Research Methods

Virtual & Enhanced Reality in the Library Space- a Brownbag with Matt Bernhardt

Jun 21, 12:00pm to 1:00pm

Location: 

E25-401

Abstract TBA

 

Information Science Brown Bag talks, hosted by the Program on Information Science, consists of regular discussions and brainstorming sessions on all aspects of information science and uses of information science and technology to assess and solve institutional, social and research problems. These are informal talks. Discussions are often inspired by real-world problems being faced by the lead discussant.  We will provide lunch, please bring your own drink and your questions.

IAP- Managing Confidential Research Data

Apr 25, 12:00pm to 3:00pm

Location: 

E25-401

This class is on research design and research methods related to confidential information. In this class we’ll discuss how to recognize sensitive information; prepare for IRB review; reduce risks in data collection; evaluate information threats and vulnerabilities; organize and store sensitive data;  understand data use agreements; and create data management plans.  If you’re a researcher, whether a late career grad student, faculty, or professional research staff, this class is for you.

 

Altman M, Amos B, McDonald MP, Smith D.

Revealing Preferences: Why Gerrymanders are Hard to Prove, and What to Do about It

. Social Science Research Network [Internet]. Working Paper. Publisher's VersionAbstract
Gerrymandering requires illicit intent. We classify six proposed methods to infer the intent of a redistricting authority using a formal framework for causal inferences that encompasses the redistricting process from the release of census data to the adoption of a final plan. We argue all proposed techniques to detect gerrymandering can be classified within this formal framework. Courts have, at one time or another, weighed evidence using one or more of these methods to assess racial or partisan gerrymandering claims. We describe the assumptions underlying each method, raising some heretofore unarticulated critiques revealed by laying bare their assumptions. We then review how these methods were employed in the 2014 Florida district court ruling that the state legislature violated a state constitutional prohibition on partisan gerrymandering, and propose standards that advocacy groups and courts can impose upon redistricting authorities to ensure they are held accountable if they adopt a partisan gerrymander.
Altman M, Amos B, McDonald MP, Smith DA.

Revealing Preferences: Why Gerrymanders are Hard to Prove, and What to Do about It

 

. [Internet]. Working Paper. Download Paper from SSRNAbstract
Gerrymandering requires illicit intent. We classify six proposed methods to infer the intent of a redistricting authority using a formal framework for causal inferences that encompasses the redistricting process from the release of census data to the adoption of a final plan. We argue all proposed techniques to detect gerrymandering can be classified within this formal framework. Courts have, at one time or another, weighed evidence using one or more of these methods to assess racial or partisan gerrymandering claims. We describe the assumptions underlying each method, raising some heretofore unarticulated critiques revealed by laying bare their assumptions. We then review how these methods were employed in the 2014 Florida district court ruling that the state legislature violated a state constitutional prohibition on partisan gerrymandering, and propose standards that advocacy groups and courts can impose upon redistricting authorities to ensure they are held accountable if they adopt a partisan gerrymander.

IAP 2015: Getting Started, Getting Funded

Jan 08, 1:00pm to 5:00pm

Increasingly, conducting innovative research requires resources that exceed those readily on-hand to the individual scholar. You can use research funding to access a wider set of research methods, to accelerate your research project, expand its scope and depth, and increase its impact. This short course provides an overview of the types and sources of funding available for research support, and introduces the fundamental elements of planning, proposal writing, and management for "sponsored" projects.

Altman M, McDonald MP. How to set a random clock: remarks on Earnest (2006). PS: Political Science and Politics [Internet]. 2006;39(1):795. Publisher's VersionAbstract
We read with interest David C. Earnest's recent (July 2006) PS article about the pedagogical challenges surrounding the statistical computation of pseudo-random numbers (PRNGs). We write to clarify some issues regarding the testing and setting of PRNG seeds, and to direct readers' attention to a set of resources for configuring computationally accurate simulations and statistical analyses.
Altman M, McDonald MP. Replication with Attention to Numerical Accuracy. Political Analysis [Internet]. 2003;11:302–307. Publisher's VersionAbstract
Numerical issues matter in statistical analysis. Small errors occur when numbers are translated from paper and pencil into the binary world of computers. Surprisingly, these errors may be propagated and magnified through binary calculations, eventually producing statistical estimates far from the truth. In this replication and extension article, we look at one method of verifying the accuracy of statistical estimates by running these same data and models on multiple statistical packages. We find that for two published articles, Nagler (1994, American Journal of Political Science 38:230-255) and Alvarez and Brehm (1995, American Journal of Political Science 39:1055-1089), results are dependent on the statistical package used. In the course of our replications, we uncover other pitfalls that may prevent accurate replication, and make recommendations to ensure the ability for future researchers to replicate results.
Altman M. Computational Modeling. In Kurian GT The Encyclopedia of Political Science CQ Press; 2011. pp. 291–292. Publisher's VersionAbstract
In political economy, computational models are used to simulate the behavior of institutions or individuals. Researchers use these models to explore emergent patterns in the behavior of individuals and institutions over time. Computational models are used as a complement to mathematical models -and as a form of independent theory construction in their own right.
Altman M, Gill J, McDonald MP. A Comparison of the Numerical Properties of EI Methods. In King G, Rosen O, Tanner MA Ecological Inference: New Methodological Strategies Cambridge: Cambridge University Press; 2005. pp. 383–409. Publisher's VersionAbstract
All statistical techniques place limitations on the types of data and the range of inferences that can be accomodated. All computional implementations of these statistical techniques impose further limitations due to algorithmic and low-level computational implementations. Failure to understand these issues can lead to gross misperceptions and seriously incorrect inferences. In this work we examine the numerical accuracy of King's (1997) approach, to ecological inference by using data perturbation, error analysis, and comparative reliability assessment.

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