The paper examines the relationship between neighborhood and alcohol consumption among college students. Globally, consumption of alcohol has resulted in public health concerns in many nations, and it is among the risk behaviors in many college students. Alcohol consumption has a significant impact on the overall performance of students, such as risky sexual behaviors, substance abuse, fights, poor academic performance, and injuries. The research utilizes the survey method of data collection to identify the relationship between environment and alcohol consumption. The survey covers the demographic, alcohol consumption, and educational information to understand the research problem. Data analysis is done using SPSS, and linear regression and chi-square tests are performed. The result shows that there is a correlation between the neighborhood and the consumption of alcohol among students.
Globally, young people are the most alcohol and substance users for recreational and other purposes. Many tertiary students tend to experiment with alcoholic drinks and beverages for various reasons such as academic, social, and emotional health (Gant et al., 2020). There are very many reasons that force students to engage in alcoholism either by choice or by purpose. Other reasons include workload, stress, boredom, peer pressure, and experience of negative life events. Stress is a major factor that forces students to engage in alcohol to cope with the changes. College life is usually very stressful and demanding, which exposes students to various forms of stress such as financial, social, and academic.
This research focuses on the type of neighborhood college students live in and alcohol consumption among the students. The environment can be stressful, especially when it is demanding. Various types of surroundings tend to impact the performance of individuals as it impacts their day-to-day activities. For example, research studies show that many people who live in a gang neighborhood tend to have higher chances of family members getting involved with the gang activities because of the environmental conditions (Patton & Weigold, 2020). The papers use the same concept to identify whether the neighborhood has a similar impact on students engaging in alcoholism.
This research aims to identify the impact of neighborhood on alcohol consumption among students. The research study utilizes a linear regression model to identify whether the predictor variables influence alcohol consumption among college students. Linear regression results show that the relationship between alcohol consumption and the neighborhood that the students live is positively related despite the model used being statistically insignificant for the research (Chekole, 2020). However, despite the model being week, the relationship between alcohol and neighborhood has been trending among researchers. Haardörfer et al. (2021) show that social life stress is derived from our environment, and it makes students use various methods to get hold of it, such as smoking, drinking, and using other drugs.
Participants and procedure
The current study involved college students with an age bracket of 20 to 54 years old. The survey was conducted at California university between January 2021 to May 2021. The students who were willing to participate in the research study were informed and required to sign up using the online google form questionnaire system. The students were provided with the link to the survey form, where they were required to read the consent form of the survey before proceeding with the survey. The consent form had an accept and reject button where they were required to either accept to continue with the research survey or decline and exit the survey process.
The participants were then forwarded to the online survey form, which took approximately 15 to 20 minutes. After completing the online survey, the participants were provided with the debriefing form, where they were informed of the purpose of the research. A total of 260 responses were downloaded as the data set for the project. The data set was then subjected to screening and cleaning, where 44 records were discarded. The data cleaning process removed 32 incomplete records, and 12 duplicate files were removed, making the final dataset have 220 records. The research study was approved by the Institutional Review Board (IRB).
Demographic and contextual information
Demographic and other contextual information were collected using questions that focused on educational background and personal characteristics. Personal characteristic information was collected on race, gender, and marital status. The gender of the participant was coded into binary (male and female). The marital status was coded into never married, married, other, cohabiting, divorced, separated, and others. Ethnicity was coded into white, Asian, Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, Taiwanese, other Asian, Guamanian, Samoan, black/African American, Tongan, Micronesian, other pacific islanders, multi-ethnic, other ethnicities, Mexican, Puerto Rican, Cuban, other Hispanic/Latino, American Indian, Native Hawaiian, Portuguese. Educational background information was collected on the current year at college, which was coded into freshman, sophomore, junior, senior. The type of student was also recorded, whether international or local.
Usage of alcohol was assessed using the Alcohol Use Disorder Test (AUDIT). The survey question used was 18 on alcohol usage and outcome after using the alcohol. The aim of using AUDIT is to identify alcohol usage among college students and the alcohol-related consequences. Furthermore, the AUDIT is essential in portraying the hazardous drinking patterns among the students and the impact on the neighborhood.
The relational ethics was measured using the Relational Ethics Scale (RES). It consisted of 15 items that were measured on varying Likert scale measurements between 3 and 7. An example of a question in relational ethics is “My family is willing to help me make decisions.” The higher the score in the RES, the higher the overall performance.
Data analysis was conducted using SPSS software version 22. The data was screened and cleaned before coding. The chi-square test and linear regression were conducted to identify the relationship between alcohol consumption and neighborhood. The Anova results and significance of the model were also considered to ensure that the model is significant. The bivariate model helped in the creation of the multivariate model and the linear relationship between the variables.
Table 1.0 shows that the respondents who filled the age section were 216 and the minimum age was 20 years, and the maximum age was 54 years. College students are mature enough to make their own decisions despite being associated with alcoholism, resulting in risky behaviors.
|age||Valid N (listwise)|
Table 1.0: Age of the respondents.
From the Chi-square tests in table 2.0, the relationship between students drinking alcohol and the neighborhood selling or using drugs is insignificant. This is because the p-value is greater than 0.05. However, this differs from Jones-Webb and Karriker-Jaffe (2013), their findings show that neighborhood with high drugs activity increases the consumption alcohol among students.
|Value||df||Asymp. Sig. (2-sided)|
|N of Valid Cases||220|
|a. 5 cells (25.0%) have expected count less than 5. The minimum expected count is 1.69.|
Table 2.0: How often do you have a drink containing alcohol? * People sell or use drugs in my neighborhood.
|Value||df||Asymp. Sig. (2-sided)|
|N of Valid Cases||220|
|a. 9 cells (45.0%) have expected count less than 5. The minimum expected count is.65.|
Table 3.0: How often do you have a drink containing alcohol? * People in this neighborhood generally get along with each other.
From table 4.0, the value of R square is 0.026, which means that 2.6% of the variation on students drinking alcohol can be explained by the combined variation of people in neighborhood getting along with each other, people sell or use drugs in the neighborhood, I feel safe being out alone in my neighborhood, people often get mugged, robbed or attacked in my neighborhood, and people in my neighborhood could be trusted. However, the adjusted R square, which considers the number of variables and the sample size, shows that only 0.3% of the variation in the dependent variable can be explained by combined variations of the dependent variable (Ramachandran & Tsokos, 2021).
From table 5.0, the p-value is 0.342, which is greater than 0.05. This means that the regression analysis is not significant to explain the dependent variable using the independent variables (Maneejuk & Yamaka, 2020). From the correlations results in table 3.0; I feel safe being out alone in my neighborhood, people often get mugged, robbed, or attacked in my neighborhood, and people in my neighborhood generally got along with each other have a weak positive correlation with how often do you have a drink containing alcohol? People sell or use drugs in my neighborhood, and People in my neighborhood could be trusted has a weak negative correlation with how often do you have a drink containing alcohol? (da Silva Filho et al., 2021).
Table 4.0: Model summary.
|Model||R||R Square||Adjusted R Square||Std. Error of the Estimate||Change Statistics|
|R Square Change||F Change||df1||df2||Sig. F Change|
|a. Predictors: (Constant), People in my neighborhood generally got along with each other., People sell or use drugs in my neighborhood., I feel safe being out alone in my neighborhood during the night., People often get mugged, robbed, or attacked in my neighborhood., People in my neighborhood could be trusted.|
|b. Dependent Variable: How often do you have a drink containing alcohol?|
Table 5.0: ANOVA.
|Model||Sum of Squares||df||Mean Square||F||Sig.|
|a. Dependent Variable: How often do you have a drink containing alcohol?|
|b. Predictors: (Constant), People in my neighborhood generally got along with each other., People sell or use drugs in my neighborhood., I feel safe being out alone in my neighborhood during the night., People often get mugged, robbed, or attacked in my neighborhood., People in my neighborhood could be trusted.|
Table 6.0: Coefficients.
|(Constant)||I feel safe being out alone in my neighborhood during the night.||People often get mugged, robbed, or attacked in my neighborhood.||People sell or use drugs in my neighborhood.||People in my neighborhood could be trusted.||People in my neighborhood generally got along with each other.|
|95.0% Confidence Interval for B||Lower Bound||1.825||-.105||-.184||-.289||-.484||-.091|
|a. Dependent Variable: How often do you have a drink containing alcohol?|
The dependent variable for this study is alcohol consumption among students (Freitas et al., 2020). The study has five independent variables; firstly, is people often get mugged, robbed, or attacked in the neighborhood. Secondly, people in my neighborhood look out for each other, and thirdly, people sold or used drugs in the neighborhood. Fourthly, people in my neighborhood generally got along with each other, and lastly, I feel safe being out alone in my neighborhood during the night.
The neighborhood that students reside in impacts their social life performance in day-to-day activities. This may result in stress, making them unable to meet their educational and social expectations (Chekole, 2020). Despite stress being part of daily life, college students experience increased stress because of the complexity of college academic and social life. These complexities include crime, unsafe sexual practices, and physical assault. Various reasons have been linked with the neighborhood the students live in, such as inadequate parental or guardian supervision and psychosocial predisposition to alcohol abuse such as inability to cope with the new environment and bullying.
According to Nekgotha et al. (2020), college students come from various backgrounds with differences in financial stability and physical and social environments. The differences create a mixture of cultures, and when they interact in the same neighborhood, they tend to influence one another. The influence may not be rapid, but with time, some of the students tend to copy-paste habits, culture, and the way of life of other people. This may force some students to engage in activities that will push them towards smoking, drinking, and using many kinds of drugs (Patton & Weigold, 2020). Furthermore, many students prefer living in neighborhoods favorable to their living conditions. Neighborhood with high poverty and increased resident mobility is known to increase the risk of alcohol problems.
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