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spss17.0神经网络教程（有案例，很详细）
官方公共微信台湾的老师同学们做的。在网上找到后重新排了排版和加了点注释（一点点）里面似乎对相关关系这个词的定义很宽泛，把回归、因子分析等到包括进去了（虽然确实涉及到相关）。有些部分讲了以前没注意的地方，如复回归（大陆这边好像应该叫多元回归）的相关系数，其实就是决定系数开平方。不过算是理一次跟相关有&相关&的思路了，要全部用spss实现这里提到的东西似乎有点难度。有人知道怎么用spss算肯德尔和谐系数吗？如有兴趣，请e-mail
国庆节期间恰好兴起，以一份点名游戏的题目为蓝图，上下查询，看看能不能找到谁出的题。顺便了解所谓的六度连接有多大。结果发现这个过程很漫长。由于有些关键的点名人深藏不露，加上能检索到的信息有限，所以有很多的分支没办法连接起来。（而且我很懒，没有特别去校对被点名人的重复、其他叫法的问题）那么一下就是从07.7.6左右到到07.9.20左右一段，部分人的点名关系图。看看你在哪里？你跟谁是六度连接？图片比较大，请等完全打开后另存到电脑上再看。736 KB (753,664 字节)说明：如果你觉得有任何不妥或建议，请email: 。我会尽快修改、更正。谢谢！！【请点击&全文&查看图片】
&域&法：在[插入]菜单项下选[域...]，在[域代码]处输入：EQ &开关&，确定。常用&开关&：分数\f(x,y)，根号\r(x,y)，上下标\s(u,d).。例如，输入 X 平均值(X的上划线)，插入域为EQ \x \to (X)。&--之前搞了十来分钟都搞不定，因为很多网页都没有提到最后的X要加一个括号注：也可以在文档中按ctrl+F9调出域俺更喜欢公式编辑器，上面提到的这种方法会导致行距加宽，但是公式编辑器不会。不过编辑器不是默认装的，没有的话将就一下吧
今天在spss交流群上遇到有人问为什么Explore里面的正态性检验结果和非参里面K-S的正态性检验结果不同。Explore里面的正态性检验非参里面的one&sample&K-S&test从上面两个图可以看到，Explore里面的检验显著而非参的没有。为什么会出现这样的情况呢？结论是：Explore里面的其实是改良的K-S检验（就是进行了the&Lilliefors&correction，暂时没查到具体的计算方式），而非参里面是原汁原味的K-S检验。前者对分布比较敏感（就像LSD），后者相对保守、不敏感（就像Bonferroni）。那么对于这个例子是否意味着这个数据的分布是正态呢？因为后者保守嘛，也是经典算法不放心，于是看看Skewness和Kurtonsis。（.181,&-.947）和0很接近嘛，似乎是正态，似乎经典算法是对的。正当我有点信心告诉别人很可能是正态的时候。对方发了茎叶图过来&&&&&1.00&&&&&&&&3&.&&0&&&&&1.00&&&&&&&&4&.&&0&&&&12.00&&&&&&&&5&.&&&&&&&7.00&&&&&&&&6&.&&0000000&&&&12.00&&&&&&&&7&.&&&&&&&6.00&&&&&&&&8&.&&000000&&&&&3.00&&&&&&&&9&.&&000&&&&10.00&&&&&&&10&.&&垮了~~~&看起来明显非正态于是跟对方说，从茎叶图来看，Lilliefors&test是正确的。确实应该拒绝零假设。但是为了避免出现更多错误，我说，&看看数据的基本情况，看看图的样子和两种检验哪个的结果符合一些，然后确定一个标准&&当我开始写这个日志的时候，忽然从一类、二类错误的角度把这个问题看清一些。其实敏感就意味着一类错误可能性要大，因为容易错误拒绝；保守意味着二类错误可能性大，因为容易错误接受。象以上这个问题就是很明显的一、二类错误的抉择。在这种情况下，必须要多依靠别的参考数据，比如说峰-偏度（有书说这两个估计可能过分乐观，而且前者样本至少在1000以上后者200以上才比较可靠――据张厚粲的《现代心理教育统计学》）、p-p图、q-q图。至于K-S正态检验的话，只要样本够大，一些并不重要的偏移也可能造成拒绝正态的结果（就像t-test自由度问题）。另外explore里面的Shapiro-Wilks&W&test检验适合30-2000之间的样本附一段评述，来自：&& &&&&&&&by.NewOccidentalTests&for&NormalityThe&simplest&method&of&assessing&normality&is&to&look&at&the&frequency&distribution&histogram.&The&most&important&things&to&look&at&are&the&symetry&and&peakiness&of&the&curve.&In&addition&be&aware&of&curves&that&indicate&two&or&more&peaks&this&would&show&a&bimodal&distribution&and&are&not&very&friendly&in&statistics.Visual&appraisals&must&only&be&used&as&an&indication&of&the&distribution&and&subsequently&better&methods&must&be&used.&Values&of&skew&and&kurtosis&as&found&in&Excels&Function&Wizard&(SKEW&and&KURT&respectively)&are&another&good&indicator,&but&can&be&over&optimistic&regarding&the&datas&match&with&normality.&Before&the&advent&of&good&computers&and&statistical&programs,&users&could&be&forgiven&for&trying&to&avoid&any&surplus&calculations.&Now&that&both&are&available&and&much&easier&to&use,&tests&for&normality&(and&homogeneity&of&variance)&should&always&be&carried&out&as&a&best&practice&in&statistics.&SPSS&and&Minitab&contain&the&Kolmogorov-Smirnov&test,&which&is&the&principal&goodness&of&fit&test&for&normal&and&uniform&data&sets.&Alternatively,&if&you&are&a&whizz&on&the&calculator&or&in&Excel&and&have&a&day&or&two&spare&or&have&access&to&UNISTAT,&you&may&wish&to&use&the&Shapiro-Wilk&test&which&is&more&reliable&when&n&50.Both&of&the&above&tests&use&the&same&hypotheses:HO:&there&is&no&difference&between&the&distribution&of&the&data&set&and&a&normal&one&HA:&there&is&a&difference&between&the&distribution&of&the&data&set&and&normal&The&P-value&will&be&provided&by&SPSS&or&Minitab,&if&below&0.05&reject&the&HO.Kurtosis&is&the&peakedness&of&a&distribution.&A&common&rule-of-thumb&test&for&normality&is&to&run&descriptive&statistics&to&get&skewness&and&kurtosis,&then&divide&these&by&the&standard&errors.&Kurtosis&also&should&be&within&the&+2&to&-2&range&when&the&data&are&normally&distributed&(a&few&authors&use&+3&to&-3).&Negative&kurtosis&indicates&too&many&cases&in&the&tails&of&the&distribution.&Positive&kurtosis&indicates&too&few&cases&in&the&tails.&Note&that&the&origin&in&computing&kurtosis&is&3&and&a&few&statistical&packages&center&on&3,&but&the&foregoing&discussion&assumes&that&3&has&been&subtracted&to&center&on&0,&as&is&done&in&SPSS&and&LISREL.&The&version&with&the&normal&distribution&centered&at&0&is&Fisher&kurtosis,&while&the&version&centered&at&3&is&Pearson&kurtosis.&SPSS&uses&Fisher&kurtosis.&Various&transformations&are&used&to&correct&kurtosis:&cube&roots&and&sine&transforms&may&correct&negative&kurtosis.&In&SPSS&13,&one&of&the&places&kurtosis&is&reported&is&under&Analyze,&Descriptive&Statistics,&D&click&O&select&kurtosis.Dichotomies.&By&definition,&a&dichotomy&is&not&normally&distributed.&Many&researchers&will&use&dichotomies&for&procedures&requiring&a&normal&distribution&as&long&as&the&split&is&less&than&90:10.Shapiro-Wilks&W&test&is&a&formal&test&of&normality&offered&in&the&SPSS&EXAMINE&module&or&the&SAS&UNIVARIATE&procedure.&This&is&the&standard&test&for&normality.&W&may&be&thought&of&as&the&correlation&between&given&data&and&their&corresponding&normal&scores,&with&W&=&1&when&the&given&data&are&perfectly&normal&in&distribution.&When&W&is&significantly&smaller&than&1,&the&assumption&of&normality&is&not&met.&That&is,&a&significant&W&statistic&causes&the&researcher&to&reject&the&assumption&that&the&distribution&is&normal.&Shapiro-Wilks&W&is&recommended&for&small&and&medium&samples&up&to&n&=&2000.&For&larger&samples,&the&Kolmogorov-Smirnov&test&is&recommended&by&SAS&and&others.&In&SPSS&13,&Shapiro-Wilks&test&is&found&under&Analyze,&Descriptive&Statistics,&E&select&Both&or&Plots&in&the&Display&&click&Plots&and&select&at&least&one&plot.Kolmogorov-Smirnov&D&test&or&K-S&Lilliefors&test,&is&an&alternative&test&of&normality&for&large&samples,&available&in&SPSS&EXAMINE&and&SAS&UNIVARIATE.&This&is&sometimes&called&the&Lilliefors&test&as&a&correction&to&K-S&developed&by&Lilliefors&is&now&normally&applied.&SPSS&(as&of&Version&9),&for&instance,&automatically&applies&the&Lilliefors&correction&to&the&K-S&test&for&normality&in&the&EXAMINE&module&(but&not&in&the&NONPAR&module).&This&test,&with&the&Lilliefors&correction,&is&preferred&to&the&chi-square&goodness-of-fit&test&when&data&are&interval&or&near-interval.&When&applied&without&the&Lilliefors&correction,&K-S&is&very&conservative:&that&is,&there&is&an&elevated&likelihood&of&a&finding&of&non-normality.&Note&the&K-S&test&can&test&goodness-of-fit&against&any&theoretical&distribution,&not&just&the&normal&distribution.&Be&aware&that&when&sample&size&is&large,&even&unimportant&deviations&from&normality&may&be&technically&significant&by&this&and&other&tests.&For&this&reason&it&is&recommended&to&use&other&bases&of&judgment,&such&as&frequency&distributions&and&stem-and-leaf&plots.&In&SPSS&13,&Shapiro-Wilks&test&is&found&under&Analyze,&Descriptive&Statistics,&E&select&Both&or&Plots&in&the&Display&&click&Plots&and&select&at&least&one&plot.&The&Kolmogorov-Smirnov&test&in&SPSS&13,&is&found&under&Analyze,&Descriptive&Statistics,&E&select&Both&or&Plots&in&the&Display&&click&Plots&and&select&at&least&one&plot.oxplot&tests&of&the&normality&assumption:&The&SPSS&boxplot&output&option&(see&the&EXAMINE&command&in&SPSS&Base)&produces&charts&in&which&the&the&Y&axis&is&the&interval&dependent&and&categories&of&the&independent&are&arrayed&on&the&X&axis.&Inside&the&graph,&for&each&X&category,&will&be&a&rectangle&indicating&the&spread&of&the&dependents&values&for&that&category.&If&these&rectangles&are&roughly&at&the&same&Y&elevation&for&all&categories,&this&indicates&little&difference&among&groups.&Within&each&rectangle&is&a&horizontal&dark&line,&indicating&the&mean.&If&most&of&the&rectangle&is&on&one&side&or&the&other&of&the&mean&line,&this&indicates&the&dependent&is&skewed&(not&normal)&for&that&group&(category).&Further&out&than&the&rectangle&are&the&&whiskers,&&which&mark&the&smallest&and&largest&observations&which&are&not&outliers&(defined&as&observations&greater&than&1.5&inter-quartile&ranges&[IQRs&=&boxlengths]&from&the&1st&and&3rd&quartiles).&Note&you&can&display&boxplots&for&two&factors&(two&independents)&together&by&selecting&Clustered&Boxplots&from&the&Boxplot&item&on&the&SPSS&Graphs&menu.Graphical&methods.&A&histogram&of&a&variable&shows&rough&normality,&and&a&histogram&of&residuals,&if&normally&distributed,&is&often&taken&as&evidence&of&normality&of&all&the&variables.&A&graph&of&empirical&by&theoretical&cumulative&distribution&functions&(cdfs)&simply&shows&the&empirical&distibution&as,&say,&a&dotted&line,&and&the&hypothetical&distribution,&say&the&normal&curve,&as&a&solid&line.&A&P-P&plot&is&found&in&SPSS&13&under&Graphs,&P-P&plots.&One&may&test&if&the&distribution&of&a&given&variable&is&normal&(or&beta,&chi-square,&exponential,&gamma,&half-normal,&Laplace,&Logistic,&Lognormal,&Pareto,&Students&t,&Weibull,&or&uniform).&he&P=P&plot&plots&a&variables&cumulative&proportions&against&the&cumulative&proportions&of&the&test&distribution.The&straighter&the&line&formed&by&the&P-P&plot,&the&more&the&variables&distribution&conforms&to&the&selected&test&distribution&(ex.,&normal).&Options&within&this&SPSS&procedure&allow&data&transforms&first&(natural&log,&standardization&of&values,&difference,&and&seasonally&difference).&A&quantile-by-quantile&or&Q-Q&plot&forms&a&45-degree&line&when&the&observed&values&are&in&conformity&with&the&hypothetical&distribution.&Q-Q&plots&plot&the&quantiles&of&a&variables&distribution&against&the&quantiles&of&the&test&distribution.From&the&SPSS&menu,&select&Graphs,&Q-Q.&The&SPSS&dialog&box&supports&testing&the&following&distributions:&beta,&chi-square,&exponential,&gamma,&half-normal,&Laplace,&Logistic,&Lognormal,&normal,&pareto,&Students&t,&Weibull,&and&uniform.
据一项最新的研究发现，喝了咖啡的人更容易被说服。澳大利亚研究人员指出，咖啡因的确能够改善我们处理信息的能力，扩展我们倾听的范围，采纳富有说服力的信息。为了测试这一理论，研究人员让参与测试的人员喝两杯咖啡，另一部分人员喝两杯安慰剂，分别在喝前和喝后进行测试，问一问他们对安乐死和堕胎的看法，在他们喝完咖啡后还让他们阅读一些有争议的材料，结果发现，喝过咖啡的人不但情绪变得更好，而且更容易被人说服。& 研究该项目的专家指出，咖啡因增强了我们观察信息的能力。这一研究有助于广告者提高广告的作用，广告人员应该在人们喝咖啡的时候打出广告，比如在早餐时，人们往往会喝咖啡，所以这个时间做广告效果更好。未查证
construct&validity&-&the&crux&for&test&validity&Topic:& Date:&&Topic&author:&psymassey&Subject:&construct&validity&-&the&crux&for&test&validity&Posted&on:&&07:02:09&Message:&What&is&a&construct?&In&which&sense&is&construct&validity&the&crux&of&all&evidence&for&test&validity?&The&value&of&a&psychological&assessment&is&determined&by&its&reliability&first&and&by&its&validity&later&(Anastasi&&&Urbina,&1997).&While&reliability&indicates&the&degree&to&which&an&assessment&is&measuring&some&attribute&in&a&consistent&manner,&it&does&not&provide&evidence&that&we&are&measuring&what&we&intend&to&measure&(Aiken,&2000).&The&term&validity&as&applied&to&a&psychological&test&refers&to&the&extent&to&which&a&test&actually&measures&the&characteristic&that&it&was&designed&to&measure&(Cohen&&&Swerdlik,&1999).&Therefore,&validity&is&absolutely&indispensable&to&any&psychological&assessment.&Murphy&and&Davidshofer&(2005)&stated&that&the&measurement&of&psychological&construct&is&seriously&concerned&in&the&design&of&the&test.&It&is&essential&to&a&test&that&it&provides&meaningful&and&valid&measures&of&constructs.&Anastasi&(1986)&notes&that&a&psychological&test&is&merely&an&indirect&or&operational&way&of&attempting&to&describe&the&extent&to&which&individuals&or&environments&possess&some&theoretically&postulated&construct.&Thus,&the&way&to&guarantee&the&test&validity&is&firstly&to&have&a&better&understanding&of&what&we&mean&by&a&construct&(Cronbach&&&Meehl,&1955).&Kline&(2000)&defines&that&a&construct&is&a&theoretical,&intangible&quality&or&trait&in&which&individuals&differ.&For&example,&intelligence&is&a&construct&assumed&to&be&present&to&varying&degrees&in&different&people.&A&construct&is&not&restricted&to&one&set&of&observable&indicators&or&attributes&(Aiken,&2000).&This&point&can&be&easily&comprehended&by&an&example&that&a&person&s&intelligence&is&indicated&neither&on&one&s&face&nor&on&one&s&driver&s&license.&In&other&words,&we&are&not&able&to&observe&intelligence&directly.&The&purpose&for&developing&tests&is&that&we&intend&to&reflect&a&person&s&intelligence&in&an&indirect&way.&Construct&validation&is&the&process&of&gathering&data&to&support&this&contention&that&the&test&of&intelligence&is&actually&a&reflection&of&the&construct&which&is&designed&to&study&(Messick,&1988).&In&a&nutshell,&constructs&theoretically&have&some&form&of&independent&existence&and&some&extent&to&predictable&influences&on&behavior&(Murphy&&&Davidshofer,&2005).&Kline&(2000)&also&suggests&that&a&test&purporting&to&assess&a&construct&must&estimate&the&existence&of&an&underlying&trait&based&on&limited&samples&of&behavior.&All&psychological&constructs&have&two&common&characteristics.&The&first&one&is&that&the&construct&cannot&be&operationally&defined.&There&is&no&external&referent&sufficient&to&validate&the&existence&of&the&construct.&Another&is&that&a&network&of&connected&suppositions&can&be&derived&from&existing&theory&about&the&construct&(Cronbach&&&Meehl,&1955).&These&points&can&be&illustrated&by&reference&to&the&construct&of&learning,&a&process&of&acquiring&knowledge&(Anastasi&&&Urbina,&1997).&Learning&is&also&a&construct,&like&intelligence,&it&cannot&be&observed&directly.&However,&we&can&define&learning&in&terms&of&performance&on&a&written&test&or&a&maze.&We&can&count&the&number&of&correct&responses&or&errors.&We&can&also&use&the&time&taken&to&complete&a&task&as&an&index&of&learning.&Thus,&a&network&of&interlink&suppositions&which&derive&from&theory&about&learning&provide&us&a&method&to&validate&a&measure&of&learning&(Anastasi&&&Urbina,&1997).&With&a&good&understanding&of&what&a&construct&is,&we&are&now&able&to&comprehend&the&concept&of&construct&validity.&Construct&validity&is&difficult&to&define.&Messick&(1988)&reports&that&this&type&of&validity&is&concerned&with&the&association&between&test&scores&and&theoretical&prediction.&Construct&validity&has&traditionally&been&defined&as&the&degree&to&which&an&operational&definition&accurately&represents&the&construct&it&is&intended&to&measure&(Cronbach&&&Meehl,&1955).&Cronbach&and&Meehl&(1955)&emphasize&that&the&crucial&point&to&understand&about&construct&validity&is&that&no&criterion&is&accepted&as&entirely&adequate&to&define&the&quality&to&be&measured.&Hence,&to&evaluate&the&construct&validity&of&a&test,&we&must&accumulate&a&series&of&evidence&from&numerous&sources&(Murphy&&&Davidshofer,&2005).&Construct&validity&for&a&test&is&probably&best&demonstrated&by&an&accumulation&of&supportive&evidence,&from&different&sources&and&over&some&period&of&time,&of&what&the&test&measures.&The&evidence&shows&that&a&test&has&some&concurrent,&predictive&and&content&validity&will&support&the&existence&of&construct&validity&for&the&test&(Cohen&&&Swerdlik,&1999).&Cohen&and&Swerdlik&provide&us&following&evidence&of&construct&validity&and&the&procedures&to&gain&it.&To&determine&if&the&test&items&are&homogeneous&and&therefore&measure&a&single&construct.&According&to&study&the&developmental&changes&to&determine&whether&they&are&consistent&with&the&theory&of&the&construct?&Research&must&ascertain&that&group&differences&on&test&scores&are&consistent.&To&do&an&analysis&to&determine&if&intervention&effects&on&test&scores&are&consistent.&The&evidence&of&construct&validity&should&be&considered&are&that&the&correlation&of&the&test&with&other&related&and&unrelated&tests&and&measures,&and&factor&analysis&of&test&scores&in&relation&to&other&sources&of&information.&There&is&a&concise&discussion&of&some&crucial&evidence&of&construct&validity&in&detail&on&following.&The&component&items&are&homogeneous,&when&a&test&measures&a&single&construct&only.&The&aim&of&test&development&is&to&select&items&that&form&a&homogeneous&scale&(Kline,&2000).&Kline&has&pointed&out&that&homogeneity&is&an&important&first&step&in&confirming&the&construct&validity&of&a&test,&but&standing&alone&it&is&inadequate&to&evidence.&Hence,&we&have&to&utilize&multiple&sources&of&construct&validity&to&demonstrate&test&homogeneity.&Construct&validity&has&often&been&viewed&as&an&accumulation&of&convergent&and&discriminative&validity&(Kline,&2000).&Convergent&validity&refers&to&that&two&tests&are&believed&to&measure&closely&related&variables&(Anastasi&&&Urbina,&1997).&In&other&words,&in&demonstrating&construct&validity&we&must&show&that&a&test&meets&theoretical&expectations&and&is&associated&with&variables&which&it&should&be&reasonably&correlated.&For&instance,&two&different&tests&rank&students&&algebra&solving-capability&in&similar&way.&Discriminative&validity&consists&of&providing&evidence&that&two&tests&do&not&measure&strongly&related&variables&(Anastasi&&&Urbina,&1997).&In&other&words,&to&demonstrate&construct&validity&we&must&indicate&that&a&test&is&not&related&to&other&variables&which&it&is&not&be&reasonably&correlated.&That&is&to&say,&a&test&of&algebra&should&primarily&measure&algebra-correlated&constructs&and&neither&history&knowledge&nor&reading&comprehension&constructs.&Both&convergent&and&discriminative&validity&provide&important&evidence&of&construct&validity&(Cohen&&&Swerdlik,&1999).&Thus,&in&order&to&determine&the&construct&validity&of&a&particular&algebra&test,&we&probably&should&demonstrate&that&the&correlations&of&scores&on&the&test&with&results&on&other&algebra&tests&rather&than&the&correlations&with&scores&on&reading-comprehension&tests.&Factor&analysis&is&another&method&that&can&also&provide&some&evidence&supporting&the&construct&validity.&The&results&of&a&factor&analysis&of&test&items&yield&information&about&how&many&dimensions&or&traits&are&needed&to&explain&test&performance&(Kline,&2000).&If&a&test&is&constructed&to&measure&one&characteristic,&and&items&are&sampled&only&from&behaviors&reflecting&that&characteristic,&a&factor&analysis&yielding&a&single&general&factor&would&be&an&evidence&of&construct&validity&(Kline,&2000).&Therefore,&the&results&of&factor&analysis&study&should&be&in&agreement&with&the&assumed&dimensionality&of&the&construct,&if&evidence&of&construct&validity&is&to&be&provided.&A&test&with&construct&validity&is&a&foundation&for&test&validity&(Anastasi,&1986).&Test&validity&refers&to&the&degree&which&the&inferences&bases&on&test&scores&are&meaningful,&useful,&and&appropriate&(Cronbach&&&Meehl,&1955).&Hence,&test&validity&is&a&characteristic&of&a&test&when&it&is&applied&to&a&particular&population.&There&are&some&traditional&methods&to&gather&test&validity.&Test&validity&has&usually&been&classified&into&three&categories&which&are&content-related,&criterion-related,&and&construct-related&validity&(Messick,&1988).&Messick&also&states&that&there&are&no&strict&distinctions&between&them.&In&other&words,&they&are&not&distinct&types&of&validity.&Messick&s&study&indicates&the&evidence&that&criterion-related&and&content-related&validity&may&also&be&relevant&in&the&construct-related&validity.&Murphy&and&Davidshofer&(2005)&also&point&out&that&studies&pertinent&to&the&establishment&of&construct&validity&include&criterion-related&and&content-related&validity.&Criterion-related&validity&demonstrates&that&test&scores&are&systematically&related&to&one&or&more&outcome&criteria&(Murphy&&&Davidshofer,&2005).&In&terms&of&a&college&entrance&examination,&for&instance,&it&is&reasonably&accurate&in&reflecting&the&future&academic&scores&of&examinees&would&achieve&criterion-related&validity.&Criterion-related&validity&contains&two&different&approaches.&One&is&concurrent&validity&which&can&be&defined&as&a&measurement&device&s&ability&to&vary&directly&with&a&measure&of&the&same&construct&or&indirectly&with&a&measure&of&an&opposite&construct&(Murphy&&&Davidshofer,&2005).&It&shows&that&a&test&is&valid&by&comparing&it&with&an&already&valid&test.&Another&one&is&predictive&validity,&which&refers&to&the&usefulness&of&test&scores&to&predict&future&performance&(Murphy&&&Davidshofer,&2005).&For&example,&students&who&are&not&native&English&speakers&receive&high&score&on&the&IELTS&tend&to&obtain&high&grades&in&an&English-language&spoken&university.&The&evidence&of&content-related&validity&refers&to&the&extent&to&which&the&test&questions&represent&the&abilities&in&the&particular&subject&area.&Content&validity&is&concerned&with&a&test&s&ability&to&include&or&represent&all&of&the&content&of&a&specific&construct&(Aiken,&2000).&For&example,&a&test&of&calculus&would&seem&to&be&valid&if&it&was&composed&of&items&requiring&calculus.&Construct-related&validity&indicates&that&the&test&measures&the&right&psychological&constructs&(Anastasi&&&Urbina,&1997).&Intelligence&is&an&example&of&such&psychological&trait.&Evidence&in&support&of&construct-related&validity&can&take&many&forms.&One&approach&is&to&demonstrate&that&the&items&within&a&measure&are&correlated&and&therefore&measure&a&single&construct&(Anastasi,&1986).&Another&approach&is&to&demonstrate&that&the&test&behaves&as&it&would&expect&a&measure&of&the&construct&to&behave&(Anastasi,&1986).&Undoubtedly,&test&validity&is&affected&by&construct&validity&significantly.&Test&validity&is&easily&be&ruined&when&construct&validity&is&not&carefully&concerned.&There&are&two&major&threats&to&test&&construct&unrepresentativeness&and&construct-irrelevant&variance.&Construct&unrepresentativeness&exhibits&that&the&tasks&which&are&measured&in&the&assessment&fail&to&include&important&dimensions&or&facets&of&the&construct&(Messick,&1988).&Thus,&the&test&results&are&unlikely&to&reveal&an&examinees&true&ability&within&the&construct&which&was&indicated&as&to&be&measured&by&the&test.&Construct-irrelevant&variance&means&that&the&test&measures&too&many&variables,&many&of&which&are&irrelevant&to&the&interpreted&construct&(Messick,&1988).&Two&reasons&can&lead&to&this&type&of&invalidity.&One&possible&cause&is&that&when&extraneous&clues&in&items&permit&some&individuals&to&respond&correctly&or&appropriately&in&ways&that&are&irrelevant&to&the&construct&being&assessed.&On&the&other&hand,&the&invalidity&may&occur&when&extraneous&aspects&of&the&test&make&the&test&irrelevantly&difficult&for&some&individuals&or&groups&(Messick,&1988).&In&other&words,&construct-irrelevant&variance&can&cause&one&to&score&higher&than&one&would&under&normal&circumstance,&and&it&may&also&cause&a&remarkably&lower&score.&It&is&worth&while&to&note&Messick&s&(1988)&study&about&validity.&Messick&presents&a&unified&and&expanded&theory&of&validity,&which&contains&the&evidential&and&consequential&bases&of&test&interpretation&and&use.&The&study&mainly&emphasizes&that&the&evidential&basis&for&interpreting&tests&involves&the&empirical&study&of&construct&validity,&which&is&defined&as&the&theoretical&context&of&implied&relationships&to&other&constructs.&Messick&establishes&a&model&of&facets&of&test&validity&that&indicates&the&evidential&basis&for&using&tests&involves&the&empirical&investigation&of&both&construct&validity&and&utility,&which&are&defined&as&the&theoretical&contexts&of&implied&applicability&and&usefulness.&In&conclusion,&the&establishment&of&construct&validity&is&essential&to&psychological&tests,&and&the&more&evidence&such&assessments&enable&us&to&obtain&about&the&test&s&construct&validity,&the&greater&the&meaning&and&usefulness&of&the&test&will&be.&Only&if&we&have&information&concerning&relationships&of&the&construct&to&the&phenomena&in&real-world&setting,&we&are&able&to&explain&and&understand&the&meaning&of&the&construct&and&to&use&the&test&for&applied&purposes.&Therefore,&construct&validity&is&not&only&a&necessary&part&of&a&test&s&quality&but&is&also&the&trait&by&which&the&test&acquires&meaning&and&usefulness.&Construct&validity&is&the&major&basis&on&which&tests&contribute&to&theoretical&understanding&and&to&solve&applied&problem.&In&all&sense,&construct&validity&is&a&crucial&factor&that&directly&indicates&the&degree&of&the&test&validity.&References&Aiken,&L.R.&(2000).&Psychological&testing&and&assessment.&(10th&ed.).&Needham&Heights:&Allyn&&&Bacon,&Inc.&Anastasi,&A.&(1986).&Evolving&concepts&of&test&validation.&Annual&Review&of&Psychology,&37,&1-15.&Anastasi,&A.,&&&Urbina,&S.&(1997).&Psychological&testing&(7th&ed.).&Upper&Saddle&River:&Prentice-Hall,&Inc.&Cohen,&R.J.,&&&Swerdlik,&M.E.&(1999).&Psychological&testing&and&assessment:&An&introduction&to&tests&and&measurement.&(4th&ed.).&Mountain&View:&Mayfield&Publishing&Company.&Cronbach,&L.J.,&&&Meehl,&P.E.&(1955).&Construct&validity&in&psychological&tests.&Psychological&Bulletin,&52,&281-302.&Kline,&P.&(2000).&The&handbook&of&psychological&testing.&(2nd&ed.).&London:&Routledge.&Messick,&S.&(1988).&The&once&and&future&issues&of&validity:&Assessing&the&meaning&and&consequences&of&measurement.&In&H.&Wainer&&&H.&Brown,&(Ed.).&Test&validity&(pp.19-32).&Hillsdale,&NJ:&Erlbaum.&Murphy,&K.R.,&&&Davidshofer,&C.O.&(2005).&Psychological&testing:&principles&and&applications.&(6th&ed.).&Upper&Saddle&River:&Pearson&Education,&Inc.&
上测量学习班的时候有个学员提到在使用MMPI进行研究时发现效度指标Q量表呈偏态分布，为了和常模进行比较想尽办法。因为在问卷的编制中偏态数据无可避免地会出现偏态数据，所以怎么处理也就成为一个难题。针对不同需要，可以有不同的处理办法。（假设数据无法与常用的函数模型进行拟合，比如卡方、二项分布等）需要一：设定划界分&&& 1、均数&标准差。偏态数据往往有均数大于标准差的情况，比如说我模拟出的数据就是4.4&7.5。最不厚道就是根据这个划定。&&& 2、百分位数，根据需要选定一定百分位下的分数（比如在19分以下的人群约为95%）。容易受到样本的影响，合理性不十分高，但是比前者好。&&& 3、实验法得出。通过模拟缺失不同数量的题目对最终结果的影响，确定最高的容忍度。比如MMPI就是最多30题不回答，超过这个数就对剖面图有比较严重的影响，大多数的剖面图将无法解释。需要二：与其它研究进行比较。&&& 1、非参秩和检验。只能有两组原始数据的前提下进行。不然的话，会不符合统计学原则。&&& 2、那位学员说最后用非参中的中位数比较搞定了。因为我只是隐约记得见过，具体怎么做不是很记得。先单独列出来，等以后再补。ps.在analysis--&nonparametric analysis--&1-sample K-S analysis里面有：正态、泊松、均态和指数分布的模型拟合。如果sig的话就是不符合这个分布。很好用。11.07后记，翻阅统计书后。&&& 中位数检验是检验两个独立样本是否从具有相同中数的总体中抽取&&& 计算过程（现代心理与教育统计学，张厚粲，徐建平编著，北师大出版社，04年2版，p383）&&&&&&& 1、将两个样本数据混合从小到大排列&&&&&&& 2、求混合排列的中数&&&&&&& 3、分别找出每一个样本中大于混合中数及小于混合中数的数据个数，列成四格表&&&&&&& 4、对四格表进行卡方检验。若卡方检验结果显著，就说明两样本的集中趋势差异显著
好奇中。如果搞的话，你会选择什么题目？怎么搞呢？感觉这个课题会很好玩，什么动机啊，认知啊，决策啊，情绪啊，应激啊都在里面。会很有趣的样子。
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