Nonresponse bias when estimating victimization rates: A nonresponse analysis using latent class analysis
| DOI | 10.1177/02697580211014781 |
| Date | 01 January 2022 |
| Author | Nathalie Leitgöb-Guzy |
| Published date | 01 January 2022 |
| Subject Matter | Articles |
Article
Nonresponse bias when
estimating victimization
rates: A nonresponse analysis
using latent class analysis
Nathalie Leitgo
¨b-Guzy
Federal Criminal Police Office, Germany
Abstract
The study expands empirical knowledge on nonresponse bias when estimating victimization rates
by using latent class analysis (LCA). Based on information about proxy-nonrespondents (hard-to-
reach respondents and soft refusals), the study identifies subgroup(s) of persons who are sys-
tematically underrepresented by refusal and unreachability and determines whether an over- or
underestimation of different offense-specific crime rates (prevalence and incidence rates) is to be
expected. Therefore, a broad review of the current state of research is carried out, followed by a
nonresponse analysis of a large-scale victimization survey conducted in Germany (n ¼35,503). The
paper illustrates that a variety of factors must be considered when analyzing nonresponse in
victimization surveys and that the current state of research does not allow definitive conclusions
about the amount and direction of nonresponse bias. The following analysis shows that LCA
constitutes an excellent approach to determine nonresponse bias in surveys. In each sample, one
class of person was identified that is systematically underrepresented, both by refusal and
unreachability. Here, victimization rates of violent crime tend to be significantly higher, indicating
an underestimation of crime rates.
Keywords
Victimization surveys, nonresponse bias, non response analysis, victimization rates, la tent class
analysis, proxy-nonrespondents
Introduction
Looking at the current state of research, it is common methodological knowledge that nonresponse
can cause nonresponse bias if the probability of nonresponse relates to one of the variables of
Corresponding author:
Nathalie Leitgo
¨b-Guzy, Federal Criminal Police Office, IZ33 Police Crime Statistics (PCS), Research on Unreported Crime,
Research and Advisory Unit, 65173 Wiesbaden, Germany.
Email: Nathalie.Leitgoeb-Guzy@bka.bund.de
International Review of Victimology
2022, Vol. 28(1) 109–133
ªThe Author(s) 2021
Article reuse guidelines:
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DOI: 10.1177/02697580211014781
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interest. This can be the case not only for point estimates (Groves and Peytcheva, 2008) but also for
the estimation of bi- and multivariate relations (Billiet et al., 2007; Peytchev et al., 2009), although
the latter is known to be much smaller (Amaya and Presser, 2017; Heggestad et al., 2015).
Nevertheless, current empirical knowledge about the amount and direction of nonresponse bias
for specific variables of interest is very limited, especially in criminological research. Although a
variety of theoretical assumptions and evidence make nonresponse bias in victimization surveys
likely, as yet, there is a lack of reliable empirical evidence on the overall level and direction of
nonresponse bias when estimating victimization risks.
Consequently, neither researchers nor political decision makers have validation as to whether
victimization rates from surveys are over- or underestimated in victimization surveys and how
large these biases may be. The political importance of victimization surveys as a crucial source of
crime data, as well as the steadily rising nonresponse rates to surveys (in Europe, surveys with
response rates below 50%are usual), suggest that this lack of research is unsustainable for survey
methodology in general and for criminological research in particular.
1
In the context of victimization surveys, two principal effects of nonresponse when estimating
victimization rates—first described by van Dijk in 1989—are commonly discussed in the
research literature: a) the interest in the survey topic and the so-called ‘eager-to-tell hypothesis’,
whereby victims are willing to speak about their victimization experiences (which should lead to
ahigher participation rate of victims and thus an underestimation of victimization risks); and b)
the so-called ‘lifestyle hypothesis’, which assumes that persons who go out more frequently have
greater victimization risks while being harder to reach in surveys (which should lead to a lower
participation rate of victims and thus an underestimation of victimization risks). However, no
empirical validation of these relationships is available. Neither van Dijk et al. (1990) nor Griggs
et al. (2018) found a correlation between response and victimization rates. Schnell (2002)
demonstrated a positive correlation between the number of contact attempts and the number
of different victimization experiences; however, the relation was curvilinear for most offenses.
Sparks et al. (1977) found that victims and non-victims do not differ in terms of their refusal
frequency but rather in terms of their accessibility and lifestyles. Nevertheless, the conclusion of
this research, indicating the absence of a serious nonresponse bias, is insufficient for several
reasons:
1. For an adequate analysis of nonresponse in victimization surveys, it appears necessary to
differentiate between two major reasons for nonresponse: unavailability and refusal. It is
commonly understood that these two factors are the result of different processes and thus
related to different predictors (see also Couper and de Leeuw, 2003; Groves and Couper,
1998). Van Dijk et al. (1990) have also referenced the necessity of differentiating between
reasons for nonresponse when thinking about nonresponse bias in victimization surveys.
Nevertheless, recent research has not systematically differentiated between processes relat-
ing to refusal and unavailability in victimization surveys.
2. Currently, a variety of variables (which exceed the above-mentioned approaches) are
known to influence the probability of non response. Worth mentioning are sociode mo-
graphic variables, fear of crime, and socio-environmental variables. Given that these vari-
ables may also be related to the probability of victimization experiences (partly in opposite
directions), a systematic review of these effect s should be considered when analyzing
nonresponse bias.
110 International Review of Victimology 28(1)
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