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Victimization, Social Structure and Psychosocial Variables: The Case of Spain in 1999 and 2016

Gonzalo Herranz de Rafael
1 and
Juan Sebastián Fernández-Prados
Department of State Law and Sociology, Sociology Area, University of Malaga, 29071 Málaga, Spain
Department of Geography, History and Humanities, Sociology Area, University of Almería, 04120 Almería, Spain
Author to whom correspondence should be addressed.
Soc. Sci. 2019, 8(3), 102;
Submission received: 8 November 2018 / Revised: 14 March 2019 / Accepted: 20 March 2019 / Published: 22 March 2019
(This article belongs to the Special Issue Crime Prevention through Pro-Social Design)


This article reviews the research on the factors influencing victimization and explores the case of Spain. The first section presents a comparative analysis of the data for 1999 and 2016 in terms of perceptions, profiles and the most significant sociodemographic and socioeconomic variables. The second section shows an explanatory analysis based on multivariate logistical regression models, using as independent variables sociodemographic and psychosocial items, and the dependent variable is whether one is described as one is described as a victim or no in the 2016 survey. The results point towards an explanatory model of victimization in which sociodemographic variables play a less important role, whereas psychosocial variables related to lifestyle and subjective perceptions make a significant contribution to greater understanding of the nature of being the victim of a crime and offer suggestions on how to improve Crime Prevention Through Environmental Design (CPTED).

1. Introduction

A major focus of research in the area of victimization relates to how lifestyles shape victimization (Von Hentig 1948; Hindelang et al. 1978; Joutsen 1987). This “scientific victimology” approach differs from others such as “humanistic victimology” (Fattah 1992) or even so-called “radical victimology,” which is mainly concerned with human rights and societal power structures (Elias 1986, 1993; Dignan 2005; Dixon et al. 2006; Walklate 2015). In this sense, while it is believed that the connection between victimology and human rights through the analysis of victims’ movements and power structures in society is a very important perspective, it is essentially unviable impossible to explore given the state of the available data. One limitation in the field concerns the lack of proposals made from a feminist perspective (Genn 1988; Crawford et al. 1990; Walklate 2007; Sharp 2009).
While initially the tendencies of such research may be referred to as “positivist victimology” (Miers 1989), “conservative victimology” (Karmen 1990) or “conventional victimology” (Walklate 1989), the methodology utilized in national surveys rules out other options. In this realm, the objectives of victimology coincide with the classical proposal (Mendelsohn 1976) to prevent victimization from occurring, to minimize damage resulting from victimization, and to prevent repeated victimization.
Although the vulnerability of potential victims has more to do with their appeal to possible delinquents than with their objective characteristics, it is also true that lifestyle, understood as routine vocational (work, school, etc.) and leisurely daily activities (going out at night, frequenting certain locales, etc.), conditioned by the subject’s social structure such as his or her age, sex, race or area of residence, and the frequency of contact with unknown persons are key to understanding the victimological profile of a society in this case of Spain in 1999 and 2016. Wolfgang (1957) conducted an empirical study of homicides, including those involving victim precipitation and provocation. In the former, the victim is a direct and positive precipitator of the crime; 26% of the 588 cases he analysed were precipitated by victims. He also established a series of variables that affect precipitation: sex, race, marital status and substance abuse. As Frederick Wertham states in his work, The Show of Violence, “One cannot understand the psychology of the murderer if one does not understand the sociology of the victim” (as cited in Clevenger et al. 2018, p. 3).
In Spain, 12 surveys have been conducted by the Centre for Sociological Research with questions referring to victimization, addressing the period from January of 1978 to 2016 (visit data bank While some of these surveys have already been analysed (Alvira and Rubio 1982; Torrente 2001), a comparative analysis remains to be done, especially regarding variables that have traditionally been defined as victimological in nature in relation to the lifestyles of Spaniards.
Although in this case, due to methodological issues, the present work focuses on two surveys (those for 1999 and 2016), curiously, while Spain’s crime rate has varied over the years, the rates were nearly identical in 1999 and 2016 at 44% and 43%, respectively. The surveys mentioned lack data and do not include homogeneous series for a given frequency. On the other hand, the two surveys selected are methodologically like victimization surveys, as both use a large sample size: the 1999 survey has 12,994 cases and the 2016 survey has 5290 cases. However, one of the objectives of this article is to determine whether perceptions, profiles and social variables are coincident or divergent for the two periods.
This article is divided into two sections. The first presents a comparative analysis of the data for 1999 and 2016 in terms of perceptions, profiles and most significant sociodemographic and socioeconomic variables; the second presents an explanatory analysis based on a multivariate logistical regression model using the lifestyle of the population and socioeconomic variables as independent variables, and an individual’s susceptibility to becoming a victim of certain crimes as the dependent variable.
The applied approach is used as part of the broader framework of lifestyle exposure theory, which focuses on the actions and behaviours of potential victims that increase their susceptibility to becoming the victim of a crime. As stated by Clevenger et al. (2018), “LET was proposed by Hindelang, Gottfredson and Garofalo in 1978 and is very similar to the routine activity theory (RAT) by Cohen and Felson.” In fact, routine activity theory is viewed as an extension of lifestyle exposure theory (Choi 2008).
Lifestyle exposure theory states that the risk of becoming a victim varies in societies depending on the position of individuals in the social structure depending on their age, sex, race or ethnicity, and social class. Meanwhile, individuals and their activities and lifestyles are interwoven with roles and their expectations. More recently, the theory has been successfully applied to study victimization in cases of cybercrime (Reyns et al. 2011). Recent studies (Navarro and Jasinski 2015) have demonstrated the vulnerability of becoming a victim through exposure to social networks such as Facebook and susceptibility to being exposed to cyberbullying through the daily use of such networks. In a similar vein, evidence of the harassment of young women using online videogames has been observed (Breuer et al. 2015; Tang and Fox 2016).
This model has not been free of criticism (Walklate 2007) given the problematic nature of measuring lifestyles; the ambiguities concerning whether structural variables contributing to patterns of victimization include age, sex, social class and ethnicity rather than ageing, sexism, classism or racism; and the model’s systematic disregard of power relations.
However, the model has been used in most victimization surveys to address the patterns of victimization to reduce the risk of crime on the one hand (Hough and Mayhew 1983; Felson 2006) and to consider the impacts of victimization on the most socially disadvantaged groups on the other (Kinsey et al. 1986; Crawford et al. 1990).
National surveys normally include information that is both consistent and discordant. For example, in the case of Spain (Alvira and Rubio 1982), consistent data are used for living area, age and sex (men between the ages of 26 and 35), and there is a positive relationship between victimization and higher rent and higher professional level. Alvira and Rubio (1982) established that behaviour in Spain is different from that observed in the Anglo-Saxon world. In this sense, the notion is consistent with Walklate (2007). For example, in the United States, people in urban areas with incomes of less than $7500 are more likely to be the subject of robberies and assaults within and outside of the home (Catalona 2005).
The groups most vulnerable to victimization over the last decade (Mawby and Walklate 1994; Walklate 2007) include the following: low-income people, ethnic minorities, renters, the elderly, young people, single-parent families and women (especially older women). However, the picture becomes more complex when we take into account people and homes. Pease and Tseloni (2014) argue that the two differ based on risk factors such as location, visible goods, a lack of employment and lifestyles, but a greater risk of victimization is borne by both reiterative subjects and homes. Several projects, such as that led by Kikholt in Rochdale, Lancashire, have proven predictive vigilance to be successful (Forrester et al. 1988). Pease and Tseloni (2014) considered the frequency of domestic crimes and their predictions based on the following traits: three or more cars; single parents; inner city location; social housing; duplex or townhouse; between five and 15 years of age; and urban areas.
In short, citizens’ lifestyles are objective facts that we can determine from different variables such as habitats, neighbourhoods of residence, locales frequented, night-time outings, age, social class, race or ethnicity, belonging to unstructured families and socially disadvantaged groups, etc. However, lifestyles can also be determined subjectively from individuals’ perceptions and opinions about citizens’ insecurity or from perceptions they have of being victims of a crime.

2. Methods and Data

To summarize, our hypothesis points towards an explanatory model of victimization in which sociodemographic variables play an increasingly less important role, while variables related to lifestyle and subjective perceptions make a significant contribution to a greater understanding of the nature of being the victim of a crime.

2.1. Data and Instruments

As stated above, this article contains a comparative analysis of the 1999 and 2016 surveys conducted in Spain and an explanatory analysis of victimization based on the second survey. The two surveys were carried out by the Centre for Sociological Research (CIS, for its initials in Spanish; more information is at with a similar methodology (see Table 1). Both studies focus on the national environment and on the population over 18 years of age with large samples in Spain, although the 1999 sample is especially large, with nearly 13,000 interviewees. Sample selection with a broad network of sampling points by municipality and with systematic sampling conducted over several phases culminated with face-to-face interviews. The sampling error was measured at roughly 1%, with that for the 1999 survey being ±0.88% and that for the 2016 survey being ±1.4% for the entirety of the corresponding sample (Herranz and Fernández-Prados 2018).
All methodological information drawn from the two surveys such as data sheets, questionnaires, data tables and descriptive results are available for download from the corresponding link (see Table 1). The instrument or questionnaire of each survey presented a set of items measuring different types of crimes experienced within the last year (12 for the 1999 survey and 19 for the 2016 survey), making it possible to construct the dependent or victimization variable. The list of items of victimization derived from the 2016 questionnaire was reduced to 17, as there had been no cases of “terrorism” in the last year and “rape” was added to the category “sexual aggression,” as it appeared on the questionnaire during the 1990s. The independent variables cover traditional sociodemographic issues (sex, age, subjective social class, education and area of residence), and other questions used to explain victimization (e.g., lifestyles or free time spent outside the home covering four items, perceptions of the safety of one’s neighbourhood or locality and the probability of being the victim of a crime) of the same crimes were measured on a scale of 0–10.

2.2. Methodology and Analysis

On the one hand, the analysis of the data applied describes the evolution of victimization and of the reporting of different crimes experienced through the two surveys described, which address a period of 17 years; on the other hand, it is used to explain or construct a model with which to understand which variables cause one to become or not become a victim of a crime based on the 2016 survey alone because the psychosocial variables were not available for the 1999 data. For the descriptive analysis, the victimization percentage or the fraction of victims of each type of crime who have reported a crime is used. Meanwhile, for the explanatory study, a logistic regression analysis is carried out with a dichotomous dependent variable (having been a victim of a crime in the last 12 months or never having experienced any crime) and with the independent variables described above.

3. Results

A comparison of the victimization percentages derived from the 1999 and 2016 surveys offers several results of interest to us. First, the crimes most frequently reported are the same in both: the theft of objects from vehicles, the theft of a purse or wallet, and burglary of residential premises. Second, the crimes varying most significantly are muggings (−87.8%), vehicle thefts (−84.4%) and thefts of objects from vehicles (−67.1%). Finally, total aggregate victimization levels declined from 15.5% to 9.6%, reflecting a reduction of nearly six percentage points or a negative variation of 38.2% (see Table 2).
According to both studies, the crimes most frequently reported were the same and with roughly the same percentages: thefts of vehicles (reporting rates of 86.4% for 1999 and 83.2% for 2016) and burglary of residential premises (reporting rates of 74.7% for 1999 and 79.7% for 2016). While a percentage increase is observed in the reporting of crimes, of note are crimes involving thefts of objects from vehicles, cases of abuse or coercion, and cases of fraud and scams, which show increases of over 40%. Thus, for the aggregate set, the rate of reporting increased significantly by 43.2% or nearly 20 percentage points.
In sum, a comparison of the survey for the end of the 1990s and the more recent one most notably shows that along with a reduction in the victimization percentage (−38.3%) there has been an increase in the proportion of reporting (43.2%). Thus, from these data and the surveys, we can conclude that, while the statistics denote a similar percentage of reported crimes in these two years (1999 and 2016), behind this lies a reduction in the number of crime victims and an increase in reporting by victims, each compensating for the other.
These results are consistent with official studies and statistics, as well as with theories on crime in the West. The study of the Crime Balance of 2016 reflects decreases in practically all the objective crime indicators, but also in the social perception of insecurity (for more information, see The perception of citizen insecurity has experienced a positive evolution over the last few years: it has gone from representing 9.7% registered by the CIS in May 2010 to be a concern for only 3.2% of the population in December 2016. As additional significant evidence of this balance, it should be noted that: (a) crime declined by 1.2% during 2016; (b) the crime rate stands at 43.2 crimes per 1000 inhabitants; (c) intentional homicide amounted to 300 cases a year; (d) Spain is the country in the European Union with the second-lowest rate of violent deaths, behind only Austria.
Moreover, other international studies and theorists present the same trends in Western countries (Tonry 2014; Hunter and Tseloni 2016; Ignatans and Pease 2016, etc.). For example, Ignatans and Pease (2016) examine the changed distribution of crime across households in England and Wales, which mirrors the crime drop common to Western countries in the last two decades and explain that crime is being concentrated at the individual level (hot dots) as well as at area level (hot spots) (Pease et al. 2018).
The evolution of the sociodemographic profiles of those who have experienced a crime in recent months between one survey and the other allows us to confirm which variables are significantly associated with victimization and to determine whether this has varied in the long term. Thus, the profile of a victim according to the 1999 survey is a man 18 to 30 years of age, of high social standing, with higher education and residing in a city with more than 100,000 inhabitants. Additionally, when we analyse the differences between the differences (see Table 3), the values of the sociodemographic variables highlight a decline with respect to the total for the 2016 survey (male, −0.5; 18–30 years of age, −1.8; etc.).
The binary multivariate logistic regression allows us to evaluate how the dependent variable “having been or not been a victim of a crime in the last 12 months” is presumably related to the independent variables and to build a model or equation for predictive purposes. The independent variables are those used in the descriptive analysis and are considered continual variables except for sex, which is treated as categorical (male = 1; female = 0), and three psychosocial variables are added regarding lifestyles or free time, perceptions of the safety neighbourhoods and probability of becoming the victim of a crime. The psychosocial questions were only asked on the second survey (2016). The variable on lifestyle and leisure covers four items regarding the frequency of a series of free time activities occurring outside of the home (“I’m going to read you a series of leisure activities, and I would like you to tell me, for each one, if you often engage in them: go out to meet friends; go to the movies, to the theatre or the concerts; go to museums or exhibits; and attend conferences or colloquia”). A frequency scale was created, adding four variables and using a linear transformation, where 0 means never and 10 almost every day. Perceptions of safe neighbourhoods were measured with a question rated on a scale of 0 to 10 (“In thinking about these types of problems or situations, on a scale from 0 to 10, where 0 denotes ‘I feel very safe’ and 10 denotes ‘I feel very unsafe,’ how would you characterize the feelings that you experience when you are in your neighbourhood/locality?”). Finally, the probability of being the victim of a crime is determined by adding the scores from 0 (no probability) to 10 (high probability) from the interviewees for the 17 types of crimes (“to what extent do you think it is possible for you to be a victim of the following crimes in the neighbourhood/locality in which you live?”). The main descriptive results of psychosocial variables are shown on Table 4.
The results demonstrate that six of the eight variables contribute significantly to the explanatory model on being the victim of a crime over the last 12 months (see Table 5). The eight variables correspond to five sociodemographic variables and the other three are related to psychological variables, to free time spent outside of the home, to subjective perceptions of the safety of one’s neighbourhood, and to the probability of being a victim. The latter variable generates the highest Wald test statistic (49.480), followed by lifestyle (12.865); both are psychosocial variables and highly statistically significant (p < 0.001), although sex, education, and area of residence are also significant, but less so (p < 0.05). In any case, the omnibus test is highly statistically significant (p < 0.000), although the proportion of the explained variability of being a victim of a crime according to our model is not excessively high (between 12.3% for Cox and Snell’s R-squared and 20.8% for Nagelkerke’s R-squared).

4. Conclusions

The first conclusion drawn from the descriptive and comparative study verifies the evolution of the sociodemographic variables relating to victimization from the 1999 and 2016 surveys for Spain. While sex (being male), age (young), education (higher education or university) and living area (urban) maintain associations with being a victim of crime for both surveys, it has been demonstrated that the sociodemographic or classificatory variables have declined in intensity, and some have even lost their associations, as in the case of social class.
Second, the explanatory model obtained again demonstrates that, apart from sex, education and area of residence, the other sociodemographic variables of age and social class did not achieve statistical significance. By contrast, psychosocial variables related to lifestyle or leisure, free time spent outside of the home, subjective perceptions of the safety of one’s neighbourhood and the probability of being a victim of crime do grow in importance in the explanatory model and particularly in the case of the latter.
This study also shows that there is a need for future work to expand on the probability of being a victim or fears of experiencing a crime. Thus, research on perceptions of fear and their relationship to victimization is salient, as confirmed by recent studies (Chadee et al. 2017; Chadee 2016; Doran and Burgess 2012), and psychosocial variables and subjective perceptions of crime are clearly associated with variables regarding lifestyles, neighbourhood locations and social contexts (Tseloni 2006). Building on this work, future studies may also examine the evolution of victimization and its relationship to reporting crimes, as the data demonstrate a reduction in the victimization percentage and an increase in the proportion of reporting. In this sense, researching the causes of this process and potential hypotheses (increased civic responsibility, social pressures, a loss of fear, increased ease of reporting, etc.) may be an area of interest for the social sciences (Turanovic et al. 2018).
There are other methodological aspects to consider in future studies, like comparing surveys and statistical data. In fact, victimization and self-report surveys, which ask people about their criminal victimization, or offending, over a specified period, yield better estimates of the prevalence of crime than do official statistics (Gartner 2015). It is also necessary to confirm the trends and their causes from an international, comparative point of view (Fernández-Prados et al. 2018). The explanations are simultaneously simple and complex (Tonry 2014): (a) simple explanations deal with situational crime prevention initiatives and target hardening; (b) complex explanations concern interactions among cultural and secular changes that have influenced capacities for informal social control and individual self-control. Finally, it is important to make a difference analysis depending on the dependent variable for a defensible crime recording system. In that sense, Pease and Tseloni (2014) consider as an absolute minimum requirement breaking down the crime rate (incidence) into its components: prevalence (or risk), the proportion of units (people and places) available to be victimised that are in act victimised; and concentration—the number of victimisations that the average victimised unit (person or place) suffers.

Author Contributions

Conceptualization, G.H.d.R.; Formal analysis, J.S.F.-P.; Methodology, J.S.F.-P.; Writing—original draft, G.H.d.R.; Writing—review & editing, J.S.F.-P.


This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Alvira, Francisco, and María A. Rubio. 1982. Victimización e Inseguridad: La perspectiva de las encuestas de victimización en España. Revista Española de Investigaciones Sociológicas 18: 29–50. [Google Scholar]
  2. Breuer, Johannes, Rachel Kowert, Ruth Festl, and Thorsten H. Quandt. 2015. Sexist Games = Sexist Gamers? A Longitudinal Study on the Relationship Between Video Game Use and Sexist Attitudes. Cyberpsychology, Behavior, and Social Networking 18: 192–202. [Google Scholar] [CrossRef] [PubMed]
  3. Catalona, Shannan M. 2005. Criminal Victimization 2004; Washington: Bureau of Statistics, US Department of Justice.
  4. Chadee, Derek. 2016. Psychology of Fear, Crime, and the Media. New York: Routledge. [Google Scholar]
  5. Chadee, Derek, Stacia Ali, Ariel Burke, and Jason Young. 2017. Fear of crime and community concerns: Mediating effect of risk and pragmatic fear. Journal of Community and Applied Social Psychology 27: 450–62. [Google Scholar] [CrossRef]
  6. Choi, Kyung-shick. 2008. An empirical assessment of an integrated theory of computer crime victimization. International Journal of Cyber Criminology 2: 308–33. [Google Scholar]
  7. Clevenger, Shelly H., Jordana N. Navarro, Catherine D. Marcum, and George E. Higgins. 2018. Understanding Victimology. An Active-Learning Approach. New York: Routledge. [Google Scholar]
  8. Crawford, Adam, Trevor Jones, Tom Woodhouse, and Jack Young. 1990. The Second Islington Crime Survey. London: Centre for Criminology, Middlesex Polytechnic. [Google Scholar]
  9. Dignan, James. 2005. Understanding Victims and Restorative Justice. Maidenhead: Open University Press. [Google Scholar]
  10. Dixon, Mike, Reed Howard, Ben Rogers, and Lucy Stone. 2006. CrimeShare: The Unequal Impact of Crime. London: IPPR. [Google Scholar]
  11. Doran, Bruce J., and Melissa B. Burgess. 2012. Putting Fear of Crime on the Map. Investigating Perceptions of Crime Using Geographic Information Systems. New York: Springer. [Google Scholar]
  12. Elias, Robert. 1986. The Politics of Victimisation. Oxford: Oxford University Press. [Google Scholar]
  13. Elias, Robert. 1993. Victims Still. London: SAGE. [Google Scholar]
  14. Fattah, Ezzat A., ed. 1992. Critical Victimology. London: Macmillan. [Google Scholar]
  15. Felson, Marcus. 2006. Crime and Nature. Thousand Oaks: Sage Publications. [Google Scholar]
  16. Fernández-Prados, Juan S., Cristina Cuenca, and María J. González. 2018. International public opinion surveys and public policy in Southern European democracies. Journal of International and Comparative Social Policy, 1–11. [Google Scholar] [CrossRef]
  17. Forrester, David, Mike Chatterton, and Ken Pease. 1988. The Kirkholt Burglary Prevention Project, Rochdale. London: Home Office. [Google Scholar]
  18. Gartner, Rosemary. 2015. Crime: Knowledge about and Prevalence. In International Encyclopedia of the Social & Behavioral Science, 2nd ed. Edited by James Wright. Amsterdam: Elsevier, vol. 5, pp. 164–69. [Google Scholar]
  19. Genn, Hazel. 1988. Multiple victimisation. In Victims of Crime: A New Deal? Edited by Mike Maguire and John Pointing. Milton Keynes: Open University Press. [Google Scholar]
  20. Herranz, Gonzalo, and Juan S. Fernández-Prados. 2018. Subterranean Values and Deviance: An Empirical Investigation of the Case of Spain. Social Sciences 7: 149. [Google Scholar] [CrossRef]
  21. Hindelang, Michael J., Michael R. Gottfredson, and James Garofalo. 1978. Victims of Personal Crime: An Empirical Foundation for a Theory of Personal Victimization. Cambridge: Ballinger. [Google Scholar]
  22. Hough, Mike, and Pat Mayhew. 1983. The British Crime Survey: First Report. London: HMSO. [Google Scholar]
  23. Hunter, James, and Andromachi Tseloni. 2016. Equity, justice and the crime drop: The case of burglary in England and Wales. Crime Science 5: 1–13. [Google Scholar] [CrossRef]
  24. Ignatans, Dainis, and Ken Pease. 2016. On whom does the burden of crime fall now? Changes over time in counts and concentration. International Review of Victimology 22: 55–63. [Google Scholar] [CrossRef]
  25. Joutsen, Matti. 1987. The Role of the Victim of Crime in European Criminal Justice Systems. Helsinki: Helsinki Institute for Crime Prevention and Control. [Google Scholar]
  26. Karmen, Andrew. 1990. Crime Victims: An Introduction to Victimology. Belmont: Wadsworth. [Google Scholar]
  27. Kinsey, Richard, John Lea, and Jock Young. 1986. Losing the Fight against Crime. Oxford: Basil Blackwell. [Google Scholar]
  28. Mawby, Rob I., and Sandra Walklate. 1994. Critical Victimology: International Perspectives. London: SAGE Publications Ltd. [Google Scholar]
  29. Mendelsohn, Benjamin. 1976. Victimology and contemporary society’s trends. Victimology: An International Journal 1: 8–28. [Google Scholar] [CrossRef]
  30. Miers, David. 1989. Positivist victimology: A critique part 1. International Review of Victimology 1: 1–29. [Google Scholar] [CrossRef]
  31. Navarro, Jordana N., and Jana L. Jasinski. 2015. Demographic and motivation differences among online sex offenders by type of offense: An exploration of routine activities theories. Journal of Child Sexual Abuse 24: 753–71. [Google Scholar] [CrossRef] [PubMed]
  32. Pease, Ken, and Andromachi Tseloni. 2014. Using Modelling to Predict and Prevent Victimization. London: Springer. [Google Scholar]
  33. Pease, Ken, Dainis Ignatans, and Lauren Batty. 2018. Whatever happened to repeat victimisation? Crime Prevention and Community Safety 20: 256–67. [Google Scholar] [CrossRef]
  34. Reyns, Bradford W., Billy Henson, and Bonnie S. Fisher. 2011. Being pursued online: Applying cyberlifestyle-routine activities theory to cyberstalking victimization. Criminal Justice and Behavior 38: 1149–69. [Google Scholar] [CrossRef]
  35. Sharp, Susan F. 2009. Feminist criminology. In 21st Century Criminology: A Reference Handbook. Edited by Miller J. Mitchell. Thousand Oaks: Sage Publications, pp. 245–52. [Google Scholar] [CrossRef]
  36. Tang, Wai Y., and Jesse Fox. 2016. Men’s Harassment Behavior in Online Video Games: Personality Traits and Game Factors. Wiley Periodicals 42: 513–21. [Google Scholar] [CrossRef] [PubMed]
  37. Tonry, Michael. 2014. Why Crime Rates Are Falling Throughout the Western World. Crime and Justice 43: 1–63. [Google Scholar] [CrossRef]
  38. Torrente, Diego. 2001. Desviación y delito. Madrid: Alianza. [Google Scholar]
  39. Tseloni, Andromachi. 2006. Multilevel modelling of the number of property crimes: Household and area effects. Journal of the Royal Statistical Society Series A—Statistics in Society 169: 205–33. [Google Scholar] [CrossRef]
  40. Turanovic, Jillian J., Travis C. Pratt, and Alex R. Piquero. 2018. Structural Constraints, Risky Lifestyles, and Repeat Victimization. Journal of Quantitative Criminology 34: 251–74. [Google Scholar] [CrossRef]
  41. Von Hentig, Hans. 1948. The Criminal and his Victim. New Haven: Yale University Press. [Google Scholar]
  42. Walklate, Sandra. 1989. Victimology: The Victim and the Criminal Justice Process. London: Unwin Hyman. [Google Scholar]
  43. Walklate, Sandra. 2007. Imagining the Victim of Crime. London: Open University Press. [Google Scholar]
  44. Walklate, Sandra. 2015. Jock Young, left realism and critical victimology. Critical Criminology 23: 179–90. [Google Scholar] [CrossRef]
  45. Wolfgang, Martin E. 1957. Victim precipitated criminal homicide. Criminal Law, Criminology, and Police Science 48: 1–11. [Google Scholar] [CrossRef]
Table 1. Surveys of victimization in Spain (1999 and 2016).
Table 1. Surveys of victimization in Spain (1999 and 2016).
Study:CIS No. 2315 Citizen security and victimizationCIS No. 3123 Spanish General Social Survey
Scope:NationalNational (excluding Ceuta and Melilla)
Universe:Spanish population, both sexes, 18 years old and overSpanish population, both sexes, 18 years old and over
Sample:12,994 interviews5290 interviews
Sampling points:513 municipalities and 52 provinces523 municipalities and 48 provinces
Sampling procedure:Multi-stage, stratified by clusters with the random proportional selection of primary sampling units (municipalities) and secondary units (sections) and with the selection of final units (individuals) by random routes and sex and age quotas.Two stages and stratified by cluster. The selection of primary sampling units (sections) is proportional to the resident population, and the selection of final units (individuals) is carried out through a systematic selection of individuals residing in the area by house number.
Sampling error:For a confidence level of 95.5% (two sigma) and where P = Q, the error is ±0.88% for the whole sample.For a confidence level of 95.5% (two sigma) and where P = Q, the real error is ±1.4% for the whole sample and for the simple random sampling association.
Date:From 11 January to 28 February 1999From 22 December 2015 to 12 April 2016
Source: The authors.
Table 2. Comparison of the victimization and reporting rates by type of crime derived from the 1999 and 2016 surveys for Spain.
Table 2. Comparison of the victimization and reporting rates by type of crime derived from the 1999 and 2016 surveys for Spain.
1999 *2016 *Diff. (2016–1999)
Violent purse theft0.7%62.6%0.2%57.9%−73.0%−7.6%
Purse or wallet theft2.6%58.7%1.6%66.0%−37.7%12.4%
Burglary of residential premises2.3%74.7%1.3%79.7%−42.7%6.8%
Car theft1.7%86.4%0.3%83.2%−84.4%−3.7%
Theft of objects from a vehicle4.7%42.6%1.6%64.3%−67.1%50.8%
Fraud or scams1.2%28.5%0.8%41.0%−28.4%44.0%
Intimidation or threats1.8%28.8%0.8%34.6%−54.9%20.2%
Physical aggression1.1%35.0%0.3%48.9%−70.2%39.6%
Rape or sexual aggression0.1%40.0%0.0%50.0%−50.9%25.0%
Disorderly behaviour or vandalism2.0%26.9%1.8%37.6%−13.5%39.9%
Abuse or coercion by authorities1.4%13.5%0.6%19.7%−58.8%45.5%
Total (aggregate)15.5%46.2%9.6%66.1%−38.3%43.2%
Rate victimization (100,000)15,5009600
Source: The authors based on CIS studies 2315 (1999) and 3123 (2016). * Victimization = percentage of victims among respondents; Reporting = percentage of victims surveyed who report to the police; Victims: extrapolation from survey; Population: over 18 years old in Spain.
Table 3. Comparison of the sociodemographic profiles of people who have experienced a crime in the last 12 months in Spain (1999 and 2016).
Table 3. Comparison of the sociodemographic profiles of people who have experienced a crime in the last 12 months in Spain (1999 and 2016).
19992016Difference in the Difference 1
AGE 5.9
 Over 608.85.72.8
 Higher education21.313.6−1.8
 Less than 10,000106.72.6
 Over 100,00019.411.1−2.4
Source: The author based on CIS studies 1999 and 2016. 1 Difference in the difference = (v2016 − total2016) − (v1999 − total1999).
Table 4. Description of psychosocial variables by the dependent variable, “having been a victim or not” in the last 12 months (Spain, 2016).
Table 4. Description of psychosocial variables by the dependent variable, “having been a victim or not” in the last 12 months (Spain, 2016).
Dependent Variable
VictimNot a VictimTotal
 Mean (0–10)
 Standard deviation1.601.571.63
 Mean (0–10)
 Standard deviation1.852.101.93
 Mean (0–10)
 Standard deviation1.651.761.73
Source: The authors, based on CIS study (2016).
Table 5. Logistic regression analysis with “having been a victim or not” in the last 12 months, as the dependent variable (Spain, 2016).
Table 5. Logistic regression analysis with “having been a victim or not” in the last 12 months, as the dependent variable (Spain, 2016).
Model with Sociodemographic VariablesModel with Sociodemographic and Psychosocial Variables
B 1Sig. 2Exp (B) 3B 1Sig. 2Exp (B) 3
Sex (1 = male)−0.2650.0110.768−0.5060.0020.603
Social class−0.0340.4350.9670.0320.6461.032
Area of residence0.1930.0001.2130.1230.0191.131
Lifestyle or free time 0.2120.0001.236
Perceptions of neighbourhood safety 0.1520.0011.164
Probability of being a victim 0.3480.0001.416
Cox and Snell’s R-squared4.9%12.3%
Nagelkerke’s R-squared8.2%20.8%
Source: The authors, based on CIS study (2016). 1 This is the coefficient for the constant (also called the “intercept”) in the null model. 2 This is the Wald chi-square test that tests the null hypothesis that the constant equals 0. This hypothesis is rejected because the p-value (listed in the column called “Sig.”) is smaller than the critical p-value of 0.05 (or 0.01). 3 This is the exponentiation of the B coefficient, which is an odds ratio.

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Herranz de Rafael, G.; Fernández-Prados, J.S. Victimization, Social Structure and Psychosocial Variables: The Case of Spain in 1999 and 2016. Soc. Sci. 2019, 8, 102.

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Herranz de Rafael G, Fernández-Prados JS. Victimization, Social Structure and Psychosocial Variables: The Case of Spain in 1999 and 2016. Social Sciences. 2019; 8(3):102.

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Herranz de Rafael, Gonzalo, and Juan Sebastián Fernández-Prados. 2019. "Victimization, Social Structure and Psychosocial Variables: The Case of Spain in 1999 and 2016" Social Sciences 8, no. 3: 102.

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