KM log11 A content analysis of literature regarding knowledge management opportunities for librarians

PART I
1. Knowledge management is about creating, capturing, organising, retrieving, disseminating, sharing and re-using knowledge for the benefit of organizations.
2. The concept of corporate memory refer to the collective tacit and explicit knowledge.
3. Librarians need to get engaged with issues that have not been part of their tasks previously, and change and adapt their duties from gatekeepers to gateways of information to fulfill the role of knowledge manager successfully.
4. Traditional information management principles include organizing, retrieving, repackaging and uilising information, which are important for effective knowledge management applications.
5. Librarians understand the information seeking behaviour of users, which give them an advantage over those people who deal exclusively with the technology of information because they add human value to information.
6. Enhancing the role and employers expectations of the profession depends on the efforts of individual librarians.

PART II
The authors used content analysis to discuss the opportunities of KM for librarians. They said, the librarians need to improve their value to rejucenate profession.
Because of the training and specialty of librarians, they are suitable to be consultants for company which want to implement KM. But there were still something need to learn, like socail skill, IT knowledege and manage skill .etc..

PART III
Topic: KM careers.

From this article, we realize the outline of abilities except librarian of KM. For instance, socail interaction, risking taking, management skill and bussiness knowledge, technology skill.

PART IV
In terms of the author's oppinion, librarian could be a KM consultant for company. Although they described a lot of abilities of librarian for knowledge manangement, I still think that librarian is not the only one candidate for the career of KM. More exactly, the expert of business or management are more suitalbe for that kind of job. After all, the business environment is a extremly practicle place. If librarians didn't invole in there for enough time, they might not engage in the job very well, because the surrounding is totally different from library.

log10 Mission impossible? Communicating and sharing knowledge via infomaiton technology & Knowledge management in practice

PART I

Too great an emphasis on technologically based knowledge management initiatives has been shown to reinforce existing cultures rather than help transform them.

Trust between individuals has been shown to be necessary in order to facilitate knowledge sharing.

The objectivist epistemology will be shown as being founded on one foundational assumption: the distinction between tacit and explicit knowledge.

Tacit and explicit knowledge do not represent the extremes of a spectrum, but instead represent two pure and separate forms of knowledge

Practice perspective suggests that tacit knowledge and explicit knowledge are inseparable and are mutually constituted.

Knowledge is highly tacit; the effective sharing of it requires a significant amount of intense social interaction.
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Although these executives understand that knowledge is highly people-based, they are stuck with an investment model that is geared primarily toward technology implementations.

When asked about the biggest difficulties in managing knowledge in their organization, 56 percent of the study participants cited “changing people’s behavior”.

They generally start with the implementation of a technological capability.

Only after the technological capability exists that many firms realize how vital the people factors are.

If technology solves your problem, yours was not a knowledge problem.

Technology won’t bring down your greatest knowledge sharing barriers. This doesn’t mean that IT can’t lead the effort, but there had better be plenty of folks involved who are ready to resist the strong pull of the technology-only solutions.

PART II

Hislop emphasized on social interaction, and Ruggles emphasized that human involved in IT. Both of these papers emphasis on human, but from different prospection.

Hislop’s paper distinguished the difference between objectivist and practiced knowledge management. The most different feature between both approaches was theory of knowledge. Objectivist’s knowledge was a well know theory that knowledge could be represented by two pure and separate forms, that is, explicit and tacit. Another’s knowledge was that the two forms of knowledge were indivisible. If the knowledge was based on the later approach, the sharing of explicit knowledge would have some trouble, because explicit Knowledge included some tacit knowledge. That is, when the theory of the knowledge is challenged, some conflictions of KM will arise, especially KM by IT.

Ruggle’s paper used empirical study to implement. Through the study of 431 U.S. and European organization, he found that although these executives understand that knowledge is highly people-based, they are stuck with an investment model that is geared primarily toward technology implementations.

PART III
Topic: KM technology

Mission impossible? Communicating and sharing konwledge via informaiton technology? My answer to this question is that it's possible. But remember that IT, obvioursly, is a auxiliary character, it make the KM process more efficent. The main character is still human. Since we have a KM class this semaster, many cases told us the balance of technology and human is importante.

PART IV

Hislop's notion almost denied the benefits of IT. I think no matter the objectivist or the practiced KM, they have the same objective, that is, make more change to let knowledge managed. KM is a kind of approach of management, and the value of management are make things more efficienct and profitable. If the researchers always care about the original philosophy of knowledge, the things will be complicated. That's say, whatever approach was used, the value of it is the thing which should be considered.

In this class, I heard a notion which I am impressive with it. Knowledge has somewhere ambiguous, even in explict knowledge. This question could be arison forever, but if you really want to implement KM, you had better endure the ambiguous boundary of knowledge.

Log9 Learning from notes: Organizational Issues in Groupware Implementation

PART I

1. Metal models and structural properties significantly influence how groupware technology is implemented and used.
2. The findings suggest that where people's mental models do not understand or appreciate the collaborative nature of groupware, such technologies will be intepreted and used as if they were more familiar technologies, such as personal, stand-alone software.
3. Where the premises underlying the groupware technology are counter-cultural to an organization's structural properties, the technology will be unlikely to facilitate collective use and value.
4. Cognitive elements are the mental models or frames of references that individuals have about the world, their organization, work, technology, and so on.
5. How users change their technological frames in response to a new technology is influenced by (i) the kind and amount of information about the product communicated to them, and (ii) the nature and form of training they receive on the product.
6. If people have a poor or inappropriate understanding of the unique and different features of a new technology they may resist using it, or may not integrate it appropriately into their work practices.
7. Training users on new technology is central to their understanding of its capabilities and appreciating how it differs from other technologies with which they are familiar.
8. Structural properties of organizations encompass the reward systems, policies, work practices, and norms that shape and are shaped by the everyday action of organizational members.
9. The pyramidal structure and the hierarchical "up or out" career path promote and reinforce an individualistic culture among consultants, where those who have not yet attained principal status vie with each other to get the relatively few promotions handed out each year.

PART II

This paper explores the introduction of a groupware technology - Lotus Corporation's Notes - into one office of a large organization to understand the chages in work practices and social interaction facilitated by the technology.

The finding are that mental model and structural property are important elements which influence IT in one organization.

In mental model, regarding as cognitive elements, in Alpha was not performed well. They didn't communicatie about Note and train their workers. In structural elements, the Alpha didn't establish reward system, police and procedure of access. In addition, the firm culture and norms in Alpha were totally opposite to Note. Consequently, at the initial time when Note was launched was almost failure.

PART III

Topic: Organizational Issues

The communication of new IT is important. It's will influence the usage and notion of employees. In addition, the structure of a company is also an important issue. If the policy and culture do not fit the IT, the resualt of it won't be controled as the beginning as you thought.

PART IV

Although this paper didn't metion any knowledge management, but the purpose is the same as KM.

After I read this paper, I thought the culture in a organization need to change to fit the KM. But I remembered that couple weeks ago, we had an article about culture barriers. The conclusion was that there is no need to change culture to fit KM, but find the way to link with the core values. So, I reconsidered the paper we read this week, culture changing is not really easy, and may course some serious damage. Like the Alpha, it not suitalble to change their work style or to force them to share something. On the other hand, if they had found the core value and made the Note improve or fit it, Note could have been successful in another way.

KM8 Building a learning organization

PART I
1. In the absence of learing, campanies - and individuals - simply repeat old practices.
2. Their discussion of learning organizations have often been reverential and utopian, filled with near mystical terminology.
3. Three critical issue: 1st is the question of meaning; 2nd the question of management; 3rd the question of measurement.
4. Without accompanying changes in the way that work gets done, only the potential for improvement exitsts.
5. All these organizations have been effective at creating or acquiring new knowledge but notalby less successful in applying that knowledge to their own activities.
6. Learning organizations are skilled at five main activities: 1st: systematic problem solving, 2nd: experimenttation with new approaches, 3rd: learning from their own experience and past history, 4th learning form the experiences and best practicies of others, 5thtransferring knowlege quickly and efficiently throughout the organization.
7. If you cannot measure it, you cannot manage it.
8. Most successful examples are the products of carefully cultivated attitudes, commitments, and management processes that have accrued slowly and steadily over time.
9. The first step is to foster an environment that is conducive to learning. And to open up boundaries and stimulate the exchange of ideas.

PART II
There are numbers of companies doing continuous improvement programs, but few of them are succucefull. Because they didn't grasp a substance of whole things, that is "Learning".
This paper started from the 3M of learning organization, Meaning, Management and Measurement. And suggest that base on these 3M, skilling the activities of solving problem systematicly, experiment of new business approach, learning from the history, learning from others, and distributing knowledge effiently.
In the end, it said that most successful examples are the products of carefully cultivated attitudes, commitments, and management processes that have accrued slowly and steadily over time.

PART III

TOPIC: Organizational Learning

T do organizational learning, in order to be a Learning Organization. But how to let your employees learn in organization? Building a sharing environment is the first step, and then make your stuffs comfortable and free when they share their idea. Gradually, your organization will become a learning organizaion.
The most important factor of organizational learning is employees, all magangers can do are support, encourage, and supervise them.

PART IV
This paper used many examples for each part, it makes me understand clearly what learning organization is.
After I read it, I have a conclusion that stratagy is important. The stratagy include the environment, rewarding system, measurment, etc, anything can make your empolyees involve learning further. And these stratagies are made by top supervisors which are usually managers, so the manager level of an organization is important.
In terms of it, this paper was wroten through manager's vision to let them follow. But I still think the employees' vision is also essential to discuss. Maybe the Measurement mentioned in this paper is the way to care about employees. If it can describe measurements like the detail of questionnaire and interview's question more clearly, it will make these two side (manager and stuff) more balanced.

KM7 Overcoming cultural barriers to sharing knowledge

PART I

1. Culture is often seen as the key inhibitor of effective knowledge sharing.
2. Potential users said that they liked share system online, but just didn't have time for it.
3. Sharing was not built into the culture enough for people to actually take the time to do it.
4. However strong your commitment and approach to knowledge management, your culture is stronger.
5. Companies that successfully implement knowledge management do not try to change their culture to fit their knowledge management approach.
6. We defined culture as the shared values, beliefs and practice of the people in the organization.
7. The main reason knowledge management progams fail is a lack of a clear connection with a business goal.
8. It is the most important for the style of your effort to match how things get done in your organization.
9. Link these invisible values and visible elements of knowledge management is the behavior of peer and managers.

PART II

Culture is often seen as the key inhibitor of effective knowledge sharing. This paper interview 5 companies to find their organization culture.

Culture of organization can split into two dimensions - visible and invisible. The visible one is like espouse values, philosophy or mission. Even the stories, space and sturcture of one company is also regard as visible culture. And the invisible cultures such as their simple precepts are seen but unspoken background of the company.

Although culture affect knowledge sharing deeply, companies need not to change their culture to fit knowledge approach. Because culture is the foundation of a company, it is stronger as well. Instead, try to make KM approachs to fit your own culture and existent network, in addition, to let your employee get used to them. It will exert knowledge management more successfully.

PART III

TOPIC: Organizational Culture

This paper didn't define the organizational cuture clearly. But after Dr.Pheobe's interpretion, I knew more about this paper.

We can observe Organizational Culture by three things, that are atifacts & behaviors, values and assumptions. These three things is like an onion's skins. From the outside to inside is as the order above. These three can be split into visible and invisible dimension as well.

PART IV

I think there is only one insight in this paper, that is, building KM on solving problems.

And in my opinion, that's OK even your culture need to fit KM approach, if you can really solve problems and get profit from KM. Because, as a whole, there are still number of companies do not have that kind culture which suit to fit KM. In this case, the company need to create a new one for KM approachs.

The conclusions were only made from the companies which have knowledge sharing already. This is the biggest blind part of this paper, the authors didn't considered about other companies which have not had KM yet.

There is another interesting thing, that is, don't let your employees feel you are undergoing KM activities. Make KM approach as a routine of their work, and encourage them to get used to. I agree with it, and I think if the employees know what you required is about KM at first, they will feel overloaded. But I still think it is need to let them know what is KM step by step. It may make them agree more what you did.

KM6 Assessing Knowledge Assets: A review of the models used to measure intellectual capital

PART I

1. Stewart defines intellectual capital as intellectual material - Knowledge, information, intellectual property and experience - that can be put to use to create wealth.
2. IC could be an addendum accompanying with traditional financial reports.
3. The value an organization place on its IC is wholly dependent upon the goals of the organization and the state of the market.
4. A 500-year-old system of accounting must make way for a system of non-financial knowledge flow and intangible assets that use new proxies.
5. The pursuit of measuring IC assets objetively is a noble but difficult one.
6. The measurement examples thus far have been too firm-specific and no set of indicators could hope to be general enough to encompass the needs of a variety of international and industry settings.
7. Pursuing standards at this point might be more harmful given the nascent stage of research development.

part II

This paper reviews six assessments of IC and interprets their strengths and weaknesses. In the content, the author mentioned that many companies agree IC is important, but a few companies really practice it.

According to this paper, IC have different definitions among scholars. But it still has some similar parts, for example, human capital, finance of firm, renewal and development, customer etc. IC assessment the paper listed are trying to measure value of above items, even it is intangible.

In conclusion, IC assesment is almost firm-based, but pursuing standards might be more harmful given the nascent stage of research development. Academic should keep push this field into advance.

PART III

TOPIC: Knowledge assets assesment

This paper consider knowledge assets as intellectual capital. We could notice that there are many ways to assess IC and each of them have strenghs and weakness. It is also firm-based.

PART IV

After reading this paper, I felt something seems to be lost. First, I still cannot practice IC assesment, it is too abstract. Second, IC assesments are usually customization, but the author didn't talk about how to choose or create an appropriate one for each company. Third, because of the firm-based assesment, how to judge the way you used is right and efficient? After all, right or wrong, good or bad, those concept is relative as well as basing on comparing. But firm-base assesments can't compare with each other.

Now that it cannot compare, and the items which be assessed resemble knowledge map. I think, somewhere, knowledge mapping is enough and can be replaced IC assesment with it.

[統計] 第七章 抽樣分佈與估計式

第一節 抽樣誤差

1. 不對母體抽樣的原因：

• 母體太大
• 無法知道母體的範圍
• 破壞性檢測

2. 要如何抽樣才能有效的推估母體? 在於估計誤差的大小。

3. 估計誤差來源：

• 抽樣誤差： 使用恰當的樣本數來估計母體
• 非抽樣誤差：樣本無代表性，計算錯誤等

第二節 抽樣方法

1. 簡單隨機抽樣
先將母體編號，再以抽籤方式抽出

2. 間隔抽樣
每隔幾個就取幾個

3. 分層抽樣
要先決定有哪些重要的"層"

4. 集群抽樣
抽樣前，先將母體分為好幾個相似的集群，再以集群為單位來抽樣。如從全國小學10000所裡抽20所。

5. 分段抽樣
使用複合式的抽樣方法。如先進行集群抽樣後，再由每個集群裡，簡單隨機抽樣。

6. 配額抽樣(主觀)
類似分層抽樣，但事先不知道母群的各層比例

7. 判斷抽樣
最為主觀無根據的抽樣方法。

[統計] 第六章 連續機率分佈

第一節 連續變項

1. 連續變項是連續的，如溫度、時間、身高、收入等，表示任兩個數值之間，會存在著第三個值。
2. 明天中午12點溫度為30度的機率為多少？這個問題是無意義的，機率為0。應該說溫度到達30度上下（+ - 5）的機率為多少，有一個區間才能表達連續變項的特性。
3. 機率密度並無多大意義，通常關心的是累積機率密度。

第二節 連續機率分佈

1. 均勻分佈

• 一連續變項x，其值介於a, b之間。假設每一點出現的機率都是均等，則此變數的機率分佈為連續均勻分佈。
• 均勻分佈的機率為圖形面積。
• 平均數 = (a+b)/2, 變異數 = (b-a)平方/12

2. 常態分佈

如果一個連續變項X，具有公式6.8的性質，就是常態分佈。

2.1 超幾何分佈、二項式分佈、波式分佈與常態分佈之間的關係

• 超幾何分佈的樣本數是母體的0.05以下的話，可用二項式分佈取代
• 在二項式中，若樣本n夠大，則可用常態分佈取代；即使樣本n不大，機率p接近0.5 也可。若樣本數大而機率小時，可用波式分佈取代。
• 當樣本到達無限大時，超幾何分佈、二項式分佈與波式分佈皆會變為常態分佈

3. 標準常態分佈(Z分佈)

• 在常態分佈的狀況下，另其平均數為0，標準差為1，就可轉換為標準常態分佈。
• Z分數 = (該變數-平均數)/變異數

4. 伽瑪分佈

• 如果一隨機連續變數擁有公式6.10的特性，就是伽瑪分佈。
• 平均數 = a*b, 變異數 = a*(b平方)
• 伽瑪分佈可用來計算等候時間。在波式歷程裡，單位時間成功次數為入，那麼等候第一個成功事件出現的時間，平均就要 b=1/入。若要等候至第n個成功(a=n)，等候的時間就是伽瑪分佈。
• = GAMMADIST(時間X, 第a次成功, 第一次成功時間b, TRUE)，TRUE為累積機率，FALSE為機率密度函數。

5. 指數分佈

• 為伽瑪分佈的特例，令 a=1, b=1/入 就是指數分佈。
• 平均數 = 1/入, 變異數 = 1/(入平方)
• 伽瑪分佈是在算第n次成功的等待時間，指數分佈是在算第一次成功的等待時間。
• = EXPONDIST(時間X, 第一次成功時間b, TRUE)。

6. 卡方分布

• 為伽瑪分佈的特例，令 a=v/2, b=2，就是卡方分布。其中 v(nu)為正整數的自由度
• 平均數 = ab = v, 變異數 = a(b平方) = 2v，與伽瑪分佈相同。
• 若一連續變項X為標準常態分布，則該變項的平方(X平方)是為自由度1的卡方分布。多個獨立變項 X1~Xn均是標準常態分布，則自由度可累加起來變成n。
• 自由度越大，卡方分布越接近常態分布。
• 卡方分布常用來檢定資料與模式的吻合度
• P(x>a) = CHIINV(P,v)，給正無限大累積到a點的機率，計算a點是多少。
• P(x>a)= CHIDIST(點a, v)，給a點，計算大於a點數值的累積機率。

7. F分布

• 兩變數U, V互相獨立，且均有卡方分布的性質，其自由度分別為V1和V2，則隨機變數X= (U/v1) / (V/v2)
• 平均數 = v2/(v2-2), 變異數 = P115頁
• F分布常用於檢驗兩變異數是否相等
• P(X>a)= FINV(P, V1, V2)，給正無限大到a點的機率，計算a點是多少
• P(X>a)= FDIST(a點, V1, V2)，給a點，計算大於a點的累積機率。
• 若要計算a點的累積機率1 - P(X < a)

8. t分布

• 若變數U和變數Z互相獨立，而U為自由度v的卡方分布，Z為標準常態分布，則變數X = Z/ [(U/v)根號]的分布就是t分布
• t分布平方 = F分布
• 當自由度很大時，曲線就會接近標準常態分布
• t分布和z分布常用於檢驗母體平均數
• P(a < color="#ff6600">兩尾端面積和, v)，給兩尾端面積和，求a點和b點(a, b兩點為正負關係)
• P(X <> a) = TDIST(點a, v, tails)，給點a，求大於a點的累積機率。若tails = 1 則可直接算出累積機率，tails = 2可算出兩尾端面積和。

※ 在Excel中關於卡方分布(CHIDIST, CHIINV)、F分布(FDIST, FINV)和t分布(TDIST, TINV)的函數，所算得的累積機率是由正無限大到該點的累積機率。而在常態分布函數的情況下(NORMDIST, NORMINV, NORMSDIST, NORMSINV)所計算出的累積機率，則是由負無限大開始累積。

KM log5 Managing codified knowledge & KM in three organizations

PART I

Manageing Codified Knowledge

1. Even knowledge and expertise that can be shared is often quickly made obsolete.
2. Knowledge about something is called declarative knowledge
Knowledge of how something occurs or is performed is called procedural knowledge.
Knowledge why something occurs is called causal knowledge.
3. Knowledge that is inherently inarticulable yet which firms attempt to make explicit may result in the essence of the knowledge being lost, and performance suffering.
4. Articulable knowledge that has been made explicit represents an exploited opportunity, while leaving inarticulable knowledge in its native form respects the power of tacit knowledge. Both indicate appropriate mangement of the balance between tacit and explicit knowledge.
5. Imagination and flexibility are important, knowledge routinization may be inappropriate.
6. KM is 10% technology and 90% people.

Knowledge Management in three organization: an exploratory study
1. the character of the client and the way that the organization interacted with the client set the framework of the knowledge structures in very important ways.
2. communication culture was very dependent on the management policies adopted by each orgnization, and the commercial nature of their interaction with other organizations.
3. Despite the limited awareness of knowledge management theory and retoric, there was a pervasive understanding of the role of knowledge in ther organization, with some quite well-developmed strategies of embedding knowledge in the organization's operation.
4. the nature of knowledge and knowledge process were intimately related to the nature of the organization, its function, culture, structure and position in the market.
5. the specific nature of the organization, its structure and its specific context must be considered when developing models or theoretical frameworks of kownledge management.

PART II
Both of these articles used practical cases to interpret KM. The first one provided a framwork for configuring a firm's organizational and technical resources capabilities to leverage its codified knowledge. Another one used a exact research method to study different firm's kownledge management.

KM in the first article was separated into 4 parts :
1. the context of knowledge management
2. new organizational roles
3. managing knowledge prcessing applications
4. benefits

The important opinions in this article, I think, are that
1. codified knowledge is explicit knowledge
2. appropriate management of the balance between tacit and explicit knowledge
3. respecting the role of knowledge and learning may be the most effective approach to building a solid and enduring competitive foundation for business organizations.

KM in the second article was separated into 7 parts(in terms of Devenport & Prusak's opinion):
1. the organization: environment and functions
2. governing structures
3. the client
4. knowledge stratege
5. staff skills and development
6. the concept of knowledge
7. information service

The important opinions in this article, I think, are that
1. processes and value of KM are different among organizational culture, commercial structure and other features of each firm.
2. theory and practice need bridge the gap.

PART III
TOPIC: Practices of Knowledge Management

This week, we saw 5 examples: TRI, BL, Law firm, Educational institute, Local Council. In those cases we can realize that there are various dimentions to operate KM, and the dimentions which were choosen by each firms must different in terms of their strategies and organizational ecology.

PART IV

Through those cases mentioned in the articles, we can realize that different organization had different requirements of KM. As the same as the second article said: Knowledge structures and cultures differed substantially between organizations, and were heavily influenced by the commercial enviroment.

The firms, TRI & BL, recited in first article used KM process to develop their KM structure. On contrary, those three firms studied in the second article didn't know KM theory very well, but they still had some activities like KM. This reaffirm the inherence of KM that "KM is an old and new subject". Each organization must have some original activities to treat their own particular knowledge, even they didn't know what KM is. We might consider that KM is an old thing which is rewritten and basic on daily procedure of organizations.

The one makes me most impressive is that "theory and practice need bridge gap", theories usually don't relate practices very well. This situation not only occur in KM, but in many fields.

統計 第五章 間段機率分布

第一節 機率理論
1. 事前機率(古典機率)
假設在一樣本空間有N個樣本點，且每一個樣本點出現的機會相等。某事件A出現的機率為 A/N。

2. 經驗機率
實際重覆的做實驗，將出現次數除以實驗次數。
當實驗的次數越大，經驗機率就越接近先天機率(大數法則)

3. 主觀機率
憑自己的知識與經驗加以猜測。

以上三種理論必須遵守：

• 樣本空間中任一事件的機率不小於0
• 互斥事件聯集的機率就是各事件機率之和
• 樣本空間內所有機率總和為1

第二節 聯合、邊緣、條件機率

1. 聯合機率：
兩個或兩個以上事件同時發生的機率，ex：全部的人之中，吃檳榔又患口腔癌的機率 f(x,y)。

2. 邊緣機率：
只考慮一個樣本空間中事件的發生機率。ex:全部的人之中，吃檳榔的機率 f(y)

3. 條件機率：
固定在一個樣本空間內，另一樣本空間中事件所發生的機率。ex: 在吃檳榔的人之中，患口腔癌的機率 f(x│y)。

條件機率 = 聯合機率/邊緣機率

第四節 間斷機率分布

1. 白努力分布

• 實驗只有兩種結果，成功(機率為p)與不成功(機率為1-p)。
• 平均數 u=p, 變異數=p(1-p)

2. 二項式分布

• 為白努力事件，成功的機率是 p，那麼在n次嘗試中，共成功X次的機率就是二項式分布
• 平均數 = np, 變異數=np(1-p)
• = BINOMDIST(成功次數x, 實驗次數n, 成功機率p, FALSE), FALSE為該次數的機率，TRUE為該次數的累積機率。

3. 負二項分布

• 成功的機率是p，那麼在r次成功之前，已有X次失敗的機率。
• 平均數 = (r/p)-r, 變異數 = r(1-p)/(p平方)
• = NEGBINOMDIST(失敗次數x, 成功次數r, 成功機率p)

4. 超幾何分布

• 一個有限的母體大小為N，其中有M個是成功，如果從這個母體以不放回抽樣法，抽取大小為n的樣本，其中含有X的成功機率
• 平均數 = nM/N, 變異數 = nM(N-M)(N-n)/( N三方 - N平方 )
• = HYPGEOMDIST(要成功的次數X, 樣本大小n, 母體成功的個數M, 母體大小N)
• 當超幾何分布的母體大小N為無限大，就可用二項式分布取代

5. 波氏分布

• 發生於單位時間內的成功次數(入)已知，且成功次數與時間的長短成正比，且兩段不重覆時間內所發生的成功機率是獨立的，且在極短時間內超過一次以上的成功，其機率可以不計。
• 平均數 = 入, 變異數= 入
• = POISSON(事件出現的次數X, 成功的次數(入), FALSE)，TRUE為該次數的機率，FALSE為該次數的累積機率。
• 當二項式分布裡的母數n趨近於無限大，且機率p很小，則二項式分布會趨近波氏分布

6. 間斷均勻分布

KM log4 Knowledge Management and the Dynamic Nature of Knowledge

PATR I
1. Konwledge management or knowledge sharing in organizations is based on an understanding of knowledge creation and knowledge transfer.

2. Knowledge requires knowers, so its processes are interteined with human activity and experience.

3. Knowledge is enriched information with insight into its contxt showing how information and knowledge are closely associated and how they used to define each other.

4. Communicating knowledge is primarily a process, but in order to capture and share knowledge conveniently, its representations are often placed into a storage and retrieval system.

5. One reason knowledge is more valuable than data or information is that it is closer to action.

6. In a kownledge management program it is the knowledge artifact, or the thing, that is managed, not knowledge itself.

7. Instead of the constant initiatives to extract knowledge from within the employees to creat new explicit knowledge artifacts, it might be more productive for organizations to invest effort in creating a kownledge culture.

PART II

This article argue that effective knowledge management in many disciplinary contexts must be based on understanding the dynamic nature of knowledge itself.

The author emphasized that knowledge is dynamic, that is, knowledge is always changing with the human experience and learning. Because of dynamic nature, how to manage knowledge is mentioned on this article. Author said that what in a knowledge management prgram is the "Knowledge Artifact".

And then author addressed three problematic aspects of knowledge management:
1. Knowledge originates and resides in the mind
Separating the mind, body and spirit in defining knowledge and recognizing only the intellectual dimention ignores ignores essential aspects of human nature and presents a fractured picture of knowledge.
As far as knowledge management is concerned about the wholeness of human experience.

2. The technological imperative
IT is just a TOOL, not a solution.

3. Knowledge as a social value
This part mentioned about "organizational knowledge".
Knowledge can make profit if knowledge could be distributed within organization. But it can also be a disadvantage to the organization if it is wrong or if it is inhibiting, or if it is not used for the fulfillment of the organisation's mission.

Anyway, this article gave a good opinion that organizations need to manage knowledge both as "object and process".

PART III

Topic: Knowledge Creation

According to this article, Kownledge is the awareness of what one knows through study, reasoning, experience or association, or through various other types of learning. On the other hand, knowledge is a result of a varid set of prcosses. Through those process, knowledge could be created.

PART IV

"Without person involvment in understanding, knowledge has little value". Although this sentence has been overwrited on other articles, it also make great sense. Activities among people create knowledge and distribute knowledge. If there were no people in there, knowledge would become meaningless.

And I very agree the opinion on "Knowledge Artifact". The last article brought up that knowledge cannot be documented but can be passed through social activities. And this article told us precisely that what we documented or what we handle in KM programs were knowledge artifacts and it is dynamic, changing overtime.

統計 第四章 常態分布

第一節 常態分布的特性

1. 常態分布就是以平均數為中心點，往兩旁漸低的左右對稱分布。常態分布下，中心的最高點就是平均數，也就是眾數、和中位數。
在現實中，並沒有連續的曲線存在，頂多只是類似常態分布，但當樣本數很大時，會越接近常態分布。

2. 常態分布曲線公式(圖4.1)，有平均數和變異數(或標準差)就可知道常態分布的形狀。

• 標準差決定y軸，標準差越小，data越集中
• 平均數決定x軸，平均數不同，圖型會左右位移

3. 讀法：p(X=3)=1/6 → 參數3的出現機率是1/6。只有間斷變項才會有這樣的表達方式，若是連續變項，如身高，就不會說170公分出現的機率是多少。這時候就必須使用「機率密度」

4. 機率密度：

• 適用於連續變項。如平均數170公分，標準差5的常態分布中，170(+-5，165~175)的機率密度為0.0798。但機率密度無多大意義，大家比較關心的是170公分以下的機率，或是165~175的機率。
• = NORMDIST(160, 170, 5, FALSE) → 平均數170，標準差5的常態分布下，160的機率密度。

5. 累積分布函數：

• = NORMDIST(170, 170, 5, TRUE) → 平均數170，標準差5的常態分布下，170以下的累積機率為0.5(50%)。
• 累積分布反函數： 90% = NORMINV(0.9, 170, 5)= 176.41

第二節 標準常態分布 (Z分布)

1. 將平均數定為0，變異數訂為1的常態分布。

2. 將X參數利用線性公式4.25轉換為z分數後，使用 = NORMSDIST(z) 會得到該參數的累積機率。也可利用 = NORMSINV(累積機率) 回求該參數。

第三節 峰度與偏態 (用來描述常態分布的形狀)

1. 常態分布的峰度為0

• 若資料峰度大於0，呈現高峽峰
• 若資料峰度小於零，呈現低闊峰
• KURT(range) 就可得到峰度

3. 偏態

• 偏態值>0，表示資料集中在左邊，右偏態
• 偏態值<0，表示資料集中在右邊，左偏態
• = SKEW(range)

統計 第三章 變異量數與分佈形狀

只用集中量數來描述資料是不夠的，若忽略資料點的分布情形，可能會做出錯誤的判斷。

1. 全距

• 最大值減最小值
• = MAX(range) - MIN(range)
• 優點：容易計算
• 缺點：只用大小值，無法精確反應資料的分布情形

2. 四分位距

• Q=(Q3-Q1)
• 和全距一樣，沒有用到所有資料

3. 平均絕對離差

• 各個數值減掉平均數後絕對值再取平均值
• = AVEDEV(1,2,3,4,5)
• 有絕對值不好計算

4. 變異數

• 平均絕對離差的變異狀，有分為母體變異數和樣本變異數
• 可四則運算，也可推估母體，是推論統計的基石
• 容易受到平均數的極端值影響，因有平方
• 母體 = VARP(range)
• 樣本= VAR(range)

5. 標準差

• 為變異數開根號，分為母體標準差和樣本標準差
• 可四則運算，也可推估母體，是推論統計的基石
• 同樣會受到平均數的極端值影響
• 母體 = STDEVP(range)
• 樣本 = STDEVP(range)

6. 變異係數

• 樣本標準差除以樣本平均數就是變異係數
• 計算標準差或變異數時，每個值都要減去平均數，因此會受到平均數的影響。變異係數可避免過於極端的平均數。
• 變異係數是對於樣本而言的數值，不用來推估母數狀況

KM log3 Knowledge Management: Hype, Hope or help?

PART I

1. Kownledge management, it seems, has two part:

‧there is the management of supporting data and information
‧there is the management of individual with specific abilities.

2. Knowledge is different from data and information, only a person can have and exercise knowledge.

3. Knowledge Management is not so much the management of tangible assets such as data or information, but the active management and support of expertise.

4. Knowledge is not something that can usually be written down, knowledgeable individuals must be encourage to pass their expertise to other through personal contact.

5. For the goal of kownledge worker is not so much to manage kownledge but to solve problems.

PART II

The purpose of this discussion is to look at KM carefully and try to understand what it is, or at least what it could be.
Author used five questions to discuss this topic: KM, hype, hope or help?

Q1: what is knowledge
In this part, author tell different from data, information and knowledge.
He thought Knowledge is not something tangible that we can possess, exchange or lose the way that we can with data or information. And only person can excise it.

Q2: Why are people, especially managers, thinking about knowledge management now?
Every afternoon our corporate knowledge walks out the door and I hope to God they will come back tomorrow. This sentence almost expound why they want to do KM.
And then he started to talk about comminities and IT. He mentioned sharing something is an essential thing although it is hard to creat this kind of culture. And in the IT regard, the author thought the failue of DSS and ES was that people wanted to use them to replace something what can be done only by person.

Q3: What are the enabling technologies for KM?
Store and transmit system. That your workers can find and share something which they want.

Q4: What are the prerequisites for KM?

• Knowledge map
• Knowledge worker

Q5: What are the major challenges for KM?

• culture of sharing
• the treatment of tacit knowledge
• intellecture properties between employees and organization

PART III

Topic: Knowledge and Knowing

This article used "observation (how people use it)" to distinguish the diference between data, information and knowledge. And I use the followed instance to realize these three words.

Before you present something about Tacit Knowledge, you must search a lot of aticles. These articles were composed by words. We can regard these words as DATA. Then I collect these data, and sum up the definition of Tacit Knowledge. This definition is kind of INFORMATION to the others. But for me, I can present it without the summary, so it has turned into KNOWLEDGE. And then I must decide which way to present - by PPT, video or other else - to let the others understand the meaning of Tacit Knowledge, it would be my WISDOM.

Through this instance, we can realize that knowledge is formed from knowing process. Book is not a knowledge until you read it, after this knowing process - reading, something can be internalized become knowledge.

PART IV

Author metioned that knowledge cannot be documented but can be passed through social activities. This opinion is different from last three articles which just mentioned UNDOCUMENTED. And I also agree the opinon about DSS and ES, that is, system can't replace the thing only person can do.

Just as teacher said, the author used words carfully to discuss those questions. But in the conclusion, he told us clearly that Knowledge is not a management but a action. Do something to solve problem. It's realy makes me impressive.

After reading the practical experience of 台積電. I remenber that my friends in StarBucks just felt only StarBucks' coffee is good coffee. They were all assimilated by the company culture, even they had been just hired for 6 months. Consequently, I consider that the culture is not such a difficulte part as the author said. It depends on how deos the culture be distribute in the company.

統計 第二章 集中量數

描述資料的兩種數據：集中量數、變異量數

集中量數(資料集中的情形)
1. 平均數

• 算數平均數：mu=資料為母體，Xbar=資料為樣本。=AVERAGE(1,2,3,4,5,) or = AVERAGE(A1:A5)
• 幾何平均數：適用於平均改變率、平均成長率或平均比率。如近年的經濟成長率為1%,2%,3%，就要用這個公式。=GEOMEAN(5,14,40,125,350)
• 調和平均數：也稱倒數平均數，若資料成等差數列，就使用此平均數。=HARMWAN(80,90)

只有算數平均數才會有母體跟樣本之分，其他則無。

2. 眾數
如果所有的數值都只出現一次，那就沒有眾數，眾數也可能有兩個以上。=MODE(1,1,2,3,4)

3. 中位數
先將數字由大至小排序，中間的數即是中位數。=MEDIAN(1,2,3,4)

4. 截尾平均數
過於極端的值會影響到平均數，所以才將數列排序後，以四分位去掉頭尾，將Q1以下Q3以上的數值排除後，再計算其平均數就是截尾平均數。
Q1= QUARTILE({1,2,3,4,5,6},1)
Q3= QUARTILE({1,2,3,4,5,6},3)

5. 溫賽平均數
與截尾平均數的概念相同，只是溫賽平均數是用四分位數來取代極端值。

量尺的特性

• 名義量尺：眾數
• 順序量尺：眾數、中位數
• 等距量尺：眾數、中位數、平均數

集中量數的優缺點

眾數

• 優點：真實存在、多數意見、容易猜中。
• 缺點：未必能代表集中趨勢

中位數

• 優點：不受極端值影響
• 缺點：不適合四則運算

平均數

• 優點：
• 可以進行四則運算，是推論統計的基礎 。
• 使用了資料所有的數值，因此具有代表性。
• 用平均數來猜測所有數值，產生的誤差最小。
• 樣本的平均數是母體的平均數的最佳估計式（estimator）。
• 平均數較不會受到抽樣變動的影響
• 缺點：
• 容易受到極端值的影響

統計 第一章 序論

第一節 統計學的意義

1. 描述統計學

• 注重資料整理、分析、展示與解釋
• 出處是母體而不是樣本

2. 推論統計學

• 透過整理與分析，藉此推論母體的狀況
• EX: 1萬個燈泡的檢測，樣本必須「充分代表」母體，也就是要隨機取樣，差異性不能太大

3. 樣本數要多大?
見仁見智，求準，樣本就要大。

第二節 變項的分類

1. 質的變項(類別變項)

• 數字無分大小等區別，如宗教、性別
• 必是間斷變項

2. 量的變項

• 如人數、身高等，數字具有量的意義
• 間斷變項：如班級人數，他必是整數
• 連續變項：兩數之間可能有第三數的存在，如身高

3. 其他變項分類
實驗研究：自變項、依變項

第三節 四種測量量尺

1. 名義量尺
性別男1女2，月份1~12(除非限定年份，否則無計算意義)，數字只是代號，沒有順序的差別。

2. 順序量尺

• 有著大小意義
• 如成績90>89，但他不構成等距的條件。因89與88也是差1，但這兩者的1無法描述期間的差異。
• 又如李克特氏量表，非常不同意到非常同意由1~5表示，這也只是順序量尺

3. 等距量尺

• 等距量尺不僅有順序意義，還有差距意義。
• 如攝氏11>10，10>9，這兩者差1度是等量的。

4. 比率量尺

• 最高階的量尺
• 除了有順序、等距意義外，還有「自然零點」。
• 如身高200 cm是100cm的兩倍，換算成其他公制，還是差兩倍
• 但寬鬆一點的說法，如果有個共識基礎，等距量尺就是比率量尺，如溫度雖然會隨公制不同而有差異，但如果共識為攝氏0度，這樣也能達成自然零點的條件

嚴格來說，凡是會使用到平均數和標準差的統計，都不可以使用順序量尺(如智力、成績)。

KM log2

PART I

Artical: What is knowledge management?

1. Concepts are best defined from how people use them.
2. IT-Track KM (management of information) :

• computer and information science
• Knowledge = object

People-Track KM (management of information) :

• philosophy, psychology, sociology......
• Knowledge = Processes

3. Because of their different origins, the two tracks use different language in their dialogues and thus tend to confuse each other when they meet.

4. Anyone can buy a new KM software, but very few have the ability to create sustainable creative organisations.

Part II:

The opinion of IT Track and People Track is come from this article.

IT-Track KM means that the organization focus on computer and information science, and knowledge can be handled by information system. And People-Track KM is that the organization concern about education or training of employees. Learning is the most essencial thing.

The author dislike the notion "KM" personally. He uesed "Knowledge Perspective" or "to be Knowledge Focused" to instead KM. And he seems tended to People Track.

After describing these two tracks, the author started to introduce some companies which take effect on KM.

PART III:

This Week's Topic is : Definition of KM

• In the opinion of Karl, KM includes two tracks.
• In the opinion of Prusak, KM is intergraded by intellectural and practice antecedents.

PART IV:

This week, I learn the development background of KM from Prasuk, and two tracks of KM from Karl. Especially the tracks, it tell difference from two tracks to tell us KM is not only IT but People.

Regarding to the presenter, I very agree her opinion in her conclusion. She said that "you must know what you want to get from knowledge management, and then you can get something from it." As I mentioned in course, companies is too relay on IT to run KM succesfully, especially in Esten countries. They don't really understand the goal of KM, and just set up a lot of system. Then waiting something comes effort automatically. They don't realize that the profit of KM is almost hardly being etimated.

In the conclusion of Prasuk's article, he wanted to see KM would be internalized in organizations, but it is pity that we are still on the origin. We don't make KM into slogan like re-engineer, and we also don't make it into nature. We can't predict what KM will be after 10 years, but we still want to see KM can be naturalized.

統計1 統計的用途

普查是最簡單的方法，也是最不用學習統計的量化研究。
而學習統計是為了「推論」，比方說有一萬人的國小老師，要「抽樣」100人來「推論」到10000人，統計就是在這時候來做「檢定」用。
統計可以幫助你不用去做到1萬人次的普查，可以節省金費和心力。

網路問卷是最難做分析的，因為無法得知母群體的來源與特性(是否常態分布等)，若用傳統的統計分析，則會出現爭議，若使用「無母數統計」會增加分析的難度。

KM LOG1: The Nonsense of Knowledge Management

Part I:

Data and information may be managed, and information resource may be managed, but knowledge can never be managed.

When employees leave a company, their knowledge goes with them, no matter how much they've shared.

Can "Tacit knowledge" be captured? The answer, of course, is that it cannot be "captured" - it can only be demonstrated through our expressible knowledge an through our acts.

The conclusion is reached that "knowledge management" is an umbrella term for a variety of organization. Those activities that are not concerned with the management of information are concerned with the management of work practice.

Part II

KM is a papular work in business work, but even in acdamic community, they still use 'knowledge' as a synonym for 'information'. A lot of KM activities are just a kind of Information Management.

Even though, it is not saying that people to contribute effectively the management of organizations is impossible and that sharing knowledge is impossible. It's just to differcult to do it.

Part IV

The opinion of this article made me shock. I have never heard opinions like this.

Before I read this report, I knew KM was a kind of work to make the knowledge of a company can be stored and shared. Especilly sharing, it was the most important activity of KM. But now, I agree that 'Tacit Knowledge' can't be captured, the knowledge will go with retired employees, no matter how much they shared before. I also argee that 'Information' and 'Knowledge' these two words make people confused.

In this condition, KM may be a fad. But I still believe KM should be developed. Because of the economy of knowledge, in this term, we never use ‘information’ to replace ‘knowledge’. It is a new word and this is a new world, the important element of companies (or others) is not only information but knowledge. So we do need a new discipline to deal with this new element.

I believe Wilson didn’t mean to offend the people who advocate managing knowledge. He just wanted to remind us the knowledge couldn’t be handled as like as we handled information before.

W18 The End Talk

最後一堂課，老師用讓我們用interview的方式，說明自己在這個學期學到了什麼。

Round 1 ：由映竹訪問我關於期末報告論文的議題
由於我們是用聊天的方式進行，所以沒有甚麼很明確的問題，以下簡略的將對話內容摘要：

你寫這篇研究的動機大概是什麼？
因為之前當過國科會的研究助理，所以大概知道有些人在數位典藏國家型計畫結束後，會被強迫遣散，所以想要了解他們在計畫結束後的規畫是什麼。

你真的有這麼關心這一群人嗎？
嗯......其實也還好啦..只是想說有些認識的學長姐在裡面工作，而且是第一份工作，不知道他們被遣散後，該怎麼辦，想要稍微了解一下。順便也看一下這些研究助理們往後會不會繼續留在數位典藏相關產業裡面貢獻。

所以你的研究對象是鎖定在老師底下的學生嗎？
嗯..不是耶，因為老師底下的學生通常都是任務性質的幫忙完成工作。我比較想知道自願被招聘進去的那些研究助理未來該怎麼走。是要回家帶小孩？還是自己開公司？或是其他出路之類的

那國外類似計畫的人呢，他們有比較早結束的計畫，那他們的人去哪裡了？
ㄜ....這個禮拜一的時候小蝶老師也有問到我，老實說，我還真的忘了有國外的狀況可以做相關的文獻探討。沒有注意到這一個部分....不過我在想，應該怎麼找到那些檔案呢？這感覺會比較像一個研究報告...不知道這些報告該去哪裡拿...希望國外會有人跟我做相關的研究...

那你比較想看哪個國家的？
我覺得應該是英國吧，因為美國最近一次的大型國家型典藏計畫是美國記憶計畫，但那已經結束有將近10年了....而英國的AHDS最近才剛結束，影響應該會比較深刻一點。不過AHDS似乎是一個機構...也不像是一個計畫...總之會再找國外類似的案例來研究。

感想：
跟同學重新講了自己的研究之後，發現中間有許多矛盾和不足的地方，由其是最後一個問題，我打從心裡沒有想到可以去探討相關案例，而且國內的應該也有早已結束的長期大型計畫，也可以作為survey的對象。
原來在做研究的時候，很容易的會陷入象牙塔裡面，在自己認知的世界出不來...還是要多跟其他人討論才可以海闊天空......

Round 2：由我訪問叔華關於這學期的課程學到了什麼

請問學到最有印象的是？

1. 從課本和讀論文的練習中，知道了做研究的流程與原因該是怎麼樣的。比如說：以前只知道在寫文章前要先文獻探討，而現在知道為什麼寫文章需要文獻探討了。
2. 了解到研究需要有相當嚴謹度，從架構到用詞，都會影響文章的品質

請問最沒把握的是？
有了研究方法的相關名詞知識，但是在操作上還是會碰到問題。由其是在解釋資料的部分，自己所認為的推理過程解釋，在其他老手的眼中看起來像是笑話一樣，考慮到的層面還不足，而且會陷入數字與語句的迷思當中。

感想：
在跟叔華聊天的過程中，我們有互相聊到對於這堂課的心得，他說：與其他系所相對起來，我們所得到的知識無法用考試來衡量，比方說調查研究，其他系的學生會從定義開始下手，說明調查研究的定義、特性、問題該怎麼發展等等，像在寫書一樣(外顯知識)。但我們學到的，會比較著重在研究意識跟研究問題的發展，相對來講，是比較哲學上的思路(內隱知識)。

我也有相同的感想，突然要我講出一個研究法名詞的定義跟特性，我可能沒辦法講的很詳細完整。但是要我思考一個研究議題，我會想的比較深入。應該這樣說，我覺得這學期學到比較多的是研究方法的意涵與思考，而非研究工具的使用。這兩個部分是需要合而為一的，但相信在工具的部分，往後的學習歷程能慢慢的補足，畢竟思考的能力還是比工具使用能力重要許多。

[W17] 量化分析

今天這堂課主要學習的是：
1.整理原始數據(編碼簿)：問卷上的答案，紀錄的表格
Code Book VS Coding Book：Code Book是定義Code的地方；Coding Book是擺放raw data的地方
2.量化資料分析：研究者作一些事情將些原始資料，變成能夠看到他在假設上所陳述的為何(描述統計)
3.解釋數據(最難的地方)：最後能夠解釋或是給予理論一些有意義的結果

資料處理

• 資料編碼 (原來量化也是需要編碼的!!)
• 輸入資料
• 清除資料

資料分析

1.單變項分析

• 次數分配(Frequency)
  每一種category裡面，有幾個次數出現
























































變項	值	次數	百分比
income	1	25	6.25%
	2	50	12.5%
	3	100	25%
	4	150	37.5%
	5	50	12.5%
	6	25	6.25%
gender	1	100	25%
	2	300	75%

• 集中量數測量
  主要概念關鍵字：最小值、最大值、中位數、平均數、眾數
  


• 離散趨勢測量
  用來描述變項分數的分散情形，主要觀念關鍵字有：全距、四分差、變異數

2. 雙變項分析
想了解變項之間的關係是什麼
在解讀時可從從散佈圖中看三樣東西：形狀、方向、密度

3.多變項分析

• 統計控制
• 百分比的表格設計
• 多元迴歸分析

推論統計

需要注意的有：統計的目的、統計顯著度(顯著水準為.05 .01 .001)
另外在敘述上，光是X跟Y有相關，這樣的敘述不夠好，必須寫說X跟Y有統計上的顯著相關


老師不斷提醒我們量化的概念不應該分散的來讀，但我今天所理解到的概念還是非常的分散。最懂的部分是量化分析的步驟：資料處理→量化分析→解釋量化。但是這三個part的內涵還是一知半解，尤其是分析的部分。知道甚麼是單變項、雙變項，知道怎麼算中位數、平均數、眾數，但這些概念在甚麼時候用得上呢，更正確的說，應該是這些概念要怎麼拼湊才會得到問題的答案，甚麼樣的問題需要有哪些概念？不知道…這是我目前量化最大的障礙。

除此之外，老師也一再提到統計工具的重要性，量化這個部份要把握的是各個方法的概念與使用時機，其他的就交給高科技來處理。

W16 investigation II

這次的調查過程比Foster教授的還要棘手很多，表面上只是看四篇文章寫個分析心得，但實質上卻有重大的內涵。若是個人一篇文章的分析還容易完成，但當四篇研究放在一起的時候，它們所擦出的火花，是很複雜的。

整個作業的過程中我們一直在想，如何把四篇研究放在一起比較，但發現其實實驗研究之間的比較是沒什麼意義的，尤其是在資料處理與實驗進行過程方面，因為這些都會隨著研究者的研究目的與假設而改變，並非說我這篇文章有多做一次共變數分析多做一次T檢定，而你的沒有，就代表你的文章寫的不好，這是我們這次報告所獲得的最大心得。

我們也曾經想過用柏堯一組的方式作呈現，但當把表格列出來後，發現我們根本沒有能力去說明為什麼這篇有T檢定，而另一篇沒有；為什麼你的是用組內迴歸同質性分析，我的卻是組間？這當中牽扯到太多太多的統計概念，所以決定放棄這種表達方式，因為我們所能告訴大家的僅是為什麼這四篇都是用共變數分析，共變數的用途與共變數需要注意的事項。

其實今天的報告懂的，仔細想想沒幾樣，唯一比較懂的新概念就是老師所講解的虛無假設與對立假設之間的差異以及Type Error I & II的概念。

	犯罪事實有	犯罪事實無
調查犯罪有	correct	Type I Error(信心水準0.1)
調查犯罪無	Type II error(信心水準.05)	correct

因為沒有實際看過該報告，直接聽同學們的簡短敘述，我還是沒有辦法擷取出當中的重點，往往發現還在弄懂該研究在講什麼的時候，就報告完了....看來我離鑑賞報告的功力還有一甲子之多....