Hispanics, Health Insurance and Health Care Access - Statistics Project Example

HISPANICS, HEALTH INSURANCE AND HEALTH CARE ACCESS Our objective in this study is to test statistically whether there exist differences in the given phenomena or testing the association between two attributes. First let us take up a statistical hypothesis about the interest the Hispanics are taking care of answering in English or Spanish according to gender. The null hypothesis here is H0: There is no association between the opinion about health and status of coverage of health insurance. Alternative hypothesis H1: There is an association between the opinion about health and status of coverage of health insurance Level of significance: 5% level or α=0.05. Test Statistic used: The test statistic for association of attributes is chi square test with the formula χ02=Σ(Oi–Ei)2/Ei where Oi is the observed frequency and Ei is the expected frequency with the exception for 2x2 contingency table Table 1: Table describing model contingency table of 2x2 type C1 C2 Total R1 a b a+b R2 c d c+d Total a+c b+d N The formula for chi square is χ02=(ad–bc)2N/[(a+b)(c+d)(a+c)(b+d)] The results are as follows: Table 2: Table describing gender vs. status of health insurance coverage [Gender Vs. 91. Are you, yourself, now covered by any form of health insurance or health plan? This would include any private insurance plan through your employer or that you purchase yourself, as well as a government program like Medicare or Medicaid?] Gender vs. Question No.91 91. Are you, yourself, now covered by any form of health insurance or health plan? This would include any private insurance plan through your employer or that you purchase yourself, as well as a government program like Medicare or Medicaid? Total Yes No Dont know Refused Gender Male 1244 698 15 59 2016 Female 1290 658 8 41 1997 Total 2534 1356 23 100 4013 The chi square observed value for 3 d.f. at 5% level of significance is 7.296 with probability 0.063>0.05, hence it is concluded that there is no association between gender and status of coverage under health insurance. Table 3: Table describing language proficiency vs. status of health insurance coverage [Language proficiency Vs. 91. Are you, yourself, now covered by any form of health insurance or health plan? This would include any private insurance plan through your employer or that you purchase yourself, as well as a government program like Medicare or Medicaid?] Language proficiency vs. Question No.91 91. Are you, yourself, now covered by any form of health insurance or health plan? This would include any private insurance plan through your employer or that you purchase yourself, as well as a government program like Medicare or Medicaid? Total Yes No Dont know Refused Language proficiency British dominant 501 142 3 18 664 Bilingual 966 383 11 29 1389 Spanish dominant 1067 831 9 53 1960 Total 2534 1356 23 100 4013 The chi square observed value for 6 d.f. at 5% level of significance is 140.736 with probability 0.0000.05) in the duration since last checked BP between the origin of coverage of insurance which indicates that whether the insurance is covered in USA or not covered in USA. Table 6: Table describing the descriptive statistics coverage of insurance genderwise for the variable: How long ago was the last time BP checked Gender N Mean SD SE(Mean) 76b. How long ago was the last time? (Years) Males 423 2.75 3.85 0.19 Females 311 2.12 2.15 0.12 Table 5: Table showing Independent Samples Test Levenes Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Diff. SE(Diff.) 95% Confidence Interval of the Difference 76b. How long ago was the last time? (Years) Equal variances assumed 9.354 0.002 2.593 732 0.01 0.63 0.24 0.15 1.11 Equal variances not assumed 2.813 685.6 0.005 0.63 0.22 0.19 1.07 Results and discussion: From the above table, it is clearly observed that there is a significant difference (P>0.05) in the duration since last checked BP between gender which indicates that females are checking BP more often than males do. ANOVA ANOVA is a statistical technique used to test various group means by splitting up the total variation into two parts 1. Between group variances, 2. Within group variances. Here in our study, our objective is to test the significance in the duration since BP checked (qn76b) between various health positions (qn7). Null Hypothesis H0: There is no significant difference in the mean weight between various health insurance coverage groups. Alternative Hypothesis H1: There is a significant difference in the mean weight between various health insurance coverage groups. Level of significance: 5% level or α=0.05. Procedure: ANOVA Table 7: Table of Descriptive Statistics for the variable Weight vs. are you covered under any health insurance N Mean SD SE 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound Yes 2534 1.015 0.644 0.013 0.980 1.03 0.209 4.000 No 1356 0.981 0.613 0.017 0.949 1.014 0.211 4.000 Don’t know 23 1.36 0.996 0.208 0.929 1.791 0.377 4.000 Refuse 100 1.032 0.425 0.0425 0.948 1.117 0.456 2.362 Overall 4013 1 0.632 0.01 0.980 1.019 0.201 4.000 Table 8: Table of ANOVA for the variable Weight Source of variation Sum of Squares df Mean Square F Sig. Between Groups 3.626 3 1.209 3.033 0.028 Within Groups 1597.414 4009 0.398 Total 1601.039 4012 Chart 1: Chart showing the mean weight of various insurance coverage groups From the above table of ANOVA, we observe that there is a significant difference in weight between the various groups of health insurance coverage at 5% level of significance. The group which says that they don’t know about the health insurance coverage has more weight with mean 1.36 whereas the group saying no health insurance coverage has the least weight 0.981. Table 9: Significant difference in weight between various categories of coverage of insurance (I) 91. Are you, yourself, now covered by any form of health insurance or health plan? This would include any private insurance plan through your employer or that you purchase yourself, as well as a government program like Medicare or Medicaid? Mean difference Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound Yes No 0.024* 0.021 .258 -0.018 0.066 Dont know -0.354* 0.132 .007 -0.614 -0.095 Refused -0.027* 0.064 .676 -0.153 0.099 No Dont know -0.378* 0.133 .004 -0.639 -0.118 Refused -0.051* 0.065 .436 -0.179 0.077 Dont know Refused 0.327* 0.146 .025 0.041 0.614 *. The mean difference is significant at the 0.05 level. Null Hypothesis H0: There is no significant difference in the mean duration since last time BP checked between language proficient groups. Alternative Hypothesis H1: There is a significant difference in the mean duration since last time BP checked between language proficient groups. Level of significance: 5% level or α=0.05. Procedure: ANOVA Table 10: Table of Descriptive Statistics for the variable time since last time BP checked vs. language proficient groups Language proficiency group N Mean SD SE 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound English dominant 115 2.11 1.92 0.18 1.76 2.47 1 10 Bilingual 233 2.48 3.01 0.20 2.09 2.86 1 20 Spanish dominant 386 2.60 3.72 0.19 2.23 2.98 1 50 Overall 734 2.49 3.27 0.12 2.25 2.72 1 50 Table 11: Table of ANOVA for the variable Weight Source of variation Sum of Squares df Mean Square F Sig. Between Groups 21.358 2 10.679 0.996 0.37 Within Groups 7840.006 731 10.725 Total 7861.364 733 Chart 2: Mean plots for time since last time BP checked by language proficient groups From the above table of ANOVA, we observe that there is no significant difference in mean time since last BP checked weight between the various language proficient groups at 5% level of significance. All the language proficient groups are checking the BP at equal regular intervals. Conclusion: 1. There is no association between gender and status of coverage under health insurance at 5% and 1% levels of significance. 2. There is an association between language proficiency and status of coverage under health insurance at 5% and 1% levels of significance. 3. There is no significant difference (P>0.05) in the duration since last checked BP between the origin of coverage of insurance which indicates that whether the insurance is covered in USA or not covered in USA 4. There is a significant difference (P>0.05) in the duration since last checked BP between the gender which indicates that females are checking more often BP than males do. 5. There is a significant difference in weight between the various groups of health insurance coverage at 5% level of significance. 6. There is no significant difference in mean time since last BP checked weight between the various language proficient groups at 5% level of significance. Read More