Peer Pressure in Adolescent Alcohol Use

A study was recently conducted in 2017, on students, monitoring adolescent alcohol use. The article Peer Influence, Peer Selection and Adolescent Alcohol Use: A Simulation Study Using a Dynamic Network Model of Friendship Ties and Alcohol Use, was tested and written by Cheng Wang, John Hipp, Carter Butts, Rupa Jose, and Cynthia Lakon. The study focused on individuals’ alcohol behavior correlating with peers or if they will adopt the same alcohol behavior of their peers. These researchers wanted to compare the results of this study with previous work involving peer influence and cigarette smoking. They utilized a parallel methodology and the same sample for both studies to maximize comparability. The researchers expected to see a similar outcome, from varying the strength of peer selection that they found in the smoking study (Wang, Hipp, Butts, Jose, & Lakon, 2017). There was a strong correlation between smoking and friendship ties.

The population used in this study was the two largest schools from the Add Health study saturation sample. They used adolescents in grades 7-12 from this school. Assessments were collected from all the consented students over three survey waves. There was an in-school survey, two in-home surveys, and a parent survey as part of an in-home survey. Friendship networks and alcohol use were measured from these surveys. The students were asked to list the names of up to five female and five male best friends. The friendship tie variable is based on whether a tie is present between any two students. The alcohol use part of the surveys was looking for answers to specify nondrinkers, casual drinkers, light drinkers, medium drinkers, and heavy drinkers. They found their answers by asking two questions in the surveys. In wave one of alcohol use, the question asked was: “During the past twelve months, how often did you drink beer, wine, or liquor?” In waves two and three, the question asked was “During the past twelve months, on how many days did you drink alcohol?” The researchers used all the data found and turned it into a summary of statistics using analytic methods. The two schools were analyzed with SAB Models.

This strategy estimates how drinking behavior impacts friendship networks and how friendship networks impact future drinking. The model creates two equations. The first equation, the outcome variable is the choice of forming or dissolving a tie to another adolescent in the school. The second equation, the outcome is the decision to either increase, stay the same, or decrease one’s drinking level. The network equation model was used for individual alcohol use, affecting network evolution. This was used in three ways, as the main effect on attractiveness (drinking alters), the main effect on network activity (drinking ego), and as the peer selection effect (drinking similarity). The behavior equation specification model measured the tendency for adolescents to change their alcohol use levels to match their friends’ levels (Wang, Hipp, Butts, Jose, & Lakon, 2017). This is the population and the procedures used in this study.

Using the different models and calculations, the researchers found the results in this case study. In both schools, a positive correlation was found between friendship ties and alcohol use. Increasing peer influence has a strong effect on drinking behavior. When the network is simulated, assuming there is zero peer influence effect, the percentage of drinkers decreases (Wang, Hipp, Butts, Jose, & Lakon, 2017). So, friendship groups who drink, influence each other to drink. If there is no friendship group, drinking decreases because peer pressure is eliminated. Although in some cases, drinking decreased when an adolescent who consumes alcohol becomes friends with an adolescent who does not consume alcohol. The researcher’s predictions were correct. They had based the prediction on a previous study, tying friendship groups and cigarette smoking, which had a positive correlation (Wang, Hipp, Butts, Jose, & Lakon, 2017). The results supported the hypothesis that was made by the researchers.

Parental support is unrelated to drinking, while it is negatively related to smoking in adolescents. Communication between children and parents about cigarette smoking and alcohol usage is important. If a parent uses one or both substances, the more likely an adolescent child will use them as well. Peer influence can decrease alcohol use among a school-based population. This can help with prevention efforts. If most students in a school do not use alcohol, the peer influence effect will possibly make the school alcohol consumption decrease overall. Intervention groups can consider the strategy of boosting peer influence among youth populations, using the overall level of alcohol usage in a school as a guide. The SAB modeling strategy is a cost-effective way for an intervention to create changes in the level of peer influence or sections in a network. A successful intervention can substantially change the level of drinking among adolescents (Wang, Hipp, Butts, Jose, & Lakon, 2017). This study is important in the real world to prevent adolescents from being peer influenced by alcohol.

The researcher’s conclusions to this case study is that peer influence does affect whether or not adolescents choose to drink (Wang, Hipp, Butts, Jose, & Lakon, 2017). I agree with the conclusion for many reasons. The first reason is that I was an adolescent and I have faced peer pressure in my life. There was always a lot of peer pressure in order to fit in with a group. Peer pressure is a big part of life; everyone faces it because most people want to fit in. The second reason I agree with the conclusions is based on social influence theory and conformity. Social influence is how our thoughts, feelings, and behaviors change when we are in the presence of other people. Conformity is changing how you behave to be more like others. Conformity can run very deep and can even change our beliefs and values to be like those of our peers (Waude, 2017). Both social influence and conformity play big parts in this case study. Culture, locality, and education shape our behavior, so the researchers could take this study and their findings even further if they sampled groups from more than one area. For example, they could find schools in poorer areas or more wealthy areas to do this study. Overall, this article was very interesting in my opinion. I expected the results to come out how they did and agreed with the researchers’ predictions.