4.4.2. Linking data in Stata

Example 1: Merging micro and macro data files in Stata
How to merge two files microdata.dta and macrodata.dta, both including the macrounit identifier but with no other variable in common.


4.4.3 Linking data in SPSS

Example 2: SPSS syntax
How to merge two files microdata.dta and macrodata.dta, both including the macrounit identifier but with no other variable in common.


4.5.2 Producing weights to equalize the size of each macro unit

Example 3: produce a weight variable
How to produce a weight variable that you can use for analysis where you want each macro unit to contribute equally.


4.5.3 Producing weights to respect to population as a whole

Example 4: producing a weight variable that you can use for analysis where you want to make inferences about the population
How to produce a weight variable for micro units within a given macro unit are weighted according to the true population for that macro unit.


4.6.2.2. Adjusting weights to account for survey error on one variable separately for each macro unit

Example 5: Creating a suitable weight


4.7.1. Starting simple: separate analyses for each macro unit

Example 6: Separate analyses for each macro unit


4.7.2 Regression analysis with both micro and macro variables

Example 8: Regression analysis with both micro and macro variables
Testing whether there is a statistically significant effect of a particular macro level variable on a particular micro level outcome.


4.7.3 Regression analysis to test cross-level interactions

Example 9: Regression analysis to test cross-level interactions


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.