Specifying and Interpreting Latent State–Trait Models With Autoregression: An Illustration

Latent state–trait models are valuable tools for representing the longitudinal stability and variability of individuals’ relative standing on a construct (e.g., a behavior or psychological process). Specifically, state–trait models partition construct variance into time-varying and time-invariant components, enabling one to examine the relations between these components and other variables. Such partitioning of construct variance has a number of valuable applications including the improvement of risk-outcome research. The trait–state–occasion (TSO) model and latent state–trait model with autoregression (LST–AR) are ideal for use with constructs with relative stability that decreases with increasing durations, but relative stability that does not decrease to 0 even over long durations. Despite the fact that this pattern of relative stability expression is observed for a wide variety of constructs, there are relatively few applications of the TSO and LST–AR models. Thus, this article describes the TSO and LST–AR models and illustrates application of these models.     

A Simplified Regression Formulation of Tukey’s Test

A simplified regression approach is used for Tukey’s test for multiple comparisons. By constructing k - 1 regression equations (where k is the number of means) in such a way that each mean is being compared to every other mean, the computed t-values that result from testing the significance of each regression weight can be compared to a modified value (q)/√2 from the studentized range statistic.     

A New Parameter Estimation Algorithm of Multivariable Time Series Model

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Dog——A Friend of Man

Dogs are friends of man. They work for us. They hunt with us. They also play with us.But a long time ago,in all over the world,dogs were wild.Dogs can     

Song of Solomon——A Journey of Milkman Dead

American Scholar Joseph Campbell puts forward a more detailed model of heroes in which a successful journey usually contains three major phases:separationinitiationandreturn.In Song of Solomon written by Toni Morrison,the hero,Milkman Dead leaves his family to the south to find his family’s lost tradition and memory and the journey of Milkman Dead turns out to be a journey of understanding himself,his ancestry and African heritage.The journey also fits into the legendary model of hero’s journeySeparation—Initiation—Returnmodel.     

Song of Solomon——A Journey of Milkman Dead

American Scholar Joseph Campbell puts forward a more detailed model of heroes in which a successful journey usually contains three major phases:separationinitiationandreturn.In Song of Solomon written by Toni Morrison,the hero,Milkman Dead leaves his family to the south to find his family’s lost tradition and memory and the journey of Milkman Dead turns out to be a journey of understanding himself,his ancestry and African heritage.The journey also fits into the legendary model of hero’s journeySeparation—Initiation—Returnmodel.     

Extending the Trait–State–Occasion Model: How Important Is Within-Wave Measurement Equivalence?

Abstract

Trait–State–Occasion (TSO) covariance models represent an important advance in methods for studying the longitudinal stability of latent constructs. Such models have only been examined under fairly restricted conditions (e.g., having only 2 tau-equivalent indicators per wave). In this study, Monte Carlo simulations revealed the effects of having 2 versus 3 indicators per wave and relaxing the within-wave tau equivalence. These modifications were examined under conditions that varied with regard to the within-wave trait variance and the cross-wave stability of occasion influences. In general, the TSO model performed well (i.e., few convergence problems or out-of-range parameter estimates) under most of these conditions; however, the likelihood of improper solutions increased when only 2 indicators were used per wave, when factor loadings were small, when the proportion of trait variance was either very high or very low, and when the occasion factor was highly stable. Based on these findings, recommendations are made for successful use of TSO models with real data.     

Mars—A Planet of Mystery

It was August 27th,2003 at about 10 p.m.My daughter-in-law calledme."Mom,don’t you want to see the Mars?"     

What Do Test Score Really Mean? A Latent Class Analysis of Danish Test Score Performance

Latent class Poisson count models are used to analyse a sample of Danish test score results from a cohort of individuals born in 1954–1955, tested in 1968, and followed until 2011. The procedure takes account of unobservable effects as well as excessive zeros in the data. We show that the test scores measure manifest or measured ability as it has evolved over the life of the respondent and is, thus, more a product of the socioeconomic status of the parents and the human capital formation process than some latent or fundamental measure of pure cognitive ability. We find that variables which are not closely associated with traditional notions of intelligence explain a significant proportion of the variation in test scores. This adds to the complexity of interpreting test scores and suggests that school culture and possible incentive problems make it more difficult to understand what the tests measure.     

Mars—A Planet of Mystery

<正>It was August 27th, 2003 at about 10 p. m. My daughter-in-law called me.“Mom, don’t you want to see the Mars”     

Obama＇s State of the Union Address——奥巴马国情咨文演讲片段欣赏

Mr. Speaker, Mr. Vice President, members of Congress, my fellow Americans：
Today in America, a teacher spent extra time with a student who needed it, and did her part to lift America＇s graduation rate entrepreneur flipped on the lights in her to its highest level in more than three decades. An tech startup, and did her part to add to the more than 8 million new jobs our businesses have created over the past four years.     

The British State’s production of the Muslim School: A simultaneity of categories of difference analysis

Recently, national populist politics has been translated with the emergence of two overlapping narratives of Islamophobia and anti-EU immigration media discourses. Such discourses have been made highly visible in the increased spike in hate crimes that have been a hidden cost of the national(ist) debate about Britain leaving the European Union (Brexit). This is the context in which we can trace a remarkable shift in the state representation of the schooling of the South Asian/Muslim community and the reclassification from promoting South Asians as central to the future of a modernised multicultural Britain (1970s) to positioning Muslims as a ‘suspect community’ (2020). This article unpacks two cultural moments in the critical exploration of the State production of the Muslim School within a post-Trojan Horse era. First, a national dominant image of the Muslim School operating within the State ascribed ‘no-go’ ethno-religious Muslim neighbourhoods, as a repository of regressive (extreme) Islamist religiosity; thus, reconstructing their religious belief as racialised identities, as they disconnect from British values. Second, the No Outsiders programme, with the accompanying framing of intolerant (Muslim) parents. In this case, the ensuing tension between ‘homophobic Muslims’ and ‘British values’ sets in place a homogenisation of differences. Deploying a simultaneity of categories of difference perspective, we address an underlying discursive re-politicising of South Asian ethnic communities as religious communities, which is resulting in a perpetual negotiation of the meanings attached to being Muslim. This will enable an international application of the article beyond the specific focus on the city of Birmingham, identified in the international media as the Jihadi capital of Europe.     

A New Recursive Parameter Estimation Algorithm of Multi－Variable Time－Varying AR Model

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Application of RBF Neural Network in OptimizingMachining Parameters

In machining processes, errors of rough in dimension, shape and location lead to changes in processing quantity, and the material of a workpiece may not be uniform. For these reasons, cutting force changes in machining, making the machining system deformable. Consequently errors in workpieces may occur. This is called the error reflection phenomenon. Generally, such errors can be reduced through repeated processing while using appropriate processing quantity in each processing based on operator‘s experience.According to the theory of error reflection, the error reflection coefficient indicates the extent to which errors of rough influence errors of workpieces. It is related to several factors such as machining condition, hardness of the workpiece, etc. This non-linear relation cannot be worked out using any formula. RBF neural network can approximate a non-linear function within any precision and be trained fast. In this paper, non-linear mapping ability of a fuzzy-neural network is utilized to approximate the non-linear relation. After training of the network with swatch collection obtained in experiments, an appropriate output can be obtained when an input is given. In this way, one can get the required number of processing and the processing quantity each time from the machining condition. Angular rigidity of a machining system,hardness of workpiece, etc., can be input in a form of fuzzy values. Feasibility in solving error reflection and optimizing machining parameters with a RBF neural network is verified by a simulation test with MATLAB.     

Longitudinal Measurement Non-Invariance with Ordered-Categorical Indicators: How are the Parameters in Second-Order Latent Linear Growth Models Affected?

Models of change typically assume longitudinal measurement invariance. Key constructs are often measured by ordered-categorical indicators (e.g., Likert scale items). If tests based on such indicators do not support longitudinal measurement invariance, it would be useful to gauge the practical significance of the detected non-invariance. The authors focus on the commonly used second-order latent growth curve model, proposing a sensitivity analysis that compares the growth parameter estimates from a model assuming the highest achieved level of measurement invariance to those from a model assuming a higher, incorrect level of measurement invariance as a measure of practical significance. A simulation study investigated the practical significance of non-invariance in different locations (loadings, thresholds, uniquenesses) in second-order latent linear growth models. The mean linear slope was affected by non-invariance in the loadings and thresholds, the intercept variance was affected by non-invariance in the uniquenesses, and the linear slope variance and intercept–slope covariance were affected by non-invariance in all three locations.     

A Pragmatic Study of Gender Differences in Verbal Communication——A Case Study of Desperate Housewives

Male and Female languages refer to those which are usually used by men and women or those which characterize gender differences. Based on the theories of sociolinguistics and pragmatics, this thesis mainly provides a contrastive analysis of gender differences in conversation of Desperate Housewives from the perspectives of politeness principles. It aims at investigating how the pragmatic principles are violated or observed in the conversations of the characters in the series, finding out some to minimize the misunderstandings in inter-gender communication and achieve effective inter-gender communication.     

Do Students with Varying Academic Ability Benefit Equally from Personal Qualities? Applying a Trait and State Perspective

Using multilevel models, this study examined whether students with varying academic ability benefit equally from perseverance and intellectual curiosity in terms of academic achievement. In addressing this question two perspectives were applied: a trait perspective, focusing on differences between students, and a state perspective, focusing on differences within students across semesters. By means of an online questionnaire, undergraduate students (N = 2272) were asked to rate themselves on perseverance and intellectual curiosity at the beginning of five consecutive semesters. Results indicate that academic ability but also personal qualities have to be taken into account to explain the differences between students in academic achievement. In particular perseverance was found to be important in explaining differences both between students and within students across semesters. Also, individual students fluctuate quite substantially in their reported perseverance and intellectual curiosity from semester to semester.