A New Model for Acquiescence at the Interface of Psychometrics and Cognitive Psychology

ABSTRACT

When measuring psychological traits, one has to consider that respondents often show content-unrelated response behavior in answering questionnaires. To disentangle the target trait and two such response styles, extreme responding and midpoint responding, Böckenholt (2012a Böckenholt, U. (2012a). Modeling multiple response processes in judgment and choice. Psychological Methods, 17, 665–678. doi:10.1037/a0028111[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) developed an item response model based on a latent processing tree structure. We propose a theoretically motivated extension of this model to also measure acquiescence, the tendency to agree with both regular and reversed items. Substantively, our approach builds on multinomial processing tree (MPT) models that are used in cognitive psychology to disentangle qualitatively distinct processes. Accordingly, the new model for response styles assumes a mixture distribution of affirmative responses, which are either determined by the underlying target trait or by acquiescence. In order to estimate the model parameters, we rely on Bayesian hierarchical estimation of MPT models. In simulations, we show that the model provides unbiased estimates of response styles and the target trait, and we compare the new model and Böckenholt’s model in a recovery study. An empirical example from personality psychology is used for illustrative purposes.     

Improved Insight into and Prediction of Network Dynamics by Combining VAR and Dimension Reduction

To understand within-person psychological processes, one may fit VAR(1) models (or continuous-time variants thereof) to multivariate time series and display the VAR(1) coefficients as a network. This approach has two major problems. First, the contemporaneous correlations between the variables will frequently be substantial, yielding multicollinearity issues. In addition, the shared effects of the variables are not included in the network. Consequently, VAR(1) networks can be hard to interpret. Second, crossvalidation results show that the highly parametrized VAR(1) model is prone to overfitting. In this article, we compare the pros and cons of two potential solutions to both problems. The first is to impose a lasso penalty on the VAR(1) coefficients, setting some of them to zero. The second, which has not yet been pursued in psychological network analysis, uses principal component VAR(1) (termed PC-VAR(1)). In this approach, the variables are first reduced to a few principal components, which are rotated toward simple structure; then VAR(1) analysis (or a continuous-time analog) is applied to the rotated components. Reanalyzing the data of a single participant of the COGITO study, we show that PC-VAR(1) has the better predictive performance and that networks based on PC-VAR(1) clearly represent both the lagged and the contemporaneous variable relations.     

Modeling Interactions Between Latent Variables in Research on Type D Personality: A Monte Carlo Simulation and Clinical Study of Depression and Anxiety

Several approaches exist to model interactions between latent variables. However, it is unclear how these perform when item scores are skewed and ordinal. Research on Type D personality serves as a good case study for that matter. In Study 1, we fitted a multivariate interaction model to predict depression and anxiety with Type D personality, operationalized as an interaction between its two subcomponents negative affectivity (NA) and social inhibition (SI). We constructed this interaction according to four approaches: (1) sum score product; (2) single product indicator; (3) matched product indicators; and (4) latent moderated structural equations (LMS). In Study 2, we compared these interaction models in a simulation study by assessing for each method the bias and precision of the estimated interaction effect under varying conditions. In Study 1, all methods showed a significant Type D effect on both depression and anxiety, although this effect diminished after including the NA and SI quadratic effects. Study 2 showed that the LMS approach performed best with respect to minimizing bias and maximizing power, even when item scores were ordinal and skewed. However, when latent traits were skewed LMS resulted in more false-positive conclusions, while the Matched PI approach adequately controlled the false-positive rate.     

Evaluating Structural Equation Models for Categorical Outcomes: A New Test Statistic and a Practical Challenge of Interpretation

This research is concerned with two topics in assessing model fit for categorical data analysis. The first topic involves the application of a limited-information overall test, introduced in the item response theory literature, to structural equation modeling (SEM) of categorical outcome variables. Most popular SEM test statistics assess how well the model reproduces estimated polychoric correlations. In contrast, limited-information test statistics assess how well the underlying categorical data are reproduced. Here, the recently introduced C₂ statistic of Cai and Monroe (2014) is applied. The second topic concerns how the root mean square error of approximation (RMSEA) fit index can be affected by the number of categories in the outcome variable. This relationship creates challenges for interpreting RMSEA. While the two topics initially appear unrelated, they may conveniently be studied in tandem since RMSEA is based on an overall test statistic, such as C₂. The results are illustrated with an empirical application to data from a large-scale educational survey.     

Trait anxiety and trait anger measured by ecological momentary assessment and their correspondence with traditional trait questionnaires

Ecological momentary assessments (EMA) of anxiety and anger/hostility were obtained every 25–30 min over two 24-h periods, separated by a median of 6 months, from 165 employees at a university in the Northeast. We used a multilevel trait-state-error structural equation model to estimate: (1) the proportion of variance in EMA anxiety and anger/hostility attributable to stable trait-like individual differences; (2) the correspondence between these trait-like components of EMA anxiety and anger/hostility and traditional questionnaire measures of each construct; and (3) the test–retest correlation between two 24-h averages obtained several months apart. After adjustment for measurement error, more than half the total variance in EMA reports of anxiety and anger/hostility is attributable to stable trait-like individual differences; however, the trait-like component of each construct is only modestly correlated with questionnaire measures of that construct. The 6-month “test–retest” correlations of latent variables representing the true 24-h EMA average anxiety and average anger are quite high (r ? 0.83). This study represents the longest follow-up period over which EMA-based estimates of traits have been examined. The results suggest that although the trait component (individual differences) of EMA momentary ratings of anxiety and anger is larger than the state component, traditional self-report questionnaires of trait anxiety and anger correspond only weakly with EMA-defined traits.     

Spearman's law of diminishing returns: A statistical artifact?

Spearman's Law of Diminishing Returns (SLODR) is the idea that the structure of human cognitive ability is more differentiated and g a weaker determinant of cognitive performance at higher levels of ability. In this study, we distinguish between ‘traditional’ methods of testing SLODR and ‘contemporary’ methods of testing SLODR. It is the former set of methods from which the vast majority of the evidence base for SLODR derives. We demonstrated that it is easy to mimic SLODR and reverse SLODR effects in these traditional methods of assessing SLODR by using data with skewed observed variable distributions. The skewness magnitudes did not need to be large to produce these effects and they fell well within the range of values that are usually considered unproblematic for parametric statistic analysis. In simulated datasets, positive subtest skewness resulted in SLODR and negative subtest skewness resulted in reverse SLODR. In contemporary methods of testing SLODR, non-linear g-loadings or a skewed g are assumed to reflect evidence for SLODR. When we applied contemporary methods of testing SLODR to these data, there was evidence of heteroscedastic residuals but no evidence of non-linear g-loadings or skewed g distributions. We broadly replicated the effects of subtest skew from these simulated datasets in real data from the Minnesota Study of Twins Reared Apart. Results imply that traditional methods of assessing SLODR cannot distinguish between effects due to subtest characteristics that have nothing to do with differences in ability structure at different levels of g and true SLODR effects. This calls into question the empirical support for SLODR.     

音乐知觉的神经基础：脑成像研究的元分析

本研究根据音乐加工的层级结构, 对现有的脑成像研究进行了元分析, 探讨了音乐知觉的神经基础。具体而言, 对特异于音乐知觉加工的两个层级, 音程分析和结构分析的神经基础进行了分析, 并在此基础上对比了参与两个层级加工的脑区。结果发现, 音程分析主要的激活分布在双侧颞上回和右侧额下回, 在中央前回、角回和脑岛等脑区也有分布。音程分析在颞上回激活最多, 可能表明颞上回为音程分析的核心区域。结构分析激活分布较广, 主要激活颞上回、颞横回和前额叶区域, 此外, 还激活了下顶叶、缘上回和舌回等顶枕区域。结构分析在前额叶激活最多, 可能表明前额叶为结构分析的核心区域。最后, 对比两层级激活的脑区发现, 二者仅在后侧颞上回存在着重合, 而在绝大部分脑区则表现出分离, 这暗示了音程分析和结构分析通过颞上回进行交流, 并负责音乐不同层面的加工。     

Effect of elasto-plastic compatibility of grains on void-initiation criteria in low-carbon steel

The present study provides evidence for the role of ferrite-grain-size distributions on the occurrence of void initiation in a low-carbon steel. Various thermomechanical treatments were undertaken to create ultrafine, bimodal and coarse distributions of ferrite grain sizes. A two-parameter characterisation of probable void initiation sites is proposed, namely an elastic modulus difference and a difference in Schmid factor of the grains surrounding the void. All microstructures were categorised based on the ability to facilitate or resist void nucleation. For coarse grains, the elastic modulus and the Schmid factor differences are the highest, while they are intermediate for ultrafine grains and the lowest for the bimodal microstructure.     

Ab initio investigation of electron-phonon interaction in LaSn3 and CaSn3

ABSTRACT

We present results for the structural, electronic, vibrational, and electron-phonon coupling properties of LaSn₃ and CaSn₃ adopting the simple cubic AuCu₃-type structure obtained using the the generalised gradient approximation of the density functional theory and plane wave ab initio pseudopotential method. Our electronic results show that both materials display metallic character with several bands, which have mainly Sn 5p character, crossing the Fermi level. The calculated phonon spectrum of LaSn₃ accords very well with reported experimental measurements. The weights of the peaks in the Eliashberg spectral function of both compounds are enhanced with the use of experimental lattice constant in our electron-phonon calculation, increasing the value of average electron phonon coupling parameter from 0.876 to 0.937 for LaSn₃ (by 7%) and from 0.642 to 0.725 for CaSn₃ (by 13%). The use of experimental lattice constant also improves the agreement between theoretical and experimental values of the superconducting temperature for both compounds.