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Journal of Happiness Studies
Erschienen in: Journal of Happiness Studies 4/2021

31.07.2020 | Research Paper

Household Crowding Measures: A Comparison and External Test of Validity

verfasst von: Eilya Torshizian, Arthur Grimes

Erschienen in: Journal of Happiness Studies | Ausgabe 4/2021

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Abstract

Analysts may have multiple measures of household crowding, and so need to know which measure to emphasise. We analyse the relationships between alternative subjective and objective crowding measures and assess how well these alternative measures predict a measure of residential satisfaction. Statistically, a perceived crowding (PC) measure outperforms the people per bedroom (PPBR) measure, an objective measure of crowding. However, there may be bias in the relationship between PC and the residential satisfaction variable. Amongst objective measures, the Canadian National Occupancy Standard also outperforms PPBR. Nevertheless, all three measures are highly correlated and each helps to predict levels of residential satisfaction. Thus, any of the three measures provides a valid indicator of household crowding when assessing housing stress.
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Fußnoten
2
In that review, there is no information available about the use of CNOS.
 
3
This measure is also called room density. To avoid confusion, we refer to it in this study as people per bedroom.
 
4
Based on CNOS, a household is considered to be crowded if any of the following criteria is violated: the number of people per bedroom should not exceed two, however parents or couples may share a bedroom; children aged less than five years may share a bedroom; children aged over four and less than 18 years and of the same sex may share a bedroom; children aged between five and 17 years should not share a bedroom with children aged less than or equal to five years and of the opposite sex; single adults aged more than or equal to 18 years and any unpaired children should have their separate bedroom.
 
5
While the NZ housing regulations are the only legal standards for crowding they are not generally used.
 
6
This measure is also called perceived density. To avoid confusion, we refer to it in this paper as perceived crowding.
 
8
The distribution is very negatively skewed such that 84 per cent of observations fit into two groups: ‘very satisfied’ and ‘satisfied’. Considering the high proportion of the ‘satisfied’ group, we distinguish between those who are ‘very satisfied’ and all others – i.e. the residential satisfaction variable is dichotomous.
 
9
We compare the impacts of the explanatory variables between a model using the five-category output and a model using the binary output. The results are not significantly different. For identifying the correct re-categorisation, we use the Brant test (Brant 1990).
 
10
The categories defined on the horizontal axis of Fig. 1 are aggregate categories for the PPBR values that fit within each defined bin. Size of each bin is 0.3.
 
11
We ignore ‘Other’ in the discussion since this group is very small.
 
12
In addition to PPBR, we also include PPBR2. The assumption of a quadratic relationship between this measure of crowding and residential satisfaction is consistent with Rodgers’ (1982) findings that these relationships are better described in a curvilinear form than a linear one.
 
13
Different methods of resampling may serve to increase the precision of estimates using survey designs by deriving more robust standard errors, proportions, odds ratios and regression coefficients. To do this, a random set of observations is left out at each time of estimation. Replication of this leads to the estimation of the bias of a statistic. This method is called jackknifing.
The NZGSS provides us replicate weights produced by the delete-a-group Jackknife method (Kott 2001). In the dataset, 100 groups are derived by using primary sampling units (PSUs) randomly sorted into each stratum. This strategy results in 100 replicate samples in each, of which one of the groups is omitted and weights are adjusted accordingly. Using these weights in our estimation leads to estimates that tend asymptotically to true values. For more information see Statistics New Zealand (2013).
 
14
A lower value of the Akaike Information Criterion (AIC) indicates a better fitting model after taking into account the number of regressors.
 
15
The null hypothesis is that the two coefficients of interest (on PPBR and PPBR2) are simultaneously equal to zero. In Model (1), for example, the result of the PPBR Wald test suggest that we can reject the null hypothesis at better than the 1 per cent significance level.
 
16
The estimated odds ratios indicate the chance of gaining a higher level of the dependent variable (RS) for a one unit increase in the independent variable (PC). The odd ratios provide an understanding of the relative chance of being satisfied versus being dissatisfied. When the chance of being satisfied is equal to being dissatisfied, the odds ratio is equal to one. Hence, our results suggest that with PC being equal to one (i.e. perceiving house to be small), the odds of people being satisfied with their residential environment (relative to being dissatisfied) is equal to 0.199. This implies 80 per cent lower chance of being satisfied compared to being dissatisfied.
 
17
The reported marginal effect of PPBR accounts for the non-linear relationship between RS and PPBR.
 
18
The joint marginal effect of PPBR and PPBR2 is reported on a row below ‘squared PPBR’.
 
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Metadaten
Titel
Household Crowding Measures: A Comparison and External Test of Validity
verfasst von
Eilya Torshizian
Arthur Grimes
Publikationsdatum
31.07.2020
Verlag
Springer Netherlands
Erschienen in
Journal of Happiness Studies / Ausgabe 4/2021
Print ISSN: 1389-4978
Elektronische ISSN: 1573-7780
DOI
https://doi.org/10.1007/s10902-020-00302-z

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