Results and Findings

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**Results and Findings**

In particular, this dissertation examines non-medical Out of Pocket Expenses impact on middle income family’s quality of life. To proceed with the purpose, three questions were developed.

**Research Question 1 (RQ1): What is the relationship between NOOPEs and the ability of middle-income families in Houston with a cancer parent to pay for rent and mortgage?**

Null hypothesis: A statistically significant relationship does not exist between NOOPEs and the ability of middle-income families in Houston with a cancer parent to pay for rent and mortgage.

Research Hypothesis: A statistically significant relationship does exist between NOOPEs and the ability of middle-income families in Houston with a cancer parent to pay for rent and mortgage.

Several test have been performed in order to provide statistical decision for association between NOOPEs and the ability of middle income families to pay for utilities. Test includes test of normality, bivariate correlation and regression analysis. Test of normality has been performed to check the nature of data either it is parametric or non-parametric while correlation and regression analysis has been performed to find the association between NOOPEs and ability of middle income families to pay for utilities.

## Test for parametric assumptions

Decision to use parametric or non-parametric test is based on distribution function like normal distribution. If there has been found normal distribution of data than Pearson correlation test will be used to find association between two variables otherwise Spearman correlation coefficient will be used to determine the relationship. Test of normality used to test parametric assumption that either data is normally distributed or not. Normal distribution of data has been find through test of normality at 95% confidence interval that means if decision value has less than 0.05, provides the strong statistical evidences to reject null hypothesis that states data values is normally distributed thus the non-parametric test will be used to find association between above mentioned variables.

**Tests of Normality**

Table 1.1 shows test for normality with; Kolmogorov-Smirnov and Shapiro-Wilk tests to check for normality. As our data is discrete and not continuous, Sharpiro-Wilk is used to analyze a continuous variable. The Likert scale data is in the form of whole number hence it will be called as discrete variable. In this case, Kolmogorov-Smirnov test will be used and it provides firm statistically significant evidence that at 95% confidence interval our p-value is less than 0.05 for each variable which indicates that the null hypothesis is rejected and the data is not normally distributed. As it was discussed earlier that when the data shows signs of non-normality it also confirms that the data is non parametric so proceeding towards spearman correlation test will be the appropriate. (statistics.laerd, n.d.)

Table 1.1 Tests of Normality |
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reduced ability to pay rent or mortgage | Kolmogorov-Smirnova | Shapiro-Wilk | |||||

Statistic | df | Sig. | Statistic | df | Sig. | ||

hardship paying kids daycare | Strongly agree | .396 | 23 | .000 | .601 | 23 | .000 |

agree | .346 | 11 | .001 | .774 | 11 | .004 | |

Neither agree nor disagree | .327 | 8 | .012 | .810 | 8 | .037 | |

Disagree | .385 | 3 | . | .750 | 3 | .000 | |

difficulty paying travel cost | Strongly agree | .300 | 23 | .000 | .632 | 23 | .000 |

agree | .353 | 11 | .000 | .649 | 11 | .000 | |

Neither agree nor disagree | .300 | 8 | .032 | .872 | 8 | .156 | |

Disagree | .385 | 3 | . | .750 | 3 | .000 | |

hardship paying food cost | Strongly agree | .323 | 23 | .000 | .819 | 23 | .001 |

agree | .287 | 11 | .012 | .754 | 11 | .002 | |

Neither agree nor disagree | .305 | 8 | .027 | .860 | 8 | .120 | |

Disagree | .385 | 3 | . | .750 | 3 | .000 | |

diffuclty paying for lodging expense | Strongly agree | .305 | 23 | .000 | .833 | 23 | .001 |

agree | .279 | 11 | .017 | .822 | 11 | .018 | |

Neither agree nor disagree | .327 | 8 | .012 | .810 | 8 | .037 | |

Disagree | .385 | 3 | . | .750 | 3 | .000 | |

less productive | Strongly agree | .248 | 23 | .001 | .796 | 23 | .000 |

agree | .334 | 11 | .001 | .826 | 11 | .021 | |

Neither agree nor disagree | .377 | 8 | .001 | .693 | 8 | .002 | |

Disagree | .385 | 3 | . | .750 | 3 | .000 | |

a. Lilliefors Significance Correction |

## Spearman Correlation Test

As it has been clear that the research is aimed towards finding a relationship between non-medical Out of Pocket Expenses and middle income family’s quality of life, spearman correlation test has been used to determine the strength of relationship between independent variable, reduced ability to pay rent or mortgage and dependent variables; less productive, difficulty paying travel cost, difficulty paying for lodging expense, hardship paying food cost and hardship paying kids daycare. Correlation coefficient of 0.052 between reduced ability to pay rent or mortgage and less productivity shows a very weak link between the two variables. It means that the chances of variability between the two variables are of just 5.2%. The correlation coefficient of 0.332 between the independent variable and difficulty paying travel cost is again weak to moderate signifying not much movement in the “reduced to ability to pay rent or mortgage”, this means that if there is difficulty paying travel cost it won’t have any effect on ability to paying rent or mortgage. The coefficient correlation of 0.631 between difficulty paying lodging expense and reduced ability to pay rent or mortgage means a moderate to relationship between the two variables. If the patient will have difficulty paying lodging expense when traveling abroad for treatment, it will have a slight positive impact on the reduced ability to pay rent or mortgage. Furthermore, the relationship between hardship paying food cost and reduced ability to pay rent or mortgage is weak to moderate at 0.399 correlation coefficient. The person’s ability to pay rent or mortgage will not have much affect when there will be change in hardship paying food cost bills. In the last, the relationship between the dependent variable, hardship paying kids daycare and dependent variable, reduced ability to pay rent or mortgage is yet again at weak to moderate level as coefficient correlation is a mere 0.441. This again identifies that if a person is facing hardship in paying for kids’ daycare it doesn’t have any effect on his/her ability to pay rent or mortgage. Table 1.2 shows the result for spearman correlation test.

Correlations |
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less productive | difficulty paying travel cost | diffuclty paying for lodging expense | hardship paying food cost | hardship paying kids daycare | reduced ability to pay rent or mortgage | |||

Spearman's rho | less productive | Correlation Coefficient | 1.000 | .116 | -.022 | .000 | .252 | .052 |

Sig. (2-tailed) | . | .450 | .885 | .998 | .094 | .732 | ||

N | 45 | 45 | 45 | 45 | 45 | 45 | ||

difficulty paying travel cost | Correlation Coefficient | .116 | 1.000 | .441** | .457** | .542** | .332* | |

Sig. (2-tailed) | .450 | . | .002 | .002 | .000 | .026 | ||

N | 45 | 45 | 45 | 45 | 45 | 45 | ||

diffuclty paying for lodging expense | Correlation Coefficient | -.022 | .441** | 1.000 | .648** | .330* | .631** | |

Sig. (2-tailed) | .885 | .002 | . | .000 | .027 | .000 | ||

N | 45 | 45 | 45 | 45 | 45 | 45 | ||

hardship paying food cost | Correlation Coefficient | .000 | .457** | .648** | 1.000 | .466** | .399** | |

Sig. (2-tailed) | .998 | .002 | .000 | . | .001 | .007 | ||

N | 45 | 45 | 45 | 45 | 45 | 45 | ||

hardship paying kids daycare | Correlation Coefficient | .252 | .542** | .330* | .466** | 1.000 | .441** | |

Sig. (2-tailed) | .094 | .000 | .027 | .001 | . | .002 | ||

N | 45 | 45 | 45 | 45 | 45 | 45 | ||

reduced ability to pay rent or mortgage | Correlation Coefficient | .052 | .332* | .631** | .399** | .441** | 1.000 | |

Sig. (2-tailed) | .732 | .026 | .000 | .007 | .002 | . | ||

N | 45 | 45 | 45 | 45 | 45 | 45 | ||

**. Correlation is significant at the 0.01 level (2-tailed). | ||||||||

*. Correlation is significant at the 0.05 level (2-tailed). |

**Table 1.2**

**Regression Analysis:**

The test for regression analysis has been conducted to examine the relationship between dependent variable, reduced ability to pay rent or mortgage and all the independent variables combined. Continuing with the analysis, the goodness of fit can be determined by R Square as it shows the degree of variability in independent variable due to change in dependent variability. Now table 1.3 shows us the R-square of 0.416, indicating a weak to moderate relationship between our dependent variable and all the independent variables. Thus implying, that there is only 41.6% chance that change in independent variables; less productive, difficulty paying travel cost, difficulty paying for lodging expense, hardship paying food cost and hardship paying kids daycare will hinder reduction in person’s ability to pay rent or mortgage.

Table 1.3 Model Summary |
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Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |

1 | .645a | .416 | .341 | .786 |

a. Predictors: (Constant), less productive , hardship paying food cost , difficulty paying travel cost, hardship paying kids daycare, diffuclty paying for lodging expense |

** **

**Research Question:** What is the relationship between NOOPEs and the ability of middle-income families who live in Houston with a cancer parent to pay for utility expenses (water, gas, phone and electricity)?

**Null Hypothesis:** A statistically significant relationship does not exist between NOOPEs and the ability of middle-income families living in Houston with cancer parent to pay for utility expenses (water, gas, phone and electricity).

**Research Hypothesis:** A statistically significant relationship does exist between NOOPEs and the ability of Houston-dwelling middle-income families with a cancer parent to pay for utility expenses (water, gas, phone and electricity).

**Test for Normality:**

Table 2.1 provides results about test of normality which are derived through SPSS, showing significance level less than 0.05 at each level. Kolmogorov-Smirnov test has been used to check distribution function that provides statistically strong evidences for rejecting null hypothesis that states that the data is normally distributed because p-value is less than 0.05. At this level, test of normality exposes the distribution free shape of data because it provides significant evidences to reject null hypothesis at 95% confidence level that means data do not include the normal or symmetric data and do not consistent for the use of parametric test in order to find the association between NOOPEs and the ability of middle income families to pay utility expenses.

Table 2.1: Tests of Normalityb |
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reduced ability to utility bills | Kolmogorov-Smirnova | Shapiro-Wilk | |||||

Statistic | df | Sig. | Statistic | df | Sig. | ||

hardship paying kids daycare | Strongly agree | .377 | 24 | .000 | .622 | 24 | .000 |

agree | .209 | 12 | .045 | .824 | 12 | .018 | |

Disagree | .473 | 5 | .001 | .552 | 5 | .000 | |

difficulty paying travel cost | Strongly agree | .276 | 24 | .000 | .635 | 24 | .000 |

agree | .352 | 12 | .000 | .729 | 12 | .002 | |

Neither agree nor disagree | .441 | 4 | . | .630 | 4 | .001 | |

Disagree | .367 | 5 | .026 | .684 | 5 | .006 | |

hardship paying food cost | Strongly agree | .311 | 24 | .000 | .822 | 24 | .001 |

agree | .321 | 12 | .001 | .699 | 12 | .001 | |

Neither agree nor disagree | .283 | 4 | . | .863 | 4 | .272 | |

Disagree | .367 | 5 | .026 | .684 | 5 | .006 | |

difficulty paying for lodging expense | Strongly agree | .271 | 24 | .000 | .859 | 24 | .003 |

agree | .209 | 12 | .045 | .824 | 12 | .018 | |

Neither agree nor disagree | .441 | 4 | . | .630 | 4 | .001 | |

Disagree | .231 | 5 | .020* | .881 | 5 | .314 | |

less productive | Strongly agree | .259 | 24 | .000 | .788 | 24 | .000 |

agree | .314 | 12 | .002 | .829 | 12 | .020 | |

Neither agree nor disagree | .307 | 4 | . | .729 | 4 | .024 | |

Disagree | .367 | 5 | .026 | .684 | 5 | .006 | |

*. This is a lower bound of the true significance. | |||||||

a. Lilliefors Significance Correction | |||||||

b. hardship paying kids daycare is constant when reduced ability to utility bills = Neither agree nor disagree. It has been omitted. |

**Non-Parametric / Spearman Correlation:**

As sufficient and appropriate evidences for the normal distribution of data has not been found, therefore Spearman correlation coefficient has been used to check association between NOOPEs and ability of middle income families to pay utilities expenses. NOOPEs are divided into five different variables which are, “less productive at work, difficulty in paying travel cost, difficulty in paying lodging expense, food costs and kids daycare cost”. **Table 2.2** shows Spearman correlation coefficient for five variables related to non-medical, out of pocket expenses with the ability of middle income families to pay its utility bills.

**Table 2.2** shows that a weak relationship exists between less productivity at work with reduction in the ability of paying utility bills which means that less productivity at workplace due to cancer treatment has no impact upon the ability of middle income families to pay utility expense.

A strong positive relationship has not been found between variables, “difficulty in paying travel cost, lodging cost, food expenses and kid’s daycare expenses” with the ability for middle income families to pay utilities expense because p-value is less than 0.01 which shows correlation is significant at 0.01 significance level. It also clarifies that non-medical, out of pocket expenses like traveling, lodging, food and kid’s daycare have a strong impact upon the ability of middle income families to pay their utility bills.

Table 2.2: Correlations |
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less productive | difficulty paying travel cost | difficulty paying for lodging expense | hardship paying food cost | hardship paying kids daycare | reduced ability to utltiy bills | |||

Spearman's rho | less productive | Correlation Coefficient | 1.000 | .116 | -.022 | .000 | .252 | .162 |

Sig. (2-tailed) | . | .450 | .885 | .998 | .094 | .287 | ||

N | 45 | 45 | 45 | 45 | 45 | 45 | ||

difficulty paying travel cost | Correlation Coefficient | .116 | 1.000 | .441** | .457** | .542** | .450** | |

Sig. (2-tailed) | .450 | . | .002 | .002 | .000 | .002 | ||

N | 45 | 45 | 45 | 45 | 45 | 45 | ||

diffuclty paying for lodging expense | Correlation Coefficient | -.022 | .441** | 1.000 | .648** | .330* | .531** | |

Sig. (2-tailed) | .885 | .002 | . | .000 | .027 | .000 | ||

N | 45 | 45 | 45 | 45 | 45 | 45 | ||

hardship paying food cost | Correlation Coefficient | .000 | .457** | .648** | 1.000 | .466** | .527** | |

Sig. (2-tailed) | .998 | .002 | .000 | . | .001 | .000 | ||

N | 45 | 45 | 45 | 45 | 45 | 45 | ||

hardship paying kids daycare | Correlation Coefficient | .252 | .542** | .330* | .466** | 1.000 | .459** | |

Sig. (2-tailed) | .094 | .000 | .027 | .001 | . | .002 | ||

N | 45 | 45 | 45 | 45 | 45 | 45 | ||

reduced ability to utltiy bills | Correlation Coefficient | .162 | .450** | .531** | .527** | .459** | 1.000 | |

Sig. (2-tailed) | .287 | .002 | .000 | .000 | .002 | . | ||

N | 45 | 45 | 45 | 45 | 45 | 45 | ||

**. Correlation is significant at the 0.01 level (2-tailed). | ||||||||

*. Correlation is significant at the 0.05 level (2-tailed). |

**Regression Analysis:**

Regression analysis has been used to measure variability between variables. **Table 2.3** shows model summary that encloses r-square. R-square shows variability between the data that how much dependent variable will change if any change will occur in independent variables. **Table 2.3** also exhibits that r-square is 0.333 which means that there is a moderate relationship between independent and dependent variables and any change in NOOPE variables will impact 33.3% on the ability for middle income families to pay their utilities expenses.

Table 2.3: Model Summary |
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Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |

1 | .577a | .333 | .247 | .885 |

a. Predictors: (Constant), less productive , hardship paying food cost , difficulty paying travel cost, hardship paying kids daycare, difficulty paying for lodging expense |

**Research Question:** What is the relationship between NOOPEs and the ability of middle-income families who live in Houston with a cancer parent to pay for expenses that maintain quality of life including vacation, entertainment and additional classes for kids?

**Null Hypothesis:** A statistically significant relationship does not exist between NOOPEs and the ability of middle-income families who live in Houston with cancer parent to pay for expenses that maintain quality of life including vacation, entertainment and additional classes for kids.

**Research Hypothesis: **A statistically significant relationship does exist between NOOPEs and the ability of middle-income families who live in Houston with a cancer parent to pay for expenses that maintain quality of life including vacation, entertainment and additional classes for kids.

**Test for Normality:**

**Table 3.1** provide results about the distribution of data that has been derived from SPSS. At 95% confidence interval, Kolmogorov-Smirnov test has been used to check the validity of null hypothesis about data if it is normally distributed at more than 0.05 significance level. As significance level is less than 0.05 for each variable, therefore we have sufficient evidence to reject null hypothesis and claim that the data is not normally distributed.

Table 3.1: Tests of Normalityb,c,d,e,f |
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reduced ability to entertainment cost | Kolmogorov-Smirnova | Shapiro-Wilk | |||||

Statistic | df | Sig. | Statistic | df | Sig. | ||

hardship paying kids daycare | Strongly agree | .325 | 31 | .000 | .667 | 31 | .000 |

agree | .342 | 13 | .000 | .766 | 13 | .003 | |

difficulty paying travel cost | Strongly agree | .232 | 31 | .000 | .674 | 31 | .000 |

agree | .352 | 13 | .000 | .646 | 13 | .000 | |

hardship paying food cost | Strongly agree | .326 | 31 | .000 | .819 | 31 | .000 |

agree | .288 | 13 | .004 | .766 | 13 | .003 | |

diffuclty paying for lodging expense | Strongly agree | .279 | 31 | .000 | .848 | 31 | .000 |

agree | .303 | 13 | .002 | .778 | 13 | .004 | |

less productive | Strongly agree | .221 | 31 | .001 | .834 | 31 | .000 |

agree | .311 | 13 | .001 | .808 | 13 | .008 | |

a. Lilliefors Significance Correction | |||||||

b. hardship paying kids daycare is constant when reduced ability to entertainment cost = Neither agree nor disagree. It has been omitted. | |||||||

c. difficulty paying travel cost is constant when reduced ability to entertainment cost = Neither agree nor disagree. It has been omitted. | |||||||

d. hardship paying food cost is constant when reduced ability to entertainment cost = Neither agree nor disagree. It has been omitted. | |||||||

e. diffuclty paying for lodging expense is constant when reduced ability to entertainment cost = Neither agree nor disagree. It has been omitted. | |||||||

f. less productive is constant when reduced ability to entertainment cost = Neither agree nor disagree. It has been omitted. |

**Non-Parametric / Spearman Correlation:**

As significant evidences has not been found to accept the normal distribution of data, non-parametric / Spearman correlation coefficient has been used to test the association between NOOPEs and to maintain middle income family’s quality of life including vacation, entertainment and additional classes for kids.

**Table 3.2** provides that there is a weak negative relationship (near to zero) between being less productive at work and to maintain middle income family’s quality of life including vacation, entertainment and additional classes for kids that reveals that correlation is not significant at 0.05 significance level because p-value is less than 0.05 that shows the less productivity of cancer patient at his workplace has no impact upon the ability to maintain quality of life.

It has been found that the relationship between variables which are difficulties in paying travel cost, lodging cost, food expenses and kid’s daycare cost with ability to maintain quality of life is strong. The evidences has been taken out from the p-value which is less than 0.01 that indicates correlation is significant at 0.01 significance level. That means difficulties in paying travel cost, lodging cost, food expenses and kid’s daycare cost have a strong impact upon the ability for middle income level to maintain their quality of life.

Table 3.2: Correlations |
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less productive | difficulty paying travel cost | diffuclty paying for lodging expense | hardship paying food cost | hardship paying kids daycare | reduced ability to entertainment cost | |||

Spearman's rho | less productive | Correlation Coefficient | 1.000 | .116 | -.022 | .000 | .252 | -.005 |

Sig. (2-tailed) | . | .450 | .885 | .998 | .094 | .976 | ||

N | 45 | 45 | 45 | 45 | 45 | 45 | ||

difficulty paying travel cost | Correlation Coefficient | .116 | 1.000 | .441** | .457** | .542** | .420** | |

Sig. (2-tailed) | .450 | . | .002 | .002 | .000 | .004 | ||

N | 45 | 45 | 45 | 45 | 45 | 45 | ||

diffuclty paying for lodging expense | Correlation Coefficient | -.022 | .441** | 1.000 | .648** | .330* | .621** | |

Sig. (2-tailed) | .885 | .002 | . | .000 | .027 | .000 | ||

N | 45 | 45 | 45 | 45 | 45 | 45 | ||

hardship paying food cost | Correlation Coefficient | .000 | .457** | .648**
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