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Although the concept of leadership is often studied, researched, and discussed. The art of leadership is still misunderstood, debated, and often neglected. It is known, however, that leadership skills can be developed and more intentional endeavors must be made to cultivate bright, young leaders for the future. Developing Leadership Potential in Gifted Students offers insight into developing leadership skills in gifted students and provides definitions and theories of leadership, looks at trends and changing paradigms, and suggests screening and identification tools for leadership, as well as instructional programs and materials to incorporate into the regular curriculum. (PsycINFO Database Record (c) 2016 APA, all rights reserved) (Source: cover)

The editors envision this volume as a handbook for researchers in psychology and education who target exceptional populations in their work, and particularly individuals exhibiting gifts and talents. The research tools described here can help to move the scholarship on giftedness and talent development to a new level of rigor and encourage the testing of program models, as well as predictive validity of theoretical or conceptual frameworks (see for example Sternberg & Davidson, 2005). Of course, the methods described in these chapters can also be usefully applied in general populations or in studies dealing with specialized subpopulations other than gifted and talented. The scholars who contributed to the first nine chapters of this volume are pioneers in the field of measurement and statistics, and they show us most elegantly how the methodologies they have mastered or developed can apply to samples that, by their nature, make statistical analysis more challenging. We tasked our contributors to address the following four barriers, and one facilitator, associated with gifted education research: Definitions of giftedness and talent are not standardized; Test ceilings can be too low to measure progress or growth; Comparison groups are difficult to find for extraordinary individuals; Participant attrition in longitudinal studies may compromise tests of hypothesized effects; and, Qualitative research conducted with gifted populations can be enhanced due to highly articulate study participants. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: introduction)

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)

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)

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)

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)

Dr. Dai systematically redefines giftedness and proposes a new framework for the field of gifted education. He identifies nine essential tensions, revolving around three core questions: What do we know about the respective roles of natural ability, environment and experiences, and personal effort in talent development? How do we identify the gifted and talented, and study the process of gifted and talent development? And finally, how do we define the aims of gifted education and promote excellence? Sure to be a milestone in the field, this book: Scrutinizes some of the deeply held assumptions about the nature of giftedness and explains why a contextual, developmental approach is a more viable alternative to the traditional psychometric approach. Takes stock of the past, defines the present, and looks into the future in terms of understanding high potential and educating youths. Tackles tensions between the gifted child and talent development movements and between excellence and equity, and responds to the "elitism" criticisms in a constructive and comprehensive way. (PsycInfo Database Record (c) 2023 APA, all rights reserved) (Source: cover)

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)

We live in a society in which people are subjected to an explosion of information, with the emergence of social problems, this justifies the need for a response from the world of education where there is a deep gap between the thinking of the schools and thinking required to make decisions in the real world. In our research, we wanted to measure the intellectual capacity, and the different skills of critical thinking in students of 5th and 6th elemental education (induction, deduction, observation and assumptions) as well as to analyze a possible relationship between the intellectual capacity and the overall capacity of critical thinking. The sample consisted of 86 students and the instruments were the Test Badig E3· and the Cornell Test of critical thinking X. We found that there are significant differences in the overall capacity of critical thinking, within a threshold of intelligence, it is, we have not found significant differences between students with high IQ and students with average IQ, but there are significant differences between students with high IQ and students with low IQ, and there are significant differences between students with average IQ and students with low IQ.

This study was conducted to investigate differences between Korean and U.S. gifted and general students perceptions of classroom quality. The Student Perceptions of Classroom Quality (SPOCQ) (Gentry & Owen, 2004) was used to assess student perceptions on their classes on the five constructs: Appeal, Challenge, Choice, Meaningfulness, and Academic Self-Efficacy. The sample included 882 10 th and 11 th grade high school students (221 Korean gifted and 220 Korean general students, 221 U.S. gifted and 220 U.S. general students). Multi-group confirmatory factor analysis (MCFA) was used to check measurement equivalence between the original version and the Korean version of the SPOCQ; then 2×2 multivariate analysis of variance (MANOVA) was employed to examine differences between gifted and general students, and between Korean and U.S. students. Finally, a second 2×2 MANOVA was conducted to examine differences between Korean gifted institutions and grade levels. Results indicated that the original and Korean versions of SPOCQ had the same constructs but did not show invariance across countries. The result of the MANOVA for giftedness by nationality revealed that differences existed between gifted and general students, as well as Korean and the U.S. students. Follow-up discriminant function analysis (DFA) indicated that the Challenge, Choice, and Meaningfulness factors predicted gifted status well; and Appeal, Choice, and Meaningfulness factors made significant contributions for discriminating nationality. The MANOVA for Korean gifted institution by grade level showed that differences existed between a Science Academy and a Foreign Language High School, as well as between 10th and 11th grade students at the Science Academy. DFA revealed that the Choice and Meaningfulness subscales were strong factors to discriminate between the science academy and the foreign language high school; and the Self-Efficacy, Challenge, and Choice subscales predicted grade levels. The findings provide researchers with information on cross-cultural use of SPOCQ and educators with insight into ways to improve their classroom to encourage students' learning and achievement. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

This study investigated characteristics of five IMO winners and influences from their formal and informal educational experiences. In particular, this study provides in-depth understanding of former Korean IMO winners' characteristics and environmental influences. Also, implications including education for parents of the gifted, professional development for pre- and in-service teachers, and changes in schools to accommodate the needs of exceptional students are discussed. Mathematical giftedness is in the focus of educational research for decades. One important contribution was made by Krutetskii (1976) who outlined five elements of gifted "readiness for an activity" –positive attitudes, characteristic traits, positive mental state, knowledge, and ability - to understand characteristics of the mathematically gifted. Efforts have been made to understand various influential factors on their talent development. One of the recent examples of such studies important for designing this study was conducted by Muratori et al. (2006). They conducted an in-depth study of two successful mathematicians' development and careers. A flexible school system, parents, and mentors were revealed as highly influential for their talent development. This study employed the case study design and aspects of ethnographical methodology. Partially structured interviews were conducted them, parents of three Olympians, and a professor who led the Korean IMO teams. The grounded theory was employed for data analysis. Results of the study confirmed Krutetskii's (1976) findings of characteristics of the mathematically gifted. Various combinations of readiness for an activity were found in each Olympian. In terms of formal education, deficiencies in supporting and encouraging the mathematically gifted within various aspects of Korean compulsory education were found. Among aspects of informal education, parents' support and participation in competitions were found to be the most influential in identifying students' interest, in guiding students talent development, and in keeping them on the right track. Also, distinctive educational practices such as group tutoring institutions, called Ha-Gwon, and weekly mathematics workbooks were acknowledged while the influence from mentors and peers was minimal. Findings and contributions of this investigation may be of help for teachers and parents of mathematically gifted students assisting them in building environment that will develop students' talent to the fullest. (PsycInfo Database Record (c) 2022 APA, all rights reserved)