you should have 20 numbers. I want you to discuss the Gad Yair method of collection was its systematic convenience. Cluster, cluster stratified simple random. What are some false? I know you highlighted this in week five, but now I want you to talk about what are some faults with the type of data collection, okay. What other types of data collections could you used instead? And how might that affected your survey? So this right here is the really important part. This is what I am trying to see what you have learned, okay? So it’s about telling me again what type of method you chose. Telling me what faults come with that method, some research might be involved. And then telling me another type of method that you would’ve liked to try if you could have and how that would affect your study. Okay. Then you’re going to tell me what the point estimate was, the point estimates, your mean. So in our case when we’re talking about the midterm, whereas my lecture notes here again, this right here, maybe I should write this down. The mean, the 76.9, that’s your point estimate. This is what we’re basing it off of. Okay? So the point estimate is the mean. Okay? So give a point estimate for the mean of the average height of all people at the place of your work. If that’s where you pull your sample and start by putting the 20 heights you are working with into the blue data. So we’ve done this already. You should already have your data in and you know what your mean is in your sample standard deviation. Then what you’re gonna do is you’re going to construct a 95% confidence interval for the true mean height of all the pupil, the place of your work, what is the interval? And provide a screenshot which we did today. Then you’re going to give me a practical interpretation what this means. So what you’re explaining is your 95% confident that the true mean height of all the people that you work with or the population that you have is between this and this, okay? Then you’re gonna put, of course going to post a screenshot. Then you’re going to change your confidence levels. With 99% confidence level, you’re going to take a screenshot and provide that as well. You’re going to compare the margins of error. So this is where you’re going to talk about what happened to your margin of error when we went from a 95 to 99% confidence interval, what happened to your interval when he went from 95 to 99% confidence interval? There should be a good paragraph explaining what happened, why it’s happening, and try to explain it in the context of the data and the lab.
WEEK 6 INFORMATION NEEDED FOR THIS POST
Use excel spreadsheet also for interpreting results of Height data (inches per person) for 20 people…60, 60, 62, 64, 65, 65, 65, 66, 67, 67, 68, 68, 69, 71, 71, 72, 72, 73, 74, 75
Thinking of the many variables tracked by hospitals and doctors’ offices, confidence intervals could be created for population parameters (such as means or proportions) that were calculated from many of them. Choose a topic of study that is tracked (or that you would like to see tracked) from your place of work. Discuss the variable and parameter (mean or proportion) you chose, and explain why you would use these to create an interval that captures the true value of the parameter of patients with 95% confidence.
Consider the following:
How would changing the confidence interval to 90% or 99% affect the study? Which of these values (90%, 95%, or 99%) would best suit the confidence level according to the type of study chosen? How might the study findings be presented to those in charge in an attempt to affect change at the workplace
A: A confidence interval (CI) is a measure of the uncertainty around the effect estimate. It is an interval composed of a lower and an upper limit, which indicates that the true (unknown) effect may be somewhere within this interval. A larger sample size will provide a smaller margin of error, a narrower interval, and greater precision (Anderson et al., 2019). Confidence interval (CI) has two dimensions which are Upper Bound and Lower Bound. The distance from the test statistic can be found with CI. Mean values, standard deviation, sample size and confidence levels are helps in determining the confidence interval (CI). The sample standard deviation is used for unknown population standard deviation and for known population the standard deviation of the population is used. CI are applied in various settings such as government policy decisions (Hadad et al,., 2021), clinical research (Schober et al, 2018), healthcare (Trkuljia and Hrabac, 2019), and teaching (Petrusch et al, 2018). Confidence intervals are influenced by sample size and heterogeneity of the data. When sample size is inversely proportional to degree of uncertainty, heterogeneity of the data is directly proportional to the degree of uncertainty. Majority of the studies use 95% confidence level for determining the CI which means 95% confident that the estimate would like within the lower and upper limits.
Table I
Commonly used confidence intervals
Confidence Level
α
α/2
Zα/2
90%
0.10
0.05
1.645
95%
0.05
0.025
1.960
99%
0.01
0.005
2.576
(Source: Anderson et al., 2019)
Application at my workplace
Patient satisfaction at my workplace is measured using five-point Likert-type scale. A patient at the time of discharge or within 10 days after discharge is given opportunity to give rating for the service quality. The population is unknown because it is tough to determine the exact number of patients because it is very big organization. A sample mean and sample standard deviation are appropriate choice in such scenario. Based on 65 reports from the patients, I have found the mean value of patient satisfaction is 3.8 with standard deviation of 0.85. The following formula had been used for calculating the confidence interval for the sample mean estimate.
CI = X̄ ± Z× s/√n
CI = Confidence Interval
Z = Confidence alpha value
S = standard deviation
N = sample size
CI = 3.8± 1.96*(0.85/√65)
Lower Limit = 3.8 – 0.207 = 3.593
Upper Limit = 3.8 + 0.207 = 4.007
Interpretation
Confidence intervals have been calculated for the sample mean estimate (M = 3.8, SD = 0.85, n = 65). The formula had been used for calculating the lower limit and upper limit at 95% confidence level. The confidence interval [3.593; 4.007] denotes that the mean estimate value falls between 3.593 and 4.007. Hence with 95% confidence, the upper bound of estimate would be 4.007 and lower bound is 3.593.
Table II
Confidence intervals at 90%, 95% and 99% confidence levels
CI
CL
z(α/2)
(X bar)
(s)
(n)
z*(s/SQRT(N))
Lower Limit
Upper Limit
ME
90%
1.645
3.8
0.85
65
0.173
3.627
3.973
95%
1.960
3.8
0.85
65
0.207
3.593
4.007
99%
2.576
3.8
0.85
65
0.272
3.528
4.072
(CI = Confidence INTERVAL, x bar = Mean, s = standard deviation, n – Sample size, CL = Confidence Level, ME = Margin of Error)
Interpretation
The confidence intervals have been calculated for 90% confidence and 99 % confidence and the results are presented in above Table II. For the sample mean estimate (M = 3.8, SD = 0.85), the confidence interval at 90% confidence [3.627; 3.973] and at 99% confidence [5.528; 4.072]. The margin of error is low (ME = 0.173) at 90% confidence and high (ME = 0.272) at 99% confidence level and ideal (ME = 0.207) at 95% confidence. Hence the organization is recommended to conduct the study at 95% confidence level for estimating confidence intervals to the sample estimate mean. Further the in charge at the organization is suggested to increase the sample size for reducing the margin error. It means the sample estimate value will be nearer to the limits in confidence interval. However in social sciences 95% confidence level is enough but for clinical research studies 99% confidence level is suggested. The findings from this study provide insights for practicing managers in health care organizations specifically to my workplace.
References
Anderson, D. R., Ohlmann, J. W., Thomas, T. A., Camm, J. D., Cochran, J. J., Sweeney, D. J. , Fry M.J. (2019). Statistics for Business & Economics. United States: Cengage Learning.
Hadad, V., Hirshberg, D. A., Zhan, R., Wager, S., & Athey, S. (2021). Confidence intervals for policy evaluation in adaptive experiments. Proceedings of the National Academy of Sciences, 118(15).
Petrusch, A., Roehe Vaccaro, G. L., & Luchese, J. (2018). They teach, but do they apply?: An exploratory survey about the use of Lean thinking in Brazilian higher education institutions. International Journal of Lean Six Sigma, 10(3), 743-766.
Schober, P., Bossers, S. M., & Schwarte, L. A. (2018). Statistical significance versus clinical importance of observed effect sizes: what do P values and confidence intervals really represent?. Anesthesia and analgesia, 126(3), 1068.
Trkulja, V., & Hrabač, P. (2019). Confidence intervals: what are they to us, medical doctors?. Croatian medical journal, 60(4), 375.
