Teacher views on the effects of the change from coursework to controlled assessment in GCSEs

When General Certificates of Secondary Education (GCSEs) were introduced in the late 1980s, coursework was included as a requirement in many subjects. Coursework was intended to help best represent students' attainments, but concerns about various issues (e.g., tasks becoming formulaic, authenticity of student work, workload) led to a review and ultimately to its replacement with “controlled assessments”. This research investigated the effects of this change. A questionnaire was completed by 346 teachers across 6 subjects. Most teachers felt the risks of plagiarism are similar or have reduced. Two thirds of teachers reported that the introduction of controlled assessment has affected the way they teach, and around 60% feel it has affected the nature of the knowledge and skills learnt by students. There are reportedly practical challenges relating to timetabling, student absence, and increased administration. However, over 70% of teachers consider it “important” or “very important” to have some form of internal assessment in their subject.     

Exploratory analyses to improve model fit: Errors due to misspecification and a strategy to reduce their occurrence

Two Lagrange multiplier (LM) methods may be used in specification searches for adding parameters to models: one based on univariate LM tests and respecification of the model (LM‐respecified method) and the other based on a partitioning of multivariate LM tests (LM‐incremental method). These methods may result in extraneous parameters being included in models due to either sampling error or the model being misspecified. A 2‐stage specification search may be used to reduce errors due to misspecification. In the 1st stage, parameters are added to models based on LM tests to maximize fit. Second, parameters added in the 1st stage are deleted if they are no longer necessary to maintain model fit. Illustrations are presented to demonstrate that errors due to misspecification occur with the LM‐respecified method and are even more likely with the LM‐incremental approach. These illustrations also show how the deletion stage can help eliminate some of these errors.     

Let’s Stay Together

全球许多传媒实况报道了奥巴马第二任就职大典的过程,包括祷告会、宣誓就职、检阅仪仗队、午餐、就职庆祝游行、晚餐以及舞会。在整个就职大典中,舞会扮演了十分重要的角色。在今年的舞会上,奥巴马夫妇跳了第一支舞,由著名演员、歌唱家詹妮弗·哈德森(Jennifer Hudson)伴唱,歌曲是"让我们在一起"(Let’s Stay Together),这是一首七十年代的当红流行曲,被列入美国五百首最了不起歌曲排行榜的第六十名。春节、情人节,一中一西两个节日,总是让整个虽然还春寒料峭的2月天都洋溢着温暖甜蜜的气氛,这首歌很是应景呢。     

Stereotype threat in manual labor settings for African American and Caucasian participants

Stereotype threat has primarily been studied with regard to test performance in academic settings, testing aptitude, ability, and intelligence, and it has been found to cause both behavioral and cognitive decrements. Although there is research on stereotype threat in the workplace, this too is usually conducted in upper‐level or more academically based job tasks. This article concentrates on how stereotype threat affects those in manual labor workplace settings. This research, however, sought to test subjects on a behavioral task in a workplace setting to see if the results mirror those in academia. Stereotype threat in academic settings has been shown to cause both behavioral and cognitive decrements. It was theorized that stereotype threat would cause performance decrements for the African American participants. Participants were undergraduate students—60 Caucasian and 60 African American. All performed two manual labor tasks, sorting and assembling nuts and bolts, and a math test, half while under stereotype threat and half without stereotype threat manipulation. Results yielded significant differences between the two conditions for African Americans on both the academic and nonacademic/manual labor tasks.     

Does adviser mentoring add value? A longitudinal study of mentoring and doctoral student outcomes

This study of the impact of doctoral adviser mentoring on student outcomes was undertaken in response to earlier research that found (a) students with greater incoming potential received more adviser mentoring, and (b) adviser mentoring did not significantly contribute to important student outcomes, including research productivity [Green, S. G., and Bauer, T. N. (1995). Personnel Psychology 48(3): 537–561]. In this longitudinal study spanning 5 1/2 years, the effect of mentorship on the research productivity, career commitment, and self-efficacy of Ph.D. students in the ‘hard’ sciences was assessed, while controlling for indicators of ability and attitudes at program entry. Positive benefits of mentoring were found for subsequent productivity and self-efficacy. Mentoring was not significantly associated with commitment to a research career.     

Incorporating Uncertainty Into Parallel Analysis for Choosing the Number of Factors via Bayesian Methods

A number of psychometricians have suggested that parallel analysis (PA) tends to yield more accurate results in determining the number of factors in comparison with other statistical methods. Nevertheless, all too often PA can suggest an incorrect number of factors, particularly in statistically unfavorable conditions (e.g., small sample sizes and low factor loadings). Because of this, researchers have recommended using multiple methods to make judgments about the number of factors to extract. Implicit in this recommendation is that, when the number of factors is chosen based on PA, uncertainty nevertheless exists. We propose a Bayesian parallel analysis (B-PA) method to incorporate the uncertainty with decisions about the number of factors. B-PA yields a probability distribution for the various possible numbers of factors. We implement and compare B-PA with a frequentist approach, revised parallel analysis (R-PA), in the contexts of real and simulated data. Results show that B-PA provides relevant information regarding the uncertainty in determining the number of factors, particularly under conditions with small sample sizes, low factor loadings, and less distinguishable factors. Even if the indicated number of factors with the highest probability is incorrect, B-PA can show a sizable probability of retaining the correct number of factors. Interestingly, when the mode of the distribution of the probabilities associated with different numbers of factors was treated as the number of factors to retain, B-PA was somewhat more accurate than R-PA in a majority of the conditions.     

Loglinear analysis of cross-classified ordinal data: applications in developmental research

This article provides an introduction to loglinear analysis of cross-classification tables, including tables with nominal and ordinal variables. Loglinear models offer several advantages over the more commonly used chi-square test of independence, including the ability to analyze 3-, 4-, and higher-way interactions, the ability to determine whether the association between variables is linear or nonlinear, and the ability to interpret scale scores assigned to categories of an ordinal variable. After a review of the advantages of loglinear modeling, the chi-square test of independence is compared with the loglinear model of independence. This comparison serves to introduce the notation and terminology of loglinear modeling. The overall strategy of loglinear modeling is introduced next; then special loglinear models for ordinal data are reviewed. Each model discussed in the article is applied to data from the developmental literature.