Posted on March 27th, 2019
A group of researchers undertook an experiment to find out which vaccine was more effective in the prevention of flu. The vaccines included a nasal spray and a shot. The hypothesis was tested using the statistical hypothesis tests. The null hypothesis and the alternative hypothesis are first developed before carrying out the hypothesis testing. These hypotheses would involve the effect of the two types of vaccines in the population. In this case, the null hypothesis of the research would be that the nasal spray is the most suitable vaccine to prevent getting flu. While the alternative hypothesis would be that the nasal spray is not the best vaccine to prevent getting flu. The results of the research were significant. This was because the both the vaccines succeeded to prevent the flu to a significant number of those treated. Similarly, there was a significant different between the results of the two vaccines and also the p-value was to way below the level of significant hence an indication of the high level of significant. In addition, the researchers rejected the null hypotheses since the p-level of 0.0008 was too small as compared to the significant level of 0.5. In this case, the results of the research are evidence enough to justify that the shot vaccines are the best options for preventing flu infection since only a verysmall proportion of the treated group developed the flue.
The sample used for the research was appropriate for the study. This is because the sample was large enough for this kind of study and the participants of the study were picked randomly to avoid biasness of the data collected. However, the research could be faced with limitations such as problems in making follow-ups after treatment as well as convincing the public to participate in the research. It could also be expensive to carry out the research. I would carry out a follow-up study by trying to locate as many participants of the initial study to determine whether they developed the flu after the initial results. Then, I would come up with the new p-value.
Practical significant is the determination on whether the observed effect can find application in real context while statistical significant determines whether the observed results are larger than the expected results.
The correlation between the IQ and the grade point averages (GPA) is moderately high. A correlation of .75 is moderate high since the highest IQ-GPA correlation is of .90. The correlation between IQ and GPA is positive. It implies that the individual participating in the research had high Intelligence quotients and could score high grades in exams. The correlation implies that it is expected that the people with high intelligence quotients are expected to score high grades in a test (Coon, 2015). However, the correlation does not provide evidence that high IQ could lead to higher GPA. The relationship could be affected by other factors that could affect grade point averages. Such factors that could affect the grades scored by individuals include their health status, their preparedness to the test and the psychological conditions they are experiencing.
The correlation between variable does not always mean that one of the variables causes the other (Kenny, 1997). However, two events occurring together have a cause and effect relationship. The correlation size between IQ and GPA can be affected by a number of factors. The preparedness of the individuals involved could either increase the correlation or decrease the correlation. An individual with high IQ but not adequately prepared for the test may have a lower correlation than an individual with a similar IQ and who is properly prepared. Therefore, the correlation is not a good test to predict GPA. Other methods such as regression can be used to predict the GPA. Regression may use a number of variables such as high school GPA, college major and SAT scores to predict the current GPA (Archdeacon, 1994).
Research Study Critique:
Effect of HIV/AIDS-related mortality on household dependency ratios in rural South Africa, 2000 to 2005
The deaths caused by HIV/AIDs in Africa are quite alarming. Thestudyinvestigatedwhetherthemortality as a result of HIV/AIDs handanydifferences from othermortalitycauses in its effects on thefamilydependencyratioandtheextent in which theeffect is arbitrated by the baseline dependencyratio. The relationship between mortality and family composition is a topic of under numerous researches. The researcher in thisstudytooktheviewthatcoming up with appropriatemeasuresaimed at assisting HIV/AIDs affectedfamilies would benefit from knowledge of theforms of thechanges that might occureither before or after HIV/ AIDs relateddeaths.
Hypotheses: HIV/AIDS has a greater negative effect on any parameter of family welfare than other causes of death (Madhavan, 2009).
The data used was collected from Agincourt, which is a sub-district in the north-east of Johannesburg in Mpumalanga Province. The sub-district has an HIV prevalence of 32.1 percent; thus it is among the most affected regions in South Africa. The researchers used a longitudinal AHDSS data collected since 1992 in 21 villages with an estimated population of about 70,000 people in 11,600 families. This annual information provided all the changes in the family membership, and the cause of deaths of family members. The researchers also used the verbal autopsy to distinguish the deaths caused by HIV/AIDS. They conducted a structured interview administered the relatives of the dead one year of when the death takes place. The data was independently examined by two trained physicians to prove the cause of death and if there was any disagreement in their decisions, a third party was involved. Then the researchers interpreted the dependency ratio as the ratio of those who are economically productive and those who rely on them. Later, linear regression wasused to determinetheeffects on thedependencyratio in 2005 in regard to whetherthedeathoccurred, thetype of death in families that experiencedthedeathandtheeffects of thedeathscaused by HIV/AIDS on dependencyratio. The age group of 45-59 was used as the reference age at death in order to the variation of only those that are of productive age (Madhavan, 2009).
The researchers found out that there were about 4150 deaths from 2000-2005 and those who died from HIV/AIDS were on average 14 years younger than those who died from other causes. They also found out that about 8.7 percent of the households had experienced at least one death related to HIV/AIDS during the five-year period. The occurrence of the death and the cause of the death have significant changes over the period from 2000-2005. The total number of families headed by mothers increased for families that experienced any kind of death as the mothers took up the head of the family after the male died. They also found out that the families that experienced death as a result of HIV/AIDS become poorer. The proportion of persons under 15 decreased over the period due to lower fertility. Similarly, the families that experienced deaths of elderly members did not have increased proportion of the elderly people. There was also an improvement in the dependency ratio for all categories either due to the reduced fertility or the aging of dependants. However, the rate of dependency ratio decrease was low in the families that experienced deaths as a result of HIV/AIDS may be because the dead member was in the productive age group. Thus, their entirefindingssuggestthateventhoughdeathsespeciallythe HIV/AIDS-related deathshadled to thechanges of a number of compositional measures, thechanges may not translate into theincreases in thedependencyratio (Madhavan, 2009).
The findings suggest that an HIV/AIDS related death has an insignificant positive effect compared to other causes of death, but it is overpowered by the positive effect of productive age death. The effects of the cause of death, the sex of the dead person, and age at death on the 2005 dependency ratio are mediated by the baseline dependency ratio. These findings challenge the conventional thought that assumes that HIV/AIDS has devastating effects on any measure of the household than any other cause of death. This implies that age at death would more significantly affect the dependency ratio than the cause of death. During the research, the deaths and their causes for the entire sub-district for the years from 2000 and 2005 were recorded. This provided a good sample size to carry out the research. The time period in which the data was collected was also appropriate for this kind of research. However, the research had some weaknesses. Using a single district would not give a clear picture of what could be the effects of HIV/AIDS related deaths on the dependency ratio. Future research should be carried out on a more distributed sample to in order to assist in drawing the conclusion. Similarly, more tests such as the chi-square to measure whether the results observed are anything close to the expected results.
Archdeacon (1994) Correlation and Regression Analysis: A Historian's Guide; Univ of Wisconsin Press; USA
Coon (2015) Introduction to Psychology: Gateways to Mind and Behavior; Cengage Learning; USA
Kenny (1997) Correlation and causality; Wiley publishers; USA
Madhavan, S., Schatz, E., & Clark, B. (2009). Effect of HIV/AIDS-related mortality on household dependency ratios in rural South Africa, 2000-2005. Population Studies, 63(1), 37-51. doi:10.1080/00324720802592784
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