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.
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).
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:
- Social Factors.
- 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|
|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.
|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|
|Number of Children in Household||68.59||36.019||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||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), 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
|Model||Sum of Squares||df||Mean Square||F||Sig.|
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.
|Model||Unstandardized Coefficients||Standardized Coefficients||t||Sig.||95% Confidence Interval for B|
|B||Std. Error||Beta||Lower Bound||Upper Bound|
|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|
|NUMBER OF CHILDREN IN HOUSEHOLD||-.004||.001||-.042||-3.270||.001||-.007||-.002|
Dependent Variable: Number of Attempts
|Model||Beta In||T||Sig.||Partial Correlation||Collinearity Statistics|
|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).
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.
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.
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.
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.
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.