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1 Heidelberg Institute of Global Health Methods Courses for Doctoral Students September 2018
Transcript of Heidelberg Institute of Global Health Methods Courses for ...
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Heidelberg Institute of Global Health
Methods Courses for Doctoral Students
September 2018
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The rigor and innovation of our research rests on our methods expertise. A key reason for doctoral
studies is to acquire competence in scientific research methods.
This document is a structured outline and introduction to courses that we think are useful
and important for students enrolled in the doctoral programs at the Heidelberg Institute of Global Health (HIGH). These courses are appropriate for both doctoral and medical students in the Dr.
med. and Dr. sc. hum. programs. Courses included in this guidebook aim to enhance the
methods rigor and efficiency of doctoral students at HIGH and teach core topics in
methodological competencies and skills.
Specifically, the courses in this document were selected to provide doctoral students with the key
skills needed for conducting original population health and intervention research in global settings.
The courses are intended as part of a methods foundation and cover analytical skills and the technical
expertise required to design population health studies, manage scientific projects, analyze data as
well as interpret results, and identify and address the limitations of different approaches and
analyses.
The courses in this guidebook do not teach specific domain knowledge, because in our opinion the
domain knowledge needs are much more diverse across students and projects than the foundational
scientific methods skills required to be a successful researcher. Put otherwise, the skills and methods
that the courses in this document teach can be applied to a broad range of health research topics,
across diseases, interventions, and cultural and geographic contexts.
This document is meant as a guide for discussions between doctoral mentors and students and to
provide inspiration and stimulate aspiration for a bespoke methods curriculum for each doctoral
student. In addition to a doctoral core curriculum, we provide a wide array of elective courses.
The following methods themes are covered in the document:
Core Methods: Statistical Inference; Regression Analysis; Causal Inference; Measurement;
Study and Survey design; Qualitative and Mixed Methods
Core Skills: Quantitative Software (Stata, R); Qualitative Software (ATLAS.ti, NVivo); Paper
Writing; Grant Writing; Scientific Project Management
Concentrations: Impact Evaluation; Performance Evaluation; Economic Evaluation; Policy
Analysis and Translation; Data Science
Specialist Certificates: Methods and Statistics in Social Sciences; Study and Survey Design;
Quantitative Software; Data, Economics, and Development Policy; Data Science and
Bioinformatics
For each methods topic we list and introduce courses in three categories of existing expertise:
introductory, intermediary, and advanced. We also distinguish between physical on campus
courses which are highlighted in a grey shaded frame, and on-line only courses or resources.
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Feedback? We welcome any questions, comments, or suggestions on the content of this guidebook. Faculty contacts: Till Bärnighausen ([email protected]) and Jan-Walter De Neve ([email protected]).
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TABLE OF CONTENTS
TABLE OF CONTENTS ............................................................................................................................................................ 4
I. CORE METHODS AND SEMINAR – FOR ALL DOCTORAL STUDENTS ............................................................ 5
II. ELECTIVE METHODS COURSES – FOR SPECIALIZATION ................................................................................. 7
II.1 Statistical inference ................................................................................................................................................. 7
II.2 Causal inference ...................................................................................................................................................... 11
II.3 Measurement ............................................................................................................................................................ 12
II.4 Study and survey design ...................................................................................................................................... 13
II.5 Qualitative and mixed methods ........................................................................................................................ 15
III. ELECTIVE SKILLS COURSES ....................................................................................................................................... 17
III.1 Quantitative software: Stata ............................................................................................................................. 17
III.2 Quantitative software: R .................................................................................................................................... 18
III.3 Qualitative software: ATLAS.ti and NVivo .................................................................................................. 19
III.4 Paper writing .......................................................................................................................................................... 19
III.5 Grant writing ........................................................................................................................................................... 20
III.6 Scientific project management ........................................................................................................................ 20
IV. ELECTIVE CONCENTRATION COURSES ................................................................................................................ 21
IV.1 Impact evaluation .................................................................................................................................................. 21
IV.2 Performance evaluation ..................................................................................................................................... 21
IV.3 Economic evaluation ............................................................................................................................................ 21
IV.4 Policy analysis and translation ........................................................................................................................ 22
IV.5 Data science ............................................................................................................................................................. 23
V. SPECIALIST CERTIFICATES ......................................................................................................................................... 26
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I. CORE METHODS AND SEMINAR – FOR ALL DOCTORAL STUDENTS
The core of the doctoral curriculum is an integrated set of methods and skills subjects in population
health sciences. These courses have dual roles: they provide breadth and serve as a basis for
subsequent specialized study. In other words, with these courses, we aim to educate what has been
called “T-shaped” scientists1, who are knowledgeable about a specific subject matter (i.e., a primary
field or major), but who also have the crosscutting component, the horizontal part of the “T.” We
recommend students complete core curriculum courses at least at the intermediary level (listed here
on pages 5-6)2. The suggested time frame is on average roughly 5 weeks per course.
In addition to the core curriculum listed below, we provide a weekly seminar, “Research Methods in
Global Health”, using a flipped classroom model3. The discussions focus on understanding the
methods and discussing how the methods might be implemented. This methods seminar
meets bi-weekly for 1.5 hours on campus and is organized on Wednesday afternoons during the
Winter and Summer Semesters of the University of Heidelberg.4 Both doctoral and medical
students in the Dr. med. and Dr. sc. hum. programs are welcome to join any of these sessions.
I.1 Statistical inference
Competencies: Applied Probability; Statistical Inference; Exploratory Data Analysis
Content: This course focuses on probability and analysis of one and two samples. Topics
include discrete and continuous probability models; expectation and variance; the central
limit theorem; inference, including hypothesis testing and confidence for means, proportions,
and counts; sample size determinations; as well as bootstrapping.
Intermediary course link: https://www.coursera.org/learn/statistical-inference
Introductory course (only if helpful): https://www.edx.org/course/statistics-unlocking-
world-data-edinburghx-statsx#!
I.2 Multivariable regression analysis
Competencies: Multivariable Regression; Confounder; Mediator
Content: This course introduces two key concepts in statistical analysis (confounding and
effect modification) and covers simple regression linear and logistic regression analysis with
a binary or continuous predictor, as well as Cox proportional hazard models. The course
extends these methods to multiple predictors in a single regression model.
Intermediary course link: https://www.coursera.org/learn/statistical-reasoning-2#
Introductory course (only if helpful): https://www.coursera.org/learn/regression-models
1 Frenk J, Hunter DJ, Lapp I. A renewed vision for higher education in public health. Am J Public Health. 2015. 2 Students should be able to take these courses without a certificate (e.g., to avoid course fees - payment is not required). 3 Students view the core curriculum videos on their own time but in-class time is devoted to questions and discussions. 4 Please contact Jan-Walter De Neve if you are interested in participating.
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I.3 Study designs in population health
Competencies: Descriptive Study Statistics; Observational Study Designs
Content: This course introduces measures of disease frequency and association such as risks
and rates and key study designs in population health, such as the cross-sectional, case-
control, cohort, as well as ecologic study design. The course introduces the concept of
causality and the experimental design, which is explored in depth in the next core course.
Intermediary course link: http://theopenacademy.com/content/epidemiologic-methods-ii
Introductory course (only if helpful): https://www.coursera.org/learn/epidemiology
I.4 Approaches for causal inference
Competencies: Instrumental Variables; Difference-in-differences; Regression Discontinuity
Content: These two courses further introduce study designs to look at causal effects as
opposed to spurious relationships. The courses introduce “quasi-experimental” methods to
assess causal effects including instrumental variables and difference-in-difference designs.
Intermediary course links: https://www.coursera.org/learn/causal-effects
https://www.edx.org/course/policy-analysis-using-interrupted-time-ubcx-itsx-2#!
Introductory course (only if helpful):
https://www.edx.org/course/data-analysis-social-scientists-mitx-14-310x-1#!
I.5 Qualitative research and mixed methods
Competencies: Qualitative Data Collection and Analysis; Mixed Methods Analysis
Content: This course introduces data collection, description, analysis and interpretation in
qualitative research. Furthermore, it covers data triangulation and mixed methods research,
as well as ethical practices in qualitative research.
Intermediary course link: https://www.coursera.org/learn/qualitative-methods
Introductory course (only if helpful): [Not available]
I.6 Software resources
Competencies: Quantitative and Qualitative Software Skills
Content: These resources introduce commonly used software packages in quantitative and
qualitative research. They describe programming language concepts and cover practical
issues in statistical computing such as reading data into a software package, cleaning data,
accessing packages, debugging, as well as organizing and commenting code.
Links for resources:
MOOC on R Programming: https://www.coursera.org/learn/r-programming
Video tutorials on using Stata: http://www.stata.com/links/video-tutorials/
SAS tutorial: https://support.sas.com/edu/elearning.html?ctry=us&productType=library
Free NVivo resources: http://www.qsrinternational.com/nvivo-learning
ATLAS.ti in the classroom: http://atlasti.com/learning-old/classroom/
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II. ELECTIVE METHODS COURSES – FOR SPECIALIZATION
II.1 Statistical inference
Statistical inference are methods used for drawing conclusions about a population based on the
information contained in a sample of observations drawn from that population. These techniques are
applied when the time and/or resources necessary to examine each member of a population are not
available. Important applications include properties of a sample mean, diagnostic testing using
probability theory, extrapolating findings from sample data to the larger population using confidence
intervals and hypothesis testing. These methods courses cover nonparametric techniques (which
relax the assumptions underlying traditional hypothesis tests); inferential methods for counts;
comparison of means and proportions; the relationships among a number of different variables using
regression models; and the basic principles underlying survival analysis.5
Introductory
Epidemiology and Biostatistics for Doctoral Students. [HIGH. contact: Dr. Andreas Deckert]6
Statistical Methods in Epidemiology. [HIGH]
Research Foundations: Epidemiology, Biostatistics. [HIGH]
Biostatistics and Epidemiology. [University of Heidelberg]
Biostatistics Methods. [University of Heidelberg]
Principles of Statistical Testing. [Faculty of Medicine, Mannheim]
Probability and Binomial Distribution. [Faculty of Medicine, Mannheim]
Normal Distribution and Estimation Methods. [Faculty of Medicine, Mannheim]
Tests for Comparing Frequencies. [Faculty of Medicine, Mannheim]
Univariate Data Description: Frequencies and Parameters. [Faculty of Medicine, Mannheim]
http://www.uni-heidelberg.de/studium/imstudium/vorlesungen/
Statistical Inference.
https://www.coursera.org/learn/statistical-inference
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/statinference/coursePage/index/
Epidemiology: The Basic Science of Public Health.
https://www.coursera.org/learn/epidemiology
Statistical Reasoning for Public Health 1: Estimation, Inference, & Interpretation.
https://www.coursera.org/learn/statistical-reasoning-1
Understanding Clinical Research: Behind the Statistics.
https://www.coursera.org/learn/clinical-research
5 Source: Rosner B. Fundamentals of Biostatistics, 7th Edition, 2011. 6 Brackets refer to physical on-campus courses (e.g., [XXX]). Please click here for more information.
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Introduction to Statistics: Descriptive Statistics.
https://www.edx.org/course/introduction-statistics-descriptive-uc-berkeleyx-stat2-1x#!
Introduction to Statistics: Probability.
https://www.edx.org/course/introduction-statistics-probability-uc-berkeleyx-stat2-2x
Statistics: Unlocking the World of Data.
https://www.edx.org/course/statistics-unlocking-world-data-edinburghx-statsx#!
Intro to Descriptive Statistics.
https://www.udacity.com/course/intro-to-descriptive-statistics--ud827
Introduction to Probability - The Science of Uncertainty.
https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2#!
Inferential Statistics.
https://www.coursera.org/learn/inferential-statistics-intro
Intro to Inferential Statistics.
https://www.udacity.com/course/intro-to-inferential-statistics--ud201
Introduction to Probability and Data.
https://www.coursera.org/learn/probability-intro#pricing
Basic Statistics.
https://www.coursera.org/learn/basic-statistics
Mathematical Biostatistics Boot Camp 2.
https://www.coursera.org/learn/biostatistics-2
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Intermediary
Bivariate Data Analysis: Correlation and Regression. [Faculty of Medicine, Mannheim]
Multivariable Statistics. [Faculty of Economics, Mannheim]
Exercises: Chi-squared Test, Fisher Test, McNemar Test. [Faculty of Medicine, Mannheim]
Exercises: Bivariate data Description. [Faculty of Medicine, Mannheim]
Exercises: Normal distribution and Estimation procedures. [Faculty of Medicine, Mannheim]
Exercises: Univariate Data Description. [Faculty of Medicine, Mannheim]
Exercises: t-tests and Rank Tests. [Faculty of Medicine, Mannheim]
Exercises: Probabilities and Binomial Distribution. [Faculty of Medicine, Mannheim]
http://www.uni-heidelberg.de/studium/imstudium/vorlesungen/
Regression Models.
https://www.coursera.org/learn/regression-models
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/RegMods/coursePage/index/
Statistical Reasoning for Public Health 2: Regression Methods.
https://www.coursera.org/learn/statistical-reasoning-2
Introduction to Applied Biostatistics: Statistics for Medical Research.
https://www.edx.org/course/introduction-applied-biostatistics-osakaux-med101x-0#!
Health in Numbers: Quantitative Methods in Clinical & Public Health Research.
https://www.edx.org/course/health-numbers-quantitative-methods-harvardx-ph207x
Population Survey Analysis.
http://www.populationsurveyanalysis.com/full-course/
Introduction to Statistics.
https://www.edx.org/course/introduction-statistics-inference-uc-berkeleyx-stat2-3x
Statistical Inference and Modeling for High-throughput Experiments.
https://www.edx.org/course/statistical-inference-modeling-high-harvardx-ph525-3x#!
Bayesian Statistics: From Concept to Data Analysis.
https://www.coursera.org/learn/bayesian-statistics
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Advanced
Epidemiology and Statistics for Advanced. [HIGH]
Quantitative Methods: Applied Panel Data Analysis. [Faculty of Medicine, Mannheim]
Prognosis Studies: Kaplan-meier Curves. [Faculty of Medicine, Mannheim]
Biomathematics: Foundations of Statistics. [Faculty of Medicine, Mannheim]
Advanced Econometrics. [Faculty of Economics, Mannheim]
Advanced PhD Seminar in Experimental Econometrics. [Faculty of Economics, Mannheim]
http://www.uni-heidelberg.de/studium/imstudium/vorlesungen/
Advanced Linear Models for Data Science 1: Least Squares.
https://www.coursera.org/learn/linear-models
Methods in Biostatistics I.
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/MethodsInBiostatisticsI/coursePa
ge/index/
Essentials of Probability and Statistical Inference IV.
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/EssentialsProbabilityStatisticalInf
erence/coursePage/index/
Advanced Quantitative Research Methodology.
http://projects.iq.harvard.edu/gov2001/book/lecture-notes-advanced-quantitative-
political-methodology
Multilevel modelling online course.
http://www.bristol.ac.uk/cmm/learning/online-course/
Fitzmaurice, Nan Laird & James Ware. Applied Longitudinal Analysis, 2nd Edition.
https://content.sph.harvard.edu/fitzmaur/ala2e/
Gelman A and Hill J (2006). Data Analysis Using Regression and Multilevel/Hierarchical
Models. Cambridge University Press.
https://www.cambridge.org/core/books/data-analysis-using-regression-and-
multilevelhierarchical-models/32A29531C7FD730C3A68951A17C9D983
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II.2 Causal inference
Causal inference is concerned with how and when we can make causal claims from empirical
research. Although observational research has an important role to play (e.g., to assess exposures
that cannot be randomized or cannot be randomized ethically7), a causal relationship is useful for
making predictions about the consequences of changing circumstances or policies. It tells us what
would happen in alternative or counterfactual worlds. The ideal research design in causal inference
uses random assignment of an exposure. In the absence of randomized interventions, however,
additional applications of causal inference include methods to evaluate “natural” or “quasi-”
experiments, such as interrupted time series, difference-in-differences, regression discontinuity, and
instrumental variable techniques, which are increasingly used in population health research.8
Introductory
Measuring Causal Effects in the Social Sciences.
https://www.coursera.org/learn/causal-effects
Causal Diagrams: Draw Your Assumptions Before Your Conclusions.
https://www.edx.org/course/causal-diagrams-draw-assumptions-harvardx-ph559x#!
Intermediary
Policy Analysis Using Interrupted Time Series. [includes regression discontinuity]
https://www.edx.org/course/policy-analysis-using-interrupted-time-ubcx-itsx-2#!
Econometrics: Methods and Applications.
https://www.coursera.org/learn/erasmus-econometrics
Econometrics.
https://ocw.mit.edu/courses/economics/14-32-econometrics-spring-2007/
Advanced
Data Analysis for Social scientists.
https://www.edx.org/course/data-analysis-social-scientists-mitx-14-310x-0#!
Statistics for Psychosocial Research: Structural Models.
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/structuralmodels/coursePage/ind
ex/
7 Such as social position or smoking. 8 Source: Hernán MA, Robins JM (2017). Causal Inference. Boca Raton: Chapman & Hall/CRC, forthcoming.
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II.3 Measurement
Methods in measurement introduce the conceptual, methodological and empirical basis for
quantifying levels of health in individuals and populations, including the construction of a range of
different summary measures that combine information on mortality and non-fatal health outcomes.
These methods courses provide an understanding of the technical basis for measurement in
population health and an appreciation of the uses and limitations of these methods in policy-making
and priority-setting. Important applications are measuring individuals’ health status along various
dimensions of health and methods for combining multi-dimensional information into measures of
summary health-state levels. Topics covered include measurement scales, life table analysis, factor
analysis, healthy life expectancy and health gap analysis, and impact evaluation.9
Introductory
Using Summary Measures of Population Health to Improve Health Systems.
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/summarymeasures/coursePage/i
ndex/
Population Change and Public Health.
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/PopulationChange/coursePage/in
dex/
Measuring Health Outcomes in Field Surveys.
https://www.edx.org/course/measuring-health-outcomes-field-surveys-mitx-jpal350x
Quantitative Methods.
https://www.coursera.org/learn/quantitative-methods
Questionnaire Design for Social Surveys.
https://www.coursera.org/learn/questionnaire-design
Data Collection: Online, Telephone and Face-to-face.
https://www.coursera.org/learn/data-collection-methods
Intermediary
Quantitative Methods: Social Science Indicators. [Faculty of Economics, Mannheim]
http://www.uni-heidelberg.de/studium/imstudium/vorlesungen/
9 Source: Murray, C.J.L. (1996). Rethinking DALYs. In: Murray CJL, Lopez AD, eds. The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries and risk factors in 1990 and projected to 2020. Cambridge, MA: Harvard School of Public Health, 1996: 1-98.
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Statistics in Psychosocial Research: Measurement.
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/statisticspsychosocialresearch/co
ursePage/index/
Introduction to Demographic Methods.
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/demographicmethods/coursePag
e/index/
II.4 Study and survey design
Why and when to conduct a randomized controlled trial—and what are the key components of a well-
designed study? Study design methods cover training on how to design and conduct rigorous studies.
These methods provide insights on how to implement a study in the field, including questionnaire
design, piloting, quality control, data collection and management. Survey design methods cover the
process of designing a survey, including potential sources of bias, respondent recruitment, data
collection methods, instrument design, and field administration. Information from surveys has been
used to describe and monitor a population’s health status and to build the case for health policy and
systems reform. Indeed, surveys serve as a base for research and provide information on a range of
population health, economic, social and behavioral outcomes.10
Introductory
Study Designs in Biostatistics and Epidemiology. [Heidelberg University]
Study Design in Quantitative Research. [Heidelberg University]
Clinical Epidemiology: Principles, Methods, and Applications. [Heidelberg University]
Foundations of Study Design in Epidemiology. [Faculty of Medicine, Mannheim]
Prevention Studies and Screening. [Faculty of Medicine, Mannheim]
Randomized Therapeutic Studies. [Faculty of Medicine, Mannheim]
http://www.uni-heidelberg.de/studium/imstudium/vorlesungen/
Issues in Survey Research Design.
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/surveyresearchdesign/coursePag
e/index/
Designing and Running Randomized Evaluations.
https://www.edx.org/course/designing-running-randomized-evaluations-mitx-jpal102x#!
10 Source: Deaton A. (1997). The design and content of household surveys. In: Deaton, A. The analysis of household surveys: a microeconometric approach to development policy. Baltimore: Johns Hopkins University Press, 7-62.
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/statisticspsychosocialresearch/coursePage/index/
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Intermediary
Econometrics and RCTs in Development Economies. [Faculty of Economics, Mannheim]
http://www.uni-heidelberg.de/studium/imstudium/vorlesungen/
Epidemiologic Methods II.
http://theopenacademy.com/content/epidemiologic-methods-ii
Statistical Methods for Sample Surveys.
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/StatMethodsForSampleSurveys/c
oursePage/index/
Sampling People, Networks and Records.
https://www.coursera.org/learn/sampling-methods
Design and Interpretation of Clinical Trials.
https://www.coursera.org/learn/clinical-trials
Advanced
Behavioral Economics in Action.
https://www.edx.org/course/behavioral-economics-action-university-torontox-be101x-0
Aday LA, Cornelius LJ. Designing and Conducting Health Surveys: A Comprehensive Guide.
http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1118046676.html
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II.5 Qualitative and mixed methods
Qualitative methods are characterized by approaches which seek to describe and analyze human
culture and behavior.11 These techniques place emphasis on providing a holistic understanding of the
social settings in which research is conducted and rely on a research strategy that is flexible and
iterative. These techniques allow the exploration and the “discovery” of unexpectedly important
topics (i.e., which may not have been visible if the researcher had been limited to a strictly pre-
defined study design, such as in the case of an RCT). Important methods and skills include the design
of qualitative study protocols, individual interviewing, developing interview guides, focus group
techniques, using theory-driven and grounded theory, category construction and software aided data
analysis, and effectively using qualitative and quantitative research in combination.12
Introductory
Research Foundations: Qualitative Methods. [HIGH]
http://www.uni-heidelberg.de/studium/imstudium/vorlesungen/
Mixed Methods in International Health Research. [HIGH]
https://www.klinikum.uni-heidelberg.de/Courses.9214.0.html
Public Health Anthropology: Concepts and Tools. [HIGH]
https://www.klinikum.uni-heidelberg.de/Courses.9214.0.html
Qualitative Methods. [Faculty of Economics, Mannheim]
http://www.uni-heidelberg.de/studium/imstudium/vorlesungen/
Qualitative Data Analysis.
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/QualitativeDataAnalysis/coursePa
ge/index/
Qualitative Research Methods.
https://www.coursera.org/learn/qualitative-methods
Auerbach, C. F., & Silverstein, L. B. (2003). Qualitative data, an introduction to coding and
analysis. New York: New York University Press.
https://nyupress.org/books/9780814706954/
11 Source: Hudelson P (1994). Qualitative Research for Health Programs. Geneva: World Health Organization. 12 Source: Cresswell J (2014). A Concise Introduction to Mixed Methods Research. Thousand Oaks: Sage Publications.
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Intermediary
Qualitative Research: Design and Methods.
https://ocw.mit.edu/courses/political-science/17-878-qualitative-research-design-and-
methods-fall-2007/index.htm
Advanced
Issues in Mental Health Research in Developing Countries.
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/MHDevCo/coursePage/index/
*****
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III. ELECTIVE SKILLS COURSES
III.1 Quantitative software: Stata13
Introductory
Video tutorials on using Stata.
http://www.stata.com/links/video-tutorials/
Resources for learning Stata. User-written and official resources.
http://www.stata.com/links/resources-for-learning-stata/
Princeton University Stata Tutorial.
http://data.princeton.edu/stata/
http://www.princeton.edu/~otorres/Stata/
UCLA Institute for Digital Research and Education.
http://stats.idre.ucla.edu/stata/
University of North Carolina Population Center Introduction to Stata.
http://www.cpc.unc.edu/research/tools/data_analysis/statatutorial
NetCourse® 101: Introduction to Stata.14
http://www.stata.com/netcourse/enroll-future-nc/
http://www.stata.com/training/
Intermediary
Statalist – the official Stata forum.
http://www.statalist.org/
Population Survey Analysis.
http://www.populationsurveyanalysis.com/full-course/
The Demographic and Health Surveys (DHS) Program User Forum.15
http://userforum.dhsprogram.com/
SALDRU Online Stata Course: The Analysis of South African Household Survey Data.
https://www.saldru.uct.ac.za/training/online-stata-course
13 MOOCs were not available for Stata, so we provide a comprehensive list of (free) Stata resources. 14 Has a small fee for enrollment. 15 Discusses many Stata related issues.
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Advanced
Stata “User’s Guide” StataCorp. 2015. Stata: Statistical Software.
http://www.stata.com/manuals14/u.pdf
Stata Journal.
http://www.stata-journal.com/
III.2 Quantitative software: R
Introductory
R Programming.
https://www.coursera.org/learn/r-programming
Explore Statistics with R.
https://www.edx.org/course/explore-statistics-r-kix-kiexplorx-0#!
Foundations of Data Analysis - Part 1: Statistics Using R.
https://www.edx.org/course/foundations-data-analysis-part-1-utaustinx-ut-7-11x#!
Foundations of Data Analysis - Part 2: Inferential Statistics Use R to learn the fundamental
statistical topic of basic inferential statistics.
https://www.edx.org/course/foundations-data-analysis-part-2-utaustinx-ut-7-21x#!
Introduction to R for Data Science Learn the R statistical programming language, the lingua
franca of data science in this hands-on course.
https://www.edx.org/course/introduction-r-data-science-microsoft-dat204x-3#!
Data Analysis with R.
https://www.udacity.com/course/data-analysis-with-r--ud651
Intermediary
Statistics and R. An introduction to basic statistical concepts and R programming skills
necessary for analyzing data in the life sciences.
https://www.edx.org/course/statistics-r-harvardx-ph525-1x#!
Programming with R for Data Science.
https://www.edx.org/course/programming-r-data-science-microsoft-dat209x-2#!
R for Data Science by Garrett Grolemund and Hadley Wickham.
http://r4ds.had.co.nz/
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III.3 Qualitative software: ATLAS.ti and NVivo16
[One course introduces ATLAS.ti – see above under Qualitative Data Analysis.]
III.4 Paper writing
Introductory
Scientific Writing I. [Faculty of Medicine, Mannheim]
http://www.uni-heidelberg.de/studium/imstudium/vorlesungen/
Designing Research Posters. [Graduate Academy, Uni-Heidelberg]
http://www.graduateacademy.uni-heidelberg.de/workshops/ga/index_en.html
Writing in the Sciences.
https://lagunita.stanford.edu/courses/Medicine/SciWrite-SP/SelfPaced/about
Introduction to Research for Essay Writing.
https://www.coursera.org/learn/introduction-to-research-for-essay-writing
How to Write and Publish a Scientific Paper (Project-Centered Course).
https://www.coursera.org/learn/how-to-write-a-scientific-paper
Booth W, Colomb G., Williams J, Bizup J, Fitzgerald W. The Craft of Research, Fourth Edition.
http://www.press.uchicago.edu/ucp/books/book/chicago/C/bo23521678.html
Zelazny Z. Say It With Charts: The Executive’s Guide to Visual Communication, 4th Edition.
https://www.safaribooksonline.com/library/view/say-it-with/9780071369978/
Tufte E. The Visual Display of Quantitative Information.
https://www.edwardtufte.com/tufte/books_vdqi
Milller J. The Chicago Guide to Writing about Multivariate Analysis, Second Edition.
http://www.press.uchicago.edu/ucp/books/book/chicago/C/bo15506942.html
Harvard Writing Project. Harvard University.
https://writingproject.fas.harvard.edu/
16 Few MOOCs are available on this topic.
20
Reference management
How to use EndNote in 5 Minutes.
http://endnote.com/training#start
Zotero tutorial: screencasts demonstrating many of the basic functions of Zotero.
https://www.zotero.org/support/screencast_tutorials
Mendeley: videos and tutorials.
https://www.mendeley.com/guides/videos
III.5 Grant writing17
Proposal Writing as a Consultancy Skill. [HIGH]
https://www.klinikum.uni-heidelberg.de/Courses.9214.0.html
Strategies for Successful Grant-Writing as a Scientific Career Booster. [Uni-Heidelberg]
www.uni-heidelberg.de/einrichtungen/zuv/weiterbildung/bildungsprogramm/index.html
Writing Grant Proposals. [Graduate Academy, Uni-Heidelberg]
http://www.graduateacademy.uni-heidelberg.de/workshops/ga/index_en.html
Scientific Writing II "Application for Funding”. [Faculty of Medicine, Mannheim]
http://www.uni-heidelberg.de/studium/imstudium/vorlesungen/
Grant Proposal.
https://www.coursera.org/learn/grant-proposal
Writing Successful Grant Proposals.
http://elevatehealth.eu/course/writing-successful-grant-proposals
III.6 Scientific project management18
Consultancy Skills in International Health. [HIGH]
https://www.klinikum.uni-heidelberg.de/Courses.9214.0.html
Evidence-Based Project Management.
https://www.edx.org/course/evidence-based-project-management-anux-ebm07x#!
*****
17 Few MOOCs are available on this topic. 18 Few MOOCs are available on this topic.
21
IV. ELECTIVE CONCENTRATION COURSES
IV.1 Impact evaluation
Intermediary
Evaluating Social Programs.
https://www.edx.org/course/evaluating-social-programs-mitx-jpal101x-4
Foundations of Development Policy: Advanced Development Economics.
https://www.edx.org/course/foundations-development-policy-advanced-mitx-14-740x-0
Pragmatic Randomized Controlled Trials in Health Care.
https://www.edx.org/course/pragmatic-randomized-controlled-trials-kix-kipractihx-1#!
Advanced
Abdul Latif Jameel Poverty Action Lab Executive Training: Evaluating Social Programs 2011.
https://ocw.mit.edu/resources/res-14-002-abdul-latif-jameel-poverty-action-lab-
executive-training-evaluating-social-programs-2011-spring-2011/
IV.2 Performance evaluation
Intermediary
Introduction to Methods for Health Service Research and Evaluation.
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/HSRE/coursePage/syllabus/
Fundamentals of Program Evaluation.
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/fundamentalsprogramevaluation/
coursePage/index/
IV.3 Economic evaluation
Introductory
Doctoral Seminar in Health Economics and Health Economics. [HIGH]
Health Policy, Health Economics and Evaluation in Health. [HIGH]
http://www.uni-heidelberg.de/studium/imstudium/vorlesungen/
22
Introduction to Health Policy.
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/IntroHealthPolicy/coursePage/in
dex/
Intermediary
Concepts in Economic Evaluation.
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/ConceptsEconomicEvaluation/co
ursePage/index/
Understanding Cost-Effectiveness Analysis in Health Care.
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/UnderstandingCostEffectiveness/
coursePage/index/
To Screen or not to Screen? Methods and health policies through case studies.
https://www.coursera.org/learn/screening
Microeconomic Theory and Public Policy.
https://ocw.mit.edu/courses/economics/14-03-microeconomic-theory-and-public-policy-
fall-2010/
Joint Learning Network. Costing of Health Services.
http://www.jointlearningnetwork.org/news/creating-digital-tools-to-augment-
practitioner-to-practitioner-
learning?utm_source=Copy+of+October+2017&utm_campaign=October+2017+Monthly&ut
m_medium=email
IV.4 Policy analysis and translation
Introductory
Decision-making in Public Health: Evidence, Politics, or Diplomacy. [HIGH]
https://www.klinikum.uni-heidelberg.de/Courses.9214.0.html
Leadership and Change Management in International Health. [HIGH]
https://www.klinikum.uni-heidelberg.de/Courses.9214.0.html
Health for All Through Primary Health Care.
https://www.coursera.org/learn/health-for-all
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/UnderstandingCostEffectiveness/coursePage/index/
23
Global Health Policy.
https://www.coursera.org/learn/global-health-policy
Global Health Diplomacy.
https://www.coursera.org/learn/global-health-diplomacy
Community Change in Public Health.
https://www.coursera.org/learn/community-public-health
Systems Thinking In Public Health.
https://www.coursera.org/learn/systems-thinking
Introduction to Public Speaking.
https://www.coursera.org/learn/public-speaking/
Joint Learning Network. High-quality, resources on health systems reforms.
http://www.jointlearningnetwork.org/resources
Intermediary
Political Economy and Economic Development.
https://ocw.mit.edu/courses/economics/14-75-political-economy-and-economic-
development-fall-2012/index.htm
IV.5 Data science
Introductory
The Data Scientist’s Toolbox.
https://www.coursera.org/learn/data-scientists-tools
Data Science Orientation. Get started on your Data Science journey.
https://www.edx.org/course/data-science-orientation-microsoft-dat101x-1#!
Intro to Data Science.
https://www.udacity.com/course/intro-to-data-science--ud359
A Crash Course in Data Science.
https://www.coursera.org/learn/data-science-course#pricing
Learning From Data (Introductory Machine Learning).
https://www.edx.org/course/learning-data-introductory-machine-caltechx-cs1156x#!
24
Data Science in Real Life.
https://www.coursera.org/learn/real-life-data-science
Statistical Thinking for Data Science and Analytics Learn how statistics plays a central role in
the data science approach.
https://www.edx.org/course/statistical-thinking-data-science-columbiax-ds101x-0#!
Biostatistics for Big Data Applications.
https://www.edx.org/course/biostatistics-big-data-applications-utmbx-stat101x#!
Probability: Basic Concepts & Discrete Random Variables.
https://www.edx.org/course/probability-basic-concepts-discrete-purduex-416-1x-0
Probability: Distribution Models & Continuous Random Variables.
https://www.edx.org/course/probability-distribution-models-purduex-416-2x-0#!
Intermediary
Machine Learning.
https://www.coursera.org/learn/machine-learning
Intro to Machine Learning.
https://www.udacity.com/course/intro-to-machine-learning--ud120
Machine Learning by Georgia Tech: Supervised, Unsupervised & Reinforcement.
https://www.udacity.com/course/machine-learning--ud262
Practical Machine Learning.
https://www.coursera.org/learn/practical-machine-learning
Machine Learning for Data Science and Analytics.
www.edx.org/course/machine-learning-data-science-analytics-columbiax-ds102x-0#!
Demystifying Biomedical Big Data.
https://www.edx.org/course/demystifying-biomedical-big-data-users-georgetownx-biox-
201-01x#!
Machine Learning With Big Data.
https://www.coursera.org/learn/big-data-machine-learning
Introduction to Computational Thinking and Data Science.
https://www.edx.org/course/introduction-computational-thinking-data-mitx-6-00-2x-5
25
Principles of Machine Learning.
https://www.edx.org/course/principles-machine-learning-microsoft-dat203-2x-2#!
Data Science Essentials.
https://www.edx.org/course/data-science-essentials-microsoft-dat203-1x-2
Statistical Inference and Modeling for High-throughput Experiments.
https://www.edx.org/course/statistical-inference-modeling-high-harvardx-ph525-3x
Model Building and Validation.
https://www.udacity.com/course/model-building-and-validation--ud919
Machine Learning: Unsupervised Learning: Conversations on Analyzing Data.
https://www.udacity.com/course/machine-learning-unsupervised-learning--ud741
Advanced
High-Dimensional Data Analysis.
https://www.edx.org/course/high-dimensional-data-analysis-harvardx-ph525-4x#!
Applied Machine Learning.
https://www.edx.org/course/applied-machine-learning-microsoft-dat203-3x-0#!
Maps and the Geospatial Revolution.
https://www.coursera.org/learn/geospatial
*****
26
V. SPECIALIST CERTIFICATES
V.1 Methods and Statistics in Social Sciences
Methods and Statistics in Social Sciences Specialization: “This Specialization covers research
methods, design and statistical analysis for social science research questions. In the final
Capstone Project, you’ll apply the skills you learned by developing your own research
question, gathering data, and analyzing and reporting on the results using statistical
methods.”
https://www.coursera.org/specializations/social-science
V.2 Study and Survey Design
Survey Data Collection and Analytics Specialization: “This specialization covers the
fundamentals of surveys as used in market research, evaluation research, social science and
political research, official government statistics, and many other topic domains. In six
courses, you will learn the basics of questionnaire design, data collection methods, sampling
design, dealing with missing values, making estimates, combining data from different
sources, and the analysis of survey data. In the final Capstone Project, you’ll apply the skills
learned throughout the specialization by analyzing and comparing multiple data sources.”
https://www.coursera.org/specializations/data-collection
V.3 Quantitative Software: R
Statistics with R Specialization: “In this Specialization, you will learn to analyze and visualize
data in R and created reproducible data analysis reports, demonstrate a conceptual
understanding of the unified nature of statistical inference, perform frequentist and Bayesian
statistical inference and modeling to understand natural phenomena and make data-based
decisions, communicate statistical results correctly, effectively, and in context without
relying on statistical jargon, critique data-based claims and evaluated data-based decisions,
and wrangle and visualize data with R packages for data analysis.”
https://www.coursera.org/specializations/statistics
27
V.4 Data, Economics, and Development Policy
Data, Economics, and Development Policy MicroMasters: “The MicroMasters credential in
Data, Economics, and Development Policy equips learners with the practical skills and
theoretical knowledge to tackle some of the most pressing challenges facing developing
countries and the world’s poor. Through a series of five online courses and in-person exams
learners will gain a strong foundation in microeconomics, development economics,
probability and statistics, and engage with cutting-edge research in the field. The program is
unique in its focus on the practicalities of running randomized evaluations to assess the
effectiveness of social programs and its emphasis on hands-on skills in data analysis.”
JPAL micromasters
https://micromasters.mit.edu/dedp/
V.5 Data Science, Bioinformatics
Data Science: “In this MicroMasters program, you will develop a well-rounded understanding
of the mathematical and computational tools that form the basis of data science and how to
use those tools to make data-driven business recommendations. This MicroMasters program
encompasses two sides of data science learning: the mathematical and the applied.
Mathematical courses cover probability, statistics, and machine learning. You will learn how
to collect, clean and analyse big data using popular open source software will allow you to
perform large-scale data analysis and present your findings in a convincing, visual way.”
https://www.edx.org/micromasters/data-science
Data Science Specialization: “A nine-course introduction to data science, developed and
taught by leading professors. This Specialization covers the concepts and tools you'll need
throughout the entire data science pipeline, from asking the right kinds of questions to
making inferences and publishing results. In the final Capstone Project, you’ll apply the skills
learned by building a data product using real-world data. At completion, students will have a
portfolio demonstrating their mastery of the material.”
https://www.coursera.org/specializations/jhu-data-science
Data Analyst Nanodegree: “We built this program with expert analysts and scientists at
leading technology companies to ensure you master the exact skills necessary to build a
career in data science. Learn to organize data, uncover patterns and insights, make
predictions using machine learning, and clearly communicate critical findings.”
https://www.udacity.com/course/data-analyst-nanodegree--nd002
28
Machine Learning Specialization: “This Specialization introduces you to the exciting, high-
demand field of Machine Learning. Through a series of practical case studies, you will gain
applied experience in major areas of Machine Learning including Prediction, Classification,
Clustering, and Information Retrieval. You will learn to analyze large and complex datasets,
create systems that adapt and improve over time, and build intelligent applications that can
make predictions from data.”
https://www.coursera.org/specializations/machine-learning
Master of Computer Science in Data Science: “This MCS-DS is one of the most affordable
gateways to one of the most lucrative and fastest growing careers of the new millennium. The
MCS-DS builds expertise in four core areas of computer science: data visualization, machine
learning, data mining and cloud computing, in addition to building valuable skill sets in
statistics and information science with courses taught in collaboration with the University’s
Statistics Department and iSchool (ranked #1 among Library and Information Studies
Schools.)”
https://www.coursera.org/university-programs/masters-in-computer-data-science
*****
