Using a Few Snapshots to Distinguish Mountains from Waves: Weak Factorial Invariance in the Context of Trait-State Research

In this article, we show that the underlying dimensions obtained when factor analyzing cross-sectional data actually form a mix of within-person state dimensions and between-person trait dimensions. We propose a factor analytical model that distinguishes between four independent sources of variance: common trait, unique trait, common state, and unique state. We show that by testing whether there is weak factorial invariance across the trait and state factor structures, we can tackle the fundamental question first raised by Cattell; that is, are within-person state dimensions qualitatively the same as between-person trait dimensions? Furthermore, we discuss how this model is related to other trait-state factor models, and we illustrate its use with two empirical data sets. We end by discussing the implications for cross-sectional factor analysis and suggest potential future developments.     

A Comparison of Population-Averaged and Cluster-Specific Approaches in the Context of Unequal Probabilities of Selection

Sampling designs of large-scale survey studies are typically complex, involving multiple design features such as clustering and unequal probabilities of selection. Single-level (i.e., population-averaged) methods that use adjusted variance estimators and multilevel (i.e., cluster-specific) methods provide two alternatives for modeling clustered data. Although the literature comparing these methods is vast, comparisons have been limited to the context in which all sampling units are selected with equal probabilities (thus circumventing the need for sampling weights). The goal of this study was to determine under what conditions single-level and multilevel estimators outperform one another in the context of a two-stage sampling design with unequal probabilities of selection. Monte Carlo simulation methods were used to evaluate the impact of several factors, including population model, informativeness of the design, distribution of the outcome variable, intraclass correlation coefficient, cluster size, and estimation method. Results indicated that the unweighted estimators performed similarly across conditions, whereas the weighted single-level estimators tended to outperform the weighted multilevel estimators, particularly under nonideal sample conditions. Multilevel weight approximation methods did not perform well when the design was informative. An empirical example is provided to demonstrate how researchers might investigate the implications of the simulation results in practice.     

Trans employees,transitioning, and job satisfaction

While trans employees have become more widely recognized in society, our understanding of their work experiences remains underdeveloped. In the current study, we investigate whether transitioning is associated with job satisfaction in England, Wales and Scotland. Using longitudinal data collected before and after sex reassignment surgery associations between job satisfaction and mental health/life satisfaction are examined for trans men and women. The estimations suggest that employees experience higher job satisfaction, mental health and life satisfaction after sex reassignment surgery than before. In addition, the estimations suggest that after sex reassignment surgery, the associations between job satisfaction and mental health/life satisfaction are stronger than before. Moreover, the estimations suggest that employees not only experience higher job satisfaction after sex reassignment surgery, but also during transitioning. The results suggest that, firms should not treat transitioning employees in a biased way, since their transition might entail positive personal and workplace advancements.     

Person‐specific versus multilevel autoregressive models: Accuracy in parameter estimates at the population and individual levels

This paper compares the multilevel modelling (MLM) approach and the person‐specific (PS) modelling approach in examining autoregressive (AR) relations with intensive longitudinal data. Two simulation studies are conducted to examine the influences of sample heterogeneity, time series length, sample size, and distribution of individual level AR coefficients on the accuracy of AR estimates, both at the population level and at the individual level. It is found that MLM generally outperforms the PS approach under two conditions: when the sample has a homogeneous AR pattern, namely, when all individuals in the sample are characterized by AR processes with the same order; and when the sample has heterogeneous AR patterns, but a multilevel model with a sufficiently high order (i.e., an order equal to or higher than the maximum order of individual AR patterns in the sample) is fitted and successfully converges. If a lower‐order multilevel model is chosen for heterogeneous samples, the higher‐order lagged effects are misrepresented, resulting in bias at the population level and larger prediction errors at the individual level. In these cases, the PS approach is preferable, given sufficient measurement occasions (T ≥ 50). In addition, sample size and distribution of individual level AR coefficients do not have a large impact on the results. Implications of these findings on model selection and research design are discussed.     

基于多传感器数据的航空管制员情境意识的计算

航空管制人员保持良好的情境意识是确保空中交通以安全、高效方式运行的必要条件。如果在管制员工作过程中, 能准确监测其情境意识状态, 并在情境意识丧失前给予及时告警, 那么将会极大地提高管制绩效和减少空中两机相撞的概率。目前, 国外评价管制员情境意识的方法虽然不少, 但这些方法基本都是诊断性质和回溯性质的, 无法对管制员的情境意识进行监测或预警。管制员的情境意识受个体差异、任务等多种因素的影响, 同时, 其也可以由某些指标来表征, 如某些作业绩效指标和生理指标。为了能够实现对航空管制员情境意识的准确监控及告警, 拟通过雷达管制模拟实验、心理测量、专家评定等方法采集多传感器数据, 分别建立基于表征指标和基于影响变量的情境意识计算模型, 并比较其预测性能。该研究结果可为管制员情境意识的实时监测提供支持。     

Exploring the effects of congruence and Holland’s personality codes on job satisfaction: An application of hierarchical linear modeling techniques

This study applied hierarchical linear modeling to investigate the effect of congruence on intrinsic and extrinsic aspects of job satisfaction. Particular focus was given to differences in job satisfaction by gender and by Holland’s first-letter codes. The study sample included nationally represented 1462 female and 1280 male college graduates who held full-time jobs in 2000. Congruence was measured by using actual occupational and academic major codes. After controlling for the differences across three-letter Holland personality codes, results indicate that job satisfaction was largely a function of individual attributes rather than disparities in personality types. Holland’s congruence was most effective in explaining intrinsic dimensions of job satisfaction for females, and less effective for extrinsic job satisfaction for both genders. Social females and males were less satisfied with extrinsic satisfaction, while Conventional and Social females were more satisfied with intrinsic features in their vocational environments.     

CONSISTENT VISUAL ANALYSES OF INTRASUBJECT DATA

Visual inspection of single‐case data is the primary method of interpretation of the effects of an independent variable on a dependent variable in applied behavior analysis. The purpose of the current study was to replicate and extend the results of DeProspero and Cohen (1979) by reexamining the consistency of visual analysis across raters. We recruited members of the board of editors and associate editors for the Journal of Applied Behavior Analysis to judge graphs on a 100‐point scale of experimental control and by providing a dichotomous response (i.e., “yes” or “no” for experimental control). Results showed high interrater agreement across the three types of graphs, suggesting that visual inspection can lead to consistent interpretation of single‐case data among well‐trained raters.