Multivariate analysis: UIC Professor teaches methods and techniques for students to handle complex data structures in her EPSY 583 course

Multivariate analysis simultaneously examines multiple variables to uncover relationships and patterns in a dataset. This technique is widely used in various fields such as statistics, economics, biology, psychology, and market research.

In the University of Illinois Chicago (UIC) online Measurement, Evaluation, Statistics, and Assessment (MESA) programs, students will be introduced to multivariate statistical methods through EPSY 583: Multivariate Analysis of Educational Data, taught by UIC Professor Yue Yin. The course covers introductory and intermediate statistics, along with practical applications such as using SAS, matrix algebra, and various multivariate analysis techniques.

The goal of multivariate analysis is to explore how different variables interact with each other and to identify underlying structures or patterns. In this course, students will learn these techniques to handle more complex data structures and address more complicated questions.

Learn more about the EPSY 583 course, and how students can apply these techniques to accomplish different research goals.

Why is it important for MESA students to be introduced to the concepts and methods of statistics?

Statistics provides the tools and techniques necessary for analyzing and interpreting data. In many fields, from science to social sciences to business, decision-making should be made based on data. Statistics help to analyze data and make sense of the data to find patterns, draw conclusions, and make decisions.

The MESA curriculum helps students build on their statistical knowledge. Examples outside of the EPSY  583 course includes the EPSY 550: Rating Scale and Questionnaire Design and Analysis course which helps students decide which instruments to use for data collection, including surveys and open-ended questions and develop the instruments. Then, the EPSY 546: Educational Measurement course will help students learn to examine whether the instruments accurately and reliably measure what they are intended to measure.

Why would you recommend students take the EPSY 583: Multivariate Analysis of Educational Data course?

When students need to analyze a complex data set, they will find multivariate analysis handy. If MESA students have taken EPSY 543: Advanced Analysis of Variance in Educational Research and EPSY 547: Multiple Regression in Educational Research, multivariate analysis will help them learn more sophisticated statistical techniques to handle more complex data structures and address more complicated questions. In addition, SAS is a powerful statistical tool that will be used in this class. As a side benefit, students can acquire basic SAS skills which will help them with data analysis and interpretation, data management, statistical modeling, and more.

Can you explain the multivariate statistical methods taught in the EPSY 583 course, and why it’s important for students to learn them?

Multivariate analysis is particularly valuable when dealing with data sets where multiple variables or factors are interrelated, which univariate analysis cannot handle. For example, Multivariate Analysis of Variance (MANOVA) can compare multiple groups on multiple dependent variables simultaneously without inflating type I error. Factor analysis (FA) can examine the relationship between multiple constructs and each construct is measured by multiple test items. Principal component analysis (PCA) helps reduce the number of variables by identifying underlying components and creating meaningful composite scores, which can be further used for other analyses. Discriminant analysis can be used to classify observations into different groups based on multiple variables.

Can you give an example of how you teach students to run analyses for these different multivariate analysis methods? What can students expect?

Students will be provided with both demonstration data and real data to practice what was covered in class. I usually demonstrate examples to students and then I will give students similar examples to try out. In the homework assignments, I will give students more examples to practice. In addition to running the analyses, I give students conceptual questions to determine what multivariate analysis method is needed to solve different questions.

How can MESA students apply their statistical skills from the EPSY 583 course to a future job role?

When confronted with the task of dissecting intricate data sets, students will discover the utility of multivariate analysis. This encompasses various techniques, including factor analysis, multivariate analysis of variance, principal component analysis, profile analysis, and discriminant analysis. With these methods, researchers can accomplish many research goals. For example, comparing participants in different groups on both achievement and motivation variables; grouping participants based on multiple behavioral, cognitive, or affective measures; and identifying the constructs underlying a survey about a university’s learning environment.

Anything else you’d like to share?

For students’ convenience, SPSS will also be used concurrently with SAS. So, students who prefer to use SPSS instead can continue to use SPSS to analyze most data in this class.