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	<title>数据范儿 &#187; 0.数据分析</title>
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	<description>- 菜鸟分析师的成长点滴</description>
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		<title>用ExcelVBA做编码合并</title>
		<link>http://datafanr.com/data/excel-vba-recode.html</link>
		<comments>http://datafanr.com/data/excel-vba-recode.html#comments</comments>
		<pubDate>Fri, 20 Apr 2012 11:00:07 +0000</pubDate>
		<dc:creator>icewent</dc:creator>
				<category><![CDATA[0.数据分析]]></category>
		<category><![CDATA[Excel]]></category>
		<category><![CDATA[VBA]]></category>
		<category><![CDATA[数据分析]]></category>
		<category><![CDATA[无提示提及率]]></category>
		<category><![CDATA[编码]]></category>

		<guid isPermaLink="false">http://datafanr.com/?p=2730</guid>
		<description><![CDATA[问卷调查中，一般会有这样的题： 提到某种消费品，您首先想到的是哪个品牌，第二想到的是哪个品牌，第三想到的是哪个品牌……还能想到哪些品牌？ 也就是我们常说的“无提示提及率”，如果你用了一些标准化的市场调查问卷，考查品牌市场份额等，一定会遇到。 对答题者而言，这种主观题基本想答就答，不想答就不答，知道多少写多少，哪个写在前面哪个写在后面没关系。 而分析的人则比较头疼：答题者天马行空，答案五花八门。（更可怕的是，还有神一级的人会写“问卷设计不合理”） 收回数据以后，要进行一下人工编码（其实是人肉编码）。 编码的事情在此不提，要讨论的是如何对答案进行合并——数据里呈现的是“答题者第1个空写了哪个品牌，第2个空写了哪个品牌……第n个空写了哪个品牌”，而我们想要的是“1号品牌提到没有，2号品牌提到没有……n号品牌提到没有，即‘A1_1,A1_2&#8230;A1_n’这样一些虚拟变量（01变量）”（从“以答题者为中心”变为“以品牌为中心”）。 这里提供一种解决方法，用Excel强大的VBA。步骤如下： 1.把编码后的数据粘到Excel里（也可以到文章结尾下载模板文件）。如下图所示（A1d_1~A1d_5为原始数据，也就是每个Case在每个空里填了什么品牌，A1_1~A1_n是我们要编成的样子，每一列代表一个品牌，初始为空） 2.按Alt+F11进入VBA界面，在对应的Sheet里添加如下代码： [crayon-4fb88b0d51ebd/] 3.按照实际情况，对初始变量做一些修改。需要改的变量有这几个（对归前面的图对应一下）： [crayon-4fb88b0d522a3/] 4.改好之后，按F5运行（光标在代码内时）或点工具栏中的绿色播放小按钮，程序自动将我们想要得到的变量填好了。 也可以直接用我写的这个模板[下载]。 思路很简单也很粗暴，先把所有品牌初始化为未选状态（A1_j=0），再遍历一下答题者所有的选项，看他选了几号，就把对应的n号品牌变量标记为已选（A1_j=1）。这样不用担心顺序与重复的问题。 如果有问题，请评论交流。 &#160; 也许你还喜欢：在Excel 2007中插入柱状-折线复合图看IBM如何做数据分析服务广告Excel做矩阵图的三种方法]]></description>
			<content:encoded><![CDATA[<p>问卷调查中，一般会有这样的题：</p>
<p><span style="color: #ff6600;">提到某种消费品，您首先想到的是哪个品牌，第二想到的是哪个品牌，第三想到的是哪个品牌……还能想到哪些品牌？</span></p>
<p>也就是我们常说的“无提示提及率”，如果你用了<a href="http://datafanr.com/data/uanda-survey.html" target="_blank">一些标准化的市场调查问卷</a>，考查品牌市场份额等，一定会遇到。</p>
<p>对答题者而言，这种主观题基本想答就答，不想答就不答，知道多少写多少，哪个写在前面哪个写在后面没关系。</p>
<p>而分析的人则比较头疼：答题者天马行空，答案五花八门。（更可怕的是，还有神一级的人会写“问卷设计不合理”）</p>
<p><span id="more-2730"></span>收回数据以后，要进行一下人工编码（其实是人肉编码）。</p>
<p>编码的事情在此不提，要讨论的是如何对答案进行合并——数据里呈现的是“答题者第1个空写了哪个品牌，第2个空写了哪个品牌……第n个空写了哪个品牌”，而我们想要的是“1号品牌提到没有，2号品牌提到没有……n号品牌提到没有，即‘A1_1,A1_2&#8230;A1_n’这样一些虚拟变量（01变量）”（从“以答题者为中心”变为“以品牌为中心”）。</p>
<p>这里提供一种解决方法，用Excel强大的VBA。步骤如下：</p>
<p><strong>1.把编码后的数据粘到Excel里（也可以到文章结尾下载模板文件）。</strong>如下图所示（A1d_1~A1d_5为原始数据，也就是每个Case在每个空里填了什么品牌，A1_1~A1_n是我们要编成的样子，每一列代表一个品牌，初始为空）</p>
<p><img class="alignnone size-full wp-image-2738" title="用ExcelVBA做编码合并" src="http://datafanr.com/wp-content/uploads/2012/04/vbaRecode.png" alt="" width="652" height="411" /></p>
<p><strong>2.按Alt+F11进入VBA界面，在对应的Sheet里添加如下代码：</strong><br />
[crayon-4fb88b0d51ebd/]<br />
<img class="alignnone size-full wp-image-2739" title="在ExcelVBA界面中添加代码" src="http://datafanr.com/wp-content/uploads/2012/04/vba.png" alt=""  /></p>
<p><strong>3.按照实际情况，对初始变量做一些修改。</strong>需要改的变量有这几个（对归前面的图对应一下）：<br />
[crayon-4fb88b0d522a3/]<br />
<strong>4.改好之后，按F5运行</strong>（光标在代码内时）或点工具栏中的绿色播放小按钮，程序自动将我们想要得到的变量填好了。</p>
<p><img class="alignnone size-full wp-image-2740" title="程序运行结果" src="http://datafanr.com/wp-content/uploads/2012/04/result.png" alt="" width="795" height="410" /></p>
<p>也可以直接用我写的这个模板[<a href="http://vdisk.weibo.com/s/4n68U" rel="external nofollow"  target="_blank">下载</a>]。</p>
<p>思路很简单也很粗暴，先把所有品牌初始化为未选状态（A1_j=0），再遍历一下答题者所有的选项，看他选了几号，就把对应的n号品牌变量标记为已选（A1_j=1）。这样不用担心顺序与重复的问题。</p>
<p>如果有问题，请评论交流。</p>
<p>&nbsp;</p>
<div  class="related_post_title">也许你还喜欢：</div><ul class="related_post"><li><a href="http://datafanr.com/data/excel-mixed-figure.html" title="在Excel 2007中插入柱状-折线复合图">在Excel 2007中插入柱状-折线复合图</a></li><li><a href="http://datafanr.com/market/ibm-dada-ad.html" title="看IBM如何做数据分析服务广告">看IBM如何做数据分析服务广告</a></li><li><a href="http://datafanr.com/data/excel-matrix.html" title="Excel做矩阵图的三种方法">Excel做矩阵图的三种方法</a></li></ul>]]></content:encoded>
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		<title>GoogleForm免费在线问卷工具再解禁（附教程三篇）</title>
		<link>http://datafanr.com/google-computer/googleform-free-from-gfw.html</link>
		<comments>http://datafanr.com/google-computer/googleform-free-from-gfw.html#comments</comments>
		<pubDate>Sun, 18 Mar 2012 15:40:59 +0000</pubDate>
		<dc:creator>icewent</dc:creator>
				<category><![CDATA[0.数据分析]]></category>
		<category><![CDATA[6.恋恋股沟]]></category>
		<category><![CDATA[Google]]></category>
		<category><![CDATA[工具]]></category>
		<category><![CDATA[问卷]]></category>

		<guid isPermaLink="false">http://datafanr.com/?p=2580</guid>
		<description><![CDATA[前几天上Gmail的时候，发现左边侧栏里自己添加的Google Documents竟然不再是“无法载入”，而是列出了我近一年前的文档，欣喜若狂。 Google Documents（当然你也可以叫谷歌文档）是Google推出的免费在线Office套件，在线编辑、云端存储、随时随地同步，多种文档软件及在线应用都对它有很好的支持。套件当中，我最喜欢的是Google Form（中文版叫Google表单），是一款强大的在线问卷工具，可以可视化设计问卷，支持多种常见题型，逻辑简单，非常易用，发布后把地址分享给好友或者你的受访者，然后样本数就开始彪升啦！ 当然你可能说，这不是和那个什么星，什么客，什么派的差不多吗？Google大爷环视了一下四周的问卷星呀、问卷通呀、OQSS呀、问道网呀、题客调查呀……，冷笑一声，留下一句“玄都观前桃千树，尽是刘郞去后栽”，扬长而去。要是没有GFW，这些国产在线调研服务根本就不可能发展起来。 前台问卷 后台统计 Google Form是最好的在线问卷工具，没有之一。强大的技术支持，让你永无后顾之忧（前提是GFW不捣乱），没有商业属性，干干净净。如果你的调查符合以下条件： 1.不想花钱（这一点其实最重要） 2.问卷没有复杂的跳转及筛选逻辑（比如“循环选项”功能，这个真没有；再比如第x题选了1或者19的，答这个题，选了24的不答下个题）[其实跳转的功能，只要你不嫌麻烦，都能实现。用编程的思路讲，这里支持所有的if和break和goto，要实现复杂逻辑可以调整问卷来实现] 3.自己有样本来源（不需要付费使用样本库，或者根本不需要严格抽样，比如在线预订、自己顾客的满意度调查等） 那么，Google Form一定是你最好的选择。 Google Form提供基本功能，你可以选择官方提供的外观，也可以自定义CSS精细修改样式。 我大学期间，曾用Google Form做了不少工作，比如： 1.一个做作业时弄着玩的手机小调查（Demo） 2.一个关于“妻管严”的大学生调查（社会学概论的作业）（男生版、女生版） 3.某年北大新闻与传播学院毕业衫的征订（Demo） 4.某年北大新闻与传播学院院衫征订（Demo） 5.某公关课做的旅行社项目调研（Demo） 6.推荐：某次纸质问卷多人协作数据录入（Demo） 7.某团委进行的学生成才需求调研（Demo） 8.某学院毕业生就业情况汇总（Demo） …… 通过上面的这些例子，我只是想说：Google Form强大到我现在都不知道我还能用它做什么。 至于一些使用上的细节问题，请参阅我曾经写的教程： Google Form笔记（1）：基本操作 Google Form笔记（2）：自定义CSS样式 Google Form笔记（3）：新增页面跳转功能 也许你还喜欢：[冷月刊]2012年4月消费者使用习惯与态度（U&#038;A）研究问卷模板CNNIC网民基本人口变量界定及网站/软件分类理想中的手机地图[冷月刊]2011年4-5月我们都做过的首字母填空百度不做第一个吃螃蟹的小白鼠谷歌的遗书我享受的Google免费午餐]]></description>
			<content:encoded><![CDATA[<p>前几天上Gmail的时候，发现左边侧栏里自己添加的Google Documents竟然不再是“无法载入”，而是列出了我近一年前的文档，欣喜若狂。</p>
<p><a href="https://docs.google.com/" rel="external nofollow"  target="_blank">Google Documents</a>（当然你也可以叫谷歌文档）是Google推出的免费在线Office套件，在线编辑、云端存储、随时随地同步，多种文档软件及在线应用都对它有很好的支持。套件当中，我最喜欢的是Google Form（中文版叫Google表单），是一款强大的在线问卷工具，可以可视化设计问卷，支持多种常见题型，逻辑简单，非常易用，发布后把地址分享给好友或者你的受访者，然后样本数就开始彪升啦！</p>
<p>当然你可能说，这不是和那个什么星，什么客，什么派的差不多吗？Google大爷环视了一下四周的问卷星呀、问卷通呀、OQSS呀、问道网呀、题客调查呀……，冷笑一声，留下一句“玄都观前桃千树，尽是刘郞去后栽”，扬长而去。要是没有GFW，这些国产在线调研服务根本就不可能发展起来。</p>
<p><span id="more-2580"></span></p>
<p><img class="aligncenter size-full wp-image-2583" title="Google Form前台问卷" src="http://datafanr.com/wp-content/uploads/2012/03/qgy2.png" alt="" width="500" height="498" /></p>
<p style="text-align: center;">前台问卷</p>
<p><img class="aligncenter size-full wp-image-2582" title="Google Form后台统计" src="http://datafanr.com/wp-content/uploads/2012/03/qgy.png" alt="" width="500" height="366" /></p>
<p style="text-align: center;">后台统计</p>
<p>Google Form是最好的在线问卷工具，没有之一。强大的技术支持，让你永无后顾之忧（前提是GFW不捣乱），没有商业属性，干干净净。如果你的调查符合以下条件：</p>
<blockquote><p>1.不想花钱（这一点其实最重要）</p>
<p>2.问卷没有复杂的跳转及筛选逻辑（比如“循环选项”功能，这个真没有；再比如第x题选了1或者19的，答这个题，选了24的不答下个题）[其实跳转的功能，只要你不嫌麻烦，都能实现。用编程的思路讲，这里支持所有的if和break和goto，要实现复杂逻辑可以调整问卷来实现]</p>
<p>3.自己有样本来源（不需要付费使用样本库，或者根本不需要严格抽样，比如在线预订、自己顾客的满意度调查等）</p></blockquote>
<p>那么，Google Form一定是你最好的选择。</p>
<p>Google Form提供基本功能，你可以选择官方提供的外观，也可以自定义CSS精细修改样式。</p>
<p>我大学期间，曾用Google Form做了不少工作，比如：</p>
<blockquote><p>1.一个做作业时弄着玩的手机小调查（<a href="https://docs.google.com/spreadsheet/ccc?key=0AnGYfdym1sredHdRRHIyR1dPNXVaRnhEZ2YwY1VjZHc#gid=0" rel="external nofollow"  target="_blank">Demo</a>）</p>
<p>2.一个关于“妻管严”的大学生调查（社会学概论的作业）（<a href="https://docs.google.com/spreadsheet/viewform?formkey=cjg0cExNTThVNHVHVHY5S2hYVEZXMVE6MA" rel="external nofollow"  target="_blank">男生版</a>、<a href="https://docs.google.com/spreadsheet/viewform?formkey=cktreWs2ZjhHSzY0emtZaDNfWnRVQUE6MA..#gid=0" rel="external nofollow"  target="_blank">女生版</a>）</p>
<p>3.某年北大新闻与传播学院毕业衫的征订（<a href="https://docs.google.com/spreadsheet/viewform?formkey=cnlKT1prb3VBY2J5TlFrNVZ1SkM2cVE6MA..#gid=0" rel="external nofollow"  target="_blank">Demo</a>）</p>
<p>4.某年北大新闻与传播学院院衫征订（<a href="https://docs.google.com/spreadsheet/viewform?formkey=cmc5cERtZVhXaXBpNkxBTW1PdzZ6b3c6MA..#gid=0" rel="external nofollow"  target="_blank">Demo</a>）</p>
<p>5.某公关课做的旅行社项目调研（<a href="https://docs.google.com/spreadsheet/viewform?formkey=ckI1dUduX0pkalNNN0Vzd2dzN1lUS2c6MA..#gid=0" rel="external nofollow"  target="_blank">Demo</a>）</p>
<p>6.<span style="color: #ff0000;">推荐：某次纸质问卷多人协作数据录入</span>（<a href="https://docs.google.com/spreadsheet/viewform?formkey=cmZ5V2wyZ1pqcUg5ZDRTbWpWSGI4UXc6MA..#gid=0" rel="external nofollow"  target="_blank">Demo</a>）</p>
<p>7.某团委进行的学生成才需求调研（<a href="https://docs.google.com/spreadsheet/viewform?formkey=cDRYVGtKZEZJWjB0Y1BydHlZTS15THc6MA..#gid=0" rel="external nofollow"  target="_blank">Demo</a>）</p>
<p>8.某学院毕业生就业情况汇总（<a href="https://docs.google.com/spreadsheet/viewform?formkey=cDRYVGtKZEZJWjB1al95TjJmSHdyS1E6MA..#gid=0" rel="external nofollow"  target="_blank">Demo</a>）</p>
<p>……</p></blockquote>
<p>通过上面的这些例子，我只是想说：Google Form强大到我现在都不知道我还能用它做什么。</p>
<p>至于一些使用上的细节问题，请参阅我曾经写的教程：</p>
<blockquote><p><a title="Permanent Link to Google Form笔记（1）:基本操作" href="http://datafanr.com/google-computer/google-form-1.html" rel="bookmark">Google Form笔记（1）：基本操作</a></p>
<p><a title="Permanent Link to Google Form笔记（2）：自定义CSS样式" href="http://datafanr.com/media/google-form-2.html" rel="bookmark">Google Form笔记（2）：自定义CSS样式</a></p>
<p><a title="Permanent Link to Google Form笔记（3）：新增页面跳转功能" href="http://datafanr.com/google-computer/googleform-jump.html" rel="bookmark">Google Form笔记（3）：新增页面跳转功能</a></p></blockquote>
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		<slash:comments>18</slash:comments>
		</item>
		<item>
		<title>消费者使用习惯与态度（U&amp;A）研究问卷模板</title>
		<link>http://datafanr.com/data/uanda-survey.html</link>
		<comments>http://datafanr.com/data/uanda-survey.html#comments</comments>
		<pubDate>Sat, 10 Mar 2012 09:22:02 +0000</pubDate>
		<dc:creator>icewent</dc:creator>
				<category><![CDATA[0.数据分析]]></category>
		<category><![CDATA[U&A]]></category>
		<category><![CDATA[问卷]]></category>

		<guid isPermaLink="false">http://datafanr.com/?p=2383</guid>
		<description><![CDATA[好多同学做毕业论文的时候喜欢做问卷，好多朋友在设计营销方案的时候也会想到调查，这个时候他们首先想到的是如何设计一份问卷。 问卷设计是一门很深的学问，曾经本科做作业时设计的问卷，被老师质疑地开始怀疑人生。作为新手或者门外汉，最快最有效的方式是找一份成熟的模板，研究修改之。这里给大家分享一份网上广为流传（因为流传太广了，我也找不到出处，但无论如何也要千方百计地感谢原作者，请大家使用之前一定要五体投地拜一拜）的日用品消费者使用习惯与态度研究问卷，供参考。 这份问卷是针对日用品的，涉及品牌、产品的认知、购买、使用等诸多问题，确实很全。题目中多次提到“产品名”、“甲”“乙”“丙”“丁”等，都是虚指，按实际情况替换掉即可。另外，严格地说，这份问卷是结构式问卷，并非直接给被访者自行填写，而是由访问员提问记录（访问员读一道题，被访者答一道题），因此，问卷中有“出示卡片”、“追问”、“检查”等说法，特定题目还标明了回答此问题的对象。这样的方式虽说并不是直接的最终问卷（比如我们常用的网络问卷形式），需要作适当的转化，但也给了我们一定的提示，可以清楚某些题需要怎么分析。 您可以下载此问卷，也可以继续阅读（在线浏览）。 第一部分 品牌和广告认知 A1．请问您听说过哪些品牌的&#8221;产品名&#8221;?[追问]还有呢?(复选) [将第一提及的品牌记录在第一提及栏，将其他提及的品牌记录在其他提及栏中] A2．请问您过去三个月中听说过哪些&#8221;产品名&#8221;品牌的广告呢? [追问]还有呢? (复选) [出示卡片] A3．除了您刚才提及的品牌外，您还听说过卡片上哪些品牌的&#8221;产品名&#8221;呢?[追问]还有呢?(复选) [将第一提及的品牌记录在第一提及栏，将其他提及的品牌记录在其他提及栏中] A4．除了您刚才提及的广告品牌外，您还听说过卡片上哪些品牌的广告呢?[追问] 还有呢?(复选) 品牌知名 广告认知 第一提及 其他提及 提示后 第一提及 其他提及 提示后 品牌名称 （A1） （A1） （A3） （A2） （A2） （A4） （ ） （ ） （ ） （ ） （ ） （ ） A 1 1 1 1 1 1 B 2 2 2 2 [...]]]></description>
			<content:encoded><![CDATA[<p>好多同学做毕业论文的时候喜欢做问卷，好多朋友在设计营销方案的时候也会想到调查，这个时候他们首先想到的是<strong><a href="http://datafanr.com/tag/%E9%97%AE%E5%8D%B7">如何设计一份问卷</a></strong>。</p>
<p>问卷设计是一门很深的学问，曾经本科做作业时设计的问卷，被老师质疑地开始怀疑人生。作为新手或者门外汉，最快最有效的方式是找一份成熟的模板，研究修改之。这里给大家分享一份网上广为流传（因为流传太广了，我也找不到出处，但无论如何也要千方百计地感谢原作者，请大家使用之前一定要五体投地拜一拜）的日用品消费者使用习惯与态度研究问卷，供参考。</p>
<p>这份问卷是针对日用品的，涉及品牌、产品的认知、购买、使用等诸多问题，确实很全。题目中多次提到“产品名”、“甲”“乙”“丙”“丁”等，都是虚指，按实际情况替换掉即可。另外，严格地说，这份问卷是结构式问卷，并非直接给被访者自行填写，而是由访问员提问记录（访问员读一道题，被访者答一道题），因此，问卷中有“出示卡片”、“追问”、“检查”等说法，特定题目还标明了回答此问题的对象。这样的方式虽说并不是直接的最终问卷（比如我们常用的网络问卷形式），需要作适当的转化，但也给了我们一定的提示，可以清楚某些题需要怎么分析。</p>
<p>您可以<a href="#beforedownload" rel="external nofollow" >下载此问卷</a>，也可以继续阅读（在线浏览）。</p>
<p><span id="more-2383"></span></p>
<h3>第一部分 品牌和广告认知</h3>
<p>A1．请问您听说过哪些品牌的&#8221;产品名&#8221;?[追问]还有呢?(复选)</p>
<p>[将第一提及的品牌记录在第一提及栏，将其他提及的品牌记录在其他提及栏中]</p>
<p>A2．请问您过去三个月中听说过哪些&#8221;产品名&#8221;品牌的广告呢? [追问]还有呢? (复选)</p>
<p>[出示卡片]</p>
<p>A3．除了您刚才提及的品牌外，您还听说过卡片上哪些品牌的&#8221;产品名&#8221;呢?[追问]还有呢?(复选)</p>
<p>[将第一提及的品牌记录在第一提及栏，将其他提及的品牌记录在其他提及栏中]</p>
<p>A4．除了您刚才提及的广告品牌外，您还听说过卡片上哪些品牌的广告呢?[追问] 还有呢?(复选)</p>
<table border="0">
<colgroup>
<col />
<col />
<col />
<col />
<col />
<col />
<col /></colgroup>
<tbody valign="top">
<tr>
<td rowspan="2"></td>
<td colspan="3">品牌知名</td>
<td colspan="3">广告认知</td>
</tr>
<tr>
<td>第一提及</td>
<td>其他提及</td>
<td>提示后</td>
<td>第一提及</td>
<td>其他提及</td>
<td>提示后</td>
</tr>
<tr>
<td rowspan="2" valign="middle">品牌名称</td>
<td>（A1）</td>
<td>（A1）</td>
<td>（A3）</td>
<td>（A2）</td>
<td>（A2）</td>
<td>（A4）</td>
</tr>
<tr>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
</tr>
<tr>
<td>A</td>
<td>1</td>
<td>1</td>
<td>1</td>
<td>1</td>
<td>1</td>
<td>1</td>
</tr>
<tr>
<td>B</td>
<td>2</td>
<td>2</td>
<td>2</td>
<td>2</td>
<td>2</td>
<td>2</td>
</tr>
<tr>
<td>C</td>
<td>3</td>
<td>3</td>
<td>3</td>
<td>3</td>
<td>3</td>
<td>3</td>
</tr>
<tr>
<td>D</td>
<td>4</td>
<td>4</td>
<td>4</td>
<td>4</td>
<td>4</td>
<td>4</td>
</tr>
<tr>
<td>E</td>
<td>5</td>
<td>5</td>
<td>5</td>
<td>5</td>
<td>5</td>
<td>5</td>
</tr>
</tbody>
</table>
<p>[出示卡片]</p>
<p>[将A1和A3中提到的品牌在A5品牌上打"X"，然后从打"√"的品牌处循环]</p>
<p>A5．请问您是从哪些渠道知道A1和A3中所提及的品牌的呢?(复选)</p>
<div>
<table width="520" border="0">
<colgroup>
<col />
<col />
<col />
<col />
<col />
<col /></colgroup>
<tbody valign="top">
<tr>
<td rowspan="3" valign="middle">认知渠道</td>
<td colspan="5" valign="middle">品牌名称</td>
</tr>
<tr>
<td valign="middle">A</td>
<td valign="middle">B</td>
<td valign="middle">C</td>
<td valign="middle">D</td>
<td valign="middle">E</td>
</tr>
<tr>
<td valign="middle">（ ）</td>
<td valign="middle">（ ）</td>
<td valign="middle">（ ）</td>
<td valign="middle">（ ）</td>
<td valign="middle">（ ）</td>
</tr>
<tr>
<td>电视</td>
<td valign="middle">1</td>
<td valign="middle">１</td>
<td valign="middle">１</td>
<td valign="middle">１</td>
<td valign="middle">１</td>
</tr>
<tr>
<td>电台</td>
<td valign="middle">2</td>
<td valign="middle">２</td>
<td valign="middle">２</td>
<td valign="middle">２</td>
<td valign="middle">２</td>
</tr>
<tr>
<td>报纸</td>
<td valign="middle">3</td>
<td valign="middle">３</td>
<td valign="middle">３</td>
<td valign="middle">３</td>
<td valign="middle">３</td>
</tr>
<tr>
<td>杂志</td>
<td valign="middle">4</td>
<td valign="middle">４</td>
<td valign="middle">４</td>
<td valign="middle">４</td>
<td style="text-align: left;" valign="middle">４</td>
</tr>
<tr>
<td>亲戚／朋友介绍</td>
<td valign="middle">5</td>
<td valign="middle">５</td>
<td valign="middle">５</td>
<td valign="middle">５</td>
<td valign="middle">５</td>
</tr>
<tr>
<td>在商店／产品展销会看到</td>
<td valign="middle">６</td>
<td valign="middle">６</td>
<td valign="middle">６</td>
<td valign="middle">６</td>
<td valign="middle">６</td>
</tr>
<tr>
<td>汽车广告／街边广告</td>
<td valign="middle">７</td>
<td valign="middle">７</td>
<td valign="middle">７</td>
<td valign="middle">７</td>
<td valign="middle">７</td>
</tr>
<tr>
<td>霓虹灯</td>
<td valign="middle">８</td>
<td valign="middle">８</td>
<td valign="middle">８</td>
<td valign="middle">８</td>
<td valign="middle">８</td>
</tr>
<tr>
<td>街招</td>
<td valign="middle">９</td>
<td valign="middle">９</td>
<td valign="middle">９</td>
<td valign="middle">９</td>
<td valign="middle">９</td>
</tr>
<tr>
<td>其他（请注明）</td>
<td valign="middle">（ ）</td>
<td valign="middle">（ ）</td>
<td valign="middle">（ ）</td>
<td valign="middle">（ ）</td>
<td valign="middle">（ ）</td>
</tr>
</tbody>
</table>
</div>
<h3>第二部分 产品使用情况</h3>
<p>B１.请问您是否使用过&#8221;产品名&#8221;呢?( )</p>
<ul>
<li>有 1 →跳问B2</li>
<li>没有 2</li>
</ul>
<p>Bla．请问您为什么从来不使用&#8221;产品名&#8221;呢?( )→跳问C1</p>
<p>B2．请问您上次使用&#8221;产品名&#8221;是多久以前呢?(单选)( )</p>
<ul>
<li>过去一周内</li>
<li>过去一至两周内</li>
<li>过去三至四周内</li>
<li>过去一至三个月内</li>
<li>过去三至六个月内</li>
<li>过去六至十二个月内</li>
<li>超过十二个月</li>
<li>不知道／不记得</li>
</ul>
<p>[B3只问过去六个月内没有使用过该产品的人]</p>
<p>B3．请问您为什么在过去六个月内没有使用过&#8221;产品名&#8221;呢?( )</p>
<p>[出示卡片]</p>
<p>B4．请问您曾经使用过哪些品牌的&#8221;产品名&#8221;呢?[追问]还有呢?(复选)</p>
<p>[B5只问过去六个月内使用过产品的人]</p>
<p>B5. 请问您在过去六个月内，使用过哪些品牌的&#8221;产品名&#8221;呢?[追问]还有呢?还有呢?(复选)</p>
<p>[B6只问过去三个月内使用过产品的人，可以从B3中判断]</p>
<p>B6．请问您在过去三个月内，使用过哪些品牌的&#8221;产品名&#8221;呢?[追问]还有呢?还有呢?(复选)</p>
<p>[ 以下问题只问过去六个月内使用过产品的人 ]</p>
<p>B7．请问您在过去六个月内，最常使用哪一种品牌的&#8221;产品名&#8221;呢?(单选)</p>
<p>B8．在您最经常使用<br />
(填入B7的答案)品牌的&#8221;产品名&#8221;前，请问您最常用的品牌是什么呢?(单选)</p>
<table width="575" border="0">
<colgroup>
<col />
<col />
<col />
<col />
<col />
<col /></colgroup>
<tbody valign="top">
<tr>
<td rowspan="3" valign="middle">品牌名称</td>
<td valign="middle">曾经用过</td>
<td valign="middle">过去六个月内用过</td>
<td valign="middle">过去三个月内用过</td>
<td valign="middle">最常用</td>
<td valign="middle">以前最常用</td>
</tr>
<tr>
<td valign="middle">B4</td>
<td valign="middle">B5</td>
<td valign="middle">B6</td>
<td valign="middle">B7</td>
<td valign="middle">B8</td>
</tr>
<tr>
<td valign="middle">（ ）</td>
<td valign="middle">（ ）</td>
<td valign="middle">（ ）</td>
<td valign="middle">（ ）</td>
<td valign="middle">（ ）</td>
</tr>
<tr>
<td valign="middle">A</td>
<td valign="middle">1</td>
<td valign="middle">1</td>
<td valign="middle">1</td>
<td valign="middle">1</td>
<td valign="middle">1</td>
</tr>
<tr>
<td valign="middle">B</td>
<td valign="middle">2</td>
<td valign="middle">2</td>
<td valign="middle">2</td>
<td valign="middle">2</td>
<td valign="middle">2</td>
</tr>
<tr>
<td valign="middle">C</td>
<td valign="middle">3</td>
<td valign="middle">3</td>
<td valign="middle">3</td>
<td valign="middle">3</td>
<td valign="middle">3</td>
</tr>
<tr>
<td valign="middle">D</td>
<td valign="middle">4</td>
<td valign="middle">4</td>
<td valign="middle">4</td>
<td valign="middle">4</td>
<td valign="middle">4</td>
</tr>
<tr>
<td valign="middle">E</td>
<td valign="middle">5</td>
<td valign="middle">5</td>
<td valign="middle">5</td>
<td valign="middle">5</td>
<td valign="middle">5</td>
</tr>
<tr>
<td>不知道</td>
<td valign="middle">X</td>
<td valign="middle">X</td>
<td valign="middle">X</td>
<td valign="middle">X</td>
<td valign="middle">X</td>
</tr>
<tr>
<td>其他(请注明)</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
</tbody>
</table>
<p>[如果没有转换品牌，即B8和B7的答案相同，跳问B10] ：</p>
<p>B9．请问您为什么从(填入B8的答案) 转向(填入B7的答案)呢?( )</p>
<p>B10．请问您现在使用什么类型的&#8221;产品名&#8221;呢?(单选)</p>
<ul>
<li>甲</li>
<li>乙</li>
<li>丙</li>
<li>丁</li>
<li>其他(请注明)</li>
</ul>
<p>B11．请问您现在使用哪种包装规格的&#8221;产品名&#8221;呢?(单选)</p>
<ul>
<li>甲 1</li>
<li>乙 2</li>
<li>丙 3</li>
<li>丁 4</li>
<li>其他(请注明)</li>
</ul>
<p>B12．请问您上次使用这种包装大小的产品用了多少天?</p>
<p>[列出B11已选答案]____</p>
<p>B13．请问您平均多久使用&#8221;产品名&#8221;一次呢?(单选)</p>
<ul>
<li>每天</li>
<li>每周5-6次</li>
<li>每周3-4次</li>
<li>每周1-2次</li>
<li>每两周一次</li>
<li>每个月一次</li>
<li>每三个月一次</li>
<li>每过三个月一次</li>
<li>不知道</li>
</ul>
<p>（注：答案选项要根据产品的一般消费时间的长度来考虑。）</p>
<p>B14．请问您一般会在一天里的什么时间内使用&#8221;产品名&#8221;呢?</p>
<ul>
<li>早上起床时</li>
<li>早餐时</li>
<li>早餐后午餐前</li>
<li>午餐时</li>
<li>午餐后</li>
<li>放学／下班后</li>
<li>晚餐前</li>
<li>晚餐时</li>
<li>晚餐后</li>
<li>睡觉前</li>
<li>午夜</li>
<li>任何时间</li>
<li>其他(请注明)</li>
</ul>
<p>（注：答案选项根据产品的特点来确定。）</p>
<p>B15．请问您是如何使用&#8221;产品名&#8221;呢?</p>
<p>（注：答案根据产品特点来确定。）</p>
<p>B16．请问您用&#8221;产品名&#8221;时，还和其他什么东西一起用?还有呢?</p>
<p>B17．请问您通常在什么地方使用&#8221;产品名&#8221;的呢?</p>
<ul>
<li>在自己家里</li>
<li>在亲朋家里</li>
<li>在工作单位／学校</li>
<li>在汽车／火车／飞机</li>
<li>在公园／旅游点</li>
<li>在宾馆</li>
<li>在电影院／剧场</li>
<li>其他(请注明)</li>
</ul>
<p>（注：答案选项要根据产品特点来确定。）</p>
<p>B18．请问您通常在什么情况下会使用&#8221;产品名&#8221;呢?</p>
<ul>
<li>家人团聚时</li>
<li>朋友聚会时</li>
<li>外出旅游时</li>
<li>在娱乐场所</li>
<li>在餐厅吃晚饭时</li>
<li>无特别原因</li>
<li>其他(请注明)</li>
</ul>
<p>（注：答案选项要根据产品的特点来确定。）</p>
<p>B19．请问您家里除了您以外，还有什么人使用&#8221;产品名&#8221;呢?请您告诉我他(她)的年龄、性别。</p>
<p>小孩</p>
<ul>
<li>10岁以下</li>
<li>10-14岁</li>
<li>15-17岁</li>
</ul>
<p>成人男</p>
<ul>
<li>18-24岁</li>
<li>25-34岁</li>
<li>35-50岁</li>
<li>50岁以上</li>
</ul>
<p>成人女</p>
<ul>
<li>18-24岁</li>
<li>25-34岁</li>
<li>35-50岁</li>
<li>50岁以上</li>
</ul>
<h3>第三部分 产品购买情况</h3>
<p>C1.请问您上次购买&#8221;产品名&#8221;是多久以前？（单选）</p>
<ul>
<li>今天</li>
<li>昨天</li>
<li>一个星期内</li>
<li>一个星期以上至四个星期</li>
<li>一个月以上至三个月</li>
<li>三个月以上至六个月</li>
<li>六个月以上</li>
<li>没有买过</li>
</ul>
<p>（注：答案选项要根据产品特点来设计。）</p>
<p>C1a.请问您为什么没有购买过&#8221;产品名&#8221;呢？（）跳问D1</p>
<p>[检查C1题,C1b只问过去六个月内没有购买过的人]</p>
<p>C1b.请问您为什么在过去六个月内没有购买过&#8221;产品名&#8221;呢？（）跳问D1</p>
<p>C2.请问您在过去六个月内最常购买哪个品牌的&#8221;产品名&#8221;呢？（单选）</p>
<p>C3.假如您最常购买的 品牌（读出C2的答案）&#8221;产品名&#8221;买不到了，您要寻找一个替代品牌，请问哪一个是最可能的替代品牌？（单选）</p>
<table width="326" border="0">
<colgroup>
<col />
<col />
<col /></colgroup>
<tbody valign="top">
<tr>
<td></td>
<td>C2</td>
<td>C3</td>
</tr>
<tr>
<td>品牌名称</td>
<td>最常购买</td>
<td>替代品牌</td>
</tr>
<tr>
<td></td>
<td>（ ）</td>
<td>（ ）</td>
</tr>
<tr>
<td>A</td>
<td>1</td>
<td>1</td>
</tr>
<tr>
<td>B</td>
<td>2</td>
<td>2</td>
</tr>
<tr>
<td>C</td>
<td>3</td>
<td>3</td>
</tr>
<tr>
<td>D</td>
<td>4</td>
<td>4</td>
</tr>
<tr>
<td>E</td>
<td>5</td>
<td>5</td>
</tr>
<tr>
<td>不找替代</td>
<td></td>
<td>Y</td>
</tr>
</tbody>
</table>
<p>C4.请问您上一次购买的是什么类型的&#8221;产品名&#8221;呢？</p>
<p>类型 （ ）</p>
<ul>
<li>甲</li>
<li>乙</li>
<li>丙</li>
<li>丁</li>
<li>其他(请注明)</li>
</ul>
<p>C5.请问您上一次购买的是哪种包装规格的&#8221;产品名&#8221;呢？</p>
<ul>
<li>甲</li>
<li>乙</li>
<li>丙</li>
<li>丁</li>
<li>其他(请注明)</li>
</ul>
<p>C6.请问您上一次买了多少这种包装规格的&#8221;产品名&#8221;呢？</p>
<p>C7.请问这种包装规格的&#8221;产品名&#8221;每包多少钱？</p>
<p>C8.请问您平均多久购买一次&#8221;产品名&#8221;呢？（单选）</p>
<ul>
<li>一星期最少一次</li>
<li>二星期最少一次</li>
<li>三星期最少一次</li>
<li>一个月最少一次</li>
<li>二个月最少一次</li>
<li>三个月最少一次</li>
<li>超过三个月一次</li>
</ul>
<p>（注:答案选项要根据产品的特点来设计.）</p>
<p>C8a.请问您为什么不经常购买&#8221;产品名&#8221;呢？</p>
<p>C9.请问您上次是在什么场合下购买&#8221;产品名&#8221;的呢？</p>
<ul>
<li>家里存货已用完</li>
<li>需要转换品牌</li>
<li>看到促销活动</li>
<li>过节</li>
<li>送礼</li>
<li>其他（请注明）</li>
</ul>
<p>C10.请问您经常到什么地方购买&#8221;产品名&#8221;呢？（单选）</p>
<ul>
<li>甲</li>
<li>乙</li>
<li>丙</li>
</ul>
<p>（注：C9、C10、C12、C12a也可以问上一次。）</p>
<p>C11.请问您上次购买&#8221;产品名&#8221;时，和什么一起买？<br />
[出示卡片]</p>
<p>C12.您购买&#8221;产品名&#8221;时，是属于卡片上哪一种情况呢？（单选）</p>
<ul>
<li>我通常在进入商店之前已决定买哪个品牌，最后也买这个品牌</li>
<li>我通常在进入商店之前，已决定买哪个品牌，但最后改变了</li>
<li>预先没有决定品牌，最后到商店才决定→跳问D1</li>
</ul>
<p>C13.上次您去的商店中有没有找到您预先决定买的品牌？</p>
<ul>
<li>有1<br />
没有 2→跳问D1</li>
</ul>
<p>C14.假如您经常去购买的商店里没有您最常使用的&#8221;产品名&#8221;品牌，请问您通常会怎样办呢？(单选)</p>
<ul>
<li>暂不买等到人这个品牌时再买</li>
<li>去别的商店买这个品牌</li>
<li>买同一品牌的其他品种</li>
<li>买其他品牌的类似品种</li>
<li>买其他品牌，不管品种</li>
</ul>
<h3>第四部分 对品牌的态度测试</h3>
<p>[将被访者在B4中回答的品牌在D1的品牌名处打"×"，然后从打"√"处循环读出打"×"的品牌，提问D1]</p>
<p>[出示卡片]</p>
<p>D1.现在我想了解一下您对一些&#8221;产品名&#8221;品牌的使用情况，卡片上有一些关于这方面的句子。现在我读出一些品牌，请从四个句子中选择答案。[注意每列单选]</p>
<div>
<table width="522" border="0">
<colgroup>
<col />
<col />
<col />
<col />
<col />
<col /></colgroup>
<tbody valign="top">
<tr>
<td></td>
<td colspan="5" valign="middle">品牌名称</td>
</tr>
<tr>
<td></td>
<td valign="middle">A</td>
<td valign="middle">B</td>
<td valign="middle">C</td>
<td valign="middle">D</td>
<td valign="middle">E</td>
</tr>
<tr>
<td></td>
<td valign="middle">( )</td>
<td valign="middle">( )</td>
<td valign="middle">( )</td>
<td valign="middle">( )</td>
<td valign="middle">( )</td>
</tr>
<tr>
<td>我曾用过这个牌子，但不想再用</td>
<td valign="middle">1</td>
<td valign="middle">1</td>
<td valign="middle">1</td>
<td valign="middle">1</td>
<td valign="middle">1</td>
</tr>
<tr>
<td>它不是我喜欢的牌子，偶尔会使用</td>
<td valign="middle">2</td>
<td valign="middle">2</td>
<td valign="middle">2</td>
<td valign="middle">2</td>
<td valign="middle">2</td>
</tr>
<tr>
<td>它是我经常使用的几个牌子之一</td>
<td valign="middle">3</td>
<td valign="middle">3</td>
<td valign="middle">3</td>
<td valign="middle">3</td>
<td valign="middle">3</td>
</tr>
<tr>
<td>它是我惟一使用的牌子</td>
<td valign="middle">4</td>
<td valign="middle">4</td>
<td valign="middle">4</td>
<td valign="middle">4</td>
<td valign="middle">4</td>
</tr>
</tbody>
</table>
</div>
<p>[访问员注意：请根据下面的说明进行访问]</p>
<p>如果在D1中的&#8221;××&#8221;品牌（研究品牌）中：圈3或4，则问D2a，然后跳问D3；圈2则问D2b，然后跳问D3；圈1则问D2c，然后跳问D3。</p>
<p>D2a.您说&#8221;××&#8221;品牌是您经常使用的几个牌子之一／唯一会使用的牌子，请问它在哪些方面是您喜欢的呢？[追问]还有呢？还有呢？<br />
D2b.请问您&#8221;××&#8221;品牌有哪些方面是您不喜欢的呢？[追问]还有呢？还有呢<br />
D2c.请问您为什么不想使用&#8221;××&#8221;品牌呢？[追问]还有呢？还有呢<br />
[出示卡片]</p>
<p>D3.下面我会出示一些人们在购买&#8221;产品名&#8221;时会考虑的各种因素，请告诉我下面每一因素在您购买&#8221;产品名&#8221;时的重要程度。您可以用1至10分来表示每一因素的重要程度，分数越高表示越重要，越低表示越不重要。（从打&#8221;√&#8221;处循环读出每一因素）</p>
<p>[将被访者在B4中回答的品牌在D5的品牌名称处打"×"]</p>
<p>[出示卡片]</p>
<p>D4.下面我想知道您对曾经使用过的品牌，在上面D3中所列出的每一因素上的评价。当我读出每一因素时，请您告诉我每一个品眚在这个因素上的表现，我们还是采用10分评分方法来表示您对每一个品牌的评价，分数越高表示评价越高。</p>
<p>（从打&#8221;√&#8221;处循环读出每一因素，对每一因素从打&#8221;√&#8221;的品牌处循环读出打&#8221;×&#8221;的品牌）</p>
<div>
<table width="576" border="0">
<colgroup>
<col />
<col />
<col />
<col />
<col />
<col />
<col /></colgroup>
<tbody valign="top">
<tr>
<td rowspan="3" valign="middle">因素</td>
<td rowspan="2" valign="middle">D3</td>
<td colspan="5">D4</td>
</tr>
<tr>
<td>A</td>
<td>B</td>
<td>C</td>
<td>D</td>
<td>E</td>
</tr>
<tr>
<td>重要程度</td>
<td>评分</td>
<td>评分</td>
<td>评分</td>
<td>评分</td>
<td>评分</td>
</tr>
<tr>
<td>甲</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
</tr>
<tr>
<td>乙</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
</tr>
<tr>
<td>丙</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
</tr>
<tr>
<td>丁</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
</tr>
<tr>
<td>戊</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
</tr>
</tbody>
</table>
</div>
<p>（D4中用10分制对每个品牌进行评分，在品牌较多时，被访者回答是十分麻烦的，因而较难取得合作，即使勉强回答也较难取得准确的答案，为此可将D4的评分形式改变为选择形式，如问题D5所示。）</p>
<p>[出示卡片]</p>
<p>D5.下面我想知道您对曾经使用过的品牌是否具有D3所列出的因素，当我读出每一因素时，您可以选择一个或一个以上的品牌，也可以一个品牌也不选。现在开始，请问您认为哪些品牌是（从打&#8221;√&#8221;处开始循环读出每一个因素），还有没有其他品牌呢？还有呢？</p>
<p>D6.现在请您想一下理想的&#8221;产品名&#8221;品牌形象，您认为一个理想的&#8221;产品名&#8221;品牌，应具备卡片上哪些因素呢？[追问]还有呢？还有呢？</p>
<div>
<table width="595" border="0">
<colgroup>
<col />
<col />
<col />
<col />
<col />
<col />
<col /></colgroup>
<tbody valign="top">
<tr>
<td rowspan="3">因素</td>
<td colspan="5">品牌名称（D5）</td>
<td rowspan="2">理想的品牌（D6）</td>
</tr>
<tr>
<td>A</td>
<td>B</td>
<td>C</td>
<td>D</td>
<td>E</td>
</tr>
<tr>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
<td>（ ）</td>
</tr>
<tr>
<td>甲</td>
<td>1</td>
<td>1</td>
<td>1</td>
<td>1</td>
<td>1</td>
<td>1</td>
</tr>
<tr>
<td>乙</td>
<td>2</td>
<td>2</td>
<td>2</td>
<td>2</td>
<td>2</td>
<td>2</td>
</tr>
<tr>
<td>丙</td>
<td>3</td>
<td>3</td>
<td>3</td>
<td>3</td>
<td>3</td>
<td>3</td>
</tr>
<tr>
<td>丁</td>
<td>4</td>
<td>4</td>
<td>4</td>
<td>4</td>
<td>4</td>
<td>4</td>
</tr>
<tr>
<td>戊</td>
<td>5</td>
<td>5</td>
<td>5</td>
<td>5</td>
<td>5</td>
<td>5</td>
</tr>
</tbody>
</table>
</div>
<h3>第五部分 产品态度测试</h3>
<p>[出示卡片]</p>
<p>E1.下面我将读出一些有关人们对&#8221;产品名&#8221;态度的语句，请您告诉我您对每个语句的同意程度。如果您非常同意这个语句，则给5分，如果您非常不同意，则给1分，或根据您的意见在1-5分钟选择一个恰当的值。（横向单选，从打&#8221;√&#8221;的语句开始循环提问）请问您对<br />
（读出语句）的同意程度怎么样呢？</p>
<p>表2-7</p>
<div>
<table width="599" border="0">
<colgroup>
<col />
<col />
<col />
<col />
<col />
<col />
<col /></colgroup>
<tbody valign="top">
<tr>
<td></td>
<td>非常不同意</td>
<td>不同意</td>
<td>不能确定</td>
<td>同意</td>
<td>非常同意</td>
<td>不知道</td>
</tr>
<tr>
<td>语句甲</td>
<td>1</td>
<td>2</td>
<td>3</td>
<td>4</td>
<td>5</td>
<td>6</td>
</tr>
<tr>
<td>语句乙</td>
<td>1</td>
<td>2</td>
<td>3</td>
<td>4</td>
<td>5</td>
<td>6</td>
</tr>
<tr>
<td>语句丙</td>
<td>1</td>
<td>2</td>
<td>3</td>
<td>4</td>
<td>5</td>
<td>6</td>
</tr>
<tr>
<td>语句丁</td>
<td>1</td>
<td>2</td>
<td>3</td>
<td>4</td>
<td>5</td>
<td>6</td>
</tr>
<tr>
<td>语句戊</td>
<td>1</td>
<td>2</td>
<td>3</td>
<td>4</td>
<td>5</td>
<td>6</td>
</tr>
</tbody>
</table>
</div>
<h3>附：关于U&amp;A研究</h3>
<p>消费者使用习惯与态度研究（U&amp;A研究）可以提供有关消费者的使用和购买习惯，以及对产品和品牌的态度方面的信息，也可以提供各品牌在市场上的竞争态势力面的信息。有了这些信息，企业就可以科学地解决下述营销管理问题：</p>
<ol>
<li>为现有产品或新产品寻找市场机会；</li>
<li>有效地细分市场，选择目标市场和确定产品定位；</li>
<li>制定营销组合策略；</li>
<li>评价企业的市场营销活动。</li>
</ol>
<p>通过U&amp;A研究，我们可以得到下列营销信息：</p>
<ol>
<li>产品渗透水平和渗透深度。</li>
<li>产品使用者和购买者的人口统计特征：①全部使用者和购买者的人口统计特征；②重度使用者的人口统计特征；③目标市场的人口统计特征；④不同品牌最常使用者的人口统计特征。</li>
<li>使用习惯和购买习惯：①使用和购买的产品类型；②使用和购买的包装规格；③使用和购买的频率；④使用和购买的时间；⑤使用和购买的地点；⑥使用和购买的场合；⑦使用和购买的数量；⑧购买金额；⑨使用方法。</li>
<li>主要竞争品牌的市场表现：①品牌认知；②广告认知；③品牌渗透率；④品牌最常使用率；⑤品牌忠诚度；⑥品牌引力和产品引力；⑦品牌形象；⑧品牌的优势和弱点。</li>
</ol>
<div id="beforedownload"></div>
<div id="download">下载消费者态度与行为（U&amp;A）研究问卷模板：<a href="http://dl.dbank.com/c0dhbsub8b" rel="external nofollow"  target="_blank">Dbank</a> | <a href="http://vdisk.weibo.com/s/35bZG" rel="external nofollow"  target="_blank">微盘</a> | <a href="http://www.box.com/s/micqzlons7jb2l07ra2k" rel="external nofollow"  target="_blank"> Box</a> | <a href="http://datafanr.com">来自数据范儿</a></div>
<div  class="related_post_title">也许你还喜欢：</div><ul class="related_post"><li><a href="http://datafanr.com/google-computer/googleform-free-from-gfw.html" title="GoogleForm免费在线问卷工具再解禁（附教程三篇）">GoogleForm免费在线问卷工具再解禁（附教程三篇）</a></li><li><a href="http://datafanr.com/data/cnnic-def.html" title="CNNIC网民基本人口变量界定及网站/软件分类">CNNIC网民基本人口变量界定及网站/软件分类</a></li></ul>]]></content:encoded>
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		</item>
		<item>
		<title>CNNIC网民基本人口变量界定及网站/软件分类</title>
		<link>http://datafanr.com/data/cnnic-def.html</link>
		<comments>http://datafanr.com/data/cnnic-def.html#comments</comments>
		<pubDate>Mon, 27 Feb 2012 11:18:01 +0000</pubDate>
		<dc:creator>icewent</dc:creator>
				<category><![CDATA[0.数据分析]]></category>
		<category><![CDATA[人口变量]]></category>
		<category><![CDATA[学历]]></category>
		<category><![CDATA[收入]]></category>
		<category><![CDATA[职业]]></category>
		<category><![CDATA[问卷]]></category>

		<guid isPermaLink="false">http://datafanr.com/?p=2354</guid>
		<description><![CDATA[市场调查中非常重要的几个人口统计学变量有性别、年龄、学历、收入、职业等。其实不止如此，北大的刘老师在课堂上曾讲过16个重要的人口统计学变量，我在《SPSS数据清洗小记》这篇文章里提到过。 性别是简单的虚拟变量（值只有0和1），学历也只有那个4到6种可能，但年龄、收入就要复杂得多，为了数据展示和描述方便，我们不得不将年龄分成几个区间，比如20岁以下是一段，20到40岁是一段等等。对于年龄分段，不同公司不同机构不同研究者有不同的分法，没有形成统一的标准，但如果非要拿个标准出来的话，恐怕官方的口径最有说服力，哪怕它看上去，也不怎么合理。 之前博客《你算白领吗？你算高收入吗？》中提到过CNIDP（CNNIC的数据平台）里对于“白领”、“高收入”等典型群体的定义。现在把其更一般的概念界定梳理如下。（出自CNIDP） 年龄分段 10岁以下 10-19岁 20-29岁 30-39岁 40-49岁 50-59岁 60岁及以上 职业 学生 党政机关事业单位领导干部 党政机关事业单位一般职员 企业/公司管理者 企业/公司一般职员 专业技术人员 农村外出务工人员 产业、服务业工人 个体户/自由职业者 农民 退休 无业、下岗、失业 其他 学历 小学及以下 初中 高中/中专/技校 大专 大学本科 硕士及以上 收入 无收入 500元及以下 501～1000元 1001～1500元 1501～2000元 2001～3000元 3001～5000元 5001～8000元 8001～12000元 12000元以上 （Datafanr注：这个划分方式是存在一定问题的，应该采取左闭右开的区间，如“500~999元，1000~1499元”，因为通常说挣一千多，指的一千，或更多。） 地域划分 东部 中部 西部 东北 区域划分 中南 华东 东北 华北 西南 [...]]]></description>
			<content:encoded><![CDATA[<p>市场调查中非常重要的几个人口统计学变量有性别、年龄、学历、收入、职业等。其实不止如此，北大的刘老师在课堂上曾讲过16个重要的人口统计学变量，我在<a href="http://datafanr.com/data/data-cleansing.html" target="_blank">《SPSS数据清洗小记》</a>这篇文章里提到过。</p>
<p>性别是简单的虚拟变量（值只有0和1），学历也只有那个4到6种可能，但年龄、收入就要复杂得多，为了数据展示和描述方便，我们不得不将年龄分成几个区间，比如20岁以下是一段，20到40岁是一段等等。对于年龄分段，不同公司不同机构不同研究者有不同的分法，没有形成统一的标准，但如果非要拿个标准出来的话，恐怕官方的口径最有说服力，哪怕它看上去，也不怎么合理。</p>
<p>之前博客<a title="Permanent Link to 你算白领吗？你算高收入吗？" href="../data/are-you-white-collar.html" rel="external nofollow"  rel="bookmark">《你算白领吗？你算高收入吗？》</a>中提到过CNIDP（CNNIC的数据平台）里对于“白领”、“高收入”等典型群体的定义。现在把其更一般的概念界定梳理如下。（出自CNIDP）</p>
<p><span id="more-2354"></span></p>
<h3>年龄分段</h3>
<ul>
<li>10岁以下</li>
<li>10-19岁</li>
<li>20-29岁</li>
<li>30-39岁</li>
<li>40-49岁</li>
<li>50-59岁</li>
<li>60岁及以上</li>
</ul>
<h3>职业</h3>
<ul>
<li>学生</li>
<li>党政机关事业单位领导干部</li>
<li>党政机关事业单位一般职员</li>
<li>企业/公司管理者</li>
<li>企业/公司一般职员</li>
<li>专业技术人员</li>
<li>农村外出务工人员</li>
<li>产业、服务业工人</li>
<li>个体户/自由职业者</li>
<li>农民</li>
<li>退休</li>
<li>无业、下岗、失业</li>
<li>其他</li>
</ul>
<h3>学历</h3>
<ul>
<li>小学及以下</li>
<li>初中</li>
<li>高中/中专/技校</li>
<li>大专</li>
<li>大学本科</li>
<li>硕士及以上</li>
</ul>
<h3>收入</h3>
<ul>
<li>无收入</li>
<li>500元及以下</li>
<li>501～1000元</li>
<li>1001～1500元</li>
<li>1501～2000元</li>
<li>2001～3000元</li>
<li>3001～5000元</li>
<li>5001～8000元</li>
<li>8001～12000元</li>
<li>12000元以上</li>
</ul>
<p>（Datafanr注：这个划分方式是存在一定问题的，应该采取左闭右开的区间，如“500~999元，1000~1499元”，因为通常说挣一千多，指的一千，或更多。）</p>
<h3>地域划分</h3>
<ul>
<li>东部</li>
<li>中部</li>
<li>西部</li>
<li>东北</li>
</ul>
<h3>区域划分</h3>
<ul>
<li>中南</li>
<li>华东</li>
<li>东北</li>
<li>华北</li>
<li>西南</li>
<li>西北</li>
</ul>
<h2>城市级别划分</h2>
<ul>
<li>一级</li>
<li>二级</li>
<li>三级</li>
<li>四级</li>
<li>五级</li>
</ul>
<h3>网站类别</h3>
<ul>
<li>资讯门户</li>
<li>搜索引擎</li>
<li>导航网站</li>
<li>新闻网站</li>
<li>财经资讯</li>
<li>游戏网站</li>
<li>体育网站</li>
<li>主流媒体</li>
<li>政府网站</li>
<li>企业网站</li>
<li>B2B网站</li>
<li>网上购物</li>
<li>电子支付</li>
<li>网上银行</li>
<li>基金证券</li>
<li>SNS</li>
<li>微博客</li>
<li>博客空间</li>
<li>社区论坛</li>
<li>婚恋交友</li>
<li>求职招聘</li>
<li>电子邮箱</li>
<li>生活资讯</li>
<li>旅游出行</li>
<li>房产网站</li>
<li>汽车网站</li>
<li>IT数码</li>
<li>女性时尚</li>
<li>母婴网站</li>
<li>网络视频</li>
<li>音乐网站</li>
<li>文学网站</li>
<li>技术社区</li>
<li>下载站</li>
<li>数字文献</li>
<li>站长统计</li>
<li>团购</li>
</ul>
<h3>软件类别</h3>
<ul>
<li>浏览器</li>
<li>下载工具</li>
<li>即时通信</li>
<li>视频播放</li>
<li>音频播放</li>
<li>杀毒软件</li>
<li>网络安全</li>
<li>输入法</li>
<li>词典翻译</li>
<li>游戏对战</li>
<li>股票证券</li>
</ul>
<div  class="related_post_title">也许你还喜欢：</div><ul class="related_post"><li><a href="http://datafanr.com/google-computer/googleform-free-from-gfw.html" title="GoogleForm免费在线问卷工具再解禁（附教程三篇）">GoogleForm免费在线问卷工具再解禁（附教程三篇）</a></li><li><a href="http://datafanr.com/data/uanda-survey.html" title="消费者使用习惯与态度（U&#038;A）研究问卷模板">消费者使用习惯与态度（U&#038;A）研究问卷模板</a></li></ul>]]></content:encoded>
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		<slash:comments>9</slash:comments>
		</item>
		<item>
		<title>你算白领吗？你算高收入吗？</title>
		<link>http://datafanr.com/data/are-you-white-collar.html</link>
		<comments>http://datafanr.com/data/are-you-white-collar.html#comments</comments>
		<pubDate>Wed, 15 Feb 2012 12:11:18 +0000</pubDate>
		<dc:creator>icewent</dc:creator>
				<category><![CDATA[0.数据分析]]></category>
		<category><![CDATA[CNIDP]]></category>
		<category><![CDATA[CNNIC]]></category>
		<category><![CDATA[中国互联网数据平台]]></category>
		<category><![CDATA[白领]]></category>
		<category><![CDATA[高收入]]></category>

		<guid isPermaLink="false">http://datafanr.com/?p=2326</guid>
		<description><![CDATA[一直以为白领是个珍稀物种，白领的生活多少都还挺幸福；也一直以为自己挺穷的，算是中低收入吧。直到有一天，我看见一个神奇的网站——CNIDP（中国互联网数据平台）。 昨天接到一个GG电话， 问我是不是注册了中国互联网数据平台，这才想起来半个月前自己确实注册过，只是一直在审核状态（官网说假期不审核）。情人节那天，他们可能开始上班了，这样的单位真好。 平台中规中矩，毕竟是CNNIC的，干调查这行的大家都懂，不多说。 网站有一个比较实用的功能，叫“媒介计划”，在这里，广告主可以选择目标人群（比如年龄多少，收入多少，学历怎样），做好限定后，就会给推荐几个网络媒体，再进一步，可以得到覆盖率等指标。 “媒介计划”的“定义目标用户”功能中，有一些推荐的“典型用户群”，白领当然是其中之一。 按它的说法，在企业/公司工作的一般职员或专业技术人员，而且收入超过1000元，就算是白领。这样定义，对吗？因为没有统一标准，也就无所谓对错。 维基百科上说，“白领族是一个从西方传来的生活型态定义，经常拿来代表领较多薪水的专业人士。这个词可能最早是从1928年启用的，当时是代表非体力劳动的工作者，如公务员、教职人员等。”以及“如果以薪水支 付方式区分，大多白领族是领固定的月薪或周薪，而蓝领族可能相对更多地是以计时来算工资。此外，过去认为白领族享有比蓝领族较高薪水的想法，今日逐渐的变 化当中。有许多白领族的薪水结构被新的企业政策重新设计，也形成了高奖金、低底薪的结构方式，如果没有达到一定的工作成效，薪水不见得就比蓝领族高。” 百度百科基本抄袭了维基，又加入了一些杂乱的内容，“白领（white-collar worker）是指有教育背景和工作经验的、从事脑力劳动的阶层，是西方社会对企业中不需做大量体力劳动的工作人员的通称，又称白领阶层，与蓝领对应。”以及“按美国的标准，白领是指年薪在8万美元、从事纯粹脑力劳动的人。除了个别技术性特强的行业，大多数的“白领”都是有生存危机的。劳动制度的不够完善，使白领无法有应有的保障。但是到1996年，8万的数字已经赶不上时代的发展速度。当然各国甚至是各地区收入水平差异很大，因此白领没有统一的标准。 ” 一级城市（不知道是否对应一线城市）里，本科以上学历，收入超过3000元，就算是高收入。 把我勉强算作白领，也就认了，但说我是高收入，我可不从。依这个标准，中国得有多少垂死挣扎的高收入白领啊。 还有其他几个典型用户群，看看里面有你吗？ 二级城市高收入人群：收入在1000元以上，且城市级别为二级城市 白领：职业为企业/公司一般职员或专业技术人员，且收入在1000元以上 职业女性：性别女，职业为企业/公司一般职员、或专业技术人员、或产业服务业工人 大学生：职业为学生，学历为大专或以上 中小学生：职业为学生，学历为高中及以下 一级城市高收入人群：学历为本科及以上，收入在3000元以上，且城市级别为一级城市 中老年人：年龄在50岁及以上 76cf59db 也许你还喜欢：翠迪鸟往事非常操蛋王湛在北大小紧张了一下中华书局面面观与世隔绝的四十小时[转如何去除msn广告-[2010-04-26]过期百度不知道新浪：与世界杯一起高潮郭德纲新闻未来走势预测]]></description>
			<content:encoded><![CDATA[<p>一直以为白领是个珍稀物种，白领的生活多少都还挺幸福；也一直以为自己挺穷的，算是中低收入吧。直到有一天，我看见一个神奇的网站——CNIDP（<a href="http://www.cnidp.cn" rel="external nofollow"  target="_blank">中国互联网数据平台</a>）。</p>
<p>昨天接到一个GG电话， 问我是不是注册了中国互联网数据平台，这才想起来半个月前自己确实注册过，只是一直在审核状态（官网说假期不审核）。情人节那天，他们可能开始上班了，这样的单位真好。</p>
<p>平台中规中矩，毕竟是CNNIC的，干调查这行的大家都懂，不多说。</p>
<p>网站有一个比较实用的功能，叫“媒介计划”，在这里，广告主可以选择目标人群（比如年龄多少，收入多少，学历怎样），做好限定后，就会给推荐几个网络媒体，再进一步，可以得到覆盖率等指标。</p>
<p><span id="more-2326"></span><img title="中国互联网数据平台媒介计划" src="http://datafanr.com/wp-content/uploads/2012/02/cnidp.png" alt="中国互联网数据平台媒介计划" width="500" height="292" /></p>
<p>“媒介计划”的“定义目标用户”功能中，有一些推荐的“典型用户群”，白领当然是其中之一。</p>
<p>按它的说法，在企业/公司工作的一般职员或专业技术人员，而且收入超过1000元，就算是白领。这样定义，对吗？因为没有统一标准，也就无所谓对错。</p>
<p><a href="http://zh.wikipedia.org/wiki/%E7%99%BD%E9%A0%98" rel="external nofollow"  target="_blank">维基百科</a>上说，“<strong>白领族</strong>是一个从西方传来的<a title="生活型态" href="http://zh.wikipedia.org/wiki/%E7%94%9F%E6%B4%BB%E5%9E%8B%E6%85%8B" rel="external nofollow" >生活型态</a>定义，经常拿来代表领较多薪水的专业人士。这个词可能最早是从<a title="1928年" href="http://zh.wikipedia.org/wiki/1928%E5%B9%B4" rel="external nofollow" >1928年</a>启用的，当时是代表非体力劳动的工作者，如公务员、教职人员等。”以及“如果以<a title="薪水" href="http://zh.wikipedia.org/wiki/%E8%96%AA%E6%B0%B4" rel="external nofollow" >薪水</a>支 付方式区分，大多白领族是领固定的月薪或周薪，而蓝领族可能相对更多地是以计时来算工资。此外，过去认为白领族享有比蓝领族较高薪水的想法，今日逐渐的变 化当中。有许多白领族的薪水结构被新的企业政策重新设计，也形成了高奖金、低底薪的结构方式，如果没有达到一定的工作成效，薪水不见得就比蓝领族高。”</p>
<p><a href="http://baike.baidu.com/view/1696.htm" rel="external nofollow"  target="_blank">百度百科</a>基本抄袭了维基，又加入了一些杂乱的内容，“白领（<a href="http://baike.baidu.com/view/1689061.htm" rel="external nofollow"  target="_blank">white-collar worker</a>）是指有教育背景和工作经验的、从事脑力劳动的阶层，是西方社会对企业中不需做大量<a href="http://baike.baidu.com/view/327865.htm" rel="external nofollow"  target="_blank">体力劳动</a>的工作人员的通称，又称白领阶层，与蓝领对应。”以及“按美国的标准，白领是指年薪在8万美元、从事纯粹<a href="http://baike.baidu.com/view/327858.htm" rel="external nofollow"  target="_blank">脑力劳动</a>的人。除了个别技术性特强的行业，大多数的“白领”都是有生存危机的。劳动制度的不够完善，使白领无法有应有的保障。但是到1996年，8万的数字已经赶不上时代的发展速度。当然各国甚至是各地区收入水平差异很大，因此白领没有统一的标准。 ”</p>
<p>一级城市（不知道是否对应一线城市）里，本科以上学历，收入超过3000元，就算是高收入。</p>
<p>把我勉强算作白领，也就认了，但说我是高收入，我可不从。依这个标准，中国得有多少垂死挣扎的高收入白领啊。</p>
<p>还有其他几个典型用户群，看看里面有你吗？</p>
<blockquote><p>二级城市高收入人群：收入在1000元以上，且城市级别为二级城市</p>
<p>白领：职业为企业/公司一般职员或专业技术人员，且收入在1000元以上</p>
<p>职业女性：性别女，职业为企业/公司一般职员、或专业技术人员、或产业服务业工人</p>
<p>大学生：职业为学生，学历为大专或以上</p>
<p>中小学生：职业为学生，学历为高中及以下</p>
<p>一级城市高收入人群：学历为本科及以上，收入在3000元以上，且城市级别为一级城市</p>
<p>中老年人：年龄在50岁及以上</p>
<p><span style="color: #ffffff;">76cf59db</span></p></blockquote>
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		</item>
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		<title>Excel做矩阵图的三种方法</title>
		<link>http://datafanr.com/data/excel-matrix.html</link>
		<comments>http://datafanr.com/data/excel-matrix.html#comments</comments>
		<pubDate>Mon, 09 Jan 2012 15:21:10 +0000</pubDate>
		<dc:creator>icewent</dc:creator>
				<category><![CDATA[0.数据分析]]></category>
		<category><![CDATA[Excel]]></category>
		<category><![CDATA[图表]]></category>
		<category><![CDATA[矩阵图]]></category>

		<guid isPermaLink="false">http://datafanr.com/?p=2196</guid>
		<description><![CDATA[引子：波士顿矩阵 不久之前，网上流传着这样一则笑话： 波士顿咨询集团（BCG）17日宣布，已聘请行业排名第一的麦肯锡做战略咨询，希望能取代麦肯锡成为行业第第一。麦肯锡欣然接受了这一单子，并希望能保住波士顿这个客户至少五年。更有戏剧性的是，麦肯锡已经决定用著名的波士顿矩阵来做这一战略。 笑话归笑话，我们也能从中看出波士顿矩阵已为人所熟知。波士顿矩阵根据相对市场份额和市场增长率的高低，把第一象限分成四个区域，落在四个区域产品（或者品牌）依次被称为明星产品、金牛产品、问题产品和瘦狗产品。 图片出处：方少华等编著《管理咨询工具箱》 当然，各家都有各家定制或者改善的矩阵模型，波士顿矩阵只是其中名气比较大的。众多矩阵模型大同小异，关键的部分是用几条分割线把整个区域分开，今天不讨论别的，单说这几条线怎么画，以两条线分四部分为例。 要实现矩阵图里的分割线，常用的有三种方法： 1.插入形状，手动画线 方法：选中图表，点菜单栏“插入&#8211;&#62;形状中的直线”，提醒一点，画线的时候按住Shift键拖动鼠标，可以使画出的线保持水平或竖直。这里分割线所处的位置是横轴或纵轴的均值（下同）。 注：为了区分，我将中间两条线画成了橙色（可以根据需要改成灰色），方框上面和右面的线是通过“双击图表&#8211;&#62;设置绘图区格式”设置的。 这种方法的优点是直接，容易想到，缺点是需要人工调整线的长短和位置（要求精确的话，长短和位置的调整并不简单直观），另外，如果调整了图表大小、数据或者坐标轴的最大最小值，分割线需要再移动位置。 2.移动坐标轴，以轴为线 方法：双击坐标轴，在“坐标轴选项&#8211;&#62;纵坐标轴交叉”中填入分割线的位置。 效果如下： 注：双击横轴，设置“纵坐标交叉”，使纵轴平移；双击纵轴，设置“横坐标”交叉，使横轴平移。 为了显示操作步骤，坐标轴上的刻度线及标签未删除，可以双击坐标轴将刻度线类型及坐标轴标签设为“无”，以获得更好的显示效果。如果还需要原来位置的横纵坐标，可以手动绘制。 这种方法的优点是较为简洁，分割线的位置也可以自动调节，如果对坐标轴及刻度线要求不高，可以采用。如果需要原有的坐标轴，则稍微有些麻烦。 3.散点连线，点动成线 方法： （1）在原x轴及y轴数据后插入四列，分别是x均值、y均值、x分布和y分布。顾名思义，均值表示的是x或y的均值，所有行都是一样的值。而x分布可以填入x可能取的所有值，比如本例中x取0到9（这个完全可以从无限小取到无限大，超出的部分不会显示在图中）。 （2）选中图表，单击菜单栏“设计&#8211;&#62;选择数据”。 （3）在弹出的对话框中选左侧“添加”图例项。系列名称可以随便填，例如xmean，X轴系列值选x的均值那一列，Y轴系列值选择y分布那列值（都不含首行标题）。 两次确定后，得到如下图： （4）选中新出来的这个方框序列，右键&#8211;&#62;更改系列图表类型，选择散点图中的第三个（带平滑线的散点图）。得到如下图： （5）调整一下这条线的样式，按相同的思路画出纵轴均值。最终效果如下： 注：添加这两条线的过程中，如果没有给坐标轴选择最大及最小值，可能会出现错位（如本例中纵坐标最大值可能会变成10），不用担心，手动设置一下最大值即可。 这种方法的优点最多，既能精确画出分割线的位置（不随图像调整而变化），又能保留原有坐标轴。缺点当然是，操作步骤最多。 需要的话，可以下载我作图时的文件参考：微盘下载 &#124; Dbank下载 附：本例采用散点图加标签工具http://t.excelhome.net/thread-1075-1-1.html 也许你还喜欢：用ExcelVBA做编码合并在Excel 2007中插入柱状-折线复合图]]></description>
			<content:encoded><![CDATA[<h3>引子：波士顿矩阵</h3>
<p>不久之前，网上流传着这样一则笑话：</p>
<p>波士顿咨询集团（BCG）17日宣布，已聘请行业排名第一的麦肯锡做战略咨询，希望能取代麦肯锡成为行业第第一。麦肯锡欣然接受了这一单子，并希望能保住波士顿这个客户至少五年。更有戏剧性的是，麦肯锡已经决定用著名的波士顿矩阵来做这一战略。</p>
<p><span id="more-2196"></span>笑话归笑话，我们也能从中看出波士顿矩阵已为人所熟知。波士顿矩阵根据相对市场份额和市场增长率的高低，把第一象限分成四个区域，落在四个区域产品（或者品牌）依次被称为明星产品、金牛产品、问题产品和瘦狗产品。</p>
<p><img class="size-full wp-image-2198 aligncenter" title="boston_matrix" src="http://datafanr.com/wp-content/uploads/2012/01/boston_matrix.jpg" alt="" width="578" height="289" /></p>
<p style="text-align: center;">图片出处：方少华等编著《管理咨询工具箱》</p>
<p>当然，各家都有各家定制或者改善的矩阵模型，波士顿矩阵只是其中名气比较大的。众多矩阵模型大同小异，关键的部分是用几条分割线把整个区域分开，今天不讨论别的，单说这几条线怎么画，以两条线分四部分为例。</p>
<p>要实现矩阵图里的分割线，常用的有三种方法：</p>
<h3>1.插入形状，手动画线</h3>
<p>方法：选中图表，点菜单栏“插入&#8211;&gt;形状中的直线”，提醒一点，画线的时候按住Shift键拖动鼠标，可以使画出的线保持水平或竖直。这里分割线所处的位置是横轴或纵轴的均值（下同）。</p>
<p><img src="data:image/png;base64,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" alt="" /></p>
<p>注：为了区分，我将中间两条线画成了橙色（可以根据需要改成灰色），方框上面和右面的线是通过“双击图表&#8211;&gt;设置绘图区格式”设置的。</p>
<p>这种方法的优点是直接，容易想到，缺点是需要人工调整线的长短和位置（要求精确的话，长短和位置的调整并不简单直观），另外，如果调整了图表大小、数据或者坐标轴的最大最小值，分割线需要再移动位置。</p>
<h3>2.移动坐标轴，以轴为线</h3>
<p>方法：双击坐标轴，在“坐标轴选项&#8211;&gt;纵坐标轴交叉”中填入分割线的位置。</p>
<p><img 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" alt="" /></p>
<p>效果如下：</p>
<p><img src="data:image/png;base64,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" alt="" /></p>
<p>注：双击横轴，设置“纵坐标交叉”，使纵轴平移；双击纵轴，设置“横坐标”交叉，使横轴平移。</p>
<p>为了显示操作步骤，坐标轴上的刻度线及标签未删除，可以双击坐标轴将刻度线类型及坐标轴标签设为“无”，以获得更好的显示效果。如果还需要原来位置的横纵坐标，可以手动绘制。</p>
<p>这种方法的优点是较为简洁，分割线的位置也可以自动调节，如果对坐标轴及刻度线要求不高，可以采用。如果需要原有的坐标轴，则稍微有些麻烦。</p>
<h3>3.散点连线，点动成线</h3>
<p>方法：</p>
<p>（1）在原x轴及y轴数据后插入四列，分别是x均值、y均值、x分布和y分布。顾名思义，均值表示的是x或y的均值，所有行都是一样的值。而x分布可以填入x可能取的所有值，比如本例中x取0到9（这个完全可以从无限小取到无限大，超出的部分不会显示在图中）。</p>
<p>（2）选中图表，单击菜单栏“设计&#8211;&gt;选择数据”。</p>
<p><img 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" alt="" /></p>
<p>（3）在弹出的对话框中选左侧“添加”图例项。系列名称可以随便填，例如xmean，X轴系列值选x的均值那一列，Y轴系列值选择y分布那列值（都不含首行标题）。</p>
<p><img 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" alt="" /></p>
<p>两次确定后，得到如下图：</p>
<p><img src="data:image/png;base64,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" alt="" /></p>
<p>（4）选中新出来的这个方框序列，右键&#8211;&gt;更改系列图表类型，选择散点图中的第三个（带平滑线的散点图）。得到如下图：</p>
<p><img src="data:image/png;base64,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" alt="" /></p>
<p>（5）调整一下这条线的样式，按相同的思路画出纵轴均值。最终效果如下：</p>
<p><img src="data:image/png;base64,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" alt="" /></p>
<p>注：添加这两条线的过程中，如果没有给坐标轴选择最大及最小值，可能会出现错位（如本例中纵坐标最大值可能会变成10），不用担心，手动设置一下最大值即可。</p>
<p>这种方法的优点最多，既能精确画出分割线的位置（不随图像调整而变化），又能保留原有坐标轴。缺点当然是，操作步骤最多。</p>
<p>需要的话，可以下载我作图时的文件参考：<a href="http://vdisk.weibo.com/s/1TZg4" rel="external nofollow"  target="_blank">微盘下载</a> | <a href="http://dl.dbank.com/c0lmujb6b0" rel="external nofollow"  target="_blank">Dbank下载</a></p>
<p>附：本例采用散点图加标签工具<a href="http://t.excelhome.net/thread-1075-1-1.html" rel="external nofollow"  target="_blank">http://t.excelhome.net/thread-1075-1-1.html</a></p>
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		<title>统计让我哭：有趣中用的统计行业网站</title>
		<link>http://datafanr.com/data/stats_make_me_cry.html</link>
		<comments>http://datafanr.com/data/stats_make_me_cry.html#comments</comments>
		<pubDate>Fri, 06 Jan 2012 15:39:16 +0000</pubDate>
		<dc:creator>icewent</dc:creator>
				<category><![CDATA[0.数据分析]]></category>
		<category><![CDATA[spss]]></category>
		<category><![CDATA[博客]]></category>
		<category><![CDATA[统计]]></category>
		<category><![CDATA[网站]]></category>

		<guid isPermaLink="false">http://datafanr.com/?p=2180</guid>
		<description><![CDATA[STATS MAKE ME CRY（统计让我哭，姑且这么翻译吧），是一个关于统计的网站，有意思有态度的网站，最重要的是，有用。 我有一次搜索SPSS某个命令的时候，偶然发现了它。 网站是这么自我介绍的： Stats Make Me Cry is a place to share ideas and find answers to your statistics and data analysis questions.  Look around, tell a friend, and come back soon! For in-depth data analysis help, check out our comprehensive consulting services.  I can help if you are a graduate student, someone [...]]]></description>
			<content:encoded><![CDATA[<p><a href="http://www.statsmakemecry.com/" rel="external nofollow"  target="_blank">STATS MAKE ME CRY</a>（统计让我哭，姑且这么翻译吧），是一个关于统计的网站，有意思有态度的网站，最重要的是，有用。</p>
<p><img class="alignnone size-full wp-image-2186" title="statsmakemecry" src="http://datafanr.com/wp-content/uploads/2012/01/statsmakemecry.png" alt="" width="609" height="162" /></p>
<p>我有一次搜索SPSS某个命令的时候，偶然发现了它。</p>
<p><span id="more-2180"></span>网站是这么自我介绍的：</p>
<blockquote><p><em><strong>Stats Make Me Cry</strong></em> is a place to share ideas and find answers to your statistics and data analysis questions.  Look around, tell a friend, and come back soon! For in-depth data analysis help, check out our <a href="http://www.statsmakemecry.com/statistics-help/" rel="external nofollow"  rel="external nofollow" >comprehensive consulting services. </a> I can help if you are a graduate student, someone that is ABD (All But Dissertation), or a professional looking for some statistical perspective.</p>
<p>不靠谱翻译：<strong>统计让我哭</strong>是这么个地方，你可以分享关于统计和<span class='bm_keywordlink'><a href='http://datafanr.com/category/data'>数据分析</a></span>问题的想法、找到想要的答案。瞧一瞧，看一看，跟你的朋友说一说，然后，马上回来。需要深入的<span class='bm_keywordlink'><a href='http://datafanr.com/category/data'>数据分析</a></span>方面的帮助，可以看看俺们的<a href="http://www.statsmakemecry.com/statistics-help/" rel="external nofollow"  rel="external nofollow"  target="_blank">综合咨询服务</a>。你要是个研究僧的话，俺可以帮你，啥都能帮，只要不是写论文。你要是干这行的，想找点统计方面的东西，俺也可以帮上点忙。</p></blockquote>
<p>网站提供了统计方面的视屏、博客、论坛、社交网络、有趣的小玩意儿、所有人问俺一个人（<span class='bm_keywordlink'><a href='http://icewent.com' target="_blank">icewent</a></span>注：这页面貌似已经木有了）、焦点链接，还有统计咨询服务。（<span class='bm_keywordlink'><a href='http://icewent.com' target="_blank">icewent</a></span>注：貌似好多页面都木有了耶）。</p>
<p>网站的<a href="http://www.statsmakemecry.com/bookshelves/" rel="external nofollow"  target="_blank">书架频道</a>列出了一些统计相关的书，包括SPSS的、线性回归的，等等；<a href="http://www.statsmakemecry.com/statistics-discussion-forum/" rel="external nofollow"  target="_blank">论坛</a>里有一些关于SPSS的话题；<a href="http://www.statsmakemecry.com/stats-video/" rel="external nofollow"  target="_blank">视屏</a>频道我这里打不开，也不知道里面有什么。</p>
<p>网站的作者<strong>Jeremy</strong>目前是<a href="http://www.depaul.edu/Pages/default.aspx" rel="external nofollow"  target="_blank"><span>德保罗大学</span></a>（这个学校竟然和PKU一样，建于1898年）临床心理学的博士僧一只，自称做过护理、经管、营销、信息系统、财务等很多方面的咨询。</p>
<p>一直在找这种做咨询从业者的个人网站，终于找到这么一个，不知道靠不靠谱，需要观察一段时间。不过无论如何，也比国内某些人假大空的博客要好得多。将来的<a href="http://datafanr.com" target="_blank">数据范儿</a>，也要做成统计让我哭这个样子。</p>
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		</item>
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		<title>Variable Sets：让SPSS选择变量不再麻烦</title>
		<link>http://datafanr.com/data/variable-sets.html</link>
		<comments>http://datafanr.com/data/variable-sets.html#comments</comments>
		<pubDate>Thu, 05 Jan 2012 14:23:53 +0000</pubDate>
		<dc:creator>icewent</dc:creator>
				<category><![CDATA[0.数据分析]]></category>
		<category><![CDATA[spss]]></category>
		<category><![CDATA[Variable Sets]]></category>
		<category><![CDATA[变量]]></category>
		<category><![CDATA[方法]]></category>

		<guid isPermaLink="false">http://datafanr.com/?p=2167</guid>
		<description><![CDATA[0.为什么要用Variable Sets？ 正经使用SPSS五个月了，一直嫌弃它的各种不方便。比如用可视化方式执行一个命令时，要在几百个变量中找啊找一个。 当你用鼠标滚轮向下滚的时候，一个不小心就过去了；掉过头来向上的时候，一个不小心又过去了。要定位一个或几个变量，真是难于上青天。 当然，细心一些，多花几秒，要找到一个或几个变量不是难事。但当你要反复选择很多次的时候（做分析写报告的时候一定会反复选择很多次），那耽误的就不只是几秒钟的功夫了。 还好，SPSS提供了一个变量集合的功能，可以对变量进行分组，必要的时候，可以让变量选择框中只出现某些特定的变量。这样，选择起来，就轻松许多。百里挑一，总比万里挑一要方便很多。 1.Variable Sets是什么？ 这个命令叫Variable Sets（变量集合），SPSS的官方帮助文档里是这么描述的： Defining variable sets Define Variable Sets creates subsets of variables to display in the Data Editor and in dialog box variable lists. Defined variable sets are saved with IBM® SPSS® Statistics data files. Set Name. Set names can be up to 64 bytes. Any characters, including blanks, can be used. Variables in Set. Any combination of numeric and string variables can be included [...]]]></description>
			<content:encoded><![CDATA[<h3>0.为什么要用Variable Sets？</h3>
<p>正经使用SPSS五个月了，一直嫌弃它的各种不方便。比如用可视化方式执行一个命令时，要在几百个变量中找啊找一个。</p>
<p>当你用鼠标滚轮向下滚的时候，一个不小心就过去了；掉过头来向上的时候，一个不小心又过去了。要定位一个或几个变量，真是难于上青天。</p>
<p><span id="more-2167"></span>当然，细心一些，多花几秒，要找到一个或几个变量不是难事。但当你要反复选择很多次的时候（做分析写报告的时候一定会反复选择很多次），那耽误的就不只是几秒钟的功夫了。</p>
<p>还好，<strong>SPSS提供了一个变量集合的功能，可以对变量进行分组，必要的时候，可以让变量选择框中只出现某些特定的变量。这样，选择起来，就轻松许多。</strong>百里挑一，总比万里挑一要方便很多。</p>
<h3>1.Variable Sets是什么？</h3>
<p>这个命令叫<strong>Variable Sets（变量集合）</strong>，SPSS的官方帮助文档里是这么描述的：</p>
<blockquote>
<h1>Defining variable sets</h1>
<p>Define Variable Sets creates subsets of variables to display in the Data Editor and in dialog box variable lists. Defined variable sets are saved with IBM® SPSS® Statistics data files.</p>
<p>Set Name. Set names can be up to 64 bytes. Any characters, including blanks, can be used.</p>
<p>Variables in Set. Any combination of numeric and string variables can be included in a set. The order of variables in the set has no effect on the display order of the variables in the Data Editor or in dialog box variable lists. A variable can belong to multiple sets.</p></blockquote>
<div id="N318D8">
<div>
<blockquote>
<h2>To define variable sets</h2>
<p>1.From the menus choose:</p>
<p>Utilities &gt; Define Variable Sets&#8230;</p>
<p>2.Select the variables that you want to include in the set.</p>
<p>3. Enter a name for the set (up to 64 bytes).</p>
<p>4. Click Add Set.</p></blockquote>
</div>
</div>
<h3>2.定义Variable Sets</h3>
<p>要想使用Variable Sets，你需要先定义一个集合，先在菜单栏里选 Utilities，下拉菜单中选择Define Variable Sets。</p>
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" alt="" /></p>
<p>在弹出的对话框中，选择你想要的变量，移动到右边，然后给集合起个名字，点Add Set，一个新的变量集合就生成了。</p>
<p><img 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" alt="" /></p>
<h3>3.使用Variable Sets</h3>
<p>需要使用这个集合时，点工具栏里两个圆圈相交的按钮（Use Variable Sets），就可以在弹出的对话框中选择你想要使用的集合。</p>
<p><img 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" alt="" /></p>
<p>假设我们选择了“人口变量”，点击OK之后，变量视图里就只显示选定集合里的变量，而其他变量都被隐藏了（蓝色部分）。</p>
<p><img src="data:image/png;base64,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" alt="" /></p>
<p>做个Frequencies，变量选择框里，只出现了“人口变量”这个集合中的变量，是不是清爽许多了？</p>
<p><img 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" alt="" /></p>
<p>要选择其他集合，再点那两个小圆圈相交的按钮；要显示所有变量，点右边的两个小圆圈相交按钮（Show All Variables）。</p>
<blockquote><p>一个最简单直接的例子是，一道多选题有四十个选项，每个选项都是0,1变量，想要得到这道多选题中每个选项的比例，而且可能会多次用到这些变量，为了避免一次一次大海捞针，只要把这四十个变量放进一个集合里，选择这个集合，用Means命令，Ctrl+A，移动到右侧，OK就可以了。以后如果这四十个变量还需要做别的命令，可以再选择这个集合。</p></blockquote>
<p>有了Variables Sets，妈妈再也不用担心我的SPSS学习！</p>
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		</item>
		<item>
		<title>焦点小组的屋子长啥样?</title>
		<link>http://datafanr.com/data/focus-group.html</link>
		<comments>http://datafanr.com/data/focus-group.html#comments</comments>
		<pubDate>Sun, 04 Dec 2011 12:49:13 +0000</pubDate>
		<dc:creator>icewent</dc:creator>
				<category><![CDATA[0.数据分析]]></category>
		<category><![CDATA[单面镜]]></category>
		<category><![CDATA[定性研究]]></category>
		<category><![CDATA[焦点小组]]></category>

		<guid isPermaLink="false">http://datafanr.com/?p=2048</guid>
		<description><![CDATA[很早就在书本上知道定性研究里的焦点小组座谈，对里面的单面镜很是好奇，最近有机会看了一眼焦点小组座谈会的庐山真面目。 ❶ ： 传说中的单面镜，单面镜以上，是座谈室（自己起的名字，不知道准确的说法是啥），主持人和被访者在里面；单面镜以下是观察室，研究者或者客户坐这边。 ❷： 摄像头，在天花板上与两面墙交汇的角上，记录整个过程，还会记录下每个人说话时的神态表情。 ❸： 玻璃黑板 ❹： 圆桌，并不是课本上说的严格意义上的圆桌，圆桌一定要圆，才能保证每个人的地位都是公平的。 ❺： 速记 ❻： 录音录像的电脑设备 ❼： 打印机等办公设备 M： 主持人 ①-⑧：被访者 座谈会进行过程中，座谈室开灯，而观察室不开灯，在光线不足的情况下，座谈室里看不到玻璃的另一边，而观察室能清楚地透过单面镜观察到座谈室里所有人的一举一动（不包括主持人的表情）。但如果观察室里打开灯，或者开了门进入光线，这个时候，单面镜将不再“单面”（至少我看到的是这样的）。 图中虚线以上是阳台，窗帘是拉下的，观察室里的窗帘也不开，要保证没有光线。 坐在观察室里，有一种人在名我在暗俯瞰芸芸众生的感觉。当然，一举一动也要小心翼翼，千万不能被发现。 也许你还喜欢：叫我什么不是我叫什么[活动推荐4月12日]2012中国网络购物消费趋势论坛[贰]点十一：我写的都是闲事儿月捐的烦恼两只者老鼠的爱情微博营销群@策略：提取搜索结果中的IDiPad版红色警戒日军任务攻略 一个女人的十年北大传播学考研经验总结（转）]]></description>
			<content:encoded><![CDATA[<p>很早就在书本上知道定性研究里的焦点小组座谈，对里面的单面镜很是好奇，最近有机会看了一眼焦点小组座谈会的庐山真面目。<br />
<span id="more-2048"></span><br />
<img title="focus-group" src="http://datafanr.com/wp-content/uploads/2011/12/focus-group.jpg" alt="" width="500" height="665" /></p>
<p>❶ ： 传说中的单面镜，单面镜以上，是座谈室（自己起的名字，不知道准确的说法是啥），主持人和被访者在里面；单面镜以下是观察室，研究者或者客户坐这边。</p>
<p>❷： 摄像头，在天花板上与两面墙交汇的角上，记录整个过程，还会记录下每个人说话时的神态表情。</p>
<p>❸： 玻璃黑板</p>
<p>❹： 圆桌，并不是课本上说的严格意义上的圆桌，圆桌一定要圆，才能保证每个人的地位都是公平的。</p>
<p>❺： 速记</p>
<p>❻： 录音录像的电脑设备</p>
<p>❼： 打印机等办公设备</p>
<p>M： 主持人</p>
<p>①-⑧：被访者</p>
<p>座谈会进行过程中，座谈室开灯，而观察室不开灯，在光线不足的情况下，座谈室里看不到玻璃的另一边，而观察室能清楚地透过单面镜观察到座谈室里所有人的一举一动（不包括主持人的表情）。但如果观察室里打开灯，或者开了门进入光线，这个时候，单面镜将不再“单面”（至少我看到的是这样的）。</p>
<p>图中虚线以上是阳台，窗帘是拉下的，观察室里的窗帘也不开，要保证没有光线。</p>
<p>坐在观察室里，有一种人在名我在暗俯瞰芸芸众生的感觉。当然，一举一动也要小心翼翼，千万不能被发现。</p>
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		<title>转：熟女经济和半熟社会——手机人@2011</title>
		<link>http://datafanr.com/data/shu.html</link>
		<comments>http://datafanr.com/data/shu.html#comments</comments>
		<pubDate>Tue, 22 Nov 2011 15:45:27 +0000</pubDate>
		<dc:creator>icewent</dc:creator>
				<category><![CDATA[0.数据分析]]></category>
		<category><![CDATA[半数社会]]></category>
		<category><![CDATA[手机]]></category>
		<category><![CDATA[熟女]]></category>

		<guid isPermaLink="false">http://datafanr.com/?p=2037</guid>
		<description><![CDATA[周末呆在家里，做一个文章专题，分享给大家！ 原文链接http://www.upperplus.cn/up-special/shu 熟女经济和半熟社会——手机人@2011 文/刘向清 电力的兴起，让更多的城市与乡村居住者们，轻按开关便可以驱赶黑夜，“电”，点亮的是家庭、城市、街道，日出而作、日落而息的大自然规律嘎然而止，生活从此有了崭新的面貌，就是“卖单儿”（东北话：看风景）也是万家灯火，街灯璀璨。 科技的纯粹与简单，让人类为此目眩神迷，它能改善生活并由此变得一切皆有可能，一切理所当然：“一个人、一台电脑可以成就一份全球的事业”。 移动互联网与电力的普及运用一样深刻地改变着人的生活。C语言之父丹尼斯•里奇创造的UNIX操作系统、史蒂夫·乔布斯缔造的苹果帝国也莫不如此。科技用 最简单的理念打造了人类更纯粹的需求，颠覆并创建了具有划时代意义的生活方式，造就出手机人。 手机不仅一个物件，不仅是一个通讯工具，不仅是一个与人完全分离的数字产品，而是人身体的一部分，像肢体、像感官、像器官，于是人与手机的关系就特殊起来，离不开，剪不断，带体温，类似于人种的进化——人与机器的融合体——手机人。这是移动互联网的产物，是原来意念遐想中的机器人的新的体现方式，是时代的象征。 如果说2009年是移动互联网元年的话[注释1]，2011年则是手机深入人们生活、关系日益密切与成熟的一年，手机人成为了生活的＂常态人＂，90.2%的人“认为手机跟钥匙一样，是一定要随身带的”。 2011年，手机人崭露的特点用一个简单的字可以概括：熟。苹果“熟透”了；“熟”女可爱疯（iphone）[注释3]；新生活方式日趋“成熟”；“半熟”社会的兴起…… 手机人族群、偏好、价值取向、行为习惯、效率、生活，全新地展现了日新月异的科技对人类生活的影响。我们承认所有科技创新都不可避免地在带来便利的同时，也面临危险的挑战，但是，一日千里的科技依旧是人类追逐生活品质的引擎，永无止境。 “天时地利人和”的“熟女”经济 成熟女性们大都经历了中国物质相对匮乏，上一代父母经济不富足的年代，她们共同的族群经验创造出属于她们的思维路径、思考方式和行为习惯，这些都是由于年少的经历和周遭的环境所映射而至。 当她们还在她们青春年少、热爱美好、渴望时尚的年纪，无论是大环境抑或是每个家庭的处境都无法从身心上满足她们对物质、时尚的向往，曾经的飘一代[注释2]潜伏的需求永远都会在条件成熟时破土而生，市场迎合了“熟女”的感受，于是有了当下成熟女性的＂可爱疯＂（iPhone） 。＂熟女＂们从未像现在这样成为时尚与消费的“弄潮儿”，她们引领风气之先（不是整体人数的多少，而是率先与带动），引领移动化微博[注释4] 、引领移动化数字阅读消费、引领移动视频 [注释5]、引领带着带体温的手机购物…… 受到“熟女”的带动，更受到族群心理影响，年轻们撞上了新技术革命，互联网、移动互联网走进每个旮旯与角落，进入手机人时代，符合了年轻们拥有并享受的要求，一切随着渴望而得到满足与实现，这也是对年轻“可爱疯”（iPhone）们[注释6]的最好诠释。 在中国的消费史上，历来都是年轻人引领时尚，唯有这轮＂手机人＂消费与沟通风潮中，成熟女性，淌进了湍急的潮流中，凭借天时，地利，人和，自然而生，催发了一个新的时尚经济类型——“熟女”经济，一个历史性的时刻。 社会化网络下的“熟”体验 社会化网络下，人们的一系列生活与观念的变化日益成熟与清晰，手机人的“熟”体验在方方面面涌现出来，我们从中透视其堂奥。 “手机人”工作与休闲互相伴随 记得儿时，严厉的父亲对着我们说过最多的话就是：学就好好的学，玩就痛快地玩，不要三心二意，否则都不尽心尽兴。 父辈们总是平时努力工作，全力以赴之后，最期盼地就是带着全家人走向公园、大自然、或是风景名胜，完成他们一生追求的幸福与温暖，他们遵循的是：工作是一 回事，休闲是另一回事，泾渭分明。以工作来定义人生，享受则放在未来的岁月里。 现在的青年人早就摒弃了父辈的唠叨与经验，他们体验的是工作与休闲互相伴随，办公室里、自习室中，乐此不彼地“耳机族”们，音乐的节奏并不能阻挡他们学习 与做事的效率，轻松快乐地迎接每一次挑战。 年轻人追求的是： 找寻适合自己生活方式的工作，摒弃为了工作而妥协生活品质，希望过“流行、时尚、休闲的生活”（64.7%）；、 成功者的定义不再是生活乏味的赚钱机器，而是不断推陈出新的玩乐花样，绞尽脑汁的是如何玩得与众不同，玩得品质杰出，他们认为成功者是“第一个适应新技术 的人”（46.2%）。 自由自在体验新事物，愈不按常理常规越好，“喜欢求新求异”（47.7%），休闲的追寻能教导自省与自我改善时，那更是求之不得的事。 把休闲看成是心理需求的手段，看成寻找自我的途径。 不知国内的管理者们是否已经了解了现在的年轻们，不要武断地认为他们没有事业心，他们只是追逐属于自己的时间，掌控自己的生活，寻求事业与生活的平衡，所以，创造更好的、舒适的、娱乐的、健康的工作环境，会让他们留住心情，付出辛勤，创造非凡。 调查显示，46.7%的人随时挂在手机QQ与飞信等即时通讯软件上；45.8%的人经常手机拍照，瞬时分享给好友；28.9%的人经常关注好友动 态……很多人会认为这样会影响他们的工作，殊不知，给他们点空间，给他们点时间，给他们打游戏、聊QQ、写微博、聚人气的机会，不仅不会影响工作，还会让 他们工作并快乐着，相信他们，比任何时候、任何年代都更抱有对成功的渴望，不同的是，他们更希望在平衡的状态下收获事业，这是当下年轻者难以割舍的情怀。 他们不会为了事业放弃生活，但是，他们依旧渴望成功。他们希望在工作与休闲的平衡中获得成就感（62.8%）。 他们才华横溢，期许在更加短暂的期限里，用更加独立、更加自信的方式，更热烈的控制欲来实现自己的梦想，他们或者选择自己创业，或者选择远离熟悉寻求陌生，为了追寻那份异类的，不同以往的成就之美。 游戏日趋休闲化，玩游戏与看电影的理由一样简单 “植物大战僵尸”、“切西瓜”、“小鸡快跑”所有这些游戏已经不同于以前耗时、耗力、耗神的游戏迷式的东东，时代对于游戏的需求是短暂、快乐、激 烈、放松，手机将几岁孩童到几十岁熟男熟女一起带到了游戏乐园里，可以在工作的间隙，可以在电视机的伴随下，可以靠在沙发中，可以在会场上，可以在公共交 通工具里，可以在惬意的床上……不限时间，无所谓地点，一切简单而自然，玩游戏与看电影、遛弯儿、听音乐的理由一样，无需解释，简单、随意。 在他们眼中，人人都有权利享有自己的时间，为了追求内心的满足，而拒绝沉闷、单调是与生俱来的权利。鞋子永远都是用来走路的，因此世界上有了男男女 女，不同国度，地域的背包族穿越于江河湖海、名山大川，寰宇内多了躁动与激情，溪水中，多了柔美与婉约。享受生活，享受属于自己的生命是他们内心的渴望。 游戏也是一样，越变化多端、越丰富、越有趣，能在短时间里得到满足越好，只有那些花样更迭的放纵瞬间，时间是自己的，于是，打球、滑板、骑马、高尔夫、跳 舞、游戏都那么贴近自然——工作与休闲的平衡为成功者注入了一份无与伦比的元素。 不要再认为游戏只是年轻孩子们的喜好，随着苹果与安卓等智能手机终端的应用，游戏已经成为各个年龄段、不同文化程度、各种阶层的休闲根据地[注释7]。 半熟社会的兴起 半熟社会（很熟悉、很贴心、很相知、很意气相投、现实中不认识）的兴起，是由于网络特别是移动互联网时代的来临，促成社会化网络的大行其道，让人类有了更广泛、更多元的新型交流场所与新的交流仪式的后果。 人类最传统、最基本需求之一就是交往需求，心里满足仍然是第一要务。移动终端带领人们不受时间、国家、地域限制自由交往。新的交往纽带提供了个人与 社会的联系通道。彼此可以不相识却能相知，彼此嘘寒问暖却不一定真正熟识，或许只是一条相同“围脖”的转发，拉近了彼此的关系；或许只是SNS里的一次不 经意的围观，找到了彼此的共同；或许是一个小小的评论流露出的情感感染了对方……可能完全没有现实中的陌生与疏离，共同的兴趣、爱好，甚至是欢乐让彼此心 灵靠近。总之，我们结结实实地爱上了，并着迷其中。 [...]]]></description>
			<content:encoded><![CDATA[<p>周末呆在家里，做一个文章专题，分享给大家！</p>
<p>原文链接<a href="http://www.upperplus.cn/up-special/shu" rel="external nofollow"  target="_blank">http://www.upperplus.cn/up-special/shu</a></p>
<p><strong>熟女经济和半熟社会——手机人@2011</strong></p>
<p>文/刘向清</p>
<p>电力的兴起，让更多的城市与乡村居住者们，轻按开关便可以驱赶黑夜，“电”，点亮的是家庭、城市、街道，日出而作、日落而息的大自然规律嘎然而止，生活从此有了崭新的面貌，就是“卖单儿”（东北话：看风景）也是万家灯火，街灯璀璨。</p>
<p><span id="more-2037"></span><br />
科技的纯粹与简单，让人类为此目眩神迷，它能改善生活并由此变得一切皆有可能，一切理所当然：“一个人、一台电脑可以成就一份全球的事业”。 移动互联网与电力的普及运用一样深刻地改变着人的生活。C语言之父丹尼斯•里奇创造的UNIX操作系统、史蒂夫·乔布斯缔造的苹果帝国也莫不如此。科技用 最简单的理念打造了人类更纯粹的需求，颠覆并创建了具有划时代意义的生活方式，造就出手机人。</p>
<p>手机不仅一个物件，不仅是一个通讯工具，不仅是一个与人完全分离的数字产品，而是人身体的一部分，像肢体、像感官、像器官，于是人与手机的关系就特殊起来，离不开，剪不断，带体温，<strong>类似于人种的进化——人与机器的融合体——手机人</strong>。这是移动互联网的产物，是原来意念遐想中的机器人的新的体现方式，是时代的象征。</p>
<p>如果说2009年是移动互联网元年的话<a href="http://www.upperplus.cn/up-special/shu#ref1" rel="external nofollow" >[注释1]</a>，2011年则是手机深入人们生活、关系日益密切与成熟的一年，手机人成为了生活的＂常态人＂，90.2%的人“认为手机跟钥匙一样，是一定要随身带的”。</p>
<p>2011年，手机人崭露的特点用一个简单的字可以概括：<strong>熟</strong>。苹果“熟透”了；“熟”女可爱疯（iphone）<a href="http://www.upperplus.cn/up-special/shu#ref3" rel="external nofollow" >[注释3]</a>；新生活方式日趋“成熟”；“半熟”社会的兴起……</p>
<p>手机人族群、偏好、价值取向、行为习惯、效率、生活，全新地展现了日新月异的科技对人类生活的影响。我们承认所有科技创新都不可避免地在带来便利的同时，也面临危险的挑战，但是，一日千里的科技依旧是人类追逐生活品质的引擎，永无止境。</p>
<h2>“天时地利人和”的“熟女”经济</h2>
<p>成熟女性们大都经历了中国物质相对匮乏，上一代父母经济不富足的年代，她们共同的族群经验创造出属于她们的思维路径、思考方式和行为习惯，这些都是由于年少的经历和周遭的环境所映射而至。</p>
<p><img src="http://www.upperplus.cn/wp-content/uploads/sjr71.jpg" alt="" width="419" height="346" /></p>
<p>当她们还在她们青春年少、热爱美好、渴望时尚的年纪，无论是大环境抑或是每个家庭的处境都无法从身心上满足她们对物质、时尚的向往，曾经的飘一代<a href="http://www.upperplus.cn/up-special/shu#ref2" rel="external nofollow" >[注释2]</a>潜伏的需求永远都会在条件成熟时破土而生，市场迎合了“熟女”的感受，于是有了当下成熟女性的＂可爱疯＂（iPhone） 。＂熟女＂们从未像现在这样成为时尚与消费的“弄潮儿”，她们引领风气之先（不是整体人数的多少，而是率先与带动），引领移动化微博<a href="http://www.upperplus.cn/up-special/shu#ref4" rel="external nofollow" >[注释4]</a> 、引领移动化数字阅读消费、引领移动视频 <a href="http://www.upperplus.cn/up-special/shu#ref5" rel="external nofollow" >[注释5]</a>、引领带着带体温的手机购物……</p>
<p>受到“熟女”的带动，更受到族群心理影响，年轻们撞上了新技术革命，互联网、移动互联网走进每个旮旯与角落，进入手机人时代，符合了年轻们拥有并享受的要求，一切随着渴望而得到满足与实现，这也是对年轻“可爱疯”（iPhone）们<a href="http://www.upperplus.cn/up-special/shu#ref6" rel="external nofollow" >[注释6]</a>的最好诠释。 在中国的消费史上，历来都是年轻人引领时尚，唯有这轮＂手机人＂消费与沟通风潮中，成熟女性，淌进了湍急的潮流中，凭借天时，地利，人和，自然而生，催发了一个新的时尚经济类型——“熟女”经济，一个历史性的时刻。</p>
<h2>社会化网络下的“熟”体验</h2>
<p>社会化网络下，人们的一系列生活与观念的变化日益成熟与清晰，手机人的“熟”体验在方方面面涌现出来，我们从中透视其堂奥。</p>
<h3>“手机人”工作与休闲互相伴随</h3>
<p>记得儿时，严厉的父亲对着我们说过最多的话就是：学就好好的学，玩就痛快地玩，不要三心二意，否则都不尽心尽兴。 父辈们总是平时努力工作，全力以赴之后，最期盼地就是带着全家人走向公园、大自然、或是风景名胜，完成他们一生追求的幸福与温暖，他们遵循的是：工作是一 回事，休闲是另一回事，泾渭分明。以工作来定义人生，享受则放在未来的岁月里。 现在的青年人早就摒弃了父辈的唠叨与经验，他们体验的是工作与休闲互相伴随，办公室里、自习室中，乐此不彼地“耳机族”们，音乐的节奏并不能阻挡他们学习 与做事的效率，轻松快乐地迎接每一次挑战。</p>
<p>年轻人追求的是：</p>
<p>找寻适合自己生活方式的工作，摒弃为了工作而妥协生活品质，希望过“流行、时尚、休闲的生活”（64.7%）；、 成功者的定义不再是生活乏味的赚钱机器，而是不断推陈出新的玩乐花样，绞尽脑汁的是如何玩得与众不同，玩得品质杰出，他们认为成功者是“第一个适应新技术 的人”（46.2%）。</p>
<p>自由自在体验新事物，愈不按常理常规越好，“喜欢求新求异”（47.7%），休闲的追寻能教导自省与自我改善时，那更是求之不得的事。 把休闲看成是心理需求的手段，看成寻找自我的途径。</p>
<p>不知国内的管理者们是否已经了解了现在的年轻们，不要武断地认为他们没有事业心，他们只是追逐属于自己的时间，掌控自己的生活，寻求事业与生活的平衡，所以，创造更好的、舒适的、娱乐的、健康的工作环境，会让他们留住心情，付出辛勤，创造非凡。</p>
<p>调查显示，46.7%的人随时挂在手机QQ与飞信等即时通讯软件上；45.8%的人经常手机拍照，瞬时分享给好友；28.9%的人经常关注好友动 态……很多人会认为这样会影响他们的工作，殊不知，给他们点空间，给他们点时间，给他们打游戏、聊QQ、写微博、聚人气的机会，不仅不会影响工作，还会让 他们工作并快乐着，相信他们，比任何时候、任何年代都更抱有对成功的渴望，不同的是，他们更希望在平衡的状态下收获事业，这是当下年轻者难以割舍的情怀。 他们不会为了事业放弃生活，但是，他们依旧渴望成功。他们希望在工作与休闲的平衡中获得成就感（62.8%）。</p>
<p>他们才华横溢，期许在更加短暂的期限里，用更加独立、更加自信的方式，更热烈的控制欲来实现自己的梦想，他们或者选择自己创业，或者选择远离熟悉寻求陌生，为了追寻那份异类的，不同以往的成就之美。</p>
<h2>游戏日趋休闲化，玩游戏与看电影的理由一样简单</h2>
<p>“植物大战僵尸”、“切西瓜”、“小鸡快跑”所有这些游戏已经不同于以前耗时、耗力、耗神的游戏迷式的东东，时代对于游戏的需求是短暂、快乐、激 烈、放松，手机将几岁孩童到几十岁熟男熟女一起带到了游戏乐园里，可以在工作的间隙，可以在电视机的伴随下，可以靠在沙发中，可以在会场上，可以在公共交 通工具里，可以在惬意的床上……不限时间，无所谓地点，一切简单而自然，玩游戏与看电影、遛弯儿、听音乐的理由一样，无需解释，简单、随意。</p>
<p>在他们眼中，人人都有权利享有自己的时间，为了追求内心的满足，而拒绝沉闷、单调是与生俱来的权利。鞋子永远都是用来走路的，因此世界上有了男男女 女，不同国度，地域的背包族穿越于江河湖海、名山大川，寰宇内多了躁动与激情，溪水中，多了柔美与婉约。享受生活，享受属于自己的生命是他们内心的渴望。 游戏也是一样，越变化多端、越丰富、越有趣，能在短时间里得到满足越好，只有那些花样更迭的放纵瞬间，时间是自己的，于是，打球、滑板、骑马、高尔夫、跳 舞、游戏都那么贴近自然——工作与休闲的平衡为成功者注入了一份无与伦比的元素。</p>
<p>不要再认为游戏只是年轻孩子们的喜好，随着苹果与安卓等智能手机终端的应用，游戏已经成为各个年龄段、不同文化程度、各种阶层的休闲根据地<a href="http://www.upperplus.cn/up-special/shu#ref7" rel="external nofollow" >[注释7]</a>。</p>
<h2>半熟社会的兴起</h2>
<p>半熟社会（很熟悉、很贴心、很相知、很意气相投、现实中不认识）的兴起，是由于网络特别是移动互联网时代的来临，促成社会化网络的大行其道，让人类有了更广泛、更多元的新型交流场所与新的交流仪式的后果。</p>
<p><img title="sjr1" src="http://www.upperplus.cn/wp-content/uploads/sjr6.jpg" alt="" width="271" height="324" /></p>
<p>人类最传统、最基本需求之一就是交往需求，心里满足仍然是第一要务。移动终端带领人们不受时间、国家、地域限制自由交往。新的交往纽带提供了个人与 社会的联系通道。彼此可以不相识却能相知，彼此嘘寒问暖却不一定真正熟识，或许只是一条相同“围脖”的转发，拉近了彼此的关系；或许只是SNS里的一次不 经意的围观，找到了彼此的共同；或许是一个小小的评论流露出的情感感染了对方……可能完全没有现实中的陌生与疏离，共同的兴趣、爱好，甚至是欢乐让彼此心 灵靠近。总之，我们结结实实地爱上了，并着迷其中。</p>
<p>“通过微博、SNS社交网站进行联系”已经成为手机人的生活方式之一（43.1%），“手机的使用增加了我和不在场的朋友的情感和联系” （77.1%）的同时，“会特意通过手机微博/SNS网站去认识一些人”（33.9%），甚至在外出时，通过手机微博/SNS网站及LBS功能“和周围的 陌生人交流”（22.8%）。</p>
<p>半熟不生的你我，触摸不同的生活，剥开每一个角落，解构了费孝通关于差序格局的论断，血缘、亲缘、地缘日益屈从于趣缘、业缘、聊缘、事件缘，所有这些新的“缘分”让价值观分流与重组，形成各种各样的小小群体，也在理论上挑战了格兰诺维特提出的强关系与弱关系理论。</p>
<p>正是这种半熟关系，引领着“手机人”向着一个没有大群、只有分化的小群的生活方式转化，也在小群的价值碰撞中形成权益的新价值–蜂群思维来了。</p>
<p>（文章中所有的数据均来自于第一象限市场咨询（北京）有限公司“2011年手机人大调查”）</p>
<p><img src="http://www.upperplus.cn/wp-content/uploads/ref.png" alt="" /></p>
<div>
<p>注释1：据CNNIC2011年3月发布的《中国网民手机上网行为研究报告》：2009年我国手机网民增长 1.16亿，同比增长95.5%，手机网民在总体网民中的比例首次达到60.8%，移动互联网2009年在用户规模、基础设施以及用户习惯等方面均迅猛发 展，被称作“移动互联网元年”。</p>
<p>注释2：刘向清、刘德寰：《“飘一代”调查综述》，《新周刊》，2000年</p>
<p>注释3：详情可参见图1(2010年苹果族深描)、图2(2011年苹果族深描)</p>
<p>注释4：详情可参见图3(2010年围脖族深描)</p>
<p>注释5：详情可参见图4(2011年手机视频族深描)</p>
<p>注释6：详情可参见图1(2010年苹果族深描)、图2(2011年苹果族深描)</p>
<p>注释7：详情可参考图5(2011年手机游戏族深描)</p>
</div>
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		<title>用Stata找F(x)</title>
		<link>http://datafanr.com/data/find_fx_with_stata.html</link>
		<comments>http://datafanr.com/data/find_fx_with_stata.html#comments</comments>
		<pubDate>Tue, 15 Nov 2011 17:13:20 +0000</pubDate>
		<dc:creator>icewent</dc:creator>
				<category><![CDATA[0.数据分析]]></category>
		<category><![CDATA[spss]]></category>
		<category><![CDATA[stata]]></category>

		<guid isPermaLink="false">http://datafanr.com/?p=2029</guid>
		<description><![CDATA[第一次基本回归之后，R方还相当小，可解释的部分不多，残差里还有很多函数，现在需要把这些函数从残差里找出来。 在SPSS点了很久之后，手累了，而SPSS的Syntax功能又相当弱，循环里只能跑描述，不能跑回归，于是想到用Stata。 假定在数据文件里已经有了edu这个变量，我需要做的是，找出edu的各个变式（也就是edu的各种初级函数），然后用基本回归方程的残差对这些变式的两两组合、三三组合甚至四四组合做回归，找到显著的项。 以三三组合为例，下边的这个do文件可以实现： set more off //compute gen sqrtEdu = sqrt(edu) gen edu2 = edu^2 gen edu3 = edu^3 gen edu4 = edu^4 gen daoEdu =1/edu gen lnEdu = ln(edu) gen daoLnEdu = 1/ln(edu) gen sinEdu = sin(edu) gen tanEdu = tan(edu) gen arctanEdu = atan(edu) gen expEdu = exp(edu) local v1 &#8220;edu sqrtEdu [...]]]></description>
			<content:encoded><![CDATA[<p>第一次基本回归之后，R方还相当小，可解释的部分不多，残差里还有很多函数，现在需要把这些函数从残差里找出来。<br />
在SPSS点了很久之后，手累了，而SPSS的Syntax功能又相当弱，循环里只能跑描述，不能跑回归，于是想到用Stata。<br />
假定在数据文件里已经有了edu这个变量，我需要做的是，找出edu的各个变式（也就是edu的各种初级函数），然后用基本回归方程的残差对这些变式的两两组合、三三组合甚至四四组合做回归，找到显著的项。<br />
以三三组合为例，下边的这个do文件可以实现：<br />
<span id="more-2029"></span></p>
<blockquote><p>set more off</p>
<p>//compute</p>
<p>gen sqrtEdu = sqrt(edu)<br />
gen edu2 = edu^2<br />
gen edu3 = edu^3<br />
gen edu4 = edu^4<br />
gen daoEdu =1/edu<br />
gen lnEdu = ln(edu)<br />
gen daoLnEdu = 1/ln(edu)<br />
gen sinEdu = sin(edu)<br />
gen tanEdu = tan(edu)<br />
gen arctanEdu = atan(edu)<br />
gen expEdu = exp(edu)</p>
<p>local v1 &#8220;edu sqrtEdu edu2 edu3 edu4 daoEdu lnEdu daoLnEdu sinEdu tanEdu arctanEdu expEdu&#8221;<br />
local v2 <code>v1'<br />
local v3 </code>v1&#8242;<br />
local v4 <code>v1'<br />
/*<br />
local ih = 1<br />
local ii = 1<br />
local ij = 1<br />
*/<br />
local add=0</p>
<p>foreach h of local v1 {<br />
foreach i of local v2 {<br />
foreach j of local v3 {<br />
//if (</code>h&#8217; != <code>i' &#038; </code>h&#8217; != <code>j' &#038; </code>i&#8217; != <code>j') {<br />
if (</code>h&#8217; < <code>i&#8217; &#038; </code>h' < <code>j' &#038; </code>i' < <code>j') {<br />
//local add = </code>add' + 1<br />
//di <code>add'<br />
capture est clear<br />
reg resdbtime </code>h' <code>i' </code>j'<br />
outreg2 using reg_restdbtime_edu.txt,append<br />
}<br />
}<br />
}<br />
}<br />
*/
</p></blockquote>
</blockquote>
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		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>分类信息网站市场现状</title>
		<link>http://datafanr.com/data/fenleixinxi.html</link>
		<comments>http://datafanr.com/data/fenleixinxi.html#comments</comments>
		<pubDate>Sun, 23 Oct 2011 16:41:52 +0000</pubDate>
		<dc:creator>icewent</dc:creator>
				<category><![CDATA[0.数据分析]]></category>
		<category><![CDATA[58同城]]></category>
		<category><![CDATA[分类信息网站]]></category>
		<category><![CDATA[市场分析]]></category>
		<category><![CDATA[赶集网]]></category>

		<guid isPermaLink="false">http://datafanr.com/?p=2020</guid>
		<description><![CDATA[姚晨骑驴骑了半年多过去了，杨幂也“神奇”了半年了，分类信息网站现在变成个什么样子了？ 1.“乱”市场：进入容易，领先难 本地生活服务分类信息网站市场巨大，技术门槛低，大量企业涌入（58同城、百姓网、列表网等，以及各行业垂直分类信息网站），同质化严重。 赶集网率先引入名人代言，大规模投放广告，58同城迅速跟随模仿，攻势不亚于赶集，近期SEO超越赶集网  搜索引擎竞争状况 注：所有数据截至2011年8月中旬 2.“贱”用户：取悦容易，忠诚难 “免费”成为分类信息网站独特卖点，用户乐于使用。但多数用户选择多平台发帖，无法独家代理。平台转换成本为零，难以培养忠诚度 由于信息真实性很少审核，不及专业垂直网站规范，不法分子滥用平台 •有用户发布简历，却收到“求一夜情”骚扰短信 •有用户打着交友的幌子推销 3.“笨”经纪人：尝试容易，操作难 以北京房地产中介行业为例，有数万活跃经纪人在各种平台（搜狐焦点、安居客等、赶集网）上付费发布房租买卖租赁信息。但这群人学历较低，使用“端口”发帖时有一定困难，效率低。 简化操作流程，抓住这群人，可以为网站培养忠诚用户。 抓住这些舍得花钱的经纪人，网站网站也就有了固定收入。 还有一点想说的是，赶集网走的早，也老得快，如今58同城日新月异，而赶集网还是那个老样子，该改改了吗？ 也许你还喜欢：要忙起来了[贰]点八：让自己充实起来Love&#38;Hate流年七月和安生稳网不稳 骗钱是真（记中国稳网）全世界都在说台头话（2）:台头话常用的五十个单词[冷月刊]2011年4-5月再祝:小元万福]]></description>
			<content:encoded><![CDATA[<p align="left">姚晨骑驴骑了半年多过去了，杨幂也“神奇”了半年了，分类信息网站现在变成个什么样子了？</p>
<p align="left"><img class="alignnone size-full wp-image-2023" title="分类信息网站市场现状" src="http://datafanr.com/wp-content/uploads/2011/10/fenleixinxi.jpg" alt="" width="556" height="393" /></p>
<p><span id="more-2020"></span></p>
<h3 align="left"><strong>1.“</strong><strong>乱”市场：进入容易，领先难</strong></h3>
<p align="left">本地生活服务分类信息网站市场巨大，技术门槛低，大量企业涌入（58同城、百姓网、列表网等，以及各行业垂直分类信息网站），同质化严重。</p>
<p align="left">赶集网率先引入名人代言，大规模投放广告，58同城迅速跟随模仿，攻势不亚于赶集，近期SEO超越赶集网</p>
<p align="left"> <strong>搜索引擎竞争状况</strong></p>
<p align="left"><img class="alignnone size-full wp-image-2021" title="分类信息网站SEO现状" src="http://datafanr.com/wp-content/uploads/2011/10/ganji.jpg" alt="" width="559" height="340" /></p>
<p align="left">注：所有数据截至2011年8月中旬</p>
<h3 align="left"><strong>2.“</strong><strong>贱”用户：取悦容易，忠诚难</strong></h3>
<p align="left">“免费”成为分类信息网站独特卖点，用户乐于使用。但多数用户选择多平台发帖，无法独家代理。平台转换成本为零，难以培养忠诚度</p>
<p align="left">由于信息真实性很少审核，不及专业垂直网站规范，不法分子滥用平台</p>
<p align="left">•有用户发布简历，却收到“求一夜情”骚扰短信</p>
<p align="left">•有用户打着交友的幌子推销</p>
<h3 align="left"><strong>3.“</strong><strong>笨”经纪人：尝试容易，操作难</strong></h3>
<p align="left">以北京房地产中介行业为例，有数万活跃经纪人在各种平台（搜狐焦点、安居客等、赶集网）上付费发布房租买卖租赁信息。但这群人学历较低，使用“端口”发帖时有一定困难，效率低。</p>
<p align="left">简化操作流程，抓住这群人，可以为网站培养忠诚用户。</p>
<p align="left">抓住这些舍得花钱的经纪人，网站网站也就有了固定收入。</p>
<p>还有一点想说的是，赶集网走的早，也老得快，如今58同城日新月异，而赶集网还是那个老样子，该改改了吗？</p>
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		</item>
		<item>
		<title>朝十晚八：网民正在浏览购物网站</title>
		<link>http://datafanr.com/data/buying-time.html</link>
		<comments>http://datafanr.com/data/buying-time.html#comments</comments>
		<pubDate>Wed, 12 Oct 2011 14:42:06 +0000</pubDate>
		<dc:creator>icewent</dc:creator>
				<category><![CDATA[0.数据分析]]></category>
		<category><![CDATA[信息图]]></category>
		<category><![CDATA[网购]]></category>

		<guid isPermaLink="false">http://datafanr.com/?p=2014</guid>
		<description><![CDATA[网购这么火，那网民们每天都在什么时候浏览购物网站呢？ 研究发现，这个时间是早上十点和晚上八点。十点，刚刚开始上班，还没着手工作或者刚刚处理完昨天遗留的任务，先网购一把！下班到家干点什么呢？先看看购物网站上有什么好东西再说。 有趣的是，女性在上午和下午这两个时间点浏览网站的比例差不多，而男性则不同，他们白天看的不多，晚上再使劲儿看，还是男人热爱工作，沉得住气！ （数据来源：第一象限） &#160; 也许你还喜欢：网购这些年]]></description>
			<content:encoded><![CDATA[<p>网购这么火，那网民们每天都在什么时候浏览购物网站呢？</p>
<p><img class="alignnone size-full wp-image-2015" title="网购者每天几点浏览购物网站？" src="http://datafanr.com/wp-content/uploads/2011/10/online-buying-time.jpg" alt="" width="520" height="1092" /></p>
<p>研究发现，这个时间是早上十点和晚上八点。十点，刚刚开始上班，还没着手工作或者刚刚处理完昨天遗留的任务，先网购一把！下班到家干点什么呢？先看看购物网站上有什么好东西再说。</p>
<p>有趣的是，女性在上午和下午这两个时间点浏览网站的比例差不多，而男性则不同，他们白天看的不多，晚上再使劲儿看，还是男人热爱工作，沉得住气！</p>
<p>（数据来源：<a href="http://www.upperplus.cn" rel="external nofollow"  target="_blank">第一象限</a>）</p>
<p>&nbsp;</p>
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		</item>
		<item>
		<title>SPSS数据清洗小记</title>
		<link>http://datafanr.com/data/data-cleansing.html</link>
		<comments>http://datafanr.com/data/data-cleansing.html#comments</comments>
		<pubDate>Tue, 04 Oct 2011 07:53:06 +0000</pubDate>
		<dc:creator>icewent</dc:creator>
				<category><![CDATA[0.数据分析]]></category>
		<category><![CDATA[数据清洗]]></category>

		<guid isPermaLink="false">http://datafanr.com/?p=1967</guid>
		<description><![CDATA[从大三开始到毕业三个月的现在，我一直处于实习或者试用的状态，辗转多个公司，终于选了最小的那一个，也就是我现在供职的企业，一家刚刚起步的咨询公司，专注于移动互联网相关的行业。 在这段迷茫的时间里，博客很少更新。一方面是因为飘泊不定，另一方面总是想憋一篇长文出来，但事实上我写博客也跟便秘一个样子，生憋基本只能把屎烂在肚子里。思前想后，偶有所得时记录一下更有意义，并且可以历久弥新。 今天的所得是数据分析中清洗数据的一些小零碎，算不得经验，行家看起来肯定会嗤之以鼻，但对于我，一个数据菜鸟，却是一笔宝贵的财富。 数据清洗有两个层面：一是变量层面，一是Case层面（能有这样的想法，多半是由于SPSS的设计，它有一个Variable视图和一个Data视图），以变量为核心，再进一步检查Case。 1.变量层面 （1）针对每一个变量，检查它的Type 许多网络问卷回收的数据会把数值型的变量做成字符串型，字符串不能计算，要改回来。 （2）检查变量的Measure 变量包含的信息从高到底排列依次是定距（Scale）、定序（Ordinal）、定类（Nominal），能定距则定距，不能的话再退而求其次，变量的层次越高，能使用的分析方法也就越多。比如年龄变量一定要定距、满意度打分要定距、收入层次和文化程度可以定序。 北大刘德寰老师在传播学研究方法课上，曾提出16个重要变量，各自的变量层级可以是（个人理解）：文化程度（定序）、收入与消费（定序居多，定距更好）、职业（定类）、年龄（定距，必须定距）、性别（定类）、婚姻状况（定类）、少年时期家庭居住地（定类，也可以是定序，按一线二线城市分）、家庭规模（定序，最好定距）、家庭体制内外（定类）、家庭生命周期（定类）、工作周期（定序、定类）、是否独生子女（定类）、是否知青（定类）、是否上网（定类）、居住环境（定类）、自以为的社会阶层（定序）。 （3）检查变量的Label 标签是对一个变量的解释，原始变量可以是题目，生成变量需要解释。如果一个标签特别长的话，在分析（如跑Frequency）时会看不到题号，这个时候可以在标签开头加上题号，方便辨认。比如下图中第一题可以改成[A1]请问您现在居住的城市是。 （4）检查变量Missing（缺失值）设定 比如问卷中“不知道”设置成了99，那么，99应该列入Missing。当然一个值是否是Missing，要根据具体分析目的确定。还有一种情况，有些Missing要改成合理的值。如果一个选项，有些人选了，有些人没选，那Missing最好改成0，这样分析百分比的时候得到的才是总体情况，而不是选了该选项的群体的情况。 （5）检查变量Values设定 比如性别题，1代表男，2代表女，3代表中间（开玩笑），就必须在Values里写清。这样做一些分析（比如跑Crosstabs）时就会自动列出男女而不是1、2。 （6）还有一个比较重要的是，检查Decimals（小数点位数） 一般是0，免得多出许多不必要的小数位，比如45岁变成了45.000岁，有时可以大一点。 说了这么多，其实你已经发现了，就是把Variables视图里的每一列都看一遍。 2.Case层面 接下来需要分析每一道题。 （1）极端值 首先对所有变量跑一遍Frequency，看看所有Case的集中趋势和离散趋势，看看极端值是否合理，是否需要修改。比如问卷中有两道题问年龄，前一道在问卷的开头，问您是哪个年龄段，后面一道在最后，问具体年龄。我们发现，前一道题选了”20—29岁“，后一题却写了56，那应该有问题。看看是故意填错的，还是无心之过，以此来决定是修改继续用还是直接删除这个Case。 （2）逻辑 问卷中会有一些跳转，如果选了A，则跳到第10题，中间的题不必答。但实际调查中可能会疏忽或由于系统故障导致一些不该答而答了或者该答而未答的问题，所以需要仔细检查逻辑。 （3）配额 样本不是越多越好，比如你需要照顾比例的时候。收了3000样本，其中2000男，1000女，而你要做很多男女对比分析，那恐怕你需要删掉1000男。SPSS在Select Cases命令中提供了随机筛选的功能，可以帮助实现随机删除。 我在数据清洗的过程中，还遇到一些问题，比如要根据变量和问卷编号定位一个数，在SPSS中无法快速定位，不知道该怎样解决，求高手支招！ 写于第二次清洗数据之后，考虑不周，欢迎交流。 也许你还喜欢：[贰]点13：你是装13，还是真13小鸟的天空一只杯具的杯具[贰]点三公民社会，农民有份史航老师，作为评委，您放弃了点评《叶问》：选择怎样的英雄零点中国，壹点中国……[贰]点六]]></description>
			<content:encoded><![CDATA[<p align="left">从大三开始到毕业三个月的现在，我一直处于实习或者试用的状态，辗转多个公司，终于选了最小的那一个，也就是我现在供职的企业，一家刚刚起步的咨询公司，专注于移动互联网相关的行业。</p>
<p align="left">在这段迷茫的时间里，博客很少更新。一方面是因为飘泊不定，另一方面总是想憋一篇长文出来，但事实上我写博客也跟便秘一个样子，生憋基本只能把屎烂在肚子里。思前想后，偶有所得时记录一下更有意义，并且可以历久弥新。</p>
<p align="left">今天的所得是<span class='bm_keywordlink'><a href='http://datafanr.com/category/data'>数据分析</a></span>中清洗数据的一些小零碎，算不得经验，行家看起来肯定会嗤之以鼻，但对于我，一个数据菜鸟，却是一笔宝贵的财富。</p>
<p align="left">数据清洗有两个层面：一是变量层面，一是Case层面（能有这样的想法，多半是由于SPSS的设计，它有一个Variable视图和一个Data视图），以变量为核心，再进一步检查Case。</p>
<h3 align="left">1.变量层面</h3>
<p align="left"><strong>（1）针对每一个变量，检查它的Type</strong></p>
<p align="left">许多网络问卷回收的数据会把数值型的变量做成字符串型，字符串不能计算，要改回来。</p>
<p align="left"><img src="http://datafanr.com/wp-content/uploads/2011/10/variable_measure.jpg" alt="" /></p>
<p align="left"><strong>（2）检查变量的Measure</strong></p>
<p><span id="more-1967"></span></p>
<p align="left">变量包含的信息从高到底排列依次是定距（Scale）、定序（Ordinal）、定类（Nominal），能定距则定距，不能的话再退而求其次，变量的层次越高，能使用的分析方法也就越多。比如年龄变量一定要定距、满意度打分要定距、收入层次和文化程度可以定序。</p>
<blockquote>
<p align="left">北大刘德寰老师在传播学研究方法课上，曾提出16个重要变量，各自的变量层级可以是（个人理解）：文化程度（定序）、收入与消费（定序居多，定距更好）、职业（定类）、年龄（定距，必须定距）、性别（定类）、婚姻状况（定类）、少年时期家庭居住地（定类，也可以是定序，按一线二线城市分）、家庭规模（定序，最好定距）、家庭体制内外（定类）、家庭生命周期（定类）、工作周期（定序、定类）、是否独生子女（定类）、是否知青（定类）、是否上网（定类）、居住环境（定类）、自以为的社会阶层（定序）。</p>
</blockquote>
<p align="left"><strong>（3）检查变量的Label</strong></p>
<p align="left">标签是对一个变量的解释，原始变量可以是题目，生成变量需要解释。如果一个标签特别长的话，在分析（如跑Frequency）时会看不到题号，这个时候可以在标签开头加上题号，方便辨认。比如下图中第一题可以改成[A1]请问您现在居住的城市是。</p>
<p align="left"><img src="http://datafanr.com/wp-content/uploads/2011/10/frequency.jpg" alt="" /></p>
<p align="left"><strong>（4）检查变量Missing（缺失值）设定</strong></p>
<p align="left">比如问卷中“不知道”设置成了99，那么，99应该列入Missing。当然一个值是否是Missing，要根据具体分析目的确定。还有一种情况，有些Missing要改成合理的值。如果一个选项，有些人选了，有些人没选，那Missing最好改成0，这样分析百分比的时候得到的才是总体情况，而不是选了该选项的群体的情况。</p>
<p align="left"><strong>（5）检查变量Values设定</strong></p>
<p align="left">比如性别题，1代表男，2代表女，3代表中间（开玩笑），就必须在Values里写清。这样做一些分析（比如跑Crosstabs）时就会自动列出男女而不是1、2。</p>
<p align="left"><strong>（6）还有一个比较重要的是，检查Decimals（小数点位数）</strong></p>
<p align="left">一般是0，免得多出许多不必要的小数位，比如45岁变成了45.000岁，有时可以大一点。</p>
<p align="left">说了这么多，其实你已经发现了，就是把Variables视图里的每一列都看一遍。</p>
<p align="left"><img src="http://datafanr.com/wp-content/uploads/2011/10/clearsing.jpg" alt="" /></p>
<h3 align="left">2.Case层面</h3>
<p align="left">接下来需要分析每一道题。</p>
<p align="left"><strong>（1）极端值</strong></p>
<p align="left">首先对所有变量跑一遍Frequency，看看所有Case的集中趋势和离散趋势，看看极端值是否合理，是否需要修改。比如问卷中有两道题问年龄，前一道在问卷的开头，问您是哪个年龄段，后面一道在最后，问具体年龄。我们发现，前一道题选了”20—29岁“，后一题却写了56，那应该有问题。看看是故意填错的，还是无心之过，以此来决定是修改继续用还是直接删除这个Case。</p>
<p align="left"><strong>（2）逻辑</strong></p>
<p align="left">问卷中会有一些跳转，如果选了A，则跳到第10题，中间的题不必答。但实际调查中可能会疏忽或由于系统故障导致一些不该答而答了或者该答而未答的问题，所以需要仔细检查逻辑。</p>
<p align="left"><strong>（3）配额</strong></p>
<p align="left">样本不是越多越好，比如你需要照顾比例的时候。收了3000样本，其中2000男，1000女，而你要做很多男女对比分析，那恐怕你需要删掉1000男。SPSS在Select Cases命令中提供了随机筛选的功能，可以帮助实现随机删除。</p>
<p align="left"><img src="http://datafanr.com/wp-content/uploads/2011/10/random.jpg" alt="" /></p>
<p align="left"><strong>我在数据清洗的过程中，还遇到一些问题，比如要根据变量和问卷编号定位一个数，在SPSS中无法快速定位，不知道该怎样解决，求高手支招！</strong></p>
<p align="left">写于第二次清洗数据之后，考虑不周，欢迎交流。</p>
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		<title>在Excel 2007中插入柱状-折线复合图</title>
		<link>http://datafanr.com/data/excel-mixed-figure.html</link>
		<comments>http://datafanr.com/data/excel-mixed-figure.html#comments</comments>
		<pubDate>Thu, 12 May 2011 05:15:41 +0000</pubDate>
		<dc:creator>icewent</dc:creator>
				<category><![CDATA[0.数据分析]]></category>
		<category><![CDATA[Excel]]></category>
		<category><![CDATA[数据分析]]></category>

		<guid isPermaLink="false">http://icewent.com/?p=1901</guid>
		<description><![CDATA[经常看到一些报告中的数据图表特别专业，能够把许多数据放到一张图里，旧数据呀、新数据呀、变动百分比呀等等都能体现出来（就像下图），可自己做的图总是那么简单和丑陋。 一番探索之后，终于找到了做法，so，Google is good. 貌似在Excel 2003版本插入图的时候可以先设定插入什么样的图，而在Excel 2007里，改变了这个思路，需要先把图做出来，然后修改样式。 Step 1：输入数据，注意横纵坐标的位置。 Step 2：选中数据插入一个图，选柱形图。 Step 3：选中你想修改成折线的数据系列，右键，选择“更改图表类型”。 于是，变成了这个样子： Step 4：再次选中折线数据系列，右键，选择“设置数据系列格式”，系列选项里，调整为“次坐标轴”。 Step 5：最后调整一下，就完成了，这个图是不是稍微好看一些了？ 也许你还喜欢：用ExcelVBA做编码合并看IBM如何做数据分析服务广告Excel做矩阵图的三种方法]]></description>
			<content:encoded><![CDATA[<p>经常看到一些报告中的数据图表特别专业，能够把许多数据放到一张图里，旧数据呀、新数据呀、变动百分比呀等等都能体现出来（就像下图），可自己做的图总是那么简单和丑陋。</p>
<p><img class="alignnone size-full wp-image-1919" title="A6AB7B3451DB7C32E1F98F062C422986_500" src="http://icewent.com/wp-content/uploads/2011/05/A6AB7B3451DB7C32E1F98F062C422986_500.jpg" alt="" width="500" height="286" /></p>
<p>一番探索之后，终于找到了做法，so，Google is good.</p>
<p>貌似在Excel 2003版本插入图的时候可以先设定插入什么样的图，而在Excel 2007里，改变了这个思路，需要先把图做出来，然后修改样式。</p>
<p>Step 1：输入数据，注意横纵坐标的位置。<span id="more-1901"></span></p>
<p><img class="alignnone size-full wp-image-1913" title="Excel 2007 插入柱状-折线复合图" src="http://icewent.com/wp-content/uploads/2011/05/DD3B6016EC37223272680DB53D436243_500.jpg" alt="" width="415" height="179" /></p>
<p>Step 2：选中数据插入一个图，选柱形图。</p>
<p><img class="alignnone size-full wp-image-1914" title="67861D89359E6599D701FC937512B9C0_500" src="http://icewent.com/wp-content/uploads/2011/05/67861D89359E6599D701FC937512B9C0_500.jpg" alt="" width="500" height="327" /></p>
<p>Step 3：选中你想修改成折线的数据系列，右键，选择“更改图表类型”。</p>
<p><img class="alignnone size-full wp-image-1916" title="55A0081EE7132AC8FAE39AE100533A40_500" src="http://icewent.com/wp-content/uploads/2011/05/55A0081EE7132AC8FAE39AE100533A40_500.jpg" alt="" width="414" height="363" /></p>
<p>于是，变成了这个样子：</p>
<p><img class="alignnone size-full wp-image-1922" title="2E002807045999A7A425304DBA2BBF01_500" src="http://icewent.com/wp-content/uploads/2011/05/2E002807045999A7A425304DBA2BBF01_500.jpg" alt="" width="496" height="329" /></p>
<p>Step 4：再次选中折线数据系列，右键，选择“设置数据系列格式”，系列选项里，调整为“次坐标轴”。</p>
<p><img title="8703E29433DB43A3C33306632C05A568_500" src="http://icewent.com/wp-content/uploads/2011/05/8703E29433DB43A3C33306632C05A568_500.jpg" alt="" width="500" height="379" /></p>
<p><img class="alignnone size-full wp-image-1917" title="3D111466644162200B91BADE5B4CD938_500" src="http://icewent.com/wp-content/uploads/2011/05/3D111466644162200B91BADE5B4CD938_500.jpg" alt="" width="500" height="354" /></p>
<p>Step 5：最后调整一下，就完成了，这个图是不是稍微好看一些了？</p>
<p><img class="alignnone size-full wp-image-1918" title="5E099254B73A8A0E697E9E40C71651C1_500" src="http://icewent.com/wp-content/uploads/2011/05/5E099254B73A8A0E697E9E40C71651C1_500.jpg" alt="" width="500" height="316" /></p>
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