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Learn about courses, in a wide range of fields at a variety of institutions, where principles and resources from Project TIER have been used to teach transparent research methods.
Course syllabi, exercises, project instructions and other course documents are available for download.
Regression AnalysisDuke University
In this course students will learn to
- analyze real-world data to answer questions about multivariab…
In this course students will learn to
- analyze real-world data to answer questions about multivariable relationships.
- fit and evaluate linear and logistic regression models.
- assess whether a proposed model is appropriate and describe its limitations.
- use R Markdown to write reproducible reports and GitHub for version control and collaboration.
- communicate results from statistical analyses to a general audience.
Introduction to Archaeological Data ScienceUniversity of Washington
This course is an introduction to basic methods of archaeology and data science. Students will lear…
Introduction to Archaeological Data Science
University of Washington
This course is an introduction to basic methods of archaeology and data science. Students will learn some of the key data science tools used in day-to-day archaeological and cultural heritage work, which are also becoming increasingly valued and popular in a wide variety of other research areas and professions. Students will use these data science tools to tackle fascinating archaeological questions with contemporary relevance. This class will give students hands-on experience and prepare them for quantitative work in many other fields.
Advanced Social PsychologyUniversity of Hong Kong
The purpose of this course is for students to gain an in-depth understanding of the recent developm…
Advanced Social Psychology
University of Hong Kong
The purpose of this course is for students to gain an in-depth understanding of the recent developments in psychological science through the lens of social psychology.
After taking this course, students will:
1. Understand the recent developments in psychological science and the so-called “replication/reproducibility crisis”.
2. Gain an academic overview of main research themes in social-psychology.
3. Summarize, analyze, reflect, and apply classic experiments and findings in social-psychology.
4. Articulate process and findings, both orally and in writing, with discussion of evidence and its implications for the academic field and in everyday life.
5. Experience and lead, hands-on, high-quality academic research using the most recent methodological advances in psychological science conducting a pre-registered replication and extension of a classic study in social-psychology .
- In-depth analysis of a published academic article
- Assessment of experimental scientific methods and evidence (effect-size, confidence-intervals, power, and p-values
- Pre-registration plan
- Data analysis
- Pre-registered replication report (as an academic submission)
Course materials are available on the OSF.
Introduction to Probability and StatisticsSmith College
An application-oriented introduction to modern statistical modeling and inference: study design, de…
Introduction to Probability and Statistics
An application-oriented introduction to modern statistical modeling and inference: study design, descriptive statistics, data visualization, random variables, probability and sampling distributions, point and interval estimates, hypothesis tests, resampling procedures, and multiple regression. A wide variety of applications from the natural and social sciences will be used. Classes meet for lecture/discussion with activities and exercises that emphasize analysis of real data.
Students complete weekly lab assignments in R and RMarkdown, and a final data analysis project. The final project is worth 25% of the course grade, and must be reproducible. Students work in groups to complete the project on a topic of their choice. Students have a number of milestone assignments along the way, including an initial proposal, revised proposal, data file submission, data appendix, and a final technical report. The technical report includes all the code needed to complete the analysis.
Students worked through labs introducing R and RStudio (http://www.science.smith.edu/~amcnamara/sds220/labs/intro_to_r.html) and introducing data analysis (http://www.science.smith.edu/~amcnamara/sds220/labs/intro_to_data.html). Both labs were developed by the OpenIntro Statistics group, and include expository videos explaining some of the topics. The OpenIntro team has created an R package called oilabs, which includes a lab report template that can be accessed through RStudio.
Business AnalyticsUnion College
This is the first year Tomas has taught a new course entitled Business Analytics. The key learning …
This is the first year Tomas has taught a new course entitled Business Analytics. The key learning objective is for students to be able to manipulate and analyze business data. The course is very hands-on with students programming in R Markdown from the first day. The emphasis is on data manipulation: students need to load in data, summarize, reshape, merge and append the data in multiple ways to get insights. In this course students realize that data preparation and manipulation is 90% of the work of an empirical researcher or an analyst. Running a regression or a sophisticated algorithm is the last and fairly straightforward step. The value of any analysis lies mostly in the quality and organization of the data. Using R Markdown forces students to integrate data manipulation and analysis with text. Their final project is always reproducible because it is a knitted R Markdown document. Even if they don't end up using R Markdown in the future (e.g. their thesis), the course gives them programming skills that are essential to reproducible research.
Multiple RegressionSmith College
Theory and applications of regression techniques; linear and nonlinear multiple regression models, …
Theory and applications of regression techniques; linear and nonlinear multiple regression models, residual and influence analysis, correlation, covariance analysis, indicator variables and time series analysis. This course includes methods for choosing, fitting, evaluating and comparing statistical models and analyzes data sets taken from the natural, physical and social sciences.
Students worked in small groups to produce a data analysis on a topic of their choice. The project is 25% of the final course grade. Students had to work in R and RMarkdown, turn in a data appendix, and document all their data cleaning and analysis in their final report.