Are Robust Standard Errors the Best Approach for Interval Estimation With Nonnormal Data in Structural Equation Modeling?

When the multivariate normality assumption is violated in structural equation modeling, a leading remedy involves estimation via normal theory maximum likelihood with robust corrections to standard errors. We propose that this approach might not be best for forming confidence intervals for quantities with sampling distributions that are slow to approach normality, or for functions of model parameters. We implement and study a robust analog to likelihood-based confidence intervals based on inverting the robust chi-square difference test of Satorra (2000). We compare robust standard errors and the robust likelihood-based approach versus resampling methods in confirmatory factor analysis (Studies 1 & 2) and mediation analysis models (Study 3) for both single parameters and functions of model parameters, and under a variety of nonnormal data generation conditions. The percentile bootstrap emerged as the method with the best calibrated coverage rates and should be preferred if resampling is possible, followed by the robust likelihood-based approach.     

When Are Multidimensional Data Unidimensional Enough for Structural Equation Modeling? An Evaluation of the DETECT Multidimensionality Index

In structural equation modeling (SEM), researchers need to evaluate whether item response data, which are often multidimensional, can be modeled with a unidimensional measurement model without seriously biasing the parameter estimates. This issue is commonly addressed through testing the fit of a unidimensional model specification, a strategy previously determined to be problematic. As an alternative to the use of fit indexes, we considered the utility of a statistical tool that was expressly designed to assess the degree of departure from unidimensionality in a data set. Specifically, we evaluated the ability of the DETECT “essential unidimensionality” index to predict the bias in parameter estimates that results from misspecifying a unidimensional model when the data are multidimensional. We generated multidimensional data from bifactor structures that varied in general factor strength, number of group factors, and items per group factor; a unidimensional measurement model was then fit and parameter bias recorded. Although DETECT index values were generally predictive of parameter bias, in many cases, the degree of bias was small even though DETECT indicated significant multidimensionality. Thus we do not recommend the stand-alone use of DETECT benchmark values to either accept or reject a unidimensional measurement model. However, when DETECT was used in combination with additional indexes of general factor strength and group factor structure, parameter bias was highly predictable. Recommendations for judging the severity of potential model misspecifications in practice are provided.     

The Structure of Preschoolers’ Emotion Knowledge: Model Equivalence and Validity Using a Structural Equation Modeling Approach

Research Findings: A theory-based 2-factor structure of preschoolers’ emotion knowledge (i.e., recognition of emotional expression and understanding of emotion-eliciting situations) was tested using confirmatory factor analysis. Compared to 1- and 3-factor models, the 2-factor model showed a better fit to the data. The model was found to be equivalent for gender, race, age, and socioeconomic risk. Theory and the high correlation between the 2 latent factors suggested a hierarchical nature of development, in which a higher level of emotion knowledge is built upon a lower level. In our validity model, we found significant paths from the recognition to the situation factor and from the situation factor to teachers’ reports of preschoolers’ learning behaviors and social competence. Results provide further evidence of the significant role emotion plays in preschoolers’ school readiness. Practice or Policy: Early childhood educators can benefit from knowing that recognition of expressions and understanding of emotion-eliciting situations are appropriately teachable in this age range and can focus such teaching upon negative emotions and those that may vary across individuals. Furthermore, relations between these aspects of emotion knowledge and school readiness add to accumulating evidence that early childhood programming focusing upon emotion knowledge has multiple benefits.     

The Pooled Data Approach for the Estimation of Split-Ballot Multitrait–Multimethod Experiments

Saris, Satorra and Coenders (2004) proposed a new approach for estimating the quality of survey questions, which combines the advantages of the following two existing approaches: the multi-trait-multi-method (MTMM) approach and the split-ballot (SB) approach. In practice, this new approach led to frequent occurrences of non-convergence and improper solutions in the case of a two-group design (Revilla &; Saris, 2013 Revilla, M., &; Saris, W. E. (2013). The split-ballot multitrait-multimethod approach: Implementation and problems. Structural Equation Modeling, 20(1), 27–46.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). This was very problematic because this approach had been used in many experiments to determine the reliability, validity and method effects of questions that are used in the European Social Survey, which is used in approximately 30 countries. In this article, we propose a new estimation procedure, which we call estimation using pooled data (EUPD), to overcome the problems with this experimental design. We show that EUPD can also be applied in a general setting of structural equation modeling when the sample size is too small for precise estimation but several similar data sets are available.     

Influence of Psychosocial Classroom Environment on Students’ Motivation and Self-Regulation in Science Learning: A Structural Equation Modeling Approach

The primary aim of this study was two-fold: 1) to identify salient psychosocial features of the classroom environment that influence students’ motivation and self-regulation in science learning; and 2) to examine the effect of the motivational constructs of learning goal orientation, science task value and self-efficacy in science learning on students’ self-regulation in science classrooms. Data collected from 1360 science students in grades 8, 9 and 10 in five public schools in Perth, Western Australia were utilized to validate the questionnaires and to investigate the hypothesized relationships. Structural Equation Modeling analysis suggested that student cohesiveness, investigation and task orientation were the most influential predictors of student motivation and self-regulation in science learning. In addition, learning goal orientation, task value and self-efficacy significantly influenced students’ self-regulation in science. The findings offer potential opportunities for educators to plan and implement effective pedagogical strategies aimed at increasing students’ motivation and self-regulation in science learning.     

A Structural Analysis of the Language of School‐age Children

Westminster College is currently undertaking a research project into the language development of children of school age in conjunction with the Department of Linguistic Science, University of Reading. The collaboration represents an attempt to apply Linguistic Science to the work of classroom teachers within the mother‐tongue. The following papers indicate the nature of the project and its possible uses to the teaching profession and students of language acquisition.     

Influence of Motivating Science Class,Family, and Peer Models on Students’ Approaches to Learning Science: A Structural Equation Modeling Analysis

Research in Science Education - Classroom environment, family, and peers are important factors in influencing students’ science learning. The primary aim of this study was to examine the...     

A Conditional Process Analysis of the Teacher Confirmation–Student Learning Relationship

Students enter college with varying degrees of academic self-efficacy, which influences how they respond to effective teaching behaviors. Teacher confirmation is one behavior that has received increased attention because it is thought to indirectly enhance students’ learning by reducing their receiver apprehension in the classroom. Findings from 208 college students supported the hypothesized indirect effects between teacher confirmation and students’ perceived learning through reduced receiver apprehension, but conditional process analyses revealed these indirect effects were moderated by students’ academic self-efficacy. These findings suggest the effectiveness of teacher confirmation behaviors varies among students, with those who lack academic self-efficacy deriving fewer of the intended educational benefits.     

A critique of the Capability Approach’s potential for application to career guidance

In this article, we provide a balanced critique of Sen’s Capability Approach (CA) with reference to its potential to inform career guidance theory and practice. There are varying understandings and interpretations of the CA. Some see capabilities as universal, whilst others favour a more relativist view. The CA is also vulnerable to misunderstanding. Critiques based on misunderstanding are easily dismissed, so our focus is on substantive conceptual and practical critiques. Three main challenges are explored: conceptual debates about the nature of freedom and justice; limitations arising from the disciplinary origins of the CA; and challenges in operationalising the CA.

     

Are the Effects of Student–Faculty Interaction Dependent on Academic Major? An Examination Using Multilevel Modeling

Using multilevel models, this study examined whether and why the strength of association between student–faculty interaction and student cognitive skills development varies across academic majors. The study utilized data from the 2008 University of California Undergraduate Experience Survey (UCUES) and a sample of 43,014 students from 119 academic majors across nine campuses. The results indicate that the impact of interaction with faculty on students’ cognitive skills development significantly varies by academic major. Findings also suggest that some aspects of departmental climate can potentially magnify the effects produced by student–faculty interaction.     

A Capabilities Approach Analysis of Evangelii Gaudium and Its Significance for Catholic Learning Environments on LGBT + Issues

Abstract

This article examines the 2013 apostolic exhortation Evangelii Gaudium by Pope Francis in terms of its significance for addressing LGBT?+?issues in Catholic learning environments. Using the 10 capabilities necessary for human flourishing as outlined by Martha Nussbaum in her capability approach to civil society and questions of social justice, I argue that the implications of Evangelii Gaudium for Catholic learning environments are significant and provide necessary guidance for addressing LGBT?+?social justice issues where these issues have often been ignored or insufficiently addressed. In Evangelii Gaudium Pope Francis is challenging the global community of Catholics to consider what kind of society the Church is producing, and I argue that a capabilities approach provides a fruitful way in which to consider and respond to this challenge. Although written with Catholicism in mind, the challenges presented by Pope Francis are pertinent for consideration by other faith communities and educators more generally.     

Exploring the Structure of Teachers’ Emotional Labor in the Classroom: A Multitrait–Multimethod Analysis

This study tests whether teachers’ emotional labor in classroom settings is optimally conceptualized according to the type of emotional labor strategy involved (genuinely expressing, hiding, and faking emotions), the specific type of emotion being performed in class (e.g., enjoyment vs. pride vs. anxiety), or both strategy type and emotion type. Multitrait–multimethod analyses of 1,086 Canadian teachers’ survey responses showed teachers’ responses to emotional labor items to be most reliably differentiated according to both specific types of emotional labor strategies as well as the valence of the emotion being performed. Findings were largely consistent with common “display rules” encouraging expression of positive emotions and hiding of negative emotions by teachers in classroom settings. Results further showed teachers’ emotional labor strategies for negative emotions to be particularly contingent on the specific discrete emotion involved, highlighting the complexity of expressing negative emotions as a behavior management strategy.     

A Multilevel Analysis of Asian Immigrant Children’s Reading Achievement in the Early Years: Evidence from the ECLS-K Data

This study investigates 1.5 generation¹ Asian immigrant children’s (n = 264) early literacy achievement patterns, treating them as a heterogeneous group. Specifically, the within-group variances in reading achievement from kindergarten to third grade are examined, drawing on four waves of data from the ECLS-K class of 1998–1999. Our analysis shows that ethnicity plays a role in shaping the children’s initial reading readiness and later growth, but the effects of languages spoken at home are not significant. Our analysis also demonstrates a persistent achievement gap between low- and high-socioeconomic status (SES) Asian groups. However, gender difference in terms of children’s reading development is not found to be significant. There is also no interaction between SES and the other factors such as gender, ethnicity and language backgrounds.     

Neuroanatomy,the Achille’s Heel of Medical Students. A Systematic Analysis of Educational Strategies for the Teaching of Neuroanatomy

Neuroanatomy has been deemed crucial for clinical neurosciences. It has been one of the most challenging parts of the anatomical curriculum and is one of the causes of “neurophobia,” whose main implication is a negative influence on the choice of neurology in the near future. In the last decades, several educational strategies have been identified to improve the skills of students and to promote a deep learning. The aim of this study was to systematically review the literature to identify the most effective method/s to teach human neuroanatomy. The search was restricted to publications written in English language and to articles describing teaching tools in undergraduate medical courses from January 2006 through December 2017. The primary outcome was the observation of improvement of anatomical knowledge in undergraduate medical students. Secondary outcomes were the amelioration of long-term retention knowledge and the grade of satisfaction of students. Among 18 selected studies, 44.4% have used three-dimensional (3D) teaching tools, 16.6% near peer teaching tool, 5.55% flipped classroom tool, 5.55% applied neuroanatomy elective course, 5.55% equivalence-based instruction-rote learning, 5.55% mobile augmented reality, 5.55% inquiry-based clinical case, 5.55% cadaver dissection, and 5.55% Twitter. The high in-between study heterogeneity was the main issue to identify the most helpful teaching tool to improve neuroanatomical knowledge among medical students. Data from this study suggest that a combination of multiple pedagogical resources seems to be the more advantageous for teaching neuroanatomy.