Lethal Domestic Violence As a Sequential Process: Beyond the Traditional Regression Approach to Risk Factors

CSI0010.1177/0011392116629809Current SociologyGnisci and Pace 629809research-article2016

Article CS

Current Sociology 2016, Vol. 64(7) 1108–1123 Lethal domestic violence as a © The Author(s) 2016 Reprints and permissions: sequential process: Beyond the sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0011392116629809 traditional regression approach csi.sagepub.com to risk factors

Augusto Gnisci and Antonio Pace Department of Psychology, Second University of Naples, Italy

Abstract This contribution conducts a critical review of the many features of the traditional regression approach to domestic violence that implies a target variable (intimate partner femicide) and multiple predictors. Based on conceptual, theoretical, methodological and statistical considerations, this analysis, first, addresses the issue of how the risk factors can be properly organized; second, it claims that what are currently regarded as the independent variables might have numerous different explanatory roles, making reference to direct, indirect, mediated or moderated models, as well as to the facilitating and suppressing effect of variables; finally, the article introduces the concept of sequential behavioural patterns. Assuming that the build-up to partner femicide (PF) is a dynamic process, the analysis cautions against ignoring the sequence through which the different moves among the actors actually take place. The same events occurring in a different order may result in entirely different outcomes. This contribution is essentially theoretical, implementing worked examples from literature on risk factors and femicide. Implications for researchers and operators in the field of PF are underlined.

Keywords Domestic violence, intimate partner homicide (IPF), mediation, moderation, partner femicide (PF), risk factors, sequential process

Corresponding author: Augusto Gnisci, Department of Psychology, Second University of Naples, Viale Ellittico 31, 81100 Caserta, Italy. Email: [email protected] Gnisci and Pace 1109

Introduction This article is about partner femicide (PF), or intimate partner homicide (IPH). Consistent with the Vienna Declaration on Femicide in the United Nations Commission on Crime Prevention and Criminal Justice (2013: 2), we employ the term ‘partner femicide’ to refer to ‘the killing of women and girls because of their gender’, which takes the form of the murder of a female as a result of domestic violence by an intimate partner (husbands and lovers, ex-husbands and ex-lovers; Campbell et al., 2003). Thus, we focus on only one of the many categories included in the general definition of femicide. In fact, the target of this article has a narrower focus, addressing the lethal forms of domestic violence against the women that involve, if not necessarily premeditation and preplanning, at least a process leading to death. This progression constitutes a history comprising multiple interactions and episodes of violence, estrangement, or relation oppression (Hall, 2008), that develops over weeks, months or even years, and escalates to lethality. There are homicides that exhibit no preplanning, but which demonstrate a history of violence, prevarication and coercion. In such cases, domestic violence escalates to a lethal outcome – and it is these which fall within the scope of the present article – but there are also homicides that have no premeditation and no process (e.g. mental illness, gross negligence, crimes caused by an impulsive reaction to a new perturbing situation): these do not fall within that scope. The aim of this contribution is primarily theoretical. First, we describe one of the best studies published on femicide (Campbell et al., 2003) and review, in outline, the research on risk factors to prevent femicide that employs a regression (or multivariate) approach (we use this term to refer to multiple regression and multiple logistic regression, as we argue below). Then, we develop a number of conceptual, theoretical, methodological and statistical considerations, in order to encourage researchers to develop further this traditional method of analysis of femicide. In so doing, we focus on particular concepts that could be applied profitably to the study of femicide as the outcome of a process unfolding over time. What this contribution does not intend to do is to propose an alternative model to explain femicide, depicting which particular variables should be applied in what role; instead, it aims to encourage researchers in the area of femicide to identify such variables and promote a wider and differentiated span of models. These include not only independent and dependent variables, as in the risk factors model implemented by a regression approach, but also different connections among variables, such as mediation and moderation (see below). The many references that we make to the variables used in femicide literature throughout the text need to be understood, not as models that we wish to propose, but as reasoned examples that clarify the proposed concepts. A more accurate approach to the study of lethal domestic violence against women would, in general, offer a better understanding of the phenomenon; this, in turn, could generate interventions that are more effective and impact to reduce levels of this type of violence in society. Ultimately, we hope that these considerations will prove helpful in supporting and improving the feministic perspective on femicide (for an up-to-date description, see Taylor and Jasinski, 2011).

The regression approach to femicide What we label as the regression approach has played a very significant and productive role in the history of studies on femicide. It has provided useful explanations of femicide 1110 Current Sociology 64(7) and allowed the sensitization of the operators in the field and the victims themselves to the many ‘alarm bells’ that lead to domestic violence and femicide. Given that our aim is to encourage the development of this approach, we are not emphasizing the many posi- tive advancements it has provided in recent decades and continues to provide today: we take them for granted. In the following, we describe one of the most highly cited, influential and well-conducted studies on risk factors for femicide, the 11-city study on femicide in abusive relationships (Campbell et al., 2003). We describe its features and its outcomes as a good starting point for our discussion. Among its merits, that study includes a control sample and a wide array of variables, properly arranged in seven different levels/blocks – socio-demographic, individual, relationships, controlling behaviours, threats and stalking, preceding physical abuse and incident risk factors. In the cases group of abusive relationships leading to femicide, all the police and medical examiner records from 1994 and 2000 were sampled (N = 220) and information was also taken from at least two proxy informants. In the control group (N = 343), abused or battered women were identified by a stratified, random-digit dial- ling, and they were matched as they pertained to the same cities where the femicide occurred. The design was a retrospective case-control and the statistical procedure used was logistic regression. The authors test seven models and, in each model, one of the above-mentioned blocks of variables was entered. The blocks were organized from the most distal to the most proximal, adding the last to the former, so that only strong distal variables would remain. The main predictors of femicide were identified as: an abuser’s unemployment, which was the most important demographic risk factor; an abuser’s access to firearms and previous threats with a weapon; the presence of a child of the victim by a previous partner; a woman’s separation from the abuser after cohabitation, in particular from a high-level controlling abuser. Moreover, the study identified a number of risk-reducing factors: prior arrest for domestic violence, as well as victim and abuser never having lived together. Interestingly, in a subsequent revisitation of the study, the authors lend greater emphasis to the interaction between the estrangement by the woman and the abuser being a high-level controller (Campbell, 2009). The final model correctly predicted 81% of the cases and 95% of control women. Many other studies have identified numerous factors that may increase the risk of femicide. We list here the most important predictive variables found in literature (in the interest of brevity, we do not report contextual information of the research and the design and analysis, for which we invite the reader to refer to the original manuscripts). These are: previous partner violence (Bailey et al., 1997; Browne et al., 1998; Campbell et al., 2009; Moracco et al., 1998; Saunders and Browne, 2000); prior abuse by the perpetrator (Block, 2003); older age of the abuser, relative to the victim (Breitman et al., 2004); cohabitation vs marriage (Moracco et al., 1998); the perpetrator’s stepchild in the home (Daly et al., 1997); estrangement (Campbell, 1992; Moracco et al., 1998; Wilson and Daly, 1993); access to a firearm (Bailey et al., 1997; Campbell et al., 2009; Violence Policy Center, 2013), or threats to kill with a weapon (Campbell et al., 2003, 2007); pregnancy and being abused during pregnancy (Campbell et al., 2003, 2007); the woman being more educated, employed or a higher income earner (Buzawa and Buzawa, 2013; Renzetti, 2009); unemployment of male partner (Campbell et al., 2003, 2007; Stöckl Gnisci and Pace 1111 et al., 2013); a sense of inadequacy or loss of power in men (Anderson, 1997; Campbell et al., 2009; Hornung et al., 1981; Kaukinen, 2004; Taylor and Nabors, 2009); a high controlling abuser (Campbell et al., 2003); the woman’s diminished exposure, that is, the length of time that participants in a violent relationship are in contact with one another (Dugan et al., 2003) and leaving the abusive relationship (Bailey, 1999); jealousy, pos- sessiveness and suspicions of infidelity (Campbell et al., 2003; Wilson and Daly, 1992, 1993); forcing sexual intercourse on a partner (Campbell et al., 2007); problematic alcohol use and illicit drug use or mental health problems (Campbell et al., 2003), etc. While we have mentioned many of the factors that predispose for femicide detailed in recent research, our list does not claim to be exhaustive. As we shall see, the considerations we develop on the regression model remain valid, independently of the content and number of predictors. Usually, the statistical techniques used to identify risk factors of femicide are dual, namely, multiple regression (MR) and multiple logistic regression (LR) (Hutcheson and Sofroniou, 1999; Kline, 2011). In both, predictors are multiple and can be continuous or categorical (X1, X2, …, Xn), but in MR the criterion Y is continuous, whereas in LR it is dichotomous, as in the case versus control example mentioned above (Campbell et al., 2003). While in MR, the method of estimation for assessing the model fit is known as ordinary least squares (OLS), in LR it employs a maximum likelihood method, but applied to a unique variable that is a transformation of the binary outcome (known as a logit variable). Predictors may be entered into the equation all together (simultaneously), or in a pre- determined order (sequentially, or in stages). In the latter case, two methods of regression can be utilized: the hierarchical method, where the researcher chooses the order of inser- tion for different variables or different, conceptually organized, blocks of variables; or the stepwise method, using empirical indexes, where the procedure (e.g. the pc) automatically selects which predictors need to be inserted. The methods for entering the variables in the logistic regression are almost identical, although the statistics used to assess the model fit are different (Hutcheson and Sofroniou, 1999). Given that MR and LR are two special cases of generalized linear models, hereinafter we refer to both of them as the regression approach.

Conceptual organization of the variables and order of entry Models can sometimes present risk factors as if they were similar in value or, at least, all equivalent in degree – for example: the age difference between perpetrator and victim; the woman’s higher professional status; the perpetrator’s sense of inadequacy; and the presence of a firearm – as if they held the same theoretical and empirical status and weighting as predictors of lethal violence. Yet these are drawn from, and represent, entirely different categories. Age difference may be regarded as a background or structural variable; the woman’s higher professional status is a sociological variable; the perpetrator’s sense of inadequacy is a psychological variable; and the presence of a gun is a type of environmen- tal variable. If we insert them simultaneously into a multiple regression model, we over- look their specificity and their meaningfulness; moreover, this represents a weaker approach, in statistical terms (for reasons, see e.g. Tabachnick and Fidell, 1989). 1112 Current Sociology 64(7)

It is not our intention to imply that all the risk factors research on femicide is biased or flawed by this approach; on the contrary, we wish to encourage the entry of predictors in blocks, in a hierarchical order, into the multiple regression equation, following theoretical or rational standards, rather than empirical or statistical procedures that capitalize on chance (Kline, 2011). Indeed, ‘sometimes demographic variables are entered at the first step, and then entered at the second step as a psychological variable of interest. This order not only controls for demographic variables but also permits evaluation of the predictor power of the psychological variable, over and beyond that of simple demographic variables’ (Kline, 2011: 27).1 The last procedure is precisely the achievement demonstrated by the cited 11-city study, which sought to identify femicide risk factors in abusive relationships. Campbell et al. (2003) first identified the distal/proximal aspect of the risk factors as a basic sorting criterion, then ‘sequentially added blocks of conceptually similar explanatory variables along a risk factor continuum ranging from most distal … to most proximal’ (2003: 1090). This strategy incorporates conceptual relevance, theoretical clarity and a statistical basis. Naturally, different researchers may classify and order their relevant variables in different ways, following different theoretical approaches, but the very fact that they are organized in relevant ways provides the surplus value.

The roles of variables and models When a regression approach to risk factors is used, the lethal outcome is the target variable and the other variables can therefore only be independent variables (or predictors). This section aims at clarifying that different variables may play different roles on the path leading to the explanation of the dependent variable (target). The methodological framework is the one of structural equation modelling (SEM; Kline, 2011), a family of statistical methods and procedures designed to (dis-) confirm different models that pattern links among variables, which has its origins in sociology (Duncan, 1966, 1975) and social sciences, in general (cf. Kline, 2011). First, we introduce a basic distinction in SEM: the difference among mediation and moderation, which has been extant in the social sciences only since the 1980s (Baron and Kenny, 1986). Then, we describe some interesting consequences regarding the application of this distinction to the issue of femicide. In the discussion below, the target variable is the form of femicide that we defined at the beginning of the introduction: a history made of multiple interactions and episodes of violence, estrangement or relation oppression that develops over weeks, months or even years, and escalates to lethality. Note that we employ the singular (e.g. ‘when the woman leaves’), rather than the more generic plural (‘when women leave’), because it permits clearer reference to a specific actor (e.g. ‘she’, or ‘he’, in place of ‘they’ and ‘her’, or ‘his’, in place of ‘their’).

Mediation and indirect effects When an initial variable A (sometimes labelled exogenous, because these are always just independent variables) affects a second variable B, the link is deemed to be direct. Nonetheless, if there is also a third link – B also affects a third, final variable Y – A may Gnisci and Pace 1113 exert its effect upon Y by way of B and the link is therefore indirect or mediated. In this case, B is termed as an endogenous variable, because it can assume the role of dependent (i.e. for A) and independent (i.e. for Y) and it is always a variable internal to the model. Previously, we mentioned the reported link between the higher status of the woman and violence by the partner (cf. Taylor and Jasinski, 2011). We would like to use this example to demonstrate that a third psychological variable – a sense of inadequacy in the male partner – can be implicated in that link. According to many authors, in fact, a difference in status favouring the woman, such as higher education or income, may indeed increase the man’s sense of inadequacy; he, in turn, may resort to psychological and physical violence (Anderson, 1997; Buzawa and Buzawa, 2013; Campbell et al., 2009; Hornung et al., 1981; Kaukinen, 2004; Renzetti, 2009; Taylor and Nabors, 2009). This is a classic example of an indirect, mediated link: violence would occur when the man expe- riences a sense of loss of power (Lambert and Firestone, 2000) and/or of inadequacy (Gauthier and Bankston, 2004; Kaukinen, 2004); the latter, respectively, is determined by the woman’s higher status. This simple, indirect model, with inadequacy as the intermediate variable (mediator), may account for much of partner violence; in any case, it offers a more comprehensive and substantiated explanation of intimate partner femicide.

Moderation and interaction effects One of the major limitations of the regression approach is that it sometimes disregards interactions among predictive variables. Of course, authors may have solid theoretical or methodological reasons to not address interactions. However, interactions among the variables are characteristic of most phenomena in human society and behaviour, includ- ing specific crimes, possibly, such as femicide. Many authors report that the difference in status among partners (e.g. a more educated woman) may lead to psychological and physical violence by the man (Anderson, 1997; Buzawa and Buzawa, 2013; Campbell et al., 2009; Hornung et al., 1981; Kaukinen, 2004; Renzetti, 2009; Taylor and Nabors, 2009). However, when the woman declares her wish, or attempts, to leave the man (estrangement), the difference in status – according to the research results of many authors – may escalate into lethal violence (Taylor and Jasinski, 2011). Therefore, if this is the case, estrangement becomes a moderator. If we regard the three variables and their supposed links as a small predictive model, we see that disparity of status increases the risk of violence against women, whereas it is the act of separation by a woman, which transforms that disparity into a cause of death. To summarize, the effect of the difference in status on violence perpetrated by the man is not the same, depending on whether the woman has or has not completed the act of separation. When determining (psychological and physical vs lethal) violence, we would say that the disparities among partners interact with an estrangement. The types of interaction effects may be numerous; however, the facilitating and the suppressing effects are the more interesting ones. When the interaction effect between A and B is greater than the sum of the single component effects, the effect is termed facilitating, because the effect of a particular variable, namely A, upon the dependent Y is increased by the third variable B. Literature on 1114 Current Sociology 64(7) femicide suggests, for example, that the presence of a child of the victim by a previous partner in the home increases the risk of intimate partner femicide (Brewer and Paulsen, 1999; Campbell et al., 2003; Daly et al., 1997). When it is less than the aggregate sum, the effect is suppressing, because the third variable B acts to mitigate the effect of A upon Y. This distinction appears particularly relevant in the context of femicide, because while there may be risk factors present, there could also be protective factors, that is, elements acting to reduce rates of femicide (Buzawa and Buzawa, 2013). For example, partners who never lived together were, in general, associated with lower risk (Campbell et al., 2003).

Models of mediation and moderation combined The model patterned in the preceding sections can be further articulated, for example, if researchers consider that a femicide implies a fourth variable with a different role. Examining the effect of leaving or not leaving a relationship, we may hypothesize the former indirect model, coupled with an estrangement interacting with the man’s sense of inadequacy. Initially, the existing disparity of status may create a sense of inadequacy that, when the woman does not leave, may change into psychological and physical violence; however, when the woman does leave (estrangement), the status disparity may lead to lethal violence. In this model, estrangement interacts with the sense of adequacy, producing a different outcome and represents the moderator of the relationship between the sense of inadequacy and lethal violence, whereas the sense of inadequacy is a mediator of the link between difference in status and violence. This model, while not formulated in the terms and the lexicon employed above, has been proposed and has found empirical evidence within the feminist perspective. In essence, it perceives the submissiveness of women in our society as women’s only mode of survival (e.g. Taylor and Jasinski, 2011; Walker, 1979), predicting that the closer women and men come to equality, the higher the risk of victimization (Taylor and Jasinski, 2011). The preceding explanation, based on the mediation-moderation distinction, addresses the – sometimes – controversial role of determined risk factors, such as the presence of a firearm. Its importance is demonstrated by the afore-mentioned 11-city study (Campbell et al., 2003), which identifies, inter alia, the perpetrator’s access to a gun and previous threats with a weapon as risk factors in femicide, albeit at different levels. This, in turn, is reflected in two items of the Danger Assessment Survey (‘Does he own a gun?’; ‘Has he ever used a weapon against you or threatened you with a lethal weapon?’; Campbell et al., 2009: 655), while other items may imply its use more indi- rectly (e.g. ‘Does he threaten to harm your children?’; ‘Do you believe he is capable of killing you?’). Much has been said about this alleged predictor of homicide in general (Hemenway and Miller, 2000; United Nations Office on Drugs and Crime, 2011), and of femicide, in particular (Bailey et al., 1997; Campbell et al., 2007; Violence Policy Center, 2013). We should, however, be sceptical of considering ease of access to a firearm or a weapon as a simple, causal predictor of femicide. Gnisci and Pace 1115

As correctly stated by some scholars, in cases of domestic violence, a firearm greatly increases the risk of intimate partner homicide (Violence Policy Center, 2013). Leaving aside the complicated issue of ‘intentionality of murder’ (Block and Christakos, 1995; Campbell, 1992), domestic violence by the male partner against the woman may be strongly facilitated by access to a gun and can result in lethal violence. What is at stake here is the presence of an interaction between domestic violence and presence of a gun. Therefore, in no manner does a gun or a weapon constitute a causal factor; their role is, rather, that of moderator.

Consequences Assigning the appropriate role to each variable (independent, mediator, moderator, target) provides us with more realistic and effective models, enabling us to direct our attention to improving the focus and development of preventive measures. Furthermore, the importance of the moderators is also evident in an application to which we wish to allude briefly. In many cases, we might be making the error of over- generalizing femicide as a similar phenomenon, taking place in different contexts, societies, cultures, countries, etc. Rather, we should recognize that these elements may act as moderators and that the processes leading to lethal violence may be different if they vary: facilitating factors in one determined culture may be ineffective in another: for example, where the measure of gender equality in a society is found to be associated with femicide, but this link is valid only for White, or for White, non-Hispanic people (Vieraitis and Williams, 2002); in other cases, there is no causative effect. Interestingly, the so-called backlash hypothesis, whereby the more women gain equality, the more they are at increased risk of victimization (Taylor and Jasinski, 2011), pertains only in that population. Essentially, by identifying these kinds of mediators, we can compare directly the different processes paving the road to femicide in different contexts, societies, cultures or countries. This may also clarify some inconsistencies in findings brought by the literature. Indeed, the backlash hypothesis was debated until the studies were disaggregated by race. Briefly, the use of data disaggregated for different populations – that is, using moderators – is being encouraged increasingly (i.e. differ- ences in race, class, geographical areas, such as rural vs urban areas, etc.) (Taylor and Jasinski, 2011). There are a couple of brief but important final, statistical considerations: the different models of femicide, such as the moderation and the mediation ones, demand and inter- face with specific statistical data analysis techniques (see Baron and Kenny, 1986; Kline, 2011). If the different roles of the many variables are not recognized and a multiple regression analysis is performed instead, the results of statistical analysis may be mis- leading, if not entirely erroneous. Use of SEM needs to be founded in a strong theoretical background. Logistic regression allows the building of risk scores to assess the risk of femicide in a domestic violence setting, an extremely useful tool (e.g. Campbell et al., 2009). However, we advise that if the multiple regression approach (numerable predictors and a unique target variable) is not applied to the process involved, the scores could be biased (at least to some degree). A SEM approach could provide more adequate scores, because such scores would be based on more articulated models. 1116 Current Sociology 64(7)

Time and sequence: Femicide as a process Many authors recognize femicide as the outcome of a process that endures over time and moves dynamically, stage by stage. Authors seek to capture its elusiveness in many ways, often framing it in terms of a victim-perpetrator relationship, or relationship dynamics (Campbell, 1992; Taylor and Jasinski, 2011). This, of course, is an abstraction of the many moves developed by and between the victim and perpetrator along an intri- cate path of violence leading to a lethal outcome. Indeed, ‘Research on domestic violence has consistently found that the death of an intimate at the hands of a partner is often the result of an ongoing or continual violence in the relationship’ (Taylor and Jasinski, 2011: 345, our italics). To grasp this dynamic aspect of femicide, time needs to be understood as ‘durée’, that is, a dynamic process that allows the combination of occurrences of events in their specific order. The perpetrator’s violence and the victim’s behaviour over the course of time may be conceptualized as ‘moves and replies’ (Goffman, 1969, 1981), which become meaningful when placed in the order in which they occur. Sequentiality in interaction, communication and relationships is regarded as a basic tenet in many qualitative (e.g. Atkinson, 1992; Atkinson and Drew, 1979; Drew, 1992; Sacks, 1972; Sacks et al., 1974) and quantitative (Bakeman and Gnisci, 2005; Bakeman and Gottman, 1997; Bakeman and Quera, 2011) approaches to human behaviour. Research into ‘normal’ intimate relationships that lead or do not lead to divorce can, for instance, serve as a reference model in this context (Gottman, 1994). In these studies, during interaction, the husband’s expressions of contempt were found to be a powerful predictor of physical illness reported by the wife four years later, and the wife’s expressions of disgust predicted the length of the couple’s life in months. In this model, therefore, break-up (separation) is reached through four moves: ‘complaining and criticism’; leading to ‘contempt’; which, in turn, leads to ‘defensiveness’; which, finally, leads to ‘withdrawal’ (‘stonewalling’) (Gottman, 1993; Gottman and Krokoff, 1989; Gottman and Levenson, 1992; cf. also Bull, 2002). Needless to say, changing the order of these steps results in a completely different outcome. Research on communication in real settings – to use other examples, such as on hostage negotiation (Madrigal et al., 2009; Taylor and Donald, 2007) and on legal examina- tions during criminal trials in court (Gnisci, 2005; Gnisci and Bakeman, 2007) – also demonstrates that different sequential patterns of behaviours may lead to very different outcomes. Taylor and Thomas (2008) emphasize the importance of studying the sequence of events in many legal contexts, rather than collections of variables, as best predictors of behaviour. If we therefore apply this logic to the phenomenon of femicide as a process, we will appreciate that it is not only important that behaviour occurs (e.g. a male partner tries to choke his girlfriend), but also when it happens; that is to say, what takes place before and after that behaviour. In other words, some behaviours may be predictive of femicide by themselves, but when combined in a sequential manner with other occurring behaviours, their predictive strength may be much stronger. Some items in the previously mentioned Danger Assessment Survey (Campbell et al., 2009), for example, seek to capture precisely this dynamic. The question, ‘Have you left him after living together during the Gnisci and Pace 1117 past year?’ specifically documents the sequence whereby the woman left her partner after cohabitation.2 Finally, if we are interested in the progression towards the lethal conclusion of domestic violence, it is worth alluding to the fact that many of the previously mentioned predictor variables are of a dynamic nature, that is, they can change over time. We are referring, for example, to marital status, presence of children, alcohol dependency, and so on. Their transformation may interfere, alter or even interrupt the processes themselves and therefore demand serious consideration. Some cultural or societal variables that interfere with increase of rates of femicide may also change over time, such as gender inequality (Vieraitis et al., 2008); hence, the necessity to measure them continuously. To summarize: if we seek to understand the process leading to lethal violence against a woman by a male partner, we should endeavour to consider the different actions occurring over time, together with the pattern of sequential behaviours of both partners.

Discussion and conclusions When arguing in favour of going beyond the traditional regression approach to risk factors on femicide, we neither wish to undervalue the invaluable contribution it has provided in the comprehension of femicide, nor to criticize the choices made by the authors in their research. The majority may have had solid theoretical and methodological grounds for not having factored in interaction effects or sequential approaches. Building on that research, this contribution aims at stimulating and encouraging researchers in this area to develop the regression approach further, in order to embrace more realistic and increasingly complex models. We claim a development towards a more articulated and dynamic model in the study of the risk factors for femicide. Contextualization of risk factors in coherent and homoge- neous sets, alongside their concretization into models that assign them appropriate roles and links with other relevant variables, renders the analysis meaningful. For example: when a predictive analysis is performed, making the distinction between those variables considered to be endogenous causes (Kline, 2011) and those which are moderators or mediators (Baron and Kenny, 1986) is an important step in the right direction. If a causal chain is shown (ABY), the relation is indirect and the intermediate variable mediates the relation between the distal cause A and the target variable Y. If, however, an interaction between A and B can be demonstrated, the effect of A upon Y changes in accordance with the different levels of B (e.g. B1 and B2). In the above presentation, we have brought an example from the research literature whereby, when there is no estrangement (B1), the sense of inadequacy of the perpetrator (A) may lead probabilistically to non-lethal violence (C); whereas, when there is estrangement (B2), it may lead to lethal violence (C). Moderations, mediations, direct and indirect links may all coexist within more complex models that could be elaborated to explain processes in more realistic settings. Returning to and expanding the instance of a nexus of mediation, a disparity in the status of the two partners (A), e.g. a woman who works to earn a living, and an unemployed man, may determine the man’s sense of inadequacy (B as mediator). The latter, in turn, may lead to different kinds of (lethal vs non-lethal) violence (Y), depending on the woman’s attempts and choice, as whether to leave the relationship (C as moderator). 1118 Current Sociology 64(7)

In this instance, the moderator exacerbates the effect of inadequacy towards violence – that is to say, it has a facilitating effect; however, suppressing effects can also be found. Research should therefore also focus attention towards an understanding of the variables that can inhibit violence – namely, so-called ‘protective factors’ (Buzawa and Buzawa, 2013). The renewed challenge towards a better comprehension of femicide also calls for adequate modelling within a network of interconnected variables, in which each variable has a defined and specific role. Although what we have defined as the regression approach has improved and enlarged our knowledge of femicide in the past, there is a need to see beyond a number of its components, in order to improve understanding of femicide as a process. We should recognize that the regression approach sometimes takes a rather static view of femicide. Human behaviours and actions, however, happen over time and occur sequentially, as ‘moves and replies’ (Goffman, 1969, 1981). Our models need to incor- porate this developmental component, reconsidering sequential patterns of behaviour: while slapping is always deplorable conduct, giving a slap in return for one received carries an entirely different significance than giving a slap after a caress. Again, in order to explain the progression that leads to femicide, there is a need for recourse to a dynamic process – a ‘film’, rather than taking a static ‘image’ (see Gnisci, 2005; Taylor and Donald, 2007). More effective and more realistic models can improve future research and the comprehension of femicide. Hopefully, they may help to break the link in our society – denounced by the feministic perspective – between the survival of the women in violent relationships and the circumstances linked to their submissiveness (see Taylor and Jasinski, 2011). Moreover, such models would direct the attention of social workers in the field correctly. Intervention programmes and training for reporting officers and police officers, as well as reports in the local media, schools, etc., should focus not only on the actual presence of risk factors, but also on their co-occurrence, or sequential occurrence. We should recognize that measuring some of the afore-mentioned variables is extremely difficult (e.g. male’s sense of inadequacy), as one limitation of the present proposal; in particular, where the study is retrospective to fatalities, as with femicide. There is also difficulty in measuring these variables coherently across different cases, data sources and countries; finally, measuring data in their sequential occurrence may prove to be a very exacting assignment. A notion of time as a diachronic process that allows the combination of occurrence in their specific order needs to be incorporated into our way of conducting research on femicide. This would have important implications, both for the process of data collection and for the task of the professionals who usually record and report events of femicide. The empirical implementation of the models described above demands a very different manner of collecting data on femicide. Other than recording as many relevant facts and facets as quickly as possible, so that there will be documentation for trial and future research purposes, from the outset, recorders should address their attention to when those events occurred, in order to facilitate the reconstruc- tion of the process that led to the act of lethal violence against the woman at the hand of the male partner. Gnisci and Pace 1119

Acknowledgements The authors wish to thank the anonymous reviewers for their suggestions.

Funding The authors are grateful to Cost Action IS1206 for funding the editing of the article.

Notes 1. This is the reason why the above-mentioned stepwise procedure, with all its variants (i.e. forward inclusion and backward elimination) – a procedure that automatically selects which predictors to insert in which order on the base of statistical indexes – is often rejected: ‘death to stepwise regression, think for yourself’ (Kline, 2011: 27) as the humorous, categorical imperative for a reasoned procedure. 2. Many statistical approaches prove useful in incorporating sequence and time in their models. We provide a number of brief references, without claiming to be exhaustive in this respect: sequential analysis of interaction (Bakeman and Gnisci, 2005; Bakeman and Gottman, 1997; Bakeman and Quera, 2011), stage-sequential modelling (Kim and Böckenholt, 2000), graphical chain modelling (Borgoni et al., 2012; Cox and Wermuth, 1996; Gnisci et al., 2008), time-series analysis (Gottman, 1979, 1993, 1994), event history modelling (Vermunt, 1997; Yamaguchi, 1991).

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Author biographies Augusto Gnisci teaches psychometrics at the Department of Psychology, Second University of Naples. He is the director of the Laboratory of Observation of this department. He has applied, both traditional and innovative, techniques of sequential observation and analysis of social interaction, to different research trends within social psychology applied to language and communication (legal settings, political interviews, etc.). Antonio Pace, studied psychometrics at the Department of Psychology, Second University of Naples. His main research interest concerns the effects of non-verbal communication (in particular, hand gestures and interruptions) on the psychosocial evaluation of speaker by the listener. He studies these effects in the laboratory, creating real interactions thanks to the collaboration of confederates. He explores also other research trends within social psychology, in particular the features of political communication.

Résumé Cet article procède à un examen critique des instruments de la traditionnelle méthode de régression appliquée aux violences domestiques et impliquant l’utilisation d’une variable cible (fémicide du partenaire intime) et de multiples prédicteurs. En s’appuyant sur des analyses conceptuelles, théoriques, méthodologiques et statistiques, cette étude répond à la question de savoir comment organiser convenablement les facteurs de risque ; elle suggère que ce que l’on considère comme des variables indépendantes pourraient avoir de nombreux rôles explicatifs différents ; elle se réfère à des modèles directs, indirects, intermédiaires et modérés, ainsi qu’à la facilitation et la suppression de l’effet des variables et ; elle introduit le concept de modèles de comportement Gnisci and Pace 1123 séquentiel. En partant de l’hypothèse que la phase préliminaire du meurtre de la conjointe est un processus dynamique, cette étude recommande de tenir compte de l’ordre des différentes actions entre les protagonistes. Tout changement dans la succession de ces événements pourrait en effet aboutir à un résultat totalement différent. Cette étude essentiellement théorique analyse des cas traités dans des articles scientifiques sur les facteurs de risque et le fémicide. Elle met l’accent sur la contribution de ce travail à la recherche sur le fémicide conjugal.

Mots-clés Facteurs de risque, modèle séquentiel, modération, médiation, violence domestique létale, fémicide conjugal (Partner Femicide), meurtre de la conjointe (Intimate Partner Femicide), Modèle d’équations structurelles (SEM)

Resumen Esta contribución realiza una revisión crítica de las muchas características del método de regresión tradicional a la violencia doméstica que implica una variable objetiva (feminicidio de pareja) y varios predictores. Con base en consideraciones conceptuales, teóricas, metodológicas y estadísticas, este análisis: primero, aborda la cuestión de cómo los factores de riesgo se pueden organizar adecuadamente; en segundo lugar, afirma que lo que se considera actualmente como variables independientes pueden tener numerosos papeles explicativos diferentes, haciendo referencia a los modelos directos, indirectos, mediados o moderados, así como a la facilitación y al efecto de las variables de la supresión; por último, introduce el concepto de patrones de compor- tamiento secuencial. Suponiendo que la acumulación de femicidios de pareja es un pro- ceso dinámico, el análisis advierte sobre ignorar la secuencia a través del cual los diferentes movimientos entre los actores realmente tienen lugar. Los mismos eventos que ocurren en un orden diferente pueden finalizar en resultados totalmente diferentes. Esta contribución será esencialmente teórica, implementando ejemplos trabajados de la literatura sobre los factores de riesgo y el femicidio. Se subrayan las implicaciones para los investigadores y los operadores en materia de feminicidio pareja.

Palabras clave Factores de riesgo, modelo secuencial, moderación, mediación, violencia doméstica letal, Femicidio de pareja, Femicidio de pareja ínitma, Modelo de Ecuaciones Estructurales