Case Descriptive Statistics
Statistical parameters and measurements are employed in analysis of various sets and types of data. These approaches cut across all fields of study and in most cases statistically significant findings are extrapolated in designing policies (Campbell, Machin & Walters, 2007). In the medical arena, data may be statistically significant but not clinically significant and as such, it can not be used in concluding various findings and be employed in daily clinical approaches. Different statistical measures are employed in computing different forms of data. Measures of central tendency include the mean, mode and the median.
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Different situations require different forms of the measure of central tendency. The mean is the summation of all the data values over the total of the individual items. The mean finds more use in situations where the data is either in the form of ratios or in intervals (Das, 1989). The mean is subject to various factors, the most important being the presence of outliers which have a major influence on the mean. In this study, the researchers used the mean as the measure of central tendency. The results show presence of outliers who lived for forty-five weeks and this has a pulling effect on the mean obtained rendering the mean not to be the best statistical measure to report the findings. Furthermore, the mean alone does not provide conclusive information on the implication of the results and as such, other statistical measures would have been used by the researchers.
Other measures of central tendency, such as the median are not affected by the presence of outliers in the data set. The median finds wide application as it can be used with ordinal, interval and even ratio types of data. The median is of importance especially in data sets that have outliers that are more likely to distort the mean and result in improper conclusions (Johnson & Bhattacharyya, 2009). The researchers should have considered using the median in reporting their findings to alleviate the influence of outliers. The mode is the other entity of the measure of the central tendency. It is the item that appears regularly in a given sample of data. The mode offers a wide range of versatility as it can be used in various types of data including ordinal, interval, categorical and ratio data (Lü & Fang, 2003). However, it finds wide application in situations where data is categorical. Since the type of data in this study is not categorical, there was no need to use the mode.
Data that is “normally distributed” employs other statistical measures such as the standard deviation. Standard deviation is one of the measures of dispersion. It shows how far the data set is far from the sample mean (Feinstein, 2002). From the results obtained by the researchers, the standard deviation shows that the values are spread over a wide range from the mean. This implies that the findings obtained are not conclusive enough as they are spread widely around the mean, insinuating the irreproducibility of the intervention as compared to the results obtained in earlier studies. Furthermore, the sample size is small and such may reduce the credibility of the study. As a member of the committee involved in awarding grants to researchers, I would suggest that funding for this study is not necessary as the findings are not clinically significant.
SLP Descriptive Statistics
Is the use of cell phones and texting while driving a major reason for the increased distracted driving fatalities?
Redelmeir, D., & Tibshirani, J. (1997). Association between cellular-telephone calls and motor vehicle collisions. The New England Journal of Medicine, 336(7), 453-458.
The study was designed to ascertain the association between cell phone use and automobile incidents and accidents. The researchers employed an epidemiological design in ascertaining the association of cell phone use and increased risk of automobile collisions and they simulated this in a real world scenario. A sample 0f 699 drivers was chosen and put in a cross-over design. In addition, the chosen drivers need to have been involved in an accident that led to destruction of property but without personal injury. Consequently, there must have been a cell phone in the vehicles of interest at the time of accident. Descriptive statistics was used in analysis of circumstantial factors. The drivers acted as their own controls. The frequency of calls by or to the driver were analyzed and matched to the time of the accident. Matched pair studies were used in the estimation of the relative risk posed by cell phone use while driving. The researchers ensured that all the P-values were two tailed to achieve 95% confidence limit. The researchers found out that there was a significant increase in risk of an accident when using a cell phone while driving than when one is not using one. In addition, the researchers found out that there was no risk difference when one was using hands on or hands-free devices while driving. The study article supports my hypothesis that the use of cell phones while driving increases the occurrences of distracted driving fatalities. The study found a four fold increase in incidences of accidents when the driver was using a cell phone while driving.
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Violanti, M., & Marshall, R. (1996). Cellular phones and traffic accidents: An epidemiological approach. Accident Analysis and Prevention, 28(1), 2-4.
The researchers carried out statistical analyses to determine the association of traffic accidents and cell phone use while driving. The study was a case control one whereby those subjects involved in accidents were termed as “cases”, while those not involved were refereed to as “controls”. Each arm consisted of 100 drivers from the New York state. Multivariate and descriptive statistical analyses were carried out to test the hypotheses of the study. From the descriptive statistical analyses, the researchers found out that on average, the subjects in the “case” arm had more cell phone talk time, more business calls and more personal calls as compared to those in the “control” arm. The multivariate analyses showed a linear relationship between the amount of time spent on the phone and traffic accidents. The results showed that, drivers in the “case” arm spent twice as much time on their cell phones as compared to drivers in the “control” arm. The researchers employed descriptive statistical analyses to elaborate the reasons behind increased use of cell phones for those in the “case” arm. They attributed these on more business calls that involved concentrated conversations. This was tied to the increased cases of traffic accidents among members of this arm of the study. The study is tandem with my hypothesis as it reinforces the hypothesis of the relationship of increased distractions to traffic accidents. The study clearly elaborates that there is a direct correlation between the amounts of time spent on the cell phone while driving with the occurrence of traffic accidents.
Campbell, M., Machin, D., & Walters, J. (2007). Medical statistics: A textbook for the health sciences. Hoboken: Wiley-Interscience.
Das, M. (1989). Statistical methods and concepts. New Delhi: New Age International.
Feinstein, A. (2002). Principles of medical statistics. London: Chapman & Hall/CRC Press.
Johnson, R., & Bhattacharyya, G. (2009). Statistics: Principles and methods. Hoboken: Wiley-Interscience.
Lü, Y., & Fang, J. (2003). Advanced medical statistics. Singapore: World Scientific.