaian, S. A., & Kasim, R. M. (2007, April). Comparing Univariate and Multivariate Multilevel Meta-Analysis Methods. Paper presented at the Annual Meeting of the American Educational Research Association (AERA). Chicago, Illinois.
  • Kalaian, S. A. (2007, March). Technology Students' Perceptions of Assessment Strategies in a Qualitative Research Design Course. Poster presented at the Faculty Development Showcase. Eastern Michigan University. Ypsilanti, Michigan.
  • Shah, H. & Kalaian, S. (2006, November). Parametric Statistical Methods to Obtain Reliability in Delphi Technique. Paper presented at the 37th Annual Meeting of the Decision Science Institute (DSI). San Antonio, Texas.