A Monte Carlo method for comparing generalized estimating equations to conventional statistical techniques for discounting data

Discounting is the process by which outcomes lose value. Much of discounting research has focused on differences in the degree of discounting across various groups. This research has relied heavily on conventional null hypothesis significance tests that are familiar to psychologists, such as t‐tests and ANOVAs. As discounting research questions have become more complex by simultaneously focusing on within‐subject and between‐group differences, conventional statistical testing is often not appropriate for the obtained data. Generalized estimating equations (GEE) are one type of mixed‐effects model that are designed to handle autocorrelated data, such as within‐subject repeated‐measures data, and are therefore more appropriate for discounting data. To determine if GEE provides similar results as conventional statistical tests, we compared the techniques across 2,000 simulated data sets. The data sets were created using a Monte Carlo method based on an existing data set. Across the simulated data sets, the GEE and the conventional statistical tests generally provided similar patterns of results. As the GEE and more conventional statistical tests provide the same pattern of result, we suggest researchers use the GEE because it was designed to handle data that has the structure that is typical of discounting data.     

Applying mixed‐effects modeling to single‐subject designs: An introduction

Behavior analysis and statistical inference have shared a conflicted relationship for over fifty years. However, a significant portion of this conflict is directed toward statistical tests (e.g., t‐tests, ANOVA) that aggregate group and/or temporal variability into means and standard deviations and as a result remove much of the data important to behavior analysts. Mixed‐effects modeling, a more recently developed statistical test, addresses many of the limitations of more basic tests by incorporating random effects. Random effects quantify individual subject variability without eliminating it from the model, hence producing a model that can predict both group and individual behavior. We present the results of a generalized linear mixed‐effects model applied to single‐subject data taken from Ackerlund Brandt, Dozier, Juanico, Laudont, & Mick, 2015, in which children chose from one of three reinforcers for completing a task. Results of the mixed‐effects modeling are consistent with visual analyses and importantly provide a statistical framework to predict individual behavior without requiring aggregation. We conclude by discussing the implications of these results and provide recommendations for further integration of mixed‐effects models in the analyses of single‐subject designs.     

Trifactor Models for Multiple-Ratings Data

In this study we extend and assess the trifactor model for multiple-ratings data in which two different raters give independent scores for the same responses (e.g., in the GRE essay or to subset of PISA constructed-responses). The trifactor model was extended to incorporate a cross-classified data structure (e.g., items and raters) instead of a strictly hierarchical structure. we present a set of simulations to reflect the incompleteness and imbalance in real-world assessments. The effects of the rate of missingness in the data and of ignoring differences among raters are investigated using two sets of simulations. The use of the trifactor model is also illustrated with empirical data analysis using a well-known international large-scale assessment.     

What you are confident of determines your choices in context: Confidence in yourself versus in your ability

The notion that consumers' preference is constructed by decision context is well established. Two of such salient manifestations are compromise effect and attraction effect. Although literature has explored the moderators of these effects from the perspective of a decision maker, little is known about whether a significant difference exists between the effects of individual differences as a situational state and as a stable personality. This article approaches this question by examining how specific self‐confidence and general self‐confidence shape consumer's preference for context options. Four studies find that compromise effect is greater for consumer with high specific self‐confidence, whereas attraction effect is greater for consumer with low specific self‐confidence. The two context effects are greater for consumers with low general self‐confidence only in the presence of social influence. In addition, low (vs. high) general self‐confidence strengthens (vs. weakens) the impact of specific self‐confidence on context effects under this condition. This article concludes by discussing the theoretical and practical implications of the findings.     

位置和内容对网页广告效果影响的眼动评估

使用TobiiT120型眼动仪,以16名大学生为被试,记录他们阅读嵌有网页广告新闻网页时的眼动注视过程,以探讨广告呈现位置、新闻内容与网页广告的相关性是否影响人们对网页广告的注视。实验采用3（广告位置：上方、中间、下方）×2（相关性：相关、不相关）两因素被试内设计。结果发现：（1）新闻内容和网页广告的相关性不影响人们对网页广告的加工;（2）对位于新闻上方和新闻中间的网页广告,被试的注视次数较多,且注视时间较长;（3）对位于新闻中间的网页广告,其再认效果优于位于新闻上方和新闻下方的广告。表明只有网页广告呈现位置这一因素影响被试对网页广告的注视。     

On undesirable consequences of thinking: framing effects as a function of substantive processing

Three studies investigate the impact of effortful constructive processing on framing effects. The results replicated previous findings: Participants avoided the risky option when the scenario was framed in terms of gains, but preferred this option when the scenario was framed in terms of losses. Importantly, framing effects were most pronounced when conditions allowed for an effortful constructive processing style (i.e., substantive processing). This impact of decision frames varied when decision time served as an indicator for the elaboration extent (Study 1), and also when processing motivation (accountability; Study 2) and processing ability (decision time; Study 3) were manipulated. Moreover, effortful processing did not increase framing effects when contextual cues reduced the necessity for constructive thinking (Study 1). We suggest that decision frames may take on very different roles as a function of the ambiguity of the decision problem, and the degree and style of processing. Copyright © 2006 John Wiley & Sons, Ltd.     

Improving Service Evaluation in Clinical Genetics: Identifying Effects of Genetic Diseases on Individuals and Families

Outcome measurement in clinical genetics is challenging. Outcome attributes used currently have been developed by service providers or adapted from measures used in other areas of healthcare. Many of the ‘patients’ in clinical genetics are healthy but at risk of developing or transmitting a condition. Usually no pharmacological or surgical treatment is offered, although information-giving is an objective of most consultations. We argue that services should be evaluated on the basis of how well they alleviate the effects of disease, from a patient perspective. This paper describes a qualitative study using seven focus groups with health professionals, patients and patient representatives. Social and emotional effects of genetics diseases were identified. Some differences emerged between the effects identified by health professionals and those identified by patients. These findings will be used to inform the evaluation of existing outcome measures and develop robust measures of outcome for clinical genetics services.     

汉词和图画再认的ERP效应及脑机制

目的：比较汉词与图画材料再认过程中的事件相关电位（ERP）表现并探讨其脑机制。方法：使用经典“学习一再认”实验模式对21名大学生进行测验,同时记录EEG,离线处理后对汉词及图画再认诱发的ERP波形主要成分及新旧效应进行比较。结果：两种材料的再认均诱发出了明显的新旧效应。顶区新旧效应头皮分布基本一致,但图画再认该效应波幅大于汉词再认;两种材料诱发的额区新旧效应有所不同,图画再认该效应为正走向,而汉词再认此效应表现为负走向趋势。结论：汉词与图画再认既存在某种共同的神经加工机制,叉分别涉及一些特异性的神经活动模式。     

Cruel intentions on television and in real life: can viewing indirect aggression increase viewers' subsequent indirect aggression?

Numerous studies have shown that viewing violence in the media can influence an individual's subsequent aggression, but none have examined the effect of viewing indirect aggression. This study examines the immediate effect of viewing indirect and direct aggression on subsequent indirect aggression among 199 children ages 11 to 14 years. They were shown an indirect, direct, or no-aggression video and their subsequent indirect aggression was measured by negative evaluation of a confederate and responses to a vignette. Participants viewing indirect or direct aggression gave a more negative evaluation of and less money to a confederate than participants viewing no-aggression. Participants viewing indirect aggression gave less money to the confederate than those viewing direct aggression. Participants viewing indirect aggression gave more indirectly aggressive responses to an ambiguous situation and participants viewing direct aggression gave more directly aggressive responses. This study provides the first evidence that viewing indirect aggression in the media can have an immediate impact on subsequent aggression.     

Generalized multilevel structural equation modeling

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.gllamm can be downloaded from http://www.gllamm.org. The paper was written while Sophia Rabe-Hesketh was employed at and Anders Skrondal was visiting the Department of Biostatistics and Computing, Institute of Psychiatry, King's College London.