Guide to Course Selection
2019 PSI International
Korea: July 1 - July 26, 2019
Havana, Cuba: August 5 - August 16, 2019
The PSI International Summer Institute for Data Science, Survey Methodology and Interdisciplinary Research & Applications (PSI International) courses are offered mostly in four-week formats for university students and two-week workshops for professionals. Participants can tailor course elections to individual time constraints and interests. Four-week courses (2 credit hours) provide in-depth and intensive coverage of a topic. These courses include readings, exercises, and examinations and provide participants with an opportunity to practice survey research techniques in teams. One-week and two-week intensive workshops designed mostly for professionals offer hands-on experience of applied survey research methods, applied sampling, and subject-specific survey methods, and include readings, homework, and team workshops.
Participants new to survey research or with little statistical background will find helpful Introduction to Statistics, Interdisciplinary Survey Research Methodology, Introduction to Biostatistics and Computer Analysis of Survey Data. Participants with both statistical and survey backgrounds can choose Applied Survey Sampling, Bayesian Methods. Participants in graduate programs or those with advanced degrees can choose Advanced Data Science, Advanced Statistics, Advanced Biostatistics, and Total Survey Errors and Costs.
Most courses provide real-life examples to illustrate fundamentals of data science, survey research methodology and statistics and offer opportunities of team or individual projects that may be considered for applying to the da Vinci Grant Program. The da Vinci Grant Program awards innovative interdisciplinary survey research proposals that take advantage of survey methodology and statistics in basic and applied science and technology, finance, business, trade, agriculture, public health, and education, among other fields. Grant recipients will conduct interdisciplinary survey research projects under year-long one-on-one mentorship with PSI and hosting university faculty advisers. Some awardees with the most outstanding proposals will have their travel supported to present the research paper in renowned international conferences of data science, statistics and survey methodology.
Course List
(4-Week University Courses)
(Fundamentals)
Interdisciplinary Survey Research Methodology
Introduction to Statistics
Introduction to Biostatistics
Introduction to Data Science
Applied Sampling
Computer Analysis of Survey Data
(Advanced Courses)
Bayesian Methods
Advanced Statistics
Advanced Biostatistics
Advanced Data Science
Total Survey Errors and Costs
Statistical Use of Big Data and Administrative Records
(2-Week Intensive Workshops for Professionals)
Business Survey Methodology/Marketing Statistics
Health Survey Methodology/Biostatistics
Education Survey Methodology/Education Statistics
Census Methodology
Applied Sampling
Applied Survey Methodology
Applied Data Analysis
Applied Data Science
(1-Week Intensive Workshops for Professionals)
Survey Questionnaire Design
Data Collection Methods
Data Visualization in R
Big Data Analytics
Applied Bayesian Methods
(4-Week University Course Description)
Interdisciplinary Survey Research Methodology - 2 credit hours
(No prerequisite) This course is designed to provide an overview of the theory and practice of interdisciplinary sample survey research methods applicable to most academic disciplines and professions where statistical analysis and quantitative methods are required. For example, survey research methods are used in North America and Europe as a primary tool of collecting and analyzing survey and census data that inform public policy in economics, marketing, trade, education, and public health as well as science and information technology. The course covers fundamentals regarding each phase of the sample survey lifecycle from planning and preparation to implementation, analysis, and dissemination. Topics covered include: questionnaire design, sampling design, data collection methods, data analysis, and quality control from the total survey error perspectives which are inclusive of measurement, nonresponse, and coverage errors. For each phase of the survey lifecycle, the challenges faced when designing and implementing surveys in international and multicultural settings will also be discussed. This will include challenges faced in translation and adaptation of questionnaires, implementing proper sampling techniques, quality assurance in multi-national studies, and cultural variations in response styles. Various strategies to deal with typically encountered challenges will be addressed.
Examples will be drawn from the government surveys and censuses, academic surveys and large-scale international surveys. In addition to exercises and assignments that will be geared to each topic, course participants are required to prepare and present a team class project that involves responding to the Da Vinci Grant Program, which awards grants to proposals for interdisciplinary research that takes advantage of science, technology, finance, business, and survey research methodology, among others. The project will provide an opportunity to apply the course material in an integrated fashion. To simulate real-life survey contexts, course participants will work in teams for the class project.
Introduction to Statistics - 2 credit hours
(No prerequisite) Research in the sciences has increasingly come to rely on statistical concepts in the presentation and analysis of data. The application of a wide variety of research designs, including both experimental and non-experimental designs, requires real understanding of fundamental statistical concepts. The primary purpose of this course is to provide a rigorous introduction to statistics in the context of these differing designs. Instruction will emphasize practical understandings and uses of statistics rather than theoretical derivations. Its main objective is to develop a deep, conceptual understanding of statistical reasoning rather than to foster rote application of statistical formulae. This course exposes students to the fundamentals of statistics and assumes little to no previous knowledge of the topics. First, univariate descriptive statistics along with numerical and graphical summaries will be reviewed. Students learn level of measurement, measures of central tendency and variability, probability and the normal distribution, and properties of samples and populations. Then inferential statistics will be the main focus of the course through the use of hypothesis testing and confidence intervals in many 1-sample and 2-sample settings. Students learn Chi-Square test, Fisher’s Exact Test, nonparametric tests of significance, ANOVA, and correlation. Slightly more advanced analyses such as regression and ANCOVA will be introduced.
Advanced Statistics - 2 credit hours
(Prerequisite - Introduction to statistics, or Statistics and probability) This course covers in-depth coverage of the multiple regression model, including the statistical theory underlying it, methods to estimate it, and methods to diagnose and correct problems with it. Topics covered include maximum likelihoold estimation, bootstrapping and robust estimation, and contemporary Bayesian estimation of the regression model, diagnosing and correctin for multicollinearity, nonormal heteroscedastic, and autocorrelated errors, dealing with missing data. The course is accompanied by a lab section which teaches application of these ideas using a statistical software. In addition to reading assignments associated with each topic, students are required to prepare and present a class project that involves responding to the Da Vinci Grant Program, awarding interdisciplinary research proposals that take advantage of science, technology, finance, business, agriculture, and survey research methodology, among others. Students will be asked to apply a regression model for answering real-life statistical issues in DPRK sectors of science, technology, business, international trade, agriculture, or the student’s chosen field of interests.
Applied Sampling - 2 credit hours
(Prerequisite - Introduction to statistics, or Statistics and probability) Applied Survey Sampling will cover the fundamental techniques used in sampling practice: simple random sampling, cluster sampling, stratification, systematic selection, and probability proportional to size sampling. The course will also cover sampling frames, cost models, sampling error estimation techniques, and compensating for nonresponse. It focuses on design of survey samples and estimation of descriptive statistics rather than the analysis of collected data, the topic addressed in computer analysis of survey data. Emphasis is on practical considerations rather than on theoretical derivations, although understanding of principles requires review of statistical results for sample surveys. Examples will be drawn mostly from sampling human populations. In addition to exercises and assignments associated with each topic, students are required to prepare and present a team sampling project that involves responding to the Da Vinci Grant Program, awarding interdisciplinary research proposals that take advantage of science, technology, finance, business, agriculture, and survey research methodology, among others.
Computer Analysis of Survey Data - 2 credit hours
(No prerequisite) The course begins with a broad overview of research designs frequently used by survey researchers. It then focuses upon measures of sample variability, unbiased estimates of sampling error, kinds of sampling designs, and sampling distributions of sums, means, and percents for random samples. A short paper will be assigned in which students will be asked to analyze data from a previously conducted survey and to report their results using principles of sampling statistics learned in the course. In the last part of the course, data analytic techniques most commonly used in the context of these research designs are presented from the perspective of sampling statistics. Topics include z-tests and t-tests for one and two groups, correlation, and regression analysis. Additional course topics include normal approximations, measurement error, hypothesis testing, probability samples, and the calculation of sample size for specified precision levels.
Students will have hands-on experience in computer lab, learning application of methods taught in this course using statistical software (e.g, open-source R-software, SAS, and SPSS) to obtain results from complex sample survey data. Some attention to interpretation of results will be included. The course will cover data file preparation and manipulation, exploring data structure preparatory to index construction, index construction and evaluation, data exploration using descriptive and data visualization techniques, bivariate and multivariate regression and logistics analyses, and contingency table analysis. In addition to exercises and assignments associated with each topic, students are required to prepare and present a team data analysis project that involves responding to the Da Vinci Grant Program, awarding interdisciplinary research proposals that take advantage of science, technology, finance, business, agriculture, and satistics, among others.
Participants new to survey research or with little statistical background will find helpful Introduction to Statistics, Interdisciplinary Survey Research Methodology, Introduction to Biostatistics and Computer Analysis of Survey Data. Participants with both statistical and survey backgrounds can choose Applied Survey Sampling, Bayesian Methods. Participants in graduate programs or those with advanced degrees can choose Advanced Data Science, Advanced Statistics, Advanced Biostatistics, and Total Survey Errors and Costs.
Most courses provide real-life examples to illustrate fundamentals of data science, survey research methodology and statistics and offer opportunities of team or individual projects that may be considered for applying to the da Vinci Grant Program. The da Vinci Grant Program awards innovative interdisciplinary survey research proposals that take advantage of survey methodology and statistics in basic and applied science and technology, finance, business, trade, agriculture, public health, and education, among other fields. Grant recipients will conduct interdisciplinary survey research projects under year-long one-on-one mentorship with PSI and hosting university faculty advisers. Some awardees with the most outstanding proposals will have their travel supported to present the research paper in renowned international conferences of data science, statistics and survey methodology.
Course List
(4-Week University Courses)
(Fundamentals)
Interdisciplinary Survey Research Methodology
Introduction to Statistics
Introduction to Biostatistics
Introduction to Data Science
Applied Sampling
Computer Analysis of Survey Data
(Advanced Courses)
Bayesian Methods
Advanced Statistics
Advanced Biostatistics
Advanced Data Science
Total Survey Errors and Costs
Statistical Use of Big Data and Administrative Records
(2-Week Intensive Workshops for Professionals)
Business Survey Methodology/Marketing Statistics
Health Survey Methodology/Biostatistics
Education Survey Methodology/Education Statistics
Census Methodology
Applied Sampling
Applied Survey Methodology
Applied Data Analysis
Applied Data Science
(1-Week Intensive Workshops for Professionals)
Survey Questionnaire Design
Data Collection Methods
Data Visualization in R
Big Data Analytics
Applied Bayesian Methods
(4-Week University Course Description)
Interdisciplinary Survey Research Methodology - 2 credit hours
(No prerequisite) This course is designed to provide an overview of the theory and practice of interdisciplinary sample survey research methods applicable to most academic disciplines and professions where statistical analysis and quantitative methods are required. For example, survey research methods are used in North America and Europe as a primary tool of collecting and analyzing survey and census data that inform public policy in economics, marketing, trade, education, and public health as well as science and information technology. The course covers fundamentals regarding each phase of the sample survey lifecycle from planning and preparation to implementation, analysis, and dissemination. Topics covered include: questionnaire design, sampling design, data collection methods, data analysis, and quality control from the total survey error perspectives which are inclusive of measurement, nonresponse, and coverage errors. For each phase of the survey lifecycle, the challenges faced when designing and implementing surveys in international and multicultural settings will also be discussed. This will include challenges faced in translation and adaptation of questionnaires, implementing proper sampling techniques, quality assurance in multi-national studies, and cultural variations in response styles. Various strategies to deal with typically encountered challenges will be addressed.
Examples will be drawn from the government surveys and censuses, academic surveys and large-scale international surveys. In addition to exercises and assignments that will be geared to each topic, course participants are required to prepare and present a team class project that involves responding to the Da Vinci Grant Program, which awards grants to proposals for interdisciplinary research that takes advantage of science, technology, finance, business, and survey research methodology, among others. The project will provide an opportunity to apply the course material in an integrated fashion. To simulate real-life survey contexts, course participants will work in teams for the class project.
Introduction to Statistics - 2 credit hours
(No prerequisite) Research in the sciences has increasingly come to rely on statistical concepts in the presentation and analysis of data. The application of a wide variety of research designs, including both experimental and non-experimental designs, requires real understanding of fundamental statistical concepts. The primary purpose of this course is to provide a rigorous introduction to statistics in the context of these differing designs. Instruction will emphasize practical understandings and uses of statistics rather than theoretical derivations. Its main objective is to develop a deep, conceptual understanding of statistical reasoning rather than to foster rote application of statistical formulae. This course exposes students to the fundamentals of statistics and assumes little to no previous knowledge of the topics. First, univariate descriptive statistics along with numerical and graphical summaries will be reviewed. Students learn level of measurement, measures of central tendency and variability, probability and the normal distribution, and properties of samples and populations. Then inferential statistics will be the main focus of the course through the use of hypothesis testing and confidence intervals in many 1-sample and 2-sample settings. Students learn Chi-Square test, Fisher’s Exact Test, nonparametric tests of significance, ANOVA, and correlation. Slightly more advanced analyses such as regression and ANCOVA will be introduced.
Advanced Statistics - 2 credit hours
(Prerequisite - Introduction to statistics, or Statistics and probability) This course covers in-depth coverage of the multiple regression model, including the statistical theory underlying it, methods to estimate it, and methods to diagnose and correct problems with it. Topics covered include maximum likelihoold estimation, bootstrapping and robust estimation, and contemporary Bayesian estimation of the regression model, diagnosing and correctin for multicollinearity, nonormal heteroscedastic, and autocorrelated errors, dealing with missing data. The course is accompanied by a lab section which teaches application of these ideas using a statistical software. In addition to reading assignments associated with each topic, students are required to prepare and present a class project that involves responding to the Da Vinci Grant Program, awarding interdisciplinary research proposals that take advantage of science, technology, finance, business, agriculture, and survey research methodology, among others. Students will be asked to apply a regression model for answering real-life statistical issues in DPRK sectors of science, technology, business, international trade, agriculture, or the student’s chosen field of interests.
Applied Sampling - 2 credit hours
(Prerequisite - Introduction to statistics, or Statistics and probability) Applied Survey Sampling will cover the fundamental techniques used in sampling practice: simple random sampling, cluster sampling, stratification, systematic selection, and probability proportional to size sampling. The course will also cover sampling frames, cost models, sampling error estimation techniques, and compensating for nonresponse. It focuses on design of survey samples and estimation of descriptive statistics rather than the analysis of collected data, the topic addressed in computer analysis of survey data. Emphasis is on practical considerations rather than on theoretical derivations, although understanding of principles requires review of statistical results for sample surveys. Examples will be drawn mostly from sampling human populations. In addition to exercises and assignments associated with each topic, students are required to prepare and present a team sampling project that involves responding to the Da Vinci Grant Program, awarding interdisciplinary research proposals that take advantage of science, technology, finance, business, agriculture, and survey research methodology, among others.
Computer Analysis of Survey Data - 2 credit hours
(No prerequisite) The course begins with a broad overview of research designs frequently used by survey researchers. It then focuses upon measures of sample variability, unbiased estimates of sampling error, kinds of sampling designs, and sampling distributions of sums, means, and percents for random samples. A short paper will be assigned in which students will be asked to analyze data from a previously conducted survey and to report their results using principles of sampling statistics learned in the course. In the last part of the course, data analytic techniques most commonly used in the context of these research designs are presented from the perspective of sampling statistics. Topics include z-tests and t-tests for one and two groups, correlation, and regression analysis. Additional course topics include normal approximations, measurement error, hypothesis testing, probability samples, and the calculation of sample size for specified precision levels.
Students will have hands-on experience in computer lab, learning application of methods taught in this course using statistical software (e.g, open-source R-software, SAS, and SPSS) to obtain results from complex sample survey data. Some attention to interpretation of results will be included. The course will cover data file preparation and manipulation, exploring data structure preparatory to index construction, index construction and evaluation, data exploration using descriptive and data visualization techniques, bivariate and multivariate regression and logistics analyses, and contingency table analysis. In addition to exercises and assignments associated with each topic, students are required to prepare and present a team data analysis project that involves responding to the Da Vinci Grant Program, awarding interdisciplinary research proposals that take advantage of science, technology, finance, business, agriculture, and satistics, among others.
