About The Program
Master the power of data in education - and harness it to drive meaningful insights and improvements.
The Graduate Certificate in Advanced Educational Statistics equips you with a robust and comprehensive foundation in statistical techniques essential for educational research. You'll build expertise in quantitative research and evaluation, from sampling and data collection to cleaning, modeling, and interpreting results.
With a strong emphasis on advanced quantitative methodologies, the program explores multilevel modeling, structural equation modeling, and machine learning applications. Through a comprehensive curriculum covering quantitative research design, multivariate statistics, multilevel modeling, structural equation modeling, applied machine learning in education research, and more, totaling a minimum of 18 graduate credits, you'll acquire the skills to analyze complex social and educational data at an advanced level.
In a world inundated with data, this certificate plays a crucial role in bridging the gap by fostering expertise in research, evaluation, and institutional assessment. It equips you to design studies, apply cutting-edge methods, and make meaningful contributions in the field of educational research, producing impactful reports that resonate in academia, industry, and beyond.
Career Paths
- Educational researcher
- Quantitative analyst in education
- Statistician for educational institutions
- Research methodologist
- Data scientist in education
- Evaluation and assessment specialist
- Institutional research analyst
- Policy analyst with a quantitative focus
- Academic data consultant
- Assessment coordinator
At a Glance
Degree Earned
Post-graduate certificate
Credits: 18
Application Semesters and Deadlines
Fall: April 1
Spring: November 15
Summer: April 1
Program Modality
In-person
Estimated Completion Time
Part-time (1-8 credits): average completion in 2-4 semesters
Admission requirements
| Degree: | Semester(s) of entry: | Deadline dates: | Test requirements: |
|---|---|---|---|
| Certificate | Fall | Apr 1 | |
| Spring | Nov 15 | ||
| Summer | Apr 1 |
In addition to the general admission requirements of the VCU Graduate School, the following requirements represent the minimum acceptable standards for admission:
- Bachelor's degree
- Two letters of recommendation addressing the student’s potential for graduate study in education
- Statement of intent
- Transcripts of all previous college work
- Resume or curriculum vitae
Please visit the School of Education website for further information.
The curriculum is designed to provide students with advanced quantitative methodology skills in order to engage in research, evaluation and institutional assessment. In our rapidly changing world, in which data continues to proliferate, it is critical for students to have an understanding of how to collect, analyze and interpret many forms of educational data. This certificate fills this gap by providing students with a set of aligned skills in research and evaluation that they can use to obtain a variety of positions in academia and industry.
Degree requirements
| Course | Title | Hours |
|---|---|---|
| Required courses | ||
| EDUS 608 EDUS 608. Educational Statistics. 3 Hours.
Semester course; 3 lecture hours (delivered online, face-to-face or hybrid). 3 credits. An introductory-level statistics class focusing primarily on techniques of inferential analysis. The course focuses on basic concepts in quantitative design and analysis for educational research, probability theory, null hypothesis significance testing, inferential statistics including the t-test and analysis of variance, and applications of statistics to applied problems in education. | Educational Statistics | 3 |
| EDUS 663 EDUS 663. Applied Multivariate Statistics in Education. 3 Hours.
Semester course; 3 lecture hours. 3 credits. Prerequisite: EDUS 608 or equivalent. Examines multivariate statistical analysis and evaluation research methods with application to educational research. Emphasizes advanced regression, including moderator and mediator analysis, logistic regression, repeated measures ANOVA, factor analysis, cluster analysis and introductions to multilevel modeling and structural equation modeling as they are applied in the field of educational research. | Applied Multivariate Statistics in Education | 3 |
| EDUS 664 EDUS 664. Multilevel Modeling in Education. 3 Hours.
Semester course; 3 lecture hours. 3 credits. Prerequisite: EDUS 608 or equivalent. Examines multilevel statistical analysis and evaluation research methods with application to educational research. Emphasizes both cross-sectional and longitudinal multilevel models, as well as cross-classified and generalized linear models as they are applied in the field of educational research. | Multilevel Modeling in Education | 3 |
| EDUS 667 EDUS 667. Applied Structural Equation Modeling in Education. 3 Hours.
Semester course; 3 lecture hours (delivered face-to-face or hybrid). 3 credits. Prerequisite: EDUS 663 or equivalent. Enrollment is restricted to students enrolled in the Ph.D. in Education program. Students are expected to have some basic knowledge of multiple regression and multivariate data analysis. Most of the statistical methods in this course are an extension of regression and multivariate models. This course provides students with an understanding of basic concepts and statistical procedures of structural equation modeling in educational research. Students will learn to perform analyses in Mplus and R. These analyses will allow the class to examine the interrelationships among variables based on the proposed theoretical model and simultaneously handle measurement error issues and statistical biases. The analyses cover path analysis, measurement models (exploratory and confirmatory factor analysis), SEM with continuous and categorical variables, multi-group SEM, measurement invariance, latent growth models, latent class analysis and multilevel SEM. | Applied Structural Equation Modeling in Education | 3 |
| EDUS 668 EDUS 668. Applied Machine Learning in Education Research. 3 Hours.
Semester course; 3 lecture hours (delivered online, face-to-face or hybrid). 3 credits. Prerequisite: EDUS 608 or equivalent. This is an advanced-level course tailored for graduate students in education and related social science disciplines who are interested in integrating machine learning techniques in their research. Students will delve deep into core machine learning algorithms, data processing techniques and applications specific to education research challenges. The course will combine the technical rigor of machine learning with the nuances and needs specific to educational research. With a blend of individual mentorship, peer feedback and practical applications, students will be empowered to not just understand, but to apply these techniques to their research, producing work that stands up to academic scrutiny and has the potential for real-world impact. | Applied Machine Learning in Education Research | 3 |
| EDUS 710 EDUS 710. Quantitative Research Design. 3 Hours.
Semester course; 3 lecture hours (delivered online, face-to-face or hybrid). 3 credits. Prerequisite: EDUS 608 or equivalent. An examination of quantitative research designs and concepts commonly utilized in conducting research in applied educational settings. Fundamental principles of research are extended to cover such topics as experimental designs, quasi-experimental designs, observational designs, secondary data analysis, advanced analysis of variance designs and multiple regression analysis. | Quantitative Research Design | 3 |
| or EDUC 797 EDUC 797. Directed Research. 1-9 Hours.
Semester course; 1-9 variable hours. 1-9 credits. Enrollment restricted to students who have completed first-year Ph.D. courses in education or by permission of program director. The course provides doctoral students the opportunity to do hands-on research prior to the dissertation project that is relevant to their substantive area or individual learning needs. The topic and specific project will be initiated by the student and implemented in collaboration with a School of Education faculty member. A proposal for a directed research course must be submitted that specifies how the student will gain experience, knowledge and skills in one or more aspects of conducting a research project. Graded S/U/F. | Directed Research | |
| Total Hours | 18 | |
The minimum total of graduate credit hours required for this certificate is 18.
The VCU Bulletin is the official source for academic course and program information.