A2.1.
Notes on Census Data Sources and Calculations
The census parameters against which survey estimates are compared in chapters 3 and 5 were all sourced from data tables publicly available from Statistics New Zealand’s website (www.stats.govt.nz). Table 40 presents citations for each of the variables employed. Full bibliographic details can be found in the references (p. 157).
For three of the variables, it was not possible to limit the population figures to those aged 20 years or older (see the ‘Base’ column in the table), because the official statistics were not available with age breakdowns. Furthermore, the electoral roll, and each ISSP survey sample taken from it, covers those 18 years or older. Hence, all variables except those relating to household size differ by 2 to 3 years in their comparative bases between the census and survey sets.
Table 40: Sources for individual census parameters
Source of Census Data
Variable Base 2001 2006
% Male 20+ (Statistics New Zealand, 2007d) (Statistics New Zealand, 2007i) % 20-34 Years old 20+ (Statistics New Zealand, 2007d) (Statistics New Zealand, 2007i) % 65+ Years old 20+ (Statistics New Zealand, 2007d) (Statistics New Zealand, 2007i) % Maori 20+ (Statistics New Zealand, 2007d) (Statistics New Zealand, 2007f) % Marital: Single 15+ (Statistics New Zealand, 2007d) (Statistics New Zealand, 2007i) % Bach/PG Qual 15+ (Statistics New Zealand, 2007d) (Statistics New Zealand, 2007i) % Income <$20k 20+ (Statistics New Zealand, 2007d) (Statistics New Zealand, 2007h) % Income > $50k 20+ (Statistics New Zealand, 2007d) (Statistics New Zealand, 2007h) % Not Religious 20+ (Statistics New Zealand, 2007d) (Statistics New Zealand, 2007f) % Empl. Fulltime 15+ (Statistics New Zealand, 2007d) (Statistics New Zealand, 2007i) % 1 Person HH All (Statistics New Zealand, 2007d) (Statistics New Zealand, 2007g) % 5+ Person HH All (Statistics New Zealand, 2007d) (Statistics New Zealand, 2007g)
The discrepancy in bases means that, in some cases, survey estimates that appear to match census figures on average would actually slightly under or overestimate the population parameter if the bases were equal. In particular, this is likely to be the case for ethnicity (% Maori) and religiosity (% Not Religious), since they covary with
age (Maori have a lower life expectancy and their population is skewed toward the young).
For instance, for the ‘% Not Religious’ variable, a shift in base from 20+ to 15+ causes the population percentage to change from 28% to 29% for 2001, and 33% to 35% for 2006. All the survey estimates reported in Table 22 are below the latter figure, despite the fact that the surveys for 2005 and 2006 effectively oversampled younger people.
Turning to ethnicity, if the base for ‘% Maori’ is shifted from 20+ to 15+, the proportion rises from 11% to 12% in both 2001 and 2006. All but one of the survey estimates reported in Table 22 (p. 71) are below 12%, and the one that is not comes from a sample that deliberately overrepresented Maori. Indeed, there are also other reasons why measurement error may contribute to a smaller difference between the survey and census data than really exists for this variable. For example, classification methods for ethnicity changed between 2001 and 2006 (see Statistics New Zealand, 2007c), such that in 2006 the class ‘New Zealander’ was explicitly reported where, in the past, it was subsumed within the ‘European’ category. This occurred at a time when there was public discussion about the term ‘New Zealander’ in the months leading up to the 2006 field period. In 2001, 2.4% of people identified with the write-in ‘New Zealander’ ethnicity category, whereas 11.1% did so in 2006. To the extent that this category was used by some people who would normally have reported only Maori ethnicity (Statistics New Zealand, 2007e), the reported proportion of people with Maori ethnicity in the population for 2006 may have been reduced.
Furthermore, while the census employs a categorisation schema that counts people under multiple categories if they signal multiple ethnicities, the calculations for the ISSP survey were developed under single ethnicity prioritisation scheme (i.e., where multiple ethnicities were signalled, only one was used and Maori was given priority in selection). Had a multiple ethnicity scheme been employed for the ISSP, the gap between the census and ISSP figures is likely to have been larger. It would be possible to recode the ethnicity variable in the ISSP to make it comparable to the census classification mechanism. However, since it is unlikely to change the conclusions drawn in the studies in the thesis, this has not been done.
Other variables that are likely to be subject to measurement error are ‘Highest Qualification’ and ‘Marital Status’. Specifically, changes in the educational system in New Zealand between 2001 and 2006 meant that the classification scheme for highest educational attainment changed between the two census instances (see Appendix section A1.3, p. 169 for copies of the forms). Moreover, while qualifications beyond high school were required to be written in on the census form (and were subsequently coded), the ISSP surveys had coded options for post-high school qualifications. Thus, differences in question wording may have led to discrepancies between the census and ISSP results reported.
For ‘Marital Status’, changes in legislation regarding civil unions also led to a change in census question formats between 2001 and 2006. Furthermore, there were some differences between the ISSP question and the 2001 census format (the number of categories were the same, but the wording was simplified and they were presented in a different order in the ISSP). Thus, variations in question wording may have led to discrepancies between the census and ISSP results reported.
Of note is that, although measurement error is likely to be an issue in the ‘Highest Qualification’ and ‘Marital Status’ questions, the direction of bias was at least consistent across all of the surveys examined. Furthermore, the NPS scheme detailed in chapters 4 and 5 did have some success in moving the ‘Marital Status’ ISSP estimates closer to the census parameters (results were mixed for the ‘Highest Qualification’ variable). Hence, at least some of the bias in ‘Martial Status’ is likely to have been due to noncontact nonresponse.