To reflect the wide range of ongoing research across disciplines, we invite submissions that 1) directly examine dynamics of Tweet behavior and Twitter networks, 2) explore the representativeness and validity of Twitter data for making scientific inference, 3) develop new computational methodology for obtaining, processing, or archiving Twitter data, or 4) present applications of Twitter data for studying diverse social phenomena. Conference attendance will be open to anyone interested in learning about the wide array of Twitter data applications in current research at the University. We further seek to facilitate new connections to help U-M scholars create opportunities for future joint research, collaborative grant writing, training and other activities. #UMTweetCon2019 will connect U-M scholars across a diverse set of disciplines in an interdisciplinary exchange about common challenges and lessons learned. University wide conference at the University of Michigan (Ann Arbor, Dearborn, and Flint) on the use of Twitter data in research and analytics. Call for Abstractsĭo you use Twitter data in your research? Then, you are invited to submit an abstract for the first The conference is sponsored by the Social Science and Social Media Collaborative, the Michigan Institute for Data Science, the #Parenting Rackham Interdisciplinary Group, and coordinated by the Center for Political Studies and the Institute for Social Research. #UMTweetCon2019 By Jenny Lee | A Conference on the Use of Twitter Data for Research and Analytics Those using SPSS for Unix or Macintosh should email the instructor at enrolling. Lastly, importing and exporting data between SPSS and other statistical programs (Excel, dBase, SAS) is demonstrated.Īudience: Never before SPSS users who will be using SPSS for Windows. Next, creating aggregated data sets and applying aggregated data to individuals is covered. Then, the two main methods of merging SPSS data files are covered: adding additional variables and adding additional cases. This portion begins with a discussion of subsetting data files by drawing samples, selecting groups and excluding groups from analysis. Attention then turns to temporary transformations, conditional processing of transformations, and repetitive transformations. This portion introduces data management capabilities, including recoding variables (manual and automatic), computing new variables using formulas, and counting occurrences of values within subjects. Finally, it covers a brief introduction to obtaining frequency distributions, descriptive statistics, and cross tabulations of variables. It then addresses defining variables, attaching labels, defining missing values, and various ways to enter data into SPSS.
This portion introduces SPSS for Windows, the menu and the help systems, the three main types of files used, and printing from within SPSS.
Audience: Never before SPSS users who will be using SPSS for Windows.
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