EES2004 Data Confrontation Seminar - PowerPoint PPT Presentation

1 / 14
About This Presentation
Title:

EES2004 Data Confrontation Seminar

Description:

Analyzing electoral utilities ... Regress electoral utility on the independent variable to be transformed ... electoral utility items for 6 parties (var081 to var086) ... – PowerPoint PPT presentation

Number of Views:46
Avg rating:3.0/5.0
Slides: 15
Provided by: eeshom
Category:

less

Transcript and Presenter's Notes

Title: EES2004 Data Confrontation Seminar


1
Analyzing electoral utilities by way of a stacked
data-matrix
  • Cees van der Eijk
  • (University of Nottingham)
  • cees.vandereijk_at_nottingham.ac.uk

2
Analyzing electoral utilities
  • The philosophy of analyzing party choice via
    electoral utilities has been described in detail
    elsewhere
  • Tillie 1995
  • van der Eijk Franklin 1996 (Ch. 20)
  • van der Eijk et al. 2005
  • The procedure described in this document is
    geared towards SPSS. Stacking in STATA is
    somewhat easier, but there, too, the construction
    of independent variables requires separate
    attention

3
Constructing a stacked data-file
  • for every respondent the same question has been
    asked for each of a set of parties (e.g.,
    electoral utilities), each of these is a separate
    variable (column) in the data-matrix.
  • These separate variables are to be stacked in
    order to analyze them as a single dependent
    variable
  • Stacking involves the transformation of a file
    where records are respondents into a file where
    the records are respondent party combinations
  • Analyzing the stacked dependent variable requires
    the independent variables to be also defined in
    respondentparty terms, and to be stacked as well

4
Constructing a stacked datafile
  • If the data pertain to various countries (as is
    the case in EES data) the following procedure has
    to be performed for each country separately. If
    one would like to analyze the data from all
    countries simultaneously, this can be done by
    pooling the stacked data-files of the various
    countries (in SPSS by merge filesgtadd cases)
  • The procedure has to be performed simultaneously
    for all dependent and independent variables. If
    one wants to add another independent in a later
    stage, the process has to be started all over
    again

5
Sequence of steps
  • Identify dependent and independent variables. The
    dependent variable usually does not require any
    special treatment before stacking
  • Insert in the unstructured dataset a set of
    variables for the identification of the stacks
    (i.e., in our case parties)
  • If necessary transform the independent variables
    into an appropriate form
  • Use the Restructure option in the SPSS data menu
    for the actual stacking

6
Identification of stacks
  • Create as many identifying variables as there are
    parties. These variables have the same value for
    each respondent in the unstructured data-file
    (they are thus constants). In the case of, e.g.,
    6 parties
  • compute p11.
  • compute p22.
  • compute p33.
  • compute p44.
  • compute p55.
  • compute p66.
  • These variables will also be stacked, in order to
    yield a single identifier for parties in the
    stacked file. In the restructuring procedure in
    SPSS this stacked variable can be named at will.

7
Transformation of independents
  • Independent variables in the stacked file should
    (often) be of a nature that they pertain to a
    respondentparty combination. In other words
    they should reflect a relationship between a
    voter and a party. This can be done in different
    ways
  • Distances
  • i.e. between voter and each of the parties on the
    L/R scale, pro/anti EU scale (NB define
    distances by absolute differences!)
  • Theoretically constructed similarities, entirely
    to be justified in theoretical terms and
    contextual knowledge of the party system in
    question. For example, if religion is an
    important cleavage
  • voter is religious AND party is religious
    similarity1
  • voter is not religious AND party is not
    religious similarity1
  • voter is not religious AND party is religious
    similarity0
  • voter is religious AND party is not religious
    similarity0
  • Inductively generated independent variables
    pertaining to voterparty combinations (works
    always) Y-hat procedure (see next sheet)

8
Y-hat procedure -1-
  • Perform the following operations in the
    unstructured data-matrix for each of the parties
    in turn
  • Regress electoral utility on the independent
    variable to be transformed
  • Save the predicted value (the y-hat)
  • Determine the mean of the y-hat in question
  • Center the y-hat around 0 by subtracting mean
  • Save, and use as one the variables to be stacked
  • This should for each independent variable yield
    as many centered y-hats as there are parties to
    be stacked

9
Y-hat procedure -2-
  • NB
  • the y-hat transformation may also be used to
    combine a set of indicators into a single
    stack-able independent variable
  • e.g., define a multiple regression with utilities
    as dependent variable and as independents, e.g.,
    occupation, income, autonomy in work, etc. in
    order to derive a single y-hat for job-status
  • The y-hats contain exactly the same explanatory
    information as the original independent
    variable(s) as they are nothing else than a
    linear transformation of the original
    variable(s).
  • Further details see Tillie (1975), van der
    EijkFranklin (1996, ch.19-20), van der Eijk et
    al. (2005), van der Brug, van der Eijk Franklin
    (forthcoming)

10
Empirical example -1-
  • See dataset stacking example.sav, which is a
    subset of variables and of cases (the first 249
    cases) from the German survey in EES99. It
    contains the following variables (see the
    codebook of EES99 for question texts etc.)
  • identification of study, respondent and country
  • political interest score (var078)
  • electoral utility items for 6 parties (var081 to
    var086)
  • left/right self-placement of respondent (var117)
  • respondents perceptions of left/right positions
    of 6 parties (in the same order as above) (var118
    to var123)
  • Of this a stacked dataset can be made with the
    stacked utility items as dependent variable and
    stacked left/right distances as
    independentcontinued-

11
Empirical example -2-
  • The following syntax creates an identifier for
    parties compute p11. compute p22. compute
    p33. compute p44. compute p55. compute
    p66. execute.
  • And left/right distances are computed in SPSS for
    this dataset as follows
  • compute dist1abs(var117-var118).
  • compute dist2abs(var117-var119).
  • compute dist3abs(var117-var120).
  • compute dist4abs(var117-var121).
  • compute dist5abs(var117-var122).
  • compute dist6abs(var117-var123).
  • execute.

12
Empirical example -3-
  • Enter the restructure procedure in the SPSS data
    menu. This brings you in a wizzard, with the
    following steps
  • First a choice of the kind of restructuring.
    Chose the 1st option (restructure selected
    variables into cases)
  • 2nd step define the number of variable groups,
    this is the number of stacked variables that will
    be created in the new datafile, each from a
    number of separate variables in the unstructured
    file. In our example, thew number is 3 (i.e. the
    identification of parties see previous sheet,
    the utilities of the parties (var082 to var086),
    and the left/right distances see previous sheet)
  • In step 3 you define the variables that have to
    be stacked, and you define their name in the
    stacked datamatrix. For example
  • Name 1st target variable utility and define
    var081 to var086 as the variables from which it
    will be constructed
  • Name the 2nd variable lr_dist, and define dist1
    to dist6 as the variables from which it will be
    constructed
  • Name the 3rd variable id_pty, and define p1 to
    p6 as the variables from which it will be
    constructed
  • continued

13
Empirical example -4-
  • NB for some reason beyond my comprehension, SPSS
    sometimes refuses to activate the Nextgt button
    after all the specifications in step 3. Retry a
    couple of times with deselecting and re-selecting
    the variables that have to be stacked, until the
    Nextgt button is activated and can be pressed, so
    as to enter you into the next step of the
    procedure
  • Step 4 involves the creation of index variables
    which is the same as identifiers for the stacks.
    You have already done this by creating p1 to p6,
    so you may specify none (alternatively, you can
    not explicitly make the identifier, and here
    define 1 index variable)
  • Step 6 asks what to do with the variables that
    are not to be stacked, and what to do with
    missing data
  • When specifying keep for the non-selected
    variables, theur values are replicated for all
    new records that pertain to the same respondent
    (as you can see for yourself after completing
    this example)
  • In the 2nd box choose create a case, as
    otherwise the resulting file becomes exceedingly
    non-transparant .
  • Next step asks whether you want to restructure or
    to save syntax. In the latter case you have to
    execute the saved syntax from the syntax window
  • In the data editor view of SPSS you now find the
    desired stacked data

14
References
  • Eijk, C. van der, W. van der Brug, M. Kroh M.N.
    Franklin 2005. Rethinking the Dependent Variable
    in Voting Behavior On the Measurement and
    Analysis of Electoral Utilities, to appear in
    Electoral Studies, 2005.
  • Eijk, C. van der , M. Franklin et al. 1996.
    Choosing Europe? The European Electorate and
    National Politics in the Face of the Union. Ann
    Arbor University of Michigan Press (in
    particular Ch. 20) .
  • Tillie, J. 1995. Party Utility and Voting
    Behavior. Amsterdam Het Spinhuis.
Write a Comment
User Comments (0)
About PowerShow.com