Title: Mark Tranmer
1Multilevel models for combining macro and micro
data
Unit 5
- Mark Tranmer
- Cathie Marsh Centre for Census and Survey Research
2Introduction
- We will see how the multilevel model provides a
framework for combining individual level survey
data with aggregate group level data. - We illustrate this with an example where
individual level data from the European Social
Survey are combined with country level data from
the Eurostat New Cronos data. - The dependent variable in our example is voter
turnout in the most recent election in their
country of residence.
3Learning objectives (1)
- To introduce the idea of multilevel modelling
- To explain why multilevel modelling is useful
when linking macro and micro data. - To present the kinds of substantive research
questions that can be answered using this
approach - To outline software that permits multilevel
models to be fitted.
4Learning objectives (2)
- To give an example of linking micro and macro
data in the multilevel model framework by
combining the ESS micro data with country-level
macro data from Eurostat New Cronos - To briefly outline the various multilevel models
in this context - To explain how interactions between aggregate
(macro) and individual level (micro) measures
work in these models and why they might answer
important substantive research questions.
5Levels of analysis and inference
- Traditional regression models are used to carry
out an analysis at a single level. - Such as the individual (person level) with
individual level data - Or at the group (country level) with aggregate
data. - If we do an individual level analysis we can make
individual level inferences but, without group
level information such inferences may be made out
of the context in which the processes occur. - Sometimes this is referred to the atomistic
fallacy - Ideally we want to do the analysis in context
6Levels of analysis and inference
- We could also do a group (country level)
analysis. For example relating the voting in
each European country with the unemployment rate
in that country. - This would tell us whether countries with higher
unemployment tended to have higher (or lower)
levels of voter turnout. - But it wouldnt tell us whether unemployed people
were more (or less) likely to vote than employed
people. - To make such an inference about individuals from
a group level analysis would be an example of the
ecological fallacy. - In general the results of analyses carried out at
the group level do not apply at the individual
level.
7Multilevel models
- Multilevel models allow us to consider the
individual level and the group level in the same
analysis, rather than having to choose one or the
other. - For example we can consider the individual and
the country level in the same analysis - An alternative is to include dummy variables for
each of the groups (i.e. countries in the
analysis). A so called fixed effects approach. - However multilevel models have several advantages
over this approach
8Multilevel models
- 1. They provide an ideal framework for combining
data from several sources, such as individual
level survey data (micro data) and country level
aggregate data (macro data). - 2. They allow sophisticated hypotheses to be
tested without the need to add a lot of extra
variables and interactions to the model. E.g. it
is relatively straight forward to consider a
research question such as this is the
association of age with voter turnout stronger in
some countries than others?
9Multilevel modelling framework
- The current example involves individual level
micro data from the European Social Survey - And country level aggregate macro data from the
Eurostat New Cronos. - There are basically three ways of fitting
multilevel models for voter turnout with these
data
10Multilevel modelling framework
- Models that involve the micro data only
- Models that combine micro data and macro data and
assess the additional impact of the variables
from the macro dataset to explain variations in
voter turnout - Models that interact variables on the micro data
and macro data, such as whether or not someone is
unemployed (micro data) with the long term
unemployed in the country (macro data).
11Multilevel modelling software
- We will use software called MLwiN.
- Although to some extent SPSS can be used for
multilevel modelling, MLwiN is more flexible and
has better graphics and so on. - More details of MLwiN at www.cmm.bristol.ac.uk
- MLwiN is being made free to academics
12Part 1 multilevel models and ESS micro data
13Modelling approaches theory
- Model 1 Single level model e.g. predicting
chance of voting with age
14Modelling approaches theory
- Model 1 Single level model
15Modelling approaches theory
- Model 2 null model (multilevel) getting a
sense of where the variation in voter turnout is
between people or between countries
16Modelling approaches theory
- Model 3 Multilevel Model with varying
intercepts. - Relating age to voting and allowing overall
turnout to be higher/lower in each European
country.
17Modelling approaches theory
- Model 3 Multilevel Model with varying intercepts
18Modelling approaches theory
- Model 4 Multilevel Model with varying intercepts
and slopes relationship of age with voting can
be stronger/weaker in each country
19Model 4 graphical representations
20Using MLwiN to read in the data and set up the
binomial model
- We will set up a binomial model in MLwiN and
estimate some multilevel models (models 2-4)
using the ESS micro data only - We will use an MLwiN worksheet called
- Lmmd6.ws
21Using Mlwin to read in the data and set up the
binomial model
- Open MLwiN by locating it in the programmes
listed in the windows start menu or by clicking
on the MLwiN icon on your desktop. - The default worksheet size for this exercise is
5000 cells which is too small to permit the
analysis. However, it is easy to increase the
worksheet size. - To do this go to options and make the worksheet
10000 cells (change from 5000). NB Do not save
worksheet when prompted. - Now choose data manipulation gt names
22(No Transcript)
23Setting up the model in MLwiN
24Setting up the model in MLwiN
25Setting up the model in MLwiN
26Setting up the model in MLwiN
27Setting up the model in MLwiN
28Null model (model 2) is now set up
29Estimation type
30Model 2 results
31Model 3 results add cent_age to model by
clicking on add term
32Model 4 set up
33Model 4 results
34Part 2 combining macro and micro data in
multilevel models
35Combining data in mulitlevel models model 5
Main effects
36Combining data in mulitlevel models model 6
interactions
37Model 5 main effects results
38Model 6 Interactions results
39Summary what you have learnt in this session
- The multilevel model is an extremely useful
framework for combining macro and micro data - Multilevel logistic regression models can be used
for an outcome with two categories such as voter
turnout - We can then fit a series of models to extent the
nature and extent of individual and country level
variations in voter turnout. We can use software
such as MLwiN to do this.
40Summary what you have learnt in this session
- 4. We can then estimate multilevel models with
ESS micro data only - We can then combine micro and macro data by
adding variables from Eurostat New Cronos to
model - Finally we can also interact individual level ESS
variables with country level variables from new
Cronos data