Course Project Worksheet – Using Excel for Statistical Testing

Last week’s assignment required a review of various statistical tests. Earlier this week, in Discussion 1, you explored which test might be most appropriate given your chosen variables. Now is your time to practice running the statistical test and analyzing the data. Complete each of the items below:

1. Which statistical test (T-Test, ANOVA, Chi-Square, or Regression) did you choose for your variables?

Regression

2. Explain why you chose this test.

Regression analysis is one of the appropriate statistical methods to be used if the relationship between one or more independent variables and a dependent variable is to be measured (Sarstedt & Mooi, 2019). In this case, it is the healthcare spending attitude as represented by the variable ‘NATHEAL’, which is ordinal in nature. However, for the purpose of this analysis, the variable is assumed to be continuous to allow for regression analysis. The independent variables are the highest degree earned, which is a measure of education level or DEGREE, and political views or POLVIEWS put on a liberal-to-conservative scale. Regression is preferred to analyze how these predictors jointly affect attitudes toward healthcare spending. It is appropriate since the method has indicated the strength and statistical significance of these relationships that might be used to understand those factors that shape opinions on healthcare spending.

3. Next, using Excel (refer to the handouts from this week for step-by-step instructions), run the statistical test you chose above and paste the output below.

4. Analyze the results.

The regression analysis gave an important insight into the relationship between the level of education, political views, and attitude toward healthcare expenditure. The F-statistic with its value of 16.043 and the p-value with a value of 1.35E-07 gave support that the combined effect of independent variables causes a significant difference in the dependent variable; however, seeing the value of R², which was 0.028, the explanatory power of the model would be quite low. This suggests that although the relationship of the predictors to the outcome is statistically significant, the model accounts for only 2.82% of the variance in attitudes toward national healthcare spending. More literally, though the predictors contribute significantly to understanding healthcare spending attitudes, other factors not included in the model are likely to play a larger role.

Further contextualization is reinforced by analyzing each of the independent variables. The education level variable, DEGREE, has an estimated coefficient of 0.019; this is indicative of a weak positive slope between greater educational attainment and the likelihood of increased support for national healthcare spending. However, the p-value for this variable, at 0.168, exceeds the conventional threshold for statistical significance at p < 0.05. That might indicate some evidence that higher education can lead to more significant support for healthcare spending, but the evidence does not appear strong enough to determine with any confidence that this relationship is statistically significant in this data set. Therefore, education level might not be a reliable predictor of healthcare spending attitudes in this particular setting.

On the other hand, POLVIEWS are significantly related to attitudes toward spending on healthcare. The coefficient of political views is 0.064, indicating that every step toward conservative political views decreased the attitude of support for additional spending on healthcare. This is highly significant, as its resultant p-value stands at 2.51E-08, which is far below the 0.05 level. This is in agreement with the literature, which denotes that political ideology strongly predicts general public attitudes toward priorities in government spending and healthcare (Vilhjalmsson, 2016). More conservative subjects are for limited government intervention, and thus, they would predictably support increases in national health spending less than their opponents.

These results highlight how political ideology explains much of the variance in public attitudes about spending on healthcare, while the role of education is less influential based on this dataset. The overall low R² would also tend to support that while the political view is significant, it’s but one factor in a myriad that affects this attitude. Such factors as income, demographics, and/or personal experiences with healthcare might be considered as other predictors in constructing a complete model of attitudes toward healthcare spending in future studies. This would provide greater clarity on the complex interconnection of factors driving public opinion in this domain

References

Sarstedt, M., & Mooi, E. (2019). Regression analysis. Springer Texts in Business and Economics, 1(1), 209–256. https://doi.org/10.1007/978-3-662-56707-4_7

Vilhjalmsson, R. (2016). Public views on the role of government in funding and delivering health services. Scandinavian Journal of Public Health, 44(5), 446–454. https://doi.org/10.1177/1403494816631872

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Last week’s assignment required a review of various statistical tests. Earlier this week, in Discussion 1, you explored which test might be most appropriate given your chosen variables. Now is your time to practice running the statistical test and analyzing the data. Complete each of the items below:

1. Which statistical test (T-Test, ANOVA, Chi-Square, or Regression) did you choose for your variables?

[Enter your response here.]

2. Explain why you chose this test.

[Enter your response here.]

3. Next, using Excel (refer to the handouts from this week for step-by-step instructions), run the statistical test you chose above and paste the output below.

[Paste Excel output here.]

Course Project Worksheet – Using Excel for Statistical Testing

4. Analyze the results.

[Enter your response here.]

References

[List references according to APA guidelines.]