Combining and Analyzing Complex Data
- Skills in survey design and tool development
- Skills in data collection from different sources
- Basic EstimationAfter completing Modules 1 and 2 of this course you will understand how to estimate descriptive statistics, overall and for subgroups, when you deal with survey data. We will review software for estimation (R, Stata, SAS) with examples for how to estimate things like means, proportions, and totals. You will also learn how to estimate parameters in linear, logistic, and other models and learn software options with emphasis on R. Module 3 and 4 discuss how you can add additional data to your analysis. This requires knowing about record linkage techniques, and what it takes to get permission to link data.
- ModelsModule 2 covers how to estimate linear and logistic model parameters using survey data. After completing this module, you will understand how the methods used differ from the ones for non-survey data. We also cover the features of survey data sets that need to be accounted for when estimating standard errors of estimated model parameters.
- Record LinkageModule starts with the current debate on using more (linked) administrative records in the U.S. Federal Statistical System, and a general motivation for linking records. Several examples will be given on why it is useful to link data. Challenges of record linkage will be discussed. A brief overview over key linkage techniques is included as well.
- EthicsThis module will discuss key issues in obtaining consent to record linkage. Failure to consent can lead to bias estimates. Current research examples will be given as well as practical suggestions on how to obtain linkage consent.
- Archilla, A. R., & Madanat, S. (2001). Estimation of Rutting Models by Combining Data from Different Sources. Journal of Transportation Engineering, 127(5), 379. https://doi.org/10.1061/(ASCE)0733-947X(2001)127:5(379)
- Hensher, D., Louviere, J., & Swait, J. (1998). Combining sources of preference data. Journal of Econometrics, (1–2), 197. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.eee.econom.v89y1998i1.2p197.221
- Laura Camfield. (2018). Rigor and Ethics in the World of Big-team Qualitative Data: Experiences From Research in International Development. https://doi.org/10.1177/0002764218784636
- Conti, G., & Badin, G. (2019). Statistical Measures and Selective Decay Principle for Generalized Euler Dynamics. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsarx&AN=edsarx.1907.05069