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Health Condition and the Academic Level Relations

Abstract

To determine the relationship between health condition and the academic level completed, the study performed one-way ANOVA. Descriptive statistics indicated that the mean of academic level completed increases with health condition. Hypothesis testing holds that there are statistically significant differences in the means of academic level completed among the four categories of health conditions, F(3,737) = 19.302, p = 0.000.

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Statistical Assumptions

  1. The measuring scale of the dependent variable should be in the continuous scale such as ratio scale or interval scale (Frankfort-­Nachmias & Nachmias, 2008).
  2. The independent variable should be a nominal variable comprising three or more categories to allow post hoc analysis.
  3. The observations made on each category should be independent and free from any systematic biases.
  4. The dependent variable should not contain significant outliers because they reduce validity of outcomes (Hatcher, 2013).
  5. The data of the independent variable should follow the normal distribution to give robust inferential statistics.
  6. The variances of different categories should be homogenous to allow robust comparison of categories (Macdonald, 2015).

Variables Selected

  • The independent variable selected is health condition with three levels or categories, namely, excellent, good, fair, and poor health conditions.
  • The dependent variable selected is the highest level of academic level that the respondents have completed.

Hypotheses

  • H0: There are no statistically significant differences in the means of academic level completed among the four categories of health conditions.
  • H1: There are statistically significant differences in the means of academic level completed among the four categories of health conditions.

P-value and Confidence Intervals

The p-value of the one-way ANOVA outcome is 0.000. The confidence interval of the mean at 95% is 13.49 and 13.91 for the lower and upper limits respectively. The confidence interval implies that, at the probability of 0.95, the interval of mean falls within 13.49 and 13.91 limits.

Hypothesis Testing

In hypothesis testing, p-value is a parameter that determines whether to reject or retain the null hypothesis. Fundamentally, rejection of the null hypothesis happens when p-value is less than the significance level while retention of the null hypothesis occurs when the p-value is greater than the significance level (Field, 2013; Wilcox, 2012). In this case, the p-value is 0.000, which is less than the significance level of 0.05. Hence, the p-value rejects the null hypothesis that there are no statistically significant differences in the means of academic level completed among the four categories of health conditions. Therefore, hypothesis testing holds that there are statistically significant differences in the means of academic level completed among the four categories of health conditions.

Post Hoc Analysis

According to ­Green and Salkind (2014), post hoc analysis enables determination of multiple comparisons between two groups. Post hoc analysis shown in the multiple comparisons table (Table 3) indicates that there are statistically significant differences among means of all groups except between the groups with poor health and fair health.

Report of the Results

Descriptive statistics (Table 1) indicate that there are apparent differences in the means of the academic level completed. Evidently, the means of the academic level completed increases with the quality of health. Respondents (N = 213) with excellent health had the highest mean (M = 14.55, SD = 2.705) of the academic level completed while the ones (N = 33) with poor health had the lowest mean (M = 11.52, SD = 3.022). Respondents with good health (N= 361) ranked second (M = 13.78, SD = 2.850) whereas the ones with fair health (N = 134) ranked third (M + 12.67, SD = 2.780).

The hypothesis testing holds that there are statistically significant differences in the means of academic level completed among the four categories of health conditions, F(3,737) = 19.302, p = 0.000. Post hoc analysis shows that the differences in the means among respondents with excellent, good health, fair health, and poor health were statistically significant (p<0.05) while the difference in means between respondents with fair and poor health was statistically insignificant (p = 0.147). Overall, the findings indicate that the quality of health improves with the academic level completed.

References

Field, A. (2013). Discovering statistics using IBM SPSS statistics. London: SAGE Publication.

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Frankfort-­Nachmias, C., & Nachmias, D. (2008). Research methods in the social sciences (7th ed.). New York: Worth.

­Green, S. B., & Salkind, N. J. (2014). Using SPSS for Windows and Macintosh: Analyzing and understanding data (7th ed.). Upper Saddle River, NJ: Pearson.

Hatcher, L. (2013). Step-by-step basic statistics using SAS: Student guide. Cary: SAS Institute.

Macdonald, S. (2015). Essentials of Statistics with SPSS. Raleigh: Lulu Publisher.

Wilcox, R. (2012). Introduction to robust estimation and hypothesis testing. London: Academic.

Appendices

SPSS Analysis

Table 1

ANOVA
Highest Year of School Completed
Sum of Squares df Mean Square F Sig.
Between Groups 455.251 3 151.750 19.302 .000
Within Groups 5794.239 737 7.862
Total 6249.490 740

Table 3

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Multiple Comparisons
Dependent Variable: Highest Year of School Completed
Tukey Hsd
(I) CONDITION OF HEALTH (J) CONDITION OF HEALTH Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval
Lower Bound Upper Bound
EXCELLENT GOOD .768* .242 .009 .14 1.39
FAIR 1.878* .309 .000 1.08 2.67
POOR 3.034* .525 .000 1.68 4.38
GOOD EXCELLENT -.768* .242 .009 -1.39 -.14
FAIR 1.110* .284 .001 .38 1.84
POOR 2.266* .510 .000 .95 3.58
FAIR EXCELLENT -1.878* .309 .000 -2.67 -1.08
GOOD -1.110* .284 .001 -1.84 -.38
POOR 1.156 .545 .147 -.25 2.56
POOR EXCELLENT -3.034* .525 .000 -4.38 -1.68
GOOD -2.266* .510 .000 -3.58 -.95
FAIR -1.156 .545 .147 -2.56 .25

Syntax

Notes
Output Created 23-JAN-2016 06:55:24
Comments
Input Data C:Documents and SettingsDesktopgss04student_corrrected 30FINAL(1).sav
Active Dataset DataSet1
Filter
Weight
Split File
N of Rows in Working Data File 1500
Missing Value Handling Definition of Missing User-defined missing values are treated as missing.
Cases Used Statistics for each analysis are based on cases with no missing data for any variable in the analysis.
Syntax ONEWAY EDUC BY HEALTH
/STATISTICS DESCRIPTIVES
/MISSING ANALYSIS
/POSTHOC=TUKEY ALPHA(0.05).
Resources Processor Time 00:00:00.03
Elapsed Time 00:00:00.13

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