Family Ties: The Relation Between Siblings and Adolescent Risk-Taking Behavior
Master Thesis Forensic Child and Youth Sciences Graduate School of Child Development and Education University of Amsterdam M.A. Tabor (12459941) First Supervisor: Dr. I. N. Defoe Second Supervisor: M. J. Noom
Abstract
Risk-taking behavior plays a major role during adolescence. Understanding the mechanisms behind adolescent risk-taking behavior is therefore essential to improve behavioral outcomes. Siblings are known to have effects on multiple characteristics of the adolescent development process. In this study, the role of sibling presence and the moderating role of sibling gender composition are included as predictors of adolescent risk taking behavior. It was
hypothesized that the presence of siblings while undertaking risky behavior will predict higher overall risk-taking behavior by adolescents. Furthermore a larger effect was expected for male sibling pairs in comparison to female sibling pairs. Adolescents (n = 228, 12 – 18 years, M age = 14.61, 57% male) responded to a questionnaire regarding questions about risk-taking behavior with a sibling present and regarding risk-risk-taking behavior in general. Results showed that sibling presence, while engaging in risk-taking behavior, predicted more overall risk-taking behavior by adolescents. However, the current research did not find, a moderating factor for male and female sibling pairs. This study has provided new insights about the predictive value of sibling presence on the overall amount of risks that adolescents are likely to take. Despite some limitations, for which further research is suggested, the results of this investigation clearly show that the role of siblings needs to be taken into consideration in understanding and preventing risk-taking behavior by adolescents.
Keywords: taking behavior, sibling presence, gender moderation, adolescent risk-taking, sibling context
FAMILY TIES: THE RELATION BETWEEN SIBLINGS AND ADOLESCENT RISK-TAKING BEHAVIOR
Risk-taking behavior can lead to a number of undesirable and unwanted outcomes such as injuries, drug addiction, delinquency and unsafe sex (Defoe, 2016; Thompson et al., 2005). Risk-taking behavior is defined as behavior that can be accompanied by an unwanted outcome and chance of loss (Boyer, 2006; Furby & Beyth-Marom, 1992). Adolescents are more inclined to take risks and ‘real life’ risk behaviors, such as substance use and delinquent behavior, have shown to increase and peak during the adolescent period (Arnett, 1992; Blum & Nelson-Mmari, 2004; Boyer, 2006; Gardner & Steinberg, 2005; Kloep et al., 2009).
Peer influences play an important role in increasing the probability of risk-taking behavior amongst adolescents (Gardner & Steinberg, 2005; Shepherd et al., 2011; Smith et al., 2014; Steinberg, 2008). Siblings can also be compared to peers; both are placed on the same level in the Ecological Systems Theory of Bronfenbrenner (1979). Additionally, the effects that siblings have on behavior follow similar mechanisms as those produced by peer groups; they have their roots in the social learning theory (Bandura, 1977) in which modeling influences behavior (Whiteman et al., 2013; Whiteman et al., 2014). Research so far has found that the sibling context significantly contributes to risk-taking behavior (Buist et al., 2013; Defoe et al., 2013; Leonardi-Bee et al., 2011). Further research regarding adolescents is essential to provide more insight into the predictive value of siblings’ presence during risk-taking behavior on the amount of overall risk-risk-taking behavior that adolescents show.
Another factor that is important in assessing risk-taking behavior, and for potential moderating factors of the effect of sibling risk-taking behavior, is gender (Ellis et al., 2012). Ellis proposed that males, compared to females, have an evolutionary base of taking more risks in general. Within sibling studies researching risk-taking behavior, the effects found for gender differences were inconclusive. Some studies failed to find statistically significant
effects (Defoe et al., 2013; Morrongiello & Bradley, 1997). However, other studies found differences between same-sex and mixed-sex sibling pairs (Buist et al., 2013; Rowe & Gulley, 1992; Trim et al., 2006) and between brothers and sisters (Buist et al., 2013; Slomkowski et al., 2001). Further research is therefore necessary to assess if the gender differences that are found in risk-taking behavior (Ellis et al., 2012) also exist when siblings are present. The current study will investigate the predictive value of sibling presence on the overall amounts of risk-taking behavior (smoking, alcohol use, drug use and delinquent behavior) by adolescents, and will add gender composition as a moderating factor. Risk-Taking Behavior
Furby and Beyth-Marom (1992) define risk-taking behavior as a (deliberate or non-deliberate) action that is accompanied by the chance of loss. Although this definition is broadly used in scientific literature, Reyna and Farley (2006) argue that especially for adolescents, it is difficult to define negative outcomes. They further argue that risks such as HIV and lung cancer are overestimated by adolescents to highlight that not all risks are weighed equally. For this reason, some scholars choose for a broader and more objective definition of risk-taking behavior (Defoe et al., 2015; Figner & Weber, 2011). These scholars define risk-taking behavior as behavior that leads to a wide range of possible outcomes. The notion that there could be more potential outcomes means that there is a greater chance that one of the outcome options will be negative (Figner & Weber, 2011). The current study will adhere to this latter definition and will define risk-taking behavior as behavior that leads to a wide range of potential outcomes, which includes possible negative outcomes.
Risk-taking behavior is more likely to occur during the adolescent period (Arnett, 1992; Blum & Nelson-Mmari, 2004; Gardner & Steinberg, 2005; Kloep et al., 2009). A possible reason for this is that adolescents are exposed to more risks in ‘real-life’ situations than younger children are (Boyer & Byrnes, 2009; Defoe, Dubas & Romer, 2019). Moreover, immature
psychosocial brain networks could cause adolescents to take more risks in social situations (Steinberg, 2004, 2007, 2008). Therefore, peers tend to have a greater influence on adolescents when compared to children or adults (Gardner & Steinberg, 2005). With a clear understanding of the mechanisms that underpin risk-taking behavior within the youth population, the public health system can be improved for youth. Research targeting this age group will aid in further identifying and understanding the factors that are related to risk-taking behavior by adolescents. Sibling Presence while engaging in Risk-Taking
The social learning theory (Bandura, 1977) states that people imitate each other’s behavior, which causes people to behave differently in social situations. Previous research has likewise found that risk-taking behavior of (adolescent) youth increases in the presence and encouragement of peers (Gardner & Steinberg, 2005; Shepherd et al., 2011; Smith et al., 2014; Steinberg, 2008).
Siblings are classified as being on the micro-level of the Ecological Systems Theory of Bronfenbrenner (1979). Social influences from the most immediate environment such as family, school and peers, are placed on the micro-level of the Ecological Systems Theory and are thought to have similar influences on behavior. Similar to the case of peers, modeling also plays an important role in shaping the influence siblings have on each other. The modeling of siblings’ behavior is found to be essential for the shared norms and expectations that are present regarding risk-taking (D’Amico & Fromme, 1997; Whiteman et al., 2014) and is shown to be an important factor for the effect older siblings have on deviant and sexual risk-taking behavior by adolescents (Whiteman et al., 2013). Siblings are a crucial social influence and uniquely contribute to the developmental process of adolescents (Jenkins et al., 2009; Tucker et al., 2001; Tucker et al., 2009). Furthermore, sibling influences during the
However, compared to other social influences, there is little research on the relationship between siblings and risk-taking behavior by adolescents. Morrongiello and Bradley (1997) (see also Morrongiello et al., 2010) were one of the first to undertake
experimental research on this topic and found that older siblings have a persuasive influence on their younger sibling (8 years old) and can persuade them to choose either riskier or safer paths. More recently, a meta-analysis by Leonardi-Bee et al. (2011) found that siblings who smoke are a significant predictor of adolescent smoking. Moreover, Buist et al. (2013) found in their meta-analysis that the quality of the relationship between siblings is of importance as well. They reviewed 34 studies and found that sibling conflict leads to more externalizing problem behavior by children and adolescents. Although this meta-analysis did not include the predictive value of sibling behavior, it does provide insight into the contribution that the sibling context has on externalizing behavior by adolescents.
A longitudinal study by Defoe et al. (2013) reported that more externalizing behavior shown by older siblings predicts more externalizing behavioral problems by younger siblings (i.e. aggression and delinquency). Furthermore, other correlational research targeting sibling deviant behavior, such as drug use and rule breaking, found that this is a significant predictor for an increase of deviant behavior over time for children (aged 12 to 14), who are at risk of substance abuse and antisocial behavior (Stormshak et al., 2004). Finally, Walters (2018) found comparable results and stated that sibling delinquency predicted the amount of delinquent behavior participants (aged 9 to 17) showed five years later. He concluded that siblings can be seen as a risk-factor for future offending. It is possible that the behavior of siblings is modeled, which could explain the increase of deviant behavior and behavioral problems.
The studies mentioned above (Buist et al., 2013; Defoe et al., 2013; Leonardi-Bee et al., 2011; Morrongiello & Bradley, 1997; Morrongiello, et al., 2010; Stormshak et al., 2004;
Walters, 2018) indicate that there could be a relationship between risk-taking behavior by adolescents and their siblings. However, these studies did not include the presence of siblings while engaging in risk-taking behavior in their investigations. This study will contribute to the current knowledge by assessing the amount of risks adolescents take in the presence of their siblings and comparing this to the overall amount of risks adolescents take.
Gender Differences
Ellis et al. (2012) proposed an evolutionary model of risk-taking behavior and found evolutionary-based gender differences. Their research showed that boys had more to gain and less to lose from high-risk behavior, and that boys are more sensitive to social cues about mating and status. Indeed, several studies on risk-taking behavior show that there are clear gender differences. According to a meta-analysis by Byrnes et al. (1999), boys display more risk-taking behavior compared to girls. They found that gender differences vary by age group, with the biggest gender differences recorded for children, pre-adolescents and college students. A recent meta-analysis (Cross et al., 2013) found that gender differences are also observed for sensation seeking, which correlates with risk-taking behavior (Lauriola et al., 2014).
Studies concerning gender differences moderating the relationship between siblings and risk-taking behavior provide inconclusive results. For example, the aforementioned experimental study by Morrongiello & Bradley (1997) did not find a moderating relationship between gender and sibling presence in their research on risk-taking behavior by children of 8 years old. They did, however, find that different motives contributed to the behaviors of girls and boys (Morrongiello and Bradley, 1997). Boys used ‘fun’ as a motive for their behavior whereas girls tended to use ‘safety’ as a motive. A more recent correlational study also shows no apparent indication of gender differences on the predictive value of siblings’ externalizing problems (Defoe et al. 2013).
Even though these studies did not find significant gender effects, similarities in problem behavior between siblings are specifically shown when siblings are same-sex pairs in comparison to mixed-sex pairs (Buist, 2010; Rowe & Gulley, 1992; Trim et al., 2006). For example, same-sex sibling pairs are seen as a risk factor for delinquency. For brothers, however, this influence was less dependent on the type of relationship between the siblings than was the case for sisters (Slomkowski et al., 2001). Sisters were only found to be
influential when the relationship was characterized as hostile, whereas brothers also impacted delinquency when the relationship was characterized as warm and supportive. The
aforementioned meta-analysis by Buist et al., (2013) found similar effects and reported larger effects for brothers when examining sibling relationships. Brothers showed to react more to differential treatment with behavioral changes when being compared to sisters.
Although previous research found gender differences in risk-taking behavior (Byrnes et al., 1999; Cross et al., 2013; Stone et al., 2012), research containing sibling studies shows to be inconclusive (Buist, 2010; Buist et. al., 2013; Defoe et al., 2013; Morrongiello & Bradley, 1997; Rowe & Gulley, 1992; Trim et al., 2006). Previous research suggests that brothers could be a stronger moderator on risk-taking behavior than sisters (Buist et al., 2013; Slomkowski et al., 2001). Empirical research including a moderating role of gender
composition on sibling presence and overall risk-taking behavior is scarce and sorely needed. Current study
Research on sibling presence during risk-taking behavior is limited, but is much needed to obtain insight into the role that the presence of siblings can play in risk-taking behavior by adolescents. Siblings are an important social context during adolescence (Buist et al., 2013; Jenkens et al., 2009; Tucker et al., 2001, Tucker et al., 2009) and risk-taking
behavior shown by siblings seems to be correlated with risk-taking behavior by adolescents (Defoe et al., 2013; Leonardi-Bee et al., 2011; Stormshak et al., 2004). However, the role of
gender on risk-taking behavior in the presence of siblings is inconclusive (Buist et al., 2013; Defoe et al., 2013; Morrongiello & Bradley, 1997; Rowe & Gulley, 1992; Trim et al., 2006). This study will uniquely contribute to previous adolescent correlational studies by measuring the presence of siblings while engaging in risk-taking behavior and whether this predicts overall adolescent risk-taking behavior. Furthermore, the hypothesized moderating effect of gender will be added. Additionally, open questions on the motivational aspects behind risk-taking behaviors will be examined, and this information will be given as background information of the sample.
In the current study, a sample is used of Dutch adolescents between the ages of 12 and 18 years old. We hypothesize that the presence of siblings while engaging in risk-taking behavior will predict higher overall levels of risk-taking behavior. Additionally, we
hypothesize this link will be moderated by gender composition. It is expected that for male sibling pairs the link between engaging in risk-taking behavior in the presence of a sibling, and overall adolescent risk-taking behavior, will be stronger than will be the case for female sibling pairs. A correlational research design will be employed where risk-taking behavior will be assessed by answering questions about delinquent and substance use behaviors in the presence of siblings (independent variable) as well as questions about adolescent risk-taking behavior in general (dependent variable).
Method Sample
The data used in this study was taken from a longitudinal research project in the Netherlands called “The Adolescent Risk-Taking (ART) Project” (Defoe et al., 2016). ART was set up to collect data about different categories of risk-taking behavior by adolescents. The data collections were conducted once a year over a period of three years. For our
questions, which have shown to be more reliable in analyzing adolescents than questions answered by parents (Verhulst & Van der Ende, 1992).
The total sample size of the second wave of data collection was 582 adolescents. For the current research, a subsample of adolescents that have a same-sex sibling was used. A total of 228 valid cases were used in the analysis (57% male). The data was collected in the fall and winter of 2013. Adolescents participated at their school during school hours. The majority was in their second (42.1%) or fourth year (53.9%) of high school, on the middle-level of the secondary educational track system (i.e. vmbo and havo in Dutch). The
adolescents participating in the study were between 12 and 18 years old (M = 14.61, sd = 1.24). Their siblings varied between 7 and 21 years old1 (M = 15.52, sd = 3.10). At the start of the study, the majority of the sample was born in the Netherlands (93.20%) but 61.60% identified themselves as Dutch. The other participants identified themselves as belonging to various other ethnicities. Furthermore, 68.40% of the parents were still together (married or living together) and 24.80% of the adolescents answered that their parents were either
separated or divorced. A large number of the adolescents were unaware of the highest level of education of their parents (44.90% fathers; 46.50% mothers). The reason given was that their parents had completed their education abroad, where education systems differ from those used in the Netherlands (11% fathers; 11.80% mothers) (Defoe et al., 2016). More detailed demographic information regarding the database that has been employed in this investigation can be found in the article by Defoe et al. (2016).
To provide more background information qualitative responses that adolescents completed about themselves were used. Seven open answered questions, regarding the motives behind alcohol use, smoking and drug use, were coded and analyzed. Appendix A shows the coding procedure. Coding these open ended questions showed that acting tough to impress others, “stoer doen” in Dutch, was the most given answer for all risk-taking
behaviors. Enjoyment was the second-most mentioned for all risk-taking behaviors. The other given motives showed to be different for each risk-taking behavior and a short summary of these outcomes can be found in Appendix B.
Procedure
High schools were used to recruit the survey participants and to collect data. In the second wave, seven high schools participated in the data collection exercise. One school dropped out after the first wave due to organizational issues. Parents received a letter which they had to sign and return back to the school as a form of informed consent. A trained research assistant collected data at the different schools during school hours. After answering the questions, all participants were given the choice of either a chocolate candy (worth 2 euros) or to enter in a raffle and potentially win a gift voucher (worth 50 euros). For further details please see: Defoe et al. (2016).
Measures
Adolescent Risk-Taking Behavior
Risk-taking behavior was assessed by measuring the amount of delinquent behavior and substance use that the adolescents reported. These types of risk-taking behaviors were combined because the underage use of alcohol, smoking and soft drugs could also be
considered as “delinquency”. This is because substance use under the age of 18 is prohibited in the Netherlands. Furthermore, delinquent behavior and substance use are often assessed within the same category in the literature. For example, in the well-established ‘Youth Self Report Questionnaire’, delinquent behavior as well as substance use are both assessed as ways in which the participants would externalize behavioral problems (Verhulst et al., 1997).
Alcohol use was measured with the following question: “Do you drink alcohol?” Participants chose from a 6 point likert scale ranging from 0 (no, I’ve never drunk alcohol) to 5 (yes, every day). For the use of soft drugs and smoking, the same answer options were
given to the following questions: “Do you use (or have you used) soft drugs (cannabis, weed, hash or marijuana)?” and “Do you smoke (cigarette, cigar, shag or pipe tobacco)?” To assess the amount of delinquent behavior amongst the target group, the following three questions were administered: “Have you ever stolen something from a (department) store?”, “Have you ever stolen a wallet, purse or something else that belongs to another person?” and “Have you ever stolen a bike, scooter or motorcycle?” Participants answered these questions on a 5 point likert scale, ranging from 0 (never) to 4 (yes, in the past 12 months three times or more). An average of these three questions was computed to assess delinquent behavior. In the analysis the answer option 0 was coded as “never”, and the other answer options (1 to 4) were
collapsed into the score 1 (“at least one time”).
An average of the four risk behaviors (alcohol use, drug use, smoking and delinquent behavior) was taken to compute the variable “Adolescent Risk Taking Behavior”. Making use of average scores (instead of sum scores) was recommended to address missing values (Brace et al., 2016). A higher score on this variable indicated higher levels of different kinds of risk behaviors (i.e., alcohol use, drug use, smoking and delinquent behavior). A
Cronbach’s Alpha score of .69 was obtained, which showed that these items combined can be considered as a reliable measurement tool of adolescent risk-taking behavior (Bijleveld, 2013).
Sibling Presence During Risk-Taking Behavior
To assess the likelihood of risk-taking behavior in the presence of a sibling the following question regarding delinquent behavior was formulated: “How many times have you stolen in the presence of your brother/sister?” The answer scale ranged from 0 (never) to 4 (6 times or more). The same questions and answer options were given for drinking alcohol, smoking and the use of soft drugs in the presence of a sibling. The data was coded as 0 (never) and 1 (at least one time). An average of these four questions were combined into one
variable, which measures sibling presence (while engaging in risk-taking behavior). A higher score on this variable implied that siblings were present during risk-taking behavior on more than one dimension of risk-taking behavior (delinquency, drinking, smoking and drug use), or in other words, higher levels of risk-taking behavior with a sibling. With a Cronbach’s Alpha of .54 this measurement variable showed a relative low score of reliability (Bijleveld, 2013). Gender
Participants filled in a set of general questions about themselves, such as their gender and the gender of the brother/sister used as reference to complete the sibling-related questions in the questionnaire. Using this information, same-sex male and female sibling pairs were identified and used for further analysis. Male siblings were added with a value of 0 and female siblings were added with a value of 1.
Statistical Approach
A multiple regression model was used to analyze the predictive value of sibling presence while engaging in risk-taking behavior on risk-taking behavior in general, and to examine the possible moderating role of gender. The data observations were collected independently of each other (Field, 2018). Before undertaking the regression analysis, the data-bases were checked for the adequacy of the sample size, outliers and multicollinearity, while the residuals were examined for normality, linearity and homoscedasticity.
Sibling presence and gender were used as independent variables in the model that was estimated. In addition an interaction variable, between gender and sibling presence, was included as an independent variable using the PROCESS Macro tool (Hayes, 2018) in SPSS version 25. Sibling presence and risk-taking behavior were both added as continuous
variables and gender was included as a categorical variable. A model that combines a
categorical variable with a continuous interaction variable can be analyzed by using a simple moderation model (Hayes, 2012). The PROCESS Macro tool was used to center the variable
sibling presence to the mean as recommended for moderation analysis cases which include a continuous variable (Field, 2018).
The PROCESS Macro has been used in different comparable research projects to perform a simple moderator analysis (Konsztowicz & Lepage, 2019; Madden & Shaffer, 2019). Use of the PROCESS Macro tool is recommended for analyses which contain a moderator variable because it automatically centers the predictors, computes interaction terms and computes simple slopes (Field, 2018). Furthermore, the PROCESS Macro tool uses ordinary least square regression, and computes correlation values and standard deviations for the explanatory variables (Hayes, 2012). A multiple regression analysis was used to collect missing values that could not be computed using the PROCESS Macro tool (i.e. b and sr2).
Results Assumptions
Prior to analyzing the results of the multiple regression, the assumptions required for a multiple regression analysis were checked. First, one case was excluded from the sample because of missing values of the sibling presence variable. The sample (n = 227) that was remaining and used was, however, considered large enough (Tabachnick & Fidell, 2013). No extreme outliers were found (Hoaglin & Iglewicz, 1987), although the critical Mahalanobis distance fordf = 3 (at p = .001) of 16.266 was exceeded for multiple cases. However, in all cases, the Cooks distance was small (all below .05), and therefore the multivariate extremes were retained for this analysis (Allen et al., 2018). The assumptions of normality, linearity and homoscedasticity of the residuals appeared to have been met based on examination of the normal probability plot of standardized residuals and a scatterplot of standardized residuals against predicted values. Moreover, the residuals appeared to be independent for each observation (Brace et al., 2016). Finally, tolerance values were checked and no collinearity was found between the predictor variables (Bijleveld & Commandeur, 2009).
Descriptive Statistics
The variables that represented overall risk-taking behavior and risk-taking behavior in the presence of siblings were analyzed. The means, standard deviations and correlations of the total sample for overall risk-taking behavior and risk-taking behavior in the presence of siblings are reported in Table 1. Correlations between these variables were found to be significant (p < .001). Furthermore, the sample was split up for male and female gender pairs (Table 2) which both showed statistically significant correlations (p < .001)( Table 3). All correlations showed a large effect size according to Cohen’s criteria (Cohen, 1988) Table 1
Means and Standard Deviations of Overall Risk-Taking Behavior and Risk-Taking Behavior in the Presence of Siblings.
Variable M SD 1 2
1. Overall Risk Taking Behavior .27 .29 - .61**
2. Risk-Taking Behavior in the Presence of Siblings
.10 .19 .61** -
Note. n = 227 ** p < .001
Table 2
Means, Standard Deviations and Pearson Correlation Coefficients of Overall Risk-Taking Behavior and Risk-Taking Behavior in the Presence of Siblings for Male and Female Pairs.
Variable M SD
Male Sibling Pairs
Overall Risk Taking Behavior .29 .32
Risk-Taking Behavior in the Presence of Siblings .12 .22 Female Sibling Pairs
Overall Risk Taking Behavior .23 .26
Risk-Taking Behavior in the Presence of Siblings .08 .15 Note. Total n = 228 (male sibling pairs n = 129 and female sibling pairs n = 98);
Table 3
Pearson Correlation Coefficients of Overall Risk-Taking Behavior and Risk-Taking Behavior in the Presence of Siblings for male and female pairs.
Variable 1 2
1. Overall Risk Taking Behavior - .60**
2. Risk-Taking Behavior in the Presence of Siblings .61** - Note. n = 227; Above the diagonal the results for the male sibling pairs (n = 129) are shown. Below the diagonal the results for the female sibling pairs (n = 98) are shown.
** p < .001 Main Analyses
Table 4 shows the regression coefficients for each variable that was included into the estimated model. The overall predictive power of the model was shown to be statistically significant and an adequate representation of the data2, F(3,223) = 44.12, p < .001. The variables used in the regression model accounted for 37% of the variability of the observed
level of risk-taking behavior by adolescents (R2 = .37, adjusted R2 = .36). Considering that this model is explaining human behavior, which generally is difficult to measure, 37% of explained variance could be considered as a substantial amount (Cohen, 1988).
Table 4
Regression coefficients of the predictor variables: Sibling Presence, Gender and Interaction
Predictor b [95% CI] β SE B t p
Sibling Presence .89 [.70, 1.08] .58 .10 9.27 .000**
Gender -.02 [-.08, .04] -.04 .03 -.68 .496
Interaction between Sibling Presence and Gender .15 [-.22, .51] .05 .19 .80 .427 Note. n = 227; CI = confidence interval;
** p < .001
Sibling Presence during Risk-Taking Behavior
The presence of siblings while undertaking risk-taking behaviors was shown to be a positive significant predictor for the overall amount of risks that adolescents are likely to take (β = .58, p < .001). The analysis showed that when sibling presence during risk-taking
behavior increases with one standard deviation, the amount of overall risk that is taken
increases with a standard deviation of .58. These findings suggest that sibling presence during risk-taking behavior is predictive for more overall risk-taking behavior by adolescents.
Moreover, sibling presence during risk-taking behavior accounted for 24.20%of the total variance (sr2 = .242) in risk-taking behavior, which is considered to be a medium effect according to Cohen’s criteria (Cohen, 1988)
Gender Differences for Risk-Taking Behavior
Furthermore the relationship of gender on risk taking behavior was analyzed. The differences of risk-taking behavior by male and female adolescents was not statistically significant (β = -.04, p = .496) (Table 4). This means that no evidence was found for a
substantial difference between male and female adolescents in the amount of overall risks that are taken. The amount of total variance that gender accounted for was small (sr2 = .001). The Moderating Role of Gender on Sibling Presence and Overall Risk-Taking Behavior
Finally, the moderation of gender on sibling presence and overall risk-taking behavior was analyzed. This interaction term was not statistically significant (β = .05, p = .427) and accounted for a small amount of the total variance (sr2 = .002). No moderation effect of gender was found in the relationship between risk-taking behavior while siblings were present and risk-taking behavior in general. This means that no substantial difference was found for the predictive value of the presence of male siblings, or female siblings, on general risk-taking behavior. Therefore, the second hypothesis was not accepted.
Discussion
The main goal of this study was to gain insight into the predictive value of sibling presence in risk-taking behavior by adolescents. A composite score of questions regarding alcohol use, drug use, smoking and delinquent behaviors was used to analyze the overall risk-taking behavior and the amount of risks taken in the presence of a sibling. Our analysis provides support for the hypothesis that the presence of siblings during risk-taking behavior significantly predicts higher levels of overall risk-taking behavior by the adolescent sibling. The second aim of this study was to examine whether the predictive value of sibling presence on overall risk-taking behavior could be moderated by the gender composition of the siblings. Our investigation found no significant differences between male and female sibling pairs for the relation between risk taking with a sibling present and overall risk-taking behavior. These findings are discussed in more detail below.
Sibling Presence While Undertaking Risk-Taking Behavior
The current study found significant support for a positive relation between sibling presence and overall risk-taking behavior. These results are in line with other studies
implying the importance of siblings on adolescent behavior (Buist et al., 2013). Buist et al. (2013) found that conflict between siblings leads to more externalizing behavior by
adolescents. Furthermore, the results comply with a meta-analysis that found that siblings who smoke are a significant predictor of smoking by children and young adults (Leonardi-Bee et al., 2011). The results of this investigation are likewise consistent with studies that found externalizing and deviant behavior of siblings to predict higher levels of these
behaviors by adolescents (Defoe et al., 2013; Stormshak et al., 2004). These studies (Defoe et al., 2013; Leonardi-Bee et al., 2011; Stormshak et al., 2004) mainly compared the amount of smoking, externalizing and deviant behavior shown by the adolescents and their siblings. The current study, however, uniquely focuses on the presence of siblings while undertaking several risk-taking behaviors as independent variable. Therefore, this study provides an important scientific contribution to understanding risk-taking behavior in the presence of siblings and the impact this has on risk-taking behavior by adolescents.
Our findings further support the notion that sibling modeling can influence risk-taking behavior by adolescents, as the social learning theory would predict (Bandura, 1977). It is possible that siblings look up to each other and model each other’s behavior. Likewise, it is known that adolescents can rebel against authority as a way of forming their own identity (Koepke & Denissen, 2012). Should this process of rebelling against authority reach the levels of risk-taking behavior, then that could set a behavioral standard in the household that other siblings might aim to reach (Whiteman et al., 2014). However, further research is necessary to clearly identify the precise mechanisms that define the relationship between sibling presence and adolescent risk-taking behavior.
Gender
The second aim of this study was to investigate the moderating role of gender on the relationship between sibling presence and risk-taking behavior. Initially, we hypothesized
that the presence of a male sibling during risk-taking behavior would predict higher levels of overall risk-taking behavior by adolescents, as compared to a female sibling. However, the current study was unable to demonstrate gender as a moderating factor. This finding is unexpected when considering the evolutionary theory (Ellis et al., 2012) that suggests clear gender differences in risk-taking behavior. Furthermore, the result is contrary to the findings of Slomkowski et al. (2001), who followed younger and older siblings during a 4-year period (ages 9 to 15 during the first wave). They found that brothers are a greater risk factor for delinquency when compared to sisters. Moreover, in the meta-analysis by Buist et al. (2013) larger effects were found for male siblings, in comparison to female siblings, when
investigating the effect of sibling relationships on adolescent behavior. Conflicting results between the previous and current research could be due to the fact that the current research investigated the presence of a sibling while engaging in risk-taking behavior, which earlier studies had not focused on. It is possible that gender differences are less apparent when investigating the presence of a sibling while engaging in risk-taking behavior. At the same time, the lack of firm statistical indication of gender as a moderating factor is in accordance with previous sibling studies that failed to find supporting evidence of gender composition differences (Defoe et al., 2013).
A possible explanation for the fact that there is limited statistical evidence of gender being a moderating factor in sibling presence and risk-taking behavior is that gender is a more relevant moderator for older youth. For example, a meta-analysis by Byrnes et al. (1999) showed that gender differences within risk-taking behavior were largest for college students, when compared to other ages. Moreover, male young adults tend to show more risk-taking behavior than female young adults and therefore have a bigger risk of developing problems concerning substance use (Stone et al., 2012). However, participants included in the current study were all teenagers living at home, and their siblings were between the ages of 7
and 21. Perhaps older participants, who are no longer living at home, and have greater possibilities of making independent choices, might be more susceptible to gender influences. For example, young adult males might be tempted to go to pubs, as one form of risk-taking behaviors, while young adult females might be more likely to go to nail salons or hairdressers together, which are not considered as risk-taking behaviors. In other words, older male and female adolescents face different (i.e. gender-specific) behavioral choices because of social norms and values that affect the rites-of-passage as they age.
Strengths, Limitations and Future Directions
This investigation provides important insights into the predictive value of sibling presence for risk-taking behavior by adolescents. Sibling research has so far been scarce and our investigation contributes to more insight in this field. To achieve this, data from a large longitudinal research was used and a large sample size was investigated.
Although this research contributes to overall scientific knowledge in the field, some limitations of this investigation should be taken into account. First of all, the data for this investigation was collected during a period (October 2013-January 2014) in which the legal setting for adolescent risk-taking behavior had undergone a major change. In 2014, the Netherlands introduced a new law, which raised the legal drinking and smoking age from 16 to 18 (Tweede Kamer, 2011 – 2012). This occurred during the data collection period. As also mentioned by Defoe et al. (2016), raising the legal age of drinking alcohol could have
influenced adolescent behavior and needs to be taken into consideration.
A second, and particularly important, weakness of this investigation concerns the low alpha score of the sibling presence variable. A score below .60 is generally not accepted as a reliable measurement tool (Bijleveld, 2013). However, the sibling presence variable had an alpha score of .54. It is possible that risk-taking behaviors of different natures (i.e. alcohol use, drug use, smoking and delinquent behavior) should not be considered on one single scale
(Bijleveld, 2013). Further research could consider using single risk-taking behaviors instead of combining these items.
Furthermore, the current study only included same-sex gender composition as a moderating factor. Therefore, the possible effects for mixed-sex gender pairs was not included. The choice of only including same-sex sibling pairs was made because problem behavior showed to be more similar for same-sex sibling pairs than for mixed-sex sibling pairs (Buist, 2010; Rowe & Gulley, 1992; Trim et al., 2006) and the effects for brothers showed to be greater than for other sibling compositions (Buist et al., 2013; Slomkowski et al., 2001). Future research could include these mixed-sex sibling pairs as a moderating factor as this could have an effect on the moderating role of gender composition.
The last limitation concerns overlap between the questions that were used.
Adolescents were asked whether they displayed risk-taking behavior while siblings were present and if they show risk-taking behavior in general. These questions do exhibit some overlap, namely that whether adolescents show risk-taking behavior while siblings are
present, then they automatically also show risk-taking behavior in general. The database used did offer the option to use questions regarding risk-taking behavior when being alone instead of risk-taking behavior in general. Using the ‘alone’ question would resolve the overlap issue. However, the purpose of the current research was not to examine whether adolescents engage in risk-taking behavior alone versus with siblings. Using the ‘alone’ question was therefore not preferred. Further research could consider a different question method, where the answer options do not overlap, such as investigating risk-taking behavior in the presence of siblings versus risk-taking behavior in the absence of siblings. When framing the questions this way, other influential factors (such as peer presence) are also assessed, which is not the case when using the ‘alone’ question.
Further work in this field should consider the use of experimental research designs.
Originally, it was intended that primary data collection would be undertaken to collect data directly from students with siblings at University-level. However, the corona pandemic made this approach not feasible. Experimental approaches should be pursued in the future, since experimental research on the influence of siblings on risk-taking behavior is limited (Defoe, 2016; Morrongiello et al., 2010). This type of research is certainly needed to draw more definitive conclusions about the causal effect that siblings have on risk-taking behavior. Furthermore, experimental research could give more insight into the mechanisms behind the relationship between sibling presence and risk-taking behavior.
Moreover, the model used in this study accounts for 37% of the total variance of overall risk-taking behavior by adolescents. This means that more predictive factors play a role when understanding risk-taking behavior by adolescents. Risk-taking behavior is not seen as a trait but is influenced by multiple personal and external factors (Figner & Weber, 2011). Strategies for preventing this behavior need to be crafted based on a solid
understanding of what motivates it in the first place. More research into further predictive factors is therefore necessary to determine what other personal and external factors affect risk-taking behavior by adolescents and if these factors could affect the predictive value of sibling presence.
Finally, future research regarding the motives behind risk-taking behavior is advised. The analysis of open ended questions on the motives behind risk-taking behavior showed that for alcohol and drug use, answers were related to ‘having fun’, whereas for smoking the motive ‘addiction’ was reported more often in comparison to alcohol and drugs use. The fact that not all risk-taking behaviors are motivated by the same factors points to the need for a better understanding of the motives that drive adolescents to engage in risk-taking behavior. Investigating these behaviors separately could be preferred considering the different
motivations that drive them. Furthermore, a connection between the motives behind behavior and sibling presence effects should be examined. Are the motives of siblings similar or different? Is the predictive value of siblings larger when motivational aspects are the same? In the current study, the motivational aspects were not included as a hypothesis. Further research could consider using a motivational hypothesis to increase knowledge in this field. Conclusion
Understanding why adolescents engage in risk-taking behavior is an evolving field and has become even more important since the corona-pandemic has introduced a whole new class of risk-taking behaviors that all adolescents are exposed to. Will siblings encourage each other to follow the distancing regulations and report mild corona-symptoms, or will they encourage each other to undergo more risk-taking behavior? This is particularly relevant now that the consequences of risk-taking behavior have risen to new epidemiological heights. High-risk adolescent behavior ultimately leads to dangerous situations (Blum & Nelson-Mmari, 2004; Steinberg, 2015, Wechsler et al., 2000) and becomes an economic and social burden for society (Welsh et al., 2008).
The current research set out to investigate the predictive value of the presence of siblings while undertaking risks on risk-taking behavior by adolescents in general. Evidence was found to support this relationship. Complemented by previous research findings, the current study could contribute evidence to the social learning theory (Bandura, 1977) and the notion of modeling influences between siblings. A second aim of the current study was to identify if gender is a moderating factor in the relationship between sibling presence and overall risk-taking behavior by adolescents. The current study failed to obtain evidence for gender as a moderating factor and therefore does not provide supporting evidence for the evolutionary theory suggested by Ellis et al. (2012). Further experimental research is
predictive value of sibling presence on risk-taking behavior by adolescents. Siblings typically play an important role in the life of adolescents. Therefore, the predictive value of sibling presence on adolescent risk-taking behavior should be acknowledged and taken into account in the prevention and further understanding of adolescent risk-taking behavior.
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Footnotes
1 This analysis includes all respondents that had a same-sex brother or sister in the sample. Approximately .8% of the respondents had brothers or sisters that were less than 11 years of age. It is possible that the effects of siblings and gender on risk-taking behavior manifest more clearly in cases where respondents are adolescent or older. To take this into
consideration, a second sample was generated, which excluded all those whose siblings were 11 years of age or less. The same multiple regression analysis that was performed for the full-sample was conducted for this "adolescent or older" full-sample of siblings to determine whether the presence of young siblings had any influence on the analysis results.
2 As noted in the methodology section, the regression analysis was also performed on a sample of respondents who had siblings that were adolescents or older. The results of this regression are essentially the same as that of regression analysis conducted on the full sample. The overall model used stayed significant to explain risk-taking behavior (F = 43.806, p < .001, R2 = .373). In this case, the presence of siblings continued to significantly explain the variation in risk-taking behavior (t = 9.225, p < .001), explaining 24.1% of total variation in risk-taking (sr2 = .241). Gender continued to have an insignificant moderational effect on risk-taking behavior, with the t-variable of the interaction variable in the regression being .789, p = .431.
Appendix A Coding Information
To gain information regarding the motivational aspects behind risk-taking behaviors the following questions were coded: “Can you give reasons why you or other young people drink alcohol?”, “Can you give reasons why you or other young people smoke?” and “Can you give reasons why you or other young people use soft drugs?”.
The coding process followed the steps recommended by Boeije (2005). First, all questions were skimmed and coded openly. The list of labels were then grouped into
categories based on categories identified in the risk-taking literature. The categories that were selected will be further discussed below.
All answers were independently coded by four researchers. Afterwards, an Intraclass Correlation Coefficient (ICC) was calculated. Based on recommendations by Koo & Li (2016), a two-way mixed model (agreement) was chosen. The ICC was calculated for all questions and was identified as being excellent in terms of reliability according to the Koo & Li standard (Alcohol = .999, Smoking = 1.00 and Soft Drugs = 1.00). Also an average ICC of .998 was calculated. Before analyzing the data an average of the four scores was calculated as recommended for qualitative research (Malec, 2018; Swanborn, 2002). The coding outcomes and a short summary of the results can be found in Appendix B.
Self-focused approach motivations
The first category for classifying the responses was self-focused approach
motivations. This category consists of physical and emotional pleasure-seeking motivations. It is believed that these behaviors can be qualified as enhancement motives for risk-taking behavior (Cooper, 2015). In this category, examples of answers included: ‘for fun’, ‘good feeling’ and ‘to relax’.
The second category of self-focused avoidance motives covers coping motives. The motives represented in this category could be seen as behavior that is undertaken to avoid or minimize the likelihood of negative outcomes. Risk-taking behavior could possibly be undertaken to cope with threats or negative emotions (Cooper, 2015). Examples of answers that fell into this category included ‘to cope with problems’, ‘to handle stress’, ‘to feel better’ and ‘addiction’.
Social approach motives
Social approach motives were also coded as a motive for risk-taking behavior. Bonding with others or improving social gatherings were classified as social/affiliative motives (Cooper, 2015). Examples of answers that were classified as social approach motives included: ‘because it’s cozy (“gezellig” in dutch)’, ‘to party’ and ‘as a game’.
Social avoidance motives
When considering the social aspects of behavior, social avoidance motives were also assessed. This includes motivations that would have to do with gaining peer approval or avoidance of social censure. These motivations are otherwise also classified as approval or conformity motives (Cooper, 2015). For example, responses such as ‘acting tough (“Stoer” in dutch)’, ‘belonging to the group’ and ‘peer pressure (in a group)’ have been classified as social avoidance motives.
Environmental factors
The social context is understood to be a learning context for adolescents. Bandura’s social learning theory rests on the premise that modeling influences behavior and furthermore that social environments influence the process of learning and forgetting different behaviors (Bandura, 1977). Moreover, risk-taking behavior has been shown to be influenced by the presence of peers during the adolescent period (Gardner & Steinberg, 2005). In this category, reported motivations such as ‘others also do it’, ‘copying of behavior’ and ‘availability’ are
examples of answers that were classified as environmental motivations. Sensation-seeking
There is evidence that sensation-seeking is related to risk-taking behavior. Zang et al., (2019) found in their meta-analysis that higher measures of sensation-seeking are a predictor for risky driving behavior. They classified sensation-seeking and impulsivity as strong predictors for risk-taking behavior. Examples of answers given in this category are: ‘to try it out, ‘finding it exciting’ and ‘to get a kick’.
Behavioral change
Risk-taking behavior can also lead to positive behavioral benefits (Furby & Beyth-Marom, 1992). For example, a possible benefit of risk-taking behavior is learning from the risks that other youths have taken (Van Duijvenvoorde et al., 2016). Behavioral changes due to risk-taking behavior are further assessed in this category. Examples of answers given in this category are: ‘more self-confidence’ and ‘to dare more’.
Other reasons:
Motives that could not be coded in other categories are labeled as other reasons. For example, answers such as ‘not enough education regarding risk-taking’, ‘as a bet’ and ‘when there are no drugs’. Also financial reasons such as ‘earning money’, ‘lack of money’ and ‘financial maintenance’ were classified as other reasons.
Missing data
The category missing data contains the following answers: blank responses, responses such as ‘I don’t know’, ‘yes’, ‘no’ and other responses that did not contain a codable answer.
Appendix B Coding Outcomes Summary of the main results
The coding of open ended questions regarding the motivations behind smoking, soft drugs and alcohol use showed that for all behaviors the social avoidance motive of acting tough to impress others, “stoer doen” in Dutch, was the most given answer (mentioned 113.50 times for smoking, 90 times for alcohol use and 56.50 times for drug use). Secondly the self-focused approach motive, enjoyment, was mentioned for all risk-taking behaviors (55.75 times for smoking; 70.25 times for alcohol use and 34.50 times for drug use). Various motives for alcohol consumption were given, such as social approach motives (cozy,
“gezellig” in Dutch, = 26.75 times), self-focused motives (for fun = 26.75 times) and social avoidance motives (belonging = 23.25 times). Motives for drug use included self-focused approach motives (for the feeling = 20.50 times), social avoidance motives (belonging = 18.75 times) and sensation seeking motives (experimentation = 14.25 times). More self-focused avoidance motives were given for smoking (addiction = 43 times, stress = 21 times) when compared to the other risk-taking behaviors. Social avoidance motives were also stated as a motive for smoking (belonging = 39.25 times). See table B1 for an overview of all coding outcomes scored by four independent coders.
Table B1
Below are the average outcomes of the coding results by four independent coders of open ended questions regarding the motives behind the use of soft drugs, alcohol and smoking.
Categories / Labels Number of times given as the rational for the behaviora
Soft drugs Alcohol Smoking
Self-focused approach motives (physical/emotional pleasure) For fun 12,25 26.75 9.75 To be happy 2,50 2 .50 Funny 2,75 2.50 - Enjoyable 34,50 70.25 55.75 (Good) Feeling 20,50 10.75 1 Want to be drunk/high 2,25 7 - To feel free 1,25 3.25 - To feel looser 1,50 12.25 - To relax 5,75 2.75 1.50 To enjoy life 1,75 3.75 1
Categories / Labels Number of times given as the rational for the behaviora
Soft drugs Alcohol Smoking
Self-focused avoidance motives (avoidance of negative emotions)
Distraction 4,25 1 -
To cope with problems / To forget everything
6,25 15.75 2.50
To handle stress 10 5.75 21
Because people have problems/ pain
3,75 6.75 1.25
Because people are crazy/stupid 6,75 6 4.25
Depression 0,75 .75 -
Because life is not fun 2,25 1 -
To calm down 11,25 1.25 5.50
To feel better 8,50 5.75 1
Addiction / Junkies 18 14 43
To offset boredom / To pass the time
