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Bias attributable to missing data conditions is often ignored by applied researchers. In response, journal editors (e.g., von Elm et al., 2007) and governing bodies (e.g., Wilkinson & APA Task Force, 1999) are increasingly calling for missing data analysis as routine assessment. The term missing data is traditionally used in reference to the condition of a study participant missing one or more, but not all, survey responses, test items, or other data points. In addition, methods to control the bias resulting from nonequivalent comparison groups could be viewed as a “missing data” scenario (Dates & King, 2009), though in this chapter we emphasize the former. Consideration of the effects of missing data is critically needed in studies of giftedness. Frequent causes of missing data include study dropouts in longitudinal studies of children through adolescence and adulthood and incomplete data records due to oversight on the part of respondents, refusal to answer certain questions, or a host of other reasons. Missing values can sometimes be safely ignored if observed for only a very small percentage of items. Yet even a few omitted responses can significantly bias results if the data are missing systematically. For example, consider a hypothetical study of 10,000 children in which 5% are categorized as gifted. Observing a modest missing data rate of 2% may nonetheless be of concern should the absent values all fall within the gifted population (i.e., 40% of gifted children would have missing data). In such an instance, ignoring missing values or applying ineffective adjustments may drastically alter study outcomes and lead to invalid conclusions. It is therefore imperative that giftedness researchers carefully examine the consequences of even modest amounts of missing data and familiarize themselves with best practices for handling these conditions. This chapter proceeds by briefly describing a typology for categorizing missing data. We then provide a review of the strengths and weaknesses of a number of traditional and contemporary methodologies for handling missing data, followed by discussion and detailed application of a new, “hybrid” approach that is capable of adjusting for both item-level missing data and missing cases in a nonequivalent groups design, a scenario frequently faced by the applied researcher. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

This chapter focuses on students with a unique profile of being strong in nonverbal areas but much lower in verbal abilities, a group that is surfacing with some regularity now that nonverbal tests are being used more frequently in schools. This particular group is included in the discussion of low-income gifted because of the prevailing view that strong nonverbal aptitudes are found in culturally different youngsters (e.g., Native Americans, African Americans, English language learners). We know that culturally different children often are found in poverty at higher rates, so discussion of this subset of gifted learners offers the opportunity to learn more about how to recognize and nurture giftedness in underserved groups. A literature review exploring aspects relevant to this special population opens the chapter. Aspects of the review include a working definition of high nonverbal, low verbal giftedness; identification route influences; student performance; and learning needs and supports, styles, and preferences. Drawing on case study findings of special needs gifted learners, this chapter discusses unique characteristics, educational needs, learning opportunities, and similarities/differences in student, parent, and teacher perspectives found in this special needs group. Examples and quotations from student vignettes illustrate these perspectives. Through integration of the literature and the case study findings, this chapter then provides ideas for school-based practitioners and gifted coordinators in the development of gifted high nonverbal, low verbal children. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

To understand and better serve the needs of the gifted, the field must produce quality research to inform practice. Far too often, recommendations in the field of gifted education are based on conventional wisdom or anecdotes instead of data-driven research. This book provides accessible introductory treatments of several modern analytic methods that can be used to advance our knowledge within the field of gifted education. It also alerts researchers about potential pitfalls of inferential statistics in general as well as analytical areas of particular concern within the field of giftedness. As someone who is passionate about both gifted education and research methodology, I am honored to be able to provide some comments about the techniques contained in this volume and to provide thoughts about future methodological directions for our field. This chapter contains three sections. First, I describe some of my own research within the field of gifted education. Then, I briefly describe studies that I am planning or contemplating that utilize some of the methodologies described in this book. Finally, I offer some thoughts on methodological issues that researchers in the field of gifted education should consider as they are planning their studies. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

The history of hierarchical linear models generally traces its roots back to the work of Robinson (1950) in recognizing contextual effects. The discovery that Robinson made is sometimes thought of as the “frog pond” theory and is fairly simple to relate. Suppose that a researcher was conducting an analysis on environmental factors affecting the weight of frogs. And also suppose that two of the frogs being analyzed both weighed 500 grams. However, the first frog that weighed 500 grams was drawn from a pond where it was the largest frog in the pond. The second frog that weighed 500 grams was drawn from a lake where 500 grams was the weight of the average frog for that pond. In a typical ordinary least squares (OLS) analysis, the researcher would have no way of honoring the pond nesting structure for these two frogs and would then erroneously assume that the environmental factors affecting frog growth were the same regardless of which pond was home to the frog. This same problem often presents itself when researchers are analyzing data obtained from school research. For example, suppose that a researcher were to try to run a regression analysis in which student grade point average (GPA) scores were used to predict performance on the Stanford Achievement Test (SAT). It stands to reason that, within a given school, GPAs might be good predictors of SAT scores where higher GPAs correlate with higher scores on the SAT. However, across schools, the relationship between GPA and SAT may be dramatically different. Whereas in a low-performing school, a student with a 4.0 GPA might score only a 1,000 on the SAT, a student with a 4.0 GPA in an elite, high-performing private school might score a 1,600 on the SAT. This happens because GPA scores are not independent of the school from which they are drawn. This problem of independence of observations is often a factor when collecting data in schools, yet until the development of hierarchical linear modeling (HLM), no methods for dealing appropriately with such data existed. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

Given my interest in statistics and design, I was pleased to be able to read the chapters in Parts 1 and 2 of this book and think about applying the concepts within them to my own work. I am going to respond to the earlier chapters from two different perspectives—as a former editor of two of the major journals in the field of gifted education and as a current reviewer for several others, and as a researcher in the field of gifted education. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

In this chapter, I argue that researchers of giftedness should shift emphasis from null hypothesis significance testing (NHST) to confidence intervals (CIs) and other preferred techniques. I start with a brief summary of arguments put forward by statistical reformers and describe basic features of CIs. I then report a small survey of current statistical practices in giftedness research and a comparison with practices in psychology generally. The main part of the chapter is a discussion of p values and CIs in relation to replication; I focus on the Pearson’s r correlation, because r is so widely used in giftedness research. A simulation demonstrates the large variability in r, and even greater variability in p values, over replications of a simple experiment. Calculations confirm that a p value gives only very vague information about replication, and therefore any p value could easily have been very different, simply because of sampling variability (Cumming, 2008). The severe deficiencies of p values in relation to replication give an additional strong reason for giftedness researchers to turn from NHST and pursue statistical reform with vigor. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

The chapters in Parts 1 and 2 of this book provide considerable detail about myriad approaches to quantitative and mixed method research designs and important issues to consider when using those designs. The authors build a compelling case for the importance of using those designs in moving the research base in gifted studies toward greater sophistication. The first author’s awareness of the need to enhance the literature base by incorporating increasingly sophisticated research approaches crystallized in November 1997 at the National Association of Gifted Children conference just after a symposium on research methodology had ended, when Laurence J. Coleman, Michael Pyryt, and I stood talking about the session. I asked Michael if he thought the field of gifted studies was ready to move in a postpositivistic direction. As he was known to do, he thought about what I had asked him and carefully responded with his own question: “How can the field of gifted studies become postpositivistic when our research base is still prepositivistic?” That was Michael’s way of critiquing our field as being reliant on too many studies that were theoretically unsound and poorly conducted. The symposium had emphasized the epistemological and ontological assumptions of qualitative and quantitative approaches to research. Arguments were made during the session for the utility of both approaches, but with an emphasis being placed on the limitations of each in terms of the nature of the research questions that can and cannot be addressed by either. While few academics these days still question the usefulness of either quantitative or qualitative research (see Capraro & Thompson, 2008; Larabee, 2003), what Michael did in few words was summarize the research in the field of gifted studies circa 1997. This book is intended to be both a catalyst and a primer for future research in gifted studies to help us move from prepositivistic to positivistic and postpositivistic approaches. Michael would be pleased. In this chapter, we describe the various research approaches we have utilized over the years, including both qualitative and quantitative techniques. Other chapters in this volume have provided fodder for additional discussions about our research. As a result of the ideas generated by our fellow authors in this volume, we propose an enhancement to our existing research through the use of structural equation modeling (SEM) as described by Kline in chapter 7 of this volume. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

Generating accurate and reliable data is critical to strong scientific inquiry and essential to furthering giftedness research yet is often neglected by even the most attentive researchers. From a measurement standpoint, much of the focus has been on the question of score validity (i.e., do assessments accurately identify gifted and talented students?) and test fairness (e.g., do assessments provide an unfair advantage based on socioeconomic status, race, ethnicity, culture, language, and other contextual factors that may affect assessment results?). Although valid and fair measurement is certainly a goal within the larger fields of both measurement and giftedness research, before score validity and test fairness can be addressed, instruments must first generate consistently repeatable and replicable scores. If a measure cannot generate reliable scores across the population for which the measure is intended, it will not be able to generate valid scores. The ability of a measure to generate repeatable, consistent scores is a psychometric concept known as reliability. This chapter’s purpose is to explain the integral role of score reliability and reliability generalization across studies in advancing both the science of giftedness and the practice of identifying and measuring the progress of students that are gifted. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

The family of statistical techniques that make up structural equation modeling (SEM) offers many potential advantages in education research, including studies about gifted students. These techniques are highly versatile and permit the evaluation of a wide range of hypotheses, including those about direct or indirect effects, measurement, or mean differences on either observed or latent variables. They have also become quite popular among researchers. Indeed, it is increasingly difficult to look through an issue of an education research journal and not find at least one article in which results of SEM analyses are reported. In gifted research, SEM has been used less often, but there are more and more such studies in this area, too. However, there are some potential pitfalls of using SEM in gifted research that are inherent to the study of a special population, including range restriction, regression effects, and the need for large samples. Accordingly, the goals of this chapter are to (a) review the general characteristics of SEM with special consideration of possible advantages in educational research and (b) consider specific potential pitfalls of using SEM in gifted research. Outlined next are important issues in basically all applications of SEM. Later sections deal with problems specific to the use of SEM in education research in general and in gifted research in particular. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

In this chapter, our purpose is to present mixed research as an alternative approach toward addressing five challenges characterizing the field of gifted education research. First, we provide a critique of the dominant types of monomethods—quantitative and qualitative research—utilized in the field of gifted education. In so doing, we contend that mixed research, which involves the mixing or embedding of quantitative and qualitative research approaches within the same framework, offers a better potential to address the five challenges than do monomethod studies. Second, we present a formal definition of mixed research, followed by a discussion of the mixed research process. Third, we present a mixed data collection model applicable for identifying various rationales and purposes for conducting mixed data collection techniques within a single study or a gifted program of research. This discussion is followed by a presentation of mixed analytical techniques (i.e., techniques involving combining quantitative and qualitative analyses). Additionally, we contextualize our discussion of mixed data collection and analytical techniques with illustrative examples addressing the five gifted education research challenges. Our chapter concludes with a call for researchers to conduct more mixed studies in gifted education. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

General differences in achievement on international mathematics assessments between Korean male and female students have significantly narrowed. However, the achievement gap between boys and girls at higher performance levels is still wide, and the proportion of females enrolled in special programs for gifted students at certified institutions is less than 30 %. To rectify this trend, Korea launched the Women into Science and Engineering [WISE] Project, an education program aimed at supporting girls in the upper level score brackets to continuously improve their mathematical capability and remain in those brackets. In this chapter, the mathematical capability of three students participating in the WISE project was examined, their learning patterns and perspectives on mathematics were explored, and the support of their parents considered. There were five distinct findings that vividly portray the realities facing Korean female students who achieve high scores in mathematics and their future potential in mathematics: (1) The girls' problem-solving abilities differed according to the types and characteristics of the problems examined and not the content area covered; (2) The three girls believed it was desirable for girls to study mathematics through continuous practice, and that they must do this in order to perform as well as boys who had innate talents in mathematics; (3) The girls preferred group activities to individual activities; (4) The female participants possessed a strong will to discuss their future career paths and to realize their dreams; and (5) Even if one parent paid attention to the mathematical studies of a daughter and enthusiastically helped with problem solving, this may raise her potential immensely because the daughter might cultivate a strong will and positive attitude towards mathematical studies. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

Most of the people I evaluate for possible learning disabilities are like Rebekah, bright-to-gifted individuals whose struggles are at times inexplicable or invisible to those around them. Frequently, those who are twice-exceptional find that both their high intelligence and their learning disabilities go unrecognized, because the learning disabilities bring down the broad-based cognitive scores, and their strong cognitive abilities enable them to at least partially compensate for their weaknesses. I have often seen children who can barely decode nevertheless score in the average range on reading tests. In this case, Rebekah's parents referred her for assessment for three main reasons. First, because they home-school her, they wish to have an objective measurement of how she is progressing in the various curricular areas so as to guide their future curriculum delivery. Second, because they have observed that she has a great deal of difficulty expressing herself orally and in writing, whether about her own ideas or about what she has read, they are concerned about possible expressive language difficulties. Finally, her parents have observed some "tuning-out" behaviors, possibly consistent with auditory processing or attentional problems, and wish to have these possibilities investigated. Rebekah is a highly gifted 7-year-old girl who shows strengths in fluid reasoning, short-term and working memory, executive functioning, and receptive language. She is able to consider alternatives, is flexible in generation and use of strategies, and shows a level of comfort with abstract thought that is unusual even among adults. In contrast, her speed of information retrieval and response is exceedingly slow by comparison, causing a significant impediment to her use of expressive language. When she is pressed for time, or when she is not able to use verbatim recall as a scaffold, she has extreme difficulty in quickly finding the words and constructing the sentences she needs to express her ideas. As a twice-exceptional learner, Rebekah is at risk for having both her giftedness and her difficulties in processing speed and expressive language go unrecognized. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

ASC (Autism Spectrum Conditions) can appear on all levels of cognitive function, from profound mental retardation to intellectual giftedness. Therefore, the intelligence level is not part of the definition of any ASC. However, there is an excess of mental retardation in ASC. It is assumed that about 55% of all affected individuals also show a mental handicap (Baird et al., 2006), whereas the expected rate in the general population is approximately 3%. Thus, it is the most frequent coexisting psychiatric problem in ASC. Females with an ASC are even more likely to show a low intelligence quotient (IQ). On the other hand, most people with Asperger syndrome have an IQ in the average or high range. It is common in clinical settings today to also label people with ASC according to their general mental capacity in order to enhance communication between experts. One often hears the terms "high-functioning" (HF) or "low-functioning" (LF) in association with ASC individuals. These are still unofficial and not clearly defined subcategories. HF refers to the absence of mental retardation (i.e., IQ > 70) or sometimes even to the absence of learning disability (i.e., IQ > 85), whereas LF generally indicates mental retardation (i.e., IQ < 70). This differentiation is a little misleading and may be misunderstood by laypersons, as HF ASC does not necessarily indicate that the respective individual does particularly well in everyday life (Bolte & Poustka, 2002). (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

For the purposes of this chapter, misdiagnosis is defined as a child who is identified as having a behavioral problem or psychological diagnosis without taking his or her gifted intellect and the normal behaviors of the gifted into consideration. The hypothesis for this chapter is that gifted Hispanic and African American children commonly served in the United States public school system often are underidentified as gifted due to behaviors related to their culture, as opposed to their level of giftedness; furthermore, the underidentification can lead to misdiagnosis. This chapter presents anecdotal evidence supporting the hypothesis that giftedness often is overlooked in culturally and linguistically diverse student populations. The children's identification is then frequently misdiagnosed or misrepresented. Some of the reasons for the misdiagnosis evidenced include: language differences, cultural norm differences, multiple school placements, institutionalized racism, and the prevailing lack of knowledge about giftedness in general. Various cultural populations likely carry more psychological diagnoses due to idiosyncratic issues that are discussed in this chapter. Carrying a psychological diagnosis further obviates overlooking giftedness in the culturally diverse population. Recommendations for identification and remediation of the misdiagnosis problem also are discussed. The author posed questions related to the stated hypothesis, which guided the collection of anecdotal information for this chapter. During individual interviews the questions were posed to educators, paraprofessionals, and parents. The members of this group included an elementary school principal, the head psychologist of a school district, an elementary school teacher, a parent-educator, a parent, a school district gifted coordinator, and a pediatric neuropsychologist. The individuals interviewed included an equal representation of Hispanics, African Americans, and Anglos, and one American Indian. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

In an increasingly global world, a deliberate effort must be made to recognize the value of all people of the world. Our Western view of exceptional behavior is not necessarily shared by other people of the world, nor is our view the only valid perspective. Throughout history, giftedness has been defined in a variety of ways. In the following pages we will discuss intelligence as giftedness, giftedness beyond intelligence, the debate on whether giftedness is a human trait or a behavior, and views of giftedness by scholars who are not White and male. Finally, we will take a comprehensive look at giftedness as defined by people across the globe. The challenge for gifted education in a globalized world is to utilize learning situations, appropriate curriculum, and available resources to develop necessary skills for a thoughtful, successful citizenship. Our challenge is to be sine our students are aware of their power and their responsibility toward others in the world. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

The author discusses using mathematics as an equalizer for gifted Latino/a adolescent learners. In 1994, the Board of Directors of the National Council of Teachers of Mathematics created a task force to explore the topic of "mathematically promising" students. This period marked a new era in the discussion of what mathematical ability had traditionally meant. The task force had as its primary goal to guide discussions among mathematicians and mathematics educators about the mathematically talented student. The task force reported that an indicator of being a mathematically promising student is based on his or her potential to become a good problem solver. The team described being "mathematically promised" as individual attributes connected to four nonfixed variables: (1) ability, (2) motivation, (3) beliefs, and (4) experiences and opportunities. By examining the mathematical "potential" of students, this new definition for identifying talented students may have alluded, for the first time, to students who had not been traditionally identified as gifted, talented, intelligent, or precocious, but rather to those who have been excluded from previous definitions of gifted and talented and therefore excluded from rich mathematical opportunities. This chapter addresses: identification of mathematically promising students; the realities of today's schooling; differentiating mathematics teaching and learning in the United States; differentiated instruction within inclusive classrooms; giftedness and culture; a perspective from Latin America—classroom dynamics in mathematics; language and mathematics; multiple literacies in a mathematics classroom; writing mathematics, reading mathematics, and talking mathematics; making mathematics classrooms culturally and socially responsive; integrating the history of mathematics; effective strategies for English language learners; and teaching mathematics to talented Latino/a students. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

This chapter addresses the background of American Indian schooling and the need for cultural responsiveness to advance student achievement by defining giftedness in an American Indian context. It also examines promising models for appropriate programming and services for American Indian students who exhibit giftedness and talent. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

"Twice-exceptional" children are those children who are gifted yet also have a disability that impacts their ability to fully develop in all areas (Baum & Owen, 2004). In this chapter, the term twice-exceptional will refer not to a given definition of either giftedness or disability, but to children who "exhibit remarkable strengths in some areas and disabling weaknesses in others" (Weinfeld, Barnes-Robinson, Jeweler, & Shevitz, 2002). This chapter will focus on those children who exhibit learning and social difficulties rather than physical or sensory impairments, primarily because they constitute the largest population of twice-exceptional students (National Educational Association, 2006). The following topics are addressed: identification issues, paradoxical characteristics, paradoxical programming, and paradoxical curriculum. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: create)

This chapter focuses on a school-based concept of giftedness wherein an individual demonstrates outstanding levels of achievement or competence in one or more domains. The conception of giftedness employed by a school district is the foundation for all subsequent decisions such as identification, programming, curricula, teacher preparation, and program evaluation. Additionally, given the diverse needs of gifted and talented students, policies must be in place and programming must be flexible and responsive to the needs of particular learners at a given stage of development in order for a student's ability in a domain to be realized and demonstrated. Teachers who work with these learners must be carefully selected and trained in gifted education. Finally, gifted learners require high-powered curriculum to ensure a depth and sophistication of content, commensurate with their unique needs. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

An overview of definitions of giftedness, special populations of gifted and talented children, methods of identification, and a continuum of services are summarized in this chapter. These services include organizational strategies (such as instructional grouping options), instructional strategies (such as acceleration and enrichment options), and a variety of talent development opportunities that should be included in a continuum of services that will engage and challenge all gifted and talented students. Also included in this chapter are some social and emotional challenges that may affect gifted and high potential children, such as the potential underachievement of children who do not encounter sufficient challenge in school. The chapter ends with a summary of research about the effectiveness of grouping, instructional, and talent development strategies, as well as recommendations for the creation of a continuum of services in each school district that will challenge and engage all students. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)