my. applied data analysis, spring 2017 dr. andrew ho, harvard university

1
12/20/2016 my.harvard https://courses.my.harvard.edu/psp/courses/EMPLOYEE/EMPL/h/?tab=HU_CLASS_SEARCH&SearchReqJSON=%7B%22PageNumber%22%3A1%... 1/1 HELP SIGN IN Welcome to my.harvard 4 Courses Found Course Instructor School Department Meeting Times Displaying results 1 to 4 of (4) This course is designed for those who want to extend their data analytic skills beyond a basic knowledge of multiple regression analysis and who want to communicate their ndings clearly to audiences of researchers, scholars, and policymakers. The course contributes directly to the diverse data analytic toolkit that the well-equipped empirical researcher must possess in order to perform Applied Data Analysis EDU S052 Ho HGSE Education 2017 Spring Full Term S M T W Th F S 10:00am - 11:29am This is the rst of two sequential modules on quantitative methods for educational measurement. Students will learn and apply techniques essential for the design and analysis of educational and psychological assessments, including reliability, generalizability theory, validation, differential item functioning, item response theory, scaling, linking, standard setting, and adjustments for Statistical and Psychometric Methods for Educational Measurement (Part I) EDU S061A1 Ho HGSE Education 2016 Fall Fall 1 S M T W Th F S 10:00am - 11:59am This is the second of two sequential modules on quantitative methods for educational measurement. Students will continue their training in psychometric and statistical methods of measurement, with greater emphasis on understanding and critiquing recent research, as well as the development of an individual research proposal that has promise for advancing the eld. Training will Statistical and Psychometric Methods for Educational Measurement (Part II) EDU S061A2 Ho HGSE Education 2016 Fall Fall 2 S M T W Th F S 10:00am - 11:59am Special Reading & Research (Independent Study) EDU S999 H012 Ho HGSE Education 2016 Fall Full Term TBA Course Site Harvard Coop Applied Data Analysis EDU S052 2017 Spring Full Term 1/23/2017 to 4/26/2017 S M T W Th F S 10:00am - 11:29am Location:TBA Class Number: 32795 Course ID: 180866 Consent: No Consent Class Capacity: No Limit Description: This course is designed for those who want to e xtend their data analytic skills beyond a basic knowledge of multiple regression analysis and who want to communicate their ndings clearly to audiences of researchers, scholars, and policymakers. The course contributes directly to the diverse data analytic toolkit that the well-equipped empirical researcher must possess in order to perform sensible analyses of comple x educational, psychological, and social data. Topics in the course include more e xtensive use of transformations in regression analysis, inuence statistics, building and comparing tax onomies of regressio n models, general linear hypothesis testing, logistic regression analysis, multilevel modeling, and principal comp onents analysis, and introductions to survival analysis, generalized linear modeling, cluster analysis, and measurement theory . S-052 is an applied course that offers conceptual explanations of statistical techniques, along w ith opportunities to examine, implement, and practice them in real data. Because the course will feature the intensiv e use of Stata statistical software in all data analyses, learning the computer skills necessary to conduct these kinds of analyses, and the communication skills to discuss them, is an integral part of the course. Attendance at one of two weekly sections is required. Prerequisites: successful completion of S-040 (B+ or better allowed, A- or A recommended) or an equivalent course or courses that include 10 or mor e full hours of class time on multiple regression and its direct extensions. Students who do not meet the prerequisite should consider S-030. Class Notes: Required, 90-minute sections. School: Graduate School of Education Department: Education Subject: Education Units: 4 Grading Basis: Optional Cross Reg: Available for Harvard Cross Registr ation Learning Goals: The course is designed to de velop and extend the data-analytic skills acquired in earlier courses and to help students learn to communicate ndings clearly to audiences of other empirical researchers, scholars, policy-mak ers, practitioners, students, and parents. W e have designed S-052 to contribute to the diverse data-analytic toolkit that you will need in order to perform sensible and b elievable analyses of complex educational, psychological, and social data. Career Focus: This course supports careers that require data-analytic liter acy and data-analytic uency. Literacy goals include asking critical questions of current educational, social science, and health science research reports and peer- reviewed publications. Fluency goals include p roductive contribution to quantitative research teams and written analyses. Common ne xt steps include doctor al research trajectories, research think-tanks, governmental organizations, data journalism, and the wide arr ay of for-prot and not-for-prot organizations that value data- analytic skills. Competency: use quantitativ e-research software , write a research/analytic paper , develop research questions , write a research article, collaborate, create data visualizations , analyze quantitativ e data Content: descriptive statistics , data analysis , statistics, research methods , advanced quantitativ e methods , foundational quantitative methods , causal reasoning Pedagogy: lecture, lab sessions , problem sets , team-based learning EDU S052 EDU S061A1 Andrew Ho Course Component: Regular Course

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12/20/2016 my.harvard

https://courses.my.harvard.edu/psp/courses/EMPLOYEE/EMPL/h/?tab=HU_CLASS_SEARCH&SearchReqJSON=%7B%22PageNumber%22%3A1%... 1/1

 

 

HELP SIGN INWelcome to my.harvard

4 Courses FoundCourse Instructor School DepartmentMeeting Times

Displaying results 1 to 4 of (4)

 

This course is designed for those who want to extend their data analytic skills beyond a basic knowledge of multiple regressionanalysis and who want to communicate their �ndings clearly to audiences of researchers, scholars, and policymakers. The coursecontributes directly to the diverse data analytic toolkit that the well-equipped empirical researcher must possess in order to perform

Applied Data AnalysisEDU S052 Ho HGSE Education

2017 SpringFull Term

S M T W Th F S

10:00am - 11:29am…

This is the �rst of two sequential modules on quantitative methods for educational measurement. Students will learn and applytechniques essential for the design and analysis of educational and psychological assessments, including reliability, generalizabilitytheory, validation, differential item functioning, item response theory, scaling, linking, standard setting, and adjustments for

Statistical and Psychometric Methods for Educational Measurement (Part I)EDU S061A1 Ho HGSE Education

2016 FallFall 1

S M T W Th F S

10:00am - 11:59am…

This is the second of two sequential modules on quantitative methods for educational measurement. Students will continue theirtraining in psychometric and statistical methods of measurement, with greater emphasis on understanding and critiquing recentresearch, as well as the development of an individual research proposal that has promise for advancing the �eld. Training will

Statistical and Psychometric Methods for Educational Measurement (Part II)EDU S061A2 Ho HGSE Education

2016 FallFall 2

S M T W Th F S

10:00am - 11:59am…

Special Reading & Research (Independent Study)EDU S999 H012 Ho HGSE Education

2016 FallFull Term

- -

TBA

 

 

Course Site Harvard Coop

Applied Data AnalysisEDU S052

2017 SpringFull Term

1/23/2017 to 4/26/2017

S M T W Th F S

10:00am - 11:29amLocation:TBAClass Number: 32795 Course ID: 180866 Consent: No Consent Class Capacity: No Limit

Description: This course is designed for those who want to e xtend their data analytic skills beyond a basic knowledge of multipleregression analysis and who want to communicate their �ndings clearly to audiences of researchers, scholars, andpolicymakers. The course contributes directly to the diverse data analytic toolkit that the well-equipped empiricalresearcher must possess in order to perform sensible analyses of comple x educational, psychological, and socialdata. Topics in the course include more e xtensive use of transformations in regression analysis, in�uence statistics,building and comparing tax onomies of regressio n models, general linear hypothesis testing, logistic regressionanalysis, multilevel modeling, and principal comp onents analysis, and introductions to survival analysis, generalizedlinear modeling, cluster analysis, and measurement theory . S-052 is an applied course that offers conceptualexplanations of statistical techniques, along w ith opportunities to examine, implement, and practice them in realdata. Because the course will feature the intensiv e use of Stata statistical software in all data analyses, learning thecomputer skills necessary to conduct these kinds of analyses, and the communication skills to discuss them, is anintegral part of the course. Attendance at one of two weekly sections is required.

Prerequisites: successful completion of S-040 (B+ or better allowed, A- or A recommended) or an equivalent course orcourses that include 10 or mor e full hours of class time on multiple regression and its direct extensions. Students who do notmeet the prerequisite should consider S-030.

Class Notes: Required, 90-minute sections.

School: Graduate School of Education Department: Education Subject: Education

Units: 4 Grading Basis: Optional

Cross Reg: Available for Harvard Cross Registr ation

Learning Goals:

The course is designed to de velop and extend the data-analytic skills acquired in earlier courses and to help studentslearn to communicate �ndings clearly to audiences of other empirical researchers, scholars, policy-mak ers,practitioners, students, and parents. W e have designed S-052 to contribute to the diverse data-analytic toolkit thatyou will need in order to perform sensible and b elievable analyses of complex educational, psychological, and socialdata.

Career Focus:

This course supports careers that require data-analytic liter acy and data-analytic �uency. Literacy goals includeasking critical questions of current educational, social science, and health science research reports and peer-reviewed publications. Fluency goals include p roductive contribution to quantitative research teams and writtenanalyses. Common ne xt steps include doctor al research trajectories, research think-tanks, governmentalorganizations, data journalism, and the wide arr ay of for-pro�t and not-for-pro�t organizations that value data-analytic skills.

Competency: use quantitative-research software , write a research/analytic paper, develop research questions , write a researcharticle, collaborate, create data visualizations , analyze quantitativ e data

Content: descriptive statistics, data analysis, statistics, research methods , advanced quantitativ e methods, foundationalquantitative methods, causal reasoning

Pedagogy: lecture, lab sessions, problem sets, team-based learning

EDU S052 EDU S061A1  

Andrew Ho

Course Component: Regular Course