D. Betsy McCoach

Effect of the Number of Variables on Measures of Fit in Structural Equation Modeling

There has been relatively little systematic investigation of the effect of the number of variables on measures of model fit in structural equation modeling. There is conflicting evidence as to whether measures of fit tend to improve or decline as more variables are added to the model. We consider 3 different types of specification error: minor factors, 2-factor models, and method errors. Using a formal method based on the noncentrality parameter (NCP), we find that root mean squared error of approximation (RMSEA) tends to improve regardless of the type of specification error and that the comparative fit index (CFI) and Tucker-Lewis Index (TLI), generally, though not always, tend to worsen as the number of variables in the model increases. The formal method that we develop can be used to investigate other measures of fit and other types of misspecification.

The Performance of RMSEA in Models With Small Degrees of Freedom

Given that the root mean square error of approximation (RMSEA) is currently one of the most popular measures of goodness-of-model fit within structural equation modeling (SEM), it is important to know how well the RMSEA performs in models with small degrees of freedom (df). Unfortunately, most previous work on the RMSEA and its confidence interval has focused on models with a large df. Building on the work of Chen et al. to examine the impact of small df on the RMSEA, we conducted a theoretical analysis and a Monte Carlo simulation using correctly specified models with varying df and sample size. The results of our investigation indicate that when the cutoff values are used to assess the fit of the properly specified models with small df and small sample size, the RMSEA too often falsely indicates a poor fitting model. We recommend not computing the RMSEA for small df models, especially those with small sample sizes, but rather estimating parameters that were not originally specified in the model.

The Underachievement of Gifted Students: What Do We Know and Where Do We Go?:

The process of defining underachievement, identifying underachieving gifted students, and explaining the reasons for this underachievement continues to stir controversy among practitioners, researchers, and clinicians. Despite this interest, the underachievement of gifted students remains an enigma. This article reviews and analyzes three decades of research on the underachievement of gifted students in an attempt to clarify the present state of research. The problems inherent in defining and identifing underachieving gifted students are given special attention. The authors also include suggestions for those interested in pursuing potentially promising new lines of research and inquiry in this area.

Factors That Differentiate Underachieving Gifted Students From High-Achieving Gifted Students

The purpose of this study was to examine whether gifted achievers and gifted underachievers differ in their general academic self-perceptions, attitudes toward school, attitudes toward teachers, motivation and self-regulation, and goal valuation. The sample consisted of 56 gifted underachievers and 122 gifted achievers from 28 high schools nationwide. Gifted achievers and gifted underachievers differed in their attitudes toward school, attitudes toward teachers, motivation/self-regulation, and goal valuation, but not their academic self-perceptions. In addition, the logistic regression analysis correctly classified over 81% of the sample as either gifted achievers or gifted underachievers using their motivation/self-regulation and goal valuation self-ratings. This study represents an important step toward quantifying factors related to the underachieviement of gifted adolescents.

VOCABULARY INTERVENTION FOR KINDERGARTEN STUDENTS: COMPARING EXTENDED INSTRUCTION TO EMBEDDED INSTRUCTION AND INCIDENTAL EXPOSURE

The purpose of the two studies reported in this article was to evaluate the effectiveness of extended vocabulary instruction during storybook reading with kindergarten students within a small-group intervention setting. Extended vocabulary instruction is characterized by explicit teaching that includes both contextual and definitional information, multiple exposures to target words in varied contexts, and experiences that promote deep processing of word meanings. In Study One, we compared extended instruction of target words to incidental exposure. In Study Two, we compared extended instruction to embedded instruction (i.e., providing simple definitions within the context of the story). Our findings indicated that extended instruction resulted in greater word learning than either incidental exposure or embedded instruction. Moreover, students maintained much of their understanding of word meanings six to eight weeks after instruction. Implications are discussed in relation to a tri-level approach to vocabulary instruction and intervention for kindergarten students at risk for language and reading disabilities.

Direct Vocabulary Instruction in Kindergarten: Teaching for Breadth versus Depth

The purpose of this study was to compare 2 methods for directly teaching word meanings to kindergarten students within storybook read‐alouds that varied in instructional time and depth of instruction along with a control condition that provided students with incidental exposure to target words. Embedded instruction introduces target word meanings during storybook readings in a time‐efficient manner. Extended instruction is more time intensive but provides multiple opportunities to interact with target words outside the context of the story. Participants included 42 kindergarten students who were taught 9 target words, 3 with each method. Target words were counterbalanced in a within‐subjects design. Findings indicated that extended instruction resulted in more full and refined word knowledge, while embedded instruction resulted in partial knowledge of target vocabulary. Implications are discussed in relation to the strengths and limitations of different approaches to direct vocabulary instruction in kindergarten and the trade‐offs between instruction that focuses on teaching for breadth versus depth.

The Effects of Differentiated Instruction and Enrichment Pedagogy on Reading Achievement in Five Elementary Schools

This experimental study examined the effect of a differentiated, enriched reading program on students’ oral reading fluency and comprehension using the schoolwide enrichment model–reading (SEM-R). Treatment and control conditions were randomly assigned to 63 teachers and 1,192 second through fifth grade students across five elementary schools. Using multilevel modeling, significant differences favoring the SEM-R were found in reading fluency in two schools (Cohen’s d effect sizes of .33 and .10) and in reading comprehension in the high-poverty urban school (Cohen’s d = .27), with no achievement differences in the remaining schools. These results demonstrate that an enrichment reading approach, with differentiated instruction and less whole group instruction, was as effective as or more effective than a traditional whole group basal approach.

Increasing Student Mathematics Self-Efficacy through Teacher Training.

Teachers can modify their instructional strategies with minimal training and effort, and this can result in increases in their students’ self-efficacy. Self-efficacy judgments are based on four sources of information: an individuals own past performance, vicarious experiences of observing the performances of others, verbal persuasion that one possesses certain capabilities, and physiological states. Individuals use these four sources of information to judge their capability to complete future tasks. Teachers who capitalize on the influence of the strongest of these sources—past performances, observations of others as models, and verbal persuasion—produce more confident students. The following instructional strategies increase student self-efficacy: • Reviewing lesson accomplishments from the previous day, posting the current lessons objectives prior to instruction, drawing attention to the lesson objectives as they are covered, and reviewing the lesson objectives at the end of the lesson. • Asking students to record each day on a calendar something new they learned that day or something at which they excelled. • Prompting students who perform poorly to attribute their failures to lack of effort and encouraging them to try harder. • Drawing students’ attention to their growth and complimenting them on their specific skills. • Using student models early to demonstrate some aspects of a lesson to remind them that other students like themselves are mastering the material and therefore they can master it also. Teachers who use these strategies on a daily basis produce students who are more confident in their academic skills.

The Differential Impact of Academic Self-Regulatory Methods on Academic Achievement Among University Students With and Without Learning Disabilities

Although research on academic self-regulation has proliferated in recent years, no studies have investigated the question of whether the perceived usefulness and the use of standard self-regulated learning strategies and compensation strategies provide a differential prediction of academic achievement for university students with and without learning disabilities (LD). We developed and tested a model explaining interrelationships among self-regulatory variables and grade point average (GPA) using structural equation modeling and multiple group analysis for students with LD (n = 53) and without LD (n = 421). Data were gathered using a new instrument, the Learning Strategies and Study Skills survey. The results of this study indicate that students with LD differed significantly from students without LD in the relationships between their motivation for and use of standard self-regulated learning strategies and compensation strategies, which in turn provided a differential explanation of academic achievement for students with and without LD. These paths of influence and idiosyncrasies of academic self-regulation among students with LD were interpreted in terms of social cognitive theory, metacognitive theory, and research conducted in the LD field.

Dealing With Dependence (Part I): Understanding the Effects of Clustered Data

This article provides a conceptual introduction to the issues surrounding the analysis of clustered (nested) data. We define the intraclass correlation coefficient (ICC) and the design effect, and we explain their effect on the standard error. When the ICC is greater than 0, then the design effect is greater than 1. In such a scenario, the standard error produced under the assumption of independence is underestimated. This increases the Type I error rate. We provide a short illustration of the effect of non-independence on the standard error. We show that after accounting for the design effect, our decision about the statistical significance of the test statistic changes. When we fail to account for the clustered nature of the data, we conclude that the difference between the two groups is statistically significant. However, once we adjust the standard error for the design effect, the difference is no longer statistically significant.