Sistem Informasi Keperilakuan

Create E-Commerce web by using opensource software osCommerce

2009-07-29T21:44:00.000-07:00

About osCommerce

osCommerce is an online shop e-commerce solution that offers a wide range of out-of-the-box features that allows online stores to be setup fairly quickly with ease, and is available for free as an Open Source based solution released under the GNU General Public License.

osCommerce was started in March 2000 and has since matured to a solution that is currently powering 14,120 registered live shops around the world.

Today, osCommerce has been taken to the next level, moving towards an e-commerce framework solution that not only remains easy to setup and maintain, but also making it easier for store administrators to present their stores to their customers with their own unique requirements.

The success of osCommerce is secured by a great and active community where members help one another out and participate in development issues reflecting upon the current state of the project.

You are more than welcome to contribute to the success of osCommerce by helping out in the realization of the project, by participating in the forums, by donating to the team developers and sponsoring the project, or just by spreading the word!

osCommerce Online Merchant
Showcases
Visit the Demonstrations page for online demonstrations.

Visit the Live Shops Directory site for a list of worldwide online shops powered by osCommerce.
osCommerce Online Merchant v2.2 Release Candidate 2a
osCommerce Online Merchant is a complete online store solution that contains both a catalog frontend and an administration tool backend which can be easily installed and configured over a web-based installation procedure.

Catalog Screenshots
Administration Tool Screenshots

Downloads:
osCommerce Online Merchant v2.2 Release Candidate 2a
oscommerce-2.2rc2a.zip (2,359,052 bytes; md5: 31bb9f5f2bd93d18666f8e41c8283147)

Release Announcements
osCommerce Online Merchant v2.2 RC2 Released
osCommerce Online Merchant v2.2 RC2a Released
Server Requirements
PHP v3+ (PHP v5+ recommended)
MySQL v3+ (MySQL v5+ recommended)

osCommerce Online Merchant v3.0 Alpha 5
oscommerce-3.0a5.zip (3,803,184 bytes; sha1: 4a35e809849a596b886a485002767542c353097d)

Server Requirements
PHP v5.2.0+ (with MySQLi extension)
MySQL v4.1.13+ or v5.0.7+

Product reviews and price comparisons

2009-06-16T11:30:00.001-07:00

Appliances
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Beauty
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Cameras
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Cell phones
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Computers
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Electronics
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GPS
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MP3
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Players
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TVs
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Video Games
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Movies
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»More

Ciao is a multi-million-strong online community that critically reviews and rates millions of products and services for the benefit of other consumers. Available free of charge to consumers in local language versions in major western European and North American markets, Ciao combines unbiased consumer reviews and up-to-date price information from hundreds of online retailers to make it the most comprehensive source of intelligent shopping on the web. Testimony to Ciao's success, more than 38 million consumers visit the site every month, making it one of the largest shopping portals in North America and Europe.

How can I take an active part in Ciao?
You can register free and it's easy. As soon as your Ciao account has been activated (this usually only takes a few minutes), you can start to actively participate in Ciao and earn money!

Does taking part cost anything?
No, participating in Ciao costs you nothing and is completely obligation free – and in fact, by actively participating on Ciao, you can even earn money

What can Ciao do for users?
Ciao is the most comprehensive source of intelligent shopping on the web. Ciao presents users with a range of valuable information on consumer goods and services on the market, including authentic reviews by other users and a comparison of the prices available at the most competitive online shops. Whether you're looking for the best DVD player, the most reliable electricity provider, or the best price for a particular model of cell phone, you can be sure to find the information you need on Ciao.

What can Ciao do for businesses?
Ciao offers a highly effective marketing environment for merchants and advertisers, where they can access well-informed customers in the critical moment when they are taking purchase decisions. Of the more than 38 million monthly visitors (Source: Omniture Dec 2006) to the platform, more than 2 million move on to merchants and buy. Businesses who would like to get in touch with us should visit our corporate website for an overview of our services.

How does Ciao earn money?
Ciao earns money from online advertising with retailers who pay to have their online stores linked from the site. We never sell our members' e-mail addresses or earn money from member data in any way. To find out more about how Ciao uses your data, please read our Privacy Policy.

The Impact of Information Technology Application to the Enterprise Design Factors and Competitive Advantage of Manufacturing Industries in Indonesia

2009-05-29T18:30:00.000-07:00

Oleh: SUTRISNO, JOKO
Email: library@lib.unair.ac.id; library@unair.ac.id
Post Graduate Airlangga University
Dibuat: 2007-01-29 , dengan 1 file(s).

Keywords: Information technology, enterprise design factors, competitive advantage of information technology, manufacturing industry.

Subject: STRATEGIC PLANNING; INFORMATION TECHNOLOGY

The current globalization era will bring manufacturing industries to a sharper global competition. The one who win in the competition, are those having competitive advantage. The competitive advantages of manufacturing industries are characterized by having operation efficiency, suitable products, quality products and opportunity to spawn new business. The manufacturing industries, those are not efficient and less competitive, they will soon disappear and out of market.

This study is assessing the application of Information Technology in manufacturing industries to increase its competitive advantage. The application of Information Technology in this study is defined in the form of computer integrated manufacturing (CIM) system, which will cover the following activities (1) office automation (2) manufacturing resources planning system (3) manufacturing communication (4) computer aided product design and (5) manufacturing process system.

Application of Information Technology in manufacturing process will leverage the competitive advantage of manufacturing industries, as it will increase business operation efficiency, faster products differentiation, products quality improvement, and spawning new business,

However investment in information technology is considered costly, require long-term preparation and need strong coordination in all aspect of the manufacturing organization. Information Technology investment should be well planned and calculated accordingly to avoid the unnecessary kit due to the disability in operating the Information Technology based equipment system

Enterprise design factors of manufacturing industries such as training, business process re-engineering, system design, technology management and production planning as part of the integrated system of manufacturing industries will support and intensify the utility of Information Technology based equipments to bring about the competitive advantage of manufacturing industries in Indonesia.
It is suggested that full and thorough understanding to the role of Information Technology based equipment will enable enterprise in reducing risk and cost involved in Information Technology investment and increase the enterprise efficiency, products quality and provide capacity in spawning new related business

Hot spot security

2009-05-15T19:27:00.000-07:00

If we make e-banking transactions by using the hotspot of Internet,not at home, will it to be a problem?. I have heard that using the E-Banking in Warnet or computer of office is very dangerous, so when using the laptop to make a personal E-Banking using in Hotspot also to be dangerous? why?

the issues are exactly in the level of security, good security and personal security of the network. if we make a transaction of e-banking through a network such as HP or telkom, the security level is also not different from their employer.

but if we do it via the cafe or hotspot, it’s security is sometimes ignored, even though not all there so that even more secure than large companies. admin warnet often fraudulent see the customer’s data. This is not actually the correct ethica. or sometimes because of the speed so the security level is revealed.

sometimes the customers themselves also make sure to logout something that is not important that make their security of their file is easily to break.

In each free hot spot, anyone can have someone that act as a "man in the middle," that resoluble retrieve any data from you via the access point.

Some banks are already adding token pin with random algorithms do, but you can imagine that if this scenario happens you, your username and password was successfully taken by someone, and he successfully log in to your internet banking. The thief tried to buy an item online, automated system will ask the bank security pin verification, the skeleton has 3 times the chance to hit the pinnya, and the third one so that your account terblokir itself.

As we do, how difficult and complex the procedures to re-enable an account lock. I once account lock that happened cause my own carelessness and take 3 DAYS to return.

Can you imagine if this happens more than 1 time, how much opportunity and the time you do terbuang only to manage the account.

Bank Permata spent U.S. $ 15 Million for IT

2009-05-15T19:06:00.000-07:00

Jakarta - PT Bank Permata Tbk allocates it’s capital expenditures for the development of information technology (IT) in 2009 of $ 15 million.

"Our IT Expenditure this year is not different from the last year, such as $ 15 million," said Director of IT & Operations Bank Permata J. Giorgino Godong, after demo PonselPay in Permata Building, Jakarta, Thursday (14/5/2009).

He breaks down, the composition of the largest IT spending of funds intended for the procurement of machinery and devices such as servers, computers, including software (software). It’s remain spent for component of communication include telephone and internet as well as the procurement of automatic teller machines (ATM).

He also explained, for given period, computer as one of the main device for their services is also improved it’s capacity.

Currently, the number of permata’s customers is about one million accounts. According Georgino, 300,000 of them actively using mobile banking services with the number of transactions up to 300,000 traffic times.

He also estimates, the number of permata’s users on mobile banking will continues increase as fast as the increasing of the ease of transaction and efficiency of the time of transaction that if it is done by it.

Bank Permata has a number of 549 ATM units and the number of branch offices reached 277 offices in 55 cities. While the ATM network of cooperation with the national banking sector reached 17,000 ATMs. (Rou / faw)

IT Internship Program for SMEs

2009-05-15T18:22:00.000-07:00

Jakarta - the financial crisis makes small medium business (SMEs) strict the budget for technology information(IT). Therefore they are very happy with the IT internship program.

The Internship program titled Industry Attachment Program (IAP) was initiated by Senada and Cisco. In Jakarta, a number of students from the University of Bina Nusantara (BiNus) get internship opportunities in a number of SMEs.

the SMEs also seem happy with this program. For example Multichem Indojasa Artaprima PT, a firm metal services penyepuhan, says that they doesn’t know how to manage their limited IT budget.

"Maybe later, if students have internships in our firm, we can manage our budget IT and spend it well. Exactly, we do not know about what IT products that we need," said Ula Fauzi, Director of Marketing Multichem Indojasa Artaprima the told to detikINET on the sidelines of the event conference kick-off in the IAP Campus BiNus Syahdan, Jakarta, Tuesday (23/12/2008).

Ula also confess, that their company is in the domino effect of the crisis that is happening at this time. The decline in orders from customers to reach 20 percent.

By this internship program,Ula said, the company is to be able to operate well. "The Students of the program can help us help us to create a company database, and payroll, and network internal.

Senior Advisor ICT Industry Farid Maruf also says that SMEs need to be encouraged to optimize the utilization of IT that can support their business activities. Meanwhile, for students, Farid hope they can replicate the program in the future.
(Wsh / wsh)

'70% IT expenditure in firm just for maintenance

2009-05-15T17:58:00.000-07:00

Jakarta – A research by VMWare’s (a virtualization technology solutions company)shows, 70% of technology information budget expenditure was spent only for treatment (maintenance).

"it is right. More than 70% of the company out of money just to ensure that IT systems run well," said Regional Director, ASEAN MWare, Koh Eng Kheng, in a press meet in Interkontinental Hotel, Jakarta, Thursday (14/5/2009).

He breaks down, 70% of the cost includes the cost of application maintenance (30%) and maintenance for infrastructure (42%). While the rest, 23% for new investment applications and 5% for infrastructure investment.

In the middle of a crisis situation such as this, IT got clear challenges. Especially in the budget is not increased but the demand to maximize the company's IT business as a means of supporting the more increased.

"That's why we offer a virtualization solution as a way out. With virtualization solutions, and the operational budget can be pressed up to 50% without reducing the features and functions of IT is required," continued Koh Eng Kheng.

VMWare just released its own new operating system virtualization product VShpere 4. This solution is considered effective to build a “cloud computing” computing system that is affordable, even for middle-class companies (SMEs).

Koh Eng Kheng rate virtual computing solutions market in Indonesia is still high potensial. Quote from Gartner research, virtual absorption of the solution in the industry market is estimated to touch a new number 15%.
(Rou / faw)

Facebook History

2009-05-09T19:03:00.001-07:00

History
http://en.wikipedia.org/wiki/FaceBook

The advent of Facebook came about as a spin-off of a Harvard University version of Hot or Not called Facemash.[9] Mark Zuckerberg, while attending Harvard as a sophomore, concocted Facemash on October 28, 2003. Zuckerberg was blogging about a girl and trying to think of something to do to get her off his mind.[10]

The Facebook on February 12, 2004According to The Harvard Crimson, Facemash "used photos compiled from the online facebooks of nine Houses, placing two next to each other at a time and asking users to choose the “hotter” person." To accomplish this, Zuckerberg hacked into the protected areas of Harvard's computer network and copied the house's private dormitory ID images. "Perhaps Harvard will squelch it for legal reasons without realizing its value as a venture that could possibly be expanded to other schools (maybe even ones with good-looking people ... )," Zuckerberg wrote in his personal blog. "But one thing is certain, and it’s that I’m a jerk for making this site. Oh well. Someone had to do it eventually ... "[11] The site was quickly forwarded to several campus group list-servers but was shut down a few days later by the Harvard administration. Zuckerberg was charged by the administration with breach of security, violating copyrights and violating individual privacy and faced expulsion, but ultimately the charges were dropped.[12] The following semester, Zuckerberg founded "The Facebook", originally located at thefacebook.com, on February 4, 2004.[13] “Everyone’s been talking a lot about a universal face book within Harvard,” Zuckerberg told The Harvard Crimson. “I think it’s kind of silly that it would take the University a couple of years to get around to it. I can do it better than they can, and I can do it in a week.”[14] Membership was initially restricted to students of Harvard College, and within the first month, more than half the undergraduate population at Harvard was registered on the service.[15] Eduardo Saverin (business aspects), Dustin Moskovitz (programmer), Andrew McCollum (graphic artist), and Chris Hughes soon joined Zuckerberg to help promote the website. In March 2004, Facebook expanded to Stanford, Columbia, and Yale.[16] This expansion continued when it opened to all Ivy League and Boston area schools, and gradually most universities in Canada and the United States.[17] In June 2004, Facebook moved its base of operations to Palo Alto, California.[16] The company dropped The from its name after purchasing the domain name facebook.com in 2005 for $200,000.[18] Facebook launched a high school version in September 2005, which Zuckerberg called the next logical step.[19] At that time, high school networks required an invitation to join.[20] Facebook later expanded membership eligibility to employees of several companies, including Apple Inc. and Microsoft.[21] Facebook was then opened on September 26, 2006 to everyone of ages 13 and older with a valid e-mail address.[22][23] In October 2008, Facebook announced that it was to set up its international headquarters in Dublin, Ireland.[24]

UNCOVERING THE INTELLECTUAL CORE OF THE INFORMATION SYSTEMS DISCIPLINE

2009-05-09T18:53:00.000-07:00

By Anna Sidorova, Nicholas Evangelopoulos,Joseph S. Valacich, Thiagarajan Ramakrishnan (MIS Quarterly,2008)

This study uses latent semantic analysis to examine a
large body of published IS research in order to address this
question. Specifically, the abstracts of all research papers
over the time period from 1985 through 2006 published in
three top IS research journals—MIS Quarterly, Information
Systems Research, and Journal of Management Information
Systems—were analyzed. This analysis identified five core
research areas: (1) information technology and organizations;
(2) IS development; (3) IT and individuals; (4) IT and
markets; and (5) IT and groups. Over the time frame of our
analysis, these core topics have remained quite stable.
However, the specific research themes within each core area
have evolved significantly, reflecting research that has
focused less on technology development and more on the
social context in which information technologies are designed
and used. As such, this analysis demonstrates that the
information systems academic discipline has maintained a
relatively stable research identity that focuses on how IT
systems are developed and how individuals, groups,
organizations, and markets interact with IT.

Meta Analysis

2009-01-13T09:52:00.000-08:00

Meta-Analysis of Correlations
By. Dr. Andy Field

Meta analysis is a statistical technique by which information from independent studies is assimilated. Traditionally, social science literatures were assimilated through discursive reviews. However, such reviews are subjective and prone to reviewer-biases such as the selective inclusion studies, selective weighting of certain studies, and misrepresentation of findings (see Wolf, 1986). The inability of the human mind to provide accurate, unbiased, reliable and valid summaries of research created the need to develop more objective methods. Meta-analysis arguably provides the first step to such objectivity (see Schmidt, 1992), although it too relies on subjective judgments regarding study inclusion (and so is still problematic because of biased selections of studies, and the omission of unpublished data-the file drawer problem). since the seminal contributions of glass (1976), Hedges and Olkin (1985), Rosenthal and Rubin (1978) and Hunter, Schmidt and Jackson (1982) there has been a meteoric increase in the use of meta analysis. Field (2001) reports that over 2200 published articles using or discussing meta-analysis were published between 1981 and 2000. of these, over 1400 have been published since 1995 and over 400 in the past year. Clearly, the use of meta-analysis is still accelerating.

Basic Principles

To summarise, an effect-size refers to the magnitude of effect observed in a study, be that the size of a relationship between variables or the degree of difference between group means. There are many different metrics that can be used to measure effect size: the Person product moment correlation coefficient, r; the effect-size index, d; as well as odds ratios, risk rates, and risk differences. of these the correlation coefficient is used most often (Law, Schmidt, & Hunter, 1994) and so is the focus of this study. Although various theorists have proposed variations on these metrics (for example, Galss's delta, cohen's d, and Hedges's g are all estimates of Thou), conceptually each metric represents the same thing: a standardized from of the size of the observed effect. Whether correlation coefficients or measures of differences are calculated is irrelevant because either metric can be converted into the other, and statistical analysis procedures for different metrics differ only in how the standard errors and bias correlations are calculated (Hedges, 1992).

In meta-analysis, the basic principle is to calculate effect sizes for individual studies, convert them to a common metric, and then combine them to obtain an average effect size. Studies in a meta-analysis are typically weighted by the accuracy of the effect size they provide (i.e. the sampling precision), which is achieved by using the sample size (or a function of it) as a weight. Once the mean effect size has been calculated it can be expressed in terms of standard normal deviations ( a Z score) by dividing by the standard error of the mean. A significance value (i.e. the probability, p, of obtaining a Z score of such magnitude by chance) can then be computed. Alternatively, the significance of the evarage effect size can be inferred from the boundaries of a confidence interval constructed around the mean effect size.

Johnson, Mullen and Salas (1995) point out that meta-analysis is typically used to address three general issues: central tendency, variability and prediction. Central tendency relates to the need to find the expected maginitude of effect across many studies (from which the population effect size can be inferred). this need is met by using some variation on the average effect size, the sinificance of the this average or the confidence interval around the average. The issue of variability pertains to difference between effect sizes acrosss studies and is generally adressed using tests of the homogenity of effect sizes. The question of prediction relates to the need to explain the variability in effect size across studies in terms of moderator variables. This issue is usually addressed by comparing study outcomes as a function of differences in characteristics that vary over all studies. As an example, differences in effect sizes could be moderated by the fact that some studies were carried out in the USA whereas others were conducted in the UK.

My GIS Project

2009-01-10T01:12:00.000-08:00

Peta Penggunaan Lahan

Note: This is just an example (in jpg format)

GIS

2009-01-03T13:55:00.000-08:00

Introduction to ArcGIS

By Kardi Teknomo, PhD.

GIS is abbreviation of Geographic Information system. Aside from buzzwords and much hype about it, GIS in a very simple term is just a combination of Maps and database. In other words, GIS is a spatial database .

Map is a very useful geographical tool to show the location of places. You may have learned about the world map with many countries in it.

You may have recognized city map with district boundary and road and railway as shown below.

For those of you who do not even know what a database is, you can imagine a database as relational tables .

Database is more than a simple table because in it you can do some query (search back for some data) and make relationship between one table and the other tables.

GIS is a combination of Maps and Database. Depending on how we represent the map, every element in the map may have an association or connection with some database table. For example, selecting a country in the world map that has an association with a table will show all the attributes data related to that selection.

Maps in GIS can be separated into several layers. Each layer map can be viewed in combination with some other layers, or it can be seen as a separate layer. You may extract some information based on these layers. For instance, you may search for some location based on several criteria, or find some pattern over several years map, query features that near to some other feature, and so on. Later in this series of GIS tutorial, you may learn further query, analysis, and modeling and enhance decision support system using the power of GIS.

My choice of ArcGIS (I am using version 8, version 9 may be slightly different in menu) is simply because its availability and my own familiarity with this software. Even if you do not have ArcGIS, you may browse this tutorial to gain understanding on what you can do using GIS. I also provide a list of other GIS software both commercial and free version in the GIS Resources page.

ArcGIS is a product of ESRI. The latest version is available in three different editions under three different names:
-ArcView (simple, Standard edition)
-ArcEditor (functional, Professional edition)
-ArcInfo (advance; Enterprise edition)

Do not be confused with the name of the editions. The three editions above share the same architecture and have the same way on how to use. The differences are prices and more tools/functions.

If you open the menu in ArcGIS, you may see many icons. The first question is, perhaps, which one to use?
Regardless your editions, ArcGIS will contain at least three main softwares
ArcCatalog,
ArcMap, and
ArcToolbox

ArcCatalog is similar to Windows Explorer. With ArcCatalog you may preview your GIS data in term of geography (map) or table. If your data contain 3D scene, you can use ArcCatalog to preview the content. You can create file, folder and meta-data of your GIS data using this software. If your data is a map file, you can double click to open it in ArcMap, otherwise, you can do drag and drop of your GIS data into ArcMap.

ArcMap is the main software that you use for GIS. Using ArcMap you can create and edit your map, layers, and charts. You may perform some analysis and query on your GIS data using ArcMap.

ArcToolbox is a collection of Analysis Tools (e.g. extract, overlay, proximity, statistics, surface cut and fill, volume and contour), Conversion Tools (i.e. import to / export from different type of files) and Data and Management Tools (e.g. Join tables, define projections, etc.)

If you ArcGIS contain ArcScene, it is useful for showing your map in 3 dimension. ArcScene is similar to ArcMap with more functional in 3D scene analysis, but you cannot create map in ArcScene.

The rest of this tutorial will cover only ArcCatalog and ArcMap since it is the main software of ArcGIS.

Build an Executive Information System

2008-12-22T02:07:00.000-08:00

Microsoft Office Excel 2007 Visual Basic for Applications Step by Step
by Reed Jacobson
Microsoft Press 2007 (384 pages)
ISBN:9780735624023

Experience learning made easy, and quickly teach yourself Microsoft Office Excel 2007 VBA--one step at a time! With help from this friendly book, you'll learn to automate spreadsheets, write functions and procedures, customize menus, toolbars, and more!
Table of Contents
Microsoft Office Excel 2007 Visual Basic for Applications Step by Step
Features and Conventions of This Book
Using the Book’s CD
Getting Help
Chapter 1 - Make a Macro Do Simple Tasks
Chapter 2 - Make a Macro Do Complex Tasks
Chapter 3 - Explore Workbooks and Worksheets
Chapter 4 - Explore Range Objects
Chapter 5 - Explore Data Objects
Chapter 6 - Explore Graphical Objects
Chapter 7 - Control Visual Basic
Chapter 8 - Extend Excel and Visual Basic
Chapter 9 - Launch Macros with Events
Chapter 10 - Use Dialog Box Controls on a Worksheet
Chapter 11 - Create a Custom Form
Appendix A - Complete Enterprise Information System
Index
List of Figures
List of Sidebars
CD Content

This is an interesting book he..he...Build your executive information system by visual basic in excel, Now.

Simulation and Monte Carlo: With applications in finance and MCMC

2008-12-16T17:38:00.000-08:00

Simulation and Monte Carlo: With applications in finance and MCMC
By. LSC forum
Paperback: 348 pages
Data: March 23, 2007
Format: PDF

Description: Simulation and Monte Carlo is aimed at students studying for degrees in Mathematics, Statistics, Financial Mathematics, Operational Research, Computer Science, and allied subjects, who wish an up-to-date account of the theory and practice of Simulation. Its distinguishing features are in-depth accounts of the theory of Simulation, including the important topic of variance reduction techniques, together with illustrative applications in Financial Mathematics, Markov chain Monte Carlo, and Discrete Event Simulation.

Each chapter contains a good selection of exercises and solutions with an accompanying appendix comprising a Maple worksheet containing simulation procedures. The worksheets can also be downloaded from the web site supporting the book. This encourages readers to adopt a hands-on approach in the effective design of simulation experiments.

Arising from a course taught at Edinburgh University over several years, the book will also appeal to practitioners working in the finance industry, statistics and operations research.

Click Download

Information System Key Issues

2008-12-15T04:43:00.000-08:00

A. Title: Key Issues in Information Systems Management:A Shift Toward Technology Infrastructure
By. James C. Brancheau, Brian D. Janz, James C. Wetherbe

1994 Key Issues Framework
1 Building a Responsive IT Infrastructure
2 Facilitating and Managing Business Process Redesign
3 Developing and Managing Distributed Systems
4 Developing and Implementing an Information Architecture
5 Planning and Managing Communication Networks
6 Improving the Effectiveness of Software Development
7 Making Effective Use of the Data Resource
8 Recruiting and Developing IS Human Resources
9 Aligning the IS Organization within the Enterprise
10 Improving IS Strategic Planning
11 Implementing and Managing Collaborative Support Systems
11 Measuring IS Effectiveness and Productivity
13 Increasing Understanding of IS Role and Contribution
14 Facilitating Organizational Learning
15 Managing the Existing Portfolio of Legacy Applications
16 Facilitating and Managing End-User Computing
17 Using Information Systems for Competitive Advantage
18 Planning and Integrating MultiVendor Open Systems
19 Developing and Managing Electronic Data Interchange
20 Outsourcing Selected Information Services
-- Implementing Decision and Executive Support Systems dropped --
-- Improving Information Security and Control dropped --
-- Developing and Implementing Object-Oriented Technology dropped --
-- Improving Disaster Recovery Capabilities dropped --
-- Developing and Implementing Multimedia Applications dropped --
-- Implementing and Integrating CASE Technology--dropped
Note: All data are from the final round of the Delphi survey (N = 83).

B. Title: Critical Issues of Information Systems Management in Hong Kong
By. Louis C.K. Ma

Critical Issues of IS Management (1999)
1. Improving the Effectiveness of Software Development
2. Building a Responsive IT Infrastructure
3. Increasing Understanding of IS Role and Contribution
4. Making Effective Use of the Data Resource
5. Developing & Implementing an Information Architecture
6. Aligning the IS Organization within the Enterprise
7. Improving IS Strategic Planning
8. Using Information System for Competitive Advantage
9. Facilitating and Managing End-User Computing
10. Managing the Existing Portfolio of Legacy Applications
11. Measuring IS Effectiveness and Productivity
12. Planning and Managing Communication Networks
13. Improving Information Security and Control
14. Facilitating and Managing Business Process Redesign
15. Recruiting and Developing IS Human Resources
16. Establishing Effective Disaster Recovery Capabilities
17. Facilitating Organization Learning
18. Implementing and Managing Collaborative Support System
19. Planning and Integrating Multi-Vendor Open System Technologies
20. Developing and Managing Distributed Systems
21. Facilitating/Managing Decision and Executive Support Systems
22. Developing and Managing Electronic Data Interchange (EDI)
23. Outsourcing Selected Information Services
24. Planning and Using CASE Technology

1. Improving the Effectiveness of Software Development
The application development backlog remains at unacceptably high levels. Traditional development methods and platforms are no longer satisfactory. New methods and platforms have not yet proven themselves. Sophisticated users are getting impatient. Improved effectiveness will be essential for next-generation applications.

2. Building a Responsive IT Infrastructure
Building a technology infrastructure that will support existing applications while remaining responsive to change is a key to long-term enterprise productivity. This task is frustrated by the continuing rapid changes in infrastructure technology and the increasing breadth and depth of applications, which need to be supported.

3. Increasing Understanding of IS Role and Contribution
IS is often viewed as an operational activity with little recognition for its strategic contribution to the organization. This can result in executive management viewing IS strictly as an overhead expense. Funding can be cut resulting in missed opportunities for using IT to solve important business problems.

4. Making Effective Use of the Data Resource
The organization’s data resource is growing in size, complexity, and value. Despite this, it remains largely unrecognized, inaccessible, and underutilized. IS must develop a climate within its department and throughout the organization which values the data resource as a corporate asset.

5.Developing & Implementing an Information Architecture
A corporate/global information architecture is needed to identify the major information categories used within an enterprise and their relationships to business processes. It is essential for guiding applications development and facilitating the integration and sharing of data.

6. Aligning the IS Organization within the Enterprise
The IS organization’s effectiveness in supporting the enterprise’s needs is dependent on its organizational location within the enterprise. Appropriate alignment may require a combination of centralized and decentralized structures. Too often IS is not located and structured appropriately.

7. Improving IS Strategic Planning
It has always been important to align long-rang IS plans with strategic business plans. Rapidly changing business environments, increased involvement of end users, and accelerated technological change underscore the need to continue improving strategic planning skills.

8. Using Information Systems for Competitive Advantage
In many businesses, long-term survival is dependent on using information systems to gain competitive advantage. Competitive advantage results from recognition of opportunities through creativity and innovation, followed by rapid implementation. These are historical weaknesses of the IS organization.

9. Facilitating and Managing End-User Computing
The proliferation of end-user computing through personal computers offers the promise of improved productivity but also the dangers of inadequate management control. Information systems management must balance control against the need for slack. Clarification of IS and end-user roles is a necessity.

10. Managing the Existing Portfolio of Legacy Applications
Most organizations have a large investment in their existing applications portfolio. Some “legacy” applications may need to be retired quickly. Others may need to be leveraged for many years before they are replaced. Integrating new technologies and migrating to new operating environments can be difficult. Too little is known about managing these problems.

11. Planning and Managing Communication Networks
Communication is the lifeblood of the organization. Using IS for competitive advantage depends heavily on access to appropriate internal and external communication networks. This task is complicated by rapid advances in underlying technology and major structural changes in the communications industry. (e.g. Internet, Intranet, Video Conferencing and Wireless Networks).

12. Measuring IS Effectiveness and Productivity
Understanding how IT use impacts the bottom-line is crucial for justifying new investment. In addition, measuring the IS organization’s performance is necessary for effective management. Measurement is becoming more important as companies attempt to reduce operating expenses to meet the competition.

13. Improving Information Security and Control
As organizations increase their dependence on information systems, there is a greater risk from destruction and alteration of data, disclosure to outside sources, and disruption of information services. Tight security controls and fault-tolerant information delivery are becoming a necessity.

14. Facilitating and Managing Business Process Redesign
To remain competitive, many organizations are radically changing the way they do business. IT plays an increasingly important role in this change process by enabling the innovative redesign of core business processes. Much has been learned about IT implementation in general which can help facilitate and manage BPR projects.

15. Recruiting and Developing IS Human Resources
Current and future shortages of qualified IS personnel threaten the organization’s ability to make effective use of information technology. More emphasis needs to be put on developing business skills such as object-oriented and multimedia applications.

16. Establishing Effective Disaster Recovery Capabilities
Downside risks are increasing daily from the potential loss of business due to a disaster. Effective recovery plans must be in place and tested regularly to ensure losses are minimized. As organizational applications grow and become more integrated, the greater the risk becomes.

17. Facilitating Organizational Learning
Organizations that prosper will need to make appropriate use of information technologies across their entire enterprise. Business practices and organizational structures will need to be modified in many cases. IS also must demonstrate its own ability to learn and use new technology.

18. Implementing and Managing Collaborative Support System
New software is needed to support the reengineered, flat, team-based organization of the future. Appropriate IT support can help teams share information and lead to faster decision making and improved team effectiveness. Such support will become even more important in a distributed ubiquitous computing environment.

19. Planning and Integrating Multi-Vendor Open System Technologies
Many companies are moving away from single-vendor proprietary operating environments to vendor-neutral environments based on industry and de facto standards. Due to large investments in legacy systems, carefully planned migration paths are critical. This task is complicated by a still-maturing technology and unstable standards.

20. Developing and Managing Distributed Systems
Client-server applications promise to offer a cost-effective alternative to centralized application. Unfortunately, they present many challenges including: maintaining consistent software versions; maintaining consistent data; controlling joint development projects with users; and administering large-scale distributed applications.

21. Facilitating/Managing Decision and Executive Support Systems
Increasing the ability to exploit situations for competitive advantage depends on enhancing the ability of management to “experiment” with decision possibilities. Many other issues also depend on this capability. Decision support tools have long been viewed as a method for introducing modelling tools to executives to improve their decision making. However, these efforts have met with mixed success.

22. Developing and Managing Electronic Data Interchange (EDI)
Electronic communication (via internet or proprietary EDI networks) with customers and suppliers may offer competitive advantage to a company or it may be a requirement for staying in business. IS executives must develop (or adapt to) standard transaction formats, keep current on technology developments, and learn to manage inter-organizational projects.

23. Outsourcing Selected Information Services
The internal information systems organization no longer has a monopoly. Outside contractors may be able to provide some services more effectively. What services should be outsourced? How should contractor relationships be managed? Fair and objective evaluation techniques are needed which assess both costs and benefits as well as potential risks from loss of control.

24. Planning and Using CASE Technology
Significant progress has been made automating business functions within organization; however, a vast productivity gain is possible if the automation process itself is automated. In principle, software systems can provide support for integrating the design efforts of project teams, standardizing representation methods, and generating code. While this technology is still being refined, providing support for systems development is extraordinarily complex and will require major changes within the IS function.

C. Title: Key issues in information systems management in China
By. Guoqing Chen, Ruipeng Wu and Xunhua Guo, 2007
(This article need permission by publisher)

Sources of Influence on Beliefs about IT Use: an Empirical Study of Knowedge Workers

2008-12-11T15:34:00.000-08:00

An Article by William Lewis,Ritu Agarwal, V. Sambamurthy

This research examined the simultaneous of individual belief, institutional, and social context of influences on beliefs about usefulness and ease of use in the context of a contemporary technology targeted at autonomous knowledge workers.

Lewis et al. 2003 stated that research on individual beliefs as the main factor for the acceptance of an information system in an organization is acceptable. However, A research that focus only on the individual beliefs without understanding why the individual has the beliefs is no longer interesting. This is because we know that the beliefs is formed by a process from collecting, processing to synthesizing. Therefore, a factor that to be as antecedents of the beliefs need to be examined. Although there have been previous empricial studies that have examined the factors, but unfortunately the studies only focus upon a specific and limited set of antecedents.

The primary purpose of Lewis et al. 2003 research, therefore, was to present empirical evidence that institutional forces, social forces, and individual characteristics exhibit significant and differential impacts on two key individual beliefs about the use of information technologies such as beliefs related to usefulness and ease of use from Davis (1989) and Davis et al. 1989.

Lewis' et al.2003 findings suggest that beliefs about technology use can be influenced by top management commitment to new technology and the individual factors of personal innovativeness and self efficacy. Surprisingly, social influences from multiple sources exhibited no significant effects.

This research used field study research by inviting 1,121 academic faculty members that use internet technology in his/her activities to fulfill a paper form which was delivered via campus e-mail. Of These, 229 respondents have participated in this research, 181 of which were completed questionnaires and used for data analysis.

The data was analyzed using structural equation modeling by using partial least square. PLS, a latent structural equations modeling technique, was utilized to test the posited research hypotheses. PLS uses a component based approach to estimation that places minimal demands on sample size and residual distributions (Chin 1998). It also permits simultaneous analysis of both the measurement model and the
structural model.

The analysis data consist of descriptive statistics, factor analysis, inter-construct correlations, and t-test.

The implication of this research are:
1. Provide additional evidence regarding salient predictors of key beliefs in technology acceptance.

2. Help sift out and provide initial insights into the relative effects of these predictors on the target beliefs. Lewis et al. 2003 posited and confirmed that the effects of all factors are not invariant across beliefs. Institutional influences were most salient for instrumental outcomes, and individual factors, in contrast, were significant antecedents of both usefulness and ease of use. Finally, the
non-significance of social influences in this study is an interesting finding. It is possible that social influences manifest effects through beliefs not specifically examined in this work, such as image. Indeed, Venkatesh and Davis (2000) found a
significant relationship between subjective norm and image beliefs.

3. From a pragmatic perspective, it is evident that the institutional context for technology use is a critical predictor of individual behavior toward information technologies, via its effects on the mediating construct of beliefs. Our findings suggest that managers need to focus careful attention on exhibiting commitment to a new technology for contingent adoption decisions. Unless individuals perceive the power elite within the organization as strongly behind the use of a new technology
through the messages conveyed as well as overt and specific resource provisioning actions, they are unlikely to develop positive beliefs about the usefulness of that technology. Managerial commitment and support serves the key role of providing structures for the signification and legitimization of technology use. As observed by others (e.g., Compeau and Higgins 1995), it is important for technology implementers to assist individuals in developing positive perceptions about their ability to use the new technology. Finally, as suggested by Agarwal and Prasad
(1998), individuals who are personally more innovative in the use of information technology could be utilized as important change agents because they are likely to exhibit positive beliefs about technology use.

Composite Reliability

2008-12-05T20:24:00.000-08:00

Composite reliability
composite reliability is a measure of the overall reliability of a collection of heterogeneous but similar items
individual item reliability (test the reliability of the items using Croinbach Alpha )vs. composite reliability (of the construct, the latent variable)
The factor loadings are simply the correlation of each indicator with the composite
(construct factor), and the factor correlations are oblained by correlating the composites.
calculate composite relaibility for the latent variables, LISREL does not output the "composite reliability" directly. You have to calculate it by hand.
SEM approach for reliability analysis, the reliability estimate from the SEM approach tends to be higher than Cronbach’s α. Structural equation model for estimating
the reliability for the composite consisting of congeneric measures.
Composite reliability--- a measure of scale reliability, Composite reliability assesses the internal consistency of a measure, 2 means square, see Fornell & Larcker (1981)

(sum of standardized loading) 2 / [(sum of standardized loading) 2 + sum of indicator measurement error (the sum of the variance due to random measurement
error for each loading-- 1 minus the square of each loading ]

Let A be the standardized loadings for the indicators for a particular latent variable. Let B be the corresponding error terms, where error is 1 minus the reliability of the indicator; the reliability of the indicator is the square of the indicator's standardized loading.
The reliability of a measure is that part containing no purely random error
(Carmines & Zeller, 1979). In SEM terms, the reliability of an indicator is defined
as the variance in that indicator that is not accounted for by measurement error. It is
commonly represented by the squared standardized multiple correlation coefficient, which
ranges from 0 to 1 (Bollen, 1989; Jöreskog & Sörbom, 1993a). However, because
these coefficients are standardized, they are not useful for comparing reliability
across subpopulations.

composite reliability = [SUM(A)] 2 /[(SUM(A)] 2 + SUM(B).
Example, Suppose I have a construct with three indicators, i1, i2 and i3. When I run this construct in AMOS I get as standardized regression weights: 0.7, 0.8 and 0.9. For computing the composite reliability, I just make:
CR = (sum of standardized loading) 2 / (sum of standardized loading) 2 + sum of indicator measurement error)
CR = (0.7 + 0.8 + 0.9)2 / ((0.7 + 0.8 + 0.9)2 + (1-0.49 + 1-0.64 + 1-0.81)
CR = (5.76)/(5.76 + 1.06)
CR = 0.844
Average variance extracted (AVE), see Fornell & Larcker (1981),
The variance extracted estimate, which measures the amount of variance captured by a construct in relation to the variance due to random measurement error

sum of squared standardized loading / sum of squared standardized loading + sum of indicator measurement error--sum of the variance due to random measurement error in each loading=1 minus the square of each loading )

variance extracted = [(SUM(A 2)]/[(SUM(A 2) + SUM(ei))].
Example,
AVE = (sum of squared standardized loading) / (sum of squared standardized loading + sum of indicator measurement error)
AVE = (0.49 + 0.64 + 0.81)/((0.49 + 0.64 + 0.81) + (1-0.49 + 1-0.64 + 1-0.81) AVE = (1.94)/(1.94+1.06)
AVE = 0.647

More...take look this link
zencaroline.blogspot.com

Resources from Kardi Tekno's Space

2008-12-04T19:16:00.003-08:00

This resources consist of many materials from statistics to programming and network tutorial. They are like these following links below:

k-Means clustering
K Nearest Neighbor
Market Basket Analysis
Similarity and Distance
Normalization of Performance Index
Adaptive Learning from Histogram
Discriminant analysis
Reinforcement Learning
Monte Carlo Simulation
Bootstrap Sampling
Recursive Average
Kernel Regression
Difference equations
Summation Tricks
Ginger Bread Man and Chaos
Mean and Average
Mean, median, mode
Variance and Standard deviation
Time Average & Time Variance
Data Revival from Statistics
Sierpinski gasket
Regression Model
Generalized Mean
Graph Theory
Growth Model
Digital Root
Continued Fraction
PI
Convert Decimal to rational
Euler Number
Power rules
Logarithm Rules
Bayes Theorem
Independent Events
Conditional Probability
Kernel basis function

Visual Basic (VB) tutorial
Micrsoft Excel Tutorial
Microsoft Excel Macro
Tower of Hanoi
Newton Raphson
Excel Iteration
Finding Eigen Value
Root of Polynomial
Ordinary Differential Equation
Soving System Equation
Generalized Inverse
Runge-Kutta
Euler Integration
Prime Factor
ArcGIS tutorial
Learning from data
Data Analysis from Questionnaire
System dynamic
Break Even Point
Sensitivity and What If Analysis
Financial Analysis
Multicriteria decision making
Analytic Hierarchy Process (AHP)
LAN Connections Switch.

http://people.revoledu.com/kardi/resources/index.html

Do you know what is different between covariance based and variance based method

2008-11-29T01:31:00.001-08:00

In the research we have a flexibility to choose the tools that we will use for the purpose of research achievement. If an analysis tool is not suitable because they do not meet the criteria required, then the alternative may be used. As an example in the application of tools based covariance analysis of data such as Amos and Lisrel. This analysis tool requires that the data used in the analysis must be in normal distribution. If this is not fulfilled, whether we will stop until this stage? Certainly not, Why? because there are other tools available that is based varians analysis tools such as PLS. Do you know what the differences between them? The difference is that Amos and Lisrel objective is to try to reproduce more covariance matrix, while the PLS objective is to try to maximize the variance can be explained.

Conventional Business Model in New Format

2008-11-25T03:55:00.000-08:00

Technology has driven human's life for a change.
The digital revolution is upon us. We see it every day at home and work, in businessess, schools, and hospital, on road, and even the wars. One of its major aspects is the digital economy.
The digital economy refers to an economy that is based on digital technologies, including digital communication networks (the internet, intranets, extranet, and VAN's), computers, software, and other related information technologies. The digital economy is sometimes called the internet economy, the new economy, or the web economy. In this economy, digital networking and communications infrastructures provide a global platform over which people and organizations interact, communicate, collaborate, and search information.
The digital economy also refers to the convergence of computing and communications technologies on internet and other networks and the resulting flow of information and technology that is stimulating economic commerce and vast organizational changes.

28th years old before people conducted a business in EC, people conducted their business with their partner by face to face either directly or not. In payment for a transaction that has been made among the people, someone must have physically met each other. The people need the others to promote their product and soon. The people pay a fee for a transaction, the peole must pay a fee for a subscription. The people must pay a fee for advertising and pay a fee for a business partner, and soon. All of which conducted physically. We can say that situation as a conventional business model.

But in digital economy all of which has been changed. By using digital technologies, the model business is similar but to be different in business format or structure. The structuture of the digital economy model consist of two elements:
1. Revenue models
2. Value proposition

A revenue model outlines how the organization or the EC project will generate revenue. A company use its revenue model to describe how it will generate revenue and its business model to describe the process it will use to do. There are five common revenue model.
a. Transaction Fees model-Commissions paid on volume of transactions.
b. Subscription model- Fixed amounts are charged, usually monthly.
c. Advertisement model-payment from advertisers.
d. Affiliate model-Commissions for referring customers. for example; Wordtracker's affiliation program on the picture below

This firm offers a program affiliation for anyone is interested. The member of wordtracker's affiliation is given a fee pay per click for referring customer from member's site to wordtracker.

e. Sales model-revenue from sales of goods or services.

Value propositions also included in business model. A Value propositions refers to the benefits, including the intangible, nonquantitative ones, that a company can derive from using the model. A value proposition defines how a company's product or service fulfills the need of customers. The value proposition is an important part of the marketing plan of any product or service.

Reading: Turban, King, Viehland, and Lee,(2006). Electronic commerce-A Managerial Perspective, Pearson International Edition.

Do you know why people motivated?

2008-11-24T17:41:00.000-08:00

When the motivation word is not there in managers' vocabularies, they motivated their employees what people called it by "carrot and stick". This word relevant to relationship between a human and a donkey. Donkeys will do something that people want after they has given a carrot, but is not reversely. By doing it, the donkey does not do anything, stick is used as a punishment. This situation is like a machine, so we can say a human is machine.
In human being, this situation can not stand for a long time. The human has feeling, perception, and heart. Therefore, researcher begin to identifify, examine, and make a conclusion about motivation. Finally, we have known that there are two classification of motivation theories.
1. Content theory - this theory aims to examine a question what motivate the people. this revolve around the identification of inward reward.The answer is need. All of people must have a need to survive in their life, such as physiology and psychology need.
We can refer to some scientist like Maslow, Alderfer, and Herzberg.

2. Process theory - this theory aims to examine a question why people behave as they do. Adam's equity theory is talking about this, incorporating such factors as perception and learning.

What is different between Type and Traits?

2008-11-24T17:05:00.000-08:00

As we has known in psychologics, there are two classifications of personality that widely accepted by reseacrher. They are such as:
1. Traits and,
2. Type
Do you know what are they different?

Traits refer to any characteristics oh human being that is on a continuum ranging in a characteristic. You can say, that someone has a characteristic that can be anywhere on the continuum ranging in a characteristic whether it to be high, in the middle, or low.
Type refer to classify people into disticnt category. You can say that someone is said that as a introvert or extrovert. There is a distinct or discontinuous.

Research Proposal Design Characteristics

2008-11-22T21:20:00.000-08:00

In designing a research proposal, a researcher must pay attention for understanding about the research characteristic. These characteristics depend on the objective of the research. Understanding for these characteristics help us to make a good research to be easy. The characteristics are like in these following (Hartono, 2004/2005):

1. Type of research, wheter exploratory or hypotesting research.
2. If we select hypotesting research, make sure that is a descriptive or causal.
3. Type of research based on timing. whether cross sectional, time series, or pooled data/panel.
4. Type of research based on validity. whether case study or statistical study.
5. Data collection method, whether observation or communication.
6. Type of research based on research setting, whether field setting or laboratory/experiment.
7. Unit analysis of the research, whether individual, dyads, group, or organization.
8. Create empirical model and its variable definitions.
9. The resources needed for the research

Validity

2008-11-21T09:47:00.000-08:00

===================================================================================
What is Validity? :)
Question: What is Validity?

Answer: Validity is the extent to which a test measures what it claims to measure. It is vital for a test to be valid in order for the results to be accurately applied and interpreted.

Validity isn’t determined by a single statistic, but by a body of research that demonstrates the relationship between the test and the behavior it is intended to measure. There are three types of validity:

Content validity:
When a test has content validity, the items on the test represent the entire range of possible items the test should cover. Individual test questions may be drawn from a large pool of items that cover a broad range of topics.

In some instances where a test measures a trait that is difficult to define, an expert judge may rate each item’s relevance. Because each judge is basing their rating on opinion, two independent judges rate the test separately. Items that are rated as strongly relevant by both judges will be included in the final test.

Criterion-related Validity:
A test is said to have criterion-related validity when the test is demonstrated to be effective in predicting criterion or indicators of a construct. There are two different types of criterion validity:
* Concurrent Validity occurs when the criterion measures are obtained at the same time as the test scores. This indicates the extent to which the test scores accurately estimate an individual’s current state with regards to the criterion. For example, on a test that measures levels of depression, the test would be said to have concurrent validity if it measured the current levels of depression experienced by the test taker.
* Predictive Validity occurs when the criterion measures are obtained at a time after the test. Examples of test with predictive validity are career or aptitude tests, which are helpful in determining who is likely to succeed or fail in certain subjects or occupations.

Construct Validity:
A test has construct validity if it demonstrates an association between the test scores and the prediction of a theoretical trait. Intelligence tests are one example of measurement instruments that should have construct validity.
By Kendra Van Wagner
http://psychology.about.com/mbiopage.htm

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Added by. Pamenan Mato Nan Hilang
How to know good construct validity
Prior to this issue, Ensure that you have known about construct validity. There are three most widely accepted forms of validity are (Hair et al, 2006, p-137):
1. Convergent validity - assesses the degree to which two measures of the same
concept are correlated. Here the researcher may look
for alternative measures of a concept and then correlate
them with the summated scale. High correlations here indi
cate that the scale is measuring its intended concept.
Criteria: factor loading>0,4; or >0,5-0,6; or >0,7

2. Discriminant validity - is the degree to which two conceptually similar
concepts are disticnt. The empirical test is again
the correlation among measures, but this time the
summated scale is correlated with a similar, but
conceptually distinct measure. Now the correlation
should be low, demonstrating that the summated scale is
sufficiently different from the other similar concept
Criteria: not redundancy in factor loading; AVE; Cross loading.

3. Nomological Validity- refers to the degree that summated scale makes accurate
predictions of the other concepts in a theoritically
based model. The researcher must identify theoritically
supported relationship from prior research or accepted
principles and then assess wheter the scale has
corresponding relationship.
In summary, convergent validity confirms that the scale is correlated with other known measures of the concept; discriminant validity ensure that the scale is sufficiently different from other similar concepts to be distinct; and nomological validity determines wheter the scale demonstrates the relationships shown to exist based on theory or prior research.

Reliability

2008-11-21T09:44:00.000-08:00

Source:Google images

What Is Reliability? :)
Question: What Is Reliability?

Answer: Reliability refers to the consistency of a measure. A test is considered reliable if we get the same result repeatedly. For example, if a test is designed to measure a trait (such as introversion), then each time the test is administered to a subject, the results should be approximately the same. Unfortunately, it is impossible to calculate reliability exactly, but there several different ways to estimate reliability.

Test-Retest Reliability

To gauge test-retest reliability, the test is administered twice at two different points in time. This kind of reliability is used to assess the consistency of a test across time. This type of reliability assumes that there will be no change in the quality or construct being measured. Test-retest reliability is best used for things that are stable over time, such as intelligence. Generally, reliability will be higher when little time has passed between tests.

Inter-rater Reliability

This type of reliability is assessed by having two or more independent judges score the test. The scores are then compared to determine the consistency of the raters estimates. One way to test inter-rater reliability is to have each rater assign each test item a score. For example, each rater might score items on a scale from 1 to 10. Next, you would calculate the correlation between the two rating to determine the level of inter-rater reliability. Another means of testing inter-rater reliability is to have raters determine which category each observations falls into and then calculate the percentage of agreement between the raters. So, if the raters agree 8 out of 10 times, the test has an 80% inter-rater reliability rate.

Parallel-Forms Reliability

Parellel-forms reliability is gauged by comparing to different tests that were created using the same content. This is accomplished by creating a large pool of test items that measure the same quality and then randomly dividing the items into two separate tests. The two tests should then be administered to the same subjects at the same time.

Internal Consistency Reliability

This form of reliability is used to judge the consistency of results across items on the same test. Essentially, you are comparing test items that measure the same construct to determine the tests internal consistency. When you see a question that seems very similar to another test question, it may indicate that the two questions are being used to gauge reliability. Because the two questions are similar and designed to measure the same thing, the test taker should answer both questions the same, which would indicate that the test has internal consistency.
By Kendra Van Wagner
http://psychology.about.com/mbiopage.htm
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Added by: Pamenan Mato Nan Hilang
Internal consistency applies to the consistency among the variables in a summated scale. The rationale for internal consistency is that the individual items or indicators of the scale should all be measuring the same construct and thus be highly intercorelated (Hair et al.2006,p-137)
Because no single single item is a perfect measure of a concept, we must rely on a series of diagnostic measures to assess internal consistency.

1. The first measures we consider relate to each separate item, including the item to total correlation (the correlation of the item to the summated scale score) and the inter item correlation (the correlation among items). Rules of thumb suggest that the item-to-total correlation exceed 0.50 and that the inter-item correlation exceed 0.30.

2. The second type of diagnostic measure is the reliability coefficient that assesses the consistency of the entire scale, with cronbach's alpha being the most widely used measure. the generally agreed upon lower limit for CA is 0.70, although it may decrease to 0.60 in exploratory research. One issue in assessing CA is its positive relationship to the number of items, even with the same degree of intercorrelation, will increase the reliability value, researchers must place more stringent requirements for scale with numbers of items.

3. Also available are reliability measures derived from confirmatory factor analysis. Included in these measures are the composite reliability and the average variance extracted (AVE).