Katie J. Van Loo is a multi-method researcher with over nine years of experience in research methods, data analysis, and project management. She has a Ph.D. in Social Psychology from Indiana University and has conducted numerous studies on human decision making, leadership, and diversity. Her skills include designing experiments, statistical analysis, and communicating insights through compelling narratives. She is proficient in various research tools and seeks to use data-driven insights to improve user experiences.
The National Science Foundation’s Graduate Research Fellowship Program (https://www.nsfgrfp.org) offers fellowships to new and incoming graduate students in the sciences. The award is very prestigious and rather competitive. However, if you are eligible (see the solicitation) it is completely worth it to apply.
This introduction to the NSF-GRFP will teach you more about the fellowship, help you decide when to apply, and give you tips on crafting a winning application.
Doctoral Review of Literature Correspondence to Methodology: A general discus...DoctoralNet Limited
Part of the free ongoing series of conferences from DoctoralNet professors on Saturdays and Sundays, this set of slides covers the topic most likely to cause a person trouble at the proposal defense: the need for 1:1 correspondence between the review of literature and methodology.
The National Science Foundation’s Graduate Research Fellowship Program (https://www.nsfgrfp.org) offers fellowships to new and incoming graduate students in the sciences. The award is very prestigious and rather competitive. However, if you are eligible (see the solicitation) it is completely worth it to apply.
This introduction to the NSF-GRFP will teach you more about the fellowship, help you decide when to apply, and give you tips on crafting a winning application.
Doctoral Review of Literature Correspondence to Methodology: A general discus...DoctoralNet Limited
Part of the free ongoing series of conferences from DoctoralNet professors on Saturdays and Sundays, this set of slides covers the topic most likely to cause a person trouble at the proposal defense: the need for 1:1 correspondence between the review of literature and methodology.
Advancing Methodologies: A Conversation with John CreswelSAGE Publishing
In this presentation, best-selling author and professor John W. Creswell addresses the future of research design, qualitative research, and mixed methods research.
Meeting Story Pre-Plan for Educational Outreachcarriegaxiola
This is a recap of a meeting, June 11-14, 2019, Reno Nevada: Carrie Stewart, Sara Lediard, and Terra Graves
National Digital Newspaper Program (Stewart) and Washoe County School District (Lediard and Graves) learning about the project and preplanning how to deliver newspapers in the classroom.
Presentation for the American Sociological Association's Department Affiliates Webinar Series. Discussion of using quantitative data in courses throughout the undergraduate curriculum, including why it's a good practice, how it can be done, and where one can find resources that make it easier.
Program outcome 4 artifact key assessment 2-nsNihal Senlik
Analyze student data and apply research to adapt instruction and to select appropriate materials to improve student performance and enhance inquiry and critical thinking.
Advancing Methodologies: A Conversation with John CreswelSAGE Publishing
In this presentation, best-selling author and professor John W. Creswell addresses the future of research design, qualitative research, and mixed methods research.
Meeting Story Pre-Plan for Educational Outreachcarriegaxiola
This is a recap of a meeting, June 11-14, 2019, Reno Nevada: Carrie Stewart, Sara Lediard, and Terra Graves
National Digital Newspaper Program (Stewart) and Washoe County School District (Lediard and Graves) learning about the project and preplanning how to deliver newspapers in the classroom.
Presentation for the American Sociological Association's Department Affiliates Webinar Series. Discussion of using quantitative data in courses throughout the undergraduate curriculum, including why it's a good practice, how it can be done, and where one can find resources that make it easier.
Program outcome 4 artifact key assessment 2-nsNihal Senlik
Analyze student data and apply research to adapt instruction and to select appropriate materials to improve student performance and enhance inquiry and critical thinking.
Sharae Kalian Clinical Trainer and Education Specialist SharaeKalian
I am motivated by a self-felt, self-accepted calling to the cause of good, growth, and gain in the lives of others. Influential communication of ideas is a primary way of achieving those objectives. I am a passionate supporter of education, excellence and equity, and a fully engaged member of social change in the welfare and benefit of America’s Healthcare system.
Quanitiative Research PlanTextbooksAmerican Psychological Asso.docxamrit47
Quanitiative Research Plan
Textbooks
American Psychological Association. (2010). Publication manual of the American Psychological Association (6th ed.). Washington, DC: Author.
Frankfort-Nachmias, C., & Nachmias, D. (2008).Research methods in the social sciences (7th ed.). New York: Worth. (This textbook includes a GSS data disk that will be used in course assignments.)
Green, S. B., & Salkind, N. J. (2014). Using SPSS for Windows and Macintosh: Analyzing and understanding data (7th ed.). Upper Saddle River, NJ: Pearson.
Textbooks from RSCH 8100: Research Theory, Design, and Methods:
Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods approaches (Laureate Education, Inc., custom ed.). Thousand Oaks, CA: Sage Publications.
Reynolds, P. D. (2007). A primer in theory construction. Boston: Pearson Education.
-or-
Reynolds, P. D. (2010). A primer in theory construction (Laureate Education, Inc., custom ed.). Boston, MA: Pearson Education.
Project Instructions
Quanitiative Research Plan
My chosen topic: Participation of students in non-profit educational program
Section 1 (edit and reduce pages by making them full)
· Title
· Introduction
·
· Opening statement
· Background of study
· Problem statement
· Purpose of the study
· Research question(s) and hypotheses
· Theoretical framework
Section 2: Craft a 5 page paper in which you do the following:
· Assess the strengths and limitations of each of the research designs presented in Weeks 2 and 3.
· Recommend a quantitative design for your research plan. Include a rationale for why that design would be most appropriate.
· For the designs that you did not choose, state why each one is not appropriate for your research questions, hypotheses, and variables.
· Support your work with references to the literature.
Section 3: Craft a 5 page paper that includes the following:
· The levels of measurement that will be important for your study and why.
· How you will ensure content validity, empirical validity, and construct validity for your study. If any of these types of validity do not apply to your plan, provide a rationale.
· How you will ensure reliability for the measurement in your study.
· The strengths and limitations of the measurement instrument you have selected in terms of reliability and validity.
· Provide at least 10 references to the literature to support your choices and rationales.
Section 4: Craft a 5-page paper that includes the following:
·
· The levels of measurement that will be important for your study and why.
· How you will ensure content validity, empirical validity, and construct validity for your study. If any of these types of validity do not apply to your plan, provide a rationale.
· How you will ensure reliability for the measurement in your study.
· The strengths and limitations of the measurement instrument you have selected in terms of reliability and validity.
· Provide at least 10 references to the literature to support your choices and ...
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
1. KATIE J. VAN LOO Happy Valley, OR Ÿ vanlkj@gmail.com Ÿ (206) 696-1319
www.linkedin.com/in/katievanloo
SUMMARY
Multi-method researcher with over nine years of experience, leadership, and education in research methods, data analysis, and
project management and implementation, studying people: their preferences, motivations, limitations, decisions, and the
situational and environmental factors that affect the way they think and act. Excited by identifying objectives, using research,
and creating compelling narratives integrating quantitative and qualitative data to provide stakeholders with data-driven
insights and recommendations that improve products and user experiences. A self-motivated learner experienced in quick on-
the-job learning, diving into unfamiliar programs and content areas, and adapting to new teams and existing cultures.
Approaches challenges as opportunities for growth with curiosity, enthusiasm, and dedication.
SKILLS AND QUALIFICATIONS
• Creative critical-thinker; uses research and analytic techniques to generate digestible outputs for targeted audiences
• Extensive experience with designing experiments and creating surveys for data collection in person and online
• Skilled multi-tasker; contributes to several projects while setting and managing short- and long-term goals
• Effective team-player; Experience with cross-functional teams and multi-site collaborations
• Self-motivated learner; quickly catches onto new programs and techniques, invested in developing new skills
• Demonstrated oral and written communication skills with numerous professional publications and presentations
Technical Proficiencies
• Data Analysis: ANOVA, regression, correlation, factor analysis, reliability analyses, data tabulation, statistical mediation
• Methods: Experiments, quasi-experiments, surveys, content-coding, priming, observational methods, pilot testing
• Analytic Software: Expertise in SPSS; proficient in Microsoft Excel; experience with R and Stata
• Technologies: Qualtrics, MTurk, MediaLab, DirectRT, Microsoft Office (Proficient in Word, PowerPoint, Excel)
• Collaboration Tools: Google Calendar, Drive, Hangouts; Dropbox; Evernote
• Languages: English - advanced speaking, reading, writing; typing speed ~100 wpm
EDUCATION
Ph.D., Psychological and Brain Sciences Aug. 2009 – Sep. 2016
Indiana University, Bloomington, IN
Social Psychology with focus on experimental design and analysis
B.S., Psychology, Summa Cum Laude Sep. 2002 – Jun. 2006
Seattle Pacific University, Seattle, WA
SELECTED EXPERIENCE
Graduate Student Researcher, Indiana University, Bloomington, IN Aug. 2009-Sep. 2016
Conceptualized, designed, and conducted nationally-funded, cutting-edge research on human decision making and
performance, power and leadership, and gender and diversity. Completed 45+ studies as lead investigator and 25+ studies
as primary contributor with thousands of participants using experimental and survey designs resulting in multiple
professional publications, research collaborations, and oral presentations.
• Surveyed existing literature and drew from real-life issues to develop interesting, novel, and relevant research
questions and hypotheses
• Designed programmatic package of studies with multiple research techniques to answer research questions
• Conducted quantitative and qualitative statistical analyses and constructed data-driven narratives to communicate
research problems and insights
Graduate Instructor, Indiana University, Bloomington, IN Jan. 2013-May 2016
Created course structure and components, including syllabus design, textbook selection, grading structure, and instruction
for over 200 undergraduates in courses on Research Methods and Introduction to Social Psychology, helping non-research
students to understand and value and apply research methods, data analysis, and study findings.
• Advised teaching assistants on best practices for grading exams and assignments
• Drafted student evaluation surveys for courses, exams, and teaching to inform and improve teaching
Lab Manager, Indiana University, Bloomington, IN May 2010-May 2014
Established formal lab guidelines, experiment protocols, and scheduling system using Gmail and Google Calendar.
Created research assistant application form and interview questions that continue to be used in hiring for the lab. Led
undergraduate workshops on programming, statistical analysis, and getting into graduate school.
• Hired, managed, trained, and supervised between 8-12 undergraduate research assistants
• Coordinated multiple lab studies, research assistants, and principal investigators while adhering to ethical guidelines
2. Research Associate, Food Assistance Team, Center on Budget and Policy Priorities, Washington, DC Aug. 2006-Jul. 2009
Provided analyses of geographic and employment data to provide food stamps to vulnerable populations in nearly all 50
states. Created graphs, tables, and fact sheets for decision-makers in state and federal government and advocates using the
Food Stamp Program Quality Control dataset (40,000+ cases, 750+ variables). For example, tables for the New York
Times and 2008 Economic Stimulus Package proposal, analyses for 2008 Farm Bill policy reports, and graphs for debate
on the House floor. Led a training session on analyzing Food Stamp data for USDA Food and Nutritional Services staff.
• Contributed quantitative analyses as part of cross-functional team of policy analysts, economists, and social scientists
• Recreated and interpreted outside national reports on the Food Stamp Program from raw data, computed proxy
variables those not existing in raw data; allowed us to answer questions that otherwise would not have been possible
• Balanced multiple high-demand, time-sensitive analyses with longer-term projects and on-the-fly research requests
• Projected program costs and impact of policy changes
SELECTED LEADERSHIP AND SERVICE
Editorial Committee Member, ScIU Blog, Indiana University College of Arts and Sciences, 2015
Diversity Action Committee Member, Indiana University Department of Psychological and Brain Sciences, 2013-2016
Mentor for First-Generation Undergraduate in STEM, Groups Program, Indiana University, 2012-2013, 2015
Intern Coordinator Interviewed and oversaw program interns; trained and supervised food assistance intern, 2008-2009
SELECTED HONORS AND AWARDS
Gordon Kato Summer Research Scholarship in Social Psychology, Indiana University, 2015
Outstanding Graduate Student Instructor, Indiana University, 2015
College of Arts and Sciences Dissertation Fellowship, Indiana University, 2014-2015
Graduate Research Fellowship, National Science Foundation, 2011-2014
Outstanding Research Award, Society for Personality and Social Psychology (SPSP), 2012
Steinmetz Summer Research Award, Indiana University, 2010
PUBLICATIONS
Rydell, R. J., Van Loo, K. J., & Boucher, K. L. (in press). Stereotype threat: New insights into process and intervention. In A.
Eliot, C. Dweck, and D. Yeager (Eds.), Handbook of Competence and Motivation: Theory and Application. New York,
NY: Guilford Press.
Van Loo, K. J., & Rydell, R. J. (2014). Negative exposure: Watching another woman subjected to dominant male behavior
during a math interaction can induce stereotype threat. Social Psychological and Personality Science.
Rydell, R. J., Van Loo, K. J., & Boucher, K. L. (2014). Stereotype threat and executive functions: Which functions mediate
different threat-related outcomes? Personality and Social Psychology Bulletin, 40, 377-390.
Van Loo, K. J., Boucher, K. L., Rydell, R. J., & Rydell, M. T. (2013). Competition in stereotyped domains: Competition,
intergroup social comparison, and stereotype threat. European Journal of Social Psychology, 43, 648-660.
Van Loo, K. J., & Rydell, R. J. (2013). On the experience of feeling powerful: Perceived power moderates the effect of
stereotype threat on women’s math performance. Personality and Social Psychology Bulletin, 39, 388-400.
Boucher, K. L., Rydell, R. J., & Van Loo, K. J. (2012). Reducing stereotype threat in order to facilitate learning. European
Journal of Social Psychology, 42, 174-179.
Rydell, R. J., Shiffrin, R. M., Boucher, K. L., Van Loo, K. J., & Rydell, M. T. (2010). Stereotype threat prevents perceptual
learning. Proceedings of the National Academy of Sciences (US), 107, 14042-14047.
REPRESENTATIVE PRESENTATIONS
Van Loo, K. J., & Rydell, R. J. (January 2016). Feeling powerful decreases the use of base rate information in probability
estimates. Poster presented at the 17th
annual meeting of the Society for Personality and Social Psychology (SPSP), San
Diego, CA.
Van Loo, K. J., Boucher, K. L., Rydell, R. J., & Rydell, M. T. (June 2014). Competition can impair women’s math
performance by evoking stereotype threat. Talk presented at the 10th
Biennial meeting of the Society for the Psychological
Study of Social Issues (SPSSI), Portland, OR.
Van Loo, K. J., Rydell, R. J., & Boucher, K. L. (February 2014). Uncovering the role of specific executive functions in
stereotype threat outcomes for women. Talk presented at the 15th
annual meeting of the Society for Personality and Social
Psychology (SPSP), Austin, TX.
Van Loo, K. J., & Rydell, R. J. (June 2012). The consequences of power for women in stereotype threatening situations. Talk
presented at the 9th
biennial meeting of the Society for the Psychological Study of Social Issues (SPSSI), Charlotte, NC.
Van Loo, K. J., & Rydell, R. J. (January 2011). The interaction of power and stereotype threat on women’s math
performance. Poster presented at the 12th
annual meeting of the Society for Personality and Social Psychology (SPSP),
San Antonio, TX.