By the end of this unit you will be able to:

  • understand the potential for combining macro and micro data to solve specific research questions.
  • test for variation between macro units within an micro-level dataset
  • link a macro level data set to a micro level data set using statistical software
  • understand the different roles of certain weighting schemes for linked micro-macro data
  • produce a weight for linked micro and macro data set, so that each macro unit contributes equally to the analysis
  • produce a weight for a linked micro and macro data set, so that the analysis reflects the population as a whole
  • understand that there are a variety of reasons why the frequency distribution of an individual-level survey data may not match macro-level data for the same macro unit
  • adjust a weight variable to account for differences between the distribution of a key variable in the survey from the known population distribution
  • adjust a weight variable to account for differences, within each macro unit, between the distribution of a key variable in the survey from the known population distribution
  • test a hypothesis that outcomes on a particular micro-level variable depend on both micro and macro variables
  • test a hypothesis that the effect of one micro-level variable on another depends on the value of a macro-level variable

The University of Manchester; Mimas; ESRC; RDI

Countries and Citizens: Unit 4 Combining macro and micro data by Steve Fisher, University of Oxford is licensed under a Creative Commons Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales Licence.