Investigating whether social media consumption and neuroticism can predict fear of crime in females.
Purpose: Fear of crime (FoC) has frequently been the focus of both psychological and criminological research for decades. Fear of crime has been associated with numerous adverse effects including anxiety and low subjective wellbeing. Therefore, having knowledge and understanding of what constitutes fear of crime is important. The present research aims to explore the relationship between neuroticism, social media consumption and FoC in females. Design: 117 participants were recruited using opportunity/volunteer sampling, with ages ranging from 17-58 (Mean = 26.37). Participants completed a questionnaire consisting of altered and replicated pre-existing measures. Findings: A Pearson correlational analysis found both neuroticism and social media consumption to be significantly related to fear of crime. A multiple regression found both neuroticism and social media consumption were significantly predictive of fear of crime. Possible reasons for these findings and implications for future research are discussed. Limitations: Self-report surveys are prone to bias. Cross-sectional data only gives information from one point in time and cannot demonstrate causality.
Practical Implications: Controlling or reducing the occurrence of neurotic thoughts may lower fear of crime, and subsequently improve quality of life.
Originality/Value: This project has brought attention to two previously neglected variables in FoC research and highlighted their contribution to fear of crime in adult females.
Fear of crime (FoC) became a prominent concept during the introduction of crime and victim surveys in the 1960s (Wakefield and Fleming, 2009). Since then it has regularly been the subject of both psychological and criminological research, becoming one of the most highlyinvestigated crime subjects(Farall et al, 2000). Fear of crime remains an ongoing concern in society today, with many individuals expressing fear and anxiety about crime despite rates decreasing(Valera and Guàrdia, 2014).
Despite the long history of FoC research, the debate regarding how fear of crime should be defined and measured continues (Reyns, 2012).A rather outdated definition is that fear of crime is the negative emotional reaction to crime or its associated symbols (Ferraro and LaGrange, 1987). Fear of crime has also been defined as reflecting the worry of victimisation regardless of the actual likelihood (Hale, 1996).Therefore, fear of crime may be moulded by the psychology of risk perception (Jackson, 2006).Holloway and Jefferson (1997) contend that FoC is the consequence of individuals miscalculating the danger of victimisation creating FoC and subsequently causing behaviour modification. Warr (2000)supports this idea arguing that fear of crime occurs as an “emotional reaction to the perceived environment”. A more recent definition comes from Wakefield and Fleming (2009)who describe fear of crime as a state of anxiety emulating the conviction that one is at risk of being victimised. Despite numerous attempts from various academics to define fear of crime, there is no universal definition. The absence of an agreed upon definition means that questions tend to differ between surveys, creating a limitation in the measurement of FoC (Lim and Chun, 2015).
It has been argued that FoC can potentially be positive, through encouraging people to adjust their behaviour in ways that reduce the danger of criminal victimisation (Jackson et al, 2009).For example, according to Garofalo (1981),there are three behaviours people employ to minimise victimisation risk; avoidance, protective and insurance.However, several researchers have highlighted the numerous ways that FoC can have a detrimental effect on individuals lives and their wellbeing. Fear of crime can also impact mental health. Stafford, Chandola and Marmot (2007) found fear of crime correlated with subscales of depression and anxiety. However, the direction of this association is not known. Furthermore, this research did not have any data regarding actual crime victimisation, meaning they could not investigate whether experience of crime (opposed to fear of crime) was a factor in the associations found. Fear of crime has also been associated with low subjective wellbeing. Pearson and Breetzke (2014) carried out their research in New Zealand and found FoC independently effected both mental and physical wellbeing even after several individual and societal-level variables linked to social inequalities in wellbeing were adjusted for. These findings cannot necessarily be applied to other countries or cultures, not least because when compared internationally New Zealand has low and less varied crime rates. This therefore, diminishes the ability for this research to identify significant associations between crime levels and wellbeing.Nevertheless, earlier research conducted in the United Kingdom by Chandola (2001) found similar results, discovering an association between FoC and bad health independent of several socioeconomic variables at both the individual and household level. Research has also found FoC to negatively affect life satisfaction. Adams and Serpe (2000) found perceived vulnerability and elevated fear of crime have the potential to indirectly effect life satisfaction by lowering people’s perceived sense of control. Hanslmaier (2013)also found that fear of crime (along with previous victimisation) significantly lowered life satisfaction. County-level crime rate did not significantly affect reported life satisfaction, suggesting that fear of crime plays a more important role. However, as the database for this research was cross-sectional, providing answers to questions of causality is not possible. The authors of this study recommended that future research should consider personality factors when investigating determinants of fear and life satisfaction. Fear of crime has the potential to adversely impact both individuals and society. For example, a person’s quality of life may decrease, and the community may be damaged through the breakdown of a communal feeling of trust, unity and social control (Jackson, 2006). Due to the potential FoC has to negatively impact individuals lives it is important to try and have as much knowledge as possible of the factors that play a part in creating/adding to people’s fear of crime.
Social Media Consumption
There has been a vast amount of research investigating the association between FoC and the media. A large proportion of newspaper and television items people see involve crime, deviance or justice (Doyle, 2006).Given how the media bombards us with images demonstrating or relating to crime, it does not seem that surprising that the mass media influences people’s perceptions of crime. The portrayal of crime in the mass media is not the same image reflected in official statistics (Doyle, 2006).Several studies have demonstrated associations between media consumption and FoC.Gerbner et al (1980) found that individuals who watch a lot of television demonstrate a greater fear of their environment. This relationship was strongest for females and white people. Callanan (2012) investigated the relationship between FoC and several media types (newspapers, television news, crime dramas and crime reality programmes) and found consumption steadily increased fear of crime across all media types. However, the effects were different for participants of different ethnicities. For example, newspapers elevated fear in white respondents but had no effect for African American or Latinos. Chiricos et al (2000) also found television news impacted fear of crime and that local news had a greater effect on those living in high crime areas.Factors such as experiences are also involved in determining whether the media will influence FoC. For example, if a person viewing or reading a crime story has had a direct experience with the crime involved, they are not as likely to be influenced by it. It has been proposed that the media most impacts FoC crime when there is an absence of direct experience (Liska & Baccaglini, 1990).Gerbner et al, (1980) suggested two theories to explain the relationship between fear of crime and the media; the substitution thesis and the resonance thesis. The idea of the substitution thesis is that media depictions of crime will be used to establish views and opinions by those who lack personal experience of victimisation. On the other hand, the resonance thesis proposes that individuals who have experienced crime,will have their established opinions strengthened by the media.
In the past decade, social media sites have grown in significance when it comes to information circulation, including crime news.This trend is particularly prevalent among young adults who grew up around digital media(Awan & Gauntlett, 2014). The Pew Research Centre found that 60% of American adults receive news from social media sites (Gottfried and Shearer, 2016). Furthermore, a 2016 survey found that of those who receive news on social media; 80% clicked links to news items, 49% reposted/shared, 38% posted news stories themselves and 19% posted images of news stories (Mitchell et al, 2016). Despite this, there remains a significant gap in the fear of crime literature when it comes to social media.
Intravia et al (2017)claim that their research is the only published research exploring the relationship between social media consumption and fear of crime. In a sample of 918 undergraduate university students, overall social media consumption was significantly related to FoC. However, rather unexpectedly the quantity of time spent directly engaging with crime-related news items was not. The authors also measured whether individuals feel safe in their neighbourhoods. They found overall social media consumption was significantly positively related to FoC in those who reported feeling safe in contrast to those who reported feeling unsafe. The authors contend that this finding may be understood through the substitution thesis (Gerbner et al, 1980). This study is limited in its generalisability as the sample consisted of undergraduate university students, meaning it cannot be said that the findings apply to other populations e.g. high school students and older adults.
Personality traits have been found to predict lifestyle outcomes but the association between personality and FoC is still unclear (Ellis and Renouf, 2018). Neuroticism is one of the big five personality traits (Goldberg, 1993),it has been characterised as being the; absence of self-control and the ability to manage stress and a propensity to complain (Ormel et al, 2012).Neuroticism has also been described as a personality trait composed of a disposition to experience upsetting and negative emotions (Costa and McCrae, 1980). Neuroticism is the personality trait most often associated with worry and fear (Servaas, et al, 2014).Eysenck (1998) contends that people with high neuroticism have limbic systems that easily attain fear-related associations, making them susceptible to reporting elevated levels of adverse emotion, particularly fear.Fear of crime is associated with a sense of security and low worries of being victimised (Ferraro, 1995). Therefore, if someone feels endangered because of their personality, it seems logical to assume that this will manifest itself in a greater fear of crime. Whilst there is an established association between neuroticism and fear, the notion that fear of crime can be explained by personality factors is one that appears to have been neglected by researchers.
It has been stated that neuroticism is positively correlated with anxiety (Coen et al, 2011). Zinbarg et al (2016) found that teenagers who score higher on neuroticism scales are extremely likely to develop anxiety. Anxiety is a prevalent substrate of fear of crime because,anxious people often exhibit more fearful traits(Hatemi et al,2013). Stafford et al (2007) found that those who score high on measures of fear of crime also score higher on measures of anxiety and depression, compared to those with lower fear of crime scores. This further supports the idea that neuroticism is a factor related to fear of crime.Klama and Egan (2011) investigated contributory factors in attitudes towards punishment, part of their research examined the potential relationship between the big-five personality traits and fear of crime. Neuroticism was found to be significantly related to fear of crime (r =.29) and perceived risk (r = .23).These results support the proposed link between fear of crime and neuroticism. However, as fear of crime was not the primary subject of this research, potential confounds such as prior victimisation, which can significantly impact fear of crime (DeLisi et al., 2014), were not controlled for. Therefore, further research is required to investigate thesuggested relationship.
Research has consistently found females to have higher fear of crime than men (e.g. Callanan and Rosenberger, 2015; Jackson, 2006). English and Ray (2010) found females reported greater FoC than males in 93 out of 105 countries, suggesting that gender differences remain significant despite demographic and cultural factors. Given that it has already been established that females demonstrate a greater fear of crime than men, a hypothesis regarding gender would be superfluous. Furthermore, including both genders would create a confound, especially seeing as it has been proposed by Sutton and Farrall (2005) that one of the reasons females demonstrate greater fear of crime is that males demonstrate social desirability bias, answering questions in a manner that displays them as more masculine by deemphasizing their fear of crime. Goodey (1997) also argued that social desirability bias influences male responses on fear of crime surveys along with gender stereotypes. Sutton, Robinson and Farrall (2010)subsequently affirmed that males reported fear levels were reduced as they wanted to be viewed as ‘strong’. Cops and Pleysier (2011) supported these findings in their study. Their research discovered that those reporting more “feminine behaviours” had greater fear of crime than those reporting more “masculine behaviours”, irrespective of gender. This implies fear of crime is viewed as ‘feminine’, something many males do not want to be perceived as.In order to avoid gender becoming a confound and a potential skew in the data regarding fear of crime levels because of gender differences, only female participants will be recruited for this research.
Wilcox, Quisenberry and Jones (2003) reported a positive relationship between FoC and victimisation rates. It has been found that following criminal victimisation there is an impact upon one’s psychosocial functioning including, an elevated perception of risk and fear of crime (DeLisi et al, 2014). Therefore, this research will control for prior victimisation in order to prevent a potential confound.
As both Klama and Egan (2011) and Intravia et al (2017) had restricted samples of university students limiting the generalisability, this study will recruit through the Manchester Metropolitan University participation pool and twitter. This will hopefully give a broader demographic sample.
Whilst past research has revealed a lot about fear of crime and the various factors that play a role not only in inciting fear of crime but also in perpetuating and heightening levels, several factors remain under researched. Two of these factors have been discussed in the previous paragraphs, neuroticism and social media consumption. Despite these two areas clearly warranting research they remain enormously understudied regarding their (potential) association to fear of crime. Given the huge impact FoC can have, it is important to have as much knowledge as possible about which factors influence fear of crime in order for preventative measures that can reduce fear of crime and/or its impact to be thought out. Therefore, this research will focus on the two understudied factors; neuroticism and social media consumption. This research has two hypotheses firstly, social media consumption will predict fear of crime scores and secondly, neuroticism scores will predict fear of crime scores.
This research implemented a web-based questionnaire design delivered using the online project tool Qualtrics.The survey consisted of both altered and replicated versions of existing measures (Intravia et al, 2017; Davis-Mersey et al, 2010; Goldberg, 1993; Ferraro, 1995). This study had two independent variables, neuroticism and social media consumption and one dependent variable which was fear of crime. In order to prevent a potential confound, previous victimisation was controlled for. This was done by using a screening question at the start of the survey, participants were asked if they or anyone they know had been a victim of crime in the past 12 months. All participants completed the same questionnaire; therefore, this study implemented an independent measures design. Social media consumption was entered into the regression model first as there is more research supporting the role of the media in fear of crime than there is personality factors.
Female participants were recruited using an opportunity/volunteer sample. A hyperlink was distributed on Twitter and Manchester Metropolitans University’s research participation pool, which then took participants to the survey. Using both distribution channels allowed a wider age range among participants, with ages ranging from 17 to 58 and a mean age of 26. Participants were assured of their anonymity before taking the questionnaire reducing the risk of social desirability bias. Using Green’s (1991) formula of N > 50 + 8m (m being the number of predictors, in this case m = 2) an appropriate sample size for this research would be N > 66. The total sample size of this study was N = 118 therefore, exceeding the minimum number of participants required.
The survey was constructed and delivered using the online project tool Qualtrics. The questionnaire was comprised of; a participant information sheet (see appendix B), a consent form (see appendix C) and demographic questions (age and gender, to screen and ensure only females took the survey).There was also a screening question to check prior victimisation, as well as measures of; fear of crime, neuroticism and social media consumption. The final item presented to participants was a debrief form (see appendix D).
Neuroticism was measured using the eight items from the Neuroticism scale of the Big Five Inventory – 44 (BFI 44; Goldberg, 1993)(see appendix F).All the questions on this scale are measured on a likert scale from 1 to 5 for example, “I see myself as someone who can be moody” with 1 being strongly disagree and 5 being strongly agree. Three items on the scale including “I see myself as someone who remains calm in tense situations” are reverse scored meaning 1 becomes strongly agree and 5 strongly disagree. A higher score on this scale indicates a more neurotic personality. Research by Fossati et al (2011) found the BFI-44 to have high internal consistency across three different samples with Cronbach’s alpha scores being α = .78, .81 and .76. Furthermore, Alansari (2016) investigated the internal consistency of the BFI- 44 and found the neuroticism scale to have a Cronbach’s alpha of α = 0.83 further supporting the reliability of this scale.
To measure social media consumption questions were taken and adapted from Intravia et al (2017) (see appendix E). For example, “in a typical week how much time do you spend on social media?” with answers measured on a likert scale ranging from “none”, “60 min or less”, “61 to 120 min”, “121 to 180 min”, “181 to 240 min”, and “241 min or more ” Another example is “On Twitter, how often do you read, watch, tweet, or interact with (such as retweet, like, or reply) stories or news involving crime or violence occurring in society?” Responses were again scored on a likert scale with 1 being never and 5 being very often. Higher scores on this scale indicate more consumption.
In order to get a better idea of peoples engagement a question from Davis-Mersey et al (2010)’s study on social media engagement was also included; “I contribute to the conversation on these sites” this question is measured on a likert scale with 1 being strongly disagree and 5 being strongly agree. The authors reported high Cronbach’s alphas for the scale this question belonged to (α=0.88) with its stand loading being 0.77, indicating reliability and therefore, making it appropriate to use in this study.
Fear of crime was measured using Ferraro (1995)’s 10 item fear of crime scale (see appendix G). Ferraro (1995) argued that the goal of fear of crime measures, should be accessing the emotional aspects of FoC, opposed to focusing on the cognitive perceived risk of victimisation. This scale consists of 10 scenarios (for example, being approached on the street by a beggar) and participants are told to rate their fear by circling the most appropriate number, 1 not being afraid at all and 10 being very afraid. A higher score indicates greater fear. Traditional FoC scales often measure general FoC rather than fear of specific offences (Gray et al, 2010). Therefore, respondents might give answers based on their own personal experiences or crimes they think about. For example, someone might think of being mugged, while others might think of sexual assault. Rader (2017) states that fear of crime measurements should incorporate concepts of worry about specific crimes. It is necessary to specify the type of offence in questions about FoC, because the amount of fear generated in respondents is likely to differ depending on different offences. For example, one study found the fear of being burglarised was higher in comparison that of being murdered (Warr and Stafford, 1983). Ferraro and Jackson (1995) found the primary worry in women was being sexually assaulted. As the scale by Ferraro (1995) measures fear of 10 different types of crime it avoids this issue. Due to this and the fact that this scale has previously been found to have a Cronbach’s alpha of .94 (Edwards, 2007), demonstrating reliability, this scale was deemed appropriate to be used in this study.
Before the questionnaire could be distributed it was necessary to first obtain ethical approval, to make sure this study meets with the ethical guidelines for psychological practice as stipulated by the British Psychological Society and Manchester Metropolitan University (MMU) (see appendix A). Following the receipt of ethical approval an online project tool, Qualtrics, was used to construct the questionnaire. Following this the questionnaire was distributed via the MMU participation pool and the social media site Twitter using a hyperlink. Once clicking on the hyperlink participants are presented with an information sheet, providing details of the studies aims and purposes. Participants were then presented with a consent form and either agreed or disagreed to take part.
Following the receipt of consent, participants were instructed to create a unique identity code. This was done as all participants responses were anonymous and this gives a way to identify an individual participants data should anyone wish to withdraw. Following completion of the questionnaire participants were provided with an onscreen debrief which provided my contact details should they need any more information. Details were also provided for organisations such as the Samaritans in case any participants felt the need to seek advice and support about the issues raised in this research. Participants were also informed of their right to withdraw their data from the study and how to go about this should they wish. Participants were also advised that they could only withdraw their data up until the 15/08/2019 as this was when data analysis began.
Once all the data had been collected it was exported and downloaded as an SPSS file, ready for analysis.
Prior to the main analysis a reliability analysis was conducted on the individual survey scales. A satisfactory Cronbach’s alpha is α ≥ .70 (Coolican, 2014). Both the neuroticism and fear of crime scales had satisfactory scores of; α = 0.74 and α = 0.93 respectively, indicating that these scales have strong reliability. However, the social media scale had a score of α = 0.32 indicating this scale is not very reliable. Removing item 1 “In a typical week how much time do you spend on social media (such as Facebook, Instagram, Snapchat, Twitter, reddit)?” would increase the Cronbach’s alpha to α = 0.40. Removing item 2 “I contribute to the conversation on these sites” would increase the Cronbach’s alpha to α = 0.57. The removal of any of the other items would cause a decrease in the Cronbach’s alpha suggesting they are worthy of retention and the first two items are the least reliable. However, it is worth noting that removing these two items gave a score of α = 0.68 which is still unsatisfactory. Therefore, it seems that the scale does not have strong internal consistency.
Association between variables
Prior to the regression analysis, a Pearson correlation analysis (see Table 1) was conducted for both independent variables. The relationships between both independent variables (social media consumption and neuroticism) and fear of crime appear to be linear (see Figures 1 and 2). Shapiro-Wilk tests indicated that whilst social media consumption appears to be normally distributed (p = .011), as does neuroticism (p = .149), fear of crime (p<.001) is not. Therefore, bootstrapping procedures were used to perform the correlations. As seen in Table 1, there was a significant positive correlation between fear of crime and social media consumption (r(118) = .58, p <.001 one-tailed, 1000 BCa 95% CI [0.43, 0.69]). There was also a significant positive correlation between fear of crime and neuroticism (r(118) = .78, p <.001 one-tailed, 1000 BCa 95% CI [0.71, 0.84]). This indicates that greater levels of neuroticism are related to higher fear of crime. A correlation was also performed on the two independent variables. No significant correlation was found between social media consumption and neuroticism (r(118) = -.15, p = 0.1 two-tailed).
FoC Social media Neuroticism
. .58** 78**
Summary of correlational analysis
Note ** indicates p<.001
Multiple regression analysis
Prior to performing the regression analysis tests were carried out to ensure that the assumptions required for parametric tests and multiple regressions were met. The relationship between each predictor variable (social media consumption and neuroticism) and the criterion variable (fear of crime) appears to be linear. (See Figures 1 and 2).
Tests of multicollinearity demonstrated the tolerance was greater than 0.2 (= .70) and the value of VIF was less than 10 (= 1.43). This means multicollinearity is not problematic as the predictor variables are not too highly correlated with one another.
For the assumption that the residuals are independent to be met, Durbin Watson must be close to two. This assumption is met as the Durbin Watson score is 1.623. The next assumption is homoscedasticity that is, the variance of the residuals is constant. The variation in the residuals seems to be approximately similar, there seems to be an equal scatter (see Appendix J).
There does not appear to be a skew in the residuals in the model (see figure 3) The residuals appear to be normally distributed with a Kurtosis of -.12 and Skewness = -.54. However, the residuals are not normally distributed with Shapiro-Wilk = -.97, p =.014.
The analysis of standard residuals did not find any outliers in the data (Standardised Residual Min = -2.3, Standardised Residual Max = 2.5).
A hierarchical multiple regression analysis was then conducted to examine whether social media consumption (step 1) and neuroticism (step 2) were able to predict fear of crime. Coefficient confidence intervals (CI) were estimated using bias corrected accelerated (BCa) bootstrapping, due to residuals being distributed non-normally.
Step 1 accounted for 33% of the variance in fear of crime (R2 =.33). Including neuroticism at step 2 significantly improved the model (Fchange = 99.07, p<.001) and together with social media consumption explained 64% of the variation in fear of crime scores (R2adj = 64%).
Bootstrapped estimates indicated that both social media consumption (b= 1.92, BCaCI [1.01, 2.87]) and neuroticism (b= -2.24, BCaCI [-3.3, -1.18]) significantly predicted fear of crime scores (see Table 2).
Summary of regression analysis for predicting fear of crime
Step 1 Step 2
B SE(b) BCa 95% CI B SE(b) BCa 95% CI
Social Media Use
Note: R2 =
25.63 9.2 8.61 41.44 82.69 13.73 52.6 111.39
2.23 0.48 1.23 3.23 1.92 0.44 1.01 2.87
2.78 0.27 2.23 3.35
Note. ** indicates p < .001
As seen in Table 2, social media consumption was found to be a strong predictor of fear of crime scores, β = 0.99, t(115) = 3.2, p =.002. This supports hypothesis 1 and indicates that greater social media consumption can predict higher fear of crime in adult females. Neuroticism was also found to be a strong predictor of FoC scores, β = 2.78, t(115) = 9.9, p <.001. These findings support hypothesis 2 suggesting that greater neuroticism can predict fear of crime in females.
This research aimed to investigate whether social media consumption and neuroticism can predict fear of crime in adult females. It was hypothesised that both social media consumption and neuroticism would be predictive of fear of crime scores. Pearson correlations found a significant positive relationship between social media consumption and FoC suggesting that high social media consumption is related to greater fear of crime. A significant positive correlation was also found between neuroticism scores and fear of crime, suggesting higher levels of neuroticism are related to higher fear of crime. The results of the multiple regression showed that both social media consumption and neuroticism scores were able to predict fear of crime. Neuroticism seems to be a slightly stronger predictor of FoC than social media consumption. These findings support the hypotheses.
The findings regarding social media consumption are supportive of Intravia et al (2017), who found that overall social media consumption was significantly related to fear of crime in young adults. Unlike Intravia et al (2017) in this study participant’s ages ranged from 17 to 58 giving the findings more generalisability. However, the majority were aged between 22 and 27 (see appendix H). Furthermore, this study did not include age as a variable nor did it compare young vs old adults. Therefore, it cannot be categorically stated that social media consumption is an important factor in facilitating fear of crime for individuals of all ages.
Given the lack of research investigating social media consumption and FoC, finding an explanation for the relationship between these two variables is difficult. Given that a larger number of adults use social media to receive news (Gottfried and Shearer, 2016), it is plausible that the resonance and substitution theories proposed by Gerbner et al (1980) apply to social media in the same way they do to traditional media types (i.e. television and newspapers). Findings from Intravia et al (2017) contradict this idea, they found that neither general or crime news consumption were significantly related to fear of crime. Suggesting that opinions of crime are not being reinforced or established through social media (Gerbner et al, 1980). Nevertheless, Intravia et al (2017) had a sample of only young adults, older adults may engage more with news stories on social media therefore, the substitution and resonance theories may apply to them.
Neuroticism was found to be significantly correlated with fear of crime as well as a significant predictor of fear of crime. These findings are consistent with those of Klama and Egan (2011). This study unlike Klama and Egan (2011) has the strength of controlling for previous victimisation. These findings are not that surprising given that research has found teenagers high in neuroticism are highly likely to develop anxiety (Zinbarg et al, 2016), which is a common substrate of fear of crime (Stafford et al, 2007). Neuroticism is the personality trait most commonly associated with fear (Servaas et al, 2014) thus, it seems reasonable to assume that high levels of neuroticism will correlate with high levels of fear (and by extension fear of crime), which may explain the findings in this research.
This study has several limitations. Firstly, the methodology, the use of self-report surveys can be problematic and can lead to criticism. Many of the disadvantages of self-report measures are due to participants and their behaviour. There is a risk of social desirability bias, false answers, demand characteristics and misunderstanding questions (Hepner et al, 2016). Each of these issues may have impacted the results of this research. Further research should include lie scales throughout the survey to keep to combat some of the risks and limitations of self-report surveys. A further flaw in the methodology is the cross-sectional design, the use of which means causality cannot be assessed.
A further limitation of this research is that there are several demographic variables that have previously been found to influence fear of crime such as, ethnicity and location(Luo et al, 2016),age (Fattah and Sacco, 1989) and socioeconomic status (Miethe and Lee, 1984) that were not accounted for in this research.
It should also be noted that the scale used to measure social media consumption received a low Cronbach’s alpha score, indicating that it is not very reliable. Therefore, this scale should not be used in future research.
Finally, recruiting participants via social media appeared to be the most adequate way to obtain a wider cohort of participants and whilst there was quite a wide range of ages, other demographic information was not taken. Therefore, it is possible that the participants were limited to a particular demographic group. Therefore, the generalisability of this study’s findings are questionable, it should also be noted that as this study only had female participants, the findings cannot be generalised to males.
This research found that neuroticism can significantly predict fear of crime in female adults. Learning how to control or minimise the occurrence of neurotic thoughts and behaviours may lead to a reduction in fear of crime, and thus, improve wellbeing and quality of life.
Future research could investigate how this study’s findings stand up when the demographic variables not included in this study are considered. For example, compare age groups, ethnicity, locations to see if these variables influence the results. Including location as a variable in future research would also allow for crime rates to be compared to fear of crime levels, allowing an even broader perspective on the determinants of fear of crime.
Future research could also employ a longitudinal design, unlike a cross-sectional design this would allow a better idea of causality. This would also allow the stability of fear of crime to be investigated, as well as the exploration of what causes fluctuations if they occur. Longitudinal designs also give the option of gathering a mixture of qualitative and quantitative data, which is proposed to be crucial in really knowing what affects fear of crime (Lorenc et al, 2013). This would then allow for a more comprehensive understanding of the variables that impact fear of crime.
Future research might replicate this study with a male sample. This would allow the impact of neuroticism and social media consumption on FoC to be compared between genders.
Whilst this research found neuroticism to be a significant predictor of FoC, it would be interesting for subsequent research to investigate the other big five personality domains (openness, conscientiousness, extraversion and agreeableness) and fear of crime. Whilst as the personality trait most often associated with worry fear (Servaas, 2014) neuroticism is likely to have the most influence, it would be interesting to see how the other traits influence fear of crime, both individually and how they interact with one another.
Whilst this research found social media consumption to significantly predict fear of crime scores, it would be interesting for future research to investigate whether the effects differ between different social media platforms. For example, comparing fear of crime levels in Facebook vs Twitter users or even investigating image based social media such as Instagram and Snapchat. It would also be useful for future research to build upon Intravia et al (2017) to see if their finding that general and crime news consumption on social media do not significantly impact fear of crime. This would allow a greater understanding of how social media impacts FoC.
The findings of this research support previous literature and by investigating two previously neglected variables, provide a greater insight into what influences fear of crime in female adults. It is important to note that FoC is complicated and it would be extremely wrong to assume that these are the most important factors in constituting fear of crime in females.It is important that researchers continue investigating fear of crime. This will provide a more extensive understanding of the different variables that influence fear of crime. A greater understanding of the causes will subsequently allow a greater idea of how to reduce fear of crime, which can potentially improve wellbeing and life satisfaction.
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