The use of prospective and retrospective data in the process of causal reasoning - Assignment Example

Due Human Resources: Exercise #10 Create and present an example to illustrate the use of prospective and retrospective data in the process of causal reasoning. List any advantages or limitations associated with the use of either type of data. Prospective data is, essentially, represents the logic based assumptions made on given courses of action. The example given in the text book explains that when a patient with a dog bite asked to receive his tetanus shot in the same arm as the injury, the physician already knows, based on previous understandings and experiences, that this will result in a secondary infection and inflammatory response.

Knowing this and predetermining what the likely outcome will be is the core of prospective data. Retrospective data is different; it looks back in time to gain its answers to its questions. Not unlike writing the story in reverse. The example provided in the text explains how in the case of individuals who all got food poisoning and the use of retrospective information in order to determine the shared causes that led to the cases of food poisoning (Rosnow & Rosenthal 178-179). Like detective work, retrospective is a matter of backtracking the evidence to solve the case, while prospective data relies on typical and predictable outcomes based on given variables.

In this way prospective relies a little bit upon assumptions of what will occur, while retrospective is more concerned with what did occur. 2.Let’s say you were interested in studying what people do with their time after they retire from their career jobs. Sloane et al. (2008) is an example of a descriptive cross-sectional study on this topic using physicians as their target population. Following the example of Sloane et al., how might you go about planning a study using retired military officers as your target population?

Cross-sectional studies are those conducted at a specific point in time and is considered a descriptional form of research. It is a means for researchers to look at a diverse group with variable interests that share certain characteristics in common. It ultimately relies on the principal that the resulting variation between any two different groups can only be attributed to the differences that they do not share. For example, when studying two different age groups, any variation could be attributed to the age differences.

If I were to consider studying the retirement habits of military veterans I would first send out the cross sectional mailed surveys to gain interest and determine how many retried veterans that meet the research criteria are available to participate in this study. I would look for a commonality or pattern within the groups. Do most in the military retire at, generally, the same age? Do those that retire from the military at a younger age, also, share the same “retirement” habits, or do they seek other forms of employment to continue with into the traditional retirement age?

I would, also, determine the income variations. Do the socioeconomic aspects play a huge part in the types of retirement that military members will ultimately participate? I would, also, be certain to include elements that can have a great deal of influence on many individuals, gender, race, ethnicity, culture, and region that they live, which could all play a part on how the retired military will spend their time. Like the article provided, the weakness in this research can be found in that much of the research would be surveys that rely on the honesty of those filling them out, therefore it can be hard to validate the accuracy of these sources (Sloane, Cowen , and et al).

In order to have results that could be considered truly effected in would be necessary to conduct other studies as follow-up to really accurately assess all of the behaviors of the retired military. 3.Theta Hombody, a graduate student, has designed a research project to find out if women who major in mathematics and engineering are less likely to be mothers than women who are in less technical degree programs. Theta selected a random sample of women across the state of California who earned an undergraduate degree in mathematics or engineering within the past five years.

After interviewing the women she found that their average age is 24 and that 35 percent are mothers, 55 percent are childless by choice, and 10 percent are childless involuntarily. She concludes that a strong interest in technical subjects causes women to choose childlessness. It is obvious that the presumption of Theta’s study is flawed. She determined that of women who majored in mathematics or engineering sciences had an average age 24 and a third were already mothers. However, she declares that 55% are childless by choice, which leads her conclude that there is a direct correlation between choosing to be childless and choosing careers in these sciences.

This is first and most obvious flaw. Those women who opted against motherhood may have made that same decision for themselves regardless of what career field they had chosen to pursue. It may have nothing to do with the sciences. As far as the portion of women who were childless for medical reasons outside their control, may or may not have chosen to have children and there is no way to legitimately verify what they might have done. Also, at the age of 24, many women may not be ready to have children at that age or at that time in their career, but their feelings may change as they get older.

In order to truly determine whether or not majoring in mathematics encourages women to not have children or attracts women who do not wish to be mothers cannot be studied in a single period of time and be expected to provide feasible results. The study one might choose would be done through survey over an extended period of time, possibly years. The subjects are chosen and the surveys are given and answered. These answers would then be collected and stored. The same survey would need to be given to the same group of women over say a 5 to 7 year period.

This will determine what percentage of women in these sciences have changed their original perspectives and either have become or intend to become mothers. Many women are choosing to wait later in life to begin having children, so at the initial age of testing their opinions, wants, and desires in life may change over time. At the end of the research if the majority of women maintain their childless status intentionally over the course of the research then it would not be completely unwarranted to say that many of the women drawn to major in these sciences tend to be women disinterested in having children.

Works Cited Rosnow, Ralph, and Robert Robert Rosenthanl. Beginning Behavioral Research: A Concept Primer. 6th. Pearson, 2008. 480. Print. Sloane, Phillip D., Lauren W. Cowen , et al. "Brief Communication: Physician Interest in Volunteer Service during Retirement." American College of Physicians. 149.5 (2008): 317-322. Print.