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A Critique on Drug Testing in Employment by Joseph Desjardins and Ronald Duska Essays

A Critique on Drug Testing in Employment by Joseph Desjardins and Ronald Duska Essays A Critique on Drug Testing in Employment by Joseph Desjardins and Ronald Duska Paper A Critique on Drug Testing in Employment by Joseph Desjardins and Ronald Duska Paper Comparable to this, the creators current circumstances wherein it is permissible to demand a worker to submit to sedate testing however again it may not be expected of the representative. Additionally precluded are the utilization of coercive measures to cause the representative to submit to medicate testing, for example, the danger of losing business or even certain work benefits. It must be brought up that while the creators do stand firm for the assurance of the security of workers, the contentions that have been introduced must be dismissed for absence of adequate premise and choices for the accompanying reasons. As a matter of first importance, no right, even those conceded by the constitution, is outright. Each correct that an individual is allowed is constantly dependent upon specific impediments and limitations. Similarly that a person’s right to security might be attacked on the quality of a court order. The setting of being in a work environment isn't so very different that it is fit for being given an alternate treatment. Truth be told, more limitations on the privilege to security can even be forced in view of the setting. It must be recollected that in the circle of human rights, there is a relationship between's the privileges of one individual and that of another. One is just allowed to act inside the limits of his protection or rights as long as such acts don't unduly or unnecessarily meddle with the privileges of others. As the creators would contend, sedate testing can be actualized however the interest by the workers must be deliberate. This view can't be continued in accordance with the contention that no privilege is total. The purpose for this is there is a more noteworthy worry behind the entirety of this and this is open wellbeing. While it might be a restriction on one’s security, it is for a more prominent reason; a reason that everybody in the nation has submitted to and pledged to maintain. The second and maybe all the more convincing motivation behind why medication testing ought not be made willful is the way that it doesn't in fact disregard the established right to security. The creators expand the inclusion of the privilege to security to medicate testing. In a long queue of cases chose by the United States Supreme Court, it has been reliably held that physical testing of an individual can be constrained. There is no infringement of the privilege to security for this situation except if the test was completed without fair treatment. For this situation, requiring a worker who is associated with ingesting destructive medications or those that can influence work execution can be required without stress of abusing the employee’s right to protection. While the contentions introduced by the writers are not plainly validated in the article, the must, in any case, despite everything be commended for their endeavors in attempting to maintain the individual’s right to security. Medication testing can be utilized as a methods for irritating representatives or even as a method of terminating those workers who are inadmissible without experiencing the whole legitimate procedure of pulling out. No legitimate framework, no plan of action is great. There will consistently be a battle between privileges of representatives and that of the businesses. The arrangement might be far away however one thing stays clear. Until and except if a specific trade off can be made to accordingly adjust these relating rights there will be more contention encompassing this issue. The representative is as of now very much ensured under the Labor Laws of this land and his exertion is enormously refreshing yet one should likewise consider that without the business or capital the greater part of these workers would not have any occupations whatsoever.

Predictive Analytics How to Forecast the Future

Predictive Analytics How to Forecast the Future One of the most popular features of Big Data is predictive analytics. Far from the latest business buzzword, predictive analytics is a set of techniques that have become fundamental to the business strategies of many household name brand firms, such as Netflix, Google, and Amazon. These firms, and many others, dominate their respective markets, due in large part to the significant use of predictive analytics.Predictive analytics is a form of business intelligence gathering, the strategic business use of which is powerful enough to upend an industry. Driven by the tremendous-revenue generating potential of predictive analytics, more firms are investing in the necessary infrastructure, such as data storage and processing hardware and software and both database administrators and data analysts. As they do so, predictive analytics tools and techniques, grow in sophistication and refinement.Moreover, as more firms adopt predictive analytics, and incorporate it into their existing strategi es, they fuel its widespread adoption, as competitors must adopt it or risk losing significant market share. © Shutterstock.com | ImageFlowIn this article, we will cover 1) the definition of predictive analytics; 2) discuss data analysis; and 3) the types of predictive analytics; as well as cover 4) using predictive analytics; 5) the benefits of predictive analytics; 6) the risks of predictive analytics; and 7) a real-life example of a firm using predictive analytics.WHAT IS PREDICTIVE ANALYTICS?Predictive analytics is an assortment of statistical and mathematical techniques used to predict the probability of future events occurring. Fundamentally, statisticians and data scientists combine and standardize a variety of historical datasets to develop correlative statistical models that firms, research organizations, and even governments use to forecast a wide range of phenomena.The field’s origins lie in the beginnings of the computer age in the 1940s, specifically with the U.S. government’s use of computational models during World War II. Notable examples include the development of the Kerrison Predictor in 1940, which automated anti-aircraft weapon targeting, and the use of computer simulations by the Manhattan Project to determine the probable results of nuclear chain reactions in 1944.Just as computers and computing technology have grown exponentially since then, so too has the field of predictive analytics. In 2012 alone, technology users generated 2.5 exabytes of data per day â€" an estimated three-quarters of which is text, audio, or video messages. That’s a lot of data for firms to leverage, and with data storage prices and space requirements having shrunk exponentially since the 1940s (indeed, from even a decade ago), the adoption of predictive analytics is an increasingly cost-effective proposition â€" if not, exactly a simple one.Eric Siegel answers eight questions about predictive analytics DATA ANALYSISIn addition to either developing the necessary infrastructure in-house to leverage predictive analytics, or outsourcing their business intelligence ga thering, a firm must determine what questions they will use predictive analytics to answer. Predictive analytics, whether done externally or internally, is costly in terms of time and labor, as the answers to these questions are the result of intensive research, involving multiple datasets with many variables.It is important for data scientists to be able to link and visualize datasets in order to interpret them better. While computers have gotten faster and better at processing vast amounts of data, human insights lie at the root of the answers to Big Data questions. It is also important to understand that the answers to predictive analytics are, for the most part, correlative, not causative, by nature. This means that data scientists are looking at the probability of an event based on the event happening under similar conditions. A failure to understand the deeper underlying reasons â€" the causes â€" of the event, can lead to inaccurate predictions.TYPES OF PREDICTIVE ANALYTICSTh ere are several types of predictive analytics methods, including predictive modeling, design analysis and optimization, transaction profiling, and predictive search.Predictive ModelingWhen most laypeople discuss predictive analytics, they are usually discussing it in terms of predictive modeling. Indeed, predictive modeling is at the heart of predictive analytics, and has been popularized in science fiction as well as by the financial services industry.It involves mathematically modeling associations between variables in historical data, in order to predict or forecast the likelihood of a future event. Commonly used in the financial services industry to predict the behavior of capital markets, predictive analytics is increasingly being used for sales and revenue forecasting, dynamic pricing, online recommendation systems, strategic planning, and other business areas requiring decision-making about the future.Predictive modeling yields the probabilities of event occurrences based on previous event occurrences; as such there is no guarantee that a desired event will occur (or conversely an undesired event will fail to occur). Understanding this can reduce overreliance on the models.Decision analysis and optimizationDecision analysis and optimization is a subfield of predictive analytics that deals with reducing the uncertainty inherent in decision-making. Specifically, it involves aspects of a decision, and/or multiple decisions to determine the one likely to yield the most success. Firms often use decision analysis and optimization in functional areas, such as supply chain management to ensure the firm’s decisions maximize revenue and result in a firm achieving and/or exceeding other key performance goals.For example, a distribution chain optimization problem might involve determining the ideal mix of online and brick-and-mortar retailers to use to achieve a target revenue goal. Using SAS Analytics, IBM SPSS Modeler, another popular predictive modeling applic ation suite, or internal proprietary software, a data scientist can import multiple datasets (such as historical wholesale prices, local and online retailers, distribution costs by distribution method, and more), build models, and test and retest results.Transaction profilingTransaction profiling involves aggregating and filtering information from transactions involving enterprise software. These can include, but are not limited to, credit card transactions on an online retailer’s website, and logins to a proprietary social network; there are often isolated datapoints. This subfield involves standardizing this data and clustering it with relevant data in ways that can allow a firm to create predictive models of transactional data.Predictive searchPredictive search, fundamentally, involves creating algorithms that take one set of inputs and finds a particular output. However, the increasing sophistication, and in some cases, the incompleteness, of inputs requires algorithms that re turn the best possible answer.To illustrate this, consider two co-workers. The first asks the second for a restaurant suggestion for a business lunch. The second can make the recommendation based on their knowledge of the first co-workers personal preferences, likes/dislikes, and knowledge of the area. A search engine, hypothetically, has realms of data to make a strong recommendation, such as the user’s geographic location, online mentions of personal preferences.Further, the second co-worker might immediately realize, that the first co-worker might actually need a vegetarian restaurant for this particular meeting. Predictive search also involves deep dives into multiple datasets to provide you with a personalized output that gets at the underlying reason for your input. Ideally, a search query might “recognize” that the restaurant recommendation is likely for a particular meeting on your online calendar, further “recognize” that the client is a vegetarian, and return res taurants that fit this need. Predictive search developments will harness more and more data in assessing the best possible answer to return.USING PREDICTIVE ANALYTICSPredictive analytics can be used for a variety of business strategies, and has even give rise to many business models, such as search, search advertising, and recommendation engines. Firms must determine the costs and benefits of developing the in-house capabilities to do this, or outsourcing their Big Data needs to a third-party market research firm. Both approaches have time, cost and labor benefits and drawbacks for any firm; however, with other firms increasingly using predictive analytics, each firm will have to map its Big Data strategy now or in the near future. Once a strategy has been determined, the firm must determine what insights will best inform their strategy and then use predictive analytics to obtain them.BENEFITS OF PREDICTIVE ANALYTICSPredictive analytics benefit any decision by providing executives, managers and other decision-makers with the tools to make the best possible decision. Some applications include, but are not limited to predictions of customer purchasing likelihood, for use in targeted marketing and upselling; sales and revenue forecasting; optimize marketing channel, supply chain, distribution chain, and manufacturing optimization; and new product development.Really, there are no limits to the potential applications of predictive analytics for optimization and forecasting. Even scientific organizations and governments have begun to invest in the resources necessary to leverage predictive analytics.RISKS OF PREDICTIVE ANALYTICSThere are several risks to using predictive analytics, though most stem from overreliance on this set of tools. Executives and managers must understand that predictive analytics involves probabilities and correlation, which are not absolute. Data scientists must strive to filter out all of the noise from datasets to ensure accurate and replic able modeling results. They must further strive to present these results as actionable insights with risk parameters for each choice.Asking the wrong questionsAwash in reams of data, it is critical that firms ask the right questions. Predictive analytics is most efficient when used to determine the answer to a narrow inquiry, such as the likelihood of customer A to buy product X at time Y for price Z, rather than the likelihood of customers buying product X (as might be asked by a layman). Further, data scientists must be able to test assumptions and pivot quickly from erroneous ones. For example, if a question involves the impact of a marketing technique on sales â€" one favored by the CEO and widely assumed to have a significant impact, and later studies determine it has no effect, the data scientist must be able to assess the remainder of the question freely.Data scientists must take the general questions that may come from executives and managers and extract the root business ne ed. To fulfill this need, they must use the data to create appropriate recommendations by determining the appropriate datasets, filter out extraneous information, build models, and test and retest them.Bad dataData scientists must be aware that not all data is accurate, arrive at an estimate of bad data, and correct for it in their studies. Data can be bad for any number of reasons, including self-reporting errors, corrupted files, poorly phrased questions, incomplete data aggregation, and poor standardization methods.It is critical that data scientists quickly recognize and filter bad data from their data sets. They must also make sure they do not create bad data themselves â€" for example through an imperfectly calculated transformation function. Further, they must take the time to improve aggregation and standardization methods to limit the collection of bad data. Without reasonably accurate data, data scientists cannot build predictive analytics models whose assumptions will hol d.Complexity and unpredictabilityBig Data is messy, consisting of everything from social media mentions to traffic camera images to website logs. Predictive analytics, being a set of statistical techniques, requires all data to be standardized and quantified. Quantifying non-numeric data has its own risks and creates uncertainty.Further, data is unpredictable, especially dynamic data. A model that accurately forecasts future events could be thrown into disarray by a sudden unanticipated cascade of events, which were not initially estimated. Such was the case in 2007, when the majority of financial services firms failed in incorporate the possibility of sudden credit defaults, which triggered a series of other events that prior to 2007 would have been improbable.Privacy and securityMany privacy advocates find such data usage invasive and alarming. There is something inherently intrusive about firms collecting information about individuals in order to predict their behavior. Advocacy efforts include lobbying for limitations to data collection types, amounts and methods in nations across the globe. Executives and data managers must be aware of the ever-changing Big Data regulatory landscape.Privacy is a huge concern for another reason â€" security. Hackers target data storage devices and facilities for financial gain, ideological reasons, and thrills. With many nations holding firms at least partially responsible for the damage caused by loss of secured data, firms must ensure they keep up-to-date with the latest data security measures. If they outsource their data analysis to a business intelligence vendor, they are likewise compelled to ensure that the business intelligence vendor secures the firm’s data appropriately.CASE STUDY © pixabay | WikiImagesPredictive analytics are a major source of competitive advantage for Amazon, so much so that Amazon has taken market share from many brick and mortar retailers across the U.S., and even other parts of the world. Amazon uses predictive analytics to power its recommendation algorithms that help the retailing giant upsell, as well as to make its distribution system more efficient.Amazon provides site visitors with product recommendations based on your viewing history. As that viewing history grows, Amazons algorithms, using the increased data, create increasingly useful and accurate recommendations. The firm also offers discounted pricing, and/or package deals in order to entice you upsell, as well as premium pricing when demand is high and inventory is low.Beyond Amazon’s on-screen predictive analytics applications, the retailer has begun to ship products in advance of customer orders, based on the results of its predictive models. Amazon filed a patent on a †œmethod and system for anticipatory package shipping” in 2012, designed to increase the efficiency of its distribution chain. By harnessing this method during peak volume periods, such as the holidays, Amazon, whose predictive analytics models have already demonstrated a high probability of accuracy, can ensure that it has the inventory on hand to distribute and that goods are distributed beforehand, minimizing customer dissatisfaction.Amazon’s use of predictive analytics has been instrumental in its dominance of the online retail space in the U.S., in which it is the market leader as of 2014, with net sales of nearly $60 billion.

22 Causas de Negación de Ingreso a USA por Inadmisibilidad

Las autoridades de los Estados Unidos pueden negar a cualquier extranjero el ingreso a Estados Unidos  con una o varias  causas de inadmisibilidad. Esto aplica incluso a los residentes permanentes legales. Asimismo, puede aplicar tanto a los que està ¡n fuera del paà ­s como los que ya se encuentran en su interior. Este es una situacià ³n grave y, por ello, este artà ­culo informa sobre cuà ¡les son las causas de inadmisibilidad, cà ³mo surge el problema y dà ³nde y, finalmente, quà © se puede hacer para solucionar el problema. Antes de comenzar, seà ±alar que es muy importante distinguir las  causas de inadmisibilidad, la razà ³n que se da està ¡ seà ±alada con un nà ºmero  212(a),  de las causas de inelegibilidad  que hacen que el cà ³nsul rechace una solicitud de visa por motivo calificado en los documentos oficiales  como 214. 22 causas de inadmisiblidad que impiden el ingreso en Estados Unidos Las causas de inadmisibilidad pueden aplicar tanto a las visas no inmigrante, tipo turista, estudiante, trabajo, intercambio, etc como a las visas de inmigrante, para obtener la green card o tarjeta de residencia. Incluso pueden aplicar a personas que se encuentran legalmente en los Estados Unidos y que solicitan un cambio de estatus. Por ejemplo, una persona con visa H1B que pide la residencia permanente mediante un ajuste de estatus. Las causas de inadmisibilidad, que se conocen en inglà ©s con el nombre de grounds of inadmissibility, son: Carga pà ºblica. Sospecha de que es posible convertirse en una fuente de gasto para las arcas pà ºblicas de Estados Unidos. Por ejemplo, personas enfermas, muy mayores, etc.Sufrir una enfermedad contagiosa, como por ejemplo, tuberculosis.Sufrir una enfermedad fà ­sica o mental que convierta al enfermo en un peligro para otras personas.Consumo de drogas. La expresià ³n que utilizan las autoridades de inmigracià ³n es abusador de drogas y por eso se entiende haber consumido una sustancia ilà ­cita mà ¡s de una vez en los à ºltimos tres aà ±os.Haber cometido o haber sido condenado por un delito inmoral.Haber sido condenado por varios delitos.Haber sido condenado por delitos especà ­ficos tales como trà ¡fico de drogas.Ser familiar de un traficante de drogas si se ha beneficiado de las ganancias de esa actividad en los à ºltimos cinco aà ±os.Haber cometido espionaje o sabotaje.Haber cometido o haber sido condenado por un delito agravado.Haber sido miembro de un partido polà ­tico totalitario, como por ejemplo, un partido comunista, o de un partido nazi.Haber participado en un genocidio.Haber asegurado falsamente que se es ciudadano americano.Haber violado una ley de inmigracià ³n. Hay muchos ejemplos de esta circunstancia, por ejemplo, trabajar en Estados Unidos con una visa que no lo autoriza (turista, etc.)Haber cometido fraude migratorio. Bajo esta categorà ­a caben acciones muy distintas. Presentar documentos falsos ante un oficial migratorio o un consulado es una de ellas. Las mentiras en este contexto pueden resultar muy caras.Estar ilegalmente en Estados Unidos o haber estado si todavà ­a no se cumplià ³ el plazo de la penalidad.Haber sido deportado o expulsado.Haber ingresado a los Estados Unidos sin tener la documentacià ³n necesaria.Estar casado con mà ¡s de una persona al mismo tiempo. Estos son los casos de bigamia y poligamia.Haber realizado un secuestro internacional de nià ±os. Esto ocurre con frecuencia en el caso de papà ¡s y mamà ¡s que no se ponen de acuerdo dà ³nde deben vivir los nià ±os. El problema es que sin darse cuenta se puede estar cometiendo ese delito, que es muy grave.Si se ha tenido una visa de intercambio J-1  y se està ¡ sujeto a la obligacià ³n de residir fuera de Estados Unidos por dos aà ±os.Ser un peligro para la seguridad nacional de los Estados Unidos. Esto incluye pertenencia a pandillas  (gangas). Cabe destacar dos novedades. Por un lado, las nuevas reglas sobre carga pà ºblica, que està ¡n siendo aplicadas desde el 15 de octubre de 2019 por embajadas y consulados tanto para visas inmigrantes como visas temporales no inmigrantes. Por otro lado, esta regla de la carga pà ºblica està ¡ en suspenso, al menos por el momento, por orden judicial dentro de EE.UU. y no pueden ser aplicadas ni por USCIS ni por el Departamento de Seguridad Interna. Por otro lado, a partir del 3 de noviembre de 2019, los consulados y las embajadas podrà ¡n negar las visas de inmigrante para la residencia si el solicitante no puede demostrar en la entrevista que puede adquirir seguro mà ©dico segà ºn los parà ¡metros del Departamento de Estado en los 30 dà ­as siguientes a su ingreso en EE.UU. Quà © puede suceder en estos casos de inadmisibilidad Puede pasar tres cosas: Primero: la visa es denegada por un oficial consular. Tener en cuenta que la visa puede ser rechazada, ademà ¡s, por otras causas, cuando se cree que no se cumplen los requisitos para obtenerlos. Segundo: el oficial de Inmigracià ³n de la CBP (Policà ­a Fronteriza) en un puerto de entrada (aeropuerto, puerto o frontera terrestre) prohà ­be el ingreso tras consultar su completà ­sima base de datos. En estos casos pueden darse dos situaciones: Si se llega al puerto de entrada con una visa o una green card  và ¡lida, en algunas circunstancias es posible solicitar presentarse ante un juez de inmigracià ³n y, en su caso, apelar su decisià ³n ante la Corte de Apelaciones Migratorias.. Pero no siempre es posible. Por ejemplo, en casos de fraude o de haber asegurado falsamente ser ciudadano americano la decisià ³n del oficial de Inmigracià ³n es final. Pero tambià ©n puede suceder que se proceda a regresar a la persona inmediatamente a su lugar de procedencia. Las razones pueden ser varias, como en el ejemplo anterior. Pero tambià ©n sucede en caso como en los que no se tiene visa porque se es de un paà ­s del Programa de Exencià ³n de Visados la decisià ³n del oficial de la CBP es tambià ©n final y no se podrà ¡ solicitar comparecer ante un juez. Y tercera posibilidad: si ya se està ¡ dentro de Estados Unidos, las autoridades migratorias pueden proceder a la remocià ³n de la persona en esa situacià ³n. Quà © se puede hacer en los casos de inadmisibilidad Para algunos de estos supuestos es posible pedir un perdà ³n migratorio, tambià ©n conocido como waiver.  Por ejemplo, en casos de prostitucià ³n, enfermedades contagiosas, riesgo de ser una carga pà ºblica, condenas por delitos inmorales o, incluso, mà ºltiples condenas por delitos. Pero es muy importante entender que las reglas son distintas segà ºn los casos, que no es lo mismo pedir una visa no inmigrante que una inmigrante y que los perdones son medidas excepcionales.  Y tambià ©n hay que tener presente que el poder solicitar un perdà ³n no quiere decir que se vaya a obtener su aprobacià ³n. En estos casos es muy importante contar con la asesorà ­a de un abogado competente y con un buen rà ©cord, que no prometa cosas que, sencillamente, no pueden ser porque la ley no lo permite. Ademà ¡s, tener en cuenta que hay causas de inadmisibilidad para los que no es posible jamà ¡s pedir un perdà ³n. Por ejemplo, trà ¡fico de drogas, terrorismo o espionaje. Finalmente, es realmente aconsejable conocer cà ³mo aplica el castigo de inadmisibilidad de los 3 y de los 10 aà ±os por presencia ilegal en los Estados Unidos y el castigo de la prohibicià ³n permanente. Este à ºltimo es frecuentemente ignorado pero afecta a muchas personas y las consecuencias son muy graves. Este es un artà ­culo informativo. No es asesorà ­a legal.

Application of Ethical Theories - 12285 Words

The role of ethical theories in ethical reasoning and behavior within organizations - Research proposal Sigalit Pasternak, Phd student The Faculty of Management Tel Aviv University Supervisor: Dr. Ishak Saporta Introduction Business ethics is a specialized branch of ethics focusing on how moral standards apply to business organizations and behavior (Velasques, 1998). As such, it cannot be understood separately from the general ideas of ethics, and the general ethical theories apply to business ethics as well (Hunt Vitell, 1986; Fritzsche Becker, 1984; Schumann, 2001; Lahdesnati, 2005). Normative ethical theory offers different moral theories, each prescribing a set of moral rules that individuals can apply in the process of deciding†¦show more content†¦Finally, most of the empirical research on the connection between ethical theories and ethical reasoning is carried out in separation from research on the ethical decision-making process. Although there is a consensus as to the role of important individual and contextual components on ethical decision making within organizations (for review, see Kish – Gephart, Harrison and Trevino, 2010 Meta analysis), relatively little is known about the effect of these components on ethical reasoning within organizations. The third objective of this research is to explore the link between different individual and environmental factors and the application of different ethical theories in ethical reasoning. The proposed research can generate a theoretical contribution to the literature on ethical decision making within organizations in several ways. First, the research attempts to resolve the differences in past research finding in regard to the role of ethical theories in ethical reasoning by examining the connection between the specific content and context of ethical dilemmas and the ethical rule or theory applied by individuals to explain their resolution. Secondly, itShow MoreRelatedThe Application Of Ethical Theories Essay1443 Words   |  6 PagesPASS THE INSPECTION: THE APPLICATION OF ETHICAL THEORIES TO AN ETHICAL DILEMA Discussion with Senior Enlisted Leader This is an instance of senior enlisted, who you should be able to trust, giving you bad advice. When the situation is hypothetical and not a pressing issue, it is easy to see that it would be wrong to allow the Chief to sign off on the maintenance checks. But in the moment, there are good reasons to have the checks signed off. Having the maintenance appear to be complete makes yourRead MoreEthical Theories and Application580 Words   |  2 Pagesï » ¿ETHICAL THEORIES AND APPLICATION Virtue Ethics Virtue ethics consider only the motivation of the acts and not whether or not they are consistent with rules or whether those acts result in benefit or harm to others (Hursthouse, 2003). 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In certain cases, the right and good as well as the ethical rules and values are sometimes common to various ethical theories. Even though ethical theories have different reasons for application, there is an overlap in these theories that result in similar conduct in an ethical situation. There are various ethical theories with differences on how they

Psychology As A Science Free Essays

Psychology being categorised under the name science, can often lead to disputes within the field of sciences. Psychology is the observation of behaviour and thought process of the human mind, within itself it is a vital source of knowledge, such as how biology, chemistry and physics provides a source of knowledge that is vital to humans and the environment. Science can be seen as the study of natural behaviours and physical aspects of the world, this definition within itself accompanies itself with the idea that psychology is a science, as behaviours are studied within the field of psychology. We will write a custom essay sample on Psychology As A Science or any similar topic only for you Order Now Eysenck and Keane (2000) believed that to make something a science it must have the following features, controlled observation, in which a specific manipulation is observed to see the effects. Secondly objectivity, as when data has been collected objectively it reduces the possibility of bias, thirdly testing theoretical predictions, because if a theory is not tested there is no evidence to provide if it is right or wrong. Fourthly is falsifiability, which means the scientific theory has the potential to be proved wrong by evidence, fifthly is the unifying theory which is every subject within the sciences has a unifying approach all theories are based off. Finally there is the fact of is any research conducted replicable, as it is hard to rely on studies that could provide inconsistent findings. Although providing clear guidelines on what makes a science, there are still some aspects which make the divide not as clear as believed. For example psychology uses the scientific method in some of the studies conducted, which is used throughout science for all research, so this aspect can be seen to make psychology a science. Too many the field of psychology is classed as a science; the science of the mind, as it looks at the most complex thing on Earth, the human mind, all theories on behaviours and thoughts stem from psychology (BBC, 2013). In many areas psychology and the three sciences (physics, biology and chemistry) have similarities, for example, the sciences can be seen as reductionist as they try to take a complex behaviour or physical problem and break it down in to a simpler form. Many theories within psychology on similar problems can also be seen as reductionist as it aims to take complex behaviours and thoughts and break it down in to easier components to study. An example of this can be shown by Freud (1909), Freud believes behaviour stems from the unconscious mind, making it a reductionist as it does not take biology or other factors in to account. Reductionism can be seen to be an advantage when it comes to conducting a study as it means testable predictions can be created, and then can be carried out in a controlled experiment. Although by making a reductionist theory can also cause disadvantages such as falsifiability. Popper (1963) believed falsifiability was key to science, as science does not seek to prove its own theory right, but tries to confirm it as wrong. This means that if a theory is un-falsifiable then it is not scientific, psychology in many sectors is falsifiable through problems such as reductionism, but there are also theories that are un-falsifiable as they are untestable such as many of Freuds (1909) theories display, for example the Oedipus complex can neither be proven nor disproven. As well as having issues with falsifiability psychology also lacks the objectivity needed for science to make it truly scientific, as without objectivity the research is prone to becoming bias. Even in experiments such as Skinners (1956) rat experiment can be shown to be subjective, because although the rat is pressing the lever and the lever presses are recorded automatically, it is still down to the opinion of the researcher on when he believes the rat has learnt by pressing the lever they get a treat. This can be counteracted on the bases that psychology has the unique position of studying the human mind which in itself is difficult to operationalize, as not all parts of the behaviour and thoughts can be measured scientifically, which unlike atomic mass or miles per hour in science can be. Science within itself can also come across problematic issues over control and objectivity. An example of this is the Heisenberg Uncertainty Principle â€Å"The more precisely the position is determined, the less precisely the momentum is known in this instant, and vice versa. † (Heisenberg, 1972) which means if something is precisely measured, and a hypothesis is believed to be true, it can often distant the researcher from the actual result. An issue with measuring investigations using the scientific method in general is it can restrict and affect answers within itself. An example is it can be argued that laboratory experiments are very artificial, so do not provide a clear picture of what would happen in real life terms. As well as sharing similarities with science on the basis they both have issues with control and objectivity, they both also share the same goals. They have three aims, the prediction, understanding and control over a study. Scientists and psychologists both put a theory forward, these theories in both cases lead to a creation of a hypotheses, this is the prediction. The next step is the understanding which is when you receive results from a prediction it should give the researcher and anyone reading the report a greater understanding of that subject. Control is the final step, the knowledge gained from the proven hypothesis provides knowledge which can be used to alter certain factors in the world. The three aims of science are according to Allport (1947), psychology follows these same three aims throughout studies, reporting and publishing work just as biology, chemistry and physics do. Throughout psychology the scientific method is used, but not in all areas although science has default problems itself with the scientific method. So it cannot always be said subjects within science always stick within the scientific boundaries themselves. Another point within psychology is psychology is a ‘new’ science, biology, chemistry and physics have been in service for a good period longer, so it may be in time more likely to be classed as a science. Nevertheless Miller (1983) would argue psychology is just a pseudoscience, an approach that claims to be scientific but does not have the key principles of science, he claims this can be dangerous as psychology is claiming to be a science, it provides the false ideal that their findings is ‘fact’. Although in comparison it could be argued that there is no ultimate knowledge of human’s behaviours and thoughts, so there must be a science to take over this role of discovering behaviours and thoughts. Science may study the physical aspects of the brain e. g. hormones that can be proven through empirical evidence, but it does not study the unknown areas such as behaviours, this is where psychology can provide answers. For example Piaget’s (1966) stages of development theory, that people develop starting at the pre-concrete stage and move throughout these stages until they reach the formal stage, science does not provide an answer for how humans develop in this sense. In conclusion psychology may seem like a vague  subject with no clear goals or guidelines, but it does have aims, its aim is to study the mind, the way people behave and think. Science still has unexplainable occurrences, that have no empirical evidence so in turn cannot be falsified, which in itself should make it not scientific. Psychology can provide answers for what science cannot explain, such as how memories are stored, psychology provides a theory for this whereas science does not. In conclusion psychology can be seen as a science to explain human behaviour that other sciences cannot. How to cite Psychology As A Science, Papers

The Effects Of Steroids On Muscle Training Essays -

The Effects Of Steroids On Muscle Training What are steroids? Steroids are synthetic chemicals that mimic the hormones produced by the body. Hormones control bodily functions and are separated into various classifications such as adrenal, cortical, cardiac, bile salts, vitamins, and sex hormones. Anabolic steroids that build muscle tissue are classified as sex hormones and they stimulate the action of the male sex hormone testosterone. When testosterone is released at the appropriate time it has the natural effects of creating body size, bone size, body hair, sex organ maturation, and muscle tissue development. They often have many different trade names or brand names. Commonly used anabolic steroids are Anavar, Sustanon, and Dianabol. Anabolic steroids are prescription-only medicines. They are not controlled under the misuse of drugs act. It is not illegal to possess them for personal use. It is an offense to supply them. They can only be acquired from a chemist with a doctor's prescription. In addition, there is a large illicit market in anabolic steroids. The primary use of anabolic-androgenic steroids is in replacement therapy for male testosterone. Other medical uses include growth promotion in certain forms of stunted growth, osteoporosis, mammary carcinoma, animas, and hereditary angioneurotic edema. The use of various physical and chemical aids in performance enhancement has been a feature of athletic competition since the beginning of recorded history. The ancient Greeks ate sesame seeds, bufotenin was used by the berserks in Norwegian mythology, and the Andean Indians and the Australian aborigines chewed, respectively, coca leaves and the pituri plant for stimulating and anti-fatiguing effects (Bowman, 1980). Athletes have used anabolic steroids to enhance appearance and performance for years. The first ergogenic use of anabolic-androgenic steroids was reported back in the 1950's among weightlifters and bodybuilders. Bowman reported that one-third of a sample of elite track and field athletes in Great Britain admitted to systematic anabolic-androgenic steroid use by 1972 (Bowman, 1980). Silvester reported that 68% of a sample interviewed at the 1972 Olympic Games from 7 different countries, and who were competing in such diverse activities as throwing, jumping, vaulting, sprinting, and running up to 5000m, admitted to having used anabolic-androgenic steroids (Bowman, 1980). Although it was actually suggested early in 1973 and stressed later, it is now evident that the use of anabolic-androgenic steroids is not limited to the elite athletes but has now trickled down to the amateur, professional, college, high school, and even junior high athletes. Due to the estimated prevalence of non-med ical anabolic-androgenic steroid use and the implications for society and public health there were several scientific meetings set up. Moreover, a technical review at the National Institute on Drug Abuse in 1989 was set up, and both federal and state investigations to reclassify anabolic-androgenic steroids as controlled substances despite arguments from the American Medical Association. Patterns of anabolic-androgenic steroid use among athletes have been determined from several surveys. Hickson and Kurowski interviewed 24 weight-training athletes at a gymnasium in a metropolitan area of the southwestern United States. The Subjects surveyed took a combined steroid dose of four to eight times the recommended medical dose, Used more than one anabolic-androgenic steroid at a time, which is known as stacking, and combined the use of intravenous and oral anabolic-androgenic steroids (Hickson, 1986, p. 465). Although Hickson and Kurowski questioned a specific sample of anabolic-androgenic steroid users, they concluded that their subjects seemed to be representative of the type of athletes who used anabolic-androgenic steroids. Two other groups of people also conducted very similar surveys and found that their subjects were also taking well over the recommended medical dose. In 1990 Baldoenzi and Giada conducted a survey and found that 110 out of 250 weightlifters he interviewed in several gymnasiums in the metropolitan Chicago area, many of, which had no intentions of being competitive, also used a variety of anabolic-androgenic steroids. 50 weightlifters were interviewed in detail, a majority had no competitive interests in weightlifting, bodybuilding, or any other athletic event just used the steroids because they wanted to. Baldoenzi and Giada concluded that anabolic-androgenic steroid abuse had reached alarming proportions in noncompetitive athletes (Baldoenzi, 1990, p. 205). The Buckley survey in 1988 suggests that one-quarter to one-half million adolescents in the United States has used or