续：统计学在社会学中的应用1950-2000：一个简要的回顾
4 第三代统计方法:新数据,新挑战,新方法(New Data, New Challenges, New Methods)4.1 社会网络和空间数据(Social Networks and Spatial Data)
社会网络由成对的关系集构成，比如青少年之间的朋友关系(Udry and Bearman,1998)，成人之间的性关系(Laumann, 1994)，或者婚姻交换的结构(patterns of marriage exchange)以及跨社会群体的政治联合(White,1963; Bearman,1997; Padgett and Ansell,1993)。对与这类网络相关的数据的分析已经有很长的历史(Wasserman and Faust,1994)。Frank和Strauss(1986)针对这种社会网络发展出了正式的统计模型，这种网络包括与Bayes图表分析中所应用的马尔可夫随机域模型(Markov random field model)以及与使用Hammersley-Clifford定理(Hammersley-Clifford theorem)后的衍生模型相联系的社会网(Besag, 1974)。这导致了在社会网领域被广泛看好的“P*”类模型的产生(Wasserman and Pattison, 1996)。另一个对社会网进行统计模拟的正规方法是由Yamaguchi(1990)发展的基于Goodman类关联模型(Goodman–type association model)。
分析社会网所使用的方法大多数都针对数据完整且规模较小的数据集合。然而在实际应用中，比如性关系网络结构在性传播疾病的扩散方面(Morris,1997)，数据常常很大并且非常不完整。现有的方法对此也是一筹莫展。这正是遗传统计学中的血统分析在数年前所处的阶段，但是从那以后似然率(likelihood)和MCMC方法的使用使这一领域取得了巨大的进展(Thompson, 1998)。社会网相对于血统来说要更为复杂一些，因为血统倾向于具有一个树形的结构，而社会网常常是一种循环结构，但是社会网方面的进展也是有可能的。
多数社会性数据都具有空间性，但是在社会学研究中这一事实在很大程度上被忽略。一个重要的例外是Massey和Denton(1993)的有关人种的居住区隔(residential segregation by race)的研究，这一研究使美国社会学中一个较早期的传统——对美国社会的空间分析得以复兴(Duncan and Duncan,1957)。最近，在对亚洲的避孕和生育率的研究方面(多数重要项目关注中国，泰国和尼泊尔)，研究者对卫星图像和地理信息系统(GIS)数据进行了富有成果的分析(Entwisle, 1997)。
在社会学中更广泛使用空间统计法似乎成为可能。空间统计学在最近的40年中取得了很大的发展。模拟空间依赖关系的最有成果的两种方法是基于地理统计学(geostatistics) (Matheron, 1971, Chiles and Delfiner, 1999)，和马尔可夫随机域(Markov random field) (Besag, 1974; Besag, York and Mollie 1991)。地理统计学对距离进行了细致的空间相关模拟。在另一方面，马尔可夫随机域建立在邻居的基础上：一项观察被直接与其邻居联系起来，除了它的邻居外，这项观察与其他所有的案例都保持条件独立。如果社会数据的空间分布比较规律马尔可夫随机域便是一个对这类数据进行分析的有效工具，但是对于非均匀分布的空间个体，地理统计学或许会发现对空间依赖进行研究更为容易。对社会性数据来讲，它与地理距离之间的相关并非最为紧密，在一些新的基础上，比方说人口流动或者信息流动的基础上定义的距离，在一些研究中与数据的关系更为密切。但是我还没有看到在这种类型的距离基础上产生的有关空间统计模型的任何著作。
4.2 文本和定性数据(Textual and Qualitative Data)
许多社会学数据的最初形态都是文本化的，比如访谈，调查中对开放问题的回答，人种志的记述等。如何对这些数据进行正式的统计分析，并且从中做出推断仍旧是一个有待回答的问题。现有的在正规分析方面的研究聚焦于标准内容分析(standard content analysis)，主要包含了用不同的方法对文中的词语进行计数。如果对词语和句子所出现的情境进行分析的话可能会产生更好的结果。最近在这方面的产生了一些有益的努力，这包含了Carley(1993)的地图分析(map analysis)，Franzosi(1994)建立的理论方法，以及Roberts(1997)类别语义语法(generic semantic grammar)，但是这刚刚触及了皮毛。人的头脑在分析单独的文本方面十分优秀，但是电脑却不行，或者说至少现在不行。从这方面讲，对文本数据的分析与图像分析(image analysis)和语言辨析(speech recognition)等其他问题相类似。摆在这些问题面前的一个巨大挑战是如何对网络进行信息修补(Jones and Willett, 1997)，现有的大多数搜索引擎都是基于简单的内容分析法之上的。在社会学中更多的文脉分析方法(contextual methods)的出现或许会对这一领域的发展有所帮助。
Singer(1998)在结合了定量和定性的方法之后，对文本数据的分析进行了一次颇具启发性的尝试。他们使用了一个标准的个体层次的数据集，对每个人都进行250个变量的测量，然后将他们转化成书写形式的传记。然后他们对这些传记进行考察以发现共同的因素，然后进一步将这些因素归入更为类别化的陈述中。
最近Raudenbush和Sampson(1999)在“生态计量学”(ecometrics)的名称下，在系统地分析定性数据方面进行了先锋性的研究。他们的著作是在对毗邻环境类型的研究的推动下产生的，在这一研究中，研究者将毗邻环境的类型与犯罪联系在一起，比如自然物破败(废弃的建筑)，自然混乱(比如涂鸦)，和社会混乱(比如，街头的毒品交易)。研究这类问题的一个标准的定量方法是通过来自某一毗邻环境的应答者集合，对毗邻环境的影响效果进行分析，但是Raudenbush和Sampson令人信服的宣称，这种做法并不能在直接观察的基础上提供一个独立、客观的对环境的评价。他们的数据由对23000个街区的录像或观察日志组成(Sampson and Raudenbush,1999)。他们将这些数据进行编码并且发展出一个等级模型(hierarchical model)，用这一模型来评估和计算针对自然物和社会混乱的评价尺度的可靠性。
Raudenbush和Sampson将他们的工作牢固的建立在Reiss(1971)的系统社会观察的框架基础上，这一框架包含了允许进行复证，以及使用与观察数据相独立的观察的平均值的清晰规定。这对于分析来自定性的，文本的或者人种志的数据并从中做出推断来说是非常重要的，Carley (1993)，Franzosi (1994)和Roberts(1997)的著作表达了这一思想。
有趣的是，Raudenbush和Sampson指出对个体和生态学效果(ecological effects)的研究或许过于强调了个体因素，这是因为那些已经得到很好研究的个体心理计量学的测量数据，可能比没有经过很好研究的生态学数据更好一些。事实上，我注意到在许多社会学研究中，所报告的情境的或者毗邻环境的影响是很弱的，有趣的是，这或许是由测量水平有限所致而不是由于他们的关系原本就很弱而导致的。这类的数据迫切需要空间统计分析。Raudenbush和Sampson认识到了这一点，并且也把它作为将来研究的一个主题，但是他们的工作至今还没有将空间关联性考虑在内。
4.3 叙述和序列分析(Narrative and Sequence Analysis)
对生活史(life history)最典型的分析方法是将它们简化成变量，然后进行回归和多元分析或者用事件史方法分析。Abbott和Hrycak(1990)认为这些标准的方法使一些当生活史（比如一个专业工作）被当作一个整体看待时，所显现的一些重要的方面变得模糊。他们建议将生活史看作DNA或蛋白质序列的相似体，用从分子生物学中借鉴的最佳排列方法(optimal alignment method)以及聚类分析，来探测职业群体的共同结构。Stovel，Savage和Bearman(1996)使用这些方法描述了过去的一个世纪中职业体系在Lloyds Banks地区的变化。
后来，Dijkstra和Taris(1995)扩展了这一想法，使它能够包含独立变量，此外，Abbott和Barman(1997)使用了Lawrence(1993)的Gibbs抽样序列探测模型，这一模型也是首先在微观生物学中被发展出来的，而且似乎十分有效。
这些方法虽然很有趣，但它们同时还存在一些有待回答的统计学问题。这包括了序列方法论(alignment methodology)，例如插入，探测和置换成本如何被测定？除此之外在聚类分析方面，究竟有多少类？应该使用哪一个聚类方法？如何处理奇异值(outlier)？或许一个更加详细的模型能够有助于回答这些问题。聚类分析在很长时间内是一个特别的方法集合，将这一方法进行改进使它建立在正式的统计模型上，将有助于为这些问题提供一个原则上的回答(Banfield and Raftery, 1993; Fraley and Raftery, 1998)。另一个分析序列数据的方法是Yamaguchi和Kandel(1998)发展的基于对数线性模型的方法。
4.4 仿真模型(Simulation Models)
研究社会过程另一个更具体的方法是使用宏观或者微观的仿真模型。这些模型通常十分复杂并且具有较强的确定性，它代表了一个互相作用的不同分隔(compartment)的体系，每一个分隔由一系列的微分或者差分方程构成。这些方法能够分析的问题有，不同理论中有关国内政治和战争的互动方式的论述(Hanneman, Collins and Mordt, 1995)，集体行动的社会机制(Kim and Bearman, 1997)，以及在HIV传播过程中性关系网络的作用(Morris,1997及其引文)。
这些模型所遇到的困难是，用来对诸多变量进行估计，对模型的拟合程度进行评估以及对不同模型进行比较的方法还没有建立，所有这些都是在非正式的比较中完成的。其他学科中建立在坚实的统计学基础上的，对这些模型进行推论的方法对于社会学会有一定的帮助作用(Guttorp and Walden,1987; Raftery, Givens and Zeh, 1995; Poole and Raftery, 2000)。
4.5 宏观社会学(Macro-sociology)
宏观社会学致力于处理重大的问题，比如国家以及它们之间的互动。这一方法分析的个案的数量倾向于很小，同时回归之类的标准的统计学方法也难以应用。Ragin(1987)在他的一本颇具影响力的书中一针见血的指出了这一点。他自己提出的方法——定性比较分析(Qualitative Comparative Analysis)，看起来也并不令人满意，因为它不允许有任何形式的变化，因此它对数据中或者应用的方法中的任何微小变化都十分敏感(Lieberson,1994)。
针对这种情况，一个解决方案是获得更大的样本，Bollen和Appold(1993)正是这样做的。但是，在更多的情况下这一点难以做到，因此它不是一个普遍的解决方案。另一个解决方法是使用标准回归模型对具体的个案进行细致入微的分析，而不是在强大的先验信息存在的情况下进行Bayes估计，在这一领域这类先验信息通常来自实践(Western and Jackman,1994)。Bayes因素法通常也对此问题有所帮助，因为这一方法与标准的显著性检验相比在小样本方面更有弹性，并且允许用事实对评估结果进行校准，而不是强制性的拒绝或者接受一个假设(Kass and Raftery,1995)。他们也提供了一个考虑模型非确定性的方法，这种非确定性在这种情况中通常很大(Western,1996)。
5 讨论
统计学方法在社会学领域的应用已经走过了半个世纪的成功历程，发展出了交互表模型以及被广泛使用的分析个体层次数据集的方法。这些都对社会学获得今天这样的科学水平做出了巨大的贡献。这一充满新型数据和新挑战的领域对进一步开展统计研究来说已经成熟。
未来的方向在哪里？正如我在以上三代统计学方法的划分中所暗示的那样，我感到这一推动了第三代统计方法产生的数据类型所带来的问题，在不久后可能会激发出社会学方法论领域一些最激动人心的进展。但是还会有许多与当前技术发展过程中产生的数据类型联系在一起的新方法产生。比如，为调查者提供计算机并请他们在线回答问题，也许会成为以后一个时期一种新的调查方式。这种方式有可能产生新的方法论问题，比如在不等的时间间隔中进行重复测量的问题以及数据缺失(或者数据根本没有使用价值)等问题。总之，网络会产生大量的新形式的社会科学数据，发展一套从这些数据中得出有效结论的方法将注定是未来主要挑战的来源。
我要预言并且倡导的是，未来的发展将是多学科化的，它将跨越社会科学领域并且超越该领域。这一情况在二十世纪并不多见，在这一时期一个接一个社会科学领域在定量研究的复杂程度方面都获得了巨大的飞跃，但是这些成就都是在这些领域之间处于相对孤立的情况下取得的。心理学或许是第一个完成这一飞跃的领域，Spearman和Thurstone在二十世纪早期的著作为此做出巨大贡献，接下来Haavelmo，Tinbergen和Cowles委员会和其他一些人，在1930和1940年代发展出计量经济学，从而使经济学完成了这一过程。在此之后，1960年代随着Blalock，Duncan，Goodman和其他一些我们已经提到的社会学家相关著作的问世，社会学开始了自己的行动。1990年代，Gary King，Larry Bartels和其他一些人开始将统计方法应用到政治学研究领域，并且在此过程中发展出了一些新的方法。
这些学科的发展历程都比较相似。在定量化的转变中都倾向于关注或在一定的条件下创造当时最为先进的统计模型，并产生了一批具有很强流动性的方法论专家。而在最早进行这种转变的心理学和经济学领域的方法论研究者已经相互联合，形成他们自己的亚学科计量心理学和计量经济学。后来定量方法的发展在这些学科中都发展的比较缓慢，但是他们都与当时造成定量研究跨时代转变方法联系在一起。社会学也没有摆脱这一历程：在这一领域，定量研究工作仍然被1960，1970年代首先发展起来的方法所主导着(含有潜在变量的结构方程模型，一般线性模型，通过Cox模型进行的事件史分析)，而且仍然关注着这些方法的发展和改进。就像我已经讨论过的那样，这样做有很多好的理由，而且这样也会对整个学科产生非常正面的影响。但是，在当1990年代的统计模型，尤其是通过马尔可夫链之蒙特卡罗方法进行的Bayes分析已经被年轻的政治学家所采用，并且贯穿了他们激动人心的、激烈的定量化革命时，这一方法对社会学研究领域的渗透却还显得十分缓慢。
当前，我们处在一个比几十年前更加泛学科化的学术世界，对所有的社会科学学科而言现在是突破学科界限，共同将他们的定量方法推向前进的宝贵机会。在过去的几年中，一些主要的学术机构都建立了他们自己的跨学科的研究中心，并投入资源对社会科学的定量方法进行研究。华盛顿大学建立了统计学和社会科学研究中心。哈佛大学的社会科学基础研究中心加强了社会统计学的研究。加州大学圣巴巴拉分校建立了空间整合社会科学研究中心(Center for Spatially Integrated Social Sciences)，并将研究焦点访在空间统计学上。加州洛杉矶分校从社会统计学中产生的年轻的统计学系仍然与几个社会科学领域保持着跨学科的紧密联系。哥伦比亚大学设立了从社会科学和统计学中衍生出来的又一个跨学科专业——定量社会科学硕士培养计划。密歇根大学的定量方法计划，在统计学系和一些社会科学系之间建立起联合研究生培养协议。而在目前这些努力中最为成功的或许要数社会统计学系在南安普敦大学的建立。
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感谢一个叫yanlong的人的精彩翻译。转载。
请教
楼主，我想考社会学专业研究生，但对研究方法中的涉及高数的东西不甚理解，有没有必要学一下《概率论与数理统计》？谢谢！ 顶顶顶顶顶啦:P页:
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