Thurstonian-Based Analyses: Past, Present, and Future Utilities

Current psychometric models of choice behavior are strongly influenced by Thurstone’s (1927, 1931) experimental and statistical work on measuring and scaling preferences. Aided by advances in computational techniques, choice models can now accommodate a wide range of different data types and sources of preference variability among respondents induced by such diverse factors as person-specific choice sets or different functional forms for the underlying utility representations. At the same time, these models are increasingly challenged by behavioral work demonstrating the prevalence of choice behavior that is not consistent with the underlying assumptions of these models. I discuss new modeling avenues that can account for such seemingly inconsistent choice behavior and conclude by emphasizing the interdisciplinary frontiers in the study of choice behavior and the resulting challenges for psychometricians. The author would like to thank R. Darrell Bock whose work inspired many of the ideas presented here. The paper benefitted from helpful comments by Albert Maydeu-Olivares and Rung-Ching Tsai. The reported research was supported in parts by the Social Sciences and Humanities Research Council of Canada.     

Spatial memory in the grey mouse lemur (Microcebus murinus)

Wild animals face the challenge of locating feeding sites distributed across broad spatial and temporal scales. Spatial memory allows animals to find a goal, such as a productive feeding patch, even when there are no goal-specific sensory cues available. Because there is little experimental information on learning and memory capabilities in free-ranging primates, the aim of this study was to test whether grey mouse lemurs (Microcebus murinus), as short-term dietary specialists, rely on spatial memory in relocating productive feeding sites. In addition, we asked what kind of spatial representation might underlie their orientation in their natural environment. Using an experimental approach, we set eight radio-collared grey mouse lemurs a memory task by confronting them with two different spatial patterns of baited and non-baited artificial feeding stations under exclusion of sensory cues. Positional data were recorded by focal animal observations within a grid system of small foot trails. A change in the baiting pattern revealed that grey mouse lemurs primarily used spatial cues to relocate baited feeding stations and that they were able to rapidly learn a new spatial arrangement. Spatially concentrated, non-random movements revealed preliminary evidence for a route-based restriction in mouse lemur space; during a subsequent release experiment, however, we found high travel efficiency in directed movements. We therefore propose that mouse lemur spatial memory is based on some kind of mental representation that is more detailed than a route-based network map. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Matching the graphical display of data to avoidance versus approach motivation increases outcome expectancies

This study is the first demonstration of the effect of motivational focus (approach vs. avoidance) on the interpretation of graphical view of personal data, specifically, weight loss progress. In two experiments, participants were randomly assigned to see the bogus weight loss data charted against either a goal or a baseline reference line. In the first experiment, we accessed participants' chronic motivational focus. In the second study, we primed motivation by exposing the participants to either a gain- or loss-focused health message. The results demonstrate that participants with either chronic or manipulated approach motivation predicted higher future weight loss in the goal reference line condition than in the baseline reference line condition. The opposite pattern was observed for participants with either chronic or manipulated avoidance motivation. The potential of matching graphical data display to personality characteristics to promote effective health management is discussed.     

Conditional estimation of exponential random graph models from snowball sampling designs

A complete survey of a network in a large population may be prohibitively difficult and costly. So it is important to estimate models for networks using data from various network sampling designs, such as link-tracing designs. We focus here on snowball sampling designs, designs in which the members of an initial sample of network members are asked to nominate their network partners, their network partners are then traced and asked to nominate their network partners, and so on. We assume an exponential random graph model (ERGM) of a particular parametric form and outline a conditional maximum likelihood estimation procedure for obtaining estimates of ERGM parameters. This procedure is intended to complement the likelihood approach developed by  Handcock and Gile (2010) by providing a practical means of estimation when the size of the complete network is unknown and/or the complete network is very large. We report the outcome of a simulation study with a known model designed to assess the impact of initial sample size, population size, and number of sampling waves on properties of the estimates. We conclude with a discussion of the potential applications and further developments of the approach.     

Latent Growth Models with Floors,Ceilings, and Random Knots

In longitudinal/developmental studies, individual growth trajectories are sometimes bounded by a floor at the beginning of the observation period and/or a ceiling toward the end of the observation period (or vice versa), resulting in inherently nonlinear growth patterns. If the trajectories between the floor and ceiling are approximately linear, such longitudinal growth patterns can be described with a linear piecewise (spline) model in which segments join at knots. In these scenarios, it may be of specific interest for researchers to examine the timing when transition occurs, and in some occasions also to examine the levels of the floors and/or ceilings if they are not known and fixed. In the current study, we propose a reparameterized piecewise latent growth curve model so that a direct estimation of the random knots (and, if needed, a direct estimation of random floors and ceilings) is possible. We derive the model reparameterization using a 4-step structured latent curve modeling approach. We provide two illustrative examples to demonstrate how the proposed reparameterized models can be fitted to longitudinal growth data using the popular SEM software Mplus and we supply the full coding for applied researchers’ reference.     

Social Dominance Orientation and Right‐Wing Authoritarianism: Additive and Interactive Effects on Political Conservatism

It is commonly accepted that social dominance orientation (SDO) and right‐wing authoritarianism (RWA) are potent unique predictors of a variety of prejudice and prejudice‐related constructs. However, contrary to some predictions, there has been little evidence that these constructs interact to produce this outcome—they appear to be additive but not interactive in their prediction of prejudice. We extend the interaction hypothesis to consideration of another broadly relevant construct—political ideology. Drawing from 14 independent New Zealand–based samples, we show, through meta‐analysis and multilevel random coefficient modelling, that SDO and RWA additively and interactively predict levels of political conservatism operationalised in a variety of ways. Specifically, both constructs are associated with increasing political conservatism, and the lowest levels of conservatism (or highest levels of political liberalism) are found in those lowest in both SDO and RWA.     

阅读障碍儿童识字特点研究

通过作业分析方法,定量分析了小学二至五年级阅读障碍儿童的错别字类型,定性分析了其易错或易忘字的类型特点.初步探讨了阅读障碍儿童识记汉字的方法,从而推导障碍原因.研究结果表明:(1)汉语阅读困难儿童的错别字分五类,以形似错误为主.(2)随年级增高,形似错误的比例增加.依此结果可以推论:汉语阅读障碍儿童的识字方式是以图形式记忆为主的,未建立有效且强的形-义联结,其心理词典的词条数目少且精确性较差.     

Multilevel Model Prediction

Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling—model disturbances, random coefficients, and future response outcomes—and provides a unified procedure for predicting them. These predictors are best linear unbiased and are commonly known via the acronym BLUP; they are optimal in the sense of minimizing mean square error and are Bayesian under a diffuse prior. For parameter estimation purposes, a multilevel model can be written as a linear mixed-effects model. In this way, parameters of the many equations can be estimated simultaneously and hence efficiently. For prediction purposes, we show that it is more convenient to retain the multiple equation feature of multilevel models. In this way, the efficient BLUPs are easy to compute and retain their intuitively appealing recursive form. We also derive explicit equations for standard errors of these different types of predictors. Prediction in multilevel modeling is important in a wide range of applications. To demonstrate the applicability of our results, this paper discusses prediction in the context of a study of school effectiveness. This research was supported by a grant from the Graduate School at the University of Wisconsin at Madision and the National Science Foundation, Grant number SES-0436274. We are grateful to Norman Webb at Wisconsin Center for Education Research for making available the data used in the reported application.     

三维图形的视空间工作记忆容量研究

采用三维几何图形为材料,通过单探测变化觉察范式来测定视觉工作记忆的存储容量,并比较客体工作记忆和空间工作记忆容量的差异。实验1、2的材料分别为由颜色和形状两个基本特征组成的三维图形和由一个基本特征和一个细节特征组成的三维图形。两个实验结果显示,被试能在视觉工作记忆中存储约2-3个客体和4个空间位置,空间工作记忆容量显著大于客体工作记忆容量。进一步的比较发现,被试对由两个基本特征组成的三维图形的存储容量大于由一个基本特征和一个细节特征组成的三维图形。这表明,组成三维图形的特征类型对视空间工作记忆的存储容量有显著影响。     

Stronger effects of self-generated than cue-induced expectations when verifying predictions in data graphs

Expectations can come in different forms when analyzing and presenting data. Prior studies have documented stronger effects (behavioral and electrophysiological) of self-generated predictions as compared to cues. While participants presumably cannot help but use their own predictions, they might sometimes ignore cues (of low validity). In two experiments we compared the impact of cues (verbal and visual) and self-generated predictions on the performance of participants checking their current prediction against a presented data graph (linear upward or downward trend). Different from prior studies, the setup allowed for within-experiment comparison of different cue formats and ensured that cues could not be ignored. Nevertheless we found that self-generated predictions had a stronger impact than cues. Verbal cues had a stronger effect than visual cues without verbalization. Responses to graphs with a linear upward trend were faster and were influenced more strongly by predictions, than the response to graphs with a downward trend.