A Scanpath Analysis of the Risky Decision‐Making Process

In the field of eye tracking, scanpath analysis can reflect the sequential and temporal properties of the cognitive process. However, the advantages of scanpath analysis have not yet been utilized in the study of risky decision making. We explored the methodological applicability of scanpath analysis to test models of risky decision making by analyzing published data from the eye‐tracking studies of Su et al. (2013); Wang and Li (2012), and Sun, Rao, Zhou, and Li (2014). These studies used a proportion task, an outcome‐matched presentation condition, and a multiple‐play condition as the baseline for comparison with information search and processing in the risky decision‐making condition. We found that (i) the similarity scores of the intra‐conditions were significantly higher than those of the inter‐condition; (ii) the scanpaths of the two conditions were separable; and (iii) based on an inspection of typical trials, the patterns of the scanpaths differed between the two conditions. These findings suggest that scanpath analysis is reliable and valid for examining the process of risky decision making. In line with the findings of the three original studies, our results indicate that risky decision making is unlikely to be based on a weighting and summing process, as hypothesized by the family of expectation models. The findings highlight a new methodological direction for research on decision making. Copyright © 2016 John Wiley & Sons, Ltd.     

A note on monotonicity of item response functions for ordered polytomous item response theory models

A monotone relationship between a true score (τ) and a latent trait level (θ) has been a key assumption for many psychometric applications. The monotonicity property in dichotomous response models is evident as a result of a transformation via a test characteristic curve. Monotonicity in polytomous models, in contrast, is not immediately obvious because item response functions are determined by a set of response category curves, which are conceivably non-monotonic in θ. The purpose of the present note is to demonstrate strict monotonicity in ordered polytomous item response models. Five models that are widely used in operational assessments are considered for proof: the generalized partial credit model (Muraki, 1992, Applied Psychological Measurement, 16, 159), the nominal model (Bock, 1972, Psychometrika, 37, 29), the partial credit model (Masters, 1982, Psychometrika, 47, 147), the rating scale model (Andrich, 1978, Psychometrika, 43, 561), and the graded response model (Samejima, 1972, A general model for free-response data (Psychometric Monograph no. 18). Psychometric Society, Richmond). The study asserts that the item response functions in these models strictly increase in θ and thus there exists strict monotonicity between τ and θ under certain specified conditions. This conclusion validates the practice of customarily using τ in place of θ in applied settings and provides theoretical grounds for one-to-one transformations between the two scales.     

大学英语四、六级考试分数等值研究

对现有的大学英语四、六级考试分数等值模式中存在的若干问题进行了深入的分析,并提出了新的解决方案——一个基于铆题设计和两参数IRT模型的解决方案。主要包括：（1）用两参数逻辑斯蒂模型替代原来的Rasch模型,以改进题目模型的适合性;（2）用共同题目的等值设计取代原来的共同被试等值设计,解决共同被试等值设计中,等值考生的动机水平难以控制的难题;（3）建立专用的等值用题库,并且一次性完成其中铆题的预测和参数标定工作,以解决原来等值模式中存在的误差累积问题。同时,由于铆题的保密工作难度较小,因此,等值专用题库对保证等值结果的可靠性也具有重大意义;（4）本文还对新的分数等值方案进行了真实的考试数据等值计算实验,并得到了一个令人满意的分数等值结果。     

Regression among factor scores

Structural equation models with latent variables are sometimes estimated using an intuitive three-step approach, here denoted factor score regression. Consider a structural equation model composed of an explanatory latent variable and a response latent variable related by a structural parameter of scientific interest. In this simple example estimation of the structural parameter proceeds as follows: First, common factor models areseparately estimated for each latent variable. Second, factor scores areseparately assigned to each latent variable, based on the estimates. Third, ordinary linear regression analysis is performed among the factor scores producing an estimate for the structural parameter. We investigate the asymptotic and finite sample performance of different factor score regression methods for structural equation models with latent variables. It is demonstrated that the conventional approach to factor score regression performs very badly. Revised factor score regression, using Regression factor scores for the explanatory latent variables and Bartlett scores for the response latent variables, produces consistent estimators for all parameters.     

Causal graphical views of fixed effects and random effects models

Despite the long-standing discussion on fixed effects (FE) and random effects (RE) models, how and under what conditions both methods can eliminate unmeasured confounding bias has not yet been widely understood in practice. Using a simple pretest–posttest design in a linear setting, this paper translates the conventional algebraic formalization of FE and RE models into causal graphs and provides intuitively accessible graphical explanations about their data-generating and bias-removing processes. The proposed causal graphs highlight that FE and RE models consider different data-generating models. RE models presume a data-generating model that is identical to a randomized controlled trial, while FE models allow for unobserved time-invariant treatment–outcome confounding. Augmenting regular causal graphs that describe data-generating processes by adding the computational structures of FE and RE estimators, the paper visualizes how FE estimators (gain score and deviation score estimators) and RE estimators (quasi-deviation score estimators) offset unmeasured confounding bias. In contrast to standard regression or matching estimators that reduce confounding bias by blocking non-causal paths via conditioning, FE and RE estimators offset confounding bias by deliberately creating new non-causal paths and associations of opposite sign. Though FE and RE estimators are similar in their bias-offsetting mechanisms, the augmented graphs reveal their subtle differences that can result in different biases in observational studies.     

Hypermaturity and Immaturity of Personality Profiles in Adolescents

Five‐year longitudinal data on a cohort of early to middle adolescents (N = 923) and a cohort of middle to late adolescents (N = 390) were used to examine the correlates of hypermaturity (i.e. 12‐year‐olds with a personality profile resembling the profile of an average 20‐year‐old) and immaturity (i.e. 20‐year‐olds with a personality profile resembling the profile of an average 12‐year‐old) of personality. Analyses revealed that girls with high levels of hypermaturity exhibited high levels of internalizing problem behaviour and conflict with parents, while hypermaturity in boys was only associated with internalizing problems. Immature girls had low levels of anxiety and high levels of minor delinquency, whereas immature boys reported low levels of anxiety and high levels of physical maturity. These findings suggest that off‐time personality development is an interesting concept deserving further exploration. Copyright © 2011 John Wiley & Sons, Ltd.     

Psychological components of responses to interest inventory items

This study tests the relevance of 10 psychological traits that are assumed to exemplify responses to the items of INFORIZON, an occupational interest inventory. To accomplish this goal, the author analyzed the responses to the interest items based on a sample of 824 participants (17–25 years old), with an assessment of the items based on psychological traits using another French sample (147 participants) in the same age category. The results of multiple linear regression analyses supported most of the hypotheses presented in this study.     

Extending the Debate Between Spearman and Wilson 1929: When do Single Variables Optimally Reproduce the Common Part of the Observed Covariances?

The covariances of observed variables reproduced from conventional factor score predictors are generally not the same as the covariances reproduced from the common factors. We sought to find a factor score predictor that optimally reproduces the common part of the observed covariances. It was found algebraically that—under some conditions—the single observed variable with highest loading on a factor reproduces the non-diagonal elements of the observed covariance matrix more exactly than the conventional factor score predictors. This finding is linked to Spearman's and Wilson's 1929 debate on the use of single variables as factor score predictors. A population-based and a sample-based simulation study confirmed the algebraic result that taking a single variable can outperform conventional factor score predictors in reproducing the non-diagonal covariances when the nonzero loading size and the number of nonzero loadings per factor are small. The results indicated that a weighted aggregation of variables does not necessarily lead to an improvement of the score over the variable with the highest loading.     

On the Mathematical Relationship Between Latent Change Score and Autoregressive Cross-Lagged Factor Approaches: Cautions for Inferring Causal Relationship Between Variables

The present paper focuses on the relationship between latent change score (LCS) and autoregressive cross-lagged (ARCL) factor models in longitudinal designs. These models originated from different theoretical traditions for different analytic purposes, yet they share similar mathematical forms. In this paper, we elucidate the mathematical relationship between these models and show that the LCS model is reduced to the ARCL model when fixed effects are assumed in the slope factor scores. Additionally, we provide an applied example using height and weight data from a gerontological study. Throughout the example, we emphasize caution in choosing which model (ARCL or LCS) to apply due to the risk of obtaining misleading results concerning the presence and direction of causal precedence between two variables. We suggest approaching model specification not only by comparing estimates and fit indices between the LCS and ARCL models (as well as other models) but also by giving appropriate weight to substantive and theoretical considerations, such as assessing the justifiability of the assumption of random effects in the slope factor scores.     

地佐辛混合液对预防腭咽成形术苏醒期不良反应观察

比较应用芬太尼和地佐辛在预防腭咽成形术苏醒期,因疼痛以及口咽部呼吸道梗阻引起的患者躁动,恶心呕吐等不良反应情况,寻找最佳药物以及方法组合。200例美国麻醉师协会（ASA）分级I级～Ⅱ级患者,随机分为A、B、C、D四组,给药时间均为手术结束前15分钟,A组：单次静注芬太尼0．1μg·kg^-1;B组单次静注地佐辛0．1mg·kg^-1;C组单次静注地佐辛0．1mg·kg^-1＋氟哌利多2．5mg;D组单次静注地佐辛0．1mg·kg^-1＋氟哌利多2．5mg＋伤口涂抹复方利多卡因乳膏。记录患者苏醒时间,拔管后呼吸抑制,恶心呕吐等情况。结果D组出现恶心呕吐、头痛头晕例数明显少于A、B、C三组（P〈o．05）。手术结束前15分钟单次静注地佐辛O．1mg·kg^-1＋氟哌利多2．5mg＋伤口涂抹复方利多卡因乳膏,对于有效预防腭咽成形术苏醒期疼痛、恶心呕吐、头痛以及躁动是有效的。