Tuesday, November 26, 2019

Wimax vs Wifi Essays

Wimax vs Wifi Essays Wimax vs Wifi Paper Wimax vs Wifi Paper WiMAX or Wi-Fi: The Best Suited Candidate Technology for Building Wireless Access Infrastructure order to highlight that which technology will be better to build a wireless access infrastructure. The first part of the paper examines the both of these wireless technology in order to understand both technologies and their underlying concepts. Then, I have discussed some key characteristics to compare the both of these technologies. The last part concludes and presents a conclusion of which will be the best technology to build a wireless access infrastructure. II. OVERVIEW OF THE CANDIDATE TECHNOLOGIES. 2. 1 Wi-Fi The dream to network PCs and other devices without the cost and complexity of cable infrastructures has driven the rapid growth in the wireless market over the last few years. Wi-Fi is one of the wireless technology which appeared early in the wireless market. Wi-Fi is based on the IEEE 802. 11 wireless local area network (WLAN) specification. Actually it was designed to be used indoors at close range for example home user and office environment. The main goal of Wi-Fi technology is to provide service for mobile computing device like laptop. But recently it is used for more services for example consumer device such as televisions, digital cameras, and DVD players. A user with a mobile computing device such as a laptop, cell phone, or PDA which is Wi-Fi enabled can connect to the global Internet when it is within in range of an access point. The region which is covered by one or more access points is called a hotspot. Hotspots can range from a single room to thousand of square feet’s of overlapping hotspots. Wi-Fi can also be used to create a mesh network. Wi-Fi also allows connectivity in peer-to-peer (wireless ad-hoc network) mode, which enables devices to connect directly with each other [1]. This connectivity mode is useful in consumer electronics and gaming applications [1]. Wi-Fi products can use different radio frequencies [2]: The 802. 11a standard uses 5 GHz in an AP-to-AP interlink. Abstract: This paper presents a description of the existing wireless technology Wi-Fi and WiMAX, and try to compare Wi-Fi (IEEE 802. 11) and WiMAX (IEEE 802. 16), with respect to which technology provides a better solution to build a wireless access infrastructure. Each technology is evaluated based on some key characteristics. This paper concludes with a statement of, which technology will be the best and most cost effective solution to end user. I. INTRODUCTION With the help of many expert communication engineers IEEE has developed various wireless standards in a hierarchical fashion. Some of the deployed wireless standards are: 802. 15 (Bluetooth), 802. 11 (Wi-Fi), and 802. 16 (WiMAX) promoted by WiMAX forum. Recently a new standard, 802. 20 for WANs has been proposed, which is currently under development. Each of these IEEE standards has been deployed to fulfill certain criteria and they complement each other. IEEE 802. 11 also known as Wi-Fi standards has had a lot of commercial success, for this reason now the focus of wireless networking shifting to the wide area market. Wi-Fi has been optimized to address the requirements for home or office connectivity but the wide area market is still open to grabs. So to grab the market the low cost wireless which appears is WiMAX, short for Worldwide Interoperability for Microwave Access, is positioned as solution for outdoor and long-range last-mile solutions. Many service providers had adopted this technology as a quick and cheap option to provide connectivity between access points or base stations and their backbone network. The main goal of WiMAX is to provide cheap and fast connectivity of both voice and data communication to remote and difficult terrain locations. With the increasing market demand for WiMAX, it is now regularly compared with Wi-Fi. While both technologies have some identical technical characteristics, however they are approaching the wireless space from completely different perspectives. The purpose of this paper is to provide a technical and market comparison of Wi-Fi and WiMAX technologies in Figure 1: Wi-Fi Network ? The 802. 11b and 802. 11g standards use 2. 4 GHz. Different frequency bands are used by the 802. 11a, 802. 11b and 802. 11g standards; Different devices using these different frequency bands do not interfere with one another. However, portable devices using different bands cannot communicate with each other, for example an 802. 11a radio cannot communicate with an 802. 11b radio. The most commonly used standard in the Wireless LAN are the 802. 11b and 802. 1g standards because of their interoperability and the greater range option that they achieve in the 2. 4-GHz band. Each standard also use different types of radio-modulation technology, which is as follows [2]: The 802. 11b standard uses direct-sequence spread spectrum (DSSS) and supports bandwidth speeds up to 11 Mbps. The 802. 11a and 802. 11g standards use orthogonal frequency division multipl exing (OFDM) and support speeds up to 54 Mbps. Because OFDM is more suitable to outdoor environments and interference, that’s why it is commonly used for Wireless LAN infrastructure. 2. 2 WiMAX: IEEE standard 802. 6, also known as WiMAX, is a technology for last-mile wireless broadband as an alternative to cable and DSL and where the cost is high. It’s intended to deliver high speed data communication, and it also has the ability to maintain dedicated links and VoIP services at a reliable and high quality speed. Figure 2: WiMAX Network Not only it supports â€Å"last mile† broadband connectivity to individual home or business locations but also its data rates are comparable with cable and Digital Subscriber Line (DSL) rates. Many telephone companies also desire that WiMAX will be a replacement for their aging legacy wired networks. In fact, it is looked as the wireless replacement for a wired broadband connection. WiMAX has the ability to allow a subscriber to connect to a wireless Internet service provider even when they roam outside their offices or homes. With the large coverage range and high data transmission rate WiMAX’s attributes open the door of the technology to a variety of applications. WiMAX can be used as a backbone for IEEE 802. 11 hotspots for connecting to the global world, as well as a subscriber can connect WiMAX enabled mobile devices such as laptops PDA or cell phones directly to WiMAX base stations without using IEEE 802. 11. Currently many service providers, providing a DSL or T1/E1 service for a business customer to a relatively remote location or outer suburbs can take several months and the cost associated with it is very high. With the help of WiMAX, a service provider can provide that service in a short time and in a very cost effective way [3]. One of the main application of the WiMAX is that it can be used in disaster recovery scenes where the wired networks have broken down. In recent many disasters, WiMAX networks were installed to help in recovery missions [4]. Similarly, WiMAX also be used as a backup links where the traditional wired links breaks. WiMAX mainly operates in two frequency ranges. One is high frequency, which is between 11 – 66 GHz and another one is low frequency, which is sub 11 GHz [3]. Line-of-sight is very essential when operating in the high frequency range. This frequency range allows this wider channel, resulting in very high capacity links. For the low frequency range (sub 11 GHz) non line-of-sight is essential. WiMAX, with a theoretical data rate of 70 Mb/s in 20 MHz channels (2-11GHz spectrum) , allows a few hundreds of DSL connections but it operates up to 124Mbps in the 28MHz channel (in 10-66GHz), [5]. The maximum range WiMAX, covered is about 50 km [5]. But in practice this range may be decrease to 20 km and even 8 km when there are obstacles [5]. 3. 1 Efficiency Efficiency of wireless technology is measured in terms of bandwidth and latency. Efficiency is a major issue to determine what type of applications can be run on a network. A lessbandwidth network only feasibly for small application and normally support simple data application for example transferring text files. A higher bandwidth network normally used for big application such as audio and video and many more powerful applications. Another major issue in case of real-time applications like voice is latency which is very much crucial issue. The maximum range of latency should not be more than 20 ms, anything higher than that be warring for establishing echo free wireless network. 3. 2 Maximum Range Maximum range is calculated from the obtained distance between the two base stations, and like cell phone another major issue must consider here that the technology must have the capability to support hand-off between base stations without loosing connection from the global world. Maximum coverage range is a major issue, the reason behind that, it determines how long a contiguous wireless area can be? Also, maximum coverage range of wireless technologys is very much crucial according to cost, since operators can reduce their initial capital expenditures if they can give the coverage of the same area with smaller number of base stations. 3. 3 Dependability Dependability is defined as how much a wireless technology is dependable to the end user. Whether end user think that is it reliable to use or not? Dependability measure with few important metrics like average number of packet loss, average number of disconnects of calls, and whether the wireless technology is hampered by environmental issues such as line of sight, weather, etc. Dependability is very crucial because some applications may require a reliable connection. If a connection is not dependable, in that case packets may loss and that affect the network for that reason the speed of the network will decrease. This would have certainly impact on the performance of any applications, hence decreasing the applications that will use on the wireless network. . 4 Security Today’s internet is open for all. And user exchange many personal data in internet. So normally end user wants security. Security is obtained from the level of encryption of the data and the authentication of the device is provided by each technology. For many applications such as exchanging bank information require a secure connection to transmit confiden tial information. Mainly the end user will not want to expose themselves and they also want that the secret information not being viewed by unauthorized individuals. That’s why security is needed in wireless connection. III. KEY CHARACTERISTICS OF WIRELESS TECHNOLOGY This paper focuses on the hypothesis that which wireless technology, WiMAX or Wi-Fi provides a better solution in the wireless access infrastructure. Whether one wireless technology provides a better solution than any other or whether a combination of technologies is needed to create the desired infrastructure. The key characteristics for which the most powerful next generation wireless technology (WiMAX and Wi-Fi) is evaluated in this research paper are: efficiency, maximum range, dependability, security, market issue and mobility. These six key characteristics are the standard issue which will be used to compare these two wireless technologies. 3. 5 Mobility Mobility is one of the major issues in case of building wireless access infrastructure. It is the speed of the mobile access point at which the technology can remain connected to the global world without losing packets or service interruption. Naturally, a wireless infrastructure environment needs to be mobile to provide connection to the end user at any place they visit. The network must sustain connection at vehicular speeds. 3. Market comparison The last characteristics to consider when evaluating wireless technology is a market. Actually the popularity of any technology is determined by the market. Mainly markets certify a technology whether it is accepted by end user or not. So based upon the market we can decide which technology is most attractive to the wireless world IV. Wi-Fi VERSUS WiMAX 4. 1 Radio Technology: WiMAX differs from Wi-Fi in the radi o technology sector. The IEEE 802. 11 WLAN standards describe four radio link interfaces that operate mainly in unlicensed radio band having range from 2. G to 5 GHz [9]. The WiMAX 802. 16a standard released in January 2003 operates between 2 GHz and 11 GHz [9]. The lower frequency bands support Non-line-of-sight (NLOS) for that reason customer unit need not be aligned with base station. Wi-Fi mainly operates in unlicensed frequency bands, but WiMAX can operate in both licensed and unlicensed spectrum. Within IEEE 802. 16a’s 2-11 GHz range, four bands are most attractive [9]: * Licensed 2. 5-GHz MMDS * Licensed 3. 5-GHz Band: * Unlicensed 3. 5-GHz Band * Unlicensed 5 GHz U-NII Band. 4. 1. 1 Radio transmission Modulation techniques: The IEEE 802. 1b radio link uses a technique direct sequence spread spectrum that is called complementary coded keying (CCK) for radio transmission [9]. Bit stream is mainly processed by a special coding and modulated with the technique called Quad rature Phase Shift Keying (QPSK). The 802. 11a and 802. 11g uses the radio link technology 64-channel orthogonal frequency division multiplexing (OFDM) [9]. Here the bit streams is encoded on the 64 sub carriers using Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), or one of two levels of Quadrature Amplitude Modulation (16-, or 64- QAM) [9]. The IEEE 802. 16a specifies three techniques for radio link [9]: * SC-A: Single Carrier Channel. * OFDM: 256-Sub-Carrier Orthogonal Frequency Division Multiplexing. * OFDM-A: 2,048-Sub-Carrier Division Multiplexing. Orthogonal Frequency 4. 2 Efficiency: Maximum channel bandwidth for Wi-Fi is 25 MHz for IEEE 802. 11b and 20 MHz for either IEEE 802. 11a or g networks [9]. The maximum bit rates it’s providing is 54 Mbps. Wi-Fi has latency in the range of 50 ms hence little bit higher latency. In WiMAX, the channel bandwidths are in the range of 1. 25 MHz to 20 MHz [9]. Basically there has been lots of confusion regarding the actual bit rate of a WiMAX channel. But many articles give a range in of 70 M or 100 Mbps, basically exact transmission rate depends on the assigned bandwidth of the channel. WiMAX have latency in between the range of 25 to 40 ms, quite considerable range. Now have a close look at the Bandwidth efficiency of the both technologies. Basically it is measured by the number of bits per second that can be carried on one cycle of radio bandwidth (i. e. bps/Hertz). Lets have a data rates supported on its 25 MHz channel (1 M to 11 Mbps), 802. 1b have bandwidth efficiency in between 0. 04 to 0. 44 bps/Hertz [9]. In 802. 11 a or g on its 20 MHz have a transmission rate from 6 M to 54 Mbps yields a bandwidth efficiency in between . 24 to 2. 7 bps/Hertz [9]. In case of WiMAX, for 70-Mbps transmission rate on a 14-MHz radio channel yields bandwidth efficiency up to 5- bits/Hertz [9]. Basically the bandwidth efficiency decreases when the tran smission range increases. 4. 3 Maximum Coverage Range: OFDM modulation has a high spectral effectiveness that why WiMAX ranges 8 km (NLOS) to 50 km (LOS) [5]. It handles many users who are widely spread out. Mesh topologies and smart antenna techniques can be used to improve the coverage. The OFDM designed for the BWA and main goal is to provide long range transmission. 802. 16 is designed for high power OFDM used to maximize coverage up to tens of kilometers [5]. In contrast, IEEE 802. 11 standard have a basic CDMA and OFDM approach with a quite different vision. It required very low power consumption of energy that whys it can support very limited range of coverage. It is mainly designed for indoor use. Optimize range of this technology is around 100 meters [5]. 4. 4 Security: One of the major issues that differentiate from Wi-Fi to WiMAX is security. It’s a major issue because it protects transmissions from eavesdropping. But security has been one of the major lacking in Wi-Fi, encryption is optional here. But better encryption techniques are now available some of the different techniques used are [9]: Wired Equivalent Privacy (WEP): An RC4-based 40- or 104bit encryption technique. Wi-Fi Protected Access (WPA): A new standard from the WiFi Alliance that uses the 40- or 104-bit WEP key. IEEE 802. 1i/WPA2: It is a IEEE standard which will be based on a more robust encryption technique called the Advanced Encryption Standard. WiMAX is designed for public network so security is very much crucial here. So all the data that is transmitted in WiMAX network is virtually encrypted. The main encryption technique that is used here is 168-bit Digital Encryption Standard (3DES), the s ame encryption also used on most secure tunnel VPNs. There are also plan to include the Advanced Encryption Standard (AES) in WiMAX to maximize the security. 4. 5 Mobility Management Mobility management is supported by WiMAX. The latest IEEE 802. 16e is made for Mobile WiMAX. This standard supports mobile capability with the support of hand-offs capability, mainly for users when they moved between cells. Its support data rates up to 500 kbps, equivalent to the highest speed cellular offerings (e. g. Verizon Wireless’ 1xEV-DO service) [9]. Currently mobility management is not supported by Wi-Fi. But recently IEEE has begun to development of a roaming standard for Wi-Fi. However, WLAN switch vendors like Cisco, Aruba, and Airespace have developed their own proprietary hand-off protocols [9]. 4. Market Comparison Up to this point we have focused on technical issues here we consider, some market issues of these two products. Some market oriented works have been established for Wi-Fi service. The two examples are Wireless ISPs and Wi-Fi mesh networks. 4. 6. 1 Wireless ISPs (WISPs) The idea behind Wireless ISP (WISP) is to provide an Internet access service using WLAN technology and a shared Internet con nection in a public location designated a hot spot. TMobile and Wayport are currently providing this type of service [9]. But it have two problems, one is technical and another one is business oriented. From a technical viewpoint, to access the internet you have to be within the hot spot. From a business viewpoint, users have to pay monthly basis for the internet then the users have to be in the hot spot always to access the internet which is not a feasible solution. So markets of wireless ISP are in a threat now. 4. 6. 2 Wi-Fi Mesh Network Wi-Fi mesh networks are mainly used to support public safety applications and also to provide Internet access to end users. However, mesh technologies are not within the range of the Wi-Fi standards. 4. 6. 3 WiMAX Market The market goals of WiMAX not clear at the moment. But in a sense we can say that the major goal will be broadband wireless access or Wireless DSL. But it will succeed only if it provides lower cost service and also provide some extra features which the other broadband like DSL do not provide. WiMAX compatible chipsets first appeared in late-2004 by the Intel and consumer devices costing $100 or less [9]. But in case of WiMAX, before investing in this field, they have to think and analyze that whether they have enough demand in the market or not. 4. 7 Quality of Service (QoS) Wi-Fi is based on a contention based MAC (CSMA/CA). Hence no guaranteed QoS is provided mainly it can support best offer services. The Standard does not permit for different service level for each user. There is a plan to incorporate QoS in the 802. 11-e standard. In this standard two operating modes will be included to improve service for voice one is Wi-Fi Multimedia Extensions (WME) and another one is Wi-Fi Scheduled Multimedia (WSM) QoS in IEEE 802. 16 is based on a request/grant protocol. Its support multiple QoS which is build in MAC. It is designed to supports different service levels such as ,T1/E1 for business and best effort to consumer. This protocol support delay sensitive services such as voice and video. The dynamic TDMA based technique allows the suitable support for multicast and broadcast. In the below the key difference between Wi-Fi and WiMAX is described Table:1 Comparison between IEEE 802. 11 and IEEE 802. 16 802. 11 (Wi-Fi) Primary Application Range Coverage and Wireless LAN 802. 16 (WiMAX) Wireless MAN mainly designed for broadband wireless Designed for outdoor NLOS performance Optimized for 50 km Mesh topology is supported MAC designed to support thousands of users Licensed and Unlicensed Band 2 GHz to 11 GHz Adjustable range from 1. 25 to 20 MHz

Friday, November 22, 2019

The Chemistry of Chemical Hair Removal

The Chemistry of Chemical Hair Removal Have you ever wondered how chemical hair removal (a chemical depilatory) works? Examples of common brands include Nair, Veet and Magic Shave. Chemical hair removal products are available as creams, gels, powders, aerosol and roll-ons, yet all of these forms work the same way. They essentially dissolve the hair faster than they dissolve the skin, causing the hair to fall away. The characteristic unpleasant odor associated with chemical depilatories is the smell from breaking chemical bonds between sulfur atoms in the protein. The Chemistry of Chemical Hair Removal The most common active ingredient in chemical depilatories is calcium thioglycolate, which weakens the hair by breaking the disulfide bonds in the keratin of hair. When enough chemical bonds are broken, the hair can be rubbed or scraped off where it emerges from its follicle. The calcium thioglycolate is formed by reacting calcium hydroxide with thioglycolic acid. An excess of calcium hydroxide allows the thioglycolic acid to react with the cysteine in keratin. The chemical reaction is: 2SH-CH2-COOH (thioglycolic acid) R-S-S-R (cysteine) → 2R-SH COOH-CH2-SS-CH2-COOH (dithiodiglycolic acid). Keratin is found in skin as well as hair, so leaving hair removal products on the skin for an extended length of time will result in skin sensitivity and irritation. Because the chemicals only weaken the hair so that it can be scraped away from the skin, hair is only removed at the surface level. A visible shadow of subsurface hair may be seen after use and you can expect to see regrowth in 2-5 days.

Thursday, November 21, 2019

Key terms, Issues & Conditions for legal confessions Essay

Key terms, Issues & Conditions for legal confessions - Essay Example Lastly informants may give a wrong or unreliable testimony in return for money or special treatment. In U.S.A alone, there have been about 303 convictions that end up exonerated after proper DNA examination (Fender, 2012). In June 1994, Jacie Taylor, a 19 years old girl body was found on a bathtub in her apartment after she was raped and murdered. During the house search for any evidence that would be useful in the case, the police found a blanket with semen on the victims couch. They also spotted and collected a bloody shirt belonging to Robert Dewey, the only and major suspect, during an interview in his apartment. The shirt was then sent to an investigations DNA laboratory for further examination (Fender, 2012). A year later a scientist from a Texas laboratory was brought before the court to testify. He said that the blood on Mr. Robert Dewey was a mixture and that some of it would be Miss Jacie Taylor’s. Mr. Robert Dewey went to prison and the evidence was contained in a laboratory in California. In 2000, Colorado introduced new medical technological tests known as STR testing that could examine and show more complex DNA features such as double helix. They also began uploading the DNAs of all the convicts in the CODIS database. Six years later even a more refined and accurate DNA technology known as YSTR test was introduced (Fender, 2012). YSTR tests could separate female DNA from that of male in case the two happened to mix up. With this technology, Danyel Joffe the Dewey’s post- conviction attorney, with the help of New York Innocence Project had the evidence held in the laboratory in California to be re-tested. The case was reviewed and the evidence at hand retested using a â€Å"mini filer† technology that could pull more comprehensive profiles from ruined DNA samples. The new evidence revealed that the blood on Mr. Robert Dewey’s shirt did not contain Miss Taylor’s blood. The technology also revealed

Tuesday, November 19, 2019

Homework Coursework Example | Topics and Well Written Essays - 250 words - 11

Homework - Coursework Example The cloud disappears when the air temperature is raised by compression. The change in temperature results in evaporation of the cloud droplets. 9. It can be inferred from this investigation that in the open atmosphere where it is cloudy, air is generally rising and cooling. Burgan (123) asserts that where the atmosphere is clear, the air is generally moving in the opposite direction. 10. Generally, high pressure areas in the atmosphere tend to be clear because air in them experiences downward motion. Low pressure areas tend to have clouds because air in them experiences motion in the reverse direction. 13. In the eastern U.S., the front that had slowly been moving eastward was positioned near Buffalo, New York. The temperature and dewpoint at Buffalo at map time were 62 F and 61 F, respectively. Because the temperature and dewpoint at the surface were not equal, it indicated the air in Buffalo was not saturated. 19. On the Steve diagram, the bold irregular curve to the right is the temperature profile while the bold curve to the left is the dewpoint profile. Where the curves are superimposed, the temperatures and dewpoints are equal. The separation of the temperature and dewpoint values at and near the surface indicates that the surface air was not saturated. (From the radiosonde text data, not shown, there is a 1.7 C difference between the temperature and dewpoint at the surface.) 23. The temperatures were equal to the dewpoints from 975 mb up to about 600 mb. These equal temperature-dewpoint conditions do suggest there was an extensive, thick layer of clouds over Buffalo (Bunch,

Sunday, November 17, 2019

Management Influences on Turnover Intention of Software Developers Essay Example for Free

Management Influences on Turnover Intention of Software Developers Essay Introduction The Information Technology (IT) Age has created many opportunities for employment in the IT and IT services industry.   IT professionals are in demand all over the world.   Organizations worldwide invest money that go not only into salaries but for further training of IT professionals they hire. However, around the world, the demand, supply, selection, recruitment and particularly retention of IT professionals has threatened organizations that use, manage or deal in IT or IT services for the past few years (Parà © and Tremblay 2000; Ermel and Bohl 1997; Morello 1998; Guptill et al. 1999). This is why the departure of an IT professional from a company usually comes with disastrous effects to the organization.   When an IT professional resigns, the organization suffers loss of business process knowledge and acquired technical skills (Dorà © 2004). Since late 1996, the turnover for IT professionals has jumped from 15% to 20% annually, with only 8 of 10 IT positions being filled with qualified candidates (McNee et al. 1998).   With the annual turnover rate estimated at 20% or more (Alexander 1999; Kosseff 1999), job-hopping of IT professionals has been one of the biggest problems among managers and human resources (HR) experts (Parà © and Tremblay 2000). IT professionals seem to have a tendency to change their jobs faster than other employees when they feel dissatisfied with their current employer (Hacker 2003).   The estimated cost of replacing IT professionals range from 1.5 to 2.5 times of their annual salaries for the companies they resigned from (Kosseff 1999).   On the other hand, the cost of losing a qualified IT professional is actually 3 to 6 times more expensive than the cost of losing a manager (Kochanski and Ledford 2001). IT professionals, as also mentioned previously in this study, also tend to change jobs more quickly than other employees when they feel dissatisfied with in their current employment (Hacker 2003).   However, rational models of voluntary turnover cannot be used to explain the high turnover rates for IT professionals (Rouse 2001) since many IT professionals remain dissatisfied with their jobs even though they enjoy high financial rewards yet their creativity and expertise do not receive high respect from their peers, supervisors and companies as a whole (Fisher 2000). Furthermore, another explanation why IT professionals may resign more quickly when dissatisfied with their current employment is that â€Å"much of IT work is project oriented, the technical employee’s loyalty may be more to the project, and not necessarily to the employer† (Hacker, 2003, p. 15). These trends place intense pressure on both IT executives and HR managers.   High IT professional turnover translates to a threat not only to an organization’s IT department but to the business as a whole. Most importantly, high IT turnover poses a threat to the growth, competitive positioning and strength of the global economy (Parà © and Tremblay 2000). A dissertation by Dr. Timothy Lee Dorà © (2004) studied the relationships between job characteristics, job satisfaction and turnover intention among software developers.   These two factors – job characteristics and job satisfaction – are deemed to play crucial roles in understanding turnover intention not only among software developers but IT professionals as a whole. The current study aims to investigate the management influences on employee retention of IT professionals, focusing on job characteristics and job satisfaction, and their impact on turnover and retention.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   1.1.1  Ã‚  Ã‚   Scope and Limitations of the Study This research will study the impact of job characteristics and job satisfaction on the turnover intention of IT professionals.   Although this paper intends to replicate some of Dorà ©Ã¢â‚¬â„¢s findings, the study will not be limited to software developers only as this sector only constitutes a small sample of IT professionals as a whole. Specifically, the research study will focus on the turnover intention of IT professionals in___________. In studying the relationships between job characteristics, job satisfaction and turnover intention, this study is limited to the use of the following theoretical models and theories to support its conclusions: For the discussion on job characteristics, the research study will make use of the Job Characteristics Model developed by JR Hackman and GR Oldham (1975/1980) and the analysis on Model Employers by Minda Zetlin (2001). For the discussion on job satisfaction, as well as motivation, the paper will use the Motivator-Hygiene Theory by F. Herzberg (1968/2003) and the Synergistic Model by T.M. Amabile (1997). For the discussion on turnover, the study will use the Voluntary Turnover Model by R.M. Steers and R.T. Mowday (1987); the Rational Turnover Model by P.D. Rouse (2001); the Instinctual or â€Å"Unfolding† Model of Turnover by T.W. Lee, T.R. Mitchell, L. Wise and S. Fireman (1996); and the Conceptual Model for Investigating Turnover in IT, developed by J.B. Thatcher, L.P. Stepna and R.J. Boyle (2002-03) These models will be discussed in detail later in this chapter, as well as in Chapter 2 on Review of Related Literature. Chapter 2 Review of Related Literature This chapter will analyze the various literature which are related to this research paper. It will discuss the works of other analysts and researchers on theories/models that will be used to support this study, as well as pertinent literature on IT professionals’ turnover intentions. The chapter begins with a general discussion on motivational theories, cutlure, and leadership which are all critical factors that affect an employee’s intent to leave. The discussion them dovetails into a more specific presentation of the framework used in the current study. This chapter will also include a definition of terms incorporated into the discussion of related literature. 2.1  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Relationships between Job Characteristics, Job Satisfaction, and Turnover Intention In 2004, Timothy Lee Dorà © submitted a dissertation titled â€Å"The Relationships Between Job Characteristics, Job Satisfaction, and Turnover Intention Among Software Developers†.  Ã‚   According to Dorà ©, the factors leading to the turnover intention of software developers have been poorly understood.   His study was designed to further understand the relationships between job characteristics, job satisfaction, and turnover intention among software developers.   His study involved the use of 326 web surveys that contained questions relating to job characteristics, job satisfaction, turnover intention and demographic information. The results of Dorà ©Ã¢â‚¬â„¢s study showed that several factors can influence turnover intention, most significantly, job characteristics that may be influenced by management, such as training, autonomy, feedback, number of developers, task significance, and skill variety (Dorà © 2004).   In his study, Dorà © made use of two research questions and sixteen hypotheses to understand the job characteristics variables which contribute to the various dimensions of job satisfaction, and which of these job satisfaction dimensions, in turn, contribute to turnover intention. Dorà © made use of indirect effect tests, to determine if certain job characteristics could be linked to turnover intention through the job satisfaction scales he provided.   The results of his study indicated that ten of the indirect effects were statistically significant.   All ten of the statistically significant indirect effects were associated with only three of the seven job satisfaction scales: internal work motivation, general job satisfaction, and satisfaction with pay. The largest indirect effect, according to Dorà ©, was the effect of autonomy on turnover intention through general job satisfaction: higher levels of autonomy lead to lower levels of turnover intention by increasing general job satisfaction.  Ã‚   The next largest indirect effect was the effect of organizational training on turnover intention through general job satisfaction: organizational training decreased turnover intention through an increase in general job satisfaction.   The next three highest indirect effects in Dorà ©Ã¢â‚¬â„¢s findings were also between a job characteristic (feedback, skill, variety, and number of developers) and turnover intention through general job satisfaction (Dorà ©, 2004, p. 130). 2.2  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Measuring Turnover Intentions Among IT Professionals Guy Parà © and Michel Tremblay, in contrast to Dorà ©Ã¢â‚¬â„¢s study, completed a research covering the turnover intention of not just software developers but IT professionals as a whole.   Their study, â€Å"The Measurement and Antecedents of Turnover Intentions among IT Professionals† (2000), submitted to Cirano research center, aimed to present and test an integrated model of turnover intentions that address the unique nature of the IT profession (Parà © and Tremblay, 2000, p. 3).   The authors identified a multidimensional set of HR practices that will most likely increase retention among IT employees.  Ã‚   They emphasized citizenship behaviors as well as two distinct types of organizational commitment as key antecedents of turnover intentions. The study involved the sending of questionnaires to 394 Quebec members of the Canadian Information Processing Society.  Ã‚   The study addressed four research questions: 1) What are the essential HR practices necessary to create an effective plan for retaining IT professionals? 2) What is the impact of compensation and negotiation conditions on the turnover intentions of IT personnel? 3) What is the effect of employee demographic characteristics on the turnover intentions of IT personnel? 4) Do organizational commitment and citizenship behaviors mediate the effects of HR practices, compensation and negotiation conditions as well as demographic characteristics on the turnover intentions of IT personnel? (Parà © and Tremblay, 2000, p. 4) Parà © and Tremblay provide that IT employees who are highly committed to their organization are less likely to leave than those who are relatively uncommitted.   They attach three distinct dimensions to organizational commitment: affective, continuance and normative commitment (Meyer and Allen 1997). 1)  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Affective commitment – means an employee’s personal attachment and identification to the organization.   This results in a strong belief in an acceptance of the organization’s goals and values.   â€Å"Employees with a strong affective commitment continue employment with the organization because they want to do so† (Parà © and Tremblay, 2000, p. 5) 2)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Continuance commitment – is a tendency to engage in consistent lines of activity based on the individual’s recognition of the â€Å"costs† associated with discontinuing the activity.  Ã‚   â€Å"Employees whose primary link to the organization is based on continuance commitment remain because they need to do so.† (Parà © and Tremblay, 2000, p. 5) 3)  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Normative commitment – provides that employees exhibit behaviors solely because they believe it is the right and moral thing to do. â€Å"Employees with a high level of normative commitment feel that they ought to remain with the organization.† (Parà © and Tremblay, 2000, p. 5) In their findings, Parà © and Tremblay provide that affective commitment and continuance commitment are negatively related to turnover intentions (Parà © and Tremblay, 2000, p. 6).   In addition to these two distinct types of commitment affecting turnover intention, their studies also points to the factor they call Organizational Citizenship Behavior or OCB. OCB is considered as a key element in organizational effectiveness.   OCB is defined as â€Å"an employee’s willingness to go above and beyond the prescribed roles which they have been assigned† (Parà © and Tremblay, 2000, p. 6, quoting from Organ 1990). Based on Parà © and Tremblay’s findings, the stronger the citizenship behavior of an IT employee, the more likely they are to stay in their company.   The IT professional’s affective commitment, or attachment to his or her organization, also decreases turnover intention. 2.3  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Job Characteristics Model Hackman and Oldham’s Job Characteristics Model, as earlier introduced in Chapter 1 of this research study, predicts what aspects of jobs reflect the level of job enrichment for employees, and how these relate to employees’ individual differences and to the work outcomes required. The model includes five core job characteristics that can be applied to any job: skill variety, task identity, task significance, task autonomy and feedback. Skill variety is defined as â€Å"the number of different skills required in the job† (Hackman and Oldham 1980; Pilon 1998). Task identity means â€Å"the completeness of the tasks done in the job† (Hackman and Oldham 1980; Pilon 1998). Task significance on the other hand is defined as â€Å"the importance of the job to the served population.† (Mohamed 2004). Autonomy means â€Å"the vertical expansion of responsibility, the amount of decision-making and independence allowed for employees.† (Mohamed 2004). And lastly, feedback means â€Å"the extent that the job itself provides information about employees’ performance† (Huber 2000). These characteristics – skill variety, task identity, task significance, autonomy, and feedback – are combined into a single predictive index which is called the Motivating Potential Score (Hackman and Oldham 1980). Figure 1. Job Characteristics Model Source: A.H. Mohamed (2004)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The five core job characteristics enumerated in the previous paragraph are in continuous interaction with individual differences that evoke three critical psychological states in an employee.   These three states are: 1) when the job is structured by skill variety, task identity and task significance this could lead employees to experience meaningfulness in their work. 2) The second state, task autonomy, which leads to feelings of responsibility for the outcomes of work. 3) The third and last state is feedback, which leads employees towards knowledge of the results of their work (Douthit 2000; Huber 2000).   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   These three critical psychological states lead to a set of affective and personal outcomes:   high internal work motivation, high growth satisfaction, high general satisfaction, high work effectiveness, and low rate of absenteeism (Mohamed 2004; Donovan and Radosevich 1998).   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   These affective and personal outcomes are the results of en employee’s job characteristics.   They are defined as follows: High internal work motivation – this is the degree to which an employee is willing to work and to consider the organizational objectives as part of his or her own goals (Mohamed 2004). High growth satisfaction – this is the achievement of the employee in overcoming challenges, succeeding and growing (Steers and Black 1994) High general satisfaction – this the feeling derived from the overall satisfaction with the work itself. â€Å"This type of satisfaction is reflected mainly in decreased rates of absenteeism among employees† (Steers and Black 1994; Omachonu et al 1999). High work effectiveness – this refers to both the quality and quantity aspects of work performance (Hackman and Oldham 1980). Low rate of absenteeism. The Job Characteristics Model, also includes three attributes that are identified as Moderators: knowledge and skills, context job satisfaction, and employee growth-need strength.   These attributes indicate which employee will respond positively to the Motivating Potential Score of their job and its outcomes (Hackman and Oldham 1980). An employee’s knowledge and skills are dependent on their educational qualifications which in turn will reflect their perceptions toward their work outcomes (Sabiston and Laschinger 1995).   On the other hand, an employee’s perception of his or her context job satisfaction involves factors like pay, supervision, colleagues, and job security.   All these affect the employee’s outcomes as well (Mohamed 2004).  Ã‚   Lastly, growth-need strength is the degree in which an employee seeks opportunities in his or her job for self-direction, learning and personal accomplishment.   These elements in turn affect the employee’s level of work internal motivation (Mohamed 2004). An example of a study which made effective use of Hackman and Oldham’s Job Characteristics Model is the one conducted by A.H. Mohamed (2004) called â€Å"Using the job characteristics model to compare patient care assignment methods of nurses† for the Faculty of Nursing, University of Alexandria in Egypt.  Ã‚   The population used were the nurses in the Alexandria Main University Hospital.   Mohamed made use of a Job Diagnostic Survey (also developed by Hackman and Oldham) to determine nurses’ perceptions towards the components of the Job Characteristics Model in relation to their performance in utilizing the case and functional methods of patient care assignment (Mohamed 2004). In his study, Mohamed concludes that the jobs of intensive care unit nurses result in different expectations based also on the different categories of nurses, based on skills and challenges inherent in the work they perform (Mohamed 2004). Generally speaking thus, studies like Mohamed shows that an employee’s personal and affective outcomes are a result of the employee’s job characteristics. 2.4  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Model Employers However, management also plays a crucial role in the retention and conversely turnover of IT professionals.  Ã‚   Since IT professionals still enjoy a wide selection of employers to choose from, employers constantly compete to attract the best IT professionals by becoming â€Å"model employers†.   In her 2001 article for Computer World, called â€Å"Model employers†, Minda Zetlin outlines the strategies that make certain companies â€Å"model employers†. By compiling its eight annual list of 100 Best Places to Work in IT, Computer World roughly sums up the model employers as offering not just top compensation, but also â€Å"opportunities for career growth, investment in training, diversity in the work place, work flexibility, and, ideally, a comfortable and fun place to spend their daytime hours† (Zetlin 2001).   Zetlin in her article outlines three common themes behind the success of these model IT employers: IT is central to the best employers’ success According to Zetlin, excellence in IT is a top corporate strategy.  Ã‚   Prioritizing IT should not be limited to companies that strictly provide IT or IT services.   Companies such as Avon, for instance, which ranks 4th in Computer World’s list of 100 best employers, may be perceived to operate on a relationship-based environment.  Ã‚   Yet to process its more than 60 million custom orders every year, the company relies heavily on IT to process its complex supply chain.   The fact that is it is actually a very transactional business, dependent on technology, makes IT one of its priorities (Zetlin 2001). Management takes an active interest in employers’ careers from the day they arrive This includes having development plan for employees as soon as they join the organization.   Employees meet with their managers on a periodic basis for a formal review to assess their development plan and to evaluate its progress.  Ã‚   Orientation programs at the start of the employment are also part of this strategy.   Apart from orientation, Harley-Davidson, Inc. (ranked as No. 11) also provides for a yearly self-assessment for its employees against the established competencies for their jobs, with their supervisors doing the same (Zetlin 2001).   Such focus on career development per employee makes the employee feel that management takes an active interest in aligning its objectives with the employee’s personal goals. Model employers also provide for continuous interest on their employees’ careers throughout their employment with the company.   Knowledge mentoring programs and career mentoring programs, used by the State Farm Mutual Automobile Insurance Co. (ranked No. 13), for instance, allow employees to learn more skills and career guidance from their more experience colleagues, and help management to identify employees to fill leaderships positions in the short and long term (Zetlin 2001).   State Farm’s mentoring program is in fact so successful that it has extended the program to employees who haven’t even arrived yet – such as assigning mentors to college students who plan to join State Farm after they graduate. There are no walls between business and IT Unlike other organizations, model employers ensure that IT people and business people work side by side.   There is no division or competition.  Ã‚   IT professionals are given a better understanding that what they do helps the business succeed.   This understanding leads to career satisfaction for IT professionals.   Technology people know exactly how they contribute to the revenues of their business and how important they are in the business plan.   One advantage here is that a close relationship between IT and business allows people to switch between the two fields (Zetlin 2001).   Another strategies such as cross-functional work teams gives career development not just to IT professionals but to business people in the organization as well.  Ã‚   There are continuously different career tracks available.   An IT professional may opt to advance by taking on management roles within technology, or they may shift to business management positions (Zetlin 2001). 2.5  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Voluntary Intention Model   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   R.M. Steers and R.T. Mowday, in their study â€Å"Employee turnover and post-decision accommodation processes† (1981) analyzed turnover as rooted in voluntary intention.   Steers and Mowday viewed the interaction of intention to leave, and alternative job opportunities (ease of movement) as direct antecedents to turnover (Steers and Mowday 1981; Rouse 2001).   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   As earlier discussed in Chapter 1 of this study, the direction of the process in Steers’ and Mowday’s Voluntary Intention Model starts with Job Expectations, then Affective Responses, then Turnover Intention, then finally,   Actual Turnover (see Section 1.2.1.1 of this paper).   However, these four elements were actually grouped together by Steers and Mowday under three steps. As can be seen in the Figure 3: Each step in Figure 3 contains two constructs.   The second construct (Job Attitudes) of Step 1 becomes the first construct of Step 2.   The second construct (Intent To Leave) of Step 2 becomes the first construct of Step 3. Step 1 of the Voluntary Intention Model involves the manner in which job expectations influence an employee’s attitudes regarding his or her job.   Attitudes are composed of job satisfaction, organizational commitment, and job involvement.   Job expectations in turn are influenced by three stimuli. The first stimuli focuses on individual characteristics such as occupation, age, tenure, family concerns, and personality form (Steers and Mowday 1981; Rouse 2001). The second stimuli involves information obtained during the recruitment process and at various assessments points throughout the employee’s career (Steers and Mowday 1981; Rouse 2001). For instance, studies have shown that job expectation levels are often high when the employee first accepts a new job (Porter and Steers 1973). At these particular periods, expectations are developed from both the employee and employer’s ends. In other words, a sort of unwritten social contract is deemed to be adopted by the two parties (Prouse 2001). Lastly, the third stimuli affecting job expectations are alternative job opportunities.   Studies have shown that the more alternatives there are confronting an employee, then the more negative the employee’s attitudes becomes concerning his or her current job (Pfeffer and Lawler 1979). Step 2 in the Voluntary Intention Model involves the Affective Responses that are elicited from Step 1.   These responses include the construct of job satisfaction, and how those responses influence the employee’s desire to leave the organization.   Factors that affect the employee’s decision to leave include non-work factors such as family, hobbies, religion and political influences (Cohen 1995). Steers and Mowday also identified the potential of employees to alter their actual job, in terms of pay, working hours, environment, etc., and thus change their attitudes regarding their jobs (Prouse 2001). Chapter 3 Methodology The aim of the research is to examine the relationships between job characteristic, job satisfaction and turnover intention among IT professionals in ______________.   The proposition is that job satisfaction and job characteristics as management influences have indirect impact to the levels of turnover intention among IT professionals.   The literature review indicates that there are different factors affecting IT professionals’ turnover intention.   This research is going to study the turnover intention of IT professionals in _____________. 3.1  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Research Questions The study will answer the following two research questions: Which job characteristic variable(s) causes the job satisfaction among IT professionals in ____________? What job satisfaction variable(s) cause the turnover intention among IT professionals in ____________?   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   In answering these two primary questions, the thesis will make use of the following framework:    Hypotheses Research Question 1   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   â€Å"Which job characteristic variable(s) causes the job satisfaction among IT professionals in _______________?†   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The first research question will analyze the standardized effect of job characteristics to job satisfiers.  Ã‚   The null hypotheses tested were: Job Characteristics à   Job Satisfactions H1: The level of IT training does not affect the various measures of job satisfaction. H2: The level of user contact does not affect the various measures of job satisfaction.   Ã‚  Ã‚  Ã‚  Ã‚   H3: The job-required skills do not affect the various measures of job satisfaction.   Ã‚  Ã‚  Ã‚  Ã‚   H4: The level of task significance does not affect job satisfaction.   Ã‚  Ã‚  Ã‚  Ã‚   H5: The amount of workload does not affect job satisfaction.   Ã‚  Ã‚  Ã‚  Ã‚   H6: The amount of feedback does not affect job satisfaction. Research Question 2   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   â€Å"What job satisfaction variable(s) cause the turnover intention among IT professionals in ________________?†   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The first research question will analyze the standardized effect of the job satisfaction scales to turnover intention.  Ã‚   The null hypotheses tested were:   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Job Satisfactions à   Turnover Intention   Ã‚  Ã‚  Ã‚  Ã‚   H7: The level of internal work motivation does not affect turnover intention.   Ã‚  Ã‚  Ã‚  Ã‚   H8: The level of job security satisfaction does not affect turnover intention.   Ã‚  Ã‚  Ã‚  Ã‚   H9: The level of social job satisfaction does not affect turnover intention.   Ã‚  Ã‚  Ã‚  Ã‚   H10: The level of job growth satisfaction does not affect turnover intention.   Ã‚  Ã‚  Ã‚  Ã‚   H11: The level of satisfaction with pay does not affect turnover intention.   Ã‚  Ã‚  Ã‚  Ã‚   H12: The level of satisfaction with supervision does not affect turnover intention. Research Procedures   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   3.3.1  Ã‚  Ã‚   Data Collection   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Research is a process of studying and analyzing situational factors of a specific problem or issue in order to determine solutions of it (Cavana, Delahaye and Sekaran 2001). According to Cavana, Delahaye and Sekaran (2001), there are three research paradigms: positivist, interpretivist and critical research.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   As the research hypotheses of this study try to explore the relationships between job characteristic, job satisfaction and turnover intention among the IT professionals in __________________, the positivist approach will be adopted and it will provide the framework upon which the methodology of this study can be used.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   In this study, the research problem requires primary data to specifically address the twelve hypotheses. An Internet questionnaire will be used as it is the most effective and appropriate data collection method. â€Å"Questionnaire† is defined as a â€Å"pre-formulated written set of questions to which respondents recorded their answers within closely defined alternatives† (Cavana, Delahaye and Sekaran, 2001). A well-designed questionnaire provides accurate and useable data for analysis in order to make a conclusion of accepting / rejecting a research hypothesis.  Ã‚   A copy of the questionnaire to be used is attached as Appendix A of this study.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   After gathering the data from questionnaires, the analysis of the data (including frequency distribution, correlation analysis and regression analysis) will be performed by a quantitative data analysis tool called SPSS (Statistical Package for the Social Sciences). SPSS predictive analytics advances in usability and data access, drawing reliable conclusions from the collected quantitative data (SPSS, Inc. 2002). In depth quantitative analysis of the data will be undertaken. Frequency Distribution, Correlation Analysis, and Regression Analysis will be used to analyze the collected data.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The population of this research is the _________ professionals in the country. The research is expected to have a 10% response rate (i.e. ____ questionnaires).   A reminder email will be sent to the students to ensure reaching the planned response rate. Participants are not inconvenienced or exposed unnecessarily to potential harm by recruiting more than is required. The research conducted by Dorà © in 2004 (which this paper intends to compare itself to) only received 326 responses which is less than 0.1% of the population.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   An invitation email   will be sent to the administration managers of the participating institutions. Then the manager will forward the invitation email to all qualified IT professionals and invite them to fill in the Internet anonymous questionnaire within 10 business days. A reminder email will be sent by the manager on the 6th business day. The invitation email only contains a consent form   and a URL to the Internet anonymous questionnaire. Participation is entirely voluntary. The participant can withdraw at any time and there will be no disadvantage if the participant decides not to complete the survey.   At no time will any individual be identified in any reports resulting from this study. A copy of the consent form is attached with this application. Variables   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The variables which will used in this study can be categorized into two categories: job characteristics and job satisfaction.   The factors within each category are discussed as follows:   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The following job characteristics for IT professionals were selected for this study, based also on previous usage in similar studies as indicated in the literature review: IT Training User Contact Job-required Skills Task Significance Workload Feedback   Ã‚  Ã‚  Ã‚  Ã‚   On the other hand, the job satisfaction scales include the following: Internal Work Motivation Job Security Satisfaction Social Job Satisfaction Job Growth Satisfaction Satisfaction with Pay Satisfaction with Supervision Data Analysis   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The study will make use of descriptive and inferential analysis were used throughout the study.  Ã‚   Descriptive statistics (percentages, means, standard deviations, frequencies, and item means) were computed using the SPSS (SPSS, Inc., 2002).   This general-purpose analysis program will be used to characterize the sample in terms of demographic characteristics pertaining to gender, income, education, age, years as an IT professional, years in the current organization, and years in the current position.   SPSS will likewise used to analyze the correlation among job characteristics, the correlation between job satisfaction scales, the correlation between job satisfaction and job characteristics, and the correlation between job characteristics, job satisfaction, and turnover intention.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The primary inferential technique that will be used is bivariate correlation.   SPSS will   also be used to analyze the regression analysis for the data.   A 0.01 level of significance was adopted for testing significance.   The standardized effects of all the job characteristics for each job satisfier will also be computed.   The same method will be used to analyze the standardized effect of all the job satisfaction scales to turnover intention.   From these standardized effect analyses, the prediction of turnover intention by job satisfaction scales will be computed.    The job satisfaction scales which had a 0.60 level were considered significant to turnover intention.   The reliability coefficients ranging between 0.60 and 0.70 are deemed adequate for research purposes (Aiken, 2000, p.88).   For purposes of this study, the job satisfiers and job characteristics which have indirect effects of 0.60 above significance to turnover intention will be used.   The standardized effect of the significant job characteristic will be multiplied to the standardized effect of the particular job satisfier.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Each of the twelve hypotheses of this study will be tested in essentially six multiple regression analyses – one for each job satisfier as the constant, independent variable and its relation to each dependent variable represented by the job characteristics.   Otherwise stated, each job satisfier will represent a criterion variable and the six job characteristics will be considered predictors in each of the six regression analyses. 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Thursday, November 14, 2019

The Character Kevin in Freak the Mighty :: Rodman Philbrick

Many people struggle to be accepted in our world because of disabilities. Freak the Mighty, by Rodman Philbrick, is a dramatic and inspiring novel about how two boys, who are â€Å"different†, become friends and unite towards a common cause. Kevin, an eighth grader who lives with his mother Gwen, is one of the two protagonists in this extraordinary novel. Kevin is a very knowledgeable young boy who doesn't let his disability limit his abilities. Kevin uses his imagination to minimize his disability in his every day life. In Preschool Kevin would march around the school with his leg braces and use his imagination to pretend that he was a robot. The fact that he uses his imagination to see his leg braces as â€Å"astounding† is a great example of Kevin using his imagination to minimize his disability. Kevin displays great determination in this novel. Near the beginning of this narrative, Kevin is playing with a mechanical bird and flies it up into a tree. He repeatedly tries to free it from the tree, even though he seems to be getting nowhere. He had his mind set on it, so he wouldn't stop until it was in his hands. This section of the story shows that if Kevin sets a goal for himself, he’ll do almost anything to achieve it. Another example is that when Kevin helps the cops look for Max when he’s reported missing Kevin never gives up looking for Max, even when the cops want to give up. This example is yet another form of great determination. All of these points help indicate that Kevin displays great determination through the novel by Rodman Philbrick. Kevin doesn’t let his disease put a negative effect on his learning.

Tuesday, November 12, 2019

Conviction of Louise Woodward

In this essay i will be writing and explaining how spoken language is used and adapted to influence the jury in the closing argument that convicted Louise Woodward. The prosecutor uses a variety of features in this argument to convince the jury persecute Louse Woodward. I will be going through these techniques and explaining why he uses them to influence the jury. Gerard T Leone Jr was the prosecutor in the case of the death of Mathew Eappen. He uses repetition in the first section of the argument by repeating the victim name, â€Å"Mathew Eappen. The repetitions show that he wants the jury to focus his attention on the victim. He wants the jury to feel emotionally connected to Mathew Eappen so it would affect the decision the jury makes. The repetition of â€Å"Mathew Eappen† encourages the acceptance of the idea that he was young and already dead because of Louise Woodward. It gives Mathew and the court people a mutual bond. He talk about Mathew Eappen by saying the things he hasn’t done to make the court feel pity toward him by saying that â€Å"Mathew Eappen will never take his first step. Mathew Eappen will never say his first word because Mathew Eappen is dead. An additional example of repetition is when the repeats the word explodes† when he talks about how the victims actually died. He repeats and uses this word as he is expected to use powerful language to convince the jury and the word â€Å"explodes† has imagery so when he says â€Å"Mattie’s head explodes† people visualise this shocking image. This is effective because he could have easily put up pictures of the wound but by making the audience visualise it, in some people minds the wound might appear more serious and horrifying than it was actually in real life.Another use of imagery is when he talk about the size of the wound . He doesn’t use an adjective or a simile but uses the name of an object to represent the wounds. In this context he uses a goose’s egg. He says in form of a rhetorical question that â€Å"she would have seen that goose egg on the back of his head†. He uses a goose egg because when you visualise it is very fragile and easily broken so this is referring to poor Mattie’s skull and that fact it uses a gooses egg over a daily , normal chicken eggs that this wasn’t a ordinary crack but bigger more sever crack which unfortunately cost Mathew Eappen his life.A technique that he uses is sarcasm; during the last section of the speech is that convicted Louise Woodward. Gerard talk about the testimony Louise gave about the death of Mathew Eappen. She saying the testimony that she popped Mathew on the floor but he replies by saying â€Å"that the word popped sounds like the word dropped, that the words popped sounds a little lie tossed. †This is sarcastic because he wants the people to know for sure that Louise dropped Mathew Eappen which cause his head to â€Å"explode. His using sarcasm to tell the jury that Louise Woodward is guilty without tell the jury and court directly that she is guilty. This is effective because not is he accusing her indirectly but he is confirming that the injuries were caused by Louise Woodward. The prosecutor uses many rhetorical questions through out the argument because these make the audience think and have time to answer the questions in the minds even thought Gerard T Leone is not asking for an answer he is giving question after question so the audience can think about them and find out the point he is trying to make.In the middle of the argument he shows this by saying â€Å"Don’t you think she would have seen that goose egg on the back of his head? Don’t you think she would have preceded some swelling, some injury to the back of Mathew Head? † Another example of him using a rhetorical question is when he says â€Å"Why would Sergeant Detective Bill Burn lie? Former marine twenty five years on the for ce. †In this context he uses a rhetorical question that is leading so the court will automatically think that what Bill Burn said wasn’t a lie .The question is leading because he follows the question with â€Å"former marine twenty five years on the force. †This makes Bill sound trustworthy and Louise Woodward guiltier. Using rhetorical questions is great because as a prosecutor you would expect them to question the case and use rhetorical question to convince the court men. The prosecutor has used a variety of techniques through out his argument to convince the jury to convict Louise Woodward. I believe this is a great piece of spoken language as well as having many features the prosecutor is adding emotion and moods by changing some words.A example of this is when he says â€Å"She was bathing Mattie like she was supposed to,’ he uses the name Mattie instead of Mathew because it signifies how young he was and Mattie is the name that the people with the closest relationship would call him such as his parent, so by calling him Mattie he is creating sorrow and pity by talking about the loss of someone so loved. Overall this is a very convincing argument and is structured carefully so all details correspond with each other creating an organised power and strong argument.

Saturday, November 9, 2019

Nerwork Security Essay

SCENARIO 1 According to scenario 1, the followings are the threads and security measure to control it. THREADS SECURITY MEASURE 1.Fire outbreaks, begins just outside the data center. The attack is an internal and active attack caused by a disgruntled employee or worker i.e an unhappy or a dissatisfied employee I. Availability of fire department center II. Implementation of well programmed sprinkler system III. Building has been evacuated to prevent loss of lives SUGGESTIONS 1.Figure out the worker by investigating and either dismiss him/her or by compensating him/her by treating him right or well. 2. This can also be controlled by enforcing the physical security of the company i.e by installing cctv camera in every hook and corner of the company this will monitor all the employees activities within the vicinity of the company; of which any employee that engages in such a destructive act can be fished out easily by replaying the record. 3. RFID can also be deployed to monitor the in and out of every employee. 2.Anthrax box was detected by an employee in the lobby I. Evacuation of building has be done again to prevent loss of lives II. Health department is on scene to investigate the issues and treat people III. The sprinkler system has been implemented which caused the email and web server to stop working. SUGGESTIONS 1. Employees and visitors should be properly screened and be checked thoroughly before entering the organization or company so as to avoid them bringing in potentially dangerous object in to the company. 2. Foreign object detector technology should be deployed and implemented in the company to ensure proper screening of the people i.e visitors and employees moving in and out of the company. 3. Call the attention of crime investigators so  as to confirm the doer of the crime ; finger print test will done the box . 3.E-mail server and Web server are down I. The sprinkler system was programmed to turned off the web and email server down in case of any emergency so as to prevent data loss , explosion and destruction of the server SUGGESTION 1.E-mail and web server should always be kept in a safer area where it cannot be easily accessed by an intruders and free from disasters like thunder storm, lightening and flooding i.eit should be kept In a water proof data room around the middle level of a building. 2. There should always be an alternative stand-by server kept In another location to replace in case a server is down so ensure the proper functioning of the company e-commerce websites 3. call the right personnel i.e network security engineer to figure out the proper place servers should be kept against disaster when planning to design a network 4. The e-mail and web server should either switched on be repaired or replaced immediately to ensure the proper functioning of the company e-commerce sites so as to prevent lossof customers 4.Customer cannot place order at the company sites since the servers were down I. The company has provided an alternative call center at another location against emergency for customers who cannot place order at the company’s site. 5.Employees are afraid to resume work I. The police department intervened SCENARIO 2 According to scenario 2, the followings are the threads and control measure was taken. THREADS CONTROL MEASURES 1.Explosion occurs at a chemical plant i. They took a precautionary measures by building the headquarters two (2) miles away from the chemical plant so as to loss of lives and properties. ii. Officials took control measure by trying to confirm amount of  potentially dangerous and deadly toxins that have released to the air so as to alert people to evacuate the area if the rate were high or if the area will not fit for lives to survive 2. people were experiencing Breathing difficulties i. Public health officials took a security measure by encouraging people living in the city to â€Å"shelter in place† i.e the use of a structure and its indoor atmosphere to temporarily separate individuals from a hazardous outdoor atmosphere. 3. Company tells the employee not to leave the building i. Employees took a precautionary measure by leaving the building since they were not sure about what they were hearing and that they needs to get home to take care of their families. ii. The security also took a control measure ,by knowing what tell people so as not to create unnecessary or false alarm to the who want to take shelter in company’s lobby. 4. Authority says the explosion was an occupational hazard i.e an accident. i. Several employees were hospitalized for quality treatment People are upset that cafeteria did not have more supply at hand. i. Due to the explosion, company took a security measure by closing the cafeteria for while pending the time that the immediate danger passes. SCENARIO 3 According to scenario 3, the followings are the threads and security measure to control it. THREADS SECURITY MEASURE 1.Pandemic flu outbreaks starts in Hongkong i. The company took a precautionary measure by telling the employees to have traveled to hongkong not to return to work until they see a doctor so as to prevent the spread of the flu within the organization since it is communicable disease ii. As a precautionary measure, the company decided having security at the front door to interrogate visitors whether they have been to hongkong for the past three weeks so as to prevent the spread of disease SUGGESTTIONS i. A quarantine system should also be implemented i.e the isolation of infected animals and people from the normal people. ii. Dust mask should always be used within the company vicinity till the flu suppresses . 2. Few people were diagnosed with the flue i.e infected i. Students took a security measure by absenting from school so as not to contact the flu. ii. As a precautionary measure, some employees did not do to work because they afraid to go out in public so as not to be infected with the deadly flu . 3.The disease spreads i. Employees are demanding for vaccinations and dust mask so as to cure and minimize the spread of the flu disease. ii. As a precautionary measure, managers consider letting crucial staff volunteer for a lock down i.e restricted to a confined area so as to prevent the spread of the disease. iii. They consider directing work to another location or calling retired workers to help out in order to prevent the spread of the disease within the organization 4.The has peaked. i. As a precautionary measure , employees were not whether to return to work so as not contact the deadly flu disease.

Thursday, November 7, 2019

New SAT Conversion Chart Old 2400 to New 1600 (Official)

New SAT Conversion Chart Old 2400 to New 1600 (Official) SAT / ACT Prep Online Guides and Tips In March 2016, the SAT underwent a massive redesign, part of which included a change to its scoring system: it shifted from a 2400-point scale to a 1600-point scale. But how do you compare a new SAT score with one on the old SAT 2400 scale? What scores are colleges looking for since some still don't have data on the new SAT? The official new SAT to old SAT conversion charts below offer the most accurate score conversions from one SAT to the other. If you need to convert your new SAT score to an old SAT score, or vice versa, simply use our handy conversion tool below to find your score. After you get your SAT conversion, keep reading- I tell you why it's easier to get a higher SAT score than before due to the new SAT scoring advantage (the new SAT score is higher in certain score regions!). Disappointed with your scores? Want to improve your SAT score by 160 points? We've written a guide about the top 5 strategies you must be using to have a shot at improving your score. Download it for free now: Old 2400 SAT to New 1600 SAT Conversion Tool If you've taken both the new SAT and old SAT and want to know which test you've done better on, this tool will do that automatically for you. Enter your old SAT scores on the LEFT to get your new SAT scores on the RIGHT. Enter your old 2400 SAT here: Old Math (max 800) Old Reading (max 800) Old Writing (max 800) Get new 1600 SAT scores here: Old Total SAT (max 2400) New Math (max 800) New Reading + Writing (max 800) New Total SAT (max 1600) // 800) { $(this).val(800); } var m = parseInt($("#in_old_math").val()); var w = parseInt($("#in_old_writing").val()); var c = parseInt($("#in_old_critical").val()); var old_r = w + c var old_total = m + c + w; var new_m; var new_r; var new_total; if (isNaN(m)) { $("#out_new_math").val(''); } else { switch (m) { case 200:new_m = 200;break;case 210:new_m = 220;break;case 220:new_m = 230;break;case 230:new_m = 250;break;case 240:new_m = 260;break;case 250:new_m = 280;break;case 260:new_m = 300;break;case 270:new_m = 310;break;case 280:new_m = 330;break;case 290:new_m = 340;break;case 300:new_m = 350;break;case 310:new_m = 360;break;case 320:new_m = 360;break;case 330:new_m = 370;break;case 340:new_m = 380;break;case 350:new_m = 390;break;case 360:new_m = 400;break;case 370:new_m = 410;break;case 380:new_m = 420;break;case 390:new_m = 430;break;case 400:new_m = 440;break;case 410:new_m = 450;break;case 420:new_m = 460;break;case 430:new_m = 470;break;case 440:new_m = 480;break;case 450:new_m = 490;break;case 460:new_m = 500;break;case 470:new_m = 510;break;case 480:new_m = 510;break;case 490:new_m = 520;break;case 500:new_m = 530;break;case 510:new_m = 540;break;case 520:new_m = 550;break;case 530:new_m = 560;break;case 540:new_m = 570;break;case 550:new_m = 570;break;ca se 560:new_m = 580;break;case 570:new_m = 590;break;case 580:new_m = 600;break;case 590:new_m = 610;break;case 600:new_m = 620;break;case 610:new_m = 630;break;case 620:new_m = 640;break;case 630:new_m = 650;break;case 640:new_m = 660;break;case 650:new_m = 670;break;case 660:new_m = 690;break;case 670:new_m = 700;break;case 680:new_m = 710;break;case 690:new_m = 720;break;case 700:new_m = 730;break;case 710:new_m = 740;break;case 720:new_m = 750;break;case 730:new_m = 760;break;case 740:new_m = 760;break;case 750:new_m = 770;break;case 760:new_m = 780;break;case 770:new_m = 780;break;case 780:new_m = 790;break;case 790:new_m = 800;break;case 800:new_m = 800;break; } $("#out_new_math").val(new_m); } if (isNaN(old_r)) { $("#out_new_verbal").val(''); } else { switch (old_r) { case 400:new_r = 200;break;case 410:new_r = 210;break;case 420:new_r = 220;break;case 430:new_r = 230;break;case 440:new_r = 240;break;case 450:new_r = 260;break;case 460:new_r = 270;break;case 470:new_r = 280;break;case 480:new_r = 290;break;case 490:new_r = 300;break;case 500:new_r = 310;break;case 510:new_r = 310;break;case 520:new_r = 320;break;case 530:new_r = 320;break;case 540:new_r = 330;break;case 550:new_r = 330;break;case 560:new_r = 330;break;case 570:new_r = 340;break;case 580:new_r = 340;break;case 590:new_r = 350;break;case 600:new_r = 350;break;case 610:new_r = 360;break;case 620:new_r = 360;break;case 630:new_r = 360;break;case 640:new_r = 370;break;case 650:new_r = 370;break;case 660:new_r = 380;break;case 670:new_r = 380;break;case 680:new_r = 390;break;case 690:new_r = 390;break;case 700:new_r = 400;break;case 710:new_r = 400;break;case 720:new_r = 410;break;case 730:new_r = 410;break;case 740:new_r = 420;break;case 750:new_r = 420;break;ca se 760:new_r = 430;break;case 770:new_r = 430;break;case 780:new_r = 440;break;case 790:new_r = 440;break;case 800:new_r = 450;break;case 810:new_r = 450;break;case 820:new_r = 460;break;case 830:new_r = 460;break;case 840:new_r = 470;break;case 850:new_r = 480;break;case 860:new_r = 480;break;case 870:new_r = 490;break;case 880:new_r = 490;break;case 890:new_r = 500;break;case 900:new_r = 500;break;case 910:new_r = 510;break;case 920:new_r = 510;break;case 930:new_r = 520;break;case 940:new_r = 530;break;case 950:new_r = 530;break;case 960:new_r = 540;break;case 970:new_r = 540;break;case 980:new_r = 550;break;case 990:new_r = 550;break;case 1000:new_r = 560;break;case 1010:new_r = 560;break;case 1020:new_r = 570;break;case 1030:new_r = 570;break;case 1040:new_r = 580;break;case 1050:new_r = 580;break;case 1060:new_r = 590;break;case 1070:new_r = 590;break;case 1080:new_r = 600;break;case 1090:new_r = 600;break;case 1100:new_r = 610;break;case 1110:new_r = 610;break;case 1120:new_r = 620;break;case 1130:new_r = 620;break;case 1140:new_r = 630;break;case 1150:new_r = 630;break;case 1160:new_r = 640;break;case 1170:new_r = 640;break;case 1180:new_r = 650;break;case 1190:new_r = 650;break;case 1200:new_r = 650;break;case 1210:new_r = 660;break;case 1220:new_r = 660;break;case 1230:new_r = 670;break;case 1240:new_r = 670;break;case 1250:new_r = 680;break;case 1260:new_r = 680;break;case 1270:new_r = 680;break;case 1280:new_r = 690;break;case 1290:new_r = 690;break;case 1300:new_r = 700;break;case 1310:new_r = 700;break;case 1320:new_r = 700;break;case 1330:new_r = 710;break;case 1340:new_r = 710;break;case 1350:new_r = 710;break;case 1360:new_r = 720;break;case 1370:new_r = 720;break;case 1380:new_r = 730;break;case 1390:new_r = 730;break;case 1400:new_r = 730;break;case 1410:new_r = 740;break;case 1420:new_r = 740;break;case 1430:new_r = 740;break;case 1440:new_r = 750;break;case 1450:new_r = 750;break;case 1460:new_r = 750;break;case 1470:new_r = 760;break;case 1480:new_r = 760;break;case 1490:new_r = 760;break;case 1500:new_r = 770;break;case 1510:new_r = 770;break;case 1520:new_r = 770;break;case 1530:new_r = 780;break;case 1540:new_r = 780;break;case 1550:new_r = 780;break;case 1560:new_r = 790;break;case 1570:new_r = 790;break;case 1580:new_r = 800;break;case 1590:new_r = 800;break;case 1600:new_r = 800;break; } $("#out_new_verbal").val(new_r); } if (!isNaN(old_total)) { $("#out_old_total").val(old_total); switch (old_total) {case 600: new_total = 400; break; case 610: new_total = 410; break; case 620: new_total = 420; break; case 630: new_total = 430; break; case 640: new_total = 440; break; case 650: new_total = 450; break; case 660: new_total = 460; break; case 670: new_total = 470; break; case 680: new_total = 480; break; case 690: new_total = 490; break; case 700: new_total = 500; break; case 710: new_total = 510; break; case 720: new_total = 520; break; case 730: new_total = 530; break; case 740: new_total = 540; break; case 750: new_total = 550; break; case 760: new_total = 560; break; case 770: new_total = 580; break; case 780: new_total = 590; break; case 790: new_total = 600; break; case 800: new_total = 610; break; case 810: new_total = 620; break; case 820: new_total = 630; break; case 830: new_total = 640; break; case 840: new_total = 650; break; case 850: new_total = 660; break; case 860: new_total = 670; break; case 870: new_total = 680; break; case 880: new_total = 690; break; case 890: new_total = 690; break; case 900: new_total = 700; break; case 910: new_total = 710; break; case 920: new_total = 710; break; case 930: new_total = 720; break; case 940: new_total = 730; break; case 950: new_total = 730; break; case 960: new_total = 740; break; case 970: new_total = 740; break; case 980: new_total = 750; break; case 990: new_total = 760; break; case 1000: new_total = 760; break; case 1010: new_total = 770; break; case 1020: new_total = 780; break; case 1030: new_total = 780; break; case 1040: new_total = 790; break; case 1050: new_total = 800; break; case 1060: new_total = 800; break; case 1070: new_total = 810; break; case 1080: new_total = 810; break; case 1090: new_total = 820; break; case 1100: new_total = 830; break; case 1110: new_total = 830; break; case 1120: new_total = 840; break; case 1130: new_total = 850; break; case 1140: new_total = 850; break; case 1150: new_total = 860; break; case 1160: new_total = 870; break; case 1170: new_ total = 870; break; case 1180: new_total = 880; break; case 1190: new_total = 890; break; case 1200: new_total = 890; break; case 1210: new_total = 900; break; case 1220: new_total = 910; break; case 1230: new_total = 910; break; case 1240: new_total = 920; break; case 1250: new_total = 930; break; case 1260: new_total = 930; break; case 1270: new_total = 940; break; case 1280: new_total = 950; break; case 1290: new_total = 950; break; case 1300: new_total = 960; break; case 1310: new_total = 970; break; case 1320: new_total = 980; break; case 1330: new_total = 980; break; case 1340: new_total = 990; break; case 1350: new_total = 1000; break; case 1360: new_total = 1000; break; case 1370: new_total = 1010; break; case 1380: new_total = 1020; break; case 1390: new_total = 1020; break; case 1400: new_total = 1030; break; case 1410: new_total = 1030; break; case 1420: new_total = 1040; break; case 1430: new_total = 1050; break; case 1440: new_total = 1050; break; case 1450: new_total = 1060; break; case 1460: new_total = 1070; break; case 1470: new_total = 1070; break; case 1480: new_total = 1080; break; case 1490: new_total = 1090; break; case 1500: new_total = 1090; break; case 1510: new_total = 1100; break; case 1520: new_total = 1110; break; case 1530: new_total = 1110; break; case 1540: new_total = 1120; break; case 1550: new_total = 1120; break; case 1560: new_total = 1130; break; case 1570: new_total = 1140; break; case 1580: new_total = 1140; break; case 1590: new_total = 1150; break; case 1600: new_total = 1160; break; case 1610: new_total = 1160; break; case 1620: new_total = 1170; break; case 1630: new_total = 1180; break; case 1640: new_total = 1180; break; case 1650: new_total = 1190; break; case 1660: new_total = 1200; break; case 1670: new_total = 1200; break; case 1680: new_total = 1210; break; case 1690: new_total = 1210; break; case 1700: new_total = 1220; break; case 1710: new_total = 1230; break; case 1720: new_total = 1230; break; case 1730: new_total = 1240; break; case 1740: new_total = 1250; break; case 1750: new_total = 1250; break; case 1760: new_total = 1260; break; case 1770: new_total = 1270; break; case 1780: new_total = 1270; break; case 1790: new_total = 1280; break; case 1800: new_total = 1290; break; case 1810: new_total = 1290; break; case 1820: new_total = 1300; break; case 1830: new_total = 1300; break; case 1840: new_total = 1310; break; case 1850: new_total = 1320; break; case 1860: new_total = 1320; break; case 1870: new_total = 1330; break; case 1880: new_total = 1340; break; case 1890: new_total = 1340; break; case 1900: new_total = 1350; break; case 1910: new_total = 1350; break; case 1920: new_total = 1360; break; case 1930: new_total = 1370; break; case 1940: new_total = 1370; break; case 1950: new_total = 1380; break; case 1960: new_total = 1380; break; case 1970: new_total = 1390; break; case 1980: new_total = 1400; break; case 1990: new_total = 1400; break; case 2000: new_total = 1410; break; case 2010: new_total = 1410; break; case 2020: new_total = 1420; break; case 2030: new_total = 1430; break; case 2040: new_total = 1430; break; case 2050: new_total = 1440; break; case 2060: new_total = 1440; break; case 2070: new_total = 1450; break; case 2080: new_total = 1450; break; case 2090: new_total = 1460; break; case 2100: new_total = 1470; break; case 2110: new_total = 1470; break; case 2120: new_total = 1480; break; case 2130: new_total = 1480; break; case 2140: new_total = 1490; break; case 2150: new_total = 1490; break; case 2160: new_total = 1500; break; case 2170: new_total = 1500; break; case 2180: new_total = 1510; break; case 2190: new_total = 1510; break; case 2200: new_total = 1510; break; case 2210: new_total = 1520; break; case 2220: new_total = 1520; break; case 2230: new_total = 1530; break; case 2240: new_total = 1530; break; case 2250: new_total = 1540; break; case 2260: new_total = 1540; break; case 2270: new_total = 1550; break; case 2280: new_total = 15 50; break; case 2290: new_total = 1550; break; case 2300: new_total = 1560; break; case 2310: new_total = 1560; break; case 2320: new_total = 1570; break; case 2330: new_total = 1570; break; case 2340: new_total = 1580; break; case 2350: new_total = 1580; break; case 2360: new_total = 1590; break; case 2370: new_total = 1590; break; case 2380: new_total = 1590; break; case 2390: new_total = 1600; break; case 2400: new_total = 1600; break; } $("#out_new_total").val(new_total); var old_to_new_error_payload = "Why don't the section scores add up to the total score? Summing ".concat(new_m.toString()," and ",new_r.toString()," gives ",(new_m+new_r).toString(),", not ",new_total.toString(),"! The reason is that the College Board has one conversion table for individual sections (like Math to Math), and another for total to total conversion. They try to make each individual conversion as accurate as possible, which leads to some inconsistencies. You can read more here.Long story short? Don't worry about it. These are only meant to be estimates anyway. The two totals are ",Math.abs(new_total-new_r-new_m).toString()," points apart - just split the difference and use that value for what you need."); if (new_total != (new_r + new_m)) { document.getElementById("old_to_new_error").innerHTML = old_to_new_error_payload; } else { document.getElementById("old_to_new_error").innerHTML = ""; } } else { $("#out_old_total").val(''); $("#out_new_total").val(''); document.getElementById("old_to_new_error").innerHTML = ""; } }); }); // ]]> New 1600 SAT to Old 2400 SAT Conversion Tool Alternatively, if you want to input your new SAT scores and get old SAT scores, here's how to do it: Enter your new 1600 SAT here: New Math (max 800) New Reading + Writing (max 800) Get old 2400 SAT scores here: New Total SAT (max 1600) Old Math (max 800) Old Reading + Writing (max 1600) Old Total SAT (max 2400) // 800) { $(this).val(800); } var new_m = parseInt($("#in_new_math").val()); var new_v = parseInt($("#in_new_verbal").val()); new_total = new_m + new_v var old_m; var old_v; var old_total; if (isNaN(new_m)) { $("#out_old_math").val(''); } else { switch (new_m) { case 200: old_m = 200; break; case 210: old_m = 200; break; case 220: old_m = 210; break; case 230: old_m = 220; break; case 240: old_m = 220; break; case 250: old_m = 230; break; case 260: old_m = 240; break; case 270: old_m = 240; break; case 280: old_m = 250; break; case 290: old_m = 260; break; case 300: old_m = 260; break; case 310: old_m = 270; break; case 320: old_m = 280; break; case 330: old_m = 280; break; case 340: old_m = 290; break; case 350: old_m = 300; break; case 360: old_m = 310; break; case 370: old_m = 330; break; case 380: old_m = 340; break; case 390: old_m = 350; break; case 400: old_m = 360; break; case 410: old_m = 370; break; case 420: old_m = 380; break; case 430: old_m = 390; break; case 440: old_m = 400; break; case 450: old_m = 410; break; case 460: old_m = 420; break; case 470: old_m = 430; break; case 480: old_m = 440; break; case 490: old_m = 450; break; case 500: old_m = 460; break; case 510: old_m = 470; break; case 520: old_ m = 490; break; case 530: old_m = 500; break; case 540: old_m = 510; break; case 550: old_m = 520; break; case 560: old_m = 530; break; case 570: old_m = 550; break; case 580: old_m = 560; break; case 590: old_m = 570; break; case 600: old_m = 580; break; case 610: old_m = 590; break; case 620: old_m = 600; break; case 630: old_m = 610; break; case 640: old_m = 620; break; case 650: old_m = 630; break; case 660: old_m = 640; break; case 670: old_m = 650; break; case 680: old_m = 650; break; case 690: old_m = 660; break; case 700: old_m = 670; break; case 710: old_m = 680; break; case 720: old_m = 690; break; case 730: old_m = 700; break; case 740: old_m = 710; break; case 750: old_m = 720; break; case 760: old_m = 740; break; case 770: old_m = 750; break; case 780: old_m = 760; break; case 790: old_m = 780; break; case 800: old_m = 800; break; } $("#out_old_math").val(old_m); } if (isNaN(new_v)) { $("#out_old_critical").val(''); } else { switch (new_v) { case 200:old_v = 400;break;case 210:old_v = 410;break;case 220:old_v = 420;break;case 230:old_v = 430;break;case 240:old_v = 440;break;case 250:old_v = 440;break;case 260:old_v = 450;break;case 270:old_v = 460;break;case 280:old_v = 470;break;case 290:old_v = 480;break;case 300:old_v = 490;break;case 310:old_v = 500;break;case 320:old_v = 520;break;case 330:old_v = 550;break;case 340:old_v = 570;break;case 350:old_v = 600;break;case 360:old_v = 620;break;case 370:old_v = 640;break;case 380:old_v = 660;break;case 390:old_v = 690;break;case 400:old_v = 710;break;case 410:old_v = 730;break;case 420:old_v = 750;break;case 430:old_v = 770;break;case 440:old_v = 790;break;case 450:old_v = 800;break;case 460:old_v = 820;break;case 470:old_v = 840;break;case 480:old_v = 860;break;case 490:old_v = 880;break;case 500:old_v = 890;break;case 510:old_v = 910;break;case 520:old_v = 930;break;case 530:old_v = 950;break;case 540:old_v = 970;break;case 550:old_v = 990;break;ca se 560:old_v = 1010;break;case 570:old_v = 1020;break;case 580:old_v = 1040;break;case 590:old_v = 1060;break;case 600:old_v = 1080;break;case 610:old_v = 1100;break;case 620:old_v = 1120;break;case 630:old_v = 1150;break;case 640:old_v = 1170;break;case 650:old_v = 1190;break;case 660:old_v = 1210;break;case 670:old_v = 1240;break;case 680:old_v = 1260;break;case 690:old_v = 1290;break;case 700:old_v = 1310;break;case 710:old_v = 1340;break;case 720:old_v = 1370;break;case 730:old_v = 1390;break;case 740:old_v = 1420;break;case 750:old_v = 1450;break;case 760:old_v = 1480;break;case 770:old_v = 1510;break;case 780:old_v = 1540;break;case 790:old_v = 1560;break;case 800:old_v = 1590;break; } $("#out_old_critical").val(old_v); } if (!isNaN(new_total)) { $("#out_new_total2").val(new_total); switch(new_total) { case 400: old_total = 600; break;case 410: old_total = 610; break;case 420: old_total = 620; break;case 430: old_total = 630; break;case 440: old_total = 640; break;case 450: old_total = 650; break;case 460: old_total = 660; break;case 470: old_total = 670; break;case 480: old_total = 680; break;case 490: old_total = 690; break;case 500: old_total = 700; break;case 510: old_total = 710; break;case 520: old_total = 720; break;case 530: old_total = 730; break;case 540: old_total = 730; break;case 550: old_total = 740; break;case 560: old_total = 750; break;case 570: old_total = 760; break;case 580: old_total = 770; break;case 590: old_total = 780; break;case 600: old_total = 790; break;case 610: old_total = 800; break;case 620: old_total = 810; break;case 630: old_total = 820; break;case 640: old_total = 830; break;case 650: old_total = 840; break;case 660: old_total = 850; break;case 670: old_total = 860; break;case 680: old_total = 870; break;case 690: old_total = 880; break;ca se 700: old_total = 900; break;case 710: old_total = 910; break;case 720: old_total = 930; break;case 730: old_total = 950; break;case 740: old_total = 960; break;case 750: old_total = 980; break;case 760: old_total = 990; break;case 770: old_total = 1010; break;case 780: old_total = 1030; break;case 790: old_total = 1040; break;case 800: old_total = 1060; break;case 810: old_total = 1070; break;case 820: old_total = 1090; break;case 830: old_total = 1110; break;case 840: old_total = 1120; break;case 850: old_total = 1140; break;case 860: old_total = 1150; break;case 870: old_total = 1170; break;case 880: old_total = 1180; break;case 890: old_total = 1200; break;case 900: old_total = 1210; break;case 910: old_total = 1220; break;case 920: old_total = 1240; break;case 930: old_total = 1250; break;case 940: old_total = 1270; break;case 950: old_total = 1280; break;case 960: old_total = 1300; break;case 970: old_total = 1310; break;case 980: old_total = 1330; break;case 990: old_total = 1340; break;case 1000: old_total = 1360; break;case 1010: old_total = 1370; break;case 1020: old_total = 1390; break;case 1030: old_total = 1400; break;case 1040: old_total = 1420; break;case 1050: old_total = 1430; break;case 1060: old_total = 1450; break;case 1070: old_total = 1460; break;case 1080: old_total = 1480; break;case 1090: old_total = 1490; break;case 1100: old_total = 1510; break;case 1110: old_total = 1530; break;case 1120: old_total = 1540; break;case 1130: old_total = 1560; break;case 1140: old_total = 1570; break;case 1150: old_total = 1590; break;case 1160: old_total = 1610; break;case 1170: old_total = 1620; break;case 1180: old_total = 1640; break;case 1190: old_total = 1650; break;case 1200: old_total = 1670; break;case 1210: old_total = 1680; break;case 1220: old_total = 1700; break;case 1230: old_total = 1710; break;case 1240: old_total = 1730; break;case 1250: old_total = 1750; break;case 1260: old_total = 1760; break;case 1270: old_total = 1780; break;cas e 1280: old_total = 1790; break;case 1290: old_total = 1810; break;case 1300: old_total = 1820; break;case 1310: old_total = 1840; break;case 1320: old_total = 1850; break;case 1330: old_total = 1870; break;case 1340: old_total = 1880; break;case 1350: old_total = 1900; break;case 1360: old_total = 1920; break;case 1370: old_total = 1930; break;case 1380: old_total = 1950; break;case 1390: old_total = 1970; break;case 1400: old_total = 1990; break;case 1410: old_total = 2000; break;case 1420: old_total = 2020; break;case 1430: old_total = 2040; break;case 1440: old_total = 2060; break;case 1450: old_total = 2080; break;case 1460: old_total = 2090; break;case 1470: old_total = 2110; break;case 1480: old_total = 2130; break;case 1490: old_total = 2150; break;case 1500: old_total = 2170; break;case 1510: old_total = 2190; break;case 1520: old_total = 2210; break;case 1530: old_total = 2230; break;case 1540: old_total = 2260; break;case 1550: old_total = 2280; break;case 1560: old_total = 2300; break;case 1570: old_total = 2330; break;case 1580: old_total = 2350; break;case 1590: old_total = 2370; break;case 1600: old_total = 2390; break; } $("#out_old_total2").val(old_total); var new_to_old_error_payload = "Why don't the old section scores add up to the old total score? Summing ".concat(old_m.toString()," and ",old_v.toString()," gives ",(old_m+old_v).toString(),", not ",old_total.toString(),"! The reason is that the College Board has one conversion table for individual sections (like Math to Math), and another for total to total conversion. They try to make each individual conversion as accurate as possible, which leads to some inconsistencies. You can read more here.Long story short? Don't worry about it. These are only meant to be estimates anyway. The two totals are ",Math.abs(old_total-old_m-old_v).toString()," points apart - just split the difference and use that value for what you need."); if (old_total != (old_v + old_m)) { document.getElementById("new_to_old_error").innerHTML = new_to_old_error_payload; } else { document.getElementById("new_to_old_error").innerHTML = ""; } } else { $("#out_old_total2").val(''); $("#out_new_total2").val(''); document.getElementById("new_to_old_error").innerHTML = ""; } }); }); // ]]> Official Old SAT to New SAT Conversion Charts We created our conversion tools above using the College Board's official SAT conversion charts. Now, we give you actual conversion tables so that you can see more clearly how new SAT scores match up with old SAT scores (and vice versa). Before you use these tables, know that the most accurate conversion method is to split up the score conversion section by section. In other words, don't just use the College Board's total composite conversion chart (from 2400 to 1600); these can be inaccurate as they ignore the fact that individual sections convert scores differently. For example, if you're converting from an old SAT score to a new SAT score, you'd do the following: Get your old SAT Math score (out of 800) and convert it to a new SAT Math score (out of 800). Get your old Reading + Writing score (out of 1600) and convert it to a new SAT Reading + Writing score (out of 800). Old SAT Math to New SAT Math Conversion Table Math is straightforward because both the new SAT and old SAT Math sections are out of 800. Old SAT Math New SAT Math Old SAT Math New SAT Math Old SAT Math New SAT Math 800 800 600 620 400 440 790 800 590 610 390 430 780 790 580 600 380 420 770 780 570 590 370 410 760 780 560 580 360 400 750 770 550 570 350 390 740 760 540 570 340 380 730 760 530 560 330 370 720 750 520 550 320 360 710 740 510 540 310 360 700 730 500 530 300 350 690 720 490 520 290 340 680 710 480 510 280 330 670 700 470 510 270 310 660 690 460 500 260 300 650 670 450 490 250 280 640 660 440 480 240 260 630 650 430 470 230 250 620 640 420 460 220 230 610 630 410 450 210 220 200 200 Old SAT Reading + Writing to New SAT Reading + Writing Conversion Table On the old SAT, Reading and Writing were separate sections, each out of 800. On the new SAT, however, these two sections are combined for a total Evidence-Based Reading and Writing (EBRW) score out of 800. In this table, we added the old SAT Reading and Writing sections together to get a single Reading and Writing score out of 1600. Old R+W New R+W Old R+W New R+W Old R+W New R+W 1600 800 1200 650 800 450 1590 800 1190 650 790 440 1580 800 1180 650 780 440 1570 790 1170 640 770 430 1560 790 1160 640 760 430 1550 780 1150 630 750 420 1540 780 1140 630 740 420 1530 780 1130 620 730 410 1520 770 1120 620 720 410 1510 770 1110 610 710 400 1500 770 1100 610 700 400 1490 760 1090 600 690 390 1480 760 1080 600 680 390 1470 760 1070 590 670 380 1460 750 1060 590 660 380 1450 750 1050 580 650 370 1440 750 1040 580 640 370 1430 740 1030 570 630 360 1420 740 1020 570 620 360 1410 740 1010 560 610 360 1400 730 1000 560 600 350 1390 730 990 550 590 350 1380 730 980 550 580 340 1370 720 970 540 570 340 1360 720 960 540 560 330 1350 710 950 530 550 330 1340 710 940 530 540 330 1330 710 930 520 530 320 1320 700 920 510 520 320 1310 700 910 510 510 310 1300 700 900 500 500 310 1290 690 890 500 490 300 1280 690 880 490 480 290 1270 680 870 490 470 280 1260 680 860 480 460 270 1250 680 850 480 450 260 1240 670 840 470 440 240 1230 670 830 460 430 230 1220 660 820 460 420 220 1210 660 810 450 410 210 400 200 Using the two section tables above, you can convert any scores from the new SAT to the old SAT, and vice versa. You can then add up the scores you find to get your composite score. Want to get serious about improving your SAT score? We have the leading online SAT prep program that will raise your score by 160+ points, guaranteed. Exclusive to our program, we have an expert SAT instructor grade each of your SAT essays and give you customized feedback on how to improve your score. This can mean an instant jump of 80 points on the Writing section alone. Check out our 5-day free trial and sign up for free: Composite New SAT to Old SAT Conversion Chart This SAT conversion table is the one I recommend not using since it goes from composite score to composite score. This manner of translating scores is less accurate than splitting up your composite score section by section as recommended above. For example, here are two scenarios of a student with an 1800 score on the old SAT. If you just use the table below, you'd get 1290 as your new total SAT score. But this is just an approximation- if you use your section scores, you end up with entirely different conversions! Scenario 1 Old SAT Math: 800 Reading: 600 Writing: 400 Composite: 1800/2400 New SAT New Math: 800 New Reading + Writing: 560 New Composite: 1360/1600 Scenario 2 Old SAT Math: 600 Reading: 600 Writing: 600 Composite: 1800/2400 New SAT New Math: 620 New Reading + Writing: 650 New Composite: 1270/1600 Notice how in both scenarios, the old composite score adds up to 1800, but the new composite score varies by nearly 100 points. Once again, if you were to use the table below, you'd get 1290 for both, but this conversion is clearly less accurate since the two scenarios above yield wildly different scores when converting by section. Regardless, here's the official SAT composite score conversion chart for your reference: New SAT Old SAT New SAT Old SAT New SAT Old SAT 1600 2390 1200 1670 800 1060 1590 2370 1190 1650 790 1040 1580 2350 1180 1640 780 1030 1570 2330 1170 1620 770 1010 1560 2300 1160 1610 760 990 1550 2280 1150 1590 750 980 1540 2260 1140 1570 740 960 1530 2230 1130 1560 730 950 1520 2210 1120 1540 720 930 1510 2190 1110 1530 710 910 1500 2170 1100 1510 700 900 1490 2150 1090 1490 690 880 1480 2130 1080 1480 680 870 1470 2110 1070 1460 670 860 1460 2090 1060 1450 660 850 1450 2080 1050 1430 650 840 1440 2060 1040 1420 640 830 1430 2040 1030 1400 630 820 1420 2020 1020 1390 620 810 1410 2000 1010 1370 610 800 1400 1990 1000 1360 600 790 1390 1970 990 1340 590 780 1380 1950 980 1330 580 770 1370 1930 970 1310 570 760 1360 1920 960 1300 560 750 1350 1900 950 1280 550 740 1340 1880 940 1270 540 730 1330 1870 930 1250 530 730 1320 1850 920 1240 520 720 1310 1840 910 1220 510 710 1300 1820 900 1210 500 700 1290 1810 890 1200 490 690 1280 1790 880 1180 480 680 1270 1780 870 1170 470 670 1260 1760 860 1150 460 660 1250 1750 850 1140 450 650 1240 1730 840 1120 440 640 1230 1710 830 1110 430 630 1220 1700 820 1090 420 620 1210 1680 810 1070 410 610 400 600 What Does the Conversion Chart Say About the New SAT? The official conversion tables show that the new SAT has higher scores than expected across the entire score range. For a full explanation, read our guide on the new SAT scoring advantage. That said, I'll summarize the main points below. Without the College Board's concordance table, you might imagine that you could just multiply the old SAT score by 2/3 to get your new SAT score. For example, 2400 * 2/3 = 1600. Or, 1800 * 2/3 = 1200. In fact, new SAT scores are much higher than this simple formula would predict. An 1800 on the old SAT actually translates to 1280- that's 80 points higher than 1200. Likewise, a 1500 on the old SAT translates to 1100, or 100 points higher than 1000. This also reflects section by section. A 700 on the old SAT Math section is equivalent to a 730 on the new SAT Math section, while a 500 on the old SAT is equivalent to a 530 on the new SAT. What this means is that for the same performance on Math, you get a higher score on the new SAT than you would have on the old SAT. So what does this mean for you? Some people worry that this means grade inflation is happening, and that scores are creeping up. But I'm not personally worried about it, and you don't need to be either. The College Board will always grade the SAT in such a way that top students can be distinguished from average students, and average students from below-average students. What really matters is your score percentile, and the score that colleges believe is good. If everyone's SAT score goes up, then colleges will require higher scores for admission as well. This doesn't mean anything about how hard it is to get that score- the difficulty is likely going to stay similar. For now, just focus on studying for the SAT and getting the highest score possible! What’s Next? Curious about how the new SAT scoring system benefits you? Read our comprehensive guide to the new SAT scoring advantage to learn how the current version of the SAT gives you optically higher scores over a range of scores. Want to get a perfect SAT score? Then check out our guide on getting a 1600 SAT score, written by a perfect SAT scorer. What's a good SAT score for you? The answer to this question depends on your goals. Learn how to calculate a great SAT target score in our in-depth guide. Disappointed with your scores? Want to improve your SAT score by 160 points? We've written a guide about the top 5 strategies you must be using to have a shot at improving your score. Download it for free now: