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This is an archive of our past training offerings. We are looking to include workshops on topics not yet covered here. Is there something not currently on the list? Send us a proposal.
This workshop will familiarize attendees with the Undergraduate Research Apprentice Program (URAP) and the benefits and challenges of working with undegraduate research assistants. Expect to learn about:
URAP logistics and bureaucracy
Recruiting undergraduates
Organizing and the Qualitative Data Analysis Process
Organization in the Qualitative Data Analysis Process
This introductory workshop will introduce attendees to the reason for using a qualitative data analysis package for the coding and analysis process. We will begin by discussing the distinction between coding and analysis, the benefits of using a QDA package, and then view a brief demonstration of a specific program.
The workshop will be an intensive two-day introduction to R using RStudio. Topics will include
This intensive course will give users a familiarity with various automated text analysis approaches including classification (machine learning), clustering/topic modelling, dimension reduction, and regression. The intensive will begin with a theoretical explanation of each approach.
The workshop will cover (in a teaching computer lab environment, using the General Social Survey as a common dataset):
-Getting data into Stata,
-Stata specific tools and resources (do files, logs, help files, etc)
-Coding and cleaning data (making new variables from old variables; labeling variables and values, etc)
-Collapsing data
This two-hour workshop will identify common uses by researchers for secondary data into their work, different “flavors” of secondary data and their strengths and weaknesses, where you can find secondary data, and Berkeley resources to help you locate and use data.
This workshop will familiarize attendees with the Undergraduate Research Apprentice Program (URAP) and the benefits and challenges of working with undegraduate research assistants. Expect to learn about:
URAP logistics and bureaucracy
Recruiting undergraduates
Get an orientation to the basics of working with social science data in python. We'll start from the ground up, from the basics of naming variables, making decisions, and repeating operations to making informative (or at least fun) pictures on your screen.
An intro to the basics that instructors often assume you know, but probably never had good instruction on! After this course, you should be able to more easily start learning to program (i.e., in R or python), or communicate better with your collaborators who are programming.
Big cities, big data:
Big opportunity for computational social science
UC-Berkeley D-Lab // Hilton Union Square
Sign up by August 1
D-Lab will be hosting two workshops of the ICPSR Summer Program in Quantitative Methods of Social Research. Payment and registration are handled by ICPSR.
D-Lab will be hosting two workshops of the ICPSR Summer Program in Quantitative Methods of Social Research. Payment and registration are handled by ICPSR.
This workshop will be open to anyone interested in having the guidance, feedback and structure for writing a grant. Potential participants could be faculty who have not written an NIH grant before, postdocs or adjunct faculty, advanced graduate students, or even early stage graduate students who want to put together a dissertation grant.
This two part series includes how to organize and analyze qualitative data, and how to use Atlas.TI in the process. Researchers new to qualitative data analysis are encouraged to attend both sessions. More experienced researchers may choose to attend the second session only to learn more advanced techniques with Atlas.TI.
The workshop will cover (in a teaching computer lab environment, using the the General Social Survey as a common dataset): Getting data into StataStata specific tools and resources (do files, logs, help files, etc)Coding and cleaning data (making new variables from old variables; labeling variables and values, etc) Collapsing dataDescriptive statistics (tables and graphs)CrosstabsCorrelation ta
This INTENSIVE course will give users a familiarity with various automated text analysis approaches including classification (machine learning), clustering/topic modelling, dimension reduction, and regression.
The INTENSIVE will begin with a theoretical explanation of each approach and then demonstrate each in R, finishing with some practice work for participants.
This 2 day intensive workshop will cover basic beginner and intermediate concepts, commands and analysis in R.
This two part series includes how to organize and analyze qualitative data, and how to use NVivo in the process. Researchers new to qualitative methods are encouraged to attend both sessions. More experienced researchers may choose to attend the second session only to learn more advanced techniques with NVivo.
