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Reliability in Assessment Practice
Reliability in Assessment Practice
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Reliability in Assessment Practice
Despite being a Christian, I believe that screening does not have any notable impact on my beliefs concerning mental illness. People have all sorts of explanations about what causes mental issues but in my viewpoint, none of these reasons are related to religion. I believe that being screened for a mental illness is just like being screened for a normal illness. The only difference is that no medical and physical tests are conducted for mental illness. The professional screens one for an illness by mostly listening to the person and making observations of their behavior. The only time physical tests will be conducted for mental illness is when the condition is dire and is affecting them physically. I believe that screening does not have any effect on my beliefs concerning mental illness. At the end of the day, every individual goes through problems and needs assistance from time to time to overcome them.
Reliability refers to the level of consistency in a specific measuring test. One example of reliability would be a person expecting the same reading if they measure their weight during the day. This might only be possible if the scales used to measure the weight are kept constant throughout. However, if the scales are changed every time the weight is being measured, then the results would conflict making them unreliable. The three sources of measurement error are including random errors, systematic bias errors, and gross errors. Random error refers to the chance difference that exists between true and observed values of given measurement (Maleki, Amiri, & Castagliola, 2017). Systematic error is the proportional difference between true and observed values of a given measurement. Gross errors, also known as outliers are other errors rather than systematic and random errors.
Systematic errors mean that measurements of one single entity care vary according to predictable manners. In essence, every measurement tends to be different from true measurements in the usual direction and even by same measurements in some cases. Sources of systematic errors range from data collection procedures, research material, to individuals analysis techniques (Mayr, Schmid, Pfahlberg, Uter, & Gefeller, 2017). Examples of systematic errors are scale factor and offset errors. Unsystematic errors tend to affect the affect measurements in unpredictable manners. In random errors, once measurements tend to be lower or higher than true values. Sources of unsystematic error include poorly controlled procedures in the relationship, natural variations in experimental contexts, and individual differences.
Practice effects refer to the improvements that take place in cognitive test performance as a result of repeat evaluations from the using the same materials. Traditionally, practice effects are viewed as being sources of error variance. The carryover effect is a concept used to describe the transference of materials that are unwanted from one environment to another. Fatigue refers to a documented phenomenon that takes place when survey participants get tired of the exercise hence affecting the quality of data that comes as a result. The quality deteriorates when the attention and motivation of participants reduces.
References
Maleki, M. R., Amiri, A., & Castagliola, P. (2017). Measurement errors in statistical process monitoring: A literature review. Computers & Industrial Engineering, 103, 316-329.
Mayr, A., Schmid, M., Pfahlberg, A., Uter, W., & Gefeller, O. (2017). A permutation test to analyse systematic bias and random measurement errors of medical devices via boosting location and scale models. Statistical Methods in Medical Research, 26(3), 1443-1460.
Reliability Activity
Reliability ActivityYaime Camacho
Ball State University
SPCE 611
Part A
PRIMARY COLLECTOR: Yaime Camacho
SHOW: The Big Bang Theory
CHARACTER: Sheldon
BEHAVIOR: Cleaning
Behavior Definition:
The operational definition for cleaning is defined as any period of time in the show in which the character (Sheldon) is observed picking up objects from an area using his hands, organizes, washes or wipes and area, puts items away or discusses about cleaning an area. Non-examples: Making bed or washing dishes are non-examples for this activity.
Measurement System:
Data collection will be quantified by how often Sheldon’s cleaning behavior happens across the session in order to track Sheldon’s repetitive cleaning activity. Because it gives a particular number of times the behavior occurred during the session, frequency data will be the measurement approach that will best capture the behavior because it is measurable and observable. Session duration will be of approximately 7 minutes. Data will be collected in 12 sessions.
Session Details:
Hulu was used to retrieve all episodes of The Big Bang Theory. For this activity, the data was collected on the following episodes (which highlight Sheldon’s OCD trait of maintaining an organized or clean area/room): Season 1, Episode 2, Season 3, Episode 6 and Season 6, Episode 19. During the listed episodes, the data collector (Yaime) will keep track of the number of times Sheldon discusses cleaning or is witnessed doing so.
Part B
Table 1: Sheldon’s Data on Cleaning Behavior
Session Date Episode Video Segment
Time Occurrences Phase
1 06/03/22 Season 1, Episode 2 03:20-11:13 18 Baseline 1
2 06/03/22 Season 1, Episode 2 11:13-19:45 15 Baseline 1
3 06/03/22 Season 1, Episode 2 19:45-29:00 11 Intervention 1
4 06/03/22 Season 1, Episode 2 29:00-36:12 17 Intervention 1
5 06/03/22 Season 3, Episode 6 3:19-12:00 16 Baseline 2
6 06/03/22 Season 3, Episode 6 12:00-19:06 18 Baseline 2
7 06/03/22 Season 3, Episode 6 19:06-26:41 17 Intervention 2
8 06/03/22 Season 3, Episode 6 26:41-33:50 14 Intervention 2
9 06/04/22 Season 6, Episode 19 00:00-7:40 19 Baseline 3
10 06/04/22 Season 6, Episode 19 7:40-15:00 14 Baseline 3
11 06/04/22 Season 6, Episode 19 15:00-24:12 11 Intervention 3
12 06/04/22 Season 6, Episode 19 24:12-32:49 18 Intervention 3
Figure 1: Sheldon’s data on his OCD trait of maintaining an organized area/room is displayed graphically across an ABAB design. Data is presented as number of behavior occurrences in which he was observed engaging the behavior.
Part C
Character: Sheldon
Behavior: Cleaning
Primary: Yaime Camacho
Reli: Emily Delaney
IOA formula:
The Total Count formula was utilized to assess the dependability of the behavior since frequency is a total count of how often the behavior occurred.
IOA Calculation:
Fewer Occurrences
________________ x 100 = Total Count IOA %
Increased Occurrences
IOA Table:
Table 2
Sheldon’s Data on Cleaning Behavior
Session # IOA Date Yaime’s Data Emily’s Data IOA Percentage
1 06/05 18 17 17/18= 94%
5 06/05 16 16 16/16= 100%
11 06/05 11 13 11/13= 84%
The data collected from both observers had a high IOA percent, indicating that it was fairly accurate making it a reliable data. Because the specified behavior was clearly defined, there was a high level of agreement.
It was easier to obtain data to determine the IOA percent by employing the frequency measuring technology. Another technology that could have been used was the duration measurement.
Relevance of local government structure to inequality in the US
Relevance of local government structure to inequality in the US
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American communities are locomoting rapidly in terms of demographic structure. As the demographic structure shifts, the inequality between the urban and suburban takes control in access to education, affordable housing, and infrastructure (Benjamin et al,2001). Income inequality has increased and is beginning to affect the ability of local governments to deliver services. The creation of local government is alluded to social inequality, political fragmentation and contributes to economic growth. Economic inequality affects access to public jobs and anti-poverty programs (Frug et al.,2011). Political fragmentation is the process of reforming positions and authority by comprising independent commodities like districts. Through the local government, the commands of the jurisdictional outcome allow the residents to make decisions regarding schools, crime rates, and public services.
Without a sure way for most individuals to escape from poverty it becomes more imperative that governments especially at the local level to come in and asses’ various ways and means in which they can find a common ground to assist improvised individuals (Frug et al.,2011) With an ever-expanding income inequality issue that seems to be always expanding especially in recent decades, it becomes clear now more than ever that if adequate solutions cannot be found then a great majority of people may end up in worse economic conditions than they are in now (Benjamin et al.,2001). These forms of inequality are also seen to adversely affect our school system unequally to an overwhelmingly alarming extent. A case in point of such a place experiencing this issue is New Jersey, in which we see schools in a wealthier position in different districts perform much better than districts with less wealth and access to a proper education.
Urban areas in America with a higher number of black individuals who have less access to education, experience even harder hardships due to the ever-increasing problem of income inequality. America’s past laws on redlining and segregation of schools have undoubtedly caused this problem to become worse than it is over the years. Modern-day segregation of African American students has even caused the problem to further balloon to a situation that seems unrecoverable to most individuals experiencing these hardships (Simon, 2011). Without proper local governments and even better systems of infrastructure, these urban areas seem to be left in an inescapable position that better helps them alleviate their current dilemmas (Simon, 2011).
Although urban areas face the bulk of issues caused by inequality this does not in any way imply that suburbs experience an easier time or are even better off. Current research has noted that suburban areas have not been spared from a lack of having proper local governments to help in development and neighborhood planning (Mansfield et al.,2010). These suburban areas have been designed in such a way that most important home facilities such as hospitals and shopping areas are located so far away from residential areas in which most residents live. This poor planning on where exactly houses should be built and also where important facilities should be has created an unpleasant scenario in which most residents of suburbs are unable to gain key essential services and thus in all likelihood end up in such scenario in which they are adversely negatively affected to a point in which some may even wind up dead (Simon, 2011).
All in all, a lack of local governments is not the only factor to blame as to why most populated areas are failing in ensuring they have adequate and necessary amenities to help them thrive. A common denominating factor that has led to the current situation worse now is bad if not ignorant political decisions being made in a variety of states. Certain regions within the country such as New York give us a sense of just how bad planning can greatly lead to a system that is in no way sufficient enough to cater to or help the individuals in need (Rosenthal et al., 2017). A good enough example of this is the New York subway system which although sufficient enough to cater to a vast majority of people is still grossly mismanaged to an extent that it ends up being of no value to a large number of people dependent on it. Thus from seeing how mismanagement of this system is done we further get an understanding of just how badly a mismanaged system may end up causing more harm than good.
With the rise of modern-day billionaires and millionaires with an obscene amount of wealth we also see a rise in the number of poverty-stricken individuals facing hardships and challenges that have even left most of them homeless (Kawachi et al.,2014). Although most would think that there are no relations between those two scenarios it has become clear that there is an existing correlation between these two factors. The subsequent tax cuts to the rich have in all eventually caused this situation to become worse than it is. As a matter of fact, for every increase in a tax cut for the rich we also additionally see that the bottom half of the country is taxed even harder to ensure taxation is still adhered to and certain amounts of money are received by the government (Carafano et al.,2006). This in turn has demonstrated to most Americans the topmost priority of the government and thus made it quite harder for most individuals to rise from poverty levels and thus move to a different life class.
To help curb this issue in one way or another most American states have implemented means that help their local government have more power and thus be able to easily make changes that help the community at large (Mansfield et al.,2010). This although is a step in the right direction it is still a far cry approach to the other more immediate decisions that need to be made to help the country solve its inequality issues. A proper system of organizers and politicians seems to be the only form of an adequate strategy to help the entire country as a whole (Mansfield et al., 2010).
References
Benjamin, G., & Nathan, R. P. (2001). Regionalism and realism: A study of governments in the New York metropolitan area. Brookings Institution Press.
Carafano, J. J., & Weitz, R. (2006). Learning from disaster: the role of federalism and the importance of grassroots response. Heritage Foundation.
Frug, G. E., & Barron, D. J. (2011). City-bound. Cornell University Press.
Kawachi, I., & Subramanian, S. V. (2014). Income inequality. Social epidemiology, 126, 126-152.
Mansfield, E. D., & Solingen, E. (2010). Regionalism. Annual review of political science, 13, 145-163.
Rosenthal, B. M., Fitzsimmons, E. G., & LaForgia, M. (2017). How politics and bad decisions starved New York’s subways. New York Times, 18.
Simon, T. T. (2011). The effects of modern-day segregation of African American students in South Carolina’s public schools (Doctoral dissertation, Fielding Graduate University).
