Elective Training
Elective Training in Clinical Child Psychology
The Clinical Child Psychology major area of study is devoted to training graduate students to conduct research with and provide clinical services to children, adolescents, and families. Graduate students completing training in the Clinical Child area of study train in the Center for Intervention Research in Schools, which is dedicated to conducting cutting-edge research and providing innovative training experiences for students and professionals. Our current research focuses on the development and evaluation of school-based interventions for youth with attention-deficit hyperactivity disorder (ADHD), as well as other problems such as depression, conduct, anxiety and the impairments (e.g., peer relations, academic problems) that are common for students with these difficulties. Additional research interests include studying ADHD as a risk factor for family conflict and dating violence as well as identifying ways to engage children, parents and teachers in effective therapeutic services. The Center for Intervention Research in Schools is committed to providing high-quality training experiences that prepare graduate students to conduct interdisciplinary treatment outcome research.
Graduate students are also trained to be knowledgeable and effective clinical practitioners. Students in the Clinical Child area of study receive clinical training in evidence-based assessment and intervention techniques for children, adolescents, and families in the context of the 帝王会所 Psychology and Social Work Clinic. Child-focused traineeship sites provide additional experiential training opportunities that prepare students for research and practice in an interdisciplinary climate. Traineeship sites include schools, community mental health centers, residential treatment centers, and medical hospitals. These advanced clinical training opportunities help students develop the competencies necessary for interdisciplinary assessment, consultation, collaboration, and coordination of service delivery.
Requirements
Training in the Clinical Child area of study is designed to train graduate students interested in conducting sound clinical research as well as learning and applying evidence-based treatments for child and adolescent mental health disorders. Specific goals are as follows:
- To learn and apply evidence-based clinical assessment and intervention strategies with children and adolescents with mental health concerns across home and school settings.
- To receive training in school consultation skills and the behavioral management of child classroom behavioral problems.
- To develop skills to read and critically evaluate the theoretical and research literature in developmental psychology and in developmental psychopathology and intervention.
- To conduct independent research projects including, but not limited to, thesis and dissertation studies focusing on child or adolescent psychology, or on a topic that has implications for children鈥檚 mental health.
- To develop skills in study design and technical writing sufficient for publication in high quality peer-reviewed outlets and submission of successful grant proposals.
- To attend and present at national and/or international professional research conferences in child and adolescent psychology.
Required Courses
- PSY 6712 Psychopathology of Childhood and Adolescence
- PSY 7732 Child and Adolescent Assessment
- PSY 7742 Child and Adolescent Psychotherapy
- Complete at least three semesters (9 CR) of PSY 7920 Clinical Child Practicum
Internship
Students are required to complete an APA-accredited internship in clinical child psychology or one with a one-half year rotation (or equivalent) in clinical child psychology.
Research Experience
- Independent research typically done as a thesis or dissertation will focus on topic in child or adolescent psychology, or on a topic that has implications for children鈥檚 mental health.
- A minimum of one first-author poster or paper presentation on a topic in child psychology or at a national or international professional meeting.
- Collaboration with a faculty member on a submitted or published article or chapter or a submitted or funded grant application.
Elective Training in 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 帝王会所 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鈥檚 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
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)
Mathematics (MATH)
- 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)
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)
Engineering (EE)
- 5003 Computational Tools for Engineers
- 5213 Feedback Control Theory
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)
- PSY 7150 (Structural Equation Modeling)
- EDRE 7120 Item Response Theory and Modern Educational Measurement or EDRE 7610 Computer Science Applications in EDRE