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Quality homework for less

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Quality homework for less

Create a thesis and an outline on Computational Fluid Mechanics. Prepare this assignment according to the guidelines found in the APA Style Guide. An abstract is required.

The adoption of computational fluid mechanics has seen it exhibit various fruitful transition, from a mere field to an increasingly developed field in practice and design.

This literature review, in form of a position paper, provides a theoretical background of the CFD plus supporting views about its mechanism and its usage within our organization. This will involve studying aspects of technical challenges associated with using the tool within the organization. Further, possibilities and limitations are also presented to further the exploration of how CFD applies to urban physics. Intuitively, this will provide a benchmark for measuring the spatial and temporal scales that can be associated with the position of computational fluid mechanics. Finally, it would be important to scale the position of the review by focusing on the technical aspects of using CFD in the organization. For this reason, it would be possible to extensively discover the future of CFD in urban physics.

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In most urban areas, especially in the industries, urban physics is a core field in facilitating various physical processes. Essentially the field deals with the transmission of heat and mass experienced in the outdoor and indoor urban environment. Further, it focuses this concept of heat and mass transmission on its interaction with humans and materials used in production. Conclusively, an organization is likely to apply this field to ensure a healthy and sustainable outdoor and indoor environment through encompassing all the associated constraints such as security, energetic and health, all of which are considered as grand societal challenges. For these particular reasons, urban physics with all its components is adopted widely across engineering disciplines, scaling from mechanical to electrical engineering. One of the major components that make urban physics formidable is Computational Fluid Dynamics mainly used in numerical simulation within the particular organization.&nbsp.

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Assignment two-Quality homework for less

Compose a 2500 words assignment on text and culture

This declaration infuriates Jackie who leaves Roseanne’s house. Roseanne informs her husband Dan about the fight between her and Jackie and Dan responds by saying that even though he does not believe that Jackie has what it takes to become a cop it is likely that she would be accepted by the police department.

Roseanne and her family are having dinner and Darlene is constantly teasing Becky, she reveals that Becky has been reading her father’s magazine ‘Girls, Girls, Girls’ and that she wants to be like the girls who appear in the magazine. Roseanne asks Darlene to stop her mischief and quit bothering Becky to which Darlene becomes angry and leaves.

Darlene is shown to be at Jackie’s house where she discusses the issues at home with her aunt. Darlene recounts that no matter how hard she tries it is impossible for her to become like her sister Becky who is perfect in everything and is loved dearly by their parents, especially their mother. Jackie responds by telling Darlene about her relationship with her older sister, Roseanne and how she has tried to control her life and her decision despite her own personal failures. The two younger sisters appear to understand each other’s position and their place in the family as the younger sibling.

Darlene comes back home and Roseanne questions her about her conversations with Jackie but Darlene refuses to share the information. Jackie arrives and thanks Darlene for putting the pail near her bed because she had been drinking. Roseanne hears this and becomes infuriated. The two sisters argue and eventually start hitting each other, they take the fight to the couch and Jackie hits Roseanne and asks her to apologize for going against her wish of becoming a cop and demands that she supports her. Roseanne apologizes but insists that she has one thing to say before lending her support. Roseanne makes an imaginary gun with her hand and points at Jackie while asking her how she would respond to such a threat, Jackie is only able to raise her hand in order to slap the gun before Roseanne tells her that she is now dead.

Job analysis: Psychotherapy

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Job analysis: Psychotherapy

Write a 5 pages paper on job analysis paper.

Observably, this professional who is usually trained with psychotherapy along with family systems bestows their focus on getting a compressive understanding to determine the mental status as well as the pattern of the client’s interaction within their existing environment. Though this particular profession is integrally associated with providing mental assistance to individuals, but also provides aid and support to couples and groups. In most of the cases, these professionals seek to provide a system to the clients for their mental or emotional challenges based on the family system, as it is believed that the family-based approach is more effective as compared to other forms of intervention (American Association for Marriage and Family Therapy, 2014).

The reliability and validity of any job analysis can largely be determined by the supervisors, managers, and the people associated with this sort of profession. Notably, since my college days, I have been quite passionate about the profession of family therapist hence, I began to research every aspect associated with the profession. In this process, both secondary and primary data were taken into consideration to reach an ultimate conclusion about this particular job. Notably, from the secondary sources knowledge has been gained about the mechanisms of the job along with the activities one needs to conduct to attain the utmost growth in this particular profession. It will be crucial to mention that only reputed sources comprising of peer-reviewed journals and healthcare websites have been taken into consideration for the job analysis process. Furthermore, an effective conclusion has also been attained through gathering information from the primary sources such as interviews conducted with the people associated with this particular professional. Hence, it can be affirmed that the information that has been gathered to complete the job analysis can be considered valid and reliable for which the overall result can be relied upon.

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write an article on two suspension bridges Paper must be at least 1500 words.

The Roquemaure Bridge spanning the Rhone River in France, on the other hand, is a suspension bridge that came to replace an older bridge known as the 1835 Roquemaure Bridge, which still has a section in existence astride the present bridge, as a relic. The bridge is noteworthy for being one of 42 bridges in history that have spanned the Rhone River and have steel as its key structural suspension cable material. consisting of two lanes, and three suspended spans, one main span, and two side spans. It is currently in use and serving a useful function being used as a thoroughfare crossing the two sides of the Rhone River and providing commuters with a way to get through to the two sides with its two lanes, one going in opposite directions and serving the needs of the surrounding population through time (Denenberg (b) n.d.. Denenberg (c) n.d.).

The Verrazano-Narrows Bridge is impressive in its history and its characteristics, having been made with a keen eye to the engineering and technological considerations and complexities in building such a long suspension bridge span, and mindful of the environmental conditions that the bridge must be able to weather year after year. For instance, each of its towers has a height of 693 feet, and these towers have deviations in terms of top and bottom. That deviation has been measured to be 1 and ⅝ inches. The deviations are due to the need for the bridge to take into consideration the curvature of the earth, given that the distance between the towers is 4,260 feet. The towers have individual weights of 27,000 tons and are held&nbsp.in place each by a million bolt pieces and three million rivet pieces. This is a twin-decker bridge, and the contractions and expansions of the steel cables with the changes in the season cause a 12-foot deviation in elevation between the summer months and the winter months (Metropolitan Transit Authority 2014).

Thesis writing homework help

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Thesis writing homework help

Write 5 pages thesis on the topic various aspects of lifespan development

Physical development can be defined as the process through which the infants or rather the newborns gain control over their bodies. Thus, they get to move on their own with or without the support of other people or items (Krapp & Wilson, 2005). Between the first two to five months. these young babies can roll over in both directions and therefore they cannot move or rather walk, stand or grow on their own. This is because their bones are still very weak, and the physiological development is still at initial stages thus from this scenario it is true thus to say that a child’s physical development starts with muscular control.

This process, however, is gradual, and the parents should not get worried in case it seems to delay. In between five and seven months the children develop the first teeth. Assuming that the child has been undergoing normal breastfeeding, they now have high levels of calcium in their bodies hence they develop teeth (Festingen, 1962). After this time, the child is now between 5 and seven months. They start exhibiting the ability to sit unsupported (although not for a long time as they easily get tired). The infants under this stage also crawl on hands and knees, and they can even pull themselves up while holding on objects. This gives their parents a signal that they can now start giving the child more exercise. Parent, therefore, hold their babies hands and encouraging them to make their first steps.

Physical development can be affected by both genetic or rather hereditary and environmental factors. First I will begin by considering genetic factors. When a child is conceived, they usually take up the genetic makeup of their parents. For instance, if the genes of either of their parents or both are such that they grow slowly, it goes without saying that the child will follow suit too.

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Psychology thesis writing homework.

Create a thesis and an outline on CBT Research into Perfectionism. Prepare this assignment according to the guidelines found in the APA Style Guide. An abstract is required.

In this context, perfectionism can be a component of how problems develop and may also be an aspect that continues to fuel the problems. At its very basic level, Cognitive-Behavior Therapy seeks to understand and make changes to thoughts, beliefs, and behavior, which fuel the existence of problems (Herbert & Forman, 2011). In dealing with perfectionism, these cycles have to be identified to make changes that will result in the problem areas being reduced.

Researchers consider perfectionism as belonging to a cluster of thoughts, behaviors, and feelings that focus on the attainment of faultlessness and distinction, as well as in the achievement of high standards regardless of the associated costs (Frost & Steketee, 2002). These attributes may appear at first glance as appropriate and useful when considered from a school or work point of view, and in most cases, they are supported by the society (Egan & Wade, 2014). However, when this form of being is considered as a part of daily living, the results seem to be dysfunctional as the people who consider themselves to be perfectionists set targets that are difficult for them and others to achieve. People with this problem are often dissatisfied when they do not meet these expectations as they define their self-worth through the attainment of their set standards.

People who are associated with perfectionism are affected by self-doubt and can never be satisfied completely in a manner that will last (Hackfort & Tenenbaum, 2006). Perfectionists often fall into slippery slopes that involve their belief systems interfering with their activities including relationships and work. For instance, an individual with perfectionist characteristics may miss deadlines regularly as a result of their high work standards and may in numerous situations face writer’s block. A perfectionist individual also erodes his or her relationship with friends and family as he or she will start imposing the same impractical standards on the people who are important in their lives. Perfectionists, in most instances, continue to take part in the same actions regardless of the negative impact since they fear failure.

Sociology homework help

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Sociology homework help

Write 10 pages with APA style on Alcohol Advertising in the UK: A Critical Study of Ethical Issues and Debates.

This particular type of work is in a category that is referred as an effective labour and the commercial advertisers more of than not seek to elicit increased consumption of the products that they promote through branding that entails linking the product to particular qualities in the mind of the consumers.

Alcohol advertising is the proportion that is associated with intoxicating drinks by the companies that produce it by the use of several types of media. Alcohol advertising, as well as tobacco advertising, are considered to be the most highly controlled types of marketing and in some of the countries, there are bans on some or all the advertisements that are associated with alcohol (Frith and Mueller, 2010, p. 187). The advertisement of alcoholic beverages and the consumption of these products have a distinct connection according to health agencies, scientific research as well as universities, though there is no concrete proof that advertising of the alcoholic beverages causes people to take it more rather than simply exhibiting more public demand. Many of the people that are interested in these advertisements are of the opinion that the alcohol advertisements and campaigns merely increase the market share of the producer as well as the loyalty to the brand by the consumers.

The Advertising Standards Authority is an organization that is that self regulates itself that operates in the marketing industry in the UK (Kitchen and Proctor, 2001, p 17) and the organization is non-statutory and therefore the organization has no mandate to enforce or interpret legislation but the code of advertising practice widely exhibits the legislations in many of the cases. The organization does not receive funding and assistance from the government but they impose tariffs on the marketing segments of the economy.

The role that is assigned to it is they make sure that the content of advertisements is regulated, sales promotions as well as direct marketing in the United Kingdom through the investigation of the complaints that are made about advertisements, direct marketing or the sales promotions and making the decision whether the advertisements comply with the standards codes.

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Assignment two.

Provide a 6 pages analysis while answering the following question: Media in Laos and Thailand. Prepare this assignment according to the guidelines found in the APA Style Guide. An abstract is required

These have been done by establishing and enacting laws to guide the operation of the media in the country. Some of the government policies have brought bigger conflicts between the government and the media houses. In Laos and Thailand, the media have been depicted on similarly and different ways, these in terms of the trend in media, issues covered, and the application of technology.

Thailand has an approximate population of 62 million, with the addition of 60 million by the first quarter in the year 2013 (Singh & Lorraine, 27). The majority of the Thai population can barely read and read English, therefore, major media is targeting the locals who have attained the basic level of education of reading and writing the local languages. The people who understand English are the few learned and the foreign who are the expatriates, they can both read and write. So far two English media houses have been established for nationwide coverage, one by the new media group in 1971 and the media established by Bangkok in 1971 (Singh & Lorraine, 29).

In Thailand, there have been significant changes in the media. these are in terms of the freedom, issues, and the use of technology. Starting from the freedom, initially, Thailand had been enjoying greater freedom of expression, due to the unity that had been existing between the societies in Thailand. The situation has recently changed due to the recent war that started in 2005. The war has created indifferences among the different societies. The government also controls the traditional taboos and monarchy in Thailand. The changes have occurred because of the tough political period ruler in Thailand.

There have been existing changes in the platform trend used by the media. many social sites have come up to provide a common platform for the media and the journalist to use, initially, it was mostly so through radio and television broadcast, other platforms such as the social media sites like Facebook, and Twitter has emerged. The&nbsp.research shows that there are about 18 million users of Facebook one the social site.

Write an article on video games and their effects

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Write an article on video games and their effects Paper must be at least 1250 words.

The history of video games dates back to the mid-19th century when engineers and academics began producing and designing artificial intelligence programs as part and parcel of their computer science research projects. However, video game popularity reached its peak in the 1970s and 1980s after the introduction of arcade video games and gaming consoles. This paved way for the introduction of video games to the general public.

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Decades later, the evolution of very complex video games has led to a lot of “addiction” especially within children and young adults. It can also be attributed to the fact that parents have opted to buy video games and install them at home as a means of entertainment for their children. This has led to a lot of concerns among these parents themselves and teachers of these children who are of the view that these video games are consuming a lot of time and if the parents and guardians do not look into the matter today, these children will not grow up to be effective scholars.

As a major public health concern, anger management deserves much concern. Anger-related behavior such as aggression and violence has been named some of the many reasons children are brought to anger-management institutions for mental health services. Previous research using experimental manipulation and co relational methods has proved that exposure to violent media such as video games talked about earlier, causes children and young adults to behave in an aggressive manner.

Research on violent media has explained the term aggressive behavior as acts meant to injure or annoy another individual. According to Anderson and Bushman (2001), the use of violent video games has been related to aggressive cognition, aggressive affect, and physiological arousal. There might be bi-directional causal influences at play. already aggressive children will more likely give the violent video game priority. Months later the effect of playing these violent games is experienced.

Homework 2

submit a 1250 words paper on the topic Exploring Online Training Opportunities.

Online raining of adults therefore refers to the use of the internet in improving the technical skills of an adult. Such is a contemporary feature in learning and improving productivity in workplaces since the internet provides efficient ways of facilitating the training process (O’Toole & Essex, 2012).

This proposal designs an effective online training of employees in an organization. The employees have a definite basic education and therefore Have the ability to access and use the internet in undertaking numerous activities including the raining. Additionally, the employees are busy individuals who spend most of their tie at the organization working thus require a considerate learning time table in order to improve efficiency. Finally, the employees have the financial ability t purchase the numerous learning materials required to facilitate online training. With such understanding, it becomes possible to develop an effective online training program that will result in improving the quality of the human resource in the organization.

On job employee training is a vital tool used by the management in improving the quality of their productivity. The management must always improve productivity in the organization by ensuring that the employees have appropriate technical skills to enhance productivity. As such, the management must devise appropriate ways of enhancing the technical skills of the employees through effective job training. Among the reasons for the following online training, include enhancing the productivity of the organization by equipping the employees with appropriate technical skills required to facilitate the production. To motivate the employees, investing in employees by improving the quality of their productivity motivates them thus improving their loyalty to the organization. Finally, online training seeks to improve the appreciation of technology in the organization.

Men versus Women in Sports Media Coverage and Popularity

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Men versus Women in Sports Media Coverage and Popularity

Prepare and submit a term paper on Men versus Women in Sports Media Coverage and Popularity. Your paper should be a minimum of 1500 words in length

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At the Vancouver Olympics for example men received almost whole day prime-time coverage. This was about 13 hours higher in comparison to women’s coverage (Brown 102). Men in most cases tend to perform extremely well in various games. This boosts the reporter’s morale towards covering most of the men’s games. In the summer Olympics in 2008 as well as the preceding years, there emerged improved airtime coverage with both men and women almost getting equal airtime coverage. About 46.3% of airtime coverage went to women this year, a decrease from the previous year 2004 when the coverage was 47.9% (Beck 46). Nevertheless, coverage of women’s sports events improved heavily towards socially acceptable sports for women. The socially accepted sports such as gymnastics are always attractive as these sports involve minimal clothing hence women can be easily displayed as physically attractive. These kinds of sports, even though are highly ranked in the media coverage among the women are not morally acceptable in the media fraternity. This case study explains the impact of media coverage in comparison to men’s and women’s sports coverage and popularity.

Women who normally take part in sports that involve either power or hard body contact are more often unlikely to receive media coverage. This is due to the stereotypical assumptions involved with these kinds of feminine events thus. there are reduced women sports coverage (Chua45). The women’s court volleyball competition received absolutely no coverage in the year 2004 despite the American team’s captivating performance and securing the silver medal. This was almost equally divided between both the men and women volleyball sports coverage this year. The emphasis of the women’s sports coverage exhibited a high emphasis on the women’s attractiveness and gendered qualities that may be provided for a much more efficient male-centric coverage. The 2010 Olympics aptly inclined towards men coverage thus rendering it biased (Lewis 78). Most of the winter sports by their nature generally provide women with fewer opportunities to capture various sports. The make-up of the spectators whom NBC normally targets to please provides a more complex narrative than mere pandering to male sports fans for the summer Olympics in 2008.

Assignment 2-Sample

create a thesis and an outline on The Sub-Cultures of the Star Trek Fans. Prepare this assignment according to the guidelines found in the APA Style Guide. An abstract is required.

The studies have equally examined the methods in which fans interact with the text. In anthropological studies, comparisons have been made between Star Trek fan body and religious Movements (Jindra, 1994). On another account, cultural studies have made comparisons of fan organizations and cults (Hills, 200). This article seeks to examine and give a comprehensive report on the ordinary Star Trek fans.

The original Star Trek was the creation of Gene Roddenberry (1921-1991), a United States Television producer and author. His idea was to develop a Television series involving the futuristic prospects of science fiction with the stage show and enthusiasm of TV westerns his original heading for the television series was the Wagon Train to the Stars. Star Trek was initially aired on American Television in 1966 and continued for three series. Each chapter was in itself an adventure, but then they were all connected together by the premise of a huge spacecraft that was crewed by a various range of individuals, traveling around the galaxy on a mission that took five years to explore different life and new evolutions, to confidently visit a place where no human being has gone in the past. Even though, not expressly successful it attracted a devoted fan-base that was partially made up of male fans that were interested in the scientific and exceptional effects essentials of the show. On the other hand, the show also enticed many female fans.

Over the past three decades, Star Trek has been using the term mega-text. Star Treks mega-text involves much more than the innumerable studio-based television series and films. it also consists of novels, Internet chat groups, treaties, and fanzines among others. That Star Treks idea of space examination is a lightly hidden metaphor for imperialism has extensively undergone analysis (Bernardi, 1998). Exploration, occupation, and incorporation are not far from the surface of the Star Trek New Generation text. Less ostensible, nonetheless, are features of the series that challenge the hegemonic understanding of this story and which current a post-colonial appraisal.

Understanding Simple Linear Regression

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Exercise 14
Understanding Simple Linear Regression
Statistical Technique in Review
In nursing practice, the ability to predict future events or outcomes is crucial, and researchers calculate and report linear regression results as a basis for making these predictions. Linear regression provides a means to estimate or predict the value of a dependent variable based on the value of one or more independent variables. The regression equation is a mathematical expression of a causal proposition emerging from a theoretical framework. The linkage between the theoretical statement and the equation is made prior to data collection and analysis. Linear regression is a statistical method of estimating the expected value of one variable, y, given the value of another variable, x. The focus of this exercise is simple linear regression, which involves the use of one independent variable, x, to predict one dependent variable, y.

The regression line developed from simple linear regression is usually plotted on a graph, with the horizontal axis representing x (the independent or predictor variable) and the vertical axis representing the y (the dependent or predicted variable; see Figure 14-1). The value represented by the letter a is referred to as the y intercept, or the point where the regression line crosses or intercepts the y-axis. At this point on the regression line, x = 0. The value represented by the letter b is referred to as the slope, or the coefficient of x. The slope determines the direction and angle of the regression line within the graph. The slope expresses the extent to which y changes for every one-unit change in x. The score on variable y (dependent variable) is predicted from the subject’s known score on variable x (independent variable). The predicted score or estimate is referred to as Ŷ (expressed as y-hat) (Cohen, 1988; Grove, Burns, & Gray, 2013; Zar, 2010).

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FIGURE 14-1 GRAPH OF A SIMPLE LINEAR REGRESSION LINE
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Simple linear regression is an effort to explain the dynamics within a scatterplot (see Exercise 11) by drawing a straight line through the plotted scores. No single regression line can be used to predict, with complete accuracy, every y value from every x value. However, the purpose of the regression equation is to develop the line to allow the highest degree of prediction possible, the line of best fit. The procedure for developing the line of best fit is the method of least squares. If the data were perfectly correlated, all data points would fall along the straight line or line of best fit. However, not all data points fall on the line of best fit in studies, but the line of best fit provides the best equation for the values of y to be predicted by locating the intersection of points on the line for any given value of x.

The algebraic equation for the regression line of best fit is y = bx + a, where:

y=dependentvariable(outcome)

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x=independentvariable(predictor)

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b=slopeoftheline(beta,orwhattheincreaseinvalueisalongthex-axisforeveryunitofincreaseintheyvalue),alsocalledtheregressioncoefficient.

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a=y−intercept(thepointwheretheregressionlineintersectsthe y-axis),alsocalledtheregressionconstant(Zar,2010).

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In Figure 14-2, the x-axis represents Gestational Age in weeks and the y-axis represents Birth Weight in grams. As gestational age increases from 20 weeks to 34 weeks, birth weight also increases. In other words, the slope of the line is positive. This line of best fit can be used to predict the birth weight (dependent variable) for an infant based on his or her gestational age in weeks (independent variable). Figure 14-2 is an example of a line of best fit that was not developed from research data. In addition, the x-axis was started at 22 weeks rather than 0, which is the usual start in a regression figure. Using the formula y = bx + a, the birth weight of a baby born at 28 weeks of gestation is calculated below.

Formula:y=bx+a

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Inthisexample,a=500,b=20,andx=28weeks

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y=20(28)+500=560+500=1,060grams

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FIGURE 14-2 EXAMPLE LINE OF BEST FIT FOR GESTATIONAL AGE AND BIRTH WEIGHT
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The regression line represents y for any given value of x. As you can see, some data points fall above the line, and some fall below the line. If we substitute any x value in the regression equation and solve for y, we will obtain a ŷ that will be somewhat different from the actual values. The distance between the ŷ and the actual value of y is called residual, and this represents the degree of error in the regression line. The regression line or the line of best fit for the data points is the unique line that will minimize error and yield the smallest residual (Zar, 2010). The step-by-step process for calculating simple linear regression in a study is presented in Exercise 29.

Research Article
Source
Flannigan, C., Bourke, T. W., Sproule, A., Stevenson, M., & Terris, M. (2014). Are APLS formulae for estimating weight appropriate for use in children admitted to PICU? Resuscitation, 85(7), 927–931.

Introduction
Medications and other therapies often necessitate knowing a patient’s weight. However, a child may be admitted to a pediatric intensive care unit (PICU) without a known weight, and instability and on-going resuscitation may prevent obtaining this needed weight. Clinicians would benefit from a tool that could accurately estimate a patient’s weight when such information is unavailable. Thus Flannigan et al. (2014) conducted a retrospective observational study for the purpose of determining “if the revised APLS UK [Advanced Paediatric Life Support United Kingdom] formulae for estimating weight are appropriate for use in the paediatric care population in the United Kingdom” (Flannigan et al., 2014, p. 927). The sample included 10,081 children (5,622 males and 4,459 females), who ranged from term-corrected age to 15 years of age, admitted to the PICU during a 5-year period. Because this was a retrospective study, no geographic location, race, and ethnicity data were collected for the sample. A paired samples t-test was used to compare mean sample weights with the APLS UK formula weight. The “APLS UK formula ‘weight = (0.05 × age in months) + 4’ significantly overestimates the mean weight of children under 1 year admitted to PICU by between 10% [and] 25.4%” (Flannigan et al., 2014, p. 928). Therefore, the researchers concluded that the APLS UK formulas were not appropriate for estimating the weight of children admitted to the PICU.

Relevant Study Results
“Simple linear regression was used to produce novel formulae for the prediction of the mean weight specifically for the PICU population” (Flannigan et al., 2014, p. 927). The three novel formulas are presented in Figures 1, 2, and 3, respectively. The new formulas calculations are more complex than the APLS UK formulas. “Although a good estimate of mean weight can be obtained by our newly derived formula, reliance on mean weight alone will still result in significant error as the weights of children admitted to PICU in each age and sex [gender] group have a large standard deviation . . . Therefore as soon as possible after admission a weight should be obtained, e.g., using a weight bed” (Flannigan et al., 2014, p. 929).

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FIGURE 1 Comparison of actual weight with weight calculated using APLS formula “Weight in kg = (0.5 × age in months) + 4” and novel formula “Weight in kg = (0.502 × age in months) + 3.161” Flannigan, C., Bourke, T. W., Sproule, A., Stevenson, M., & Terris, M. (2014). Are APLS formulae for estimating weight appropriate for use in children admitted to PICU? Resuscitation, 85(7), p. 928.
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FIGURE 2 Comparison of actual weight with weight calculated using APLS formula “Weight in kg = (2 × age in years) + 8” and novel formula “Weight in kg = (0.176 × age in months) + 7.241” Flannigan, C., Bourke, T. W., Sproule, A., Stevenson, M., & Terris, M. (2014). Are APLS formulae for estimating weight appropriate for use in children admitted to PICU? Resuscitation, 85(7), p. 928.
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FIGURE 3 Comparison of actual weight with weight calculated using APLS formula “Weight in kg = (3 × age in years) + 7” and novel formula “Weight in kg = (0.331 × age in months) − 6.868” Flannigan, C., Bourke, T. W., Sproule, A., Stevenson, M., & Terris, M. (2014). Are APLS formulae for estimating weight appropriate for use in children admitted to PICU? Resuscitation, 85(7), p. 929.

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Study Questions
1. What are the variables on the x- and y-axes in Figure 1 from the Flannigan et al. (2014) study?

2. What is the name of the type of variable represented by x and y in Figure 1? Is x or y the score to be predicted?

3. What is the purpose of simple linear regression analysis and the regression equation?

4. What is the point where the regression line meets the y-axis called? Is there more than one term for this point and what is the value of x at that point?

5. In the formula y = bx + a, is a or b the slope? What does the slope represent in regression analysis?

6. Using the values a = 3.161 and b = 0.502 with the novel formula in Figure 1, what is the predicted weight in kilograms for a child at 5 months of age? Show your calculations.

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7. What are the variables on the x-axis and the y-axis in Figures 2 and 3? Describe these variables and how they might be entered into the regression novel formulas identified in Figures 2 and 3.

8. Using the values a = 7.241 and b = 0.176 with the novel formula in Figure 2, what is the predicted weight in kilograms for a child at 4 years of age? Show your calculations.

9. Does Figure 1 have a positive or negative slope? Provide a rationale for your answer. Discuss the meaning of the slope of Figure 1.

10. According to the study narrative, why are estimated child weights important in a pediatric intensive care (PICU) setting? What are the implications of these findings for practice?

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Answers to Study Questions
1. The x variable is age in months, and the y variable is weight in kilograms in Figure 1.

2. x is the independent or predictor variable. y is the dependent variable or the variable that is to be predicted by the independent variable, x.

3. Simple linear regression is conducted to estimate or predict the values of one dependent variable based on the values of one independent variable. Regression analysis is used to calculate a line of best fit based on the relationship between the independent variable x and the dependent variable y. The formula developed with regression analysis can be used to predict the dependent variable (y) values based on values of the independent variable x.

4. The point where the regression line meets the y-axis is called the y intercept and is also represented by a (see Figure 14-1). a is also called the regression constant. At the y intercept, x = 0.

5. b is the slope of the line of best fit (see Figure 14-1). The slope of the line indicates the amount of change in y for each one unit of change in x. b is also called the regression coefficient.

6. Use the following formula to calculate your answer: y = bx + a
y = 0.502 (5) + 3.161 = 2.51 + 3.161 = 5.671 kilograms
Note: Flannigan et al. (2014) expressed the novel formula of weight in kilograms = (0.502 × age in months) + 3.161 in the title of Figure 1.

7. Age in years is displayed on the x-axis and is used for the APLS UK formulas in Figures 2 and 3. Figure 2 includes children 1 to 5 years of age, and Figure 3 includes children 6 to 12 years of age. However, the novel formulas developed by simple linear regression are calculated with age in months. Therefore, the age in years must be converted to age in months before calculating the y values with the novel formulas provided for Figures 2 and 3. For example, a child who is 2 years old would be converted to 24 months (2 × 12 mos./year = 24 mos.). Then the formulas in Figures 2 and 3 could be used to predict y (weight in kilograms) for the different aged children. The y-axis on both Figures 2 and 3 is weight in kilograms (kg).

8. First calculate the child’s age in months, which is 4 × 12 months/year = 48 months.
y = bx + a = 0.176 (48) + 7.241 = 8.448 + 7.241 = 15.689 kilograms
Note the x value needs to be in age in months and Flannigan et al. (2014) expressed the novel formula of weight in kilograms = (0.176 × age in months) + 7.241.

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9. Figure 1 has a positive slope since the line extends from the lower left corner to the upper right corner and shows a positive relationship. This line shows that the increase in x (independent variable) is associated with an increase in y (dependent variable). In the Flannigan et al. (2014) study, the independent variable age in months is used to predict the dependent variable of weight in kilograms. As the age in months increases, the weight in kilograms also increases, which is the positive relationship illustrated in Figure 1.

10. According to Flannigan et al. (2014, p. 927), “The gold standard for prescribing therapies to children admitted to Paediatric Intensive Care Units (PICU) requires accurate measurement of the patient’s weight. . . . An accurate weight may not be obtainable immediately because of instability and on-going resuscitation. An accurate tool to aid the critical care team estimate the weight of these children would be a valuable clinical tool.” Accurate patient weights are an important factor in preventing medication errors particularly in pediatric populations. The American Academy of Pediatrics (AAP)’s policy on Prevention of Medication Errors in the Pediatric Inpatient Setting can be obtained from the following website: https://www.aap.org/en-us/advocacy-and-policy/federal-advocacy/Pages/Federal-Advocacy.aspx#SafeandEffectiveDrugsandDevicesforChildren. The Centers for Medicare & Medicaid Services, Partnership for Patients provides multiple links to Adverse Drug Event (ADE) information including some resources specific to pediatrics at http://partnershipforpatients.cms.gov/p4p_resources/tsp-adversedrugevents/tooladversedrugeventsade.html.

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EXERCISE 14 Questions to Be Graded
Follow your instructor’s directions to submit your answers to the following questions for grading. Your instructor may ask you to write your answers below and submit them as a hard copy for grading. Alternatively, your instructor may ask you to use the space below for notes and submit your answers online at http://evolve.elsevier.com/Grove/Statistics/ under “Questions to Be Graded.”

Name: _______________________________________________________ Class: _____________________

Date: ___________________________________________________________________________________

1. According to the study narrative and Figure 1 in the Flannigan et al. (2014) study, does the APLS UK formula under- or overestimate the weight of children younger than 1 year of age? Provide a rationale for your answer.

2. Using the values a = 3.161 and b = 0.502 with the novel formula in Figure 1, what is the predicted weight in kilograms (kg) for a child at 9 months of age? Show your calculations.

3. Using the values a = 3.161 and b = 0.502 with the novel formula in Figure 1, what is the predicted weight in kilograms for a child at 2 months of age? Show your calculations.

4. In Figure 2, the formula for calculating y (weight in kg) is Weight in kg = (0.176 × Age in months) + 7.241. Identify the y intercept and the slope in this formula.

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5. Using the values a = 7.241 and b = 0.176 with the novel formula in Figure 2, what is the predicted weight in kilograms for a child 3 years of age? Show your calculations.

6. Using the values a = 7.241 and b = 0.176 with the novel formula in Figure 2, what is the predicted weight in kilograms for a child 5 years of age? Show your calculations.

7. In Figure 3, some of the actual mean weights represented by blue line with squares are above the dotted straight line for the novel formula, but others are below the straight line. Is this an expected finding? Provide a rationale for your answer.

8. In Figure 3, the novel formula is (weight in kilograms = (0.331 × Age in months) − 6.868. What is the predicted weight in kilograms for a child 10 years old? Show your calculations.

9. Was the sample size of this study adequate for conducting simple linear regression? Provide a rationale for your answer.

10. Describe one potential clinical advantage and one potential clinical problem with using the three novel formulas presented in Figures 1, 2, and 3 in a PICU setting.

(Grove 139-150)

Grove, Susan K., Daisha Cipher. Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition. Saunders, 022016. VitalBook file.

The citation provided is a guideline. Please check each citation for accuracy before use.

 

Exercise 19
Understanding Pearson Chi-Square
Statistical Technique in Review
The Pearson Chi-square (χ2 ) is an inferential statistical test calculated to examine differences among groups with variables measured at the nominal level. There are different types of χ2 tests and the Pearson chi-square is commonly reported in nursing studies. The Pearson χ2 test compares the frequencies that are observed with the frequencies that were expected. The assumptions for the χ2 test are as follows:

1. The data are nominal-level or frequency data.

2. The sample size is adequate.

3. The measures are independent of each other or that a subject’s data only fit into one category (Plichta & Kelvin, 2013).

The χ2 values calculated are compared with the critical values in the χ2 table (see Appendix D Critical Values of the χ2 Distribution at the back of this text). If the result is greater than or equal to the value in the table, significant differences exist. If the values are statistically significant, the null hypothesis is rejected (Grove, Burns, & Gray, 2013). These results indicate that the differences are probably an actual reflection of reality and not just due to random sampling error or chance.

In addition to the χ2 value, researchers often report the degrees of freedom (df). This mathematically complex statistical concept is important for calculating and determining levels of significance. The standard formula for df is sample size (N) minus 1, or df = N − 1; however, this formula is adjusted based on the analysis technique performed (Plichta & Kelvin, 2013). The df formula for the χ2 test varies based on the number of categories examined in the analysis. The formula for df for the two-way χ2 test is df = (R − 1) (C − 1), where R is number of rows and C is the number of columns in a χ2 table. For example, in a 2 × 2 χ2 table, df = (2 − 1) (2 − 1) = 1. Therefore, the df is equal to 1. Table 19-1 includes a 2 × 2 chi-square contingency table based on the findings of An et al. (2014) study. In Table 19-1, the rows represent the two nominal categories of alcohol 192use and alcohol nonuse and the two columns represent the two nominal categories of smokers and nonsmokers. The df = (2 − 1) (2 − 1) = (1) (1) = 1, and the study results were as follows: χ2 (1, N = 799) = 63.1; p < 0.0001. It is important to note that the df can also be reported without the sample size, as in χ2(1) = 63.1, p < 0.0001.

TABLE 19-1

CONTINGENCY TABLE BASED ON THE RESULTS OF AN ET AL. (2014) STUDY

Nonsmokers n = 742 Smokers n = 57*
No alcohol use 551 14
Alcohol use† 191 43
*Smokers defined as “smoking at least 1 cigarette daily during the past month.”

†Alcohol use “defined as at least 1 alcoholic beverage per month during the past year.”

An, F. R., Xiang, Y. T., Yu., L., Ding, Y. M., Ungvari, G. S., Chan, S. W. C., et al. (2014). Prevalence of nurses’ smoking habits in psychiatric and general hospitals in China. Archives of Psychiatric Nursing, 28(2), 120.

If more than two groups are being examined, χ2 does not determine where the differences lie; it only determines that a statistically significant difference exists. A post hoc analysis will determine the location of the difference. χ2 is one of the weaker statistical tests used, and results are usually only reported if statistically significant values are found. The step-by-step process for calculating the Pearson chi-square test is presented in Exercise 35.

Research Article
Source
Darling-Fisher, C. S., Salerno, J., Dahlem, C. H. Y., & Martyn, K. K. (2014). The Rapid Assessment for Adolescent Preventive Services (RAAPS): Providers’ assessment of its usefulness in their clinical practice settings. Journal of Pediatric Health Care, 28(3), 217–226.

Introduction
Darling-Fisher and colleagues (2014, p. 219) conducted a mixed-methods descriptive study to evaluate the clinical usefulness of the Rapid Assessment for Adolescent Preventative Services (RAAPS) screening tool “by surveying healthcare providers from a wide variety of clinical settings and geographic locations.” The study participants were recruited from the RAAPS website to complete an online survey. The RAAPS risk-screening tool “was developed to identify the risk behaviors contributing most to adolescent morbidity, mortality, and social problems, and to provide a more streamlined assessment to help providers address key adolescent risk behaviors in a time-efficient and user-friendly format” (Darling-Fisher et al., 2014, p. 218). The RAAPS is an established 21-item questionnaire with evidence of reliability and validity that can be completed by adolescents in 5–7 minutes.

“Quantitative and qualitative analyses indicated the RAAPS facilitated identification of risk behaviors and risk discussions and provided efficient and consistent assessments; 86% of providers believed that the RAAPS positively influenced their practice” (Darling-Fisher et al., 2014, p. 217). The researchers concluded the use of RAAPS by healthcare providers could improve the assessment and identification of adolescents at risk and lead to the delivery of more effective adolescent preventive services.

Relevant Study Results
In the Darling-Fisher et al. (2014, p. 220) mixed-methods study, the participants (N = 201) were “providers from 26 U.S. states and three foreign countries (Canada, Korea, and Ireland).” More than half of the participants (n = 111; 55%) reported they were using the RAAPS in their clinical practices. “When asked if they would recommend the RAAPS to other providers, 86 responded, and 98% (n = 84) stated they would recommend RAAPS. The two most common reasons cited for their recommendation were for screening (n = 76, 92%) and identification of risk behaviors (n = 75, 90%). Improved communication (n = 52, 63%) and improved documentation (n = 46, 55%) and increased patient understanding of their risk behaviors (n = 48, 58%) were also cited by respondents as reasons to recommend the RAAPS” (Darling-Fisher et al., 2014, p. 222).

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“Respondents who were not using the RAAPS (n = 90; 45%), had a variety of reasons for not using it. Most reasons were related to constraints of their health system or practice site; other reasons were satisfaction with their current method of assessment . . . and that they were interested in the RAAPS for academic or research purposes rather than clinical use” (Darling-Fisher et al., 2014, p. 220).

Chi-square analysis was calculated to determine if any statistically significant differences existed between the characteristics of the RAAPS users and nonusers. Darling-Fisher et al. (2014) did not provide a level of significance or α for their study, but the standard for nursing studies is α = 0.05. “Statistically significant differences were noted between RAAPS users and nonusers with respect to provider types, practice setting, percent of adolescent patients, years in practice, and practice region. No statistically significant demographic differences were found between RAAPS users and nonusers with respect to race, age” (Darling-Fisher et al., 2014, p. 221). The χ2 results are presented in Table 2.

TABLE 2

DEMOGRAPHIC COMPARISONS BETWEEN RAPID ASSESSMENT FOR ADOLESCENT PREVENTIVE SERVICE USERS AND NONUSERS

Current user Yes (%) No (%) χ2 p
Provider type (n = 161) 12.7652, df = 2 < .00
Health care provider 64 (75.3) 55 (72.4)
Mental health provider 13 (15.3) 2 (2.6)
Other 8 (9.4) 19 (25.0)
Practice setting (n = 152) 12.7652, df = 1 < .00
Outpatient health clinic 20 (24.1) 36 (52.2)
School-based health clinic 63 (75.9) 33 (47.8)
% Adolescent patients (n = 154) 7.3780, df = 1 .01
≤50% 26 (30.6) 36 (52.2)
>50% 59 (69.4) 33 (47.8)
Years in practice (n = 157) 6.2597, df = 1 .01
≤5 years 44 (51.8) 23 (31.9)
>5 years 41 (48.2) 49 (68.1)
U.S. practice region (n = 151) 29.68, df = 3 < .00
Northeastern United States 13 (15.3) 15 (22.7)
Southern United States 11 (12.9) 22 (33.3)
Midwestern United States 57 (67.1) 16 (24.2)
Western United States 4 (4.7) 13 (19.7)
Race (n = 201) 1.2865, df = 2 .53
Black/African American 11 (9.9) 5 (5.6)
White/Caucasian 66 (59.5) 56 (62.2)
Other 34 (30.6) 29 (32.2)
Provider age in years (n = 145) 4.00, df = 2 .14
20–39 years 21 (25.6) 8 (12.7)
40–49 years 24 (29.3) 19 (30.2)
50+ years 37 (45.1) 36 (57.1)
image

χ2, Chi-square statistic.

df, degrees of freedom.

Darling-Fisher, C. S., Salerno, J., Dahlem, C. H. Y., & Martyn, K. K. (2014). The Rapid Assessment for Adolescent Preventive Services (RAAPS): Providers’ assessment of its usefulness in their clinical practice settings. Journal of Pediatric Health Care, 28(3), p. 221.

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Study Questions
1. What is the sample size for the Darling-Fisher et al. (2014) study? How many study participants (percentage) are RAAPS users and how many are RAAPS nonusers?

2. What is the chi-square (χ2) value and degrees of freedom (df) for provider type?

3. What is the p value for provider type? Is the χ2 value for provider type statistically significant? Provide a rationale for your answer.

4. Does a statistically significant χ2 value provide evidence of causation between the variables? Provide a rationale for your answer.

5. What is the χ2 value for race? Is the χ2 value statistically significant? Provide a rationale for your answer.

6. Is there a statistically significant difference between RAAPS users and RAAPS nonusers with regard to percentage adolescent patients? In your own opinion is this an expected finding? Document your answer.

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7. What is the df for U.S. practice region? Complete the df formula for U.S. practice region to visualize how Darling-Fisher et al. (2014) determined the appropriate df for that region.

8. State the null hypothesis for the years in practice variable for RAAPS users and RAAPS nonusers.

9. Should the null hypothesis for years in practice developed for Question 8 be accepted or rejected? Provide a rationale for your answer.

10. How many null hypotheses were accepted by Darling-Fisher et al. (2014) in Table 2? Provide a rationale for your answer.

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Answers to Study Questions
1. The sample size is N = 201 with n = 111 (55%) RAAPS users and n = 90 (45%) RAAPS nonusers as indicated in the narrative results.

2. The χ2 = 12.7652 and df = 2 for provider type as presented in Table 2.

3. The p = < .00 for the provider type. Yes, the χ2 = 12.7652 for provider type is statistically significant as indicated by the p value presented in Table 2. The specific χ2 value obtained could be compared against the critical value in a χ2 table (see Appendix D Critical Values of the χ2 Distribution at the back of this text) to determine the significance for the specific degrees of freedom (df), but readers of research reports usually rely on the p value provided by the researcher(s) to determine significance. Most nurse researchers set the level of significance or alpha (α) = 0.05. Since the p value is less than alpha, the result is statistically significant. You need to note that p values never equal zero as they appear in this study. The p values would not be zero if carried out more decimal places.

4. No, a statistically significant χ2 value does not provide evidence of causation. A statistically significant χ2 value indicates a significant difference between groups exists but does not provide a causal link (Grove et al., 2013; Plichta & Kelvin, 2013).

5. The χ2 = 1.2865 for race. Since p = .53 for race, the χ2 value is not statistically significant. The level of significance is set at α = 0.05 and the p value is larger than alpha, so the result is nonsignificant.

6. Yes, there is a statistically significant difference between RAAPS users and RAAPS nonusers with regard to percent of adolescent patients. The chi-square value = 7.3780 with a p = .01.You might expect that nurses caring for more adolescents might have higher RAAPS use as indicated in Table 2. However, nurses need to be knowledgeable of assessment and care needs of populations and subpopulations in their practice even if not frequently encountered. Two valuable sources for adolescent care include the Centers for Disease Control and Prevention (CDC) Adolescent and School Health at http://www.cdc.gov/HealthyYouth/idex.htm and the World Health Organization (WHO) adolescent health at http://www.who.int/topics/adolescent_health/en/.

7. The df = 3 for U.S. practice region is provided in Table 2. The df formula, df = (R − 1) (C − 1) is used. There are four “R” rows, Northeastern United States, Southern United States, Midwestern United States, and Western United States. There are two “C” columns, RAAPS users and RAAPS nonusers. df = (4 − 1)(2 − 1) = (3)(1) = 3.

8. The null hypothesis: There is no difference between RAAPS users and RAAPS nonusers for providers with ≤5 years of practice and those with >5 years of practice.

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9. The null hypothesis for years in practice stated in Questions 8 should be rejected. The χ2 = 6.2597 for years in practice is statistically significant, p = .01. A statistically significant χ2 indicates a significant difference exists between the users and nonusers of RAAPS for years in practice; therefore, the null hypothesis should be rejected.

10. Two null hypotheses were accepted since two χ2 values (race and provider age) were not statistically significant (p > 0.05), as indicated in Table 2. Nonsignificant results indicate that the null hypotheses are supported or accepted as an accurate reflection of the results of the study.

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EXERCISE 19 Questions to Be Graded
Follow your instructor’s directions to submit your answers to the following questions for grading. Your instructor may ask you to write your answers below and submit them as a hard copy for grading. Alternatively, your instructor may ask you to use the space below for notes and submit your answers online at http://evolve.elsevier.com/Grove/Statistics/ under “Questions to Be Graded.”

Name: _______________________________________________________ Class: _____________________

Date: ___________________________________________________________________________________

1. According to the relevant study results section of the Darling-Fisher et al. (2014) study, what categories are reported to be statistically significant?

2. What level of measurement is appropriate for calculating the χ2 statistic? Give two examples from Table 2 of demographic variables measured at the level appropriate for χ2.

3. What is the χ2 for U.S. practice region? Is the χ2 value statistically significant? Provide a rationale for your answer.

4. What is the df for provider type? Provide a rationale for why the df for provider type presented in Table 2 is correct.

200
5. Is there a statistically significant difference for practice setting between the Rapid Assessment for Adolescent Preventive Services (RAAPS) users and nonusers? Provide a rationale for your answer.

6. State the null hypothesis for provider age in years for RAAPS users and RAAPS nonusers.

7. Should the null hypothesis for provider age in years developed for Question 6 be accepted or rejected? Provide a rationale for your answer.

8. Describe at least one clinical advantage and one clinical challenge of using RAAPS as described by Darling-Fisher et al. (2014).

9. How many null hypotheses are rejected in the Darling-Fisher et al. (2014) study for the results presented in Table 2? Provide a rationale for your answer.

10. A statistically significant difference is present between RAAPS users and RAAPS nonusers for U.S. practice region, χ2 = 29.68. Does the χ2 result provide the location of the difference? Provide a rationale for your answer

(Grove 191-200)

Grove, Susan K., Daisha Cipher. Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition. Saunders, 022016. VitalBook file.

The citation provided is a guideline. Please check each citation for accuracy before use.

 

 

Exercise 29
Calculating Simple Linear Regression
Simple linear regression is a procedure that provides an estimate of the value of a dependent variable (outcome) based on the value of an independent variable (predictor). Knowing that estimate with some degree of accuracy, we can use regression analysis to predict the value of one variable if we know the value of the other variable (Cohen & Cohen, 1983). The regression equation is a mathematical expression of the influence that a predictor has on a dependent variable, based on some theoretical framework. For example, in Exercise 14, Figure 14-1 illustrates the linear relationship between gestational age and birth weight. As shown in the scatterplot, there is a strong positive relationship between the two variables. Advanced gestational ages predict higher birth weights.

A regression equation can be generated with a data set containing subjects’ x and y values. Once this equation is generated, it can be used to predict future subjects’ y values, given only their x values. In simple or bivariate regression, predictions are made in cases with two variables. The score on variable y (dependent variable, or outcome) is predicted from the same subject’s known score on variable x (independent variable, or predictor).

Research Designs Appropriate for Simple Linear Regression
Research designs that may utilize simple linear regression include any associational design (Gliner et al., 2009). The variables involved in the design are attributional, meaning the variables are characteristics of the participant, such as health status, blood pressure, gender, diagnosis, or ethnicity. Regardless of the nature of variables, the dependent variable submitted to simple linear regression must be measured as continuous, at the interval or ratio level.

Statistical Formula and Assumptions
Use of simple linear regression involves the following assumptions (Zar, 2010):

1. Normal distribution of the dependent (y) variable

2. Linear relationship between x and y

3. Independent observations

4. No (or little) multicollinearity

5. Homoscedasticity

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Data that are homoscedastic are evenly dispersed both above and below the regression line, which indicates a linear relationship on a scatterplot. Homoscedasticity reflects equal variance of both variables. In other words, for every value of x, the distribution of y values should have equal variability. If the data for the predictor and dependent variable are not homoscedastic, inferences made during significance testing could be invalid (Cohen & Cohen, 1983; Zar, 2010). Visual examples of homoscedasticity and heteroscedasticity are presented in Exercise 30.

In simple linear regression, the dependent variable is continuous, and the predictor can be any scale of measurement; however, if the predictor is nominal, it must be correctly coded. Once the data are ready, the parameters a and b are computed to obtain a regression equation. To understand the mathematical process, recall the algebraic equation for a straight line:

y=bx+a

image
where

y=the dependent variable(outcome)

image
x=the independent variable(predictor)

image
b=the slope of the line

image
a=y-intercept(the point where the regression line intersects the y-axis)

image
No single regression line can be used to predict with complete accuracy every y value from every x value. In fact, you could draw an infinite number of lines through the scattered paired values (Zar, 2010). However, the purpose of the regression equa­tion is to develop the line to allow the highest degree of prediction possible—the line of best fit. The procedure for developing the line of best fit is the method of least squares. The formulas for the beta (β) and slope (α) of the regression equation are computed as follows. Note that once the β is calculated, that value is inserted into the formula for α.

β=n∑xy−∑x∑yn∑x 2 −(∑x) 2

image
α=∑y−b∑xn

image
Hand Calculations
This example uses data collected from a study of students enrolled in a registered nurse to bachelor of science in nursing (RN to BSN) program (Mancini, Ashwill, & Cipher, 2014). The predictor in this example is number of academic degrees obtained by the student prior to enrollment, and the dependent variable was number of months it took for the student to complete the RN to BSN program. The null hypothesis is “Number of degrees does not predict the number of months until completion of an RN to BSN program.”

The data are presented in Table 29-1. A simulated subset of 20 students was selected for this example so that the computations would be small and manageable. In actuality, studies involving linear regression need to be adequately powered (Aberson, 2010; Cohen, 1988). Observe that the data in Table 29-1 are arranged in columns that correspond to 321the elements of the formula. The summed values in the last row of Table 29-1 are inserted into the appropriate place in the formula for b.

TABLE 29-1

ENROLLMENT GPA AND MONTHS TO COMPLETION IN AN RN TO BSN PROGRAM

Student ID x y x2 xy
(Number of Degrees) (Months to Completion)
1 1 17 1 17
2 2 9 4 18
3 0 17 0 0
4 1 9 1 9
5 0 16 0 0
6 1 11 1 11
7 0 15 0 0
8 0 12 0 0
9 1 15 1 15
10 1 12 1 12
11 1 14 1 14
12 1 10 1 10
13 1 17 1 17
14 0 20 0 0
15 2 9 4 18
16 2 12 4 24
17 1 14 1 14
18 2 10 4 20
19 1 17 1 17
20 2 11 4 22
sum Σ 20 267 30 238
image

The computations for the b and α are as follows:

Step 1: Calculate b.
From the values in Table 29-1, we know that n = 20, Σx = 20, Σy = 267, Σx2 = 30, and Σxy = 238. These values are inserted into the formula for b, as follows:

b=20(238)−(20)(267)20(30)−20 2

image

b=−2.9

image

Step 2: Calculate α.
From Step 1, we now know that b = −2.9, and we plug this value into the formula for α.

α=267−(−2.9)(20)20

image

α=16.25

image

Step 3: Write the new regression equation:

y=−2.9x+16.25

image

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Step 4: Calculate R.
The multiple R is defined as the correlation between the actual y values and the predicted y values using the new regression equation. The predicted y value using the new equation is represented by the symbol ŷ to differentiate from y, which represents the actual y values in the data set. We can use our new regression equation from Step 3 to compute predicted program completion time in months for each student, using their number of academic degrees prior to enrollment in the RN to BSN Program. For example, Student #1 had earned 1 academic degree prior to enrollment, and the predicted months to completion for Student 1 is calculated as:

y ̂ =−2.9(1)+16.25

image

y ̂ =13.35

image

Thus, the predicted ŷ is 13.35 months. This procedure would be continued for the rest of the students, and the Pearson correlation between the actual months to completion (y) and the predicted months to completion (ŷ) would yield the multiple R value. In this example, the R = 0.638. The higher the R, the more likely that the new regression equation accurately predicts y, because the higher the correlation, the closer the actual y values are to the predicted ŷ values. Figure 29-1 displays the regression line where the x axis represents possible numbers of degrees, and the y axis represents the predicted months to program completion (ŷ values).

image
FIGURE 29-1 REGRESSION LINE REPRESENTED BY NEW REGRESSION EQUATION.
Step 5: Determine whether the predictor significantly predicts y.

t=Rn−21−R 2 ‾ ‾ ‾ ‾ √

image

To know whether the predictor significantly predicts y, the beta must be tested against zero. In simple regression, this is most easily accomplished by using the R value from Step 4:

t=.638200−21−.407 ‾ ‾ ‾ ‾ ‾ √

image

t=3.52

image

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The t value is then compared to the t probability distribution table (see Appendix A). The df for this t statistic is n − 2. The critical t value at alpha (α) = 0.05, df = 18 is 2.10 for a two-tailed test. Our obtained t was 3.52, which exceeds the critical value in the table, thereby indicating a significant association between the predictor (x) and outcome (y).

Step 6: Calculate R2.
After establishing the statistical significance of the R value, it must subsequently be examined for clinical importance. This is accomplished by obtaining the coefficient of determination for regression—which simply involves squaring the R value. The R2 represents the percentage of variance explained in y by the predictor. Cohen describes R2 values of 0.02 as small, 0.15 as moderate, and 0.26 or higher as large effect sizes (Cohen, 1988). In our example, the R was 0.638, and, therefore, the R2 was 0.407. Multiplying 0.407 × 100% indicates that 40.7% of the variance in months to program completion can be explained by knowing the student’s number of earned academic degrees at admission (Cohen & Cohen, 1983).
The R2 can be very helpful in testing more than one predictor in a regression model. Unlike R, the R2 for one regression model can be compared with another regression model that contains additional predictors (Cohen & Cohen, 1983). The R2 is discussed further in Exercise 30.
The standardized beta (β) is another statistic that represents the magnitude of the association between x and y. β has limits just like a Pearson r, meaning that the standardized β cannot be lower than −1.00 or higher than 1.00. This value can be calculated by hand but is best computed with statistical software. The standardized beta (β) is calculated by converting the x and y values to z scores and then correlating the x and y value using the Pearson r formula. The standardized beta (β) is often reported in literature instead of the unstandardized b, because b does not have lower or upper limits and therefore the magnitude of b cannot be judged. β, on the other hand, is interpreted as a Pearson r and the descriptions of the magnitude of β can be applied, as recommended by Cohen (1988). In this example, the standardized beta (β) is −0.638. Thus, the magnitude of the association between x and y in this example is considered a large predictive association (Cohen, 1988).

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SPSS Computations
This is how our data set looks in SPSS.

image

Step 1: From the “Analyze” menu, choose “Regression” and “Linear.”

Step 2: Move the predictor, Number of Degrees, to the space labeled “Independent(s).” Move the dependent variable, Number of Months to Completion, to the space labeled “Dependent.” Click “OK.”

image

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Interpretation of SPSS Output
The following tables are generated from SPSS. The first table contains the multiple R and the R2 values. The multiple R is 0.638, indicating that the correlation between the actual y values and the predicted y values using the new regression equation is 0.638. The R2 is 0.407, indicating that 40.7% of the variance in months to program completion can be explained by knowing the student’s number of earned academic degrees at enrollment.

Regression
image
The second table contains the ANOVA table. As presented in Exercises 18 and 33, the ANOVA is usually performed to test for differences between group means. However, ANOVA can also be performed for regression, where the null hypothesis is that “knowing the value of x explains no information about y”. This table indicates that knowing the value of x explains a significant amount of variance in y. The contents of the ANOVA table are rarely reported in published manuscripts, because the significance of each predictor is presented in the last SPSS table titled “Coefficients” (see below).

image
The third table contains the b and a values, standardized beta (β), t, and exact p value. The a is listed in the first row, next to the label “Constant.” The β is listed in the second row, next to the name of the predictor. The remaining information that is important to extract when interpreting regression results can be found in the second row. The standardized beta (β) is −0.638. This value has limits just like a Pearson r, meaning that the standardized β cannot be lower than −1.00 or higher than 1.00. The t value is −3.516, and the exact p value is 0.002.

image
326
Final Interpretation in American Psychological Association (APA) Format
The following interpretation is written as it might appear in a research article, formatted according to APA guidelines (APA, 2010). Simple linear regression was performed with number of earned academic degrees as the predictor and months to program completion as the dependent variable. The student’s number of degrees significantly predicted months to completion among students in an RN to BSN program, β = −0.638, p = 0.002, and R2 = 40.7%. Higher numbers of earned academic degrees significantly predicted shorter program completion time.

327
Study Questions
1. If you have access to SPSS, compute the Shapiro-Wilk test of normality for months to completion (as demonstrated in Exercise 26). If you do not have access to SPSS, plot the frequency distributions by hand. What do the results indicate?

2. State the null hypothesis for the example where number of degrees was used to predict time to BSN program completion.

3. In the formula y = bx + a, what does “b” represent?

4. In the formula y = bx + a, what does “a” represent?

5. Using the new regression equation, ŷ = −2.9x + 16.25, compute the predicted months to program completion if a student’s number of earned degrees is 0. Show your calculations.

6. Using the new regression equation, ŷ = −2.9x + 16.25, compute the predicted months to program completion if a student’s number of earned degrees is 2. Show your calculations.

328
7. What was the correlation between the actual y values and the predicted y values using the new regression equation in the example?

8. What was the exact likelihood of obtaining a t value at least as extreme as or as close to the one that was actually observed, assuming that the null hypothesis is true?

9. How much variance in months to completion is explained by knowing the student’s number of earned degrees?

10. How would you characterize the magnitude of the R2 in the example? Provide a rationale for your answer.

329
Answers to Study Questions
1. The Shapiro-Wilk p value for months to RN to BSN program completion was 0.16, indicating that the frequency distribution did not significantly deviate from normality. Moreover, visual inspection of the frequency distribution indicates that months to completion is approximately normally distributed. See SPSS output below for the histograms of the distribution:

image

2. The null hypothesis is: “The number of earned academic degrees does not predict the number of months until completion of an RN to BSN program.”

3. In the formula y = bx + a, “b” represents the slope of the regression line.

4. In the formula y = bx + a, “a” represents the y-intercept, or the point at which the regression line intersects the y-axis.

5. The predicted months to program completion if a student’s number of academic degrees is 0 is calculated as: ŷ = −2.9(0) + 16.25 = 16.25 months.

6. The predicted months to program completion if a student’s number of academic degrees is 2 is calculated as: ŷ = −2.9(2) + 16.25 = 10.45 months.

7. The correlation between the actual y values and the predicted y values using the new regression equation in the example, also known as the multiple R, is 0.638.

8. The exact likelihood of obtaining a t value at least as extreme as or as close to the one that was actually observed, assuming that the null hypothesis is true, was 0.2%. This value was obtained by looking at the SPSS output table titled “Coefficients” in the last value of the column labeled “Sig.”

9. 40.7% of the variance in months to completion is explained by knowing the student’s number of earned academic degrees at enrollment.

10. The magnitude of the R2 in this example, 0.407, would be considered a large effect according to the effect size tables in Exercises 24 and 25.

330
Data for Additional Computational Practice for the Questions to be Graded
Using the example from Mancini and colleagues (2014), students enrolled in an RN to BSN program were assessed for demographics at enrollment. The predictor in this example is age at program enrollment, and the dependent variable was number of months it took for the student to complete the RN to BSN program. The null hypothesis is: “Student age at enrollment does not predict the number of months until completion of an RN to BSN program.” The data are presented in Table 29-2. A simulated subset of 20 students was randomly selected for this example so that the computations would be small and manageable.

TABLE 29-2

AGE AT ENROLLMENT AND MONTHS TO COMPLETION IN AN RN TO BSN PROGRAM

Student ID x y x2 xy
(Student Age) (Months to Completion)
1 23 17 529 391
2 24 9 576 216
3 24 17 576 408
4 26 9 676 234
5 31 16 961 496
6 31 11 961 341
7 32 15 1,024 480
8 33 12 1,089 396
9 33 15 1,089 495
10 34 12 1,156 408
11 34 14 1,156 476
12 35 10 1,225 350
13 35 17 1,225 595
14 39 20 1,521 780
15 40 9 1,600 360
16 42 12 1,764 504
17 42 14 1,764 588
18 44 10 1,936 440
19 51 17 2,601 867
20 24 11 576 264
sum Σ 677 267 24,005 9,089
image

331
EXERCISE 29 Questions to Be Graded
Name: _______________________________________________________ Class: _____________________

Date: ___________________________________________________________________________________

Follow your instructor’s directions to submit your answers to the following questions for grading. Your instructor may ask you to write your answers below and submit them as a hard copy for grading. Alternatively, your instructor may ask you to use the space below for notes and submit your answers online at http://evolve.elsevier.com/Grove/Statistics/ under “Questions to Be Graded.”

1. If you have access to SPSS, compute the Shapiro-Wilk test of normality for the variable age (as demonstrated in Exercise 26). If you do not have access to SPSS, plot the frequency distributions by hand. What do the results indicate?

2. State the null hypothesis where age at enrollment is used to predict the time for completion of an RN to BSN program.

3. What is b as computed by hand (or using SPSS)?

4. What is a as computed by hand (or using SPSS)?

332
5. Write the new regression equation.

6. How would you characterize the magnitude of the obtained R2 value? Provide a rationale for your answer.

7. How much variance in months to RN to BSN program completion is explained by knowing the student’s enrollment age?

8. What was the correlation between the actual y values and the predicted y values using the new regression equation in the example?

9. Write your interpretation of the results as you would in an APA-formatted journal.

10. Given the results of your analyses, would you use the calculated regression equation to predict future students’ program completion time by using enrollment age as x? Provide

(Grove 319-332)

Grove, Susan K., Daisha Cipher. Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition. Saunders, 022016. VitalBook file.

The citation provided is a guideline. Please check each citation for accuracy before use.

https://eazyweezyhomeworks.com/benefits-of-using-tablets-in-schools/

 

Exercise 35
Calculating Pearson Chi-Square
The Pearson chi-square test (χ2) compares differences between groups on variables measured at the nominal level. The χ2 compares the frequencies that are observed with the frequencies that are expected. When a study requires that researchers compare proportions (percentages) in one category versus another category, the χ2 is a statistic that will reveal if the difference in proportion is statistically improbable.

A one-way χ2 is a statistic that compares different levels of one variable only. For example, a researcher may collect information on gender and compare the proportions of males to females. If the one-way χ2 is statistically significant, it would indicate that proportions of one gender are significantly higher than proportions of the other gender than what would be expected by chance (Daniel, 2000). If more than two groups are being examined, the χ2 does not determine where the differences lie; it only determines that a significant difference exists. Further testing on pairs of groups with the χ2 would then be warranted to identify the significant differences.

A two-way χ2 is a statistic that tests whether proportions in levels of one nominal variable are significantly different from proportions of the second nominal variable. For example, the presence of advanced colon polyps was studied in three groups of patients: those having a normal body mass index (BMI), those who were overweight, and those who were obese (Siddiqui, Mahgoub, Pandove, Cipher, & Spechler, 2009). The research question tested was: “Is there a difference between the three groups (normal weight, overweight, and obese) on the presence of advanced colon polyps?” The results of the χ2 test indicated that a larger proportion of obese patients fell into the category of having advanced colon polyps compared to normal weight and overweight patients, suggesting that obesity may be a risk factor for developing advanced colon polyps. Further examples of two-way χ2 tests are reviewed in Exercise 19.

Research Designs Appropriate for the Pearson χ2
Research designs that may utilize the Pearson χ2 include the randomized experimental, quasi-experimental, and comparative designs (Gliner, Morgan, & Leech, 2009). The variables may be active, attributional, or a combination of both. An active variable refers to an intervention, treatment, or program. An attributional variable refers to a characteristic of the participant, such as gender, diagnosis, or ethnicity. Regardless of the whether the variables are active or attributional, all variables submitted to χ2 calculations must be measured at the nominal level.

410
Statistical Formula and Assumptions
Use of the Pearson χ2 involves the following assumptions (Daniel, 2000):

1. Only one datum entry is made for each subject in the sample. Therefore, if repeated measures from the same subject are being used for analysis, such as pretests and posttests, χ2 is not an appropriate test.

2. The variables must be categorical (nominal), either inherently or transformed to categorical from quantitative values.

3. For each variable, the categories are mutually exclusive and exhaustive. No cells may have an expected frequency of zero. In the actual data, the observed cell frequency may be zero. However, the Pearson χ2 test is sensitive to small sample sizes, and other tests, such as the Fisher’s exact test, are more appropriate when testing very small samples (Daniel, 2000; Yates, 1934).

The test is distribution-free, or nonparametric, which means that no assumption has been made for a normal distribution of values in the population from which the sample was taken (Daniel, 2000).

The formula for a two-way χ2 is:

χ 2 =n[(A)(D)−(B)(C)] 2 (A+B)(C+D)(A+C)(B+D)

image
The contingency table is labeled as follows. A contingency table is a table that displays the relationship between two or more categorical variables (Daniel, 2000):

A B
C D
With any χ2 analysis, the degrees of freedom (df) must be calculated to determine the significance of the value of the statistic. The following formula is used for this calculation:

df=(R−1)(C−1)

image
where

R=Number of rows

image
C=Number of columns

image
Hand Calculations
A retrospective comparative study examined whether longer antibiotic treatment courses were associated with increased antimicrobial resistance in patients with spinal cord injury (Lee et al., 2014). Using urine cultures from a sample of spinal cord–injured veterans, two groups were created: those with evidence of antibiotic resistance and those with no evidence of antibiotic resistance. Each veteran was also divided into two groups based on having had a history of recent (in the past 6 months) antibiotic use for more than 2 weeks or no history of recent antibiotic use.

411
The data are presented in Table 35-1. The null hypothesis is: “There is no difference between antibiotic users and non-users on the presence of antibiotic resistance.”

TABLE 35-1

ANTIBIOTIC RESISTANCE BY ANTIBIOTIC USE

Antibiotic Use No Recent Use
Resistant 8 7
Not resistant 6 21
The computations for the Pearson χ2 test are as follows:

Step 1: Create a contingency table of the two nominal variables:

Used Antibiotics No Recent Use Totals
Resistant 8 7 15
Not resistant 6 21 27
Totals 14 28 42 ←Total n
image

Step 2: Fit the cells into the formula:

χ 2 =n[(A)(D)−(B)(C)] 2 (A+B)(C+D)(A+C)(B+D)

image

χ 2 =42[(8)(21)−(7)(6)] 2 (8+7)(6+21)(8+6)(7+21)

image

χ 2 =666,792158,760

image

χ 2 =4.20

image

Step 3: Compute the degrees of freedom:

df=(2−1)(2−1)=1

image

Step 4: Locate the critical χ2 value in the χ2 distribution table (Appendix D) and compare it to the obtained χ2 value.

The obtained χ2 value is compared with the tabled χ2 values in Appendix D. The table includes the critical values of χ2 for specific degrees of freedom at selected levels of significance. If the value of the statistic is equal to or greater than the value identified in the χ2 table, the difference between the two variables is statistically significant. The critical χ2 for df = 1 is 3.84, and our obtained χ2 is 4.20, thereby exceeding the critical value and indicating a significant difference between antibiotic users and non-users on the presence of antibiotic resistance.

Furthermore, we can compute the rates of antibiotic resistance among antibiotic users and non-users by using the numbers in the contingency table from Step 1. The antibiotic resistance rate among the antibiotic users can be calculated as 8 ÷ 14 = 0.571 × 100% = 57.1%. The antibiotic resistance rate among the non-antibiotic users can be calculated as 7 ÷ 28 = 0.25 × 100% = 25%.

412
SPSS Computations
The following screenshot is a replica of what your SPSS window will look like. The data for subjects 24 through 42 are viewable by scrolling down in the SPSS screen.

image

413
Step 1: From the “Analyze” menu, choose “Descriptive Statistics” and “Crosstabs.” Move the two variables to the right, where either variable can be in the “Row” or “Column” space.

image

Step 2: Click “Statistics” and check the box next to “Chi-square.” Click “Continue” and “OK.”

image

414
Interpretation of SPSS Output
The following tables are generated from SPSS. The first table contains the contingency table, similar to Table 35-1 above. The second table contains the χ2 results.

Crosstabs
image

image

The last table contains the χ2 value in addition to other statistics that test associations between nominal variables. The Pearson χ2 test is located in the first row of the table, which contains the χ2 value, df, and p value.

Final Interpretation in American Psychological Association (APA) Format
The following interpretation is written as it might appear in a research article, formatted according to APA guidelines (APA, 2010). A Pearson χ2 analysis indicated that antibiotic users had significantly higher rates of antibiotic resistance than those who did not use antibiotics, χ2(1) = 4.20, p = 0.04 (57.1% versus 25%, respectively). This finding suggests that extended antibiotic use may be a risk factor for developing resistance, and further research is needed to investigate resistance as a direct effect of antibiotics.

415
Study Questions
1. Do the example data meet the assumptions for the Pearson χ2 test? Provide a rationale for your answer.

2. What is the null hypothesis in the example?

3. What was the exact likelihood of obtaining a χ2 value at least as extreme or as close to the one that was actually observed, assuming that the null hypothesis is true?

4. Using the numbers in the contingency table, calculate the percentage of antibiotic users who were resistant.

5. Using the numbers in the contingency table, calculate the percentage of non-antibiotic users who were resistant.

6. Using the numbers in the contingency table, calculate the percentage of resistant veterans who used antibiotics for more than 2 weeks.

416
7. Using the numbers in the contingency table, calculate the percentage of resistant veterans who had no history of antibiotic use.

8. What kind of design was used in the example?

9. What result would have been obtained if the variables in the SPSS Crosstabs window had been switched, with Antibiotic Use being placed in the “Row” and Resistance being placed in the “Column”?

10. Was the sample size adequate to detect differences between the two groups in this example? Provide a rationale for your answer.

417
Answers to Study Questions
1. Yes, the data meet the assumptions of the Pearson χ2:

a. Only one datum per participant was entered into the contingency table, and no participant was counted twice.

b. Both antibiotic use and resistance are categorical (nominal-level data).

c. For each variable, the categories are mutually exclusive and exhaustive. It was not possible for a participant to belong to both groups, and the two categories (recent antibiotic user and non-user) included all study participants.

2. The null hypothesis is: “There is no difference between antibiotic users and non-users on the presence of antibiotic resistance.”

3. The exact likelihood of obtaining a χ2 value at least as extreme as or as close to the one that was actually observed, assuming that the null hypothesis is true, was 4.0%.

4. The percentage of antibiotic users who were resistant is calculated as 8 ÷ 14 = 0.5714 × 100% = 57.14% = 57.1%.

5. The percentage of non-antibiotic users who were resistant is calculated as 7 ÷ 28 = 0.25 × 100% = 25%.

6. The percentage of antibiotic-resistant veterans who used antibiotics for more than 2 weeks is calculated as 8 ÷ 15 = 0.533 × 100% = 53.3%.

7. The percentage of resistant veterans who had no history of antibiotic use is calculated as 6 ÷ 27 = 0.222 × 100% = 22.2%.

8. The study design in the example was a retrospective comparative design (Gliner et al., 2009).

9. Switching the variables in the SPSS Crosstabs window would have resulted in the exact same χ2 result.

10. The sample size was adequate to detect differences between the two groups, because a significant difference was found, p = 0.04, which is smaller than alpha = 0.05.

418
Data for Additional Computational Practice for Questions to be Graded
A retrospective comparative study examining the presence of candiduria (presence of Candida species in the urine) among 97 adults with a spinal cord injury is presented as an additional example. The differences in the use of antibiotics were investigated with the Pearson χ2 test (Goetz, Howard, Cipher, & Revankar, 2010). These data are presented in Table 35-2 as a contingency table.

TABLE 35-2

CANDIDURIA AND ANTIBIOTIC USE IN ADULTS WITH SPINAL CORD INJURIES

Candiduria No Candiduria Totals
Antibiotic use 15 43 58
No antibiotic use 0 39 39
Totals 15 82 97
image

419
EXERCISE 35 Questions to Be Graded
Name: _______________________________________________________ Class: _____________________

Date: ___________________________________________________________________________________

Follow your instructor’s directions to submit your answers to the following questions for grading. Your instructor may ask you to write your answers below and submit them as a hard copy for grading. Alternatively, your instructor may ask you to use the space below for notes and submit your answers online at http://evolve.elsevier.com/Grove/statistics/ under “Questions to Be Graded.”

1. Do the example data in Table 35-2 meet the assumptions for the Pearson χ2 test? Provide a rationale for your answer.

2. Compute the χ2 test. What is the χ2 value?

3. Is the χ2 significant at α = 0.05? Specify how you arrived at your answer.

4. If using SPSS, what is the exact likelihood of obtaining the χ2 value at least as extreme as or as close to the one that was actually observed, assuming that the null hypothesis is true?

420
5. Using the numbers in the contingency table, calculate the percentage of antibiotic users who tested positive for candiduria.

6. Using the numbers in the contingency table, calculate the percentage of non-antibiotic users who tested positive for candiduria.

7. Using the numbers in the contingency table, calculate the percentage of veterans with candiduria who had a history of antibiotic use.

8. Using the numbers in the contingency table, calculate the percentage of veterans with candiduria who had no history of antibiotic use.

9. Write your interpretation of the results as you would in an APA-formatted journal.

10. Was the sample size adequate to detect differences between the two groups in this example? Provide a rationale for your answer.

(Grove 409-420)

Grove, Susan K., Daisha Cipher. Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition. Saunders, 022016. VitalBook file.

The citation provided is a guideline. Please check each citation for accuracy before use.

 

Need answers of questions to be graded at the end of each exercise.

Write my nursing essay

/in /by

Write my nursing essay

As a professional nurse, you are expected to apply your expertise to patient care. On occasion, you will also be expected to share that expertise.

With evolving technology and continuous changes to regulations designed to keep up these changes, there is usually a need to share information and expertise to inform colleagues, leadership, patients, and other stakeholders.

In this Assignment, you will study a recent nursing informatics-related healthcare policy, and you will share the relevant details via a fact sheet designed to inform and educate.

To Prepare:

Review the Resources on healthcare policy and regulatory/legislative topics related to health and nursing informatics.
Consider the role of the nurse informaticist in relation to a healthcare organization’s compliance with various policies and regulations, such as the Medicare Access and CHIP Reauthorization Act (MACRA).
Research and select one health or nursing informatics policy (within the past 5 years) or regulation for further study.
The Assignment: (1 page)

Create a 1-page fact sheet that your healthcare organization could hypothetically use to explain the health or nursing informatics policy/regulation you selected. Your fact sheet should address the following:

Briefly and generally explain the policy or regulation you selected.
Address the impact of the policy or regulation you selected on system implementation.
Address the impact of the policy or regulation you selected on clinical care, patient/provider interactions, and workflow.
Highlight organizational policies and procedures that are/will be in place at your healthcare organization to address the policy or regulation you selected. Be specific.

https://eazyweezyhomeworks.com/nursing-ethics-in-clinical-practice/

Discussion

1. Post an explanation of at least two opportunities that currently exist for RNs and APRNs to actively participate in policy review. Explain some of the challenges that these opportunities may present and describe how you might overcome these challenges. Finally, recommend two strategies you might make to better advocate for or communicate the existence of these opportunities. Be specific and provide examples.

 

 

2. Post a description of what you believe to be the consequences of a healthcare organization not involving nurses in each stage of the Systems Development Life Cycle (SDLC) when purchasing and implementing a new health information technology system. Provide specific examples of potential issues at each stage of the SDLC and explain how the inclusion of nurses may help address these issues. Then, explain whether you had any input in the selection and planning of new health information technology systems in your nursing practice or healthcare organization and explain potential impacts of being included or not in the decision-making process. Be specific and provide examples.

BBA Project Management Research Paper

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BBA Project Management Research Paper. Unit VI Research Paper

BBA Project Management Research Paper. Conduct research using the CSU Online Library, and find at least two articles on handling project conflict management. In your paper, identify the different styles you found in your research, and compare and contrast each style. Identify either low, medium, or high for concern for self and concern for others in your paper. Also, include a synopsis of each article to include when appropriate for projects. See exhibit 13.11, located on page 371 of your textbook, for an example.

Be sure to use APA format and cite your work. Your paper should be at least three pages in length and will include a title and reference page which are not included in the page count.

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Assignment 2

As the project manager, the project team is looking to you for direction on each of these issues. Review the comments of the key players, consult the grading rubric below and then answer the following questions.

  1. Is Bear or is ZAP responsible for the inspections? What is a reasonable course of action given that the project may have to be delayed a week or more?
  2. What carpet should be installed? Should work be stopped to evaluate this issue? What do you do to resolve this?
  3. How much is owed to Sheba? What actions would you take as the project manager?

Assignment 3

800 to 1000  MEMO with visual representation of an example supply chain network using the “Shape” or SmartArt” option in MS Word.

 

The warehouse manager thought a recent presentation on operations management was extremely valuable to the company. He now wants to shift the conversation to focus on his warehouse department. You told him a little about supply chain design and how it could improve inventory management. He is interested in hearing more about supply chain design and wants his department staff to also be aware of this valuable resource. He has asked you to create a memo that will be sent out to everyone in his department.

Draft a memo explaining to your warehouse managers how effective supply chain design could help to enhance profitability and stakeholder value for the company, including the following:

  • Provide an analysis of the behaviors of supply chain networks and supply chain drivers in your memo.
  • Explain how supply chain impacts distribution of assets and resources.
  • The memo should include a visual representation of an example supply chain network using the “Shape” or SmartArt” option in MS Word.