Opinion for modules 6.1 and 6.2
ecl569
opinions_deborah_hill_6.1_module.docxDeborah Hill
1 posts
Re:Module 6 DQ 1
Inductive statistics concerns drawing conclusions based on a sample of data available for analysis and the use of appropriate statistics, such as the mean and standard error of some variable in the data set. For a conclusion to be generally useful, the sample must be representative of the population of interest. Give an example of how sample design can be illogical. Will an illogical design automatically sabotage the usefulness of results? Why or why not?
The basis for a logical research design is to maintain fidelity to the guidelines of research framed by the American Psychological Association (APA), the monitoring organization whose purpose is to protect the community/ participants involved in studies while advancing knowledge about topics that effect individual's lives and the world at large (APA Publication, 2013). When methods of research contain errors there may be various issues involved such as over looking the not so obvious, obvious that is subject to mishaps along the way that could cause conflicts related to the items used in data analysis. A sample size my be too small to represent the issue, or even relying upon the discretion of the team members. Maintaining a certain degree of freedom concerning information of the study makes for genuine participation when members influence the results and provide participants with too much information.
The White Coat Syndrome is an example of an experiment that was developed to study how different positive and negative instructions effects physiological stress and motor performance (Turner, 2013). The literature shows how easy it is for innocence to prevail in the positive relationship between the student researcher and participant's invalid responses that project answers that are not true, but made-up based on what the the study might project about the individual.
These types of behaviors leave negative impact reporting to the community and effects the the research project in ways that may cause lost of protocol outcomes, investigation and humiliation Although disclosure is important, it is also must be limited to maintain valid outcomes based on randomness, and honest behavior (Turner, 2013).
There is always information thats subject to random questions of a team even after that Literature provides detailed accounts of a study that correlates with the facts. In my opinion, some of the main reasons for illogical errors are based on the lack of experience, poor communications among the team and lead researcher. A research the is conducted without adequate sample size or does not represent the population it is suppose to measure will result in faulty.
Not all mistakes are worthless to the advancement of knowledge many of the mistakes are ways for researchers to re-examine past material and find expanded knowledge on the subject even-though limitations presented are due to errors on the part of the team lead as well as the team member.
References:
Turner, M. J. (2013). White coat syndrome: Learning from mistakes in laboratory research. Sport & Exercise Psychology Review, 9(2), 95-98.
Kimbrilee Schmitz
1 posts
Re:Module 6 DQ 1
Inductive statistics concerns drawing conclusions based on a sample of data available for analysis and the use of appropriate statistics, such as the mean and standard error of some variable in the data set. For a conclusion to be generally useful, the sample must be representative of the population of interest. Give an example of how sample design can be illogical. Will an illogical design automatically sabotage the usefulness of results? Why or why not?
When choosing a sample population for a study enough of the total population that you are trying to obtain information on must be represented. The sample size should not be too large or too small, this can lead to calculation over or underestimation of the influence of what is being looked at. Correct sample size must be obtained so that inferences can be made about the population as a whole. If the sample size does not truly match the population then the sample design is illogical. Ensuring that there are enough representative samples and that the sampling is random helps ensure that it is more representative of the population as a whole.
An example is when surveys are sent out to random households asking what type of toothpaste is preferred by Americans . First not all of the surveys will be returned, second the surveys may be predominately filled out by either men or women, or from a certain age group. If this information is not required on the survey then the researcher may not even realize there might be a flow in the data. This type of survey may not truly represent the general population of Americans. In this case, this sample design would be illogical. Even though it is illogical and not representative of the population is may be useful. If there was one type of toothpaste that highly outranked other brands, then this might study may not be truly representative of the population, but may elude to what could be truly representative. With the use of surveys it is often difficult to get an accurate percentage of what is being looked for because not everyone fills them out and sends them back and they may be biased towards a certain sex, race or age group, which is hard to determine through the survey. The results may also be inaccurate if the person filling out the survey is not truthful.
Resource:
Faber, J. & Fonseca, L. (2014). How sample size influences research outcomes. Dental Press J. Orthodontics, 14(4). Retrieved from: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2176-94512014000400027&lng=en&tlng=en#?
Hi Elizabeth,
The emotional role in decision making should be conducted with caution due to the duplicity of negative and positive nature of thought and it's role with regards to passion for the common good. You pointed out how emotion and motivation drives thoughts as you stated "emotional motivation is known to improve or make impairments." I agree that emotion is a motivational factor in raising risk aversion. Emotions are powerful and may conflict with sound reasoning at times. I agree with reasoning where experiences relate to informed choice, however there must be adequate data to support decisions as seen in statistics where outliers show extremes or where close proximity explains standard deviations or central tendencies which are remarkable correlations in judging the hypothesis.
I believe that there should be consideration towards saving human life, but much of one's decision might be based upon the problem at hand. Such as the case of 911 where passengers saved the lives of others while fighting to maintain the least amount of destruction caused by terrorist on the lives of Americans. Risk aversion is suitable in a case by case basis where knowledge and understanding permits. However, it is difficult to make decisions under unexpected conditions and i concurr that there are cases where saving procedures are critical by the second and risk aversion is central to one's response. In this case, the best choice would be to use heuristics for life saving support as demonstrated by the heroism of the passengers of flight 93.
A research on the suitability of fast and frugal heuristics was designed to understand heuristics as a product of the best judgment to decisions related to patient care (Pieterse, & Vrise , 2013). An analysis of suitability was drawn from strategies involving two components known as "take the best" (TTB) and tallying fast and frugal (TFF) for supporting patient preference decision making. Patient though processes can be a determinate of healing and quality of life.
This study aimed at reducing patient's focus on complex issues as a strategy for improving their decisions and perceptions by discouraging thoughts of complex matters of their intuition to minimize distress and encourage less relevant information to gain clarification of best practices pose greater effect on life quality. In this case, the heuristic approach did not yield positively in limiting patience thought processes, but rather more intuitive mean of addressing their thoughts supported reduced anxieties (Pieterse, & Vrise, 2013), but it also supports the theory that algorithms and heuristics use are confined to the situation at hand in many cases. In this case, more complex thoughts improved the affect rather than focusing on the lest relevant.
Thank you for great clarification in your post!
Deborah
References:
Pieterse, A. H., & de Vries, M. (2013). On the suitability of fast and frugal heuristics for designing values clarification methods in patient decision aids: a critical analysis. Health Expectations, 16(3), e73-e79. doi:10.1111/j.1369-7625.2011.00720.x
