Write a persuasive essay Is there a difference in MRI procedures and PET/CT Procedures between Minnesota state and Wisconsin?
Write a persuasive essay Is there a difference in MRI procedures and PET/CT Procedures between Minnesota state and Wisconsin?
3. Research Question or Research Hypothesis
What is the Research Question or Research Hypothesis?
***Just in time information: Here are a few points for Research Question or Research Hypothesis
There are basically two kinds of research questions: testable and non-testable. Neither is better than the other, and both have a place in applied research.
Examples of non-testable questions are:
How do managers feel about the reorganization?
What do residents feel are the most important problems facing the community?
Respondents’ answers to these questions could be summarized in descriptive tables and the results might be extremely valuable to administrators and planners. Business and social science researchers often ask non-testable research questions. The shortcoming with these types of questions is that they do not provide objective cut-off points for decision-makers.
In order to overcome this problem, researchers often seek to answer one or more testable research questions. Nearly all testable research questions begin with one of the following two phrases:
Is there a significant difference between …?
Is there a significant relationship between …?
For example:
Is there a significant relationship between the age of managers? and their attitudes towards the reorganization?
A research hypothesis is a testable statement of opinion. It is created from the research question by replacing the words “Is there” with the words “There is,” and also replacing the question mark with a period. The hypotheses for the two sample research questions would be:
There is a significant relationship between the age of managers and their attitudes towards the reorganization.
It is not possible to test a hypothesis directly. Instead, you must turn the hypothesis into a null hypothesis. The null hypothesis is created from the hypothesis by adding the words “no” or “not” to the statement. For example, the null hypotheses for the two examples would be:
There is no significant relationship between the age of managers
and their attitudes towards the reorganization.
There is no significant difference between white and minority residents
with respect to what they feel are the most important problems facing the community.
All statistical testing is done on the null hypothesis…never the hypothesis. The result of a statistical test will enable you to either:
1) reject the null hypothesis, or
2) fail to reject the null hypothesis. Never use the words “accept the null hypothesis.”
*Source: StatPac for Windows Tutorial. (2017). User’s Guide; Formulating Hypotheses from Research Questions. Retrieved May 17, 2019 from https://statpac.com/manual/index.htm?turl=formulatinghypothesesfromresearchquestions.htm
What does significance really mean?
“Significance is a statistical term that tells how sure you are that a difference or relationship exists. To say that a significant difference or relationship exists only tells half the story. We might be very sure that a relationship exists, but is it a strong, moderate, or weak relationship? After finding a significant relationship, it is important to evaluate its strength. Significant relationships can be strong or weak. Significant differences can be large or small. It just depends on your sample size.
To determine whether the observed difference is statistically significant, we look at two outputs of our statistical test:
P-value: The primary output of statistical tests is the p-value (probability value). It indicates the probability of observing the difference if no difference exists.
Example of Welch Two Sample T-test from Exercise 1
The p-value from above example, 0.9926, indicates that we DO NOT expect to see a meaningless (random) difference of 5% or more in ‘hospital beds’ only about 993 times in 1000 there is no difference (0.9926*1000=992.6 ~ 993).
Note: This is an example from the week1 exercise.
An example from Exercise 1
The p-value from above example, 0.0001, indicates that we’d expect to see a meaningless (random) ‘number of the employees on payer’ difference of 5% or more only about 0.1 times in 1000 (0.0001 * 1000=0.1).
CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-40.82 ; 40.44]):
Confidence Interval Example
CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-382.16 ; 125.53]):
Confidence Interval Example
The boundaries of this confidence interval around the difference also provide a way to see what the upper [40.44] and lower bounds [-40.82].
As a summary:
“Statistically significant means a result is unlikely due to chance.
The p-value is the probability of obtaining the difference we saw from a sample (or a larger one) if there really isn’t a difference for all users.
Statistical significance doesn’t mean practical significance. Only by considering context can we determine whether a difference is practically significant; that is, whether it requires action.
The confidence interval around the difference also indicates statistical significance if the interval does not cross zero. It also provides likely boundaries for any improvement to aide in determining if a difference really is noteworthy.
With large sample sizes, you’re virtually certain to see statistically significant results, in such situations, it’s important to interpret the size of the difference”(“Measuring U”, 2019).
*Resource
Measuring U. (2019). Statistically significant. Retrieved May 17, 2019 from: https://measuringu.com/statistically-significant/
Small sample sizes often do not yield statistical significance; when they do, the differences themselves tend also to be practically significant; that is, meaningful enough to warrant action.
4. Research Method
Discuss the Research Methodology (in general). Describe the variable or variables that are being analyzed. Identify the statistical test you will select to analyze these data and explain why you chose this test. Summarize your statistical alternative hypothesis. This section includes the following sub-sections:
a) Describe the Dataset
d) Describe statistical package
Add one paragraph for the statistical package, e.g., Excel or RStudio.
5. Results
Discuss your findings considering the following tips:
▪ Why you needed to see the distribution of data before any analysis (e.g., check for outliers, finding the best fit-test; for example, if the data had not a normal distribution, you can’t use the parametric test, etc., so just add 1 or 2 sentences).
▪ Did you eliminate outliers? (Please write 1 or 2 sentences, if applicable).
▪ How many observations do you have in your database and how many for selected variables, report % of missing.
▪ When you are finished with this, go for the next steps:
Present the results of your statistical analysis; include any relevant statistical information (summary tables, including N, mean, std. dev.). Make sure to completely and correctly name all your columns and rows, tables, and variables. For this part you could have at least 1-2 tables and 1-2 figures (depending on your variables bar-chart, pi-chart, or scatter-plot), you can use a table like this:
Table 3. Descriptive analysis to compare % of BL in Medicare beneficiary, MD vs. VA- 2013
Variable
Obs.
Mean
SD
P-value
Per of Lipid in MD
24
83.20
2.32
0.4064
Per of Lipid in VA
124
82.69
4.41
Source: UMGC, 2019
When you have tables and plots ready, think about your finding and state the statistical conclusion. That is, do the results present evidence in favor or the null hypothesis or evidence that contradicts the null hypothesis?
6. Conclusion and Discussion
Review your research questions or hypothesis.
How has your analysis informed this question or hypothesis? Present your conclusion(s) from the results (presented above) and discuss the meaning of this conclusion(s) considering the research question or hypothesis presented in your introduction.
At the end of this section, add one or two sentences and discuss the limitations (including biases) associated with this analysis and any other statements you think are important in understanding the results of this analysis.
