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Population Health Analytics

//Population Health Analytics
Population Health Analytics 2018-04-26T20:29:31+00:00

Our Expertise

Amstat Analytics Group has become nationally recognized for helping hospitals and governments chart their new course with greater efficiency and agility. From implementation to data migration to tuning and optimization to advanced analytics, the Amstat Analytics Group Professional Services team will work with you every step of the way. Our clients cite these reasons for choosing to work with us:

  • All of our principals have doctorates at leading universities including Harvard, Stanford, and Columbia.
  • AMSTAT Analytics Group has numerous healthcare associates across multiple locations with proven domain competence.
  • The team includes doctors, clinical specialists, statisticians, and data scientists.
  • We have extensive backgrounds in healthcare analytics and over 100 years of practical experience in the healthcare field.
  • We have more than 650 skilled resources dedicated to healthcare research, reporting, and analytics practice.
  • We bring our cumulative experience working with close to 900 hospitals on population health issues. You benefit from your peers’ successes solving the same problems you face today.
  • Our consultants work closely with your staff so they have the skills and tools to keep improving performance long after we’re gone.
  • Our recommendations are based on more than 100 years of best practice research on hospital management, including intensive research into techniques to optimize patient access processes.

Doctorates at Leading Universities Including Harvard, Stanford, & Columbia

Doctorates at Leading Universities Including Harvard, Stanford, & Columbia

Extensive Backgrounds in Healthcare Analytics

Extensive Backgrounds in Healthcare Analytics

Numerous Healthcare Associates

Numerous Healthcare Associates

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Proactively addressing public health is the driving force behind population health management. More than simply delivering better care, today’s providers are looking for ways to reduce the need for reactive interventions such as emergency department visits, hospitalizations, and readmissions, which often address undetected—and therefore untreated—medical issues that can significantly drive up the cost of healthcare. It is a noble goal, but is it realistic? How can healthcare organizations reach such heights without busting their budgets?

There are many facets to population health management. Technological barriers need to be overcome and costs must be taken into account. Perhaps the biggest challenge, however, lies in changing the way people think about health care.

It is not episodic. It is trying to take a more broad view and get beyond a single episode of care. A provider of tools and services supports population health management. It is a methodology.

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The cost of care in the U.S. is expected to approach 20% of GDP by 2020.  With the shift to a value-based reimbursement model, payers, providers, and employers are facing increasing pressure to better understand the health of their populations and find solutions to proactively improve it.

This changing approach to an engaged population will enable these groups to address the continuing rise in the cost of care by emphasizing healthy living and access to more timely and detailed information about the patient activity. To do so requires a robust data platform, IoT integration, and advanced analytic models.

We can help payers, providers, and employers understand and engage their populations and drive down costs by thousands of dollars per person annually.  We can help you understand, track, manage, and create intervention strategies for a healthier population. We can help organizations implement and customize population health risk management capabilities to the unique needs within their organization for maximum cost benefit and value to the communities they serve. We promise a clearer look at aggregated patient populations for better clinical and cost management.

We can:

  • Identify and segment at-risk patients, highlighting behavioral and psychosocial traits potentially affecting compliance, outcomes, and margins
  • Pursue insights into variations in care delivery, site costs, utilization patterns, quality, and satisfaction
  • Refine patient population filters for retrospective and prospective cohort cost analysis and key performance indicator trending
  • Expand proactive disease management through clinical insights and remote monitoring for at-risk patients
  • Customize chronic disease care planning across care settings to decrease emergent care visits, hospital admissions, and/or re-admissions by 30-40 percent

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Predictive analytics has been in the population health management (PHM) space for a couple of decades. Predictive modeling vendors have come and gone, and risk scoring has been somewhat commoditized. What is next? What should PHM leaders expect to help advance program effectiveness and efficiency?

Beyond periodic vendor batch processing of administrative data

Most legacy predictive models originated for actuarial purposes. Because they focused on underwriting costs, it was important to have an accurate and complete representation of historical claims experience for a fixed period that was often limited to one year. These constraints have been built into most common predictive modeling processes and are the source of the poor timeliness and actionability that are the primary weaknesses associated with the use of claims data. Moving beyond this legacy thinking, and beyond administrative (i.e. health plan) data in general, is the beginning of an incremental move toward more advanced, higher-value predictive modeling.

The legacy approach of using health plan claims and member data has been extremely powerful. The mining of these data can be made more effective through the use of submitted (pre-adjudicated) claims and incremental, near real-time processing.

We can:

  • Use more clinically meaningful and timely data — from EMR/EHRs, HIE, etc. — to change the models’ focus and multiply their ability to impact care quality
  • Parse and identify the valuable features of its unstructured data as well as its structured content
    • This will be made even more valuable by timely (real-time) model execution and appropriately constructed, prioritized (or automated) and delivered decision support. This ultimately suggests the need to tightly integrate, or even merge, the EHR data collection application with a powerful PHM workflow engine.
  • Help you change patient and health system behavior to achieve desired outcomes
  • Use consumer and psychographic segmentation data to understand the behaviors and motivations of targeted individuals and enable more effective engagement and behavior change strategies
    • Since these data are typically external to clinical or health plan sources, segmentation may be applied independently and robust master data management will be required to assure that the correct characteristics are associated with each individual.
    • These data will also be important in understanding risks related to non-medical determinants of health and associated risks. Since medical care is believed to only determine 10 to 20 percent of health outcomes, there are significant opportunities to mine and model data representing other determinants, such as health behaviors, social/economic factors, and physical environment.
  • Capture relevant social media streams to identify person-level engagement opportunities and community-level opportunities for enhancing the brand, which may or may not be tied to clinical programs

Beyond predicting high future cost

We can:

  • Use predictive models to predict the specific events that care management programs target to achieve cost savings
  • Use predictive models to predict specific, avoidable events such as admissions, readmissions, disease progression, complications of conditions and treatments, ER visits, and high-cost imaging studies
    • The more time-sensitive these predictions are, the more important it is to have a current stream of claims or EHR data to input.
  • Predict current patient attributes that have not yet been observed
  • Create profiles based on characteristics that we can infer from data that can then be segmented to assign outreach messages, approaches, and personnel most likely to be successful and to maximize their efficiency
  • Create high levels of personalization by segmenting populations based on features such as predicted motivating factors, aspirations, and preferences, and matching members to programs, interventions, and personnel based on these features
  • Identify segments of individuals who have significant risk but are amenable to simple, direct messages delivered by mail or to their inboxes, and other segments with people of moderate risk who are not cost-effective to intervene with through personal telephone outreach but who likewise are likely to respond to appropriate, targeted messages delivered via a low-cost channel
  • Change from simple case identification (e.g., this person has diabetes and high future cost and is to be referred to ‘the diabetes program’) to case matching (e.g., this person has diabetes and, based on all of his or her characteristics and risks, will have the best chance of the desired outcome if he or she receives this specific bundle of interventions, in this way, and from these sources.)

To advanced, multi-dimensional predictive analytics embedded in highly automated decision support and PHM workflows

Real-time scoring and segmentation occurring across multiple dimensions not only greatly enhance the value of data, they also increase the complexity and time-sensitivity of the results; their value is best leveraged through extremely tight integration with PHM software. Because of the high level of personalization, it is neither practical nor efficient to generate lists in one application to be implemented in another. Instead, complex logic will assign programs and specific interventions within programs to individuals based on multiple criteria designed to create the best possible results with the fewest possible resources. That is, highly configurable automation will drive highly personalized interventions ranging from the highest intensity to the extremely low-cost, to achieve the best outcome in the most efficient way possible.

We can:

  • Automate the process of continuous improvement by embedding study design methodologies, including randomization (A-B testing) in some program settings and machine learning algorithms in others
  • Use feedback loops to validate segmentation and stratification methodologies
  • Identify priority areas for improvement, and optimize intervention and program designs

Dr. Raj Singhal, MD., Director, Pediatric Anesthesiology, Phoenix Children’s Hospital

“Dr. Ann has been instrumental in helping with our statistical needs. In addition to her professionalism, she has been prompt and thorough with all of our requests. Dr. Ann’s work is impeccable, and I would recommend her services to anyone in need of assistance with statistical methods or interpretation. We plan on using Dr. Ann for all of our future needs, and I am thrilled to have been introduced to her.”

Dr. Raj Singhal, MD., Director, Pediatric Anesthesiology

Dr. Haritha Boppana, MD, DHA, GHS Greenville Memorial Hospital 

“I am a physician and was in need of statistical analysis of research data. I found them on online search. Dr. Ann called me and explained the process involved in data analysis. Dr. Ann was always very prompt, helpful, intelligent and took time explaining the various tests used in conducting data analysis. Thank you so much!! I look forward to working with you in the future.”

Dr. Haritha Boppana, MD, DHA

Dr. Vincent Salyers, Dean, Faculty of Nursing, MacEwan University

“I have worked closely with them on the data analysis/results of two research projects so feel as though I am knowledgeable about their expertise. On all accounts, the company provided me with reliable statistical analysis and results that I could translate into publishable format. They are conscientious experts who provide keen insights into appropriate statistical analysis given various data sets. I highly recommend them for your statistical support needs!”

Dr. Vincent Salyers, Dean, Faculty of Nursing

Dr. Zamir S. Brelvi MD, PhD., CEO & Co-Founder, EndoLogic

“We have been very pleased with working with them. The service was custom tailored and on time completion. The statistical report was detailed with excellent graphics. The cost of the services was affordable for a start-up company such as EndoLogic! Dr. Ann is very detail oriented and likes to know the project thoroughly that is being analyzed.”

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Dr. David Fetterman
Dr. David FettermanAdvisory Board (Fetterman & Associates, President)
EDUCATION

Ph.D., Stanford University
Master’s Degree, Stanford University
Master’s Degree, Stanford University

EXPERIENCE

Stanford University, Professor
School of Medicine, Stanford University, Director of Evaluation

HONORS (selected)

American Educational Research Association Research on Evaluation Distinguished Scholar Award, 2013
American Evaluation Association Advocacy and Use Evaluation Award, 2014
Lazarsfield Award for Contributions to Evaluation Theory, American Evaluation Association, 2000
Mensa Education and Research Foundation Award for Excellence, 1990.
Myrdal Award for Cumulative Contributions to Evaluation Practice, American Evaluation Association, 1995
Outstanding Higher Education Professional, Neag School of Education, University of Connecticut, 2008
Who’s Who in America, 1990, 1995-1996, 1999, 2008-2012
Who’s Who in American Education, 1989 90, 1995-96, 2003
Who’s Who in Science and Engineering, 2010, 2011
Who’s Who in the World, 2011, 2012, 2013

PROJECTS (selected)

$15 Million Digital Divide Project, Hewlett-Packard Philanthropy and Education
W. K. Kellogg Foundation
Tobacco Prevention, Minority Initiative Sub-recipient Grant Office, University of Arkansas at Pine Bluff
Tsholofelo Community, South Africa
Corte Madera, Portola Valley School District, CA
Family and Children Services, Palo Alto, CA
Ministry of Health and Jimma University, Ethiopia
BUILD, Palo Alto
Case Method, Columbia School of Journalism
Digital Media Center, Knight Foundation
Knight New Media Center, Knight Foundation
Te Puni Kokiri, Ministry of Maori Development, New Zealand
National Institute of Multimedia Education, Japan
Knight Foundations, Western Knight Center for Specialized Journalism
Mosaic’s Project, California State University
Arkansas Department of Education
One East Palo Alto. City Revitalization Project Hewlett Foundation
National Indian Child Welfare Association. Intertribal Council of Michigan, Hannahville Indian Community
Independent Development Trust, Cape Town, South Africa
California Arts Council

BOOKS (selected)

Fetterman, D.M. (2013). Empowerment Evaluation in the Digital Villages: Hewlett-Packard’s $15 Million Race Toward Social Justice. Stanford: Stanford University Press. (See Stanford Social Innovations site: http://www.ssireview.org/articles/entry/empowerment_evaluation_in_the_digital_villages_hewlett_packards_15_million)
Fetterman, D.M., Kaftarian, S., and Wandersman, A. (2014) (eds.) Empowerment Evaluation: Knowledge and Tools for Self-assessment, Evaluation Capacity Building, and Accountability. Thousand Oaks, CA: Sage.
Fetterman, D.M., Rodriguez-Campos, L., and Zukowski, A. (in press). Collaborative, Participatory, and Empowerment Evaluation: Stakeholder Involvement Approaches to Evaluation. New York: Guilford Publications.
Fetterman, D.M., Kaftarian, S., and Wandersman, A. (2015). Empowerment Evaluation: Knowledge and Tools for Self-assessment, Evaluation Capacity Building, and Accountability. Thousand Oaks, CA: Sage.
Fetterman, D.M. and Wandersman, A. (2005). Empowerment Evaluation Principles in Practice. New York: Guilford Publications. (Preview.)
Fetterman, D.M. (2001). Foundations of Empowerment Evaluation. Thousand Oaks, CA: Sage. (Preview.)
Fetterman, D.M., Kaftarian, S., Wandersman, A. (Eds.) (1996). Empowerment Evaluation: Knowledge and Tools for Self-assessment and Accountability. Newbury Park, CA: Sage.
(Preview.)
Fetterman, D.M. (Ed.) (1993). Speaking the Language of Power: Communication, Collaboration, and Advocacy. London, England: Falmer Press. (Preview.)

CHAPTERS AND ARTICLES (selected – over 100)

Fetterman, D.M. (in press). Empowerment Evaluation. The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. Thousand Oaks, CA: Sage.
Fetterman, D.M. and Ravitz, J. (in press). Evaluation Capacity Building. The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. Thousand Oaks, CA: Sage.
Fetterman, D.M. (in press). Empowerment Evaluation: Linking Theories, Principles, and Concepts to Practical Steps. In Secolsky, C. and Denison, D.B. (eds.) Handbook on Measurement, Assessment, and Evaluation in Higher Education (2nd edition). New York: Routledge.
Fetterman, D.M. (2015). Empowerment Evaluation. International Encyclopedia of the Social and Behavioral Sciences, 2nd edition.
Mansh, M., White, W., Gee-Tong, L., Lunn, M., Obedin-Maliver, J., Stewart, L., Goldsmith, E., Brenman, S., Tran, E., Wells, M., Fetterman, D.M., Garcia, G. (2015). Sexual and Gender Minority Identity Disclosure During Undergraduate Medical Education: “In the Closet” in Medical School. Academic Medicine, 90(5):634-644.
Wang JY, Lin H, Lewis PY, Fetterman DM, Gesundheit N. (2015). Is a career in medicine the right choice? The impact of a physician shadowing program on undergraduate premedical students. Acad Med. May, 90(5):629-33. doi: 10.1097/ACM.0000000000000615
White, W., Brenman, S., Paradis, E., Goldsmith, E.S., Lunn, M.R., Obedin-Maliver, J., Stewart, L., Tran, E., Wells, M., Chamberlain, L.J., Fetterman, D.M., and Garcia, G. (2015). Lesbian, Gay, Bisexual, and Transgender Patient Care: Medical Students’ Preparedness and Comfort. Teaching and Learning in Medicine: An International Journal. Volume, 27, Issue 3: 254-263
Obedin-Maliver, J., Goldsmith, E.S., Stewart, L., White, W., Tran, E., Brenman, S., Wells, M., Fetterman, D.M., Garcia, G., Lunn, M.R. (2011). Lesbian, Gay, Bisexual, and Transgender-Related Content in Undergraduate Medical Education. JAMA, 306(9):971-977.
Fetterman, D.M., Kaftarian, S., and Wandersman, A. (2015). Empowerment evaluation is a systematic way of thinking: A response to Michael Patton Empowerment evaluation: Knowledge and tools for self-assessment, evaluation capacity building, and accountability. Evaluation and Program Planning 52 (2015) 10–14
Fetterman, D.M. (2011). Empowerment Evaluation and Accreditation Case Examples: California Institute of Integral Studies and Stanford University. In Secolsky, C. and Denison, D.B. (eds.) Handbook on Measurement, Assessment, and Evaluation in Higher Education. New York: Routledge.
Fetterman, D.M., Deitz, J., and Gesundheit, N. (2010). Empowerment evaluation: A collaborative approach to evaluating and transforming a medical school curriculum. Academic Medicine, 85(5):813-820.
Fetterman, D.M. (2009). Empowerment evaluation at the Stanford University School of Medicine: Using a Critical Friend to Improve the Clerkship Experience. Ensaio: Avaliação e Políticas Públicas em Educação. Rio je Janeiro, 17(63):197-204.
Fetterman, D.M. (2004). Empowerment Evaluation’s Technological Tools of the Trade. Harvard Family Research Project. The Evaluation Exchange, X 3, p. 8-9.
Fetterman, D.M. (2003). Empowerment Evaluation Strikes a Responsive Chord. In S. Donaldson & Scriven, M. (Eds.) Evaluating social programs and problems: Visions for the new millennium. Hillsdale, NJ: Erlbaum.
Fetterman, D.M. and Bowman, C. (2001). Experiential Education and Empowerment Evaluation: Mars Rover Educational Program Case Example. Journal of Experiential Education.
Fetterman, D.M. (2002). Web surveys to Digital Movies: Technological Tools of the Trade. Educational Researcher, 31(6):29-37 or http://aera.net
Fetterman, D.M. (1998). Teaching in the Virtual Classroom at Stanford University. The Technology Source.
Fetterman, D.M. (1998). Webs of Meaning: Computer and Internet Resources for Educational Research and Instruction. Educational Researcher, 27(3):22-30.
Fetterman, D.M. (1998). Learning with and about technology: A middle school nature area. Meridian, 1(1)
Fetterman, D.M. (1996). Empowerment Evaluation: An Introduction to Theory and Practice. In Fetterman, D.M., Kaftarian, S., and Wandersman, A. (eds.) Empowerment Evaluation: Knowledge and Tools for Self-Assessment and Accountability. Newbury Park, CA: Sage.
Fetterman, D.M. (1996). Conclusion: Reflections on Emergent Themes and Next Steps. In Fetterman, D.M., Kaftarian, S., and Wandersman, A. (eds.) Empowerment Evaluation: Knowledge and Tools for Self-Assessment and Accountability. Newbury Park, CA: Sage.
Fetterman, D.M. (1996). Videoconferencing On-Line: Enhancing Communication Over the Internet. Educational Researcher, 25(4)
Fetterman, D.M. (1995). In Response to Dr. Daniel Stufflebeam’s: “Empowerment Evaluation, Objectivist Evaluation, and Evaluation Standards: Where the Future of Evaluation Should Not Go and Where It Needs to Go,” Evaluation Practice, June 1995, 16(2):179-199.
Fetterman, D.M. (1994). Gifted and Talented Education Program Evaluation. In Sternberg, R.J. (ed.) Encyclopedia of Human Intelligence. New York, NY: Macmillan Publishing Company.
Fetterman, D.M. (1994). The Terman Study. In Sternberg, R.J. (ed.) Encyclopedia of Human Intelligence. New York, NY: Macmillan Publishing Company.
Fetterman, D.M. (1994). Keeping Research on Track. New Directions for Program Evaluation. No. 63, Fall. San Francisco, CA: Jossey-Bass, pp. 103-105.
Fetterman, D.M. (1994). Empowerment Evaluation. Presidential Address. Evaluation Practice, 15(1):1-15.
Fetterman, D.M. (1992). Hevrah: Our Intellectual Community. Anthropology and Education Quarterly, 23(4): 271-274.
Fetterman, D.M. (1992) Evaluate Yourself. Storrs, CT: National Research Center on the Gifted and Talented.
Fetterman, D.M. (1991). Evaluation in Multi-Site and Multi-Focus Projects. Revitalizing Rural America: New Strategies for the Nineties. Georgia Center for Continuing Education. Athens, GA: The University of Georgia.
Fetterman, D.M. (1990). Health and Safety Issues: Colleges Must Take Steps to Avert Serious Problems. The Chronicle of Higher Education, March 21, A48.
Fetterman, D.M. (1989). Anthropology Can Make a Difference. In Trueba, H., G. Spindler, and Spindler, L. (Eds.) What Do Anthropologists Have to Say About Dropouts? New York, NY: Falmer Press, 1989.
Fetterman, D.M. (1988). Stanford Special Review on Health and Safety Phase II: A Report on Allegations. Internal Audit Department. Stanford, CA: Stanford University.
Fetterman, D.M. (1988). Gifted and Talented Education. In Gorton, R.A., Schneider, G.T., and Fisher, J.C. (Eds.) Encyclopedia of School Administration and Supervision. Phoenix, AZ: Oryx Press.
Fetterman, D.M. (1986). Operational Auditing in a Teaching Hospital: A Cultural Approach, Internal Auditor, 43(2):48-54.
Fetterman, D.M. (1986). Evaluating Organizational Culture in a Teaching Hospital: The Use of Cultural Concepts and Techniques. In K. Sedgwick (Ed.), Association of College and University Auditors. Logan, Utah: Utah State University.
Fetterman, D.M. (1982). Ibsen’s Baths: Reactivity and Insensitivity (A misapplication of the treatment-control design in a national evaluation). Educational Evaluation and Policy Analysis, 4 (3):261-279.
Fetterman, D.M. (1981). Protocol and Publication: Ethical Obligations. Anthropology and Education Quarterly, 7(1):82-83.

RADIO INTERVIEWS (recent at: http://www.davidfetterman.com/RadioInterviews.htm)

Empowerment Evaluation in the Digital Villages (book), KAZI FM, Houston, Texas, March 29, 2013.
Chronicle of Philanthropy article about evaluation and nonprofit survival (Chronicle), WPFM FM, Washington, D.C. March 25, 2013.
Empowerment Evaluation in the Digital Villages (book), Kathryn Zox Show, March 13, 2013.
Empowerment Evaluation in the Digital Villages (book), Money Matters Network, Host Stu Taylor, January 28, 2013.
Empowerment Evaluation in the Digital Villages (book), WKXL-AM, Concord, New Hampshire, Host Bill Kearney, January 17, 2013.
Empowerment Evaluation in the Digital Villages (book), WPHM-AM Detroit, Host Paul Miller, January 14, 2013.
Empowerment Evaluation in the Digital Villages (book), Business Matters Radio, Host Thomas White, January 14, 2013.

BLOGS (selected)

Fetterman, D.M. (2014) David Fetterman on Google Glass Part I: Redefining Communications. AEA365. American Evaluation Association. http://aea365.org/blog/david-fetterman-on-google-glass-part-i-redefining-communications/ (April 17.)
Fetterman, D.M. (2014) David Fetterman on Google Glass Part II: Using Glass as an Evaluation Tool. AEA365. American Evaluation Association. http://aea365.org/blog/david-fetterman-on-google-glass-part-ii-using-glass-as-an-evaluation-tool/ (April 18.)
Fetterman, D.M. (2013). In These Uncertain Times, Charities Need a Survival Plan. The Chronicle of Philanthropy. March 10. http://philanthropy.com/article/In-These-Uncertain-Times/137741/
Fetterman, D.M. (2013). Surviving the Fiscal Cliff: The one thing every nonprofit should do in the face of federal tax increases and spending cuts. Stanford Social Innovation Review. http://www.ssireview.org/blog/entry/surviving_the_fiscal_cliff (January).
Fetterman, D.M. (2012). Empowerment Evaluation in the Digital Villages. Stanford Social Innovation Review. http://www.ssireview.org/articles/entry/empowerment_evaluation_in_the_digital_villages_hewlett_packards_15_million (December)
Fetterman, D.M. (2012). Corporate Philanthropy Tackles the Digital Divide. Stanford Social Innovation Review. http://www.ssireview.org/blog/entry/corporate_philanthropy_tackles_the_digital_divide (November)

ENCYCLOPEDIA (selected): The International Encyclopedia of Education and Encyclopedia of Human Intelligence

Dr. Ann E.K. Um
Dr. Ann E.K. UmPresident and CEO
EDUCATION

Doctorate Degree, Columbia University
Master’s Degree, Stanford University
Master’s Degree, Columbia University

EXPERIENCE

Harvard Medical School, DFCI, Research Data Manager
Harvard Medical School, Brigham and Women’s Hospital, Data Science Manager
The University of Texas, Assistant Professor

PUBLICATIONS (selected)

Autonomy Support, Self-Concept, and Mathematics Performance: A Structural Equation Analysis. Saarbrucken, Germany: VDM Verlag, 2010.
Motivation and Mathematics Achievement: A Structural Equation Analysis, Saarbrucken. Saarbrucken, Germany: VDM Verlag, 2008.
Motivation and Mathematics Performance: A Structural Equation Analysis. Michigan, Ann Arbor: ProQuest, 2006.
Motivation and Mathematics Performance: A Structural Equation Analysis (doctoral dissertation). Columbia University, New York, 2005.

PRESENTATIONS (selected)

Motivation and Mathematics Performance: A Structural Equation Analysis, National Council on Measurement in Education, Montreal, Quebec, Canada, 2005.
Comparing Eighth Grade Diagnostic Test Results for Korean and American Students, National Council on Measurement in Education, Chicago, Illinois, 2003.

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