Data for the Second Modelling Paper
The Use of Paradata (Field Process Data) in Cross–Sectional and Longitudinal Surveys
Project
(RES-062-23-2997)
Construction of the datafile
First, households which responded in Wave 1 and which had call record information as well
as interviewer observations together with survey variables available were selected. A
separate file with call record information was constructed for the Wave 2. As there is no such
a thing as longitudinal household in the Understanding Society data, it was possible to merge
data from Wave 1 and Wave 2 only on the basis of the individual identifiers which are stable
and traceable across waves. In order to do this, first each member of each household in both
waves obtained the same sequence of calls as the whole household would have. Then data
for the individuals were merged for both waves on the basis of the unique individual
identifier. Finally, only one record per households in Wave 2 remained in the final datafile as
our analysis needs to be conducted at the household level. Ethnic Minority Boost sample was
excluded from the analysis as the rules for the selection of this sample are different from the
main sample and a separate analysis could be performed for this specific group. However,
this is not the main aim of the current analysis. The British Household Panel Study sample
was also excluded from the analysis as none of the participants from this sample were
interviewed in the Wave 1 of the Understanding Society. All data then were cleaned and
listwise deletion of observations with missing values on covariates was performed.
This initial analysis sample contained 24,896 households including households with
sequences available in both waves. First, we carried out preliminary work using this initial
analysis sample, conducting separate analyses for all cases with at least 1, 2, 3 etc. calls. The
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aim was to evaluate after every call, how well we can predict final outcome and length as
more information about each case becomes available. As in previous analysis reported in
XXX, this preliminary analysis also suggested that 3 calls may be sufficient to reach an
acceptable level of predictability. To more formally assess this and to be able to compare
model fit and prediction across the different models -since measures of predictability are
dependent on sample size- we restrict our final analysis sample to all households from Wave
2 that received four or more calls (11,029 households). This approach enables us to employ
call record information from the beginning of the field period in Wave 2 (here the first three
calls) to predict final length and outcome. For these final models, we also excluded a small
number of cases with missing values in the explanatory variables, as follows: there are no
missing cases in any of the geographic information and design variables since these are
derived from administrative data. Date and time of a call are automatically captured using
computer assisted methods leading to no missing cases in these variables. Recordings of the
call outcome of the households of interest did not contain any missing information either.
Households in Wave 2 with missing items in the geographic and survey variables from Wave
1 as well as the interviewer observation variables from Wave 2 were deleted (399 cases
(3.6%)). The final analysis sample, including only cases with four or more calls, therefore,
contains 10,630 households with information from Wave 1 and Wave 2.
To analyse length and call outcome (and to ensure the comparability of the different
models), the following three response variables in Wave 2 are considered. Their distributions
are presented in Table 1.
1.) length of call sequence (binary), distinguishing short sequences (up to 6 calls) and long
sequences (more than 6 calls). The cut-off point at 6 calls was intentionally selected to
fit with the survey protocol requirements of conducting a minimum of 6 call attempts if
contact was not established earlier in the process.
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2.) (final) outcome of call sequence (binary), distinguishing successful call sequences with at
least one interview conducted in a household (after call 3) and unsuccessful call
sequences with no interviews achieved (after call 3).
3.) a variable combining both length and final outcome, distinguishing 4 categories - short
unsuccessful (up to 6 calls, no interview), short successful (up to 6 calls, at least one
interview after call 3), long unsuccessful (more than 6 calls, no interview) and long
successful (more than 6 calls, at least one interview after call 3).
(Different definitions of the dependent variables - such as complete household outcome
and different categorisations of sequence length - were also explored. The overall conclusions
were very similar to the ones presented in this paper.)
Table 1: Distributions of the three response variables in the final analysis sample (10,630
households).
Frequencies
Variables with categories
Length
6704
Short sequence (up to 6 calls)
3926
Long sequence (7-30 calls)
Final outcome
No single interview in a sequence after call
3110
3
At least one interview in a sequence after
7520
call 3
Combined response
5304
Short successful
1400
Short unsuccessful
2216
Long successful
1710
Long unsuccessful
Percentages
63.1
36.9
29.3
70.7
49.9
13.2
20.8
16.1
The explanatory variables used in the analysis can be split into seven main groups.
The distributions of the explanatory variables broken down by the categories of the response
variables are presented in the online Appendix (Table A2).
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1.) geographic information and design variables from Wave 1 (4 variables: urban/rural
indicator, government office region, low density area for ethnic minorities, and month
and year of household issue);
2.) interviewer observation variables from Wave 1 (12 variables, e.g. indicators of entry
barriers, conditions of surrounding area such as litter in street, abandoned buildings,
heavy traffic, type of accommodation, relative condition of the property, garden);
3.) survey variables from Wave 1 (9 variables, e.g. household characteristics such as
household income, the highest educational qualification, composition of the household,
and others);
4.) call record variables from Wave 1 (5 variables, e.g. proportion of non-contact calls in a
sequence or proportion of appointments in a sequence; length of sequence in Wave 1);
5.) call record variables from Wave 2 (11 variables, e.g. date, time of day, day of week, call
outcome; also derived variables including time between calls);
6.) interviewer observations from Wave 2 (9 variables, e.g. household split identifier,
presence of a car or a van, relative condition of the property, conditions of surrounding
area such as litter in street, abandoned buildings, heavy traffic, type of accommodation,
presence of children in a household, relative condition of the property, garden);
7.) change in interviewer observations between Waves 1 and 2 identifiers (8 variables, e.g.
change in conditions of the garden between the 2 waves, change in presence of a car or a
van between the two waves, and others).
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