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Quantitative Concentration

About the Quantitative Concentration

Sciences tend to be highly quantitative. Quantitative methods can improve theory development and representation, measurement, and data analysis. The Department of Psychology as well as other programs within Ohio University provide a breadth of courses in quantitative methods. Graduate students in both clinical and experimental psychology may want to avail themselves of this resource. To facilitate that process, a quantitative concentration is provided for those interested. Below are the requirements of the concentration and the options available to fulfill those requirements.

The requirements of the quantitative concentration include 18 hours (6 courses) of quantitative coursework as well as a completed project that includes a strong quantitative component. Note the coursework can overlap with other requirements (e.g., completing the quantitative concentration will include the third required quantitative course for all students as well as provide the scholarly tool required for experimental students) and the project can be incorporated within one’s thesis or dissertation.

All students in the quantitative concentration will take a foundational course in math. Generally, a background in calculus is needed to perform well in many of these courses. Moreover, the coursework will typically provide (a) broad exposure to analytic techniques as well as (b) deep exposure to a specific quantitative approach. Specific quantitative approaches include mathematical and computational modeling, psychometrics, and various data analysis specializations (see sample course sets below). Moreover, one can emphasize learning about basic mathematical principles as well as applied quantitative methods. The specific coursework undertaken will be determined by the student in consultation with a committee that includes the student’s advisor and no less than two faculty affiliated with the quantitative concentration. To facilitate this process a list of possible courses (not exhaustive) from various departments is provided below followed by sample programs depending on foci.

Courses

1. Department of Psychology (PSY) – All require PSY 6112 as a prerequisite

  • 6115 Introduction to Bayesian Data Analysis
  • 7110 Multivariate Statistics
  • 7120 Advanced Testing Principles
  • 7130 Advanced Regression Analysis   
  • 7150 Structural Equation Modeling
  • 7170 Health Statistics
  • 7310 Psychophysics and Theories of Perception
  • 7350 Concept Learning & Categorization
  • 7360 Mathematical Modeling of Cognition
  • 8901 Advanced seminars in psychology (must be oriented toward mathematical modeling, measurement, or statistics)

2. Department of Mathematics (MATH) – prerequisites (prereq.)

  • 5200 Applied Linear Algebra
  • 5301 Advanced Calculus I
  • 5302 Advanced Calculus II (prereq. MATH 5301)
  • 5320 Vector Analysis
  • 5500 Theory of Statistics
  • 5510 Applied Statistics (prereq. MATH 5500)
  • 5520 Stochastic Processes (prereq. MATH 5500)
  • 5530 Statistical Computing (prereq. MATH 5500)
  • 5620 Linear and Nonlinear Optimization
    Or 5630 Discrete Modeling and Optimization
  • 6510 Linear Models (prereq. MATH 5510)
  • 6520 Experimental Design (prereq. MATH 5510)
  • 6530 Time Series Analysis (prereq. MATH 5302 & MATH 5510)

3. Department of Education (EDRE) – All require PSY 6111 as a prerequisite

  • 7110 Theory and Techniques of Test Development
  • 7120 Item Response Theory and Modern Educational Measurement (prereq. EDRE 7200 or PSY 6111)
  • 7600 Multivariate Statistical Methods in Education (substitute for Psy 7110; prereq. PSY 6112)
  • 7610 Computer Science Applications in EDRE (prereq. 7600)

4. Engineering (EE)

  • 5003 Computational Tools for Engineers
  • 5213 Feedback Control Theory

5. Computer Science (CS)

  • 5800 Artificial Intelligence
  • 6420 Artificial Intelligence in Medicine (prereq. CS 5800)
  • 6800 Advanced Topics in Artificial Intelligence (prereq. CS 5800)
  • 6830 Machine Learning

Sample Programs

Option 1 (Linear modeling)        

  • MATH 5200 (Applied Linear Algebra)
  • MATH 5500 (Theory of Statistics)
  • MATH 5530 (Statistical Computing)
  • PSY 6115 (Introduction to Bayesian Data Analysis)
  • PSY 7130 (Advanced Regression Analysis)
  • PSY 7150 (Structural Equation Modeling)            

Option 2 (Observational Emphasis)

  • MATH 5500 (Theory of Statistics)
  • PSY 6115 (Introduction to Bayesian Data Analysis)
  • PSY 7130 (Advanced Regression Analysis)
  • PSY 7150 (Structural Equation Modeling)
  • PSY 8901 (Meta-analysis)
  • EDRE 7120 (Item Response Theory)

Option 3 (Longitudinal)

  • MATH 5500 (Theory of Statistics)
  • MATH 5510 (Applied Statistics)
  • MATH 6530 (Time Series Analysis)
  • PSY 7130 (Advanced Regression Analysis)
  • PSY 6115 (Introduction to Bayesian Data Analysis)
  • PSY 7150 (Structural Equation Modeling)            

Option 4 (Experimental Design)

  • MATH 5500 (Theory of Statistics)
  • MATH 5510 (Applied Statistics)
  • MATH 5530 (Statistical Computing)
  • MATH 6520 (Experimental Design)
  • PSY 6115 (Introduction to Bayesian Data Analysis) or PSY7130 (Advanced Regression Analysis)
  • PSY 7150 (Structural Equation Modeling)

Option 5 (Math & Computational Modeling with Cognitive Emphasis)

  • MATH 5200 (Applied Linear Algebra) or MATH 5320 (Vector Analysis)
  • MATH 5500 (Theory of Statistics)
  • MATH 5630 (Discrete Modeling and Optimization) or EE 5003 (Computational Tools for Engineers)
  • CS 6830 (Machine Learning) or CS 5800 (Artificial Intelligence) or EE 5213 (Feedback Control Theory)
  • PSY 7360 (Mathematical Modeling of Cognition)
  • PSY 7310 (Psychophysics & Theories of Perception) or PSY 7350 (Concept Learning and Categorization)

Option 6 (Applied Computational Modeling)

  • MATH 5620 (Linear and Nonlinear Optimization)
  • EE 5003 (Computational Tools for Engineers)
  • EE 5213 (Feedback Control Theory)
  • PSY 7130 (Advanced Regression Analysis) or PSY 6115 (Introduction to Bayesian Data Analysis)
  • PSY 7360 (Mathematical & Computational Models of Cognition)
  • CS 6830 (Machine Learning) or CS 5800 (Artificial Intelligence)

 Option 7 (Psychometrics/Measurement Emphasis)

  • MATH 5200 (Applied Linear Algebra)
  • MATH 5500 (Theory of Statistics)
  • PSY 7110 (Multivariate Statistics or EDRE 7600)
  • PSY 7120 (Advanced Testing Principles or EDRE 7110)
  • PSY7 150 (Structural Equation Modeling)
  • EDRE 7120 Item Response Theory and Modern Educational Measurement or EDRE 7610 Computer Science Applications in EDRE

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