You are now going to create and post a crosstab of your variables and a measures of association table.? Complete the following steps: Post a ?brief ex
You are now going to create and post a crosstab of your variables and a measures of association table.
Complete the following steps:
- Post a brief explanation of your topic. Include your research question and a broad research hypothesis — that is, the relationship of IV to DV. (For example, educational attainment affects family income in US adults.)
- Run a crosstab on your variables. Be sure to explain your findings, including a description of the table, a calculation of the epsilons, and a discussion of the 10% rule.
- Run the correct measure of association for your variables (Choose one – either Pearson R, Gamma, Phi, Cramer's V or Lambda). Explain what the output means in terms of strength and direction of the relationship. Interpret Proportional Reduction of Error (PRE) using the following statement: Knowing the IV will reduce error in predicting the DV by *%.
Copy the crosstab and measure of association table into the discussion window or into a document (PDF, MS Word) and attach to discussion. If your table does not fit to the page, choose "copy special" and then "images" or take a screen shot of the table to copy/past into the window.
Special note:
When a variable is continuous (interval/ratio level of measurement), for example age of respondent, we do not run crosstabs directly because it will result in a really spread-out table with lots of zeros and low frequency cells. Such a crosstab does not help us understand the data. The correct way is to reduce the level of measurement to either ordinal level or nominal level (group the numbers into categories) by recoding and then run the crosstab. (Please refer to the Lesson Recoding in SPSS for further information.)
As a reminder, here are the guidelines for choosing your measure of association:
- Both DV and IV are nominal variables: Lambda (when it is not a 2X2 table)
- Both DV and IV are nominal variables and it is a 2X2 table: Phi
- Both DV and IV are ordinal variables: Gamma
- One variable ordinal or interval/ratio AND the other variable dichotomous nominal (like Yes/No, male/female, etc.): Gamma
- One variable ordinal or interval/ratio AND the other variable nominal (not dichotomous, has more than 2 categories): Cramer's V.
- Both DV and IV are I/R variables: Pearson's r
SOCI332 Final Project
Marquita Cooper
APUS
Exploring the Relationship Between Military Service Duration and Injectable Drug Use
(A) Introduction
Substance abuse poses a significant challenge in the community. According to the studies, about 5% of health-related cases are concerned with issues related to substances. The studies also show personnel are the most affected individuals. Research studies cite various reasons as the main causes for the high prevalence of high number of substance abuse cases in the military. These cases include work-related stress and lifestyle changes (Smith, 2021). This study seeks to examine the relationship between military service and injectable drug use.
This topic is of interest since it sheds light on the vulnerable population (veterans) who face unique health challenges that come with different issues related to work. Through this study, I will seek to understand whether military services significantly contribute to substance abuse. The research question in this study: Does military service increase the chance of injectable drug use? The hypothesis of the study is that individuals with military service are more likely to report using injectable drugs as compared to those without military service.
(B) Literature Review
Several studies have shown that more than half of military retirees indulge in substance abuse. This high number is attributed to many factors, both in the service and during retirement. Some of the experiences that face during the services, especially during, lead the army personnel to resort to drug abuse(Gunst, Klaassen, & Vaart, 2023). Also, some are even carried to retirement, where they are lonely and have nobody to consult.
Veterans experience various challenges that affect their health. According to Johnson and Carter (2019), some of the health challenges that veterans experience include high stress and limited access to mental health resources. The research also found that veterans were more as likely to report substance abuse as compared to non-veterans.
Military experience exposes personnel to most of the unfriendly experiences that affect one psychology during and after the service. For instance, exposure to deaths in war can impact servicemen psychologically. Military personnel are frequently relocated to known areas, subjecting them to stress. Also, military personnel are usually separated from their loved ones for a long time. These issues lead to stress, thus forcing them to resort to substance abuse to relieve them from stress. These issues even worsen with one term in the service. According to Babor et al. (2020), sty found out that substance abuse among military veterans is mainly dependent on the number of years spent in the services.
Drug abuse varies depending on the type and the method in which they are used. The choice of one drug over the other is dependent on one preference, which is mainly related to the impact the drug has on that particular user. According to Barnett (2021), the military veteran uses injectables over other medications. This high use of injectables stems from environmental and psychological stressors. Barnett discussed many strategies that can help prevent and control abuse. One of the strategies focuses mainly on preventive measures tailored to high-risk populations, including veterans.
(C) Methods/Dataset
The General Social Survey (GSS) is a data repository that collects data on different topics nationally. This data is collected biennially, including demographic information, social behaviors, attitudes, and health outcomes (Barnett, 2021). Because of its comprehensive nature and large sample size, this data repository is an excellent source of data for exploring social science research questions (Neave, 2022).
The data was selected because it has data related to military service and drug abuse. Therefore, we can use it to find the relationship between drug abuse (injectables) and military service. The study will use the most recent data to ensure that the findings reflect the current situation in the community and are relevant, timely, and applicable (Yarnell Et al., 2020). The study will leverage reliable, well-documented variables that analyze trends and relationships within the broader U.S. population.
(D) Methods/Variables
Independent Variable
The independent variable (IV) in this study is VETYEARS, the number of years the respondent has spent in military service. Respondents were asked, “How many years have you served in the military?” Response options include:
· 0 years
· Less than 2 years
· 2–4 years
· More than 4 years
Dependent Variable
The dependent variable (DV) for the study is EVIDU, which indicates whether the respondent had ever used injectable drugs(nominal). The survey question asks, “Have you ever used an injection device for drugs?” with response choices of "Yes" or "No." These variables were chosen to analyze the potential influence of military service on substance abuse behaviors.
(E) Methods/Variable information:
VETYEARS Frequency table
|
Years in the armed forces |
|||||
|
Frequency |
Percent |
Valid Percent |
Cumulative Percent |
||
|
Valid |
NONE |
2098 |
89.4 |
89.5 |
89.5 |
|
LESS THAN 2 YEARS |
55 |
2.3 |
2.3 |
91.8 |
|
|
2 TO 4 YEARS |
101 |
4.3 |
4.3 |
96.1 |
|
|
MORE THAN 4 YRS |
91 |
3.9 |
3.9 |
100.0 |
|
|
Total |
2345 |
99.9 |
100.0 |
||
|
Missing |
NA |
3 |
.1 |
||
|
Total |
2348 |
100.0 |
The frequency table for the indicates that 89.4% of respondents had no military service, 2.3% served less than two years, 4.3% served two to four years, and 3.8% served more than four years. Among 2, 348 total respondents, only three response were missing. The valid percent shows similar trends, with cumulative percentages confirming the dominant category is no military service at 89.5%.
Frequency Table for EVIDU
|
R ever inject drugs |
|||||
|
Frequency |
Percent |
Valid Percent |
Cumulative Percent |
||
|
Valid |
YES |
41 |
1.7 |
2.9 |
2.9 |
|
NO |
1350 |
57.5 |
97.1 |
100.0 |
|
|
Total |
1391 |
59.2 |
100.0 |
||
|
Missing |
IAP |
942 |
40.1 |
||
|
NA |
15 |
.6 |
|||
|
Total |
957 |
40.8 |
|||
|
Total |
2348 |
100.0 |
The frequency table for " inject drugs" shows that 1.7% (41 respondents) reported having injected drugs, while 57.5% (1,350 respondents) reported not injecting drugs. Among valid responses, 97.1% answered "no." Of the total 2,348 cases, 40.1% had incomplete data marked as "IAP," and 0.6% were missing. Valid responses comprise 59.2% of the dataset, with a cumulative percentage of 100% for those who responded "no."
Pie chart for VETYEARS
The percentage of each valid response category for years in the armed forces is shown on each slide of this pie chart, totaling 100%. It can be seen from the blue slide (89.47% of the pie) that the majority of survey participants had no military experience. 2.35% of responders who served for less than two years are depicted in the green slice. 4.31% of responders who served for two to four years are depicted in the red slice. 3.88% of respondents, as indicated by the orange slice, had more than four years of military service.
Pie chart for EVIDU
This pie chart shows the percentage of each appropriate answer category for medications administered, totaling 100%. The green slide displays the 97.05% of participants who stated they have never used an injection device. The blue slice displays the 2.95% of respondents who acknowledged using injectable medications recreationally.
(F) Findings
To find the relationship between the number of years that a veteran served and the use of injectable drugs, we did an analysis using Chi-Square to determine the results.
Hypothesis
Null hypothesis
H0= There is no relationship between military service and injectable drug use.
Alternative hypothesis
H1=There is a positive relationship between military service and injectable drug use.
Significance level
Alpha α=0.05
Analysis
Score for comparison
The analysis's P-value is 0.866, which is the probability of observing the results when the null hypothesis is true.
Comparison of the p-value and significance
The study's significance level was set to 0.005. The decision rule states that when the p-value is less than or equal to the significance level, we reject the null hypothesis, and when it is less than the significance level, we fail to reject the null hypothesis.
In our analysis, the p-value is 0.866, which is greater than the alpha level of 0.05. Therefore, we fail to reject the null hypothesis.
(G) Findings
The analysis's findings show that there is no statistically significant relationship between the number of years that veterans spent in the service (VETYEARS) and their probability of indulgence in injectable drug use (EVIDU). The Chi-Square test in the analysis yielded a p-value of 0.866, which is greater than the set significance level (alpha) of 0.5.
The finding indicates that the null hypothesis cannot be rejected, and there is insufficient evidence to conclude that military service duration is associated with injectable drug use. The descriptive statistics and frequency tables further support this finding, as the majority of respondents reported neither serving in the armed forces nor engaging in injectable drug use.
(H) Conclusions
The findings of this study are crucial for understanding the relationship between military service and substance abuse and are key in finding the solutions to this menace. The study findings established that there is no statistically significant connection between the time spent in the military and the role in substance abuse behavior.
The study highlights serious concerns, such as stressors that might increase the risk of one using the drugs; however, the relationship is not apparent when focusing specifically on injectable drugs.
Future research could explore additional variables, such as mental health conditions, exposure to combat, post-traumatic stress disorder (PTSD), and access to healthcare resources, to better understand the nuanced factors influencing substance abuse among veterans. Moreover, examining different forms of substance abuse, including alcohol or non-injectable drug use, and their relationships with military experiences could provide more comprehensive insights. Longitudinal studies tracking individuals before, during, and after military service would also help determine causation and identify effective prevention strategies.
Another recommendation is the inclusion of different forms of substance abuse, which could include alcohol or non-injectable drug use, and their relationship with military experience could provide deep, comprehensive insights. Also, longitudinal studies could be implemented to track individuals before, during and after the military to get some deep insights that could help to determine the causation and to identify effective prevention strategies.
References
Babor, T. F., McRee, B. G., Kassebaum, P. A., Grimaldi, P. L., Ahmed, K., & Bray, J. (2023). Screening, Brief Intervention, and Referral to Treatment (SBIRT): toward a public health approach to the management of substance abuse. Alcohol/Drug Screening and Brief Intervention, 7-30.
Neave, H. R. (2022). Statistics tables: For mathematicians, engineers, economists and the behavioural and management sciences. Routledge.
Gunst, M. D., Klaassen, C., & Vaart, A. W. (2023). State of the art in probability and statistics: Festschrift for Willem R. Van Zwet. IMS.
Barnett, V. (2021). Environmental statistics: Methods and applications. John Wiley & Sons.
Yarnell, S., Li, L., MacGrory, B., Trevisan, L., & Kirwin, P. (2020). Substance use disorders in later life: a review and synthesis of the literature of an emerging public health concern. The American Journal of Geriatric Psychiatry, 28(2), 226-236.
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