1 Software
SPSS
is used for data manipulation and for estimation of correspondence analysis models.
This is the program for which we provide syntax files.
The estimation of RC models uses lEM, a freely available software package distributed by its author
Jeroen K Vermunt. (Follow the link 'Software and user manuals').
For much of the assessment and handling of results,
and also within certain stages of the data preparation processes, Microsoft Excel
is used, since this provides an adequate mainstream method of handling and browsing
data blocks.
Finally, a large amount of the data manipulation
and reporting is done in simple plain text file editors. A particularly useful
editor for PC operating systems is pfe, available
freely from its website. Alternative editors are equally capable of allowing for
the same manipulations, but we would advise against conducting the equivalent
text file editing using word processing packages, such as Microsoft Word. These
frequently add characters to files, whilst 'hiding' them from view, a practice
which can interfere with procedures that rely on text file processing.
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2 Preparing input data
The initial data are likely to take one of two
forms: either, a set of individual records showing the occupation (including employment
status where available) of the husband and that of the wife; or, a set of
aggregated records showing a particular occupation for husbands, a particular
occupation for wives and the frequency of that combination. (Cases where the data
are at the household level would require a preliminary stage where the two partners
are identified and an individual record set up for them.) The second, aggregated
form is the one that is used throughout the analyses. Instructions for converting
individual into aggregated records are given below in
subsection 2.2.
2.1 A schema of variable
names
When constructing multiple scale versions for
different countries and datasets, it can save a considerable amount of time if
the names used for the various occupational units are harmonised between versions.
The examples given in these construction pages use a set of variable names that
we would encourage other users to adopt. This greatly simplifies the process of
construction. Further details.
2.2 Translating from a 'case'
to a 'table' file
The 'individual case' file form (ie tabular data
of one record each per husband-wife case) is not particularly efficient in terms
of storage. A much more useful form (and indeed the only form readily adapted
to some of the analyses described below) is a 'table' file where each record consists
of a unique combination of husband and wife occupational units, plus an additional
'weighting' variable which indicates the number of individual cases which are
found in this combination.
Such table files are readily constructed from
the original case files, for instance in SPSS by using the 'casestovars' command (aka 'restructure', available from versions 10 onwards),
or alternatively by importing output from the 'crosstab'
procedure (the older method: see
these instructions ).
See sub-section 2.4 below
for how to deal with a possible complication.
2.3 Occupational unit 'value
labels'
Data are typically supplied with numeric occupational
title and status codes, and the scale estimation carried out on these original
codes or an 'autorecoded' translation of them (see sub-section
2.4). At the same time, documentation exists which translates the numerical
codes to their text 'value labels', as for instance in the tables of occupational
titles which are listed via the versions
page. It is possible to proceed through the CAMSIS estimation working only with
the numeric codes, cross-checking by hand on their textual meaning when specifically
required, but scale construction is much easier if automated procedures matching
numeric values and their value labels are implemented. The recommended way of
doing this is to utilise the value labels functions of SPSS and its subsequent
output options. This method is slightly cumbersome to set up, but once prepared
allows rapid linking between value labels and the estimation results associated
with numeric values. Further
details.
2.4 'Square autorecoded'
values
While the occupational base unit indexes used
here (title-only or title-by-status values) have numeric values which are held
constant between applications (and are associated with the relevant value labels),
those values tend to be numerically high (typically up to 999) and are not usually
contiguous (so that, for example, not all of the 999 possible categories are actually
used). It is considerably more convenient if values are used that start at 1 and
increase incrementally up to the maximum value of the actually used codes. SPSS
has a facility to implement this 'autorecoding' procedure. Further
details.
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3 Preliminary
models using CA
Our recommended method is to start the analysis
proper with Correspondence Analysis. Results from these models are subsequently
used as evaluative tools throughout the rest of the scale development. This stage
is particularly valuable in identifying the problematic diagonal elements that
are discussed in the overview. Further
details.
The final CAMSIS model will almost certainly be
subject to further refinements which account for various 'confounding factors'
in the social interaction structure. To generate such models, after having set
up some preliminary CA models, we generally then follow the sections 4 to 6 below,
often looping back through the stages to repeat and refine the models.
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4 Aggregation
of small groups
We choose on principle to maintain the maximum
level of differentiation between occupational base units, but survey samples or
census sub-samples typically involve a number of occupational units that are represented
by very few cases. To avoid problems of unrepresentative sampling, therefore,
a next step in almost all cases is the revision of the initial data to deal with
the most sparsely represented occupational units (also see notes from overview
page). This involves making decisions about merging sparsely represented units,
either with other unit groups which are already very well represented, or else
with a number of other sparsely represented units until the resultant aggregation
is sufficiently large. Where, as in most cases, there is a distinction in terms
of employment status as well as occupation, this also involves deciding which
to choose as the basis for aggregation (ie, whether to preserve employment status
and merge across title groups, or to preserve title group and merge between employment
status categories). Further details.
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5.1.1
Handling pseudo-diagonals in SPSS
The handling of pseudo-diagonals
is relatively straightforward in the CA approach, where we simply exclude the
relevant husband-wife combinations from the analysis. Further
details.
5.2
Row-column association modelling using LEM
The main 'RC' analyses used by the CAMSIS project
are carried out using the program LEM to model the cell frequencies. A major advantage
is that this allows the use of standard statistical criteria for evaluating the
fit of the models.The estimation of RC models in LEM requires the specification
of LEM command files for the chosen model structure. Details of all the options
involved can be found in the LEM manual, which, along with the programme itself,
has been made freely
available over the internet by the programme's author, Jereon K Vermunt..
Depending on the size of the data file analysed and the power of the processing
machine, RC models in LEM can be very quick (less than 1 minute) or very slow
(more than 1 day) to converge (although the convergence criteria can of course
be changed). Additionally, there is often more than one LEM model which could
be evaluated at any one time: for instance with or without subsidiary dimensions,
or with different coverage of pseudo-diagonals (and in fact, not discussed here,
using alternative statistical treatments of the pseudo-diagonal combinations).
The easiest way of estimating LEM models is to run a few models in sequence, often
for several hours or even days, when a computer is not needed for anything else.
This can be achieved by creating multiple command files and the appropriate data
and design matrices, then calling the command files sequentially from an MSDOS
batch file.
The accompanying file includes
the specification of a typical model for input
to LEM. Note, however, that it includes features discussed in sections 5.2.1
and 5.2.2, so it is important to read those first.
The remaining scale construction methods
become the successive loop between the estimation of models, checking for pseudo-daigonals,
if necessary revising the data with further category collapses, then re-estimating
a revised model.
5.2.1
Handling pseudo-diagonals in LEM
A further advantage of LEM's RC model facilities
is that they offer a more satisfactory way of dealing with pseudo-diagonals (as
specific husband-wife combinations) and, as an extension of that, with subsidiary
dimensions (see the following section). However, the specification of pseudo-diagonals
for an RC model in LEM is significantly more complicated than it is for CA in
SPSS. Further details.
5.2.2 Subsidiary dimensions
in LEM
As well as identifying specific
pseudo-diagonal husband-wife occupational unit combinations, there are also circumstances
where it is efficient to construct whole 'subsidiary dimensions' of social association
between occupations, which reflect a secondary factor that influences association
likelihoods, but is separable from a generalised interaction / stratification
primary dimension.
Such subsidiary dimensional structures cannot be readily
added to the SPSS CA estimation structure, and can only be added to the LEM RC
models if they form separable subgroups to the base occupational unit. The major
examples are the employment status subgroups of the title-by-status base units,
and the major groups (or sectoral cleavages denoted by them) of the occupational
schema. Further details.
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6
Assessing the results of CA and
RC models
6.1 Exporting
the results
After each iteration in the CA and RC analyses it
will be necessary to assess the results to determine whether a satisfactory solution
has been achieved, or what changes may need to be made. These
notes provide details of how the output can be transferred to other programs.
6.2 Assessing the results
After a given model has been estimated, our principal concern has usually been
to check that an adequate account of pseudo-diagonal combinations has been used,
and adjust the model if not. The methods we have used for this are covered in
the immediately preceding section.
In addition,
there are usually two further ways we evaluate interim and final CAMSIS model
results. First, we make regular substantive reviews of the derived occupational
unit scale scores. This can be achieved most readily by ranking the exported derived
scores alongside their occupational base unit titles, and reviewing the structures
derived. Aside from being of general interest, this method is also often the quickest
way of telling us whether there are any subsidiary dimension structures worth
accounting for in the derived model (as we may see that the derived score orders
for the single-dimensional models appear to be clustered around factors which
could comprise a subsidiary dimension).
A second method of review is to examine
the aggregate statistics for the interim and final models produced. With SPSS
CA, relatively few such statistics can be generated, but perhaps the most significant
data concerns the relative size of the singular value associated with the first
dimension (the larger it is compared to other dimensions, the more consistently
we can expect to have represented a primary dimension of social interaction /
stratification). With LEM RC models, on the other hand, a wealth of aggregate
statistics can be assessed. Dimension association statistics closely paralleled
CA singular values in indicating the relative influence of the first dimension
of generalised interaction / stratification. Additionally, a number of model fit
statistics allow us to compare how well different models have described the patterns
of husband-wife association found in the data. Thus models with differing accounts
of both pseudo-diagonals and subsidiary dimensions can be compared with each other,
and the results used to adjudicate on the most appropriate formulation for the
final LEM model.
In ongoing CAMSIS project results we are still working on
issues concerning the assessment of alternative model statistics, and substantive
structures, and more details will hopefully be supplied shortly. For an early
example, the project report on the Swiss
scale constructions, contains some practical examples of such discussions.
6.3
Descriptive statistics and graphical displays
When the model estimation is complete, we derive
descriptive statistics and graphical displays to summarise the estimated score
distributions. These are useful in giving an idea of the nature of the occupational
structure. In particular, the shape of these distributions is significant because
it can indicate whether the location of the occupations forms a smooth hierarchy
or is more akin to a discrete categorical structure. Further
details.
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7
Scores
By the section above, we have reached the stage
where derived CAMSIS scale scores have been exported to data files which link
them with the relevant occupational unit categories, plus some information on
the number of cases involved in each unit. In the following sections, we describe
the preparation for dissemination of those scores.
7.1
Score transformations
The initial scale construction processes generate
occupational unit scores that are scaled around the (default) parameters of the
model and are numerically small. We prefer to transform them to a more practically
useful form. In part, this simply involves making them all positive and multiplying
them up to a more amenable magnitude and precision. However, there is also an
issue of how scores for different countries, and for men and women, can best be
made comparable with one another. Further details.
7.2 Producing an
index file
A last significant step is the transformation
of the score values into 'index' files associated with each possible occupational
base unit. This process takes the form of three steps. First, an index file needs
to be generated which covers every possible occupational base unit category (7.2.1).
Second, the appropriate derived scores needs to be assigned to those occupational
units that were earlier merged in the data construction stage (7.2.2). Finally,
occupational units that were not represented by any cases in the original data
(but which might occur in other datasets) need to be given an imputed scale value,
using closely related units.
7.2.1
Generating the file: further details.
7.2.2
Attributing scores: further details.
7.2.3
Imputing scores: further details.
7.3
Disseminating index files
Finally, when a satisfactory 'index'
file has been generated, with a CAMSIS estimated score value for every occupational
unit, it should be readily possible to use this file to assign CAMSIS scores to
appropriate occupational units on other data sources. This
section of the 'usage guidelines' page shows SPSS syntax, for instance,
that can be used to match original occupational base unit data with the relevant
CAMSIS derived scores.
Finally, as any user who has struggled through the
full length of the details above will realise, there are many potential complexities
to any given CAMSIS scale construction. Most of these can be regarded as to some
extent 'optional', that is, different versions constructions may incorporate different
complexities to a greater or lesser extent, with no necessary precondition that
the most comprehensively derived set of scores will be the most satisfactory (although
it is more likely than not!). Moreover, several of these possible complexities
will reflect the input of individual researchers (for instance, choices in the
initial data merging options, the choice of pseudo-diagonals, and the choice of
final model using RC techniques). To promote good practice in the dissemination
process, therefore, we would encourage any users who estimate and make available
CAMSIS scale scores to provide the maximum amount of detail as possible on those
aspects of the scale constructions.
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