• 2009-09-03

    Fitts' Law - [HCI Theory]

    Giving You Fitts

    One of the most well-understood and salient principles underlying the ergonomics of graphical user interface design is Fitts' Law.

    Named for Paul Fitts, a psychologist at Ohio State University, Fitts' Law is a mathematical model of fine motor control which predicts how long it takes to move from one position to another as a function of the distance to and size of the target area. Papers outlining what became known as Fitts' Law were published in 1954 and 1964.

    Fitts himself was an expert in aviation psychology, and he developed his research around more ergonomic layouts for cockpit instrumentation as a way of increasing aviation safety. You can read more about the early history and mathematics behind Fitts' Law on Wikipedia.

    Fitts' model proved especially relevant to the early research on computer input devices performed in the late 1970s. Although Fitts' model was originally formulated to project how quickly a human could point at a physical button, it turns out that the same set of rules governs how quickly someone can target an area on the screen with a mouse cursor.

    Although there's a great deal of subtlety to Fitts' research, what became known as Fitts' law is a fairly simple intuitive concept.

    1. The farther away a target is, the longer it takes to acquire it with the mouse.
    2. The smaller a target is, the longer it takes to acquire it with the mouse.

    The inverse of both statements is true as well (closer and bigger targets can be more quickly acquired.)

    One common mathematical formulation of this relationship is:

    • MT is the average time taken to acquire the target.
    • a and b are empirical constants determined through linear regression.
    • A is the distance from the starting point to the center of the target.
    • W is the width of the target measured along the axis of motion (how close to the target you need to get to count as acquiring it.)
    • c is a constant which is either 0, .5, or 1, depending on the specific environment.

    Here's a cool Java-based applet which lets you play around with Fitts' Law to see how it feels in practice: http://www.tele-actor.net/fitts/

    How Fitts' Law Affects User Interface

    The key takeaway for interface designers is clear: the farther away a button is from the current mouse position, the larger it needs to be to achieve the same average acquisition speed. Put another way, there are two main ways to improve mouse efficiency: put the controls closer, or make them bigger. (There are other more avant-garde ways to alter the physics of mouse travel which I won't go into today.)

    Over the years, as monitors have gotten bigger and screen resolutions have increased, Fitts' law dictates that actual mouse efficiency has gone down.

    Think about Word 1.0, which was designed for a common maximum 640x480 screen resolution. Toolbar buttons in Word 1.0 were 20x20 buttons with 16x16 icons in them.

    Word 2003, on the other hand, is commonly run at resolutions as high as 1600x1200 and beyond--yet the toolbar buttons remain the same 20x20 size they were in Word 1.0. But because the screen is so much larger, most of the time your mouse cursor will be much farther away than it could have been on a 640x480 screen. Greater mouse distances mean an increased MT target acquisition time.

    In other words, the same button takes much longer to click than it did fifteen years ago.

    Mile-High Menus and Magic Corners

    One of the most useful aspects of applying Fitts' Law to computers is that screen size is bounded. No matter how far you move your mouse to the left, the cursor will never go farther than the left side of the screen.

    In a Fitts' Law sense, you can think of the edges of the screen as being infinitely wide.

    Think about how long it would take you to move your cursor from the right side of the screen to the left edge of the screen. Now compare that to how long it would take you to move your cursor from the right side of the screen to a spot 2 pixels from the left edge.

    Obviously, you would be much faster in the first case because you can literally slam your mouse to the left as fast and as hard as you want and you won't overshoot. It's infinitely wide.

    This is the same reason that the Mac user interface has been said to have "mile-high menus." The Mac menu bar is permanently affixed to the top of the screen regardless of what program you're in. As a result, you only have to worry about targeting a menu horizontally. Because the top edge of the screen is essentially "infinitely" tall, you can acquire the menus very quickly.

    The Windows taskbar is a mile-high the other direction: you can move your mouse to the bottom of the screen quickly and only worry about targeting horizontally. (If you resize your taskbar to be two rows high, of course, all bets are off.)

    Wait, it gets even better. There are four places on the screen that are effectively both infinitely wide and infinitely tall. You guessed it: the four corners.

    Regardless of how distant a corner is from your current mouse position, you can get to the corner in no time at all. Acquiring the corner requires very little fine motor control at all because the virtual target is so huge. In GUI terms, the corners are so good they're often called "magic."

    The Start button in Windows is seemingly located in an ideal place for fast acquisition, and in recent versions of Windows that's certainly true. Prior to Windows 2000, however, the Start button had a single "dead" pixel along the left and bottom sides of it in which clicking didn't open the Start menu. The result: slower acquisition times and a startling number of missed clicks.


    Windows 95: Missed by a pixel
    Windows XP: Good to the last drop

    Happily, the Windows team fixed this almost eight years ago.

    Office 2007 and Fitts' Law

    We've tried to pay attention to Fitts' Law throughout the redesign of the Office user interface.

    First off, most controls in the Ribbon are labeled. This helps discoverability and usability considerably, but it also makes the buttons bigger and easier to target. As your screen resolution increases, the width of the Ribbon also increases, providing room for more labels and larger buttons.


    Larger, labeled controls can be clicked more quickly

    In a sense, the Ribbon tries to keep MT from Fitts' Law relatively constant by compensating for the greater average travel distance required at higher resolutions by displaying larger controls.

    The Mini Toolbar was designed with Fitts' Law in mind as well. Whenever you select text or right-click selected text, a small toolbar appears directly next to the mouse cursor (you've seen the movie, right?) As you move closer to it, it fades in; as you move away, it fades out.


    Mini Toolbar: Close to the cursor

    The controls on the Mini Toolbar are small, but because they're located directly next to your cursor, they're easy to target. In this case, you want to have small buttons because it means you can have as many as possible located as close as possible to the cursor.

    We did look at other more radical designs (such as positioning the Mini Toolbar directly on the cursor, or a radial design) but we were also trading off with being able to see the text you've just selected and how easy it is to scan the controls on the toolbar. The design we went with provided the best overall balance of efficiency and utility.

    The Ribbon is designed to increase W; the Mini Toolbar is designed to reduce A. Both of these affordances help to reduce MT, the time it takes to click a button.

    Quick Access a Mile High

    The operating system really has the best opportunity to take advantage of the edges and corners of the screen. When Office windows are floating around on the desktop, we're sort of confined to the window we're in.

    But there's good news: according to the Customer Experience Improvement Program data, a startling number of Office windows run maximized. Even at high resolutions like 1280x1024 and 1600x1200, Office windows are maximized most of the time. And at 1024x768 and below, we're maximized almost all the time.

    Why is this good news? Because when we're maximized, we suddenly get edges of the screen to play with. The right edge of the screen is used for the scroll bar, which we are careful to make sure extends all the way to the edge of the screen so that it's a "mile wide." The left edge of the screen is used differently in each program.

    Historically, the top edge of the screen is used for the title bar. Having a title bar is probably necessary, but it's a huge waste of easily-targeted space, especially when your windows is maximized (meaning that you're not dragging it around to move it anyway!)

    So, we wanted to take advantage of the title bar space to help make certain controls faster to target; this is why the Quick Access Toolbar is located in the title bar by default.

    We designed the customizable Quick Access Toolbar to contain features people use frequently and regardless of the Ribbon tab they're on. By default, it contains Save, Undo, and Redo, but you can add any control in the Ribbon to your QAT by right-clicking it and choosing "Add to Quick Access Toolbar."

    Because the Quick Access Toolbar is in the title bar, the buttons are effectively infinitely tall. You can target and click each of the buttons very quickly; they're a "mile high." Finally, you can reclaim this valuable screen edge to put features you want to access ultra-efficiently.


    Quick Access that's a mile high...

    (Note: In Beta 2, there's a bug which keeps these controls from extending to the top of the screen; it's fixed in the upcoming Beta 2 Technical Refresh.)

    And We Didn't Leave Out the Magic Either…

    I mentioned before how special the corners of the screen are because they're effectively infinitely tall and wide.

    The bottom-left and bottom-right corners are taken up by the Windows taskbar, so those can't be used by Office. The upper-right corner is used for the window close button in each app, so it's kind of off-limits as well.

    The upper-left corner, though, in most programs is used for a system menu which is mostly intended to be used via the keyboard: not a good use of the most premium real-estate on the screen.

    In Office 2007, we decided to take advantage of that corner by using it for the Office button.

     
    Magic Corner: Office Button in the upper-left corner

    Although the button itself is round, the hit target for it actually extends on a maximized window all the way to the upper-left edge of the screen.

    As a result, accessing the Office menu to Save, Open, Print, Send (or to access one of your Recent Documents) is ultra-efficient. Fitts' Law was actually one of the driving forces behind the Office Button design.

    Summary

    Speed of target acquisition is but one of the many characteristics of a graphical user interface, but it's an important one. In Office's redesign, we've tried to take advantage of Fitts' Law in several key ways: the control layout and scaling of the Ribbon, the Mini Toolbar and other "by the cursor" contextual UI, and the usage of the edges and corners of the screen for the Quick Access Toolbar and Office Button.

  • Extreme User Research

    by Daniel Lafreniere on 2008/03/26 | [35 Comments]

    What is the biggest problem I face almost every time a client hires me to do something about a web project going awry? They don’t know a thing about their users. They don’t have a clue, whatsoever. Unbelievable but true!

     

    Good designers will certainly argue that THEY don’t need user data to do proper design. That if THEY like it, EVERYBODY will… sure! This probably explains why so many web projects fail in so many levels: Usability, aesthetics, emotions, and profitability.

     

     

    What’s the remedy to this world-wide infection? User research… but not in the typical version, meaning lengthy ethnographic studies that seem to take forever before obtaining some data. I’m talking about a simpler way, a faster way of doing it. I call it "extreme user research." What’s so extreme about it? Well, it can be done in 30 minutes per interviewee, and it generates loads of useful data that will have a real impact on design, thus making your website more profitable.

     

    Getting information from surrogate users

     

     

    Doing user research doesn’t have to be tedious and cost lots of money. In many cases, you should be able to do it in a few days, even a few hours, depending of the scope your project. The main idea behind extreme user research is that instead of going for the real users, we go for surrogate users. Those are the ones within a company who talk directly to the customers. We want to talk to the people who talk to the people.

     

    For instance, let’s say we do an e-commerce project for a cable company. The surrogate users would be those in the call centers—the first-line personnel who provide information about products and the second-line personnel who provide customer support for billing issues and technical problems. Talking to those people means having access to tens, hundreds, or even thousands of clients. Not bad at all!

     

    Doing extreme user research is simple. We simply perform individual semi-structured interviews that last no longer than 30 minutes. This time limit is a profitable constraint. It adds a stress that forces the interviewee to focus on the core, on the essentials. During those 30 minutes, we want to know as much as possible about the customers:

     

     

     

    What triggered the call? For example, was it a problem, advertisement, word of mouth, season, news in the media, life event like a birth, the first job, moving to another place?

    What is the whole purpose of the call?

    What are the callers’ main concerns? Are there any misconceptions or incomprehensions about the company’s products or services?

    What words do the callers use to express their needs?

     

     

    Do what people do during speed-dating sessions: Focus on the essence. Ask for the top ten questions from customers. Ask for the five things you should know if you’d have to replace a surrogate user for an afternoon. You’ll see, it works!

     

    Go for the individuals. Don’t, I repeat don’t go for group interviews, the infamous focus groups. Otherwise, you’ll have to deal with strong-minded individuals whose influence biases the group and thus the whole process.

     

    How many surrogate users should you interview? About five per job description. You want a certain degree of repetition among the interviewees to avoid anecdotes or personal perceptions. Because of the speed factor, you can interview up to 12-14 people in a single day, which means more than 60 interviews in a week! Yes, these will be long days. Yes, at a certain point, it will be tedious to hear the same thing over and over again. But that’s the whole point. We want to make sure we have solid data based on facts, not perceptions.

     

    Okay. Now, you have done your 40 interviews. You’re swamped with data. What’s next? Well, all you have to do is:

     

     

     

    Extract all the facts that you’ve found.

    Write them on sticky notes.

    Tag each note appropriately using a word or a symbol. I usually use words like user, goal, trigger, concern, FAQ, love factor, hate factor, and incomprehension. These tags will really help you later on for documentation. You can also use different colored sticky notes for this purpose.

    Find patterns and create groups around types of users.

    Create some first-version personas and then refine them.

    Show and tell everyone about your findings.

     

     

    Designing using facts, not opinions

     

     

    During the Québec city website redesign (which is not yet online), we interviewed five call-center employees and discovered that citizens interact mostly with city hall for:

     

     

     

    their home (garbage collection and recycling, permits, taxes)

    their street (parking, lighting, pavement and road works, snow removal)

    public services schedules (library, swimming pools, skating rinks, etc.)

     

     

    Knowing these interactions really helped us focus on what citizens really need. Garbage collection by definition is not very sexy, but when 30% of the calls are about this topic (based on interviews and call log analysis), it becomes clear that the city website has to address this subject before anything else!

     

    Call-center personnel also told us that citizens always ask the same four questions about a topic:

     

     

     

    How to get a service from the city? They want to know the procedure (for example, how to get rid of an old sofa).

    When is it going be done? What is the schedule?

    How much does it cost?

    Who’s in charge?

     

     

    Having this information helped us design page templates with placeholders answering those four questions. It’s that simple and straightforward.

     

    Conclusion

     

     

    Knowing, I mean really knowing your users has great benefits. Your design will be based on facts, not on suppositions or false perceptions.

     

    Knowing your users means that you’ll spend money on what users really need, NOT on what you suppose they would need or like. It usually leads to simpler solutions. Having facts reduces those never-ending discussions where everybody has his own solution based on his own personal needs and preferences. It has been said before but I’ll say it again: We, the designer and the client, are NOT the users.

     

    Get out of your cubicle. Get out of your meeting room. Go and get those surrogate users and know as much as you can about your users. You’ll see: Your users are not who you think they are.

  • Preparing for User Research Interviews: Seven Things to Remember

    By Michael Hawley

    Published: July 7, 2008

    “A researcher’s skill in conducting interviews has a direct impact on the quality and accuracy of research findings and subsequent decisions about design.”

    Interviewing is an artful skill that is at the core of a wide variety of research methods in user-centered design, including stakeholder interviews, contextual inquiry, usability testing, and focus groups. Consequently, a researcher’s skill in conducting interviews has a direct impact on the quality and accuracy of research findings and subsequent decisions about design. Skilled interviewers can conduct interviews that uncover the most important elements of a participant’s perspective on a task or a product in a manner that does not introduce interviewer bias. Companies hire user researchers and user-centered designers because they possess this very ability.

    There is a wide variety of literature regarding best practices for user research interviews. For example, in their book User and Task Analysis for Interface Design, Hackos and Redish devote an entire section to the formulation of unbiased questions. They advise interviewers to avoid asking leading questions, to ask questions that are based on a participant’s experience, and to avoid overly complex, lengthy questions.

    “in many interview formats, a significant portion of each session involves ad-hoc, probing, follow-up questions that require researchers to think quickly to maximize their time with participants.”

    Writing interview scripts in advance of a session lets researchers review and revise wording to elicit useful and unbiased responses from participants. However, in many interview formats, a significant portion of each session involves ad-hoc, probing, follow-up questions that require researchers to think quickly to maximize their time with participants. In my experience, this is where the potential to introduce bias is the greatest. In addition, conducting a successful interview involves more than just asking questions. There are also a number of guidelines for how researchers should interact with participants to enable successful interviews. These include monitoring body language, recognizing self-censoring, and understanding the correct balance between leading an interview and listening to a participant.

    Experienced researchers may become more comfortable in different kinds of interview situations and have an easier time interacting with participants during interview sessions. But, over time, researchers may also develop familiar patterns for asking questions and ways of interacting with participants that could prevent them from uncovering a unique perspective in the context of a particular interview. Also, the introduction of bias in an interview is often subtle, and it may be difficult even for researchers with years of experience to notice it during one of their own sessions.

    Seven Interview Best Practices

    Given everything there is to remember to ensure we conduct successful interviews, I find it helpful to remind myself of the following seven key best practices immediately before an interview session:

    1. Set proper expectations. Generally, interview participants are not experienced with the user-centered design process. A recruiter may have given them a brief description of the purpose of an interview during the recruiting process, but it’s very likely participants don’t have a clear sense of why they are there. They may be apprehensive, nervous, or skeptical about your intentions. Business stakeholders especially may come to a session with a negative attitude if they believe a researcher is there to check up on them. All of this will serve to influence the responses they give to interview questions. To minimize this impact, be sure to describe the intent of the interview, your role in the design process, and how the interview process will proceed. Include details such as why you will be taking notes and how you will compile the results.
    2. Shut up and listen. As a researcher, it is easy to get wrapped up in the interview script you developed, all of the questions you want to ask, and your own ideas about the salient points to uncover. It is easy to dominate the conversation and move through the interview at a pace that is too fast for a participant to keep up. In my experience, participants often raise the most interesting points only once they’ve had a chance to internalize and think about a researcher’s question. Listening appropriately involves minimizing interruptions and slowing down the pace of the interview to give participants an opportunity to qualify their statements or provide additional insights.
    3. Minimize biased questions. Asking leading or biased questions is all too easy to do. Even a simple question such as How did you like that process? subconsciously suggests to participants that they should like the process more than they should dislike it. In our attempt to be conversational, such questions as these often roll off the tongues of even the most experienced interviewers. I’ve found the best way of minimizing these types of leading questions is to read a set of good and bad examples before an interview session. Examples might include:

    Bad: How did you like the login screen?

    Good: What do you think about the login screen?

    Bad: Is the feature helpful to you?

    Good: Is the feature helpful or not helpful to you? Why?

    Bad: Would this be a good idea?

    Good: How valuable would this be to you in your job?

    1. Be friendly. Interview scripts are useful, because they help researchers remember all of the topics they need to cover. But reading directly from interview scripts can have a negative effect on the dialogue between an interviewer and a participant. The result: formal, unengaged conversations in which participants give the shortest, simplest possible response to a question so they can move on to the next one. Developing a friendly relationship and an open style with participants starts with the initial greeting and continues through the interview to the closing. Establish eye contact, remember each participant’s name, and develop a casual conversational style to elicit the most thoughtful, considered responses from each participant.
    2. Turn off your assumptions. It is human nature to let your perceptions of a given topic influence your questions and even the responses you hear from participants. You may also be biased by responses you’ve heard from other participants, perhaps earlier in the same study. While it may be impossible to avoid these influences altogether, reminding yourself that they exist before the start of an interview session helps minimize their effects. Especially during the last interview in a series of interviews, make it a point to be open-minded and responsive to alternate points of view.
    3. Avoid generalizations. In rare circumstances, it may be appropriate to ask participants to speak on behalf of others or predict how certain groups of people would react to particular experiences. However, for the most part, the best research interviews are those in which the participants speak about their own experiences and preferences. Researchers must recognize when participants are generalizing their responses and attempting to answer on behalf of others. In such cases, a researcher should politely ask participants to speak about their own experiences.
    4. Don’t forget the non-verbal cues. Participants communicate through more than just their verbal responses. Body language and tone of voice convey a great deal about participants’ comfort levels with the interview session in general, their perspectives on a task or product domain, or their opinions of a researcher or the goal of a project. Researchers who focus too intently on their interview scripts and miss participants’ non-verbal cues may miss the necessary clues that would suggest they should adjust some aspect of the interview. Customers might be nervous or apprehensive and limit their answers. Business stakeholders might be skeptical about a project or the context of an interview. So, researchers need to recognize the clues that indicate such emotional responses and be flexible enough to adjust an interview session to ensure they can properly interpret participants’ responses and get the maximum return on their effort.

    Conclusion

    “All of us are prone to bias or can fall into bad habits that can limit the reliability and effectiveness of results.”

    As Dumas and Loring note in their excellent new book Moderating Usability Tests, it’s difficult to conduct a perfect interview session. All of us are prone to bias or can fall into bad habits that can limit the reliability and effectiveness of results. This is especially true for the last sessions in a series of interviews, when you’re likely to be tired or already have formulated opinions on the outcome of a study. But, by reviewing a checklist of best practices before each interview session to remind yourself of the things you should avoid, you can minimize the impact of these pitfalls and maximize the return on your research effort.

    Additional Resources

    Dumas, J. and Loring, B. Moderating Usability Tests. San Francisco: Morgan Kaufmann, 2008.

    Hackos, J. and Redish, J. User and Task Analysis for Interface Design. New York: Wiley, 1998.

  • Website User Research  Guide


    V0.1

     

     

     

     

    By cnscorpio

    本文档内容仅代表本人观点

     

    写在前面

    站点的运营目标分为两个方面,对外就是为用户提供无可挑剔的购买前体验服务和购买后的SAAS产品服务,对内则是为公司创造不可量化的利润(如果可以的话:>)。而这一目标的实现,需要整个公司的每个环节都能做到真正的“以用户为中心”,从web开发部这个节点来说,就是要做好用户在站点前端页面上的体验,优化操作流程,最大化的将潜在顾客,准用户转化为真正的购买用户,让他们真正享受服务。

    几乎每个人都在谈用户体验,无论是什么岗位,什么部门,但是,大家理解:

    1.什么叫用户体验?

    2.为什么要做用户体验改善?

    3.怎么做用户体验改善?

    4.用什么方法做?

    5.在哪里做合适?

    ……………..

    这些概念,事情,方法论却鲜有人去思考,总结,研究,所以,造成了目前站点在进行视觉设计和产品开发的时候,没有足够的数据分析来支撑,去判断开发的产品好与坏,合适与否,视觉设计作品也是一千个人眼里有一千个哈姆雷特。

    所以,只有通过不断的进行用户行为研究来持续的改善站点的用户体验,才能为各种开发,视觉设计提供可信赖的决策条件。

     

     

     

     

     

     

     

     

     

     

     

     

     

     

    目录

    1.为什么要做用户研究? 5

    2.如何做用户研究? 6

    2.1问卷 6

    2.2点击行为轨迹 6

    2.3
    可用性实验室(包括眼动仪测试等) 6

    2.4 Google Analytics 7

    2.5 Focus group 7

    2.6 访谈方式 9

    3.划分被访用户的维度 9

    3.1 传统的划分 9

    3.2 产品导向的划分 9

    4.选择什么地方来做用户研究? 10

    4.1
    在公司的用户研究实验室 10

    4.2
    在休闲场所,例如咖啡厅 10

    4.3 在用户自己的环境里 11

    5.人员配备 11

    5.1参与测试的用户 11

    5.2主持人 11

    5.3记录员 11

    5.4观察员 11

    6.用户研究流程 12

     

     

     

     

     

     

     

     

     

     

     

    1.为什么要做用户研究?

    关于这个,在前言中也说到了一部分。开展这个工作,就是为了研究用户在站点前端页面上发生的行为(暂时不包括社区和后端的saas产品)根据这些特征得出某些用户想做,而做不到的,或者令用户疑惑的、易造成错误的、或者存在了却不想做的需求点,以及各种供分析的样本数据。

    而上述的这些成果将会在公司进行各种决策时提供分析依据,这里就包括了市场营销决策,功能开发决策,以及视觉交互设计决策,这样将能够避免不同的意见(正确的与不正确的)左右最终决策的现象。

    图一

     

     

     

     

     

    2.如何做用户研究?

    2.1问卷

    就是通过设计一系列的问题,可以是纸质的,也可以是电子档的,让被访者来回答,最后通过统计这一样本量的问卷得出若干数据,在这个的基础上去寻找某些共同的用户行为规律和其他有价值的结论。

    实施:问卷可以在某个同学的版本上进行改进。

    2.2点击行为轨迹

    使用点击热点,轨迹跟踪记录系统进行统计给用户的DEMO,或者目前正在运行的界面,例如目前试用的cetrk点击热点记录系统,这种方法能够很形象,很快速的记录下用户的点击行为,最终形成热点图,呈现点击分布规律。

    实施:可购买类似cetrk这样的系统,或者自主开发

    图二

    2.3
    可用性实验室(包括眼动仪测试等)

    可使用两个,或者两个以上的视频采集,同时对用户脸部和用户操作的界面进行拍摄与观察。最后将两者进行合成对比,既能够观察到用户在操作体验时所由里到外的情感流露。通过一些关键点,再对照当时的操作点,推断出某刻用户的惊喜,疑惑,担忧等情感因素。或者使用眼动仪对用户眼球视网膜的光线反射进行扑捉,以记录用户的心理活动。

    实施:如果还没有购置或租借眼动仪,也没没有可用性研究室,但可以临时搭建,可使用两台DV同时对用户脸部以及操作界面进行记录,以得到分析样本

    图三

    2.4
    Google Analytics

    Google Analytics等分析系统中提取与用户相关的数据,例如用户客户端使用的浏览器分布,什么时段是浏览站点的高峰期,那些页面的点击率是多少,离开率是多少,等等,比较粗糙,但能够结合其他的方法进行互补,估计能够得到更丰满的结果。

    实施:目前已经在使用,但是站点使用的统计分析JS系统过多会引发性能问题,如:页面加载不顺畅。

    图四

    2.5
    Focus group

    这个方法是看的老外用的,就是组织小部分人做圆桌讨论,需要一个比较精通业务又熟悉用户的主持人,把握好讨论的气氛,让每个人都能自由发问,自由回答,自由交流,随时灵巧的将偏题的用户拉回到话题中。通常旁边会有原始记录员,尽量忠于现场的记录一切,包括录音,文字,甚至视频音频。

    实施:可邀请50作于的用户进行分几组进行,预先挑选好主持人,并进行多次排练,以免冷场,如果主持的不好,可能这个方法得到的分析结果将是无效的。

    图五

    图六(摘自blueidea

    图七(摘自blueidea

    2.6
    访谈方式

    研究员与被访用户面对面的交流,通过在轻松的交流中,将问题一一进行讨论,引导用户进行思考,得到大概的用户心智模型。

    实施:此种小样本量的调研要么得到很好的效果要么偏差就很大,因为不能够保证每个接受访谈的用户都是忠实的。适合直接到用户所在的环境去进行,能够得到更多的其他地点不能获取的细节。尽量营造轻松的气氛。

     

     

     

     

     

     

     

    3.划分被访用户的维度

    3.1
    传统的划分

    传统上的划分通常是按照用户某些属性爱好进行划分,然后在统计结果中进行属性交叉分析,常见的是在问卷中的性别,年龄,职业。

     

     

    项目

    维度

    1

    性别

    2

    行业

    3

    年龄

    4

    喜欢的站点

    5

    院校学生(财会专业?非财会专业?)

    6

    购买决定权

    7

    ………….

     

    3.2
    产品导向的划分

    如果再深入点,可按“使用目标”也就是产品所用人群来划分用户群,然后去了解具有相同目标的用户Demographic。例如:

    图八

    —–在使用在线会计时,你喜欢以下的那种界面颜色?

    A.粉红色
    B.浅蓝色
    C.灰色
    (可以是
    UI界面图,比较直观)

     

    —–以下操作界面,你觉得那个比较上手?

    A. B. C. (应该为可操作的DEMO

     

     

    4.选择什么地方来做用户研究?

    4.1
    在公司的用户研究实验室

    如果是在公司的用户研究实验室,对客户比较不便,需要做好接待工作。对于我们,研究设备等比较好准备,但是并不能在用户环境中,可能会对测试数据有影响,或者与用户行为相关的细节不能捕捉。

    实施:目前移动互联公司是没有用户研究室的(或者叫可用性实验室),建议着手进行建设,如果建设完善的用户实验室成本过高,可以先从简易的开始(简易可用性实验室搭建方案会另有文档)。

    适合:问卷,DEMO测试,视频记录测试,眼动仪,Focus group

    4.2
    在休闲场所,例如咖啡厅

    也可以是公司的咖啡厅(-_-!可惜现在还么有)或者外面的,这样的环境比较轻快,被访者不会过于拘束。

    适合:单点访谈

    4.3
    在用户自己的环境里

    可以到用户的工作环境里观察,访谈,问卷等调研活动,通常来说,在用户的环境里研究用户是最合适的,对于活动发出者来说,比较辛苦,且比较耗费时间,但得到的结论确实最有价值的。

    适合:单点访谈,观察,视频记录(可以使用简易可用性实验室搭建方案在用户环境进行)

    5.人员配备

    5.1参与测试的用户

    人数要由测试项目的大小和需要样本量的多少来决定。

    1. 问卷:200人左右

    2. 点击轨迹记录:需要部署到测试demo上进行,10人左右

    3. 可用性实验室打包测试:50人左右

    4. Google Analytics:目前已经在站点使用这个统计系统,人数是无限

    5. Focus group10人左右,可以分5

    6. 访谈:20人左右,但需要是不同类型的用户分类,以保证大覆盖面

    5.2主持人

    一人即可,但需要经常排练,有做主持的经验更好,能很好的协调各方人员顺利进行测试。

    5.3记录员

    负责记录测试过程中用户的操作记录(包括在操作什么界面,会出现什么表情感受等),谈话内容。

    5.4观察员

    如果是在视频测试或者眼动仪测试中,则需要观察员来直接观察用户表情,操作等,并应用例如出声思维等方法来与用户进行沟通。

    6.用户研究流程

    图九

  • User Research Doesn’t Prove Anything

    By Steve Baty

    Published: March 20, 2007

    In user research, as with all avenues of statistical inquiry, we’re able to demonstrate only that a hypothesis is probably true—or untrue—with some specific degree of certainty.

    Recently, I was reading through a sample chapter of a soon-to-be-published book. The book and author shall remain nameless, as shall the book’s topic. However, I was disappointed to read, in what otherwise appeared at first glance to be an interesting publication, a very general, sweeping statement to the effect that qualitative research doesn’t prove anything and, if you want proof, you should perform quantitative research. The author’s basic assumption was that qualitative research can’t prove anything, as it is based on small sample sizes, but quantitative research, using large sample sizes, does provide proof.

    This may come as a shock to everyone, but quantitative research does not provide proof of anything either.

    Here, I’m using the word proof in the mathematical sense, because that is the context within which the author made those statements. In mathematics, a proof is a demonstration that, given certain axioms, some statement of interest is necessarily true. The important distinction here is the use of the word necessarily. In user research, as with all avenues of statistical inquiry, we’re able to demonstrate only that a hypothesis is probably true—or untrue—with some specific degree of certainty.

    Granted, I’m being pedantic; and you might think this just an interesting exercise in semantics. But let me take you through a brief survey of this topic, then perhaps you’ll appreciate the importance of this distinction.

    Statistical Sampling

    In general, our user research activities involve working with a small subset of our overall audience of users, to

    • gather information about a particular topic
    • test users’ response to some feature of our design solution
    • measure an increase or decrease in the efficiency of performing a certain task
    • or some other similar goal
    Our sample should reflect, as closely as possible, the composition of the full user population in the user characteristics that matter for our research.

    The size of the entire audience prohibits us from involving all of our users in our research activities.

    Our first step is to select our sample from the total population of users. If we’ve done that successfully, our sample should reflect, as closely as possible, the composition of the full user population in the user characteristics that matter for our research.

    For example, let’s say that we’re measuring the completion times for a set of tasks in a Web application. We should first think about the user characteristics that might make someone more or less able to complete the tasks. These characteristics might include manual dexterity—for mouse control—visual acuity or impairment, language comprehension skills, etcetera. We’re less interested in whether users are left-handed or right-handed, male or female. So when selecting our sample, we need to ensure that it represents the proportion of users with vision impairments, for example, rather than left-handedness. We refer to this attribute of the sample as its representation of the user population. In other words, our sample should be representative of the entire population.

    There are a few other things we need to consider. All members of our overall user population should have an equally likely chance of our selecting them for our sample. This factor of sampling is known as randomness. So-called convenience samples—where we choose participants based on the fact that they’re close to us—obviously limit the likelihood of non-proximal users participating in our study, so don’t satisfy the requirement for randomness.

    Lastly, our sampling technique should ensure that selecting one person has no affect on the chance that we’ll select another person. This factor is known as independence and is the same principle that describes the probability that a coin toss will result in a head or tail showing. The chance of getting a head on a single coin toss is half, or 50%. The chance that two heads will appear in a row is ½ x ½ = ¼. However, if our first coin toss shows a head, the chance that the second toss will show a head is back to being half, because the two events—our two coin tosses—are independent of one another. (Bear this in mind the next time you see a run of five black on a roulette table. You might hear someone say, “The next one must be red; the chances of having six black in a row are really low.” But really, the odds are fifty-fifty that the next one will be black.)

    So, once we have selected a random, independent, representative sample, we carefully conduct our user research—survey, usability testing, etcetera—then measure our test results.

    Inferential Statistics

    There are two things we can do with our data:

    • Calculate summary or descriptive measures.
    • Use our sample statistics to estimate the values of those measures for the user population as a whole.

    First, we usually calculate summary or descriptive measures such as

    • the arithmetic mean—what we commonly call the average
    • the mode—the most commonly measured value
    • the median—the middle value when we rank all measures

    We also might measure the variance in a summary or descriptive measure—and a host of other values we can calculate from our sample. We collectively refer to these as sample statistics.

    The mistake that researchers often make is to stop at this point and start talking as if we now have learned something definite about our user population as a whole. Statements such as the following are all complete nonsense:

    • “78% of users think…”
    • “85% of teenagers on MySpace believe…”
    • “The majority of users will…”
    It is a remarkably simple process to make this jump from a sample to an entire user population.

    The simple words “Of the users who completed the task/survey question/etcetera…” should preface all such statements.

    Of course, I said there were two things we can do with our data. The second is to use our sample statistics to estimate the values of those same measures—mean, variance, etcetera—for the user population as a whole. We do this, because, in most cases, our intent is to learn something about our entire user population.

    It is a remarkably simple process to make this jump from a sample to an entire user population. The key thing to understand, in taking this step, is that, if we took a second sample and measured the same values—mean, variance, etcetera—we’d expect to get numbers that were close to, but not exactly the same as our first sample. In fact, if we were to repeat this process over and over, the values we measured for the mean and variance would form a standard bell-shaped curve like that shown in Figure 1—called variously the normal distribution or Gaussian distribution—after the 19th century German mathematician who used it so extensively in his work on astronomy.

    Figure 1—A bell-shaped curve

    Bell-shaped curve

    This characteristic of sample statistics provides us with the means by which we can estimate statistics for an entire user population from a single sample. We can use the sample mean directly—as an estimate for the population mean. Because the sample means form a normal distribution, we know that the actual population mean will fall within a plus or minus range around our sample mean. That plus or minus range is based on the variance in the sample. When using the sample variance to estimate the population mean, we first transform it into a standard error, using the formula:

    Formula

    where s is the standard deviation for the sample—that is, the square root of the variance—and n is the number of users in our sample.

    Based on our sample mean, we can be 95% certain that our actual population mean will sit within the range x ± 2se, where x is the sample mean—to be precise, the actual range is x ± 1.96se for a 95% range. We can be 99.7% certain that the actual population mean will sit within the range x ± 3se.

    Note that, in each case, there exists a small chance that the actual population mean will fall outside the range that we’ve defined. This chance is why I referred earlier to the distinction between something probably being true and something necessarily being true.

    In the absence of such techniques, the conclusions you draw from your research lack adequate foundation, and those reviewing your reports can easily reject them.

    Also, our standard error (se) is a function of the number of users in our sample. The more people we sample, the smaller the value of se and the narrower the range we define for our population mean. However, because we use the number of sample users as a square root, to halve our estimated range, we need to sample four times as many users. This is why people tend to say that quantitative studies require more users than qualitative studies. Because we’re trying to minimize the range of our estimate, we want to minimize the value of the standard error.

    The techniques I’ve described work for all measured values—for example, task completion times—and allow you to start making more meaningful statements about your user research data. In the absence of such techniques, the conclusions you draw from your research lack adequate foundation, and those reviewing your reports can easily reject them. More importantly, the efforts you expend in conducting your research will be largely wasted for want of some simple analytical rigor.

    The estimated range we can provide for the population mean gives us a reasonable likelihood—95%, say—that the real population mean will actually fall within the bounds of that range. However, not only have we been unable to pinpoint exactly the population mean, there still exists a slight chance—5% in this example—that the actual value will fall outside this range. More importantly, that uncertainty exists regardless of the number of users we include in our test. All we can do is narrow the range and, in so doing, get closer to the real value for the entire user population. So quantitative studies, while providing us with a method for estimating user population statistics, cannot provide us with proof. Used carefully, however, they can tell us a great deal—and if not with certainty, at least with a known amount of uncertainty.

  • An Introduction to Science

    Scientific Thinking and the Scientific Method

    by

    Steven D. Schafersman
    January, 1994

     


     

     

    Introduction

    To succeed in this science course and, more specifically, to answer some of the questions on the first exam, you should be familiar with a few of the concepts regarding the definition of science, scientific thinking, and the methods of science. Most textbooks do an inadequate job of this task, so this essay provides that information. This information in its present form is not in your textbook, so please read it carefully here, and pay close attention to the words in boldface and the definitions in italics.

    The Definition of Science

    Science is not merely a collection of facts, concepts, and useful ideas about nature, or even the systematic investigation of nature, although both are common definitions of science. Science is a method of investigating nature--a way of knowing about nature--that discovers reliable knowledge about it. In other words, science is a method of discovering reliable knowledge about nature. There are other methods of discovering and learning knowledge about nature (these other knowledge methods or systems will be discussed below in contradistinction to science), but science is the only method that results in the acquisition of reliable knowledge.

    Reliable knowledge is knowledge that has a high probablility of being true because its veracity has been justified by a reliable method. Reliable knowledge is sometimes called justified true belief, to distinguish reliable knowledge from belief that is false and unjustified or even true but unjustified. (Please note that I do not, as some do, make a distinction between belief and knowledge; I think that what one believes is one's knowledge. The important distinction that should be made is whether one's knowledge or beliefs are true and, if true, are justifiably true.) Every person has knowledge or beliefs, but not all of each person's knowledge is reliably true and justified. In fact, most individuals believe in things that are untrue or unjustified or both: most people possess a lot of unreliable knowledge and, what's worse, they act on that knowledge! Other ways of knowing, and there are many in addition to science, are not reliable because their discovered knowledge is not justified. Science is a method that allows a person to possess, with the highest degree of certainty possible, reliable knowledge (justified true belief) about nature. The method used to justify scientific knowledge, and thus make it reliable, is called the scientific method. I will explain the formal procedures of the scientific method later in this essay, but first let's describe the more general practice of scientific or critical thinking.

    Scientific and Critical Thinking

    When one uses the scientific method to study or investigate nature or the universe, one is practicing scientific thinking. All scientists practice scientific thinking, of course, since they are actively studying nature and investigating the universe by using the scientific method. But scientific thinking is not reserved solely for scientists. Anyone can "think like a scientist" who learns the scientific method and, most importantly, applies its precepts, whether he or she is investigating nature or not. When one uses the methods and principles of scientific thinking in everyday life--such as when studying history or literature, investigating societies or governments, seeking solutions to problems of economics or philosophy, or just trying to answer personal questions about oneself or the meaning of existence--one is said to be practicing critical thinking. Critical thinking is thinking correctly for oneself that successfully leads to the most reliable answers to questions and solutions to problems. In other words, critical thinking gives you reliable knowledge about all aspects of your life and society, and is not restricted to the formal study of nature. Scientific thinking is identical in theory and practice, but the term would be used to describe the method that gives you reliable knowledge about the natural world. Clearly, scientific and critical thinking are the same thing, but where one (scientific thinking) is always practiced by scientists, the other (critical thinking) is sometimes used by humans and sometimes not. Scientific and critical thinking was not discovered and developed by scientists (that honor must go to ancient Hellenistic philosophers, such as Aristotle, who also are sometimes considered the first scientists), but scientists were the ones to bring the practice of critical thinking to the attention and use of modern society (in the 17th and 18th centuries), and they are the most explicit, rigorous, and successful practitioners of critical thinking today. Some professionals in the humanities, social sciences, jurisprudence, business, and journalism practice critical thinking as well as any scientist, but many, alas, do not. Scientists must practice critical thinking to be successful, but the qualifications for success in other professions do not necessarily require the use of critical thinking, a fact that is the source of much confusion, discord, and unhappiness in our sociey .

    The scientific method has proven to be the most reliable and successful method of thinking in human history, and it is quite possible to use scientific thinking in other human endeavors. For this reason, critical thinking--the application of scientific thinking to all areas of study and topics of investigation--is being taught in schools throughout the United States, and its teaching is being encouraged as a universal ideal. You may perhaps have been exposed to critical thinking skills and exercises earlier in your education. The important point is this: critical thinking is perhaps the most important skill a student can learn in school and college, since if you master its skills, you know how to think successfully and reach reliable conclusions, and such ability will prove valuable in any human endeavor, including the humanities, social sciences, commerce, law, journalism, and government, as well as in scholarly and scientific pursuits. Since critical thinking and scientific thinking are, as I claim, the same thing, only applied for different purposes, it is therefore reasonable to believe that if one learns scientific thinking in a science class, one learns, at the same time, the most important skill a student can possess--critical thinking. This, to my mind, is perhaps the foremost reason for college students to study science, no matter what one's eventual major, interest, or profession.

    The Three Central Components of Scientific and Critical Thinking

    What is scientific thinking? At this point, it is customary to discuss questions, observations, data, hypotheses, testing, and theories, which are the formal parts of the scientific method, but these are NOT the most important components of the scientific method. The scientific method is practiced within a context of scientific thinking, and scientific (and critical) thinking is based on three things: using empirical evidence (empiricism), practicing logical reasonsing (rationalism), and possessing a skeptical attitude (skepticism) about presumed knowledge that leads to self-questioning, holding tentative conclusions, and being undogmatic (willingness to change one's beliefs). These three ideas or principles are universal throughout science; without them, there would be no scientific or critical thinking. Let's examine each in turn.

    1. Empiricism: The Use of Empirical Evidence

    Empirical evidence is evidence that one can see, hear, touch, taste, or smell; it is evidence that is susceptible to one's senses. Empirical evidence is important because it is evidence that others besides yourself can experience, and it is repeatable, so empirical evidence can be checked by yourself and others after knowledge claims are made by an individual. Empirical evidence is the only type of evidence that possesses these attributes and is therefore the only type used by scientists and critical thinkers to make vital decisions and reach sound conclusions.

    We can contrast empirical evidence with other types of evidence to understand its value. Hearsay evidence is what someone says they heard another say; it is not reliable because you cannot check its source. Better is testimonial evidence, which, unlike hearsay evidence, is allowed in courts of law. But even testimonial evidence is notoriously unreliable, as numerous studies have shown. Courts also allow circumstantial evidence (e.g., means, motive, and opportunity), but this is obviously not reliable. Revelatory evidence or revelation is what someone says was revealed to them by some deity or supernatural power; it is not reliable because it cannot be checked by others and is not repeatable. Spectral evidence is evidence supposedly manifested by ghosts, spirits, and other paranormal or supernatural entities; spectral evidence was once used, for example, to convict and hang a number of innocent women on charges of witchcraft in Salem, Massachusetts, in the seventeenth century, before the colonial governor banned the use of such evidence, and the witchcraft trials ended. Emotional evidence is evidence derived from one's subjective feelings; such evidence is often repeatable, but only for one person, so it is unreliable.

    The most common alternative to empirical evidence, authoritarian evidence, is what authorities (people, books, billboards, television commercials, etc.) tell you to believe. Sometimes, if the authority is reliable, authoritarian evidence is reliable evidence, but many authorities are not reliable, so you must check the reliability of each authority before you accept its evidence. In the end, you must be your own authority and rely on your own powers of critical thinking to know if what you believe is reliably true. (Transmitting knowledge by authority is, however, the most common method among humans for three reasons: first, we are all conditioned from birth by our parents through the use of positive and negative reinforcement to listen to, believe, and obey authorities; second, it is believed that human societies that relied on a few experienced or trained authorities for decisions that affected all had a higher survival value than those that didn't, and thus the behaviorial trait of susceptibility to authority was strengthened and passed along to future generations by natural selection; third, authoritarian instruction is the quickest and most efficient method for transmitting information we know about. But remember: some authoritarian evidence and knowledge should be validated by empirical evidence, logical reasoning, and critical thinking before you should consider it reliable, and, in most cases, only you can do this for yourself.

    It is, of course, impossible to receive an adequate education today without relying almost entirely upon authoritarian evidence. Teachers, instructors, and professors are generally considered to be reliable and trustworthy authorities, but even they should be questioned on occasion. The use of authoritarian evidence in education is so pervasive, that its use has been questioned as antithetical to the true spirit of scholarly and scientific inquiry, and attempts have been made in education at all levels in recent years to correct this bias by implementing discovery and inquiry methodologies and curricula in classrooms and laboratories. The recently revised geology laboratory course at Miami University, GLG 115.L, is one such attempt, as are the Natural Systems courses in the Western College Program at Miami. It is easier to utilize such programs in humanities and social sciences, in which different yet equally valid conclusions can be reached by critical thinking, rather than in the natural sciences, in which the objective reality of nature serves as a constant judge and corrective mechanism.

    Another name for empirical evidence is natural evidence: the evidence found in nature. Naturalism is the philosophy that says that "Reality and existence (i.e. the universe, cosmos, or nature) can be described and explained solely in terms of natural evidence, natural processes, and natural laws." This is exactly what science tries to do. Another popular definition of naturalism is that "The universe exists as science says it does." This definition emphasizes the strong link between science and natural evidence and law, and it reveals that our best understanding of material reality and existence is ultimately based on philosophy. This is not bad, however, for, whether naturalism is ultimately true or not, science and naturalism reject the concept of ultimate or absolute truth in favor of a concept of proximate reliable truth that is far more successful and intellectually satisfying than the alternative, the philosophy of supernaturalism. The supernatural, if it exists, cannot be examined or tested by science, so it is irrelevant to science. It is impossible to possess reliable knowledge about the supernatural by the use of scientific and critical thinking. Individuals who claim to have knowledge about the supernatural do not possess this knowledge by the use of critical thinking, but by other methods of knowing.

    Science has unquestionably been the most successful human endeavor in the history of civilization, because it is the only method that successfully discovers and formulates reliable knowledge. The evidence for this statement is so overwhelming that many individuals overlook exactly how modern civilization came to be (our modern civilization is based, from top to bottom, on the discoveries of science and their application, known as technology, to human purposes.). Philosophies that claim to possess absolute or ultimate truth invariably find that they have to justify their beliefs by faith in dogma, authority, revelation, or philosophical speculation, since it is impossible to use finite human logic or natural evidence to demonstrate the existence of the absolute or ultimate in either the natural or supernatural worlds. Scientific and critical thinking require that one reject blind faith, authority, revelation, and subjective human feelings as a basis for reliable belief and knowledge. These human cognitive methods have their place in human life, but not as the foundation for reliable knowledge.

    2. Rationalism: The Practice of Logical Reasoning

    Scientists and critical thinkers always use logical reasoning. Logic allows us to reason correctly, but it is a complex topic and not easily learned; many books are devoted to explaining how to reason correctly, and we can not go into the details here. However, I must point out that most individuals do not reason logically, because they have never learned how to do so. Logic is not an ability that humans are born with or one that will gradually develop and improve on its own, but is a skill or discipline that must be learned within a formal educational environment. Emotional thinking, hopeful thinking, and wishful thinking are much more common than logical thinking, because they are far easier and more congenial to human nature. Most individuals would rather believe something is true because they feel it is true, hope it is true, or wish it were true, rather than deny their emotions and accept that their beliefs are false.

    Often the use of logical reasoning requires a struggle with the will, because logic sometimes forces one to deny one's emotions and face reality, and this is often painful. But remember this: emotions are not evidence, feelings are not facts, and subjective beliefs are not substantive beliefs. Every successful scientist and critical thinker spent years learning how to think logically, almost always in a formal educational context. Some people can learn logical thinking by trial and error, but this method wastes time, is inefficient, is sometimes unsuccessful, and is often painful.

    The best way to learn to think logically is to study logic and reasoning in a philosophy class, take mathematics and science courses that force you to use logic, read great literature and study history, and write frequently. Reading, writing, and math are the traditional methods that young people learned to think logically (i.e. correctly), but today science is a fourth method. Perhaps the best way is to do a lot of writing that is then reviewed by someone who has critical thinking skills. Most people never learn to think logically; many illogical arguments and statements are accepted and unchallenged in modern society--often leading to results that are counterproductive to the good of society or even tragic--because so many people don't recognize them for what they are.

    3. Skepticism: Possessing a Skeptical Attitude

    The final key idea in science and critical thinking is skepticism, the constant questioning of your beliefs and conclusions. Good scientists and critical thinkers constantly examine the evidence, arguments, and reasons for their beliefs. Self-deception and deception of yourself by others are two of the most common human failings. Self-deception often goes unrecognized because most people deceive themselves. The only way to escape both deception by others and the far more common trait of self-deception is to repeatedly and rigorously examine your basis for holding your beliefs. You must question the truth and reliability of both the knowledge claims of others and the knowledge you already possess. One way to do this is to test your beliefs against objective reality by predicting the consequences or logical outcomes of your beliefs and the actions that follow from your beliefs. If the logical consequences of your beliefs match objective reality--as measured by empirical evidence--you can conclude that your beliefs are reliable knowledge (that is, your beliefs have a high probability of being true).

    Many people believe that skeptics are closed-minded and, once possessing reliable knowledge, resist changing their minds--but just the opposite is true. A skeptic holds beliefs tentatively, and is open to new evidence and rational arguments about those beliefs. Skeptics are undogmatic, i.e., they are willing to change their minds, but only in the face of new reliable evidence or sound reasons that compel one to do so. Skeptics have open minds, but not so open that their brains fall out: they resist believing something in the first place without adequate evidence or reason, and this attribute is worthy of emulation. Science treats new ideas with the same skepticism: extraordinary claims require extraordinary evidence to justify one's credulity. We are faced every day with fantastic, bizarre, and outrageous claims about the natural world; if we don't wish to believe every pseudoscientific allegation or claim of the paranormal, we must have some method of deciding what to believe or not, and that method is the scientific method which uses critical thinking.

    The Scientific Method in Practice

    Now, we are ready to put the scientific method into action. Many books have been written about the scientific method, and it is a long and complex topic. Here I will only treat it briefly and superficially. The scientific method, as used in both scientific thinking and critical thinking, follows a number of steps.

    1. One must ask a meaningful question or identify a significant problem, and one should be able to state the problem or question in a way that it is conceivably possible to answer it. Any attempt to gain knowledge must start here. Here is where emotions and outside influences come in. For example, all scientists are very curious about nature, and they have to possess this emotional characteristic to sustain the motivation and energy necessary to perform the hard and often tedious work of science. Other emotions that can enter are excitement, ambition, anger, a sense of unfairness, happiness, and so forth. Note that scientists have emotions, some in high degree; however, they don't let their emotions give false validity to their conclusions, and, in fact, the scientific method prevents them from trying to do this even if they wished.

      Many outside factors can come into play here. Scientists must choose which problems to work on, they decide how much time to devote to different problems, and they are often influenced by cultural, social, political, and economic factors. Scientists live and work within a culture that often shapes their approach to problems; they work within theories that often shape their current understanding of nature; they work within a society that often decides what scientific topics will be financially supported and which will not; and they work within a political system that often determines which topics are permitted and financially rewarded and which are not.

      Also, at this point, normally nonscientific emotional factors can lead to divergent pathways. Scientists could be angry at polluters and choose to investigate the effects of pollutants; other scientists could investigate the results of smoking cigarettes on humans because they can earn a living doing this by working for tobacco companies; intuition can be used to suggest different approaches to problems; even dreams can suggest creative solutions to problems. I wish to emphasize, however, that the existence of these frankly widespread nonscientific emotional and cultural influences does not compromize the ultimate reliability and objectivity of scientific results, because subsequent steps in the scientific method serve to eliminate these outside factors and allow science to reach reliable and objective conclusions (admittedly it may take some time for subjective and unreliable scientific results to be eliminated). There exists a school of thought today in the humanities (philosophy, history, and sociology) called post-modernism or scientific constructivism, that claims that science is a social and cultural construct, that scientific knowledge inevitably changes as societies and cultures change, and that science has no inherently valid foundation on which to base its knowledge claims of objectivity and reliability. In brief, post-modernists believe that the modern, scientific world of Enlightenment rationality and objectivity must now give way to a post-modern world of relativism, social constructivism, and equality of belief. Almost all scientists who are aware of this school of thought reject it, as do I; post-modernism is considered irrelevant by scientists and has had no impact on the practice of science at all. We will have to leave this interesting topic for a later time, unfortunately, but you may be exposed to these ideas in a humanities class. If you are, remember to think critically!

    2. One must next gather relevant information to attempt to answer the question or solve the problem by making observations. The first observations could be data obtained from the library or information from your own experience. Another souce of observations could be from trial experiments or past experiments. These observations, and all that follow, must be empirical in nature--that is, they must be sensible, measurable, and repeatable, so that others can make the same observations. Great ingenuity and hard work on the part of the scientist is often necessary to make scientific observations. Furthermore, a great deal of training is necessary in order to learn the methods and techniques of gathering scientific data.
    3. Now one can propose a solution or answer to the problem or question. In science, this suggested solution or answer is called a scientific hypothesis, and this is one of the most important steps a scientist can perform, because the proposed hypothesis must be stated in such a way that it is testable. A scientific hypothesis is an informed,testable, and predictive solution to a scientific problem that explains a natural phenomenon, process, or event. In critical thinking, as in science, your proposed answer or solution must be testable, otherwise it is essentially useless for further investigation. Most individuals--noncritical thinkers all--stop here, and are satisfied with their first answer or solution, but this lack of skepticism is a major roadblock to gaining reliable knowledge. While some of these early proposed answers may be true, most will be false, and further investigation will almost always be necessary to determine their validity.
    4. Next, one must test the hypothesis before it is corroborated and given any real validity. There are two ways to do this. First, one can conduct an experiment. This is often presented in science textbooks as the only way to test hypotheses in science, but a little reflection will show that many natural problems are not amenable to experimentation, such as questions about stars, galaxies, mountain formation, the formation of the solar system, ancient evolutionary events, and so forth. The second way to test a hypothesis is to make further observations. Every hypothesis has consequences and makes certain predictions about the phenomenon or process under investigation. Using logic and empirical evidence, one can test the hypothesis by examining how successful the predictions are, that is, how well the predictions and consequences agree with new data, further insights, new patterns, and perhaps with models. The testability or predictiveness of a hypothesis is its most important characteristic. Only hypotheses involving natural processes, natural events, and natural laws can be tested; the supernatural cannot be tested, so it lies outside of science and its existence or nonexistence is irrelevant to science.
    5. If the hypothesis fails the test, it must be rejected and either abandoned or modified. Most hypotheses are modified by scientists who don't like to simply throw out an idea they think is correct and in which they have already invested a great deal of time or effort. Nevertheless, a modified hypothesis must be tested again. If the hypothesis passes the further tests, it is considered to be a corroborated hypothesis, and can now be published. A corroborated hypothesis is one that has passed its tests, i.e., one whose predictions have been verified. Now other scientists test the hypothesis. If further corroborated by subsequent tests, it becomes highly corroborated and is now considered to be reliable knowledge. By the way, the technical name for this part of the scientific method is the "hypothetico-deductive method," so named because one deduces the results of the predictions of the hypothesis and tests these deductions. Inductive reasoning, the alternative to deductive reasoning, was used earlier to help formulate the hypothesis. Both of these types of reasoning are therefore used in science, and both must be used logically.

      Scientists never claim that a hypothesis is "proved" in a strict sense (but sometimes this is quite legitimately claimed when using popular language), because proof is something found only in mathematics and logic, disciplines in which all logical parameters or constraints can be defined, and something that is not true in the natural world. Scientists prefer to use the word "corroborated" rather than "proved," but the meaning is essentially the same. A highly corroborated hypothesis becomes something else in addition to reliable knowledge--it becomes a scientific fact. A scientific fact is a highly corroborated hypothesis that has been so repeatedly tested and for which so much reliable evidence exists, that it would be perverse or irrational to deny it. This type of reliable knowledge is the closest that humans can come to the "truth" about the universe (I put the word "truth" in quotation marks because there are many different kinds of truth, such as logical truth, emotional truth, religious truth, legal truth, philosophical truth, etc.; it should be clear that this essay deals with scientific truth, which, while certainly not the sole truth, is nevertheless the best truth humans can possess about the natural world).

      There are many such scientific facts: the existence of gravity as a property of all matter, the past and present evolution of all living organisms, the presence of nucleic acids in all life, the motion of continents and giant tectonic plates on Earth, the expansion of the universe following a giant explosion, and so forth. Many scientific facts violate common sense and the beliefs of ancient philosophies and religions, so many people persist in denying them, but they thereby indulge in irrationality and perversity. Many other areas of human thought and philosophy, and many other knowledge systems (methods of gaining knowledge), exist that claim to have factual knowledge about the world. Some even claim that their facts are absolutely or ultimately true, something science would never claim. But their "facts" are not reliable knowledge, because--while they might fortuitously be true--they have not been justified by a reliable method. If such unreliable "facts" are true--and I certainly don't maintain that all such knowledge claims are false--we can never be sure that they are true, as we can with scientific facts.

    6. The final step of the scientific method is to construct, support, or cast doubt on a scientific theory. A theory in science is not a guess, speculation, or suggestion, which is the popular definition of the word "theory." A scientific theory is a unifying and self-consistent explanation of fundamental natural processes or phenomena that is totally constructed of corroborated hypotheses. A theory, therefore, is built of reliable knowledge--built of scientific facts--and its purpose is to explain major natural processes or phenomena. Scientific theories explain nature by unifying many once-unrelated facts or corroborated hypotheses; they are the strongest and most truthful explanations of how the universe, nature, and life came to be, how they work, what they are made of, and what will become of them. Since humans are living organisms and are part of the universe, science explains all of these things about ourselves.

      These scientific theories--such as the theories of relativity, quantum mechanics, thermodynamics, evolution, genetics, plate tectonics, and big bang cosmology--are the most reliable, most rigorous, and most comprehensive form of knowledge that humans possess. Thus, it is important for every educated person to understand where scientific knowledge comes from, and how to emulate this method of gaining knowledge. Scientific knowledge comes from the practice of scientific thinking--using the scientific method--and this mode of discovering and validating knowledge can be duplicated and achieved by anyone who practices critical thinking.

    Copyright © 1994 by Steven D. Schafersman

     


     

     

    Steven D. Schafersman
  • http://www.developer.com/design/article.php/2109801

  • Are you thinking of becoming a scientist? Do you want to uncover the mysteries of nature, perform experiments or carry out calculations to learn how the world works? Forget it!

     Science is fun and exciting. The thrill of discovery is unique. If you are smart, ambitious and hard working you should major in science as an undergraduate. But that is as far as you should take it. After graduation, you will have to deal with the real world. That means that you should not even consider going to graduate school in science. Do something else instead: medical school, law school, computers or engineering, or something else which appeals to you.

     

    Why am I (a tenured professor of physics) trying to discourage you from following a career path which was successful for me? Because times have changed (I received my Ph.D. in 1973, and tenure in 1976). American science no longer offers a reasonable career path. If you go to graduate school in science it is in the expectation of spending your working life doing scientific research, using your ingenuity and curiosity to solve important and interesting problems. You will almost certainly be disappointed, probably when it is too late to choose another career.

     

    American universities train roughly twice as many Ph.D.s as there are jobs for them. When something, or someone, is a glut on the market, the price drops. In the case of Ph.D. scientists, the reduction in price takes the form of many years spent in ``holding pattern'' postdoctoral jobs. Permanent jobs don't pay much less than they used to, but instead of obtaining a real job two years after the Ph.D. (as was typical 25 years ago) most young scientists spend five, ten, or more years as postdocs. They have no prospect of permanent employment and often must obtain a new postdoctoral position and move every two years. For many more details consult the Young Scientists' Network or read the account in the May, 2001 issue of the Washington Monthly.

     

    As examples, consider two of the leading candidates for a recent Assistant Professorship in my department. One was 37, ten years out of graduate school (he didn't get the job). The leading candidate, whom everyone thinks is brilliant, was 35, seven years out of graduate school. Only then was he offered his first permanent job (that's not tenure, just the possibility of it six years later, and a step off the treadmill of looking for a new job every two years). The latest example is a 39 year old candidate for another Assistant Professorship; he has published 35 papers. In contrast, a doctor typically enters private practice at 29, a lawyer at 25 and makes partner at 31, and a computer scientist with a Ph.D. has a very good job at 27 (computer science and engineering are the few fields in which industrial demand makes it sensible to get a Ph.D.). Anyone with the intelligence, ambition and willingness to work hard to succeed in science can also succeed in any of these other professions.

     

    Typical postdoctoral salaries begin at $27,000 annually in the biological sciences and about $35,000 in the physical sciences (graduate student stipends are less than half these figures). Can you support a family on that income? It suffices for a young couple in a small apartment, though I know of one physicist whose wife left him because she was tired of repeatedly moving with little prospect of settling down. When you are in your thirties you will need more: a house in a good school district and all the other necessities of ordinary middle class life. Science is a profession, not a religious vocation, and does not justify an oath of poverty or celibacy.

     

    Of course, you don't go into science to get rich. So you choose not to go to medical or law school, even though a doctor or lawyer typically earns two to three times as much as a scientist (one lucky enough to have a good senior-level job). I made that choice too. I became a scientist in order to have the freedom to work on problems which interest me. But you probably won't get that freedom. As a postdoc you will work on someone else's ideas, and may be treated as a technician rather than as an independent collaborator. Eventually, you will probably be squeezed out of science entirely. You can get a fine job as a computer programmer, but why not do this at 22, rather than putting up with a decade of misery in the scientific job market first? The longer you spend in science the harder you will find it to leave, and the less attractive you will be to prospective employers in other fields.

     

    Perhaps you are so talented that you can beat the postdoc trap; some university (there are hardly any industrial jobs in the physical sciences) will be so impressed with you that you will be hired into a tenure track position two years out of graduate school. Maybe. But the general cheapening of scientific labor means that even the most talented stay on the postdoctoral treadmill for a very long time; consider the job candidates described above. And many who appear to be very talented, with grades and recommendations to match, later find that the competition of research is more difficult, or at least different, and that they must struggle with the rest.

     

    Suppose you do eventually obtain a permanent job, perhaps a tenured professorship. The struggle for a job is now replaced by a struggle for grant support, and again there is a glut of scientists. Now you spend your time writing proposals rather than doing research. Worse, because your proposals are judged by your competitors you cannot follow your curiosity, but must spend your effort and talents on anticipating and deflecting criticism rather than on solving the important scientific problems. They're not the same thing: you cannot put your past successes in a proposal, because they are finished work, and your new ideas, however original and clever, are still unproven. It is proverbial that original ideas are the kiss of death for a proposal; because they have not yet been proved to work (after all, that is what you are proposing to do) they can be, and will be, rated poorly. Having achieved the promised land, you find that it is not what you wanted after all.

     

    What can be done? The first thing for any young person (which means anyone who does not have a permanent job in science) to do is to pursue another career.

     

    This will spare you the misery of disappointed expectations. Young Americans have generally woken up to the bad prospects and absence of a reasonable middle class career path in science and are deserting it. If you haven't yet, then join them. Leave graduate school to people from India and China, for whom the prospects at home are even worse. I have known more people whose lives have been ruined by getting a Ph.D. in physics than by drugs.

     

    If you are in a position of leadership in science then you should try to persuade the funding agencies to train fewer Ph.D.s. The glut of scientists is entirely the consequence of funding policies (almost all graduate education is paid for by federal grants). The funding agencies are bemoaning the scarcity of young people interested in science when they themselves caused this scarcity by destroying science as a career. They could reverse this situation by matching the number trained to the demand, but they refuse to do so, or even to discuss the problem seriously (for many years the NSF propagated a dishonest prediction of a coming shortage of scientists, and most funding agencies still act as if this were true). The result is that the best young people, who should go into science, sensibly refuse to do so, and the graduate schools are filled with weak American students and with foreigners lured by the American student visa.

  • Scenario Development

    At The Arlington Institute we do scenarios. We build them for our clients in various shapes and colors, for we believe that they are the most effective tool currently available for systematically considering the future. Some of our clients want to know about the future of their marketplace, or a major contributing factor to their operating environment, like technology. Others are concerned about possible big surprise events - wild cards - that might blow in unexpectedly and fundamentally shift the status quo. Perhaps your concern is a geographic area - like Africa, or you are considering the purchase of a major asset and want to have a sense of what might change the present situation that makes that a good decision - all of these are good candidates for scenario planning. Scenarios can also be used as the basis for developing an organizational vision - a particularly powerful role.

     

    Why scenarios?

    People try to think about the future in quite a variety of ways. Some try to predict what might happen. Others make forecasts or projections. Some extrapolate trends. But all of these approaches have fundamental flaws: it is impossible to predict the future (at least at this time). One can go to the fundamental mathematics of the situation and find that in highly complex situations, a very minor change in the initial conditions (or of some element during the evolving process) will result in major shifts in the end point. It is essentially impossible to identify and catalog the relationships of all of the potential contributing factors at any one time, so similarly, it is impossible to anticipate the future that will arrive with any level of confidence.

    So what do you do if you want to systematically look at the future? Right now, the best answer is scenarios - rigorously designed mental images of the most significant likely possibilities that might evolve, developed around specific issues that are most important to you or your organization. This process, if done well, will essentially produce a spectrum of plausible futures that effectively brackets the horizon. You will be able to see before you most, if not all, of the likely big possible situations.

    So armed, the effective strategist can then begin to consider the potential implications of each future world, and begin to put into place the contingency plans for dealing with both the opportunities and hazards that might arrive. This alone is usually worth the price of admission, for it quickly illuminates one’s perspective and immediately provides a larger, sophisticated, future-oriented context for evaluating day-to-day events. News stories are suddenly seen in a different light - as possible contributing factors for one or more plausible futures.

    As is the case in other disciplines as well, a significant amount of the value of scenario building resides in the process rather than the product. That is, in developing a rich perspective of what might happen, one systematically identifies and confronts the majority of the active players that influence the life of an organization and considers what the roles and potential impact are for each of them as they interact with all of the others. Changes in government regulation, international economic health, technological breakthroughs, social value shifts, environmental and population pressures - all are considered at the same time. That is something most of us don’t get a chance to do very often, so it is guaranteed to be loaded with enlightenment.

    Again, since you can’t predict the future, it is unlikely that any of the scenarios will evolve in quite the form that was considered. What will actually happen is some of one future and part of another . . . but having considered all of the possibilities before the fact, the effective scenarist will immediately recognize the evolving future in terms that are familiar and significant. The future has order when seen in this way. It is not just a random emergence of events.

    Perhaps the most powerful scenario variant is the normative scenario, or desired future. After completing the process of developing an initial set of scenarios the strategist can look across the spectrum of possible futures and begin to develop an image of the future that is particularly desired. This normative future can be systematically designed and shaped to an organization’s specific capabilities and resources and therefore become a very pivotal device for helping individuals to “see” what they can become. This story of the future puts a face on an otherwise abstract set of objectives. Instead of simply having a goal of say, a sustainable economy, a good scenario paints the picture of what a world would look like that was truly sustainable - what the players would do, what would be the results, etc. These images can be communicated, of course, using video, film, and multimedia.

    A well-crafted normative scenario allows an organization to become proactive, working specifically for their desired future, rather than sitting by and passively waiting for what ever the world delivers. It is a tool for allowing individuals and organizations to “create their own future,” a perspective that is often an epiphany for the participants.

  • Social Network Analysis

    February 21, 2002

    How do knowledge workers learn? How do they decide what to learn next? What motivates them to share?

    These questions are central to the challenges of knowledge management, and yet most corporate portals and online communities are designed in ignorance of their answers.

    The truth lies within the social fabric that connects people to people and people to content. Relationships, trust and serendipity play key roles.

    To illustrate, let me tell you a story about my recent foray into social network analysis, a strange world filled with mavens and connectors, structural holes, intensional networks, and socially translucent systems.

    The Tipping Point

    My interest in the ties between people and content isn't new. In 1995, I helped design an information architecture strategy for Dow Chemical that placed the employee directory at the center of a rich web of relationships between authors and documents.

    And two years ago, I invited Bonnie Nardi to speak at IA2K about her fascinating work on information ecologies and social networks.

    But it was two events last month that prompted my current enthusiasm.

    First, I discovered The Tipping Point while browsing Borders bookstore. The notion of Connectors (who know everyone) and Mavens (experts who love to teach) as catalysts of social epidemics really caught my attention.

    Second, I had lunch with Lou Rosenfeld, who had just been talking with Ed Vielmetti, who is now working with Valdis Krebs to distribute software for "social network analysis."

     The Tipping Point Kresge Business Administration Library Knowing What We Know (Organizational Dynamics) The Knowledge Management Puzzle Researching Organizational Systems Using SNA Organizational Network Mapping (PDF) Introduction to Social Network Analysis Knowledge Networks It's Not What You Know Google Vacuum Orgnet Borders Books & Music Peter Morville Jane Dysart Mary Lee Kennedy David Snowden Rob Cross Ed Vielmetti Louis Rosenfeld Valdis Krebs Social Network Analysis Socially Translucent Systems Bonnie Nardi

    Interactive Story Map of My SNA Learning Process

    This discussion sparked my interest and gave me names and keywords to feed into Google, producing some great articles.

    While traveling, I mentioned SNA to Jane Dysart and Mary Lee Kennedy. Both pointed me to Dave Snowden who told me about Rob Cross.

    In the period of a few weeks, I learned quite a bit about the people and ideas surrounding social network analysis.

    The ABCs of Network Analysis

    Valdis Krebs states "organization charts prescribe that work and information flow in a hierarchy, but network mapping reveals [they] actually flow through a vast web of informal channels."

    Social network analysis involves the mapping and measuring of these normally invisible relationships between people, providing an organizational X-ray for use by HR managers and consultants.

    Illustration: Kite Network

    Kite Network ideas developed by David Krackhardt and Valdis Krebs

    SNA tools such as InFlow help reveal densely connected clusters or communities of practice, and support the three most popular metrics:

    • Activity. Susan is a "connector" with 6 direct links to other nodes.
    • Betweenness. Claudia has only 3 connections but holds a powerful position as the sole "boundary spanner" between different groups.
    • Closeness. Sarah and Steven have the shortest paths to all others. They have an excellent view of what's going on.

    These tools and metrics can be applied at the level of individuals, organizations and industries. They can also be used to analyze computer networks (to optimize topology) and information systems (imagine a visual representation of Google's link analysis).

    Human Surrogates

    What ties information architecture, knowledge management and social network analysis more closely together is the reciprocal relationship between people and content.

    Illustration: Reciprocal Relationship Between People and Content

    Success in the former requires we know what other people know and who other people know. Success in the latter demands good search, navigation and content management systems.

    In information retrieval, we often use document surrogates such as abstracts to represent the knowledge contained within that content.

    We might also think of the documents themselves as "human surrogates," representing the knowledge and interests of authors.

    And of course, we humans also serve as surrogates for one another.

    In the context of enterprise KM, this suggests a need for metadata schema, tools, staff directories and incentives to enable and encourage explicit connections between documents and authors.

    Socially Translucent Systems

    My SNA research eventually led me to socially translucent systems and an instant messenger on steroids named Babble.

    Built upon the rationale that "visibility yields awareness yields accountability," Babble makes people aware of one another's presence and activity in both real-time and asynchronous modes.

    Screenshot: Babble

    Screenshot of IBM's Babble

    Notice the "marbles" in the Commons Area? These "social proxies" indicate the relative activity levels of users, whether they're speaking or just listening to the conversation. Yes, even lurkers are exposed to the light of day.

    Babble motivates by enabling people to build social capital and manages contribution quality through peer pressure and social feedback. Put simply, nobody wants to lose their marbles.

    Beyond Babble

    The concepts of network analysis and socially translucent systems are applicable far beyond the confines of text-based chat.

    In fact, these concepts are critical to the creation of truly useful knowledge economies and online communities.

    The seeds of innovation are lying all around us, from Google's Backward Links to AOL's Buddy Lists to Amazon's Purchase Circles to the incestuous source links of Blogdex.

    We humans are very social animals. It's about time more of us started recognizing this in the systems we design.

  • 2009-04-16

    Personal Space - [User Research]

    Personal space

    From Wikipedia, the free encyclopedia

    Jump to: navigation, search

    Personal space is the region surrounding each person, or that area which a person considers their domain or territory.[1] Often if entered by another being without this being desired, it makes them feel uncomfortable. The amount of space a being (person, plant, animal) needs falls into two categories, immediate individual physical space (determined by imagined boundaries), and the space an individual considers theirs to live in (often called habitat). These are dependent on many things, such as growth needs, habits, courtships, etc. Hall's spacing models, to note, were themselves based on Heini Hediger's 1955 psychological studies of zoo animals.[2]

    Diagram of Edward T. Hall's personal reaction bubbles (1966), showing radius in feet

    [edit] Overview

    Two people not affecting each other's personal space.
    Reaction of two people whose personal space are in conflict.

    Personal space is highly variable. Those who live in a densely populated environment tend to have smaller personal space requirements. Thus a resident of India may have a smaller personal space than someone who is home on the Mongolian steppe, both in regard to home and individual. For a more detailed example, see Body contact and personal space in the United States.

    It can be determined on a habitat level by profession, livelihood, and occupation. Personal space can also be heavily affected by a person's position in society, with the more affluent a person being the larger personal space they demand. While it is highly variable and difficult to measure accurately the best estimates for personal physical space place it at about 24.5 inches (60 centimeters) on either side, 27.5 inches (70 centimeters) in front and 15.75 inches (40 centimeters) behind for an average westerner.

    People usually make exceptions to, and modify their space requirements, when they see an immediate need or reason to temporarily allow a change in their usual personal space needs. Often a person's comfort zone is different depending upon where they are and who they are with. In certain circumstances people can accept having their personal space violated. For instance in romantic encounters the stress from allowing closer personal space distances can be reinterpreted into emotional fervour. Another method of dealing with violated personal space, according to psychologist Robert Sommer, is dehumanization. He argues that (for example) on the subway, crowded people often imagine those intruding on their personal space as inanimate. Differences in personal space distances by culture (such as a person from India attempting to talk to someone from the Midwestern US) can often cause situations where one person steps forward to enter what they perceive as a conversational distance, and the person they are talking to reflexively steps back to restore their personal space.

    Attitudes of people regarding someone else entering their personal space may depend on the sex of both people. Some train cars are women-only, to allow women to avoid men entering their personal space, providing privacy, and safety from the possibility of being groped. Changing perceptions about personal space and the fluctuating boundaries of public and private in European culture since the Roman Empire have been explored in A History of Private Life, under the general editorship of Philippe Ariès and Georges Duby, published in English by the Belknap Press.

    Neuropsychology further describes personal space through three subdivisions which denote the 'near-ness' to ones person. 1. Extrapersonal Space: Extrapersonal Space refers to all space that occurs outside the reach of the individual in question. 2. Peripersonal Space: Peripersonal Space refers to all space within reach of any limb of the individual. Thus to be 'within-arm's length' is to be within one's peripersonal space. 3. Pericutaneous Space: Pericutaneous Space refers to the space just outside our bodies. It is generally accepted that the visual-tactile perceptive fields overlaps in the pericutaneous space, such that in example, one might see a feather as not touching themselves, but still feel the inklings of being tickled when it hovers just about their hand.[3]

  •  Expecting to meet with Ms. Glenn, you might find yourself in a room with four other people: Ms. Glenn, two of her staff, and the Sales Director. Companies often want to gain the insights of various people when interviewing candidates. This method of interviewing is often attractive for companies that rely heavily on team cooperation. Not only does the company want to know whether your skills balance that of the company, but also whether you can get along with the other workers. In some companies, multiple people will interview you simultaneously. In other companies, you will proceed through a series of one-on-one interviews.

     
    Some helpful tips for maximizing on this interview format:
    • Treat each person as an important individual. Gain each person's business card at the beginning of the meeting, if possible, and refer to each person by name. If there are several people in the room at once, you might wish to scribble down their names on a sheet of paper according to where each is sitting. Make eye contact with each person and speak directly to the person asking each question.
    • Use the opportunity to gain as much information about the company as you can. Just as each interviewer has a different function in the company, they each have a unique perspective. When asking questions, be sensitive not to place anyone in a position that invites him to compromise confidentiality or loyalty.
    • Bring at least double the anecdotes and sound-bites to the interview as you would for a traditional one-on-one interview. Be ready to illustrate your main message in a variety of ways to a variety of people.
    • Prepare psychologically to expend more energy and be more alert than you would in a one-on-one interview. Stay focused and adjustable. 
  •  

    普通民谣吉他,普通耳机mic,Cooledit 录音,降噪。请多对音效点评,少对唱者点评。

  • 2007-09-08

    Walt Disney - [My life...]

    Aroud here, however, we don't look backwards for very long.
    We keep moving forward, opening up new doors and doing new things, because we're curious...
    And Curiosity keeps leading us down new paths.
  • 2007-09-08

    推理。。。 - [My life...]

    前几天听同学给我讲了一个故事,一个发生在我身边,和我密切相关的故事,事实上还是因我而起的故事。故事已经结束了,但它让我很无语,让我得出了一个令我很惊讶的结论:女生竟然如此注重一些不切实际的感觉。当然,这只是男生的看法,这又让我得出了另外一个显然的问题:我们男生又有多少想法在女生看来是不可理喻的呢?我觉得肯定有,而且不少,而且让女生很无语。另外,如果说上帝创造了人,那么我们可以得出这样一个推论:上帝是一个很糟糕的设计师。理由是:本来要设计男女在一起生活,却把两者的思想设计得那么不同,很多方面甚至还有冲突。但是对于我这样一个科学主义者是不会相信这个糟糕的设计师的存在的。到这里,我只有一个猜想了,不知道怎么证明:还是我们自己的原因,我们太自私,太自我,甚至为了自己一点小小的情绪,去否定一些人,一些事。我自己也犯过这样的错,而且不敢保证将来不会再犯。人,很难绝对的理性。

    不过就这样一件小事想些这些乱七八糟的事情本身就不合理,所以全当乱YY了。

  • 2007-09-02

    35岁的影子 - [My life...]

    一首表达了我部分心情的歌

    希望我的35岁开心一点,自律一点,真诚一点,稳重一点,谦虚一点,努力一点,幸福一点,不要没事乱闹情绪。年轻不是借口,每个人都年轻过,不怕.....

  • 2007-07-06

    Transformers - [My life...]

    又有N久没来这里了,但是现在还是没心情写,不久就要去看“变形金刚了”,暂时换换背景吧。。。

  • 2007-04-15

    今天。。。 - [My life...]

        今天白天勉强看了下书,傍晚出去打球了,回来想休息休息,看看书或者背背单词,可惜又经历了一场小风雨。唉,怕什么,暴风雪都经历过了,这点小雨可以忽略不计了。博客也不能总记录自己的坏心情啊,昨天丹霞传给我一首歌,确实很有青春气息,等过两天心情平静了再放上来,确实好听,听了也有好心情的。4.15,还有15天4月就结束了,每一天现在都很珍贵啊,不努力真的补不回来的。谢谢上面那个我不知道是谁的朋友的鼓励,发现人群中的鼓励还是有真的的。把鼓励放在心里,从深渊里努力的爬起来,既然这样我都没有崩溃,那我绝对不会放弃的。不过真的要跟自己的懒惰做斗争,它太可恶了!

        努力做一个宽容的人,做一个爱憎分明的人。。。

  •     换了博客,不是为了逃避以前的那段经历,不是害怕自己无法面对,而是MSN出毛病,居然写不了了,我晕。。。如果MSN好了的话还是要回去的,毕竟那是我的经历,见证了我的成长。。。

       昨天The Days in FLA的首映终于办完了,人数不多,都是那几张熟悉的面孔,看到她们心里有的不只是高兴,说实话,很伤心,感觉自己的那些年岁已经不再了,FLA给我的不只是和伙伴们共同经历的那些时光,也给了我一段伤心的回忆。

       曾经对室友说,终于发现自己是个善良的人,昨天又有了这样的感觉,很多人不知道,昨天我想说的话不只那些,还有很多想说的话,想说说做片子的时候发生的一些事,虽然不会明说,但想提提,心里有委屈,有不解,也有一点埋怨。但看到大家那么热心的为首映工作,知道那样的话虽然隐晦,但还是会让人不爽的。一时间,便忍下心来,还是留在心底吧。别人已经给了大家那么好的印象,虽然不全真实,也是努力换来的。也可以说是软弱吧,可是自己就是这样的人,话说的很夸张,但真正要做决定了,如果对不住他人的,还是狠不下心来,也都是埋在心里了。跟老师一起做事的一个同学说我不要什么事情都自己承担,室友也说我埋藏的太深,也许这真的是我的弱点吧。。。

       和很多人也许不同,我不想在博客上细数生活中的点点滴滴,不想展示那些会让人感觉好但是其实完全没必要说的东西。那些小事,也许会改变别人对你的映象,也许会让人觉得你很体贴,让人觉得你很努力,但那毕竟是自己说的,不知为什么,心里就有种不真实的感觉。

       开学一个半月了,过了不知道多少个混混顿顿的日子,不轻松,自己的错。反而好像自己想向别人证明些什么,有什么好证明的呢?别人能睡一觉就忘记那么多事情,那么多心情,自己睡了大半年还是不能忘,似乎说明这就是真的了,但现在也够了,给谁看呢?昨天的首映似乎在我心中划上了一个句号,嗯,可以开始忘记了,别人有别人的生活,自己也不会过得更差,还有好多事情要自己做,尽管FLA的伙伴们都会来安慰,会来支持,正如他们做到的,但最终还是要自己走出来。其实老友记里面的钱得勒也是这样说的,最终还是要靠自己。不过真的很感谢FLA的伙伴们,说实话,我真的不好意思当面说些让你们感动的话,也不好意思当面对你们真诚的笑一笑,只是开些傻傻的玩笑。不过心中的温暖是以前怎么也体会不到的。记得找海丹采访的时候,前面还有一些人,我们就聊了聊,让我吃惊的是,我们那么久没见面,她也能看出我当时的心情,也会不住的安慰我,鼓励我,也会拿自己的经历开涮,当时真的很惊讶,大大咧咧的海丹也如此体贴,而且给人的感觉那么的真实。FLA的伙伴也大都这样,平时不会很表面,都大大咧咧,但有困难的时候,有需要的时候,真的会挺身而出,而不是退缩。真的要谢谢黛瑛,每次找她述苦,不论多晚,她都陪着我,有时候自己亲密的人都走了,她却能这样坚持,这样的挚友,一生中有几个呢?真的是日久才见人心啊!不论好的,坏的,无法判断的,真的要花时间才能看清。上次孙喆找我说要剪片,其实也算是一次叙旧的长谈,依然可以聊的开心,她还时不时在我室友面前叫我“亲爱的!”唉,这样的友谊真的很难得。感觉她更像我高中时候的同学,或者说高中时候的女同学,都能如此不计较小节,也会无聊的闹来闹去,和张亚也是这样,就连开会的时候也会互相“嘲讽”。有人说这样很无聊,那又怎样呢?很开心能有这样的朋友,试想真正走入社会了,有多少人还能这样呢?也许这很幼稚,但纯真的心在这个被表面所迷惑的世界越来越难得了。工作室里的风气也差不多这样,大家也会大大咧咧的开玩笑。小旋风也会当着张亚的面说我傻B,大家也会吵来吵去,直言不讳。虽然这样有时候会不小心说到对方的心事,但如果大家愿意,也能一起分享的,是真正的心灵分享,而不是笑着,假装认真的点点头。

       工作室的人越来越有激情,让我很是感动。洁琦也会说拼了这条命也要跟你好好干了,这样的热情,真的希望每个人都能长久的拥有。那些不快的往事,也就忘了它吧,还有自己的将来。好久没有认真看过书了,好久没有仔细想问题了,表面的光环也无法弥补内心的空虚,有些事情也不想过多的说出去,每件事情都是一样的,可以说得好听点,也可以说得很难听,但到底怎样,却只有事实是真的。就像那段经历,我也可以说它让我的内心更加坚强了,让我更加明白人情世故,不论是好的,还是坏的,还是不好不坏的。但就像老妈这学期开学时说的,小心一点,以免留下一生的伤痛。可惜老妈的劝告来得晚了些。。。算了,这些总要忘的,不再管它说的好不好听,只等将来另一个人能听我述说。那些好听的事,不去说它,也不过如此,没什么大不了的,所以亚、瑛,还是不要把我昨天跟你们说的事跟别人说了吧,我知道那听起来很好,让人觉得很好,但我不喜欢这样的张扬,不喜欢这样表面的去维护自己的形象。其实反而希望别人能认识一个真正的我,不论在朋友面前,还是在对自己很重要的人面前,都希望给他们看的是真的我,而不是那个被装扮出来的我。室友曾说过,漂亮都是打扮出来的,真正的美在心里,那是通过长期的行动表现出来的。

       工作室的片头做了那么久,那种辛苦和心酸可能只有少数人可以体会,现在发现自己一个特点,只有两种事能让我忘记吃饭,其中一件就是CG,讲出来好像很牛,不过也只是说着好听罢了,其实自己还什么都不懂的。不过有时候不爽,别人一句“技术活”就把所有的东西盖住了,所有的辛苦,努力,似乎就值技术两个字。也没所谓了,那么多事情我都能忍,这些无关紧要的小事还不能?

       有些事情还是要做出选择的,导演实在当不了了,该放弃了,但我还是会继续为工作室努力的,大家的热情也是我的动力啊。。这一个半月真的好累好累,不仅生活累,心更累,什么乱七八糟的东西都要想,不想想的也跑来捣乱,经常都不知道自己的心情是什么,想过在这样的压力下自己到底什么时候会崩溃,真的不知道。也希望能有机会好好休息休息,可是总没有这样的机会。昨天的首映后,自己的心情就安静了一些,完成了一些事,圆满了,也许我真的很傻,花这么大精力做这样一部在别人看来毫无意义的片子,很多人置疑过我,也难受过,其实说实话,这是我到现在为止做过的最难受的一部片子,原因也有一些人知道,不过还是做完了,对海丹做过的承诺算是完成了,还是要说声抱歉,迟到了这么久。该走上自己新的路了,大三过了大半,大三对我的意义也不一般,但终究还是要继续走下去的。再静一静,重新上路。彻底的容忍也意味着彻底的淡忘,也许自己真的忍得太多,但要做点事情,真的还要忍一忍。这么久没写博客,那么多心情也是没法表达的,寒假的辛酸,工作的压力,自己的梦想,有些乱,有些远,想着亚也,那么多人说她,多少人真正理解,或者只是用来衬托自己呢?感动放在心里,真的,可以埋藏的东西就不要随便给人看了吧。4.13,明年这个时候我又在干什么呢?呵呵,加油吧,给自己打气!