Download Swiss Household Panel User Guide (1999 - 2012)

of the first year of the variable ££ in (99,…,12) on the sub-populations of respondents
still present in the latest wave as follows:
sLR ££  sL££
sLR $$  sL££ ∩sL$$
sLR11

 sL££ ∩sL12
,
££
where sL are the longitudinal respondents (original sample members) in 1999 for the
$$
SHP_I and 2004 for the combined panel SHP_I and SHP_II, and sL are the longitudinal respondents in year 20$$. Basically, we test to see if samples that still respond in a
later year are representative of the same individuals that responded in the first year. The
tests run through the most recent released version (wave 14).
One has to be cautious with the results presented below because the variables are
compared from their first year of appearance. So it is possible that there is “left hand”
bias already introduced in the sample. That is to say that a selective process may have
already occurred before the appearance of the variable, introducing bias. This is undetectable by this method. Moreover, the calculations are done on the entire sample of
longitudinal respondents and there are no comparisons on sub-populations (by sex, age
class, nationality, etc.). Such comparisons could reveal differences which are not observed at the aggregate level. Of course, the inverse is also possible.
The variables having been identified as being biased by attrition (in particular variables
related to leisure and politics) need to be studied with care by the researchers who use
them in their analyses. These results do not mean that these variables are unusable.
However, they show that the phenomenon of attrition can certainly not be ignored. The
researcher must account for this in his analyses and, if necessary, in the given interpretation.
For the first panel, there are 1108 variables that appear in at least one wave of the personal files and are thus eligible for testing. Out of these, there are 306 deemed unfit to
be tested. The following groups of variables were excluded:
 proxy variables, as it concerns reports on other household members
 variables with the same response in all waves considered, such as status
 variables with too few respondents (for categorical variables, if no category has
at least 30 respondents, and for numeric, if the total number of respondents is
less than 30)
 variables of which the modality is too high (this is for categorical variables with
more than 100 distinct responses, such as the 4 digit isco job classification)
 variables for which testing does not make sense, such as id variables, dates, and
weights.
Table 4.3 gives a summary of the results. If a variable has bias detected for any year
without weight, then it falls into the category of “Difference without weight”. If a variable
has bias detected for any year with weights then it falls into the category of “Difference
with weight”.
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