Contrasting the Effectiveness of Fluency Interventions for Students with or At Risk for Learning Disabilities: A Multilevel Random Coefficient Modeling Meta‐Analysis

This study had two purposes. First, we sought to compare the overall effectiveness of different types of fluency interventions for students with learning disabilities (LD). Second, we attempted to identify how individual‐ and class‐level characteristics moderated each intervention's effectiveness. We used multilevel random coefficient modeling to analyze results from 30 single‐subject studies involving 107 students with or at risk for LD. Doing so allowed us to assess the intervention's impact in terms of both intercept‐ (i.e., mean difference between study phases) and slope‐level (i.e., rate of growth) changes. Results indicated that two motivation‐focused interventions were significantly more effective than other types of interventions across gender, age, and placement. The least effective intervention was word recognition training; this finding was consistent across all levels of analyzed variables.     

A Comparative Demographic Study of Student Teachers from the North and South of Ireland

90.4% of the 1977 entrants to concurrent general Teacher Education courses in Ireland North and South responded to a demographic questionnaire. The results are analysed and the two regions contrasted with respect to personal, professional and geographical variables. These indicate that some interesting differences exist but that there are many overall similarities.     

Piecing together the puzzle: development of the Societal Attitudes towards Autism (SATA) scale

The rise in the prevalence of autism creates a need for a reliable and valid measure of attitudes towards autism. The current study describes the development of a brief 16‐ item measure of Societal Attitudes towards Autism (SATA) that exhibits sound psychometric properties and has a demonstrated ability to discriminate between expert and general college student samples. The final SATA was the result of pilot work on 75 items and exploratory and confirmatory factor analyses on a 45‐item version with 475 undergraduates. Knowledge and personal distance subscales yielded inconsistent reliability and validity outcomes. The SATA showed strong content and construct validity as evidenced by known groups discrimination, and predicted associations with an attitude towards disability measure, an autism preference item, and a measure of implicit attitudes towards disabilities.     

伦理学、经济学与高等教育——作为一种公共物品的大学

本文探讨了大学教育的经济学与公共物品的功利主义概念.笔者认为,现代大学的发展与演变是与物品这一概念的变化相伴随的.早期的自由主义经济学理论(代表人物如穆勒)认为,一种物品能够被供给,要么是因为它作为一个整体对社区有益,要么是因为它不能或者不应该通过私人的方式供给,比如国防.正如国家和社会在规则制定与福利供给方面变得越来越复杂,物品的定义也日益复杂.政策的合理性往往是通过它有助于公共财富的积累而得到证明的,具体来讲,政策应该要么满足社会受众的某种具体利益(这种福利是需要政府提供的,如学生的受教育经费),要么增进社会作为一个整体的利益(比如受教育人口的增长).这与现代大学的成长和进步是并行不悖且相互砥砺的.新自由主义的观点是对以上观点的一种挑战,它认为私人的投资与供给所产生的回报要远高于公共的投资与供给,而且从道义上来讲,个人与社会对于供给何种物品应该具有选择权;同时,由经济效能与个人选择所带来的混合的社会利益可以获致更多的集体公共物品.然而,这种观点没有考虑到规范的议题(如公平与社会正义)的影响.因此,本文旨在思考上述几重张力对于以下问题的影响,这些问题包括现代大学与公共物品的当代意涵之间的联系,以及大学占统治地位的经济功能能否与其在知识和文化方面的使命相协调.     

Mixed Mode Latent Class Analysis: An Examination of Fit Index Performance for Classification

This Monte Carlo study examines the performance of fit indices commonly used by applied researchers interested in finite mixture modeling for the purposes of classification. Conditions for the simulation study were selected to reflect conditions found in applied educational and psychological research. The factors included in the investigation were metric level of indicators, sample size, and class prevalence. All models contained a combination of categorical and continuous indicators. All categorical indicators were dichotomous, and continuous indicators were normally distributed. The fit indices examined were Akaike’s information criterion, Bayesian information criterion (BIC), sample size-adjusted Bayesian information criterion (SSBIC), integrated classification likelihood criterion with Bayesian-type approximation, and Lo–Mendell–Rubin likelihood ratio test. Overall, SSBIC tended to identify the correct solution with higher frequency than other indices. BIC tended to identify the correct solution with higher frequency than the other indices in models with more continuous than categorical indicators, or when rare classes were absent.