HIV Treatment Compliance Among African American Women

Subject: Immunology 6 1451 6 min College

The following chapter is created to illustrate the findings of the research paper in order to analyse and interpret the results. This chapter analyses the data through the regression analysis and descriptive statistics along with a model summary that can be used to determine the impact of the dependent variable on the independent variable of the study. This aspect can also play an imperative role in determining the positive or negative impact of variables.

In only 3 hours we’ll deliver a custom HIV Treatment Compliance Among African American Women essay written 100% from scratch

The chapter is also used to analyse the factors that are responsible for changes in the patient treatment of HIV. Moreover, the results from descriptive statistics and regression are used to determine the hypothesis of the study and to accept or reject it. In order to conduct analysis, SPSS software has been utilised (Coakes & Steed, 2009).

Data Analysis

In order to answer the given research question, we need to first identify the dependent and independent variables. From the assessment of the research question, it can be concluded that the dependent variable for the study is HIV treatment compliance. The number of HIV treatment attempts of the patient, whereas the dependent variable includes the following, can represent this:

1. Social Factors.
2. Other Factors.

The data on these independent factors are gathered from the number of sub-sectors that can be seen in the table below:

 Social Factors Other Factors Number of Adults in Households Employment Status Number of Adult Men in Households Income Level Number of Adult Women in Households Own or Rent Home Are you a veteran Education Level Marital Status Country Code Hispanic/Latino Household Density Stratum Code Number of Children in Households

In order to identify the impact of these social and other factors on the HIV treatment compliance, the regression analysis is performed in the next part of this report. The regression analysis is a statistical process which was used by the researcher in order to determine the relationship among variables (Seber, & Lee, 2012). Therefore, the regression analysis is utilized in order to analyse whether there is an impact of family support on HIV treatment among African American women with HIV.

Descriptive Statistics

 Mean Std. Deviation N Number of Attempts 3.98 3.502 6278 Household Density Stratum Code 1.13 .334 6278 Number of Adults in Household 1.76 .782 6278 Number of Adult Men in Household .74 .615 6278 Number of Adult Women in Household 1.02 .531 6278 Are You a Veteran 1.87 .412 6278 Marital Status 2.10 1.426 6278 Number of Children in Household 68.59 36.019 6278 Education Level 4.58 1.143 6278 Employment Status 4.75 2.858 6278 Income Level 20.94 32.871 6278 County Code 81.68 90.158 6278 Hispanic/Latino 2.02 .459 6278

Descriptive statistics demonstrated in the above-mentioned table can be used to illustrate various features related to data in the study. Descriptive statistics provide an overview regarding the data and the variation in the results (Tarrant, Ware, & Mohammed, 2009).

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 1 .266(a) .071 .069 3.379 .071 43.327 11 6266 .000

a Predictors: (Constant), Hispanic/Latino, County Code, Household Density Stratum Code, Number of Adult Women in Household, Marital Status, Income Level, Education Level, Are You a Veteran, Number of Children in Household, Number of Adult Men in Household, Employment Status

Academic experts available We will write a custom Immunology essay specifically for you for only \$16.00 \$11/page

ANOVA(b)

 Model Sum of Squares df Mean Square F Sig. 1 Regression 5441.794 11 494.709 43.327 .000(a) Residual 71545.383 6266 11.418 Total 76987.176 6277

Predictors: (Constant), Hispanic/Latino, County Code, Household Density Stratum Code, Number of Adult Women in Household, Marital Status, Income Level, Education Level, Are You a Veteran, Number of Children in Household, Number of Adult Men in Household, Employment Status

Dependent Variable: Number of Attempts

The model summary table illustrated above can be used to determine the changes in variables of the study, as changes statistics are demonstrated in it (Judd, McClelland, & Ryan, 2011). The R Square illustrated in the table is 0.071 or 71 percent. This concludes the fact that Independent variable has much efficiency that it can bring 71% of changes in the dependent variable. Therefore, the number of HIV patient attempts can be changed up to 71% by using the independent variable.

Furthermore, 71% of R Square determines the fact that along with the variables included in the study there are certainly other variables that can affect the number of HIV treatment of patient among African American women. In addition, the significance level of F change is 0.000 which determines that the model is a good fit and family support can affect on HIV treatment compliance among African American women.

Coefficients

 Model Unstandardized Coefficients Standardized Coefficients t Sig. 95% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) 4.820 .421 11.458 .000 3.995 5.645 HOUSEHOLD DENSITY STRATUM CODE .038 .128 .004 .297 .767 -.213 .289 NUMBER OF ADULT MEN IN HOUSEHOLD .122 .074 .021 1.640 .101 -.024 .267 NUMBER OF ADULT WOMEN IN HOUSEHOLD -.114 .083 -.017 -1.375 .169 -.276 .048 ARE YOU A VETERAN .241 .108 .028 2.245 .025 .031 .452 MARITAL STATUS .034 .031 .014 1.089 .276 -.027 .096 NUMBER OF CHILDREN IN HOUSEHOLD -.004 .001 -.042 -3.270 .001 -.007 -.002 EDUCATION LEVEL .111 .039 .036 2.849 .004 .035 .187 EMPLOYMENT STATUS -.284 .016 -.232 -17.482 .000 -.316 -.253 INCOME LEVEL -.001 .001 -.009 -.723 .470 -.004 .002 COUNTY CODE -.001 .000 -.017 -1.349 .178 -.002 .000 HISPANIC/LATINO -.088 .094 -.012 -.941 .347 -.272 .095

Excluded Variables

 Model Beta In T Sig. Partial Correlation Collinearity Statistics Tolerance 1 NUMBER OF ADULTS IN HOUSEHOLD .(a) . . . .000

Predictors in the Model: (Constant), Hispanic/Latino, County Code, Household Density Stratum Code, Number of Adult Women in Household, Marital Status, Income Level, Education Level, Are You a Veteran, Number of Children in Household, Number of Adult Men in Household, Employment Status

Dependent Variable: Number of Attempts

From the above table of the coefficient, it can be illustrated that certain independent variables tend to have a negative impact on the number of patient suffering from HIV. These variables include a number of adult women in the household, the number of children in the household, employment status, income level, county code and Hispanic/Latino. Therefore, it can be determined that an increase in one variable will deliberately bring about a decline in a number of patient attempts suffering from HIV.

However, certain other variables have a positive impact on the dependent variable, which includes Household Density Stratum, marital status, the number of adult men in household, veteran and education level. All these variables have a positive impact on the dependent variable and an increase in one variable will introduce an increasing trend in a number of patient attempts (Orme, & Combs-Orme, 2009).

15% OFF Get your very first custom-written academic paper with 15% off

Furthermore, it is important to determine the significance level of these variables which can be seen from the above table. The significance level of being a veteran, the number of children in the household, education level and employment status is lower than 0.05, which means that all these variables have an impact on the number of patient attempts. Therefore, all these factors related to family support can impact on the HIV treatment Among African American Women. However, other variables including household density stratum code, the number of adult men in the household, the number of adult women in the household, marital status, county code, and Hispanic/Latino and income level tends to show no impact on the dependent variable and their significance level is higher than the 0.05.

Conclusion

It can be concluded from the above discussion that family support has an impact on the HIV treatment compliance among African American women. Certain variables of family support tend to show an impact on HIV of women. These include ‘’veteran’’, a number of children in the household, education level and employment status. However, among these variables the number of children in the household and employment status have a negative impact on the HIV treatment compliance among African American women. Nevertheless, other variables including ‘’veteran’’ and ‘’education level’’ have a positive impact on the HIV treatment compliance among African American women.

Chapter Summary

It can be concluded from the above chapter that the data collected through the research questions show that various factors that have an impact on the number of patient attempts including independent factors of a veteran, the number of children in the household, education level and employment status. However, it is also important that other factors should also be focused which is not included in the study including the government focus, role of health care sector and other variables. These variables can be incorporated in future studies by other researchers.

References

Coakes, S. J., & Steed, L. (2009). SPSS: Analysis without anguish using SPSS version 14.0 for Windows. London, England: John Wiley & Sons.

Judd, C. M., McClelland, G. H., & Ryan, C. S. (2011). Data analysis: A model comparison approach. New York, NY: Routledge.

Orme, J. G., & Combs-Orme, T. (2009). Multiple regression with discrete dependent variables. Oxford, England: Oxford University Press.

Get your customised and 100% plagiarism-free paper on any subject done for only \$16.00 \$11/page

Seber, G. A., & Lee, A. J. (2012). Linear regression analysis (Vol. 936). London, England: John Wiley & Sons.

Tarrant, M., Ware, J., & Mohammed, A. M. (2009). An assessment of functioning and non-functioning distractors in multiple-choice questions: A descriptive analysis. BMC Medical Education, 9(1), 40.