Interconnectedness and difference between action research and a lesson design study in Shanghai,China

ABSTRACT

The professional development of teachers in China takes place, to a large extent, in Teaching Research Groups (TRG) that exist in all schools. Though there are diverse models of TRG activities, these might, on the surface, appear to resemble forms of Action Research (AR) or include elements that might resemble AR. In conducting a Lesson Design Study (LDS) with a TRG in Shanghai, we encountered the specific challenge of what might be the interconnectedness and differences between AR and our LDS. To address this issue, we applied a research-informed depiction of the distinguishing characteristics of AR to our LDS. Based on this analysis, we found that (1) in contrast to the depiction of AR that encompasses a choice of methods, our LDS follows a specific ‘design research’ methodology, (2) whereas the depiction of AR is simultaneously directed towards teacher self-change and restructuring the organisation or institution within which the teacher works, LDS concerns more than the practical questions in one local social context and aims to tackle bigger questions across the social contexts in the subject research field, and (3) whereas in the depiction of AR, teachers engage in a process of authentic collaboration with other teachers seeking to improve their practices, in the LDS community the external researchers and expert teachers play other roles in the TRG. Even though there may be differences between the depiction of AR and our LDS, the interconnectedness is important in that both AR and our LDS contribute directly to school-based teacher professional development.     

Mediation Analysis in a Latent Growth Curve Modeling Framework

This article presents several longitudinal mediation models in the framework of latent growth curve modeling and provides a detailed account of how such models can be constructed. Logical and statistical challenges that might arise when such analyses are conducted are also discussed. Specifically, we discuss how the initial status (intercept) and change (slope) of the putative mediator variable can be appropriately included in the causal chain between the independent and dependent variables in longitudinal mediation models. We further address whether the slope of the dependent variable should be controlled for the dependent variable's intercept to improve the conceptual relevance of the mediation models. The models proposed are illustrated by analyzing a longitudinal data set. We conclude that for certain research questions in developmental science, a multiple mediation model where the dependent variable's slope is controlled for its intercept can be considered an adequate analytical model. However, such models also show several limitations.     

Comparing Models of Change to Estimate the Mediated Effect in the Pretest–Posttest Control Group Design

Models to assess mediation in the pretest–posttest control group design are understudied in the behavioral sciences even though it is the design of choice for evaluating experimental manipulations. The article provides analytical comparisons of the four most commonly used models to estimate the mediated effect in this design: analysis of covariance (ANCOVA), difference score, residualized change score, and cross-sectional model. Each of these models is fitted using a latent change score specification and a simulation study assessed bias, Type I error, power, and confidence interval coverage of the four models. All but the ANCOVA model make stringent assumptions about the stability and cross-lagged relations of the mediator and outcome that might not be plausible in real-world applications. When these assumptions do not hold, Type I error and statistical power results suggest that only the ANCOVA model has good performance. The four models are applied to an empirical example.     

Consistent Estimation of Faculty Rank Effects in Academic Salary Models

Faculty rank is often included as an explanatory variable in academic salary models. Because there is reason to believe that this results in specification bias, rank effects should be estimated endogenously in salary models. A salary model in which faculty rank is endogenous is estimated in this article and the results are compared with those obtained from a conventional specification that treats rank as exogenous.     

基于大规模学业水平测试的科学学习困难学生特点分析

本研究利用大规模学业水平测试数据,分析8年级学生科学学习困难的基本特点。研究发现:女生的科学学习困难发生率高于男生;学习困难的女生的科学成绩略高于男生;多数学生的科学学习困难会在生物、物理、地理等多个学科领域同时发生;科学学习困难学生内容和认知各个维度上的成绩是与正常学生的差距大致相同,他们对科学问题的回答倾向于基于经验和直觉,并表现出科学能力低下的特点。     

城乡科学学习困难学生学业相关因素分析

本研究利用大规模学业水平测试数据,分析8年级学生科学学习困难学生学业相关因素的特点。研究发现:城乡学生科学学习困难检出率没有差异,但科学学习困难检出率的性别差异因城乡而异。城乡科学学习困难学生的学业相关因素水平与正常学生的差异因城乡和性别不同也有不同。师生关系是学生科学学习困难最有效的预测因素,并不随城乡、学生性别而异。家庭社会经济地位对科学学习困难的预测作用亦不能排除。     

Evaluating Intercept-Slope Interactions in Latent Growth Modeling

The effects of misspecifying intercept-covariate interactions in a 4 time-point latent growth model were the focus of this investigation. The investigation was motivated by school growth studies in which students' entry-level skills may affect their rate of growth. We studied the latent interaction of intercept and a covariate in predicting growth with respect to 3 factors: sample size (100, 200, and 500), 4 levels of magnitude of interaction effect, and 3 correlation values between intercept and covariate (.3, .5, and .7). Correctly specified models were examined to determine power and Type I error rates, and misspecified models were examined to evaluate the effects on power, parameter estimation, bias, and fit indexes. Results showed that, under correctly specified models, power increased as expected with increasing sample size, larger magnitude of interaction, and larger intercept-covariate correlation. Under misspecification, omitting a non-null interaction results in significant change in the estimation of the direct effects of both covariate and intercept in both magnitude and direction, with results dependent on sign of parameter values for main effects and interaction. Including a spurious interaction does not affect estimation of direct effects of intercept and covariate but does increase standard errors. The primary problem in ignoring a non-null interaction lies in misinterpretation of the model, as interactions yield important insights into the nature of the processes being studied.     

Predictions of Individual Change Recovered With Latent Class or Random Coefficient Growth Models

Popular longitudinal models allow for prediction of growth trajectories in alternative ways. In latent class growth models (LCGMs), person-level covariates predict membership in discrete latent classes that each holistically define an entire trajectory of change (e.g., a high-stable class vs. late-onset class vs. moderate-desisting class). In random coefficient growth models (RCGMs, also known as latent curve models), however, person-level covariates separately predict continuously distributed latent growth factors (e.g., an intercept vs. slope factor). This article first explains how complex and nonlinear interactions between predictors and time are recovered in different ways via LCGM versus RCGM specifications. Then a simulation comparison illustrates that, aside from some modest efficiency differences, such predictor relationships can be recovered approximately equally well by either model—regardless of which model generated the data. Our results also provide an empirical rationale for integrating findings about prediction of individual change across LCGMs and RCGMs in practice.     

Specifying Piecewise Latent Trajectory Models for Longitudinal Data

Piecewise latent trajectory models for longitudinal data are useful in a wide variety of situations, such as when a simple model is needed to describe nonlinear change, or when the purpose of the analysis is to evaluate hypotheses about change occurring during a particular period of time within a model for a longer overall time frame, such as change that occurs following onset of a treatment or some other event. However, the specification of various forms of piecewise models has not been fully explicated for the structural equation modeling (SEM) framework. This article describes piecewise models as a straightforward extension of the basic SEM model for linear growth, which makes them relatively easy both to specify and to interpret. After presenting models for 2 linear slopes (or pieces) in detail, the article discusses extensions that include additional linear slopes (i.e., a 3-piece model) or a quadratic factor (i.e., a hybrid linear-quadratic model).     

Of Twirling Dervishes and Daring New Directions: Collaborative and Practitioner Research

In recent years the educational programs of the Church of Jesus Christ of Latter-day Saints (LDS) have received praise for their role in the high rates of religiosity and denominational retention among their youth. This study offers a historical summary of the efforts of LDS to develop the most effective methods of engaging their youth in religious education. It tracks the development of LDS religious education from its origins in the United States, to its efforts on the global stage. Ultimately, LDS educators adopted a flexible approach utilizing volunteer teachers to meet the needs of this growing global faith.     

对"工科数学"教学改革的探讨

"工科数学"是工科院校重要的基础课.一个人的数学素质的高低已成为衡量其综合素质的重要因素.因此,工科院校因根据社会对人才需求的多样化和对人才规格的不断变化,利用先进的教学理念、教学内容、教学手段,对传统教学内容进行改革,以培养学生的定量思维、数学建模及计算能力.     

Residual Structures in Growth Models With Ordinal Outcomes

Growth models allow for the study of within-person change and between-person differences in within-person change. Typically, these models are applied to continuous variables where the residuals are assumed to be normally distributed. With normally distributed residuals there are a variety of residual structures that can be imposed and tested, which have been shown to affect model fit and parameter estimation. This article concerns residual structures in growth models with binary and ordered categorical outcomes using the probit link function. Different residual structures and their appropriateness for growth data are discussed and their use is illustrated with longitudinal data collected as part of Head Start’s Family and Child Experiences Survey 1997 Cohort. We close with recommendations for the specification and parameterization of growth models that use the probit link.     

Changing boards: investigating the effects of centres changing their specifications for English GCSE

The awarding bodies and their regulator carry out research to maintain the equivalence of examination standards. However, some criticisms remain, suggesting that examination boards may be lowering standards in specifications (syllabuses) in order to attract more centres (schools or colleges). This article reports our investigations on whether centres that decide to change their specifications do so to a specification with a higher percentage of candidates achieving grade C and above, based on the argument that such specification is easier. Using GCSE English data for the years 2000–2003, we show that there is insufficient evidence for this assumption; instead, alternative explanations may be more plausible. We then consider whether centres that changed their specifications improve their mean level of performance more than centres that did not change. There was not enough evidence for this second assumption either.     

The Importance of Identifying Levels in Multilevel Analysis: An Illustration of the Effects of Ignoring the Top or Intermediate Levels in School Effectiveness Research

In this article, the effect of ignoring one or more levels of variation in hierarchical linear regression analysis is explored. A model with four hierarchical levels is used as a reference model. A distinction is made between ignoring top and intermediate levels. The effects of ignoring levels on the fixed and on the random parameters of different random intercept models are explored by means of a real data set. The results show that ignoring an important level causes an effect on specific fixed coefficients, variance components and their corresponding standard error. Therefore, ignoring an important level can lead to different research conclusions.     

Examining relational engagement across the transition to high schools in three US high schools reformed to improve relationship quality

The purpose of this study was to examine changes in students’ relational engagement across the transition to high school in three schools reformed to improve the quality of student–teacher relationships. In order to analyze this data we employed latent growth curve (LGC) modeling techniques (n = 637). We ran three LGC models on three indicators of relational engagement: perceived teacher press (Middleton and Midgley in Contemp Educat Psychol 27:373–391, 2002), perceived teacher support (Goodenow in Psychol Sch 30:79–90, 1993a), and perceived school belonging (Goodenow in J Early Adolesc 13, 21–43, 1993b). For each model we estimated the latent means of the observed variables, the intercept, and the slope. Our hypothesized model for each of the variables included estimates for the contribution of team status (no team, cross-team, pure teamed), total absences in 8th grade, race (white/non-white), gender, gifted status, and socio-economic status. Alternate models were run dropping non-significant exogenous variables from the model one at a time. In each case, the hypothesized models, including five exogenous predictors, evidenced poor fit. However, alternate models including fewer exogenous predictors evidenced better fit, allowed for the estimation of intercept differences and rate of change, and accounted for significant amounts of variance in relational engagement. Findings are situated within the context of school reform designed to improve relational engagement.     

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.     

心理弹性及其作用机制的研究述评

心理弹性目前已成为心理学的一个重要研究领域,并日益为,心理学界所关注.已有研究分别从不同角度给予心理弹性不同的定义,虽没有形成统一的概念,但在进行研究时,都认可两个基本的操作性定义要素:个体遭遇逆境和个体成功应对.有关心理弹性的研究目前大多停留在第一阶段,即对心理弹性变量的探讨;对心理弹性内在机制的探讨是心理弹性研究的第二阶段,这将成为今后研究的重点.本研究在综合国外大量文献的基础上重点探讨了心理弹性作用机制的理论模型,并提出注重生态学原则、进行细化研究、拓展纵向研究、加强认知研究和实施干预研究,将可能成为心理弹性作用机制的研究趋势.     

The Effects of Ignoring a Level in Multilevel Analysis

Ignoring a level can have a substantial impact on the conclusions of a multilevel analysis. For intercept-only models and for balanced data, we derive these effects analytically. For more complex random intercept models or for unbalanced data, a simulation study is performed. Most important effects concern estimates and corresponding standard errors of the variance parameters at adjacent levels and of the coefficients of the predictors at the ignored and bordering levels. Therefore, we conclude that if the researcher is interested in a specific level, she/he should account for both the upper and lower level. Conclusions are illustrated using empirical data from educational research.     

A Short Note on Interpretation in the Dual Change Score Model

The latent change score framework allows for estimating a variety of univariate trajectory models, such as the no change, linear change, exponential forms of change, as well as multivariate trajectory models that allow for coupling between two or more constructs. A particularly attractive feature of these models is that it is easy to decompose and interpret aspects of change. One particularly flexible model, the dual change score model, has two components of change: a proportional change component that depends on scores at the previous time point, and a constant change component that is additive. We demonstrate through simulation and an empirical example that in a correctly specified model, the correlation between the proportional change parameter and the mean of the constant change component can approach either ?1 or 1, thus complicating interpretation. We provide recommendations and code to aid researchers’ ability to diagnose this issue in their own data.     

Individualization of data-segment-related parameters for improvement of EEG signal classification in brain-computer interface

In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI.