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Multilevel models for combining macro and micro data Unit 5 Mark Tranmer Cathie Marsh Centre for Census and Survey Research Introduction We will see how the ... – PowerPoint PPT presentation

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Title: Mark Tranmer


1
Multilevel models for combining macro and micro
data
Unit 5
  • Mark Tranmer
  • Cathie Marsh Centre for Census and Survey Research

2
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.

3
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.

4
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.

5
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

6
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.

7
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

8
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?

9
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

10
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).

11
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

12
Part 1 multilevel models and ESS micro data
13
Modelling approaches theory
  • Model 1 Single level model e.g. predicting
    chance of voting with age

14
Modelling approaches theory
  • Model 1 Single level model

15
Modelling approaches theory
  • Model 2 null model (multilevel) getting a
    sense of where the variation in voter turnout is
    between people or between countries

16
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.

17
Modelling approaches theory
  • Model 3 Multilevel Model with varying intercepts

18
Modelling approaches theory
  • Model 4 Multilevel Model with varying intercepts
    and slopes relationship of age with voting can
    be stronger/weaker in each country

19
Model 4 graphical representations
20
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

21
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

22
(No Transcript)
23
Setting up the model in MLwiN
24
Setting up the model in MLwiN
25
Setting up the model in MLwiN
26
Setting up the model in MLwiN
27
Setting up the model in MLwiN
28
Null model (model 2) is now set up
29
Estimation type
30
Model 2 results
31
Model 3 results add cent_age to model by
clicking on add term
32
Model 4 set up
33
Model 4 results
34
Part 2 combining macro and micro data in
multilevel models
35
Combining data in mulitlevel models model 5
Main effects
36
Combining data in mulitlevel models model 6
interactions
37
Model 5 main effects results
38
Model 6 Interactions results
39
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.

40
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
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