Mark Tranmer

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Multilevel models for combining macro and micro
data
Unit 5

• Mark Tranmer
• Cathie Marsh Centre for Census and Survey Research

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Introduction

• 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.

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Learning 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.

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Learning 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.

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Levels 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

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Levels 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.

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Multilevel 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

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Multilevel 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?

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Multilevel 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

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Multilevel modelling framework

1. Models that involve the micro data only
2. 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
3. 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).

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Multilevel 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

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Part 1 multilevel models and ESS micro data
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Modelling approaches theory

• Model 1 Single level model e.g. predicting
  chance of voting with age

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Modelling approaches theory

• Model 1 Single level model

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Modelling approaches theory

• Model 2 null model (multilevel) getting a
  sense of where the variation in voter turnout is
  between people or between countries

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Modelling 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.

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Modelling approaches theory

• Model 3 Multilevel Model with varying intercepts

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Modelling approaches theory

• Model 4 Multilevel Model with varying intercepts
  and slopes relationship of age with voting can
  be stronger/weaker in each country

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Model 4 graphical representations
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Using 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

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Using 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

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Setting up the model in MLwiN
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Setting up the model in MLwiN
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Setting up the model in MLwiN
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Setting up the model in MLwiN
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Setting up the model in MLwiN
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Null model (model 2) is now set up
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Estimation type
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Model 2 results
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Model 3 results add cent_age to model by
clicking on add term
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Model 4 set up
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Model 4 results
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Part 2 combining macro and micro data in
multilevel models
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Combining data in mulitlevel models model 5
Main effects
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Combining data in mulitlevel models model 6
interactions
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Model 5 main effects results
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Model 6 Interactions results
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Summary what you have learnt in this session

1. The multilevel model is an extremely useful
   framework for combining macro and micro data
2. Multilevel logistic regression models can be used
   for an outcome with two categories such as voter
   turnout
3. 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.

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Summary 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