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First we describe one particular model of talent development (Jarvin and Subotnik in The handbook of secondary gifted education. Prufrock Press, Waco, 2006) and situate it in perspective to other models developed in North America and Europe. We then discuss the implications of this view of giftedness on education and review related resources and approaches available in North America and Europe. We will conclude with the need for further international coordination in our understanding and promotion of talent development. (PsycINFO Database Record (c) 2016 APA, all rights reserved) (Source: journal abstract)

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)

Asher (1986) identified two factors that limit theory development and validation in gifted education research: imprecise measurement and small numbers of participants. He made this important point: “These two factors...combine to insure (sic) that results are obscure and that statistical significance is difficult to obtain” (p. 7). Asher was right about statistical significance being difficult to obtain: In fact, it may well be an unnecessarily difficult hurdle. The fact that p < .05 is difficult to obtain is only really a problem if statistical significance is considered the only acceptable evidence of result noteworthiness. In this chapter, I argue that it should not be and that falsely equating statistical significance with result noteworthiness has serious consequences. To demonstrate this, I draw lessons from other disciplines. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

Especially gifted and creative people are in relatively short supply, but are also very interesting. Because Q-technique factor analysis is especially suited for the intensive study of a small number of especially interesting people, Q-technique factor analysis is especially suitable for inquiry about giftedness and creativity. The purpose of the present chapter is to provide a primer on using Q-technique factor analysis in the intensive study of gifted or creative people, or other especially interesting people. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

This chapter addresses some of the most frequent misconceptions about the talent development framework. The misconceptions include: emphasizing domains of talent over general giftedness means that everyone is gifted in something; including a wider range of students may lead to talent development programs being watered down; talent development supporters do not value IQ or general ability; talent development ignores psychological needs by not focusing on the "whole child"; in a talent development framework, it is possible to 'lose' one's giftedness, whereas with the IQ model, once gifted, always gifted; a talent development framework does not serve underachievers; everyone could be gifted if they just had the right opportunities; and talent development only focuses on individuals who can become eminent. Gifted education will continue to evolve and change as new information is learned from basic and applied research studies. New conceptions of intelligence, giftedness, and talent development may emerge as a result. Revision of prevalent models based on new understandings keeps a field fresh and relevant. Talent development offers a new perspective on how to serve gifted and talented students with a greater focus on a broader range of students as well as a broader range of talents and abilities. (PsycInfo Database Record (c) 2022 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)

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)

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)

Factor analysis is often used to summarize relationships among many variables into a manageable, smaller set of factors. The methodology is frequently employed in instrument development and assessment of score validity, but it has other applications as well. This chapter reviews some common applications of factor analysis, provides an accessible treatment of how to conduct and interpret the analysis with a heuristic example, and discusses some potential benefits and problems that may be faced when conducting factor analysis in the study of giftedness. Factor analysis has enjoyed a long history of use across the social sciences. With most approaches, factor analysis is employed when researchers seek to reduce many variables to a smaller set of factors. The factors can then be thought of as a synthesis, or representation, of the many variables from which the factors were created. There are other uses of the methodology, such as Q-technique factor analysis, which can be used to identify types of people (see chap. 2, this volume), but most often variables are the subject of the analysis and therefore are the focus of this chapter. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

In this article we present research concerning important aspects of domain-specific giftedness. Specifically, we address the evidence regarding the relationship between specific abilities and achievement. Empirical evidence suggests that specific abilities have been used widely and validly for identification of exceptional talent in performance domains, and mathematical and spatial reasoning ability have demonstrated predictive validity for achievement in science, technology, engineering, and mathematics (STEM) domains. We note that domains of talent have unique trajectories and discuss four critical aspects of domain-specific giftedness. These include the developmental nature of giftedness (giftedness moves from potential to competency to expertise and possibly to eminence over time); the temporal nature of giftedness (that domains vary in their starting, peak, and ending points); the contextual aspect of giftedness (societal value of some domains over others, changing of domains and emergence of new domains, and the environmental influences in fostering domain-specific achievement); and the relative nature of giftedness (childhood giftedness is advancement relative to age peers, and adult giftedness is exceptional achievement relative to other domain experts). Finally, we present some implications of a domain perspective on giftedness for educational practice. (PsycINFO Database Record (c) 2017 APA, all rights reserved) (Source: journal abstract)

This article provides a definition of giftedness that is useful across all domains of endeavor and acknowledges several perspectives about giftedness on which there is a fairly broad scientific consensus. The article summarizes what we have learned about giftedness from the literature in psychological science and suggest some directions for the field of gifted education. Throughout its history, the field of gifted education has been troubled by a lack of agreement on a definition of giftedness. Outstanding performance is almost always judged relative to others in one’s peer group. Increasing the number of individuals who make path-breaking, field-altering discoveries and creative contributions by their products, innovations, and performances is the aim of proposed framework for gifted education. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

In this article, we provide a response to the Active Concerned Citizenship and Ethical Leadership (ACCEL) model put forward by Sternberg (see record 2018-07419-003). Our commentary focuses on four critical areas that do not receive sufficient attention in Sternberg’s proposed model: (a) the developmental nature of giftedness; (b) that giftedness is domain specific, particularly at later stages in the talent development process; (c) the challenge in applying ACCEL in the real world of schools; and (d) whether the arts and other performance areas are compelled to have an ethical theme in order to be valuable. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

The authors present a historical perspective on giftedness, highlighting the impact of key research studies on practice, research, and policy. A comprehensive model is presented emphasizing that giftedness is domain specific and developmental and involves the cultivation of psychosocial as well as cognitive skills. Implications for counselors are discussed, specifically which psychosocial issues and skills should be the focus of counseling at each stage of talent development and what counselors can do to support the development of gifted students. (PsycINFO Database Record (c) 2016 APA, all rights reserved) (Source: journal abstract)

In this article, the authors respond to the eight commentaries, J. Y. Jung (see record 2012-24309-003); A. Ziegler, H. Stoeger and W. Vialle (see record 2012-24309-004); M. C. Makel, M. Puttalaz and J. Wai (see record 2012-24309-005); A. Robinson (see record 2012-24309-006); A. N. Rinn (see record 2012-24309-007); M. T. McBee et al. (see record 2012-24309-008); T. C. Grantham (see record 2012-24309-009) and J. A. Plucker (see record 2012-24309-010) , on the original article “Rethinking Giftedness and Gifted Education: A Proposed Direction Forward Based on Psychological Science” (see record 2012-24309-002), using several themes to organize their response. These themes include ability, developmental trajectories, effort and opportunity, psychosocial factors, eminence, and equity. The authors reaffirm the contention that eminence is an appropriate standard for assigning the gifted label in individuals with well-developed talents. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

We all have an intuitive feel for the terms gifted, talented, high performer, expert, and eminent and often use the words interchangeably. Most have identified themselves as talent developers as well as psychologists of high performance. They have in some way engaged with programs serving children, youth, and adults who have been classified as gifted. They have been called experts themselves, and some are eminent psychologists, scientists, artists, and sporting greats. This chapter serves to frame the discussion of talent development and high performance. It begins with an overview of the talent development mega model because it provides the theoretical basis for this volume. The chapter provides brief definitions of several of the terms that reoccur in discussing high performance including potential, giftedness, talent development, high performance, expertise, and eminence. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: chapter)

Mathematical talent is an intriguing human phenomenon. From the time of Poincare (1908/1952), who linked mathematical talent to mathematical creation, the relationship between mathematical talent and mathematical creativity has been at the heart of the discussion of mathematical talent. Analysis of the criteria used for empirical examination of mathematical talent shows that researchers connect mathematical talent to general giftedness, to mathematical achievement, or to mathematical creativity. This chapter discusses the developing mathematical talent in schoolchildren. It starts by discussing the components and complexities of mathematical talent and goes on to review several frameworks for its development. It briefly addresses characteristics of mathematical content and didactical principles directed at the development of mathematical talent. Finally, it briefly addresses the proficiency required for teaching the gifted and presents examples of mathematical problems and their solutions. (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)

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 briefly discusses programming options for the primary grades, intermediate grades, and upper grades. Adopting a talent development framework for gifted education services in schools requires significant shifts in perspective and practice, but these are shifts that many administrators find appealing and that communities will support. A talent development approach expands the traditional concept of a gifted education program. Gifted education programs that emphasize domains of talent instead of a notion of global giftedness are easily understood and embraced, not only by other education professionals, but also by the public, which can lead to expanded parental, legislative, and corporate support for services for gifted students. With this support from important stakeholders, parents of gifted learners and K–12 educators are able to address a wide variety of challenges that impede school districts from providing appropriate services across all grade levels on a day-to-day basis for students with gifts and talents. (PsycInfo Database Record (c) 2022 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)