Build an Executive Information System

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

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

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

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

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

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zencaroline.blogspot.com

Resources from Kardi Tekno's Space

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

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

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?

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?

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

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

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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

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).

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Diskusi Hasil Penelitian

Diskusi Penelitian
Discussion
Your discussion section has two fundamental aims:
to explain the results of your study,
to explore the significance of your study’s findings.
Therefore you need to:
interpret and explain your results;
examine whether and how the questions raised in the introduction section have been answered;
show how your results relate to the literature;
qualify and explore the theoretical importance/significance of your results;
outline any new research questions or areas for future research that your results have suggested.
The discussion is also the place in a report where any qualifications or reservations you have about the research should be aired. Statistically significant results still require analysis and discussion. You might consider questions like the ones below.
How generally do your results apply?
How close to real life are the variables you manipulated in a laboratory situation?
Were their any defects in your experimental design or procedure?
Were their any confounding factors in your design: could some other factor explain your results?
These are the types of questions you will need to consider in terms of your results in terms of defining the generality and limitations of your results.
The discussion section requires you to use both the past tense and the present tense. The past tense is used when you need to explain particulars about your results; for example,
This group achieved this level of performance after less time studying the instructions,
The activity of the enzyme increased with temperatures up to 37°C.
The present tense is used when you are expanding on the implications of your results or drawing conclusions; for example,
The results show the effectiveness of combination drug therapy as a treatment ... .
This research provides powerful evidence that ... .
Separating the Results and Discussion sections is one way of organising this information. It is also possible to combine the Results and Discussion into one section or to include a separate conclusion or general discussion section. It is always advisable to check with your lecturer or tutor about these issues.

by: Team Unilearning
http://unilearning.uow.edu.au/

Laporah Hasil Penelitian

Hasil Penelitian
Results

This section describes but does not explain your results; it provides the reader with a factual account of your findings (Hay, 1996). You can, however, draw attention to specific trends or data that you think are important. Your aim in your results section is to make your results as comprehensible as possible for your readers/markers (Hay, 1996).

If you are presenting statistical results, place descriptive statistics first (means and standard deviations) followed by the results of any inferential statistical tests you performed. Indicate any transformations to the data you are reporting; for example, you may report percentage correct scores rather than straight scores. Raw data and lengthy whole transcripts of qualitative data should be put in the appendices, only excerpts (descriptive statistics or illustrative highlights of lengthy qualitative data) should be included in the results section.

In the results section you will need to use both the past tense and the present tense. The past tense is used to describe results and analyses; for example,

The knowledge scores were analysed ...,
The results indicated ... .

The present tense is used with results that the reader can see such as means, tables and figures; for example,

The means show that ...
The weekly growth rate illustrated in Table 3 illustrates how ... .

Since you are presenting your results, not the figures which represent the results, you should ensure you refer explicitly to your results and not just to your data figures (graphs, tables). As you describe particular results in the text of your results section, make sure you refer to the corresponding figure in brackets after you have mentioned the results. The figures should be inserted into the text as soon as possible after you mention them. Follow this link for more information on using figures.

by: Team Unilearning
http://unilearning.uow.edu.au/

Metode penelitian

Laporan metode penelitian
Method

The purpose of this section is to precisely describe method and materials used to conduct your experiment with enough detail so someone else could repeat the same procedure. You also need to explain and sometimes justify why you chose a particular method (Hay, 1996). Finally, it is important to add any extra information or observations, such as changes to the method generated via the results of a pilot test or changes caused by some accident.

The method section should be written in paragraph form with as little repetition as possible. This section will often be broken down into subsections such as participants, materials and procedure. (linked to new window) The subsections you use will depend on what is useful to help describe and explain your experiment.

In the method section of the report you should use the past tense since you are describing what you did; for example,
A dilution series was performed…,
The participants were instructed to ... .
Furthermore, as the focus in this section is on what was done rather than who did it, the passive voice is used as it aims to foreground the action, rather than the doer of the action; for example,
The elaiosomes were removed …
as opposed to
We removed the elaiosomes … .

by: Team Uni Learning
http://unilearning.uow.edu.au/

Cara Membuat Pendahuluan

Pendahuluan
Introduction

In your introduction, you need to let the readers and markers of your report know why the report is important and what exactly the report is about. It is essential to establish these things because it places the reader/marker in a better position to understand the significance of the material presented in the rest of the report (Hay, 1996). Although the introduction comes at the beginning of the report, it is not the first section you should write. It is easier to write the introduction after you have dealt with your method and results section because that way you are introducing the section with knowledge about what you did and what the results were. This knowledge allows you to shape your introduction so it leads up to your findings more specifically.

In your introduction, you need to answer questions such as
What do you hope to learn from the research?
What question is being asked?
Why is this research important?
(adapted from Hay, 1996).

This diagram provides an outline of the sequence of information that needs to be presented in the introduction.


The introduction starts generally, introducing the broad context within which your research fits. You need to provide a review of the literature that impacts on your research area. The literature needs to provide the reader/marker of your report with:

an understanding of the conceptual and theoretical background, context and justification for the research you are undertaking;
an appreciation of the significance of this area and in particular your topic; for example,
why does this question need researching and
how does it contribute to, fit in with, or differ from other available work on this subject (Hay, 1996)?

With your literature review should follow the pattern depicted in the diagram; for example, it should review studies to establish the general area, then move towards studies that more specifically define or are more specifically related to the research you are conducting. It is important to note that your literature review MUST NOT be a series of quotations strung together; instead it needs to provide a critical analysis of previous work (Hay, 1996). Your literature review uses both the past tense and the present tense; for example,

PAST tense: Gilles (1999) examined the effect of…, The model predicted that …..
PRESENT tense: A dormant seed bank is a solution for these populations to survive.

The past tense is used to refer to a particular experiment and the specific results of a particular experiment that has been carried out in the past. The present tense is used to refer to information that is not confined to a particular experiment.

The introduction ends with a statement of your specific hypothesis or hypotheses. This statement of the hypothesis should logically follow on from your literature review and you may want to make an explicit link between the variables you are manipulating or measuring in your study and previous research (O’Shea, 1996). The present tense is used to state your hypotheses; for example,

It is predicted that… ,
It is hypothesised that …

There is no need to write the title Introduction before this section: its position at the start of the report identifies it as an introduction (Findlay, 1993). Some departments or tutors may require you to include the aims or objectives of your study in a separate section after the Introduction so you should check this with your lecturer or tutor.

by: Team Uni Learning
http://unilearning.uow.edu.au/

Cara membuat abstrak

Abstrak
Abstract


The abstract is a precise summary of the whole report. Its function is to preview the contents of your report so that the reader can judge whether it is worth their while to read the whole report.

It includes a statement of the aim or objective of the experiment, a short description of the method used, the main results, and the conclusions or implications of the results. The abstract should normally be a single paragraph between 100 and 200 words. It should be titled with the word 'abstract'. Given the small amount of words allowed, each word and sentence included in your abstract needs to be meaningful (O'Shea, 1996). In addition, all the information contained in the abstract must be discussed in the main body of the report (Hay, 1996).

by: Team Unilearning http://unilearning.uow.edu.au/

Bagaimana membuat judul penelitian

Judul laporan penelitian
Title

The title of your report should be concise and informative. It should not be vague and general but should encapsulate the essence of the research.
For example:
Haemolysis of red blood cells in response to salt concentration
The limits of selective attention in tachistospcoic recognition
Social and economic consequences of homelessness for women in Sydney, Australia (1998-2001)

The progress of arthritis in the retained compartments after medial unicompartmental knee replacement
Memory for explicit and implicit information in picture stories
Antibiotic resistance of various Escherichia coli strains
A title like the one below is inappropriate because it is uninformative about the research contained in the report:

An investigation of a physical stimuli

A title like the one below is slightly more informative

An investigation of memory for sentences

but could be improved by increasing the specificity of the information it contains; for example,
Sentence memory: A constructive versus interpretive approach

Your title should be no longer than 15 words (O'Shea, 1996). The title is generally given on a separate page together with your name, course and instructor details.

by Team Unilearning http://unilearning.uow.edu.au/

Google adsense success story

Google AdSense Case Study
OSTG increased its income from international traffic with Google AdSense and AdSense link units
Case study : OSTG : SourceForge.net
OSTG

Business

Founded in 1996, OSTG (Open Source Technology Group) started out as the tech network Andover.net. Its mission was to provide unbiased content, community and commerce for an audience of Linux and open source users and developers. Over time, Andover.net grew by adding the community-centric sites Slashdot.org and Freshmeat.net to its technology group, and ThinkGeek.com to its e-commerce division. Since VA Software Corp. acquired OSTG in early 2000 and introduced SourceForge.net and Linux.com, OSTG’s network of sites has become even more widely visited among the worldwide Linux and open source communities.

SourceForge.net is among OSTG’s most successful Internet properties, with roughly 25 million unique visitors each month – a number that grows about 20 percent each year. SourceForge hosts more than 140,000 projects, and offers a centralized resource for managing projects, issues, communications and software code. "Given the broad appeal of SourceForge, we are always looking for ways to capitalize on each page and give users content and products to match their needs," says OSTG’s Vice President and General Manager of SourceForge.net, Mike Rudolph.

Approach
"Link units are relevant, potentially interesting to the user, and don’t stick out as ads. They help users get to the content they’re looking for and help advertisers get their information out there."

Mike Rudolph
Vice President and General Manager, OSTG

In 2005, OSTG decided to try link units, a text advertising product available to Google AdSense publishers. Each link unit displays a list of topics relevant to the content of the page it is running on. When visitors click on a topic, they go to a page displaying several text advertisements highly relevant to their chosen topic.

Although link units began producing results right away, OSTG realized there was a way to maximize the benefits even more. The OSTG and SourceForge.net team began to think about how to leverage Google’s expansive worldwide advertising network to better serve international users. Through Google’s geographical targeting, they knew they could maximize the relevant topics and ads delivered to customers in specific regions.

OSTG therefore began serving link units specifically to its international traffic, replacing U.S. targeted ad campaigns, on the Sourceforge.net homepage. This approach made sense: 80 percent of visitors to SourceForge.net come from outside the United States, from places as diverse as Brazil, Europe, Australia and Asia. "We needed to target German ads to German visitors, instead of sending out a generic ad that probably wouldn’t appeal to someone outside the U.S.," explains Rudolph. "We needed to improve the user experience and generate revenues from international traffic, and realized that more precisely geo-targeting link units was a great way to achieve both aims."

Rudolph notes that "the process of using link units was very simple and straightforward. The implementation took little time, and right away a relevant mix of international topics started to appear."

Results

OSTG was immediately pleased with the results from using link units, because they provided a way to earn incremental income from international traffic and improve the user experience. Over a two-month period, link units added more than 20 percent to overall AFC revenue, without any additional real estate being allotted to advertising. "Because international users make up 80 percent of our traffic on SourceForge, they are a top priority for us," says Rudolph. "Geographic targeting through link units helped us overcome the challenge of reaching these users with useful ad content."

Link units and AdSense also complement OSTG’s own ad sales efforts, says Rudolph. "There is a long tail of advertisers Google serves that we will never scale to reach. With AdSense, we can tap these advertisers in a turnkey way that is phenomenal," he notes.

Rudolph says he’s impressed with the quality of ads and the way they mesh with site content, and appreciates the fact that link units are clean and unobtrusive. "Link units are relevant, potentially interesting to the user, and don’t stick out as ads," says Rudolph. "They help users get to the content they’re looking for and help advertisers get their information out there. Trying to do what Google does with AdSense and link units would be a bad decision on any media company’s part. We could never do it with the same level of quality as Google does."

About Google AdSense

Google AdSense is a program enabling online businesses to earn revenue from serving ads precisely targeted to specific web content and search pages. With service levels ranging from online sign-up to dedicated support management, a broad range of sites profit from AdSense. Thousands of Google advertisers also benefit from AdSense by gaining exposure on sites across the Google Network, which includes many of the Top 100 Media Metrix sites such as AOL, About.com, Amazon, Ask.com, and Lycos.

A Classification of Multivariate Techniques

There are two kind of multivariate techniques; such as

1. Dependence technique- as one in which a variable or set of variable is identified as the dependent variable to be predicted or explained by other variables known as independent variables. for example; regression analysis, SEM, canocical, conjoint, MANOVA, and discrmininant analysis

2. Independent technique- is one in which no single variable or group of variables is defined as being independent or dependent. rather the procedure involves the simultaneous analysis pf all variables in the set. for example: factor analysis, CFA, cluster analysis, correspondence analysis.

Tthe Relationship between Multivariate Dependence Methods

canocical
Y1+Y2+Y3+...+Yn = X1+X2+X3+...+Xn
(METRIC, NON METRIC) (METRIC,NONMETRIC)

Multivariate analysis of variance
Y1+Y2+Y3+...+Yn = X1+X2+X3+...+Xn
(METRIC) (NONMETRIC)

ANOVA
Y1 = X1+X2+X3+...+Xn
(METRIC) (NONMETRIC)


Multiple discriminant analysis
Y1 = X1+X2+X3+...+Xn
(NONMETRIC) (METRIC)

Multiple Regression analysis
Y1 = X1+X2+X3+...+Xn
(METRIC) (METRIC,NONMETRIC)

conjoint analysis
Y1 = X1+X2+X3+...+Xn
(METRIC,NON METRIC) (NONMETRIC)

SEM
Y1 = X11+X12+X13+...+X1n
Y2 = X21+X22+X23+...+X2n
Y3 = X31+X32+X33+...+X3n
(METRIC) (METRIC,NONMETRIC)

REFERENCES: Hair et al. (2006)

Convergent calidity

by. Zencaroline
Convergent validity also requires that SMCs be equal to or greater than .5 along with pattern coeffieicnts equal to or greater than .7. Other useful assessments are composite reliability and Average Variance Extracted. Composite reliability should be equal to or greater than .7 and AVE should be greater than .5.
Convergent validity is assessed by the correlation among items which make up the scale or instrument measuring a construct (internal consistency validity)
Internal consistency is a type of convergent validity which seeks to assure there is at least moderate correlation among the indicators for a construct
Cronbach's alpha is commonly used to establish internal consistency construct validity, with .60 considered acceptable for exploratory purposes, .70 considered adequate for confirmatory purposes, and .80 considered good for confirmatory purposes.
Average variance extracted (AVE) s above 0.5 are treated as indications of convergent validity.
Convergent validity also requires that SMCs be equal to or greater than .5 along with pattern coeffieicnts equal to or greater than .7.

look at this link below:
http://zencaroline.blogspot.com/2007/05/convergent-validity.html

How to Do Business Online V. Indonesia

Be sure you follow this link

Anne Ahira: An International Online Marketer

http://www.asianbrain.com/index.php?aff_code=888999&pg=letter1

How to understand about structural equation modeling

Let's go to analyze
SEM analysis consist of two technics:
1. Covariance based
using AMOS or Lisrel -based on normality assumption
when we need amos? answer: estimation
2. Variance based
using SmartPLS, PLSGraph, etc - based on nonparametrics measurement
when we need PLS? answer: prediction
take looks these links:
http://zencaroline.blogspot.com/
http://zencaroline.blogspot.com/2007/06/useful-sem-website.html

Create a Free Forum

Create a free forum

Everyone can create his free forum, in a few seconds, without any technical knowledge, and begin to discuss immediately! The interface of the forum is easy to use and to personalize. How to create a free forum?
Simply begin with this first step by choosing a style for your forum.
Don't worry, you can always easily change to a different version anytime you want. click this link

http://www.forumotion.com/en/create-forum/