配对卡方检验及Kappa检验(一致性检验)的SPSS操作

2013-11-21 MedSci MedSci原创

一、配对卡方检验 把每一份样本平均分成两份,分别用两种方法进行化验,比较此两种化验方法的结果(两类计数资料)是否有本质的不同;或者分别采用甲、乙两种方法对同一批病人进行检查,比较此两种检查方法的结果(两类计数资料)是否有本质的不同,此时要用配对卡方检验。 操作方法:单击【Analyze按钮】---Descriptive Statistics----Crosstab,在弹出的S

一、配对卡方检验 把每一份样本平均分成两份,分别用两种方法进行化验,比较此两种化验方法的结果(两类计数资料)是否有本质的不同;或者分别采用甲、乙两种方法对同一批病人进行检查,比较此两种检查方法的结果(两类计数资料)是否有本质的不同,此时要用配对卡方检验。 操作方法:单击【Analyze按钮】---Descriptive Statistics----Crosstab,在弹出的Statistics对话框中选择McNemanr复选框,进行McNemanr检验。即配对卡方检验,只能针对方形表格进行。不能给出卡方值,只能给出P值。 二、一致性检验(Kappa检验) 诊断试验的一致性检验经常用在下列两种情况中:一种是评价待评价的诊断实验方法与金标准的一致性;另一种是评价两种化验方法对同一个样本(化验对象)的化验结果的一致性或两个医务工作者对同一组病人的诊断结论的一致性或同一医务工作者对同一组病人前后进行两次观察作出的诊断的一致性等等。 Kappa值即内部一致性系数(inter-rater,coefficient of internal consistency),是作为评价判断的

版权声明:
本网站所有内容来源注明为“梅斯医学”或“MedSci原创”的文字、图片和音视频资料,版权均属于梅斯医学所有。非经授权,任何媒体、网站或个人不得转载,授权转载时须注明来源为“梅斯医学”。其它来源的文章系转载文章,或“梅斯号”自媒体发布的文章,仅系出于传递更多信息之目的,本站仅负责审核内容合规,其内容不代表本站立场,本站不负责内容的准确性和版权。如果存在侵权、或不希望被转载的媒体或个人可与我们联系,我们将立即进行删除处理。
在此留言
评论区 (7)
#插入话题
  1. [GetPortalCommentsPageByObjectIdResponse(id=347703, encodeId=9b5a34e70306, content=请问大牛,我用配对卡方做两个蛋白的相关性,做出来的McNemar值是0.001Kappa值是0.035,P值是0.683,请教大牛这个结果改怎么分析啊?, beContent=null, objectType=article, channel=null, level=null, likeNumber=73, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=c8432306218, createdName=12218e60m53(暂无昵称), createdTime=Wed Oct 03 02:36:12 CST 2018, time=2018-10-03, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=85973, encodeId=aef7859e33a, content=有一篇英文文章解释的特别好 Understanding Interobserver Agreement: <br> The Kappa Statistic, beContent=null, objectType=article, channel=null, level=null, likeNumber=126, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://wx.qlogo.cn/mmopen/iaqwic5175nrsPthItiav2krhujqWPCa7vKqJictzMwysToPeDCiaI2Mg1C9w5S2fMzQ0jzrGdB9PuxoFCXmwUjQCNBcCSmQCEDAg/0, createdBy=d1cd1718253, createdName=maze zheng, createdTime=Mon May 16 11:34:00 CST 2016, time=2016-05-16, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=40498, encodeId=3e7940498b7, content=非常好的经验, beContent=null, objectType=article, channel=null, level=null, likeNumber=111, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=04101448103, createdName=470128510, createdTime=Fri Oct 23 20:00:00 CST 2015, time=2015-10-23, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1906820, encodeId=eaa919068208c, content=<a href='/topic/show?id=d91e1943441' target=_blank style='color:#2F92EE;'>#一致性#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=39, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=19434, encryptionId=d91e1943441, topicName=一致性)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=3138208, createdName=zhanfl, createdTime=Sun Jan 19 14:52:00 CST 2014, time=2014-01-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=8223, encodeId=03f282233d, content=原来出处在这里, beContent=null, objectType=article, channel=null, level=null, likeNumber=173, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=4bfb97947, createdName=huaqiu, createdTime=Sun Mar 09 10:42:00 CST 2014, time=2014-03-09, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=7036, encodeId=cd8ce036e8, content=好好学习,不错, beContent=null, objectType=article, channel=null, level=null, likeNumber=113, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=fc6e38265, createdName=465920538, createdTime=Thu Jan 23 11:08:00 CST 2014, time=2014-01-23, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1599771, encodeId=035c1599e710c, content=<a href='/topic/show?id=3e76365110a' target=_blank style='color:#2F92EE;'>#卡方检验#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=38, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=36511, encryptionId=3e76365110a, topicName=卡方检验)], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=eb2618750443, createdName=ycmayy, createdTime=Sat Nov 23 00:52:00 CST 2013, time=2013-11-23, status=1, ipAttribution=)]
    2018-10-03 12218e60m53(暂无昵称)

    请问大牛,我用配对卡方做两个蛋白的相关性,做出来的McNemar值是0.001Kappa值是0.035,P值是0.683,请教大牛这个结果改怎么分析啊?

    0

  2. [GetPortalCommentsPageByObjectIdResponse(id=347703, encodeId=9b5a34e70306, content=请问大牛,我用配对卡方做两个蛋白的相关性,做出来的McNemar值是0.001Kappa值是0.035,P值是0.683,请教大牛这个结果改怎么分析啊?, beContent=null, objectType=article, channel=null, level=null, likeNumber=73, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=c8432306218, createdName=12218e60m53(暂无昵称), createdTime=Wed Oct 03 02:36:12 CST 2018, time=2018-10-03, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=85973, encodeId=aef7859e33a, content=有一篇英文文章解释的特别好 Understanding Interobserver Agreement: <br> The Kappa Statistic, beContent=null, objectType=article, channel=null, level=null, likeNumber=126, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://wx.qlogo.cn/mmopen/iaqwic5175nrsPthItiav2krhujqWPCa7vKqJictzMwysToPeDCiaI2Mg1C9w5S2fMzQ0jzrGdB9PuxoFCXmwUjQCNBcCSmQCEDAg/0, createdBy=d1cd1718253, createdName=maze zheng, createdTime=Mon May 16 11:34:00 CST 2016, time=2016-05-16, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=40498, encodeId=3e7940498b7, content=非常好的经验, beContent=null, objectType=article, channel=null, level=null, likeNumber=111, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=04101448103, createdName=470128510, createdTime=Fri Oct 23 20:00:00 CST 2015, time=2015-10-23, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1906820, encodeId=eaa919068208c, content=<a href='/topic/show?id=d91e1943441' target=_blank style='color:#2F92EE;'>#一致性#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=39, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=19434, encryptionId=d91e1943441, topicName=一致性)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=3138208, createdName=zhanfl, createdTime=Sun Jan 19 14:52:00 CST 2014, time=2014-01-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=8223, encodeId=03f282233d, content=原来出处在这里, beContent=null, objectType=article, channel=null, level=null, likeNumber=173, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=4bfb97947, createdName=huaqiu, createdTime=Sun Mar 09 10:42:00 CST 2014, time=2014-03-09, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=7036, encodeId=cd8ce036e8, content=好好学习,不错, beContent=null, objectType=article, channel=null, level=null, likeNumber=113, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=fc6e38265, createdName=465920538, createdTime=Thu Jan 23 11:08:00 CST 2014, time=2014-01-23, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1599771, encodeId=035c1599e710c, content=<a href='/topic/show?id=3e76365110a' target=_blank style='color:#2F92EE;'>#卡方检验#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=38, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=36511, encryptionId=3e76365110a, topicName=卡方检验)], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=eb2618750443, createdName=ycmayy, createdTime=Sat Nov 23 00:52:00 CST 2013, time=2013-11-23, status=1, ipAttribution=)]
    2016-05-16 maze zheng

    有一篇英文文章解释的特别好 Understanding Interobserver Agreement:
    The Kappa Statistic

    0

  3. [GetPortalCommentsPageByObjectIdResponse(id=347703, encodeId=9b5a34e70306, content=请问大牛,我用配对卡方做两个蛋白的相关性,做出来的McNemar值是0.001Kappa值是0.035,P值是0.683,请教大牛这个结果改怎么分析啊?, beContent=null, objectType=article, channel=null, level=null, likeNumber=73, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=c8432306218, createdName=12218e60m53(暂无昵称), createdTime=Wed Oct 03 02:36:12 CST 2018, time=2018-10-03, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=85973, encodeId=aef7859e33a, content=有一篇英文文章解释的特别好 Understanding Interobserver Agreement: <br> The Kappa Statistic, beContent=null, objectType=article, channel=null, level=null, likeNumber=126, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://wx.qlogo.cn/mmopen/iaqwic5175nrsPthItiav2krhujqWPCa7vKqJictzMwysToPeDCiaI2Mg1C9w5S2fMzQ0jzrGdB9PuxoFCXmwUjQCNBcCSmQCEDAg/0, createdBy=d1cd1718253, createdName=maze zheng, createdTime=Mon May 16 11:34:00 CST 2016, time=2016-05-16, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=40498, encodeId=3e7940498b7, content=非常好的经验, beContent=null, objectType=article, channel=null, level=null, likeNumber=111, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=04101448103, createdName=470128510, createdTime=Fri Oct 23 20:00:00 CST 2015, time=2015-10-23, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1906820, encodeId=eaa919068208c, content=<a href='/topic/show?id=d91e1943441' target=_blank style='color:#2F92EE;'>#一致性#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=39, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=19434, encryptionId=d91e1943441, topicName=一致性)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=3138208, createdName=zhanfl, createdTime=Sun Jan 19 14:52:00 CST 2014, time=2014-01-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=8223, encodeId=03f282233d, content=原来出处在这里, beContent=null, objectType=article, channel=null, level=null, likeNumber=173, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=4bfb97947, createdName=huaqiu, createdTime=Sun Mar 09 10:42:00 CST 2014, time=2014-03-09, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=7036, encodeId=cd8ce036e8, content=好好学习,不错, beContent=null, objectType=article, channel=null, level=null, likeNumber=113, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=fc6e38265, createdName=465920538, createdTime=Thu Jan 23 11:08:00 CST 2014, time=2014-01-23, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1599771, encodeId=035c1599e710c, content=<a href='/topic/show?id=3e76365110a' target=_blank style='color:#2F92EE;'>#卡方检验#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=38, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=36511, encryptionId=3e76365110a, topicName=卡方检验)], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=eb2618750443, createdName=ycmayy, createdTime=Sat Nov 23 00:52:00 CST 2013, time=2013-11-23, status=1, ipAttribution=)]
    2015-10-23 470128510

    非常好的经验

    0

  4. [GetPortalCommentsPageByObjectIdResponse(id=347703, encodeId=9b5a34e70306, content=请问大牛,我用配对卡方做两个蛋白的相关性,做出来的McNemar值是0.001Kappa值是0.035,P值是0.683,请教大牛这个结果改怎么分析啊?, beContent=null, objectType=article, channel=null, level=null, likeNumber=73, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=c8432306218, createdName=12218e60m53(暂无昵称), createdTime=Wed Oct 03 02:36:12 CST 2018, time=2018-10-03, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=85973, encodeId=aef7859e33a, content=有一篇英文文章解释的特别好 Understanding Interobserver Agreement: <br> The Kappa Statistic, beContent=null, objectType=article, channel=null, level=null, likeNumber=126, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://wx.qlogo.cn/mmopen/iaqwic5175nrsPthItiav2krhujqWPCa7vKqJictzMwysToPeDCiaI2Mg1C9w5S2fMzQ0jzrGdB9PuxoFCXmwUjQCNBcCSmQCEDAg/0, createdBy=d1cd1718253, createdName=maze zheng, createdTime=Mon May 16 11:34:00 CST 2016, time=2016-05-16, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=40498, encodeId=3e7940498b7, content=非常好的经验, beContent=null, objectType=article, channel=null, level=null, likeNumber=111, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=04101448103, createdName=470128510, createdTime=Fri Oct 23 20:00:00 CST 2015, time=2015-10-23, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1906820, encodeId=eaa919068208c, content=<a href='/topic/show?id=d91e1943441' target=_blank style='color:#2F92EE;'>#一致性#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=39, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=19434, encryptionId=d91e1943441, topicName=一致性)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=3138208, createdName=zhanfl, createdTime=Sun Jan 19 14:52:00 CST 2014, time=2014-01-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=8223, encodeId=03f282233d, content=原来出处在这里, beContent=null, objectType=article, channel=null, level=null, likeNumber=173, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=4bfb97947, createdName=huaqiu, createdTime=Sun Mar 09 10:42:00 CST 2014, time=2014-03-09, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=7036, encodeId=cd8ce036e8, content=好好学习,不错, beContent=null, objectType=article, channel=null, level=null, likeNumber=113, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=fc6e38265, createdName=465920538, createdTime=Thu Jan 23 11:08:00 CST 2014, time=2014-01-23, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1599771, encodeId=035c1599e710c, content=<a href='/topic/show?id=3e76365110a' target=_blank style='color:#2F92EE;'>#卡方检验#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=38, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=36511, encryptionId=3e76365110a, topicName=卡方检验)], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=eb2618750443, createdName=ycmayy, createdTime=Sat Nov 23 00:52:00 CST 2013, time=2013-11-23, status=1, ipAttribution=)]
    2014-01-19 zhanfl
  5. [GetPortalCommentsPageByObjectIdResponse(id=347703, encodeId=9b5a34e70306, content=请问大牛,我用配对卡方做两个蛋白的相关性,做出来的McNemar值是0.001Kappa值是0.035,P值是0.683,请教大牛这个结果改怎么分析啊?, beContent=null, objectType=article, channel=null, level=null, likeNumber=73, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=c8432306218, createdName=12218e60m53(暂无昵称), createdTime=Wed Oct 03 02:36:12 CST 2018, time=2018-10-03, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=85973, encodeId=aef7859e33a, content=有一篇英文文章解释的特别好 Understanding Interobserver Agreement: <br> The Kappa Statistic, beContent=null, objectType=article, channel=null, level=null, likeNumber=126, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://wx.qlogo.cn/mmopen/iaqwic5175nrsPthItiav2krhujqWPCa7vKqJictzMwysToPeDCiaI2Mg1C9w5S2fMzQ0jzrGdB9PuxoFCXmwUjQCNBcCSmQCEDAg/0, createdBy=d1cd1718253, createdName=maze zheng, createdTime=Mon May 16 11:34:00 CST 2016, time=2016-05-16, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=40498, encodeId=3e7940498b7, content=非常好的经验, beContent=null, objectType=article, channel=null, level=null, likeNumber=111, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=04101448103, createdName=470128510, createdTime=Fri Oct 23 20:00:00 CST 2015, time=2015-10-23, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1906820, encodeId=eaa919068208c, content=<a href='/topic/show?id=d91e1943441' target=_blank style='color:#2F92EE;'>#一致性#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=39, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=19434, encryptionId=d91e1943441, topicName=一致性)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=3138208, createdName=zhanfl, createdTime=Sun Jan 19 14:52:00 CST 2014, time=2014-01-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=8223, encodeId=03f282233d, content=原来出处在这里, beContent=null, objectType=article, channel=null, level=null, likeNumber=173, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=4bfb97947, createdName=huaqiu, createdTime=Sun Mar 09 10:42:00 CST 2014, time=2014-03-09, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=7036, encodeId=cd8ce036e8, content=好好学习,不错, beContent=null, objectType=article, channel=null, level=null, likeNumber=113, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=fc6e38265, createdName=465920538, createdTime=Thu Jan 23 11:08:00 CST 2014, time=2014-01-23, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1599771, encodeId=035c1599e710c, content=<a href='/topic/show?id=3e76365110a' target=_blank style='color:#2F92EE;'>#卡方检验#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=38, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=36511, encryptionId=3e76365110a, topicName=卡方检验)], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=eb2618750443, createdName=ycmayy, createdTime=Sat Nov 23 00:52:00 CST 2013, time=2013-11-23, status=1, ipAttribution=)]
    2014-03-09 huaqiu

    原来出处在这里

    0

  6. [GetPortalCommentsPageByObjectIdResponse(id=347703, encodeId=9b5a34e70306, content=请问大牛,我用配对卡方做两个蛋白的相关性,做出来的McNemar值是0.001Kappa值是0.035,P值是0.683,请教大牛这个结果改怎么分析啊?, beContent=null, objectType=article, channel=null, level=null, likeNumber=73, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=c8432306218, createdName=12218e60m53(暂无昵称), createdTime=Wed Oct 03 02:36:12 CST 2018, time=2018-10-03, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=85973, encodeId=aef7859e33a, content=有一篇英文文章解释的特别好 Understanding Interobserver Agreement: <br> The Kappa Statistic, beContent=null, objectType=article, channel=null, level=null, likeNumber=126, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://wx.qlogo.cn/mmopen/iaqwic5175nrsPthItiav2krhujqWPCa7vKqJictzMwysToPeDCiaI2Mg1C9w5S2fMzQ0jzrGdB9PuxoFCXmwUjQCNBcCSmQCEDAg/0, createdBy=d1cd1718253, createdName=maze zheng, createdTime=Mon May 16 11:34:00 CST 2016, time=2016-05-16, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=40498, encodeId=3e7940498b7, content=非常好的经验, beContent=null, objectType=article, channel=null, level=null, likeNumber=111, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=04101448103, createdName=470128510, createdTime=Fri Oct 23 20:00:00 CST 2015, time=2015-10-23, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1906820, encodeId=eaa919068208c, content=<a href='/topic/show?id=d91e1943441' target=_blank style='color:#2F92EE;'>#一致性#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=39, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=19434, encryptionId=d91e1943441, topicName=一致性)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=3138208, createdName=zhanfl, createdTime=Sun Jan 19 14:52:00 CST 2014, time=2014-01-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=8223, encodeId=03f282233d, content=原来出处在这里, beContent=null, objectType=article, channel=null, level=null, likeNumber=173, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=4bfb97947, createdName=huaqiu, createdTime=Sun Mar 09 10:42:00 CST 2014, time=2014-03-09, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=7036, encodeId=cd8ce036e8, content=好好学习,不错, beContent=null, objectType=article, channel=null, level=null, likeNumber=113, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=fc6e38265, createdName=465920538, createdTime=Thu Jan 23 11:08:00 CST 2014, time=2014-01-23, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1599771, encodeId=035c1599e710c, content=<a href='/topic/show?id=3e76365110a' target=_blank style='color:#2F92EE;'>#卡方检验#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=38, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=36511, encryptionId=3e76365110a, topicName=卡方检验)], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=eb2618750443, createdName=ycmayy, createdTime=Sat Nov 23 00:52:00 CST 2013, time=2013-11-23, status=1, ipAttribution=)]
    2014-01-23 465920538

    好好学习,不错

    0

  7. [GetPortalCommentsPageByObjectIdResponse(id=347703, encodeId=9b5a34e70306, content=请问大牛,我用配对卡方做两个蛋白的相关性,做出来的McNemar值是0.001Kappa值是0.035,P值是0.683,请教大牛这个结果改怎么分析啊?, beContent=null, objectType=article, channel=null, level=null, likeNumber=73, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=c8432306218, createdName=12218e60m53(暂无昵称), createdTime=Wed Oct 03 02:36:12 CST 2018, time=2018-10-03, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=85973, encodeId=aef7859e33a, content=有一篇英文文章解释的特别好 Understanding Interobserver Agreement: <br> The Kappa Statistic, beContent=null, objectType=article, channel=null, level=null, likeNumber=126, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://wx.qlogo.cn/mmopen/iaqwic5175nrsPthItiav2krhujqWPCa7vKqJictzMwysToPeDCiaI2Mg1C9w5S2fMzQ0jzrGdB9PuxoFCXmwUjQCNBcCSmQCEDAg/0, createdBy=d1cd1718253, createdName=maze zheng, createdTime=Mon May 16 11:34:00 CST 2016, time=2016-05-16, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=40498, encodeId=3e7940498b7, content=非常好的经验, beContent=null, objectType=article, channel=null, level=null, likeNumber=111, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=04101448103, createdName=470128510, createdTime=Fri Oct 23 20:00:00 CST 2015, time=2015-10-23, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1906820, encodeId=eaa919068208c, content=<a href='/topic/show?id=d91e1943441' target=_blank style='color:#2F92EE;'>#一致性#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=39, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=19434, encryptionId=d91e1943441, topicName=一致性)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=3138208, createdName=zhanfl, createdTime=Sun Jan 19 14:52:00 CST 2014, time=2014-01-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=8223, encodeId=03f282233d, content=原来出处在这里, beContent=null, objectType=article, channel=null, level=null, likeNumber=173, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=4bfb97947, createdName=huaqiu, createdTime=Sun Mar 09 10:42:00 CST 2014, time=2014-03-09, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=7036, encodeId=cd8ce036e8, content=好好学习,不错, beContent=null, objectType=article, channel=null, level=null, likeNumber=113, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=fc6e38265, createdName=465920538, createdTime=Thu Jan 23 11:08:00 CST 2014, time=2014-01-23, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1599771, encodeId=035c1599e710c, content=<a href='/topic/show?id=3e76365110a' target=_blank style='color:#2F92EE;'>#卡方检验#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=38, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=36511, encryptionId=3e76365110a, topicName=卡方检验)], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=eb2618750443, createdName=ycmayy, createdTime=Sat Nov 23 00:52:00 CST 2013, time=2013-11-23, status=1, ipAttribution=)]

相关资讯

用SPSS进行列联表分析(Crosstabs)实例

列联表分析(Crosstabs) 列联表是指两个或多个分类变量各水平的频数分布表,又称频数交叉表。SPSS的Crosstabs过程,为二维或高维列联表分析提供了22种检验和相关性度量方法。其中卡方检验是分析列联表资料常用的假设检验方法。例子:山东烟台地区病虫测报站预测一代玉米螟卵高峰期。预报发生期y为3级(1级为6月20日前,2级为6月21-25日,3级为6月25日后);预报因子5月份平均气温x

关于卡方检验与单因素logistic回归的个人看法

关于卡方检验和单因素logistic回归,其实我个人很久以前就一直认为是等同的,从来没有觉得二者不同。但后来偶然发现有的人认为二者不同。最典型的就是有次帮别人做统计,作了单因素logistic回归,投稿后,有的审稿专家认为,从来就没有“单因素 logistic回归”这一说法,logistic回归一定是指多因素logistic回归,所谓单因素logistic回归必须用卡方检验。看了之后我承认确实没想