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Comment: The Inferential Information Criterion from a Bayesian Point of View 评论:贝叶斯观点下的推理信息准则
IF 3 2区 社会学 Q1 SOCIOLOGY Pub Date : 2018-08-01 DOI: 10.1177/0081175018794489
O. Vassend
1. The Bayesian information criterion (BIC) has been proposed as a way to carry out Bayesian hypothesis testing when there are no clear expectations. However, the BIC rests on a particular prior distribution, for which there is rarely any justification. See Raftery (1995) on the case for the BIC and Weakliem (1999) for a critique. 2. The assumption that the sample is of the same size is important. To obtain the expected prediction error in a sample of arbitrary size, it is necessary to know the true model. Consequently, there is no method of model selection that uniformly leads to better out-of-sample predictions. 3. Schultz proposes that the value should be exp(AIC2 – AIC1), or about .0025 in this example. I think this is mistaken, and it should be exp{(AIC2 – AIC1)/2}. The general point about considering the theoretical probability of a nonzero value applies regardless of which formula is correct.
1. 贝叶斯信息准则(BIC)是在没有明确期望的情况下进行贝叶斯假设检验的一种方法。然而,BIC依赖于一个特定的先验分布,很少有任何理由。参见Raftery(1995)对BIC和Weakliem(1999)案例的评论。2. 假设样本大小相同是很重要的。为了在任意大小的样本中获得预期的预测误差,必须知道真实的模型。因此,没有一种模型选择方法能均匀地导致更好的样本外预测。3.Schultz建议该值应该是exp(AIC2 - AIC1),或者在本例中约为0.0025。我认为这是错误的,它应该是exp{(AIC2 - AIC1)/2}。无论哪个公式是正确的,考虑非零值的理论概率的一般观点都适用。
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引用次数: 0
Comment: Evidence, Plausibility, and Model Selection 评论:证据、合理性和模型选择
IF 3 2区 社会学 Q1 SOCIOLOGY Pub Date : 2018-08-01 DOI: 10.1177/0081175018793654
D. Weakliem
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引用次数: 0
Prologue 开场
IF 3 2区 社会学 Q1 SOCIOLOGY Pub Date : 2018-08-01 DOI: 10.1177/0081175018799359
Anonymous
Readers who leaf through this journal's pages in early 2021 or connect to it online will, sadly, need no reminder of how COVID-19-the respiratory illness caused by a novel strain of coronavirus-spread illness, death, and fear around the world, culminating in the declaration of a pandemic by the World Health Organization on 11 March 2020 Just as spring daffodils and orange blossoms burst forth last spring in the Mediterranean region that is our shared literary-cultural patria, we found ourselves involuntarily immersed in lockdowns Last but not least, we gratefully acknowledge Editorial Board members for continuing to provide detailed and rigorous article evaluations within the requested time frame, authors at all career stages for their submissions of exciting new work, and book reviewers for keeping our readers abreast of current scholarship in comedia studies and beyond
遗憾的是,2021年初浏览本杂志页面或在线访问本杂志的读者不需要提醒新冠肺炎——一种新型冠状病毒引起的呼吸道疾病——是如何在世界各地传播疾病、死亡和恐惧的,世界卫生组织于2020年3月11日宣布疫情达到顶峰。去年春天,当我们共同的文学文化遗产地中海地区的水仙花和橙花竞相开放时,我们发现自己不由自主地陷入了封锁之中。最后但并非最不重要的是,我们感谢编委会成员在要求的时间内继续提供详细而严格的文章评估,感谢各个职业阶段的作者提交了令人兴奋的新作品,感谢书评人让我们的读者了解喜剧研究及其他领域的最新学术成果
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引用次数: 0
Dedication: Allan McCutcheon: Latent Class Analyst 奉献:Allan McCutcheon:潜在阶级分析师
IF 3 2区 社会学 Q1 SOCIOLOGY Pub Date : 2018-08-01 DOI: 10.1177/0081175018791565
A. McCutcheon, Allan Lee
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引用次数: 0
Deciding on the Starting Number of Classes of a Latent Class Tree. 决定潜在类树的起始类数。
IF 3 2区 社会学 Q1 SOCIOLOGY Pub Date : 2018-08-01 Epub Date: 2018-06-21 DOI: 10.1177/0081175018780170
Mattis van den Bergh, Geert H van Kollenburg, Jeroen K Vermunt

In recent studies, latent class tree (LCT) modeling has been proposed as a convenient alternative to standard latent class (LC) analysis. Instead of using an estimation method in which all classes are formed simultaneously given the specified number of classes, in LCT analysis a hierarchical structure of mutually linked classes is obtained by sequentially splitting classes into two subclasses. The resulting tree structure gives a clear insight into how the classes are formed and how solutions with different numbers of classes are substantively linked to one another. A limitation of the current LCT modeling approach is that it allows only for binary splits, which in certain situations may be too restrictive. Especially at the root node of the tree, where an initial set of classes is created based on the most dominant associations present in the data, it may make sense to use a model with more than two classes. In this article, we propose a modification of the LCT approach that allows for a nonbinary split at the root node, and we provide methods to determine the appropriate number of classes in this first split, based either on theoretical grounds or on a relative improvement of fit measure. This novel approach also can be seen as a hybrid of a standard LC model and a binary LCT model, in which an initial, oversimplified but interpretable model is refined using an LCT approach. Furthermore, we show how to apply an LCT model when a nonstandard LC model is required. These new approaches are illustrated using two empirical applications: one on social capital and the other on (post)materialism.

在最近的研究中,潜在类树(LCT)模型被提出作为标准潜在类(LC)分析的一种方便的替代方法。在LCT分析中,不是使用给定一定数量的类同时形成所有类的估计方法,而是通过将类依次分成两个子类来获得相互联系的类的层次结构。由此产生的树状结构可以清楚地了解类是如何形成的,以及具有不同数量的类的解是如何相互联系的。当前LCT建模方法的一个限制是它只允许二进制分割,这在某些情况下可能过于严格。特别是在树的根节点上,根据数据中最主要的关联创建一组初始类,因此使用具有两个以上类的模型可能是有意义的。在本文中,我们提出了对LCT方法的修改,允许在根节点进行非二元分割,并提供了基于理论依据或相对改进的拟合度量来确定第一次分割中适当数量的类的方法。这种新颖的方法也可以看作是标准LC模型和二元LCT模型的混合体,在二元LCT模型中,使用LCT方法对初始的、过度简化的但可解释的模型进行了改进。此外,我们还展示了在需要非标准LC模型时如何应用LCT模型。这些新方法是用两个实证应用来说明的:一个是关于社会资本的,另一个是关于(后)唯物主义的。
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引用次数: 11
Comment: Some Challenges When Estimating the Impact of Model Uncertainty on Coefficient Instability 评论:在估计模型不确定性对系数不稳定性的影响时面临的一些挑战
IF 3 2区 社会学 Q1 SOCIOLOGY Pub Date : 2018-08-01 DOI: 10.1177/0081175018790569
Robert M. O’Brien
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引用次数: 1
Comment: Bayes, Model Uncertainty, and Learning from Data 评论:贝叶斯、模型不确定性和从数据中学习
IF 3 2区 社会学 Q1 SOCIOLOGY Pub Date : 2018-08-01 DOI: 10.1177/0081175018799095
B. Western
Robert M. O’Brien is a professor emeritus at the University of Oregon. He specializes in criminology and quantitative methods. Within criminology, he focuses on the methods used to gather criminological data, on the analysis of crime rates, and on the task of extricating the effects of ages, periods, and cohorts on crime rates. His most recent publication on that topic, “Homicide Arrest Rate Trends in the United States: The Contributions of Periods and Cohorts (1965–2015),” appeared in 2018 in the Journal of Quantitative Criminology. In quantitative methods, some of his contributions involve the effects of using interval data as ordinal, generalizability theory, identification in structural equation modeling measurement models, the use of multicollinearity indices, and an obsession with age-period-cohort models. In 2015 he published a book on this topic, Age-Period-Cohort Models: Approaches and Analyses with Aggregate Data (Chapman & Hall, 2015).
Robert M.O'Brien是俄勒冈大学的名誉教授。他专门研究犯罪学和定量方法。在犯罪学领域,他专注于收集犯罪学数据的方法,犯罪率的分析,以及消除年龄、时期和群体对犯罪率的影响的任务。他关于这一主题的最新出版物《美国凶杀案逮捕率趋势:时期和群体的贡献(1965–2015)》于2018年发表在《定量犯罪学杂志》上。在定量方法中,他的一些贡献涉及使用区间数据作为序数的影响、可推广性理论、结构方程建模测量模型中的识别、多重共线性指数的使用以及对年龄段队列模型的痴迷。2015年,他出版了一本关于这一主题的书,《年龄段队列模型:聚合数据的方法和分析》(Chapman&Hall,2015)。
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引用次数: 1
Nonlinear Autoregressive Latent Trajectory Models 非线性自回归潜在轨迹模型
IF 3 2区 社会学 Q1 SOCIOLOGY Pub Date : 2018-08-01 DOI: 10.1177/0081175018789441
Shawn Bauldry, K. Bollen
Autoregressive latent trajectory (ALT) models combine features of latent growth curve models and autoregressive models into a single modeling framework. The development of ALT models has focused primarily on models with linear growth components, but some social processes follow nonlinear trajectories. Although it is straightforward to extend ALT models to allow for some forms of nonlinear trajectories, the identification status of such models, approaches to comparing them with alternative models, and the interpretation of parameters have not been systematically assessed. In this paper we focus on two forms of nonlinear autoregressive latent trajectory (NLALT) models. The first form allows for a quadratic growth trajectory, a popular form of nonlinear latent growth curve models. The second form derives from latent basis models, or freed loading models, that allow for arbitrary growth processes. We discuss details concerning parameterization, model identification, estimation, and testing for the two forms of NLALT models. We include a simulation study that illustrates potential biases that may arise from fitting alternative models to data derived from an autoregressive process and individual-specific nonlinear trajectories. In addition, we include an extended empirical example modeling growth trajectories of weight from birth through age 2.
自回归潜在轨迹(ALT)模型将潜在增长曲线模型和自回归模型的特征结合到一个单一的建模框架中。ALT模型的发展主要集中在具有线性增长成分的模型上,但一些社会过程遵循非线性轨迹。尽管扩展ALT模型以允许某些形式的非线性轨迹是很简单的,但尚未系统评估此类模型的识别状态、将其与替代模型进行比较的方法以及参数的解释。本文主要研究两种形式的非线性自回归潜在轨迹(NLALT)模型。第一种形式允许二次增长轨迹,这是非线性潜在增长曲线模型的一种流行形式。第二种形式源自潜在基础模型,或自由加载模型,允许任意增长过程。我们讨论了两种形式的NLALT模型的参数化、模型识别、估计和测试的细节。我们包括一项模拟研究,该研究说明了将替代模型拟合自回归过程和个体特定非线性轨迹得出的数据可能产生的潜在偏差。此外,我们还提供了一个扩展的经验示例,对从出生到2岁的体重增长轨迹进行建模。
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引用次数: 4
Rejoinder: Can We Weight Models by Their Probability of Being True? 复辩状:我们能根据模型为真的概率对其进行加权吗?
IF 3 2区 社会学 Q1 SOCIOLOGY Pub Date : 2018-08-01 DOI: 10.1177/0081175018796841
John Muñoz, Cristobal Young
Draper, David. 1995. “Assessment and Propagation of Model Uncertainty.” Journal of the Royal Statistical Society, Series B 57:45–97. Freedman, David A. 1983. “A Note on Screening Regression Equations.” American Statistician 37:152–55. Leamer, Edward E. 1983. “Let’s Take the Con out of Econometrics.” American Economic Review 73:31–43. Raftery, Adrian E. 1996. “Approximate Bayes Factors and Accounting for Model Uncertainty in Generalised Linear Models.” Biometrika 83:251–66. Young, Cristobal, and Katherine Holsteen. 2017. “Model Uncertainty and Robustness: A Computational Framework for Multimodel Analysis.” Sociological Methods and Research 46:3–40.
大卫·德雷柏1995。模型不确定性的评估与传播皇家统计学会学报,B辑57:45-97。弗里德曼,大卫A. 1983。关于筛选回归方程的注释。美国统计学家37:152-55。Leamer, Edward E. 1983。“让我们摒弃计量经济学的弊端。”美国经济评论73:31-43。拉特里,阿德里安E. 1996。广义线性模型中近似贝叶斯因子和模型不确定性的计算。生物统计学83:251 - 66。Young, Cristobal和Katherine Holsteen, 2017。模型不确定性和鲁棒性:多模型分析的计算框架。社会学方法与研究46:3-40。
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引用次数: 1
Rejoinder: On the Assumptions of Inferential Model Selection—A Response to Vassend and Weakliem 答辩:论推理模型选择的假设——对Vassend和Weakliem的回应
IF 3 2区 社会学 Q1 SOCIOLOGY Pub Date : 2018-08-01 DOI: 10.1177/0081175018794488
Michael Schultz
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引用次数: 0
期刊
Sociological Methodology
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