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Effective Identification through Multiple Criteria

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Title: Effective Identification through Multiple Criteria
Language: English
Authors: Matthew C. Makel (ORCID 0000-0002-3837-0088), Scott J. Peters (ORCID 0000-0003-2459-3384), Lindsay Ellis Lee (ORCID 0000-0003-4519-7209), Tamra Stambaugh (ORCID 0000-0001-5587-1506), Matthew T. McBee, D. Betsy McCoach, Kiana R. Johnson
Source: Gifted Child Today. 2024 47(2):108-118.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 11
Publication Date: 2024
Sponsoring Agency: Department of Education (ED)
Contract Number: S206A20000721
Document Type: Journal Articles
Reports - Evaluative
Descriptors: Academically Gifted, Talent Identification, Gifted Education, Evaluation Criteria, Correlation
DOI: 10.1177/10762175231222300
ISSN: 1076-2175
2162-951X
Abstract: Finding all the "gifted" students who would benefit from a gifted and talented service is a perpetual concern. In this article, we focus on how to effectively implement multiple criteria in identification. First, we provide some broad background before introducing three different ways to combine multiple data points (AND, OR, and MEAN) when identifying students for gifted services. Next, we discuss how effective use of combining multiple criteria--including using two-phase identification systems--contributes to schools saving time and money while also better identifying students. To do this, we use newly introduced criteria for evaluating gifted and talented identification systems. Finally, we provide several keys for success that can help schools accomplish their identification goals effectively.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1417264
Database: ERIC
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  Value: <anid>AN0176065278;gct01apr.24;2025Aug11.09:20;v2.2.500</anid> <title id="AN0176065278-1">Effective Identification Through Multiple Criteria </title> <p>Finding all the "gifted" students who would benefit from a gifted and talented service is a perpetual concern. In this article, we focus on how to effectively implement multiple criteria in identification. First, we provide some broad background before introducing three different ways to combine multiple data points (AND, OR, and MEAN) when identifying students for gifted services. Next, we discuss how effective use of combining multiple criteria—including using two-phase identification systems—contributes to schools saving time and money while also better identifying students. To do this, we use newly introduced criteria for evaluating gifted and talented identification systems. Finally, we provide several keys for success that can help schools accomplish their identification goals effectively.</p> <p>Keywords: equity; giftedness; identification; multiple criteria; two-phase identification</p> <hd id="AN0176065278-2">Introduction</hd> <p>"Am I identifying the right students for my programs?" and "what are the best ways to identify students?" are two omnipresent questions in gifted and talented education. It has long been recommended that using multiple criteria is an important part of a comprehensive and equitable identification system ([<reflink idref="bib16" id="ref1">16</reflink>]; [<reflink idref="bib23" id="ref2">23</reflink>]). But selecting which criteria to use and how to combine multiple data points remains an on-going question, particularly around how to improve equitable representation in gifted programs ([<reflink idref="bib4" id="ref3">4</reflink>]; [<reflink idref="bib13" id="ref4">13</reflink>]; [<reflink idref="bib20" id="ref5">20</reflink>]). Actions to accomplish these goals may be constrained by existing state policies that may limit or guide gifted identification (e.g., must use certain types of quantitative and/or qualitative criteria, universally screen for aptitude and ability; [<reflink idref="bib9" id="ref6">9</reflink>]; [<reflink idref="bib15" id="ref7">15</reflink>]). Forty-one states require gifted identification but the policies for how students are identified for each state vary widely ([<reflink idref="bib22" id="ref8">22</reflink>]). Other relevant issues for identification practices include cost, aligning identification criteria with services being provided, and student success in programs ([<reflink idref="bib21" id="ref9">21</reflink>]).</p> <p>Our goal with this paper is to help readers feel comfortable and confident making decisions about identifying students for gifted and talented programs using multiple criteria. To develop this confidence, we cover five topics. First, we briefly introduce why and how multiple criteria are applied in gifted identification. Next, we introduce three rules for combining multiple criteria. Third, we discuss how using two-phase systems is a method of applying combination rules to multiple criteria and can contribute to effective identification. Fourth, we discuss the expected consequences of various combination strategies on four evaluation criteria: cost, alignment, sensitivity, and access ([<reflink idref="bib21" id="ref10">21</reflink>]). Finally, building on several research studies (e.g., [<reflink idref="bib5" id="ref11">5</reflink>]; [<reflink idref="bib10" id="ref12">10</reflink>]; [<reflink idref="bib11" id="ref13">11</reflink>]; [<reflink idref="bib14" id="ref14">14</reflink>], [<reflink idref="bib13" id="ref15">13</reflink>]), we provide suggestions that can help schools more easily adopt identification practices that accomplish their desired goals.</p> <hd id="AN0176065278-3">Using Multiple Criteria</hd> <p>Leaders have to make many decisions when identifying students for gifted services. These include selecting assessments, determining which students should be assessed, qualifying levels on those assessments, and how to combine multiple data points to arrive at a dichotomous decision of whether a student should be placed in gifted services. All of these decisions require thoughtful consideration and can make the process feel dauntingly complex.</p> <p>The simplest gifted identification process might consist of all students taking a single assessment. This assessment could be anything: a test, a checklist, an audition, or a rating scale. Students who score above a qualification score are identified as gifted and placed in a service. In this simplified hypothetical system, all students are assessed (often called universal consideration; [<reflink idref="bib8" id="ref16">8</reflink>]) on a single assessment (often called a criterion). Many schools rely on multiple criteria. In fact, the use of "multiple measures" has long been considered best practice ([<reflink idref="bib2" id="ref17">2</reflink>]; [<reflink idref="bib3" id="ref18">3</reflink>]), including being a clear requirement in the NAGC Programming Standards ([<reflink idref="bib16" id="ref19">16</reflink>]).</p> <p>The use of multiple measures may help schools identify students who will succeed in the service. Whenever more than one data point is used to make identification decisions, schools must decide how the assessments are combined to determine who qualifies. However, the details matter ([<reflink idref="bib7" id="ref20">7</reflink>]) with huge variation in who gets identified and how many students get identified ([<reflink idref="bib5" id="ref21">5</reflink>]; [<reflink idref="bib11" id="ref22">11</reflink>]). When implemented poorly, a multiple-criteria identification system can miss many qualified students or identify too many (i.e., those who would not benefit). When implemented well, multiple criteria can help schools efficiently and effectively identify students who would benefit from the services being provided. The different options for how multiple measures can be combined and their implications are the subject of the following section.</p> <hd id="AN0176065278-4">Combination Rules: Using Multiple Criteria in Different Ways</hd> <p>In this section, we introduce three methods for combining multiple criteria in identification and discuss different factors that affect the consequences of these combination rules (e.g., the number of criteria used, relationship between criteria on the consequences of the combination rule, and other consequences). These combination rules have been discussed elsewhere by researchers (e.g., [<reflink idref="bib5" id="ref23">5</reflink>]; [<reflink idref="bib10" id="ref24">10</reflink>]; [<reflink idref="bib11" id="ref25">11</reflink>]; [<reflink idref="bib14" id="ref26">14</reflink>], [<reflink idref="bib13" id="ref27">13</reflink>]; [<reflink idref="bib12" id="ref28">12</reflink>]) but we summarize them here with application in mind. We begin this discussion in the context of a single-phase, universal consideration system where all the data points are collected on all students. A single-phase identification system (i.e., students move directly from assessment/s to identification) is less common than a two-phase system (i.e., any system in which there is an initial screening and then an identification/confirmation phase for a subset of students), but we start here for simplicity's sake. See Table 1 for an overview and some examples of how students might qualify for services using each combination rule and two-phase systems.</p> <p>Graph</p> <p>Table 1. Examples of How Combination Rules Work.</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Combination Rule</th><th align="center">Definition</th><th align="center">Examples of How Students Qualify</th></tr></thead><tbody valign="top"><tr><td align="left" rowspan="3">AND rule</td><td align="left" rowspan="2">All criteria must meet qualification threshold</td><td align="left">Students must have test scores AND referrals AND behavior checklist above qualification threshold</td></tr><tr><td align="left">Students must have test scores in fall AND spring above qualification threshold</td></tr><tr><td align="left">Multiple hurdles</td><td align="left">Services may be more specific and intense given the criteria required</td></tr><tr><td align="left" rowspan="3">OR rule</td><td align="left" rowspan="2">Any 1 criterion must meet qualification threshold</td><td align="left">Students must have test scores OR referrals OR behavior checklist above qualification threshold</td></tr><tr><td align="left">Students must have test scores in fall OR spring above qualification threshold</td></tr><tr><td align="left">Multiple pathways</td><td align="left">Services are varied and plentiful by domain and intensity</td></tr><tr><td align="left" rowspan="3">MEAN rule</td><td align="left" rowspan="2">Average of criteria must meet qualification threshold</td><td align="left">Student performance on test scores, referrals, and behavior checklist must average above qualification threshold</td></tr><tr><td align="left">Student average performance on fall and spring test must be above qualification threshold</td></tr><tr><td align="left">Compensatory model</td><td align="left">Services may be somewhat varied by domain and intensity</td></tr><tr><td align="left" rowspan="2">Two-phase system</td><td align="left" rowspan="2">Only a subset of students are given phase two assessments</td><td align="left">All students are given an achievement assessment and those who score in the 75th percentile in the school are rated on a teacher rating scale</td></tr><tr><td align="left">All students are rated using a teacher rating scale and those who score in the top 20% are given an achievement test</td></tr></tbody></table> </ephtml> </p> <hd id="AN0176065278-5">The AND Rule</hd> <p>The AND rule requires students to exceed qualification cutoffs on all identification criteria. For example, a program might require a student to score in the top 5% on an achievement test AND have high grades in a particular domain AND be recommended by her teacher.</p> <hd id="AN0176065278-6">Criteria Quantity</hd> <p>Under an AND rule, every additional criterion will result in <emph>fewer</emph> students being identified. This is because each additional criterion serves as another barrier to entry that students must surpass, potentially eliminating some qualified students from qualification. Eventually there could be so many hurdles that zero students are identified. This is particularly salient for small schools where reducing the number of students who qualify for services can create particularly small numbers of identified students. For example, if there are only 25 third grade students in a school, then the top 1% represents less than a single student. If a student must score in the top 1% on multiple assessments, the chances of anyone being identified become very small. In this case, more criteria equate to more students being missed.</p> <hd id="AN0176065278-7">Criteria Correlations</hd> <p>If the assessments used are only weakly related to each other (e.g., music performance and reading achievement) then more students will be missed (and more students being incorrectly identified) than if the assessments are strongly related to each other (e.g., reading achievement and verbal reasoning). With an AND rule, individual criteria being weakly correlated with each other also leads to fewer students being identified. This is because the lack of overlap in high performance on each assessment leads to unique students performing well on individual criteria, but not on all criteria. This results in fewer students who meet qualification levels on all criteria (which are all required under an AND rule).</p> <p>For example, imagine two identification scenarios. In the first, students qualify by performing well on a measure of math achievement AND a measure of quantitative reasoning. Alternatively, imagine a scenario where students qualify by performing well on wildly different assessments such as math achievement and their time in the 100-yard dash. In the first scenario, the assessments measure similar constructs, so even though high performance is required multiple times, the correlation between the two performances is expected to be quite high. As a result, we would expect the students who perform well on one also perform well on the other. In the second scenario, the two criteria differ from each other—the students who perform well in math achievement are not likely to be the same as those who can run a very fast 100-yard dash. Requiring students to meet high qualifications on both criteria will lead to far fewer identified students than the former scenario.</p> <p>This reduction in identified students can be a challenge in gifted identification where educators often want to measure several different and unrelated constructs (e.g., math achievement and creativity). Unfortunately, relatively few students will meet high qualifying criteria in multiple areas that are only marginally correlated ([<reflink idref="bib6" id="ref29">6</reflink>]). For example, among American elementary and middle school students, 20%–49% in English Language Arts and 14%–37% in mathematics scored 1 year or more above grade level ([<reflink idref="bib17" id="ref30">17</reflink>]). This represents millions of students. However, requiring high scores in both domains to be served would result in students scoring more than one year above grade level in only one domain would not be served.</p> <hd id="AN0176065278-8">Other Consequences</hd> <p>Compared to the two combination rules we present next, the group of students identified using an AND rule identification system will (<reflink idref="bib1" id="ref31">1</reflink>) be the smallest, (<reflink idref="bib2" id="ref32">2</reflink>) be the most homogeneous in terms of skills and demographic diversity, and (<reflink idref="bib3" id="ref33">3</reflink>) miss the largest percentage of students who would benefit from the service (discussed more below). Almost all of these outcomes are due to the "multiple hurdle" nature of AND combination rules. Every time an assessment is added (e.g., going from two criteria to three or four), the population of identified students gets smaller, more homogenous, and more students are missed due to random error. The AND combination rule will also incorrectly identify the fewest students due to error; if a student meets all identification criteria required by an AND rule, they are likely to need additional and more intense services. For reasons we will discuss at length later, the AND rule is rarely preferred despite its usage being common. Services for which AND rules might make the most sense could include those where incorrectly placing a student (despite not actually being ready), could be harmful. The best example of this might be drastic grade acceleration or early graduation from high school. If such decisions were made on accident, they would be hard to undo and might have negative effects on the student. As a result, erring on the side of caution and not placing borderline cases could be appropriate.</p> <hd id="AN0176065278-9">The OR Rule</hd> <p>Where the AND rule requires students to earn qualifying scores on all assessments, the OR rule requires a qualifying score on only one assessment, no matter how many were administered. For example, a program might require a student to either score in the top 5% on an achievement test OR have high grades in a particular domain. A student only needs to meet one criterion to qualify for services. Before we get into the details, consider how well you would run the hurdles in track if there was only a single hurdle instead of ten. If success was making it over a single hurdle, more people would make it to the finish line. Every time you add a hurdle that people must get over (AND rules), fewer people would make it.</p> <hd id="AN0176065278-10">Criteria Quantity</hd> <p>Under an OR rule, adding more criteria will result in <emph>more</emph> students being identified. This is because each additional criterion serves as another opportunity to enter a service, likely adding additional students to qualification. Although there may be diminishing returns on the number of students added once the number of criteria used is sufficiently high (e.g., going from 5 to 6 criteria may not add many newly identified students). Rather than adding a hurdle as under the AND rule, adding criteria under the OR rules adds additional doors or pathways to the service.</p> <hd id="AN0176065278-11">Criteria Correlations</hd> <p>With an OR rule, individual criteria being weakly correlated with each other (e.g., math achievement and 100-yard dash) will lead to many <emph>more</emph> students being identified. This is because the lack of overlap leads to each criterion identifying unique students. The higher the correlation between each criterion, the fewer additional students will be identified by each additional pathway. For example, different versions of the same test are intentionally designed to be quite similar to each other. Allowing students to qualify via either version has the potential to increase the number of students who qualify (e.g., they may have had a bad day when one was given). But the number of additional students identified by two versions of the same assessment will likely be smaller than if the alternative is a completely different assessment (e.g., math test score or high grades in math).</p> <hd id="AN0176065278-12">Other Consequences</hd> <p>Compared to the other two combination rules, the group of students identified using the OR rule will (<reflink idref="bib1" id="ref34">1</reflink>) be the largest, (<reflink idref="bib2" id="ref35">2</reflink>) be the most diverse in terms of skills and demographic diversity, and (<reflink idref="bib3" id="ref36">3</reflink>) miss fewer students who would benefit from the service (i.e., have higher sensitivity); the OR rule also risks incorrect placement of some students in the program depending upon the services provided. Under an OR rule, each additional criterion will result in more students being identified; each represents an additional pathway to identification. For this reason, OR rules can be thought of as multiple pathway identification systems, whereas AND rules can be thought of as multiple hurdle identification systems.</p> <p>Another important consequence that should not be overlooked is that identifying a group of students with more diverse skills means that a single service may not be sufficient to match each student's learning needs. After all, under the OR rule, one student could be identified with high creativity scores, another with high math achievement test scores, and another with high grades. These students will vary in their current level of skill mastery, domain strengths, and what they are ready to learn. Therefore, additional types and levels of services will be needed if students are served effectively.</p> <hd id="AN0176065278-13">The MEAN Rule</hd> <p>The MEAN rule is a compromise between the AND and OR rules. The MEAN rule is a little trickier to understand, but that's mostly due to the simplicity of the AND and OR rules. The MEAN rule requires students to exceed a particular threshold on the mathematical mean of all of the identification criteria. Imagine a system where a school collects achievement scores, ability scores, and teacher rating scales on all of its second graders. An AND rule requires students to meet thresholds on all three data points to be identified. An OR rule requires only one qualifying score on any of the three. With the MEAN rule, all three data points are put on a common scale and then averaged together. Students are then identified if their MEAN score on the three exceeds a set criterion.</p> <hd id="AN0176065278-14">Criteria Quantity</hd> <p>Under a MEAN rule, adding more criteria generally has little effect on the number of students being identified. It does not matter if there are two assessments or eight because if they are all averaged together the overall group mean will remain largely the same (more on this below in criteria correlations). The main downside to this is that, like the OR rule, students could be identified with relatively low scores on one particular data point. If identification is based on the mean of three data points and a student scores very high on one, then they can score lower on another and still be identified. Just like the AND and OR rules, this has implications for services.</p> <hd id="AN0176065278-15">Criteria Correlations</hd> <p>With a MEAN rule, the correlation between criteria will be subsumed into a single score that represents the mean (i.e., composite score) and the overall reliability will be higher than the reliability of any individual assessment (for more detail, see [<reflink idref="bib5" id="ref37">5</reflink>]; [<reflink idref="bib14" id="ref38">14</reflink>]). It could be considered the "goldilocks principle" of combination rules ([<reflink idref="bib6" id="ref39">6</reflink>], p. 14). For example, despite being quite different from each other, math achievement, time on the 100-yard dash, and a score on a teacher rating scale can be transformed into a common scale (i.e., z-score), then combined to a single score to determine entry into the gifted program (though we would not recommend combining measures of such wildly different constructs). The attractiveness of this particular rule is that low scores on one assessment can be compensated by high scores on another assessment ([<reflink idref="bib14" id="ref40">14</reflink>]). This rule is not nearly as affected by the correlations of all the assessments because the MEAN rule results in a single composite score where there are no longer any correlations among the various criteria like there are in AND or OR rules.</p> <hd id="AN0176065278-16">Other Consequences</hd> <p>The MEAN rule can also be thought of as the compromise rule because it falls in-between the AND and OR combination rules. Compared to the other two combination rules, the group of students identified using the MEAN rule will (<reflink idref="bib1" id="ref41">1</reflink>) be in the middle in terms of number of students identified, (<reflink idref="bib2" id="ref42">2</reflink>) be more diverse than under the AND rule, but less diverse than under the OR rule, and (<reflink idref="bib3" id="ref43">3</reflink>) miss fewer students who would benefit from the service than the AND rule, but more than the OR rule.</p> <hd id="AN0176065278-17">Two-Phase Identification Process</hd> <p>Two-phase identification systems are a special application of the AND rule. Under a two-phase identification system, all students are given an initial screening assessment. Then, students who exceed a screener threshold are advanced to a second phase where additional data are collected to make program eligibility/identification decisions. This reduces the number of students who take the actual identification assessments while still considering all students in the identification process.</p> <hd id="AN0176065278-18">Potential Benefits of Two-Phase Systems</hd> <p>One benefit to a two-phase system is cost savings. Students who are not expected to have a high likelihood of being identified as gifted are not given all the identification assessments. This can save on costs (dollars spent, teacher time, and student time). That said, <emph>a relatively inclusive low threshold or cut score should be used at the screening stage</emph>. For example, if a school is looking to eventually identify students performing at the 90th percentile, they might advance students performing at the 75th percentile into the second phase of identification. This inclusive practice helps reduce the probability that students who would have done well at the identification phase never make it because they were screened out.</p> <p>The least expensive screener is an assessment that is already being given for other purposes (e.g., an assessment already given to all students in the school). This helps make sure all students have access to the identification process while keeping costs low.</p> <hd id="AN0176065278-19">Potential Negative Consequences to Two-Phase Systems</hd> <p>If implemented poorly, two-phase systems can introduce several problems. If the screener is not strong, two-phase systems run a risk of introducing problems. <emph>A strong screener should be inexpensive to implement universally and strongly correlated with performance on the actual identification criteria.</emph> This helps make sure the screener correctly distinguishes the students who are likely to do well on the actual identification criteria. Selecting a screener that is inexpensive but lacks these benefits could produce a system that superficially appears effective but fails to identify students who would benefit from the service. One way to avoid this limitation is to make the screener also be part of the phase two identification criteria.</p> <p>Moreover, if the qualification threshold on the screener is set too high, then it will shrink the number of students who advance to the second stage, some of whom would have been identified if they had been considered. For example, if the aforementioned hypothetical school that is looking to identify students performing at the 90th percentile only advanced students performing at the 90th percentile into the second phase of identification, they would miss many students who would otherwise have been identified.</p> <p>If the screener introduces bias (e.g., it is only given on Saturday, reducing who has access to participate), it could exacerbate inequity. Similarly, if there are achievement gaps between groups (e.g., students from low-income backgrounds have had years with less opportunity to develop their talent), schools should expect the resulting identified populations to reflect these gaps. Carefully scrutinizing when identification processes reflect existing inequity versus introducing inequity will remain essential.</p> <hd id="AN0176065278-20">Combining Combination Rules</hd> <p>Combining identification criteria is not an all-or-nothing choice. Districts can also combine combination rules (AND, OR, and MEAN) as part of their identification process by offering multiple pathways for services. To illustrate one common example, schools may require students to meet two-out-of-three criteria to qualify for a gifted program (as illustrated in Table 2). For example, students might be required to score at the 95th percentile on a standardized test in two of three testing occasions (fall, winter, spring). This "2 out of 3" scenario could also be used when combining an achievement test, a checklist, and a work sample. In these scenarios OR and AND rules are combined. The full list of all eight permutations of a "2 out of 3" scenario is illustrated in Table 2. Using the "2 out of 3" pathway option results in four potential routes to identification. A straightforward AND rule requiring qualifying on all three criteria only has one path to identification whereas a straightforward application of an OR rule would have seven paths. There are other ways districts can alter which students it identifies for its gifted and talented services, but how it combines its criteria can lead to few or many paths to identification.</p> <p>Graph</p> <p>Table 2. Example Pathways When Combining Different Combination Rules in a "2 out of 3" Scenario.</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">1st Criterion Met</th><th align="center">2nd Criterion Met</th><th align="center">3rd Criterion Met</th><th align="center">ID Status</th></tr></thead><tbody valign="top"><tr><td align="left">Yes</td><td align="left">Yes</td><td align="left">Yes</td><td align="left">Qualify</td></tr><tr><td align="left">Yes</td><td align="left">Yes</td><td align="left">No</td><td align="left">Qualify</td></tr><tr><td align="left">Yes</td><td align="left">No</td><td align="left">Yes</td><td align="left">Qualify</td></tr><tr><td align="left">Yes</td><td align="left">No</td><td align="left">No</td><td align="left">Not Qualify</td></tr><tr><td align="left">No</td><td align="left">Yes</td><td align="left">Yes</td><td align="left">Qualify</td></tr><tr><td align="left">No</td><td align="left">No</td><td align="left">Yes</td><td align="left">Not Qualify</td></tr><tr><td align="left">No</td><td align="left">Yes</td><td align="left">No</td><td align="left">Not Qualify</td></tr><tr><td align="left">No</td><td align="left">No</td><td align="left">No</td><td align="left">Not Qualify</td></tr></tbody></table> </ephtml> </p> <hd id="AN0176065278-21">Deciding on How to Combine Criteria</hd> <p>As demonstrated above, combination rules have fairly predictable consequences on the number of students identified. If schools are seeking to identify more students, OR rules will help them accomplish that goal more than MEAN and AND rules. Although the number of students identified is a relevant outcome for schools to consider, it is not the only consequence that schools consider when making identification decisions. For example, the additional students identified via the OR rule will also identify a group of students with more diverse skills than the other combination rules. This means that <emph>offering only a single service may not meet the learning needs of identified students</emph>. Instead, a range of services may be needed to appropriately serve identified students. These types of consequences must be considered when making decisions about how to combine identification criteria.</p> <p>A recently introduced framework provides a broader system for assessing identification practices. [<reflink idref="bib21" id="ref44">21</reflink>] introduced the CASA criteria of Cost, Alignment, Sensitivity, and Access. <emph>The CASA criteria provide districts with a framework for evaluating identification systems and clarity about the consequences to its identification actions</emph>. Cost refers to any finite resource used to identify students for placement in each advanced learning opportunity. Common costs include funds spent on assessments as well as teacher and student time spent on identification-related activities. Alignment focuses on the agreement between the skills, dispositions, and abilities measured by the identification system and those that will be fostered in the services being provided. Two components of alignment are domain (e.g., using math related identification criteria for math related services) and level (e.g., matching intensity of services with students' demonstrated readiness).</p> <p>Sensitivity is a well-established term in psychometrics (measurement of psychological features) that assesses the accuracy of decisions. An identification is considered strong if it correctly finds students who would benefit from participating in the service; this means it has high sensitivity. Identification systems that identify a lot of students who do not do well in the service are considered to have low sensitivity. Low sensitivity can be problematic because the identification process misses students who would benefit from the service. Additionally, some research suggests that low sensitivity particularly hurts traditionally underrepresented students the most ([<reflink idref="bib1" id="ref45">1</reflink>]).</p> <p>Underrepresentation of student groups is also relevant to the Access criterion. The access criterion is concerned with removing factors that are irrelevant to success in a particular service from the identification system. For example, practices like universal screening help make sure that access to identification is not controlled by whether a student's parents are aware of the program. Removing parental awareness from the identification system improves access. Other barriers include deficit thinking, societal inequity, lack of communication to stakeholders, and arbitrarily high qualification levels to receive services. Research suggests that multiple criteria can help—but do not fully—address access issues ([<reflink idref="bib11" id="ref46">11</reflink>]). Another promising practice that has been shown to help reduce access concerns is the use of local norms ([<reflink idref="bib20" id="ref47">20</reflink>], [<reflink idref="bib18" id="ref48">18</reflink>]). Under local norms, gifted identification is based on comparing performance to students in their same school or district instead of a state or national reference group. Implementation of local norms can improve access and representation within gifted programming in many situations ([<reflink idref="bib20" id="ref49">20</reflink>]).</p> <p>Combination rules have clear implications for each of the CASA criteria (see Table 3). For example, when considering alignment to services, the more combinations for identification there are (because of multiple "or" rules) the larger the continuum of service options need to be in place. We believe that the CASA criteria can help schools evaluate how different combination rules help (or hurt) their efforts in identifying students for gifted and talented services.</p> <p>Graph</p> <p>Table 3. Combination Rules Aligned With CASA Criteria.</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Combination Rule</th><th align="center">Cost Consequences</th><th align="center">Alignment Consequences</th><th align="center">Sensitivity Consequences</th><th align="center">Access Consequences</th></tr></thead><tbody valign="top"><tr><td align="left" rowspan="2">AND rule</td><td align="left" rowspan="2">No effect on ID costs, decreases service delivery cost by missing students.</td><td align="left">Easiest population to serve—most homogenous learning needs.</td><td align="left" rowspan="2">Each data point reduces sensitivity.</td><td align="left" rowspan="2">Decreases number of students identified, including those from underrepresented populations.</td></tr><tr><td align="left">Abilities and/or skills are similar for a particular domain and/or service as long as the assessments are aligned.</td></tr><tr><td align="left" rowspan="2">OR rule</td><td align="left" rowspan="2">No effect on ID costs, increases service delivery cost.</td><td align="left">Each data point increases the range of services needed to be provided.</td><td align="left" rowspan="2">Each data point/pathway increases sensitivity.</td><td align="left" rowspan="2">Increases number of students identified, including those from underrepresented populations.</td></tr><tr><td align="left">Range of services may vary across domain (e.g., math, reading, science, and arts) and level of need (in class differentiation, cluster grouping, acceleration).</td></tr><tr><td align="left" rowspan="2">MEAN rule</td><td align="left" rowspan="2">No effect on ID costs, minimal change in service delivery cost.</td><td align="left">Broadens construct coverage.</td><td align="left" rowspan="2">Slightly increases with each assessment added; affected by reliability of assessments.</td><td align="left" rowspan="2">Slightly decreases access dependent on context; reduces diversity of students identified.</td></tr><tr><td align="left">Strength in one ability/skill in a particular domain can compensate for lower ability/skill in another.</td></tr></tbody></table> </ephtml> </p> <p>As we hope is clear, it is difficult to maximize performance on all four criteria with a single identification process. If a district wants to minimize costs, then identification pathways that rely on AND rules might be useful even if they miss a larger number of students who would benefit from the service. Alternatively, if access and sensitivity are priorities, erring more on the side of OR rules or "2 out of 3" combinations might make the most sense, especially if programs and services are flexible enough to respond to the diverse needs of the students identified. The answer to the question of "what gifted and talented identification system is best?" should always be "it depends." It depends on the available resources (cost), the services the district provides (alignment), correctly identifying them (sensitivity), and considerations of access and equity.</p> <hd id="AN0176065278-22">Keys to Success in Two-Phase Identification Systems</hd> <p>Navigating implementation of all these ideas can be difficult. To help demonstrate how decisions around combination rules can be made, we provide some brief scenarios of common identification goals that many districts may have. We focus on four potential goals districts may emphasize: increasing representation, missing fewer students (who would benefit from the service), following a fixed budget, and aligning services across domains. As you will see in Table 4, there are often similar recommendations for combining multiple criteria even when different goals are being sought.</p> <p>Graph</p> <p>Table 4. Identification Practice Suggestions by District Goal.</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left" /><th align="center">Increase Representation</th><th align="center">Miss Fewer Students</th><th align="center">Follow a Fixed Budget</th><th align="center">Fixed Number of Seats in Services</th></tr></thead><tbody valign="top"><tr><td align="left">Universal consideration</td><td align="left">Increase</td><td align="left">Increase</td><td align="left">Use an existing measure to maintain low cost</td><td align="left">Increases sensitivity</td></tr><tr><td align="left">AND rules</td><td align="left">Decrease</td><td align="left">Decrease</td><td align="left">Identifies fewer students with more homogenous readiness</td><td align="left">Identifies fewer students with more homogenous readiness</td></tr><tr><td align="left">OR rules</td><td align="left">Increase (if program size allowed to increase)</td><td align="left">Increase</td><td align="left">Identifies more students with more heterogeneous readiness</td><td align="left">Identifies more students with more heterogeneous readiness</td></tr><tr><td align="left">MEAN rules</td><td align="left">Slightly decrease</td><td align="left">Slight decrease</td><td align="left">Slight decrease</td><td align="left">Slight decrease</td></tr><tr><td align="left">2-Phase system</td><td align="left">Increase if districts use a low cutoff at phase 1; align screener with phase 2 assessments</td><td align="left">Use a low cutoff at phase 1; align screener with phase 2 assessments</td><td align="left">Use a low cutoff at phase 1; align screener with phase 2 assessments</td><td align="left">Use a low cutoff at phase 1; align screener with phase 2 assessments</td></tr></tbody></table> </ephtml> </p> <hd id="AN0176065278-23">Ways to Increase Representation in Services or Miss Fewer Students</hd> <p>Increasing representation in services and missing fewer students may be distinct goals, yet actions taken to help achieve each goal can be similar. If a district's goal is to increase representation in gifted programs or miss fewer students, several actions can be taken that will help achieve either goal.</p> <p></p> <ulist> <item> 1. Make sure all students are universally considered (no referral or nomination required). This will remove sources of inequity that arise from two-phase systems.</item> <p></p> <item> 2. Use a relatively low inclusive cutoff at the phase 1 screening stage. This will make sure that students who have a high likelihood of being identified aren't removed from the identification process.</item> <p></p> <item> 3. Use OR rules in place of restrictive AND rules for identification. Avoiding AND rules will increase the overall number of students identified, while also increasing the range of student needs and services.</item> <p></p> <item> 4. Align identification cutoffs with services needed by using local building norms instead of national norms. Using local norms at the building level (how students perform relative to others in their school) also has the potential to increase representation in districts that are highly segregated ([<reflink idref="bib20" id="ref50">20</reflink>], [<reflink idref="bib18" id="ref51">18</reflink>]).</item> <p></p> <item> 5. Provide multiple opportunities into identification (multiple pathways and/or multiple timepoints). These multiple opportunities within a grade and throughout K-12 create multiple pathways for identification and help make sure fewer barriers are between students and potential identification.</item> <p></p> <item> 6. Provide a diverse set of services and matched assessments (e.g., not just math and reading, but also leadership, arts, and creativity). Essentially, create pathways for each domain that is served in the program or increase services to include multiple domains. This diverse service offering will increase the range of domains served, which will increase the pool of students being served as well as the number of services required.</item> </ulist> <p>It is important to remember that these actions have consequences for service alignment and costs. Namely, increasing the number of students identified may also increase the range of learning needs of identified students. This may require offering a broader range of services (both in terms of domains and difficulty). Additionally, it is important to remember that many groups of students have had differential prior opportunities to learn ([<reflink idref="bib21" id="ref52">21</reflink>]). Because of this, gaps in identification rates should be expected to remain despite the best of efforts to improve identification practices ([<reflink idref="bib11" id="ref53">11</reflink>]). Actions such as frontloading talent development opportunities prior to gifted identification can help reduce such gaps (see [<reflink idref="bib21" id="ref54">21</reflink>] for more on frontloading).</p> <p>Although all the above steps will help increase identification sensitivity, many may also contribute to mistakenly identifying students who may not be successful in the service (sometimes called specificity). For example, if a school identified every student as gifted, it would not "miss" any of the students who would benefit from the service. But it would also identify many students who would likely not be successful. Having a clear plan for evaluating student performance and success within services along with responsive service plans will be an essential part of success.</p> <hd id="AN0176065278-24">Fixed Number of Seats for Services</hd> <p>Although we believe it is important for all students who would benefit from special services to receive these services, we recognize that circumstances do not always allow for this. When possible, administrators should seek to expand opportunities if more students meet qualification levels. Nevertheless, if schools are seeking to maintain service costs due to limited resources or "seats" in a program, then using the same steps as listed in the previous section in addition to using a MEAN rule may be of value to the district. MEAN rules provide reliable estimates of a student's typical performance in a domain. Yet, AND rules will result in the fewest number of students identified ([<reflink idref="bib11" id="ref55">11</reflink>]). AND rules may appear to make more sense for services that have limited seats or that require every selected student to have a high skill set. But such "risky" services are probably rare in K-12 gifted identification. However, allowing a student to skip a grade may feel riskier than allowing a student to attend a weekly hour-long pull-out enrichment program. In this case, schools may prefer to use the more cautious but not overly restrictive, MEAN combination rules. Additionally, actions like universal screening and two-phase identification can help reduce costs, increase sensitivity, and align identification with services offered. However, it should be noted that when the size of the program remains fixed, combination rules generally yield similar representation of students ([<reflink idref="bib5" id="ref56">5</reflink>]).</p> <hd id="AN0176065278-25">Fixed Identification Budget</hd> <p>Many districts have fixed operational budgets that require predictable costs to gifted identification. There are steps that can be taken to maximize return on investment in identification.</p> <p></p> <ulist> <item> 1. Use a two-phase identification system that reduces the number of students taking all assessments.</item> <p></p> <item> 2. If available, use schoolwide assessments that are already universally administered to all students, especially for the phase one screener. The result is no added cost for a screener.</item> <p></p> <item> 3. The phase one screener can also be included in the phase-two assessments.</item> </ulist> <hd id="AN0176065278-26">Conclusion</hd> <p>There is no perfect identification system. Identification is a means to an end. Only services can benefit students. The better schools are at identifying students who would benefit from the services they provide, the greater the benefit of those services. At the same time, different schools will face different challenges (e.g., state policies, limited funds, and variation in prior access to opportunity) as well as goals (e.g., increasing access and different services offered). Schools can improve their identification practices to help them accomplish their identification goals through effective use of combination rules and two-phase identification systems. Because of differing challenges and goals, the path to successful identification may vary. District leaders can combine multiple criteria in ways that support their goals while weighing factors such as cost, access, sensitivity, and alignment to meet the unique needs of academically advanced students in their district.</p> <hd id="AN0176065278-27">ORCID iDs</hd> <p>Matthew C. Makel https://orcid.org/0000-0002-3837-0088</p> <p>Scott J. Peters https://orcid.org/0000-0003-2459-3384</p> <p>Lindsay Ellis Lee https://orcid.org/0000-0003-4519-7209</p> <p>Tamra Stambaugh https://orcid.org/0000-0001-5587-1506</p> <ref id="AN0176065278-28"> <title> References </title> <blist> <bibl id="bib1" idref="ref31" type="bt">1</bibl> <bibtext> Card D., Giuliano L. (2016). Universal screening increases the representation of low-income and minority students in gifted education. Proceedings of the National Academy of Sciences of the United States of America, 113(48), 13678–13683. https://doi.org/10.1073/pnas.1605043113</bibtext> </blist> <blist> <bibl id="bib2" idref="ref17" type="bt">2</bibl> <bibtext> Erwin J. O., Worrell F. C. (2012). Assessment practices and the underrepresentation of minority students in gifted and talented education. Journal of Psychoeducational Assessment, 30(1), 74–87. https://doi.org/10.1177/0734282911428197</bibtext> </blist> <blist> <bibl id="bib3" idref="ref18" type="bt">3</bibl> <bibtext> Frasier M. M. (1997). Multiple criteria: The mandate and the challenge. Roeper Review, 20(2), A4–A6. https://doi.org/10.1080/02783199709553868</bibtext> </blist> <blist> <bibl id="bib4" idref="ref3" type="bt">4</bibl> <bibtext> Gentry M., Gray A. M., Whiting G. W., Maeda Y., Pereira N. (2019). Gifted education in the United States: Laws, access, equity, and missingness across the country by locale, Title I school status, and race. Gifted Education Research and Resource Institute, Purdue University. https://<ulink href="http://www.education.purdue.edu/geri/new-publications/gifted-education-in-the-united-states/">www.education.purdue.edu/geri/new-publications/gifted-education-in-the-united-states/</ulink></bibtext> </blist> <blist> <bibl id="bib5" idref="ref11" type="bt">5</bibl> <bibtext> Lakin J. M. (2018). Making the cut in gifted selection: Score combination rules and their impact on program diversity. Gifted Child Quarterly, 62(2), 210–219. https://doi.org/10.1177/0016986217752099</bibtext> </blist> <blist> <bibl id="bib6" idref="ref29" type="bt">6</bibl> <bibtext> Lee L. E. (2021). Evaluating program diversity and the probability of gifted identification using the Torrance Test of Creative Thinking [Doctoral dissertation, University of North Texas]. https://digital.library.unt.edu/ark:/67531/metadc1833574/m1/203/</bibtext> </blist> <blist> <bibl id="bib7" idref="ref20" type="bt">7</bibl> <bibtext> Lee L. E., Ottwein J. K., Peters S. J. (2020). Eight universal truths of identifying students for advanced academic interventions. In Jolly J. L., Robins J. H. (Eds), Methods & materials for teaching the gifted (5th ed., pp. 61-79). Prufrock Press.</bibtext> </blist> <blist> <bibl id="bib8" idref="ref16" type="bt">8</bibl> <bibtext> Lee L. E., Peters S. J. (2022). Universal screening: A process to promote equity. In Johnsen S., VanTassel-Baska J. (Eds), Handbook on assessments for gifted learners (pp. 29–43). Prufrock Press.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref6" type="bt">9</bibl> <bibtext> Lockhart K., Meyer M. S., Crutchfield K. (2022). A content analysis of selected state plans for gifted and talented education. Journal of Advanced Academics, 33(1), 3–42. https://doi.org/10.1177/1932202X211026240.</bibtext> </blist> <blist> <bibtext> Lohman D. F. (2009). Identifying academically talented students: Some general principles, two specific procedures. In Shavinina L. V. (Ed), International Handbook on Giftedness (pp. 971–997). Springer. https://doi.org/10.1007/978-1-4020-6162-2_49</bibtext> </blist> <blist> <bibtext> Long D. A., McCoach D. B., Peters S. J., Peters P. M. (2022). Implications of multiple measures on the size, ability, and diversity of gifted populations [Conference presentation]. National Association for Gifted Children Annual Conference.</bibtext> </blist> <blist> <bibtext> McBee M. T., Makel M. C. (2019). The quantitative implications of definitions of giftedness. AERA Open, 5(1), 233285841983100–233285841983113. https://doi.org/10.1177/2332858419831007</bibtext> </blist> <blist> <bibtext> McBee M. T., Peters S. J., Miller E. M. (2016). The impact of the nomination stage on gifted program identification: A comprehensive psychometric analysis. Gifted Child Quarterly, 60(4), 258–278. https://doi.org/10.1177/0016986216656256</bibtext> </blist> <blist> <bibtext> McBee M. T., Peters S. J., Waterman C. (2014). Combining scores in multiple-criteria assessment systems: The impact of combination rule. Gifted Child Quarterly, 58(1), 69–89. https://doi.org/10.1177/0016986213513794</bibtext> </blist> <blist> <bibtext> McClain M. C., Pfeiffer S. I. (2012). Identification of gifted students in the United States today: A look at state definitions, policies, and practices. Journal of Applied School Psychology, 28(1), 59–88. https://doi.org/10.1080/15377903.2012.643757</bibtext> </blist> <blist> <bibtext> National Association for Gifted Children. (2019). Pre-K to grade 12 gifted programming standards. https://cdn.ymaws.com/nagc.org/resource/resmgr/knowledge-center/nagc_2019_prek-grade_12_gift.pdf</bibtext> </blist> <blist> <bibtext> Peters S. J., Rambo-Hernandez K., Makel M. C., Matthews M. S., Plucker J. A. (2017). Should millions of students take a gap year? Large numbers of students start the school year above grade level. Gifted Child Quarterly, 61(3), 229–238. https://doi.org/10.1177/0016986217701834</bibtext> </blist> <blist> <bibtext> Peters S. J., Makel M. C., Rambo-Hernandez K. (2021). Local norms for gifted and talented student identification. Everything you need to know. Gifted Child Today, 44(2), 93–104. https://doi.org/10.1177/1076217520985181</bibtext> </blist> <blist> <bibtext> Peters S. J. (2022). The challenges of achieving equity within public school gifted and talented programs. Gifted Child Quarterly, 66(2), 82–94. https://doi.org/10.1177/00169862211002535</bibtext> </blist> <blist> <bibtext> Peters S. J., Rambo-Hernandez K. E., Makel M. C., Matthews M. S., Plucker J. A. (2019). The effect of local norms on racial and ethnic representation in gifted education. AERA Open, 5(2), 1–18. https://doi.org/10.1177/2332858419848446</bibtext> </blist> <blist> <bibtext> Peters S. J., Stambaugh T., Makel M. C., Lee L. E., McBee M., McCoach D. B., Johnson K. (2023). The CASA Criteria for evaluating gifted and talented identification systems: Cost, alignment, sensitivity, and access. Gifted Child Quarterly, 67(2), 137–150. https://doi.org/10.1177/00169862221124887</bibtext> </blist> <blist> <bibtext> Rinn A., Mun R., Hodges J. (2022). State of the States Report. National Association for Gifted Children. https://nagc.org/page/state-of-the-states-report.</bibtext> </blist> <blist> <bibtext> VanTassel-Baska J., Stambaugh T. (2007). Overlooked gems: A national perspective on promising students of poverty. National Association for Gifted Children. https://files.eric.ed.gov/fulltext/ED494579.pdf</bibtext> </blist> </ref> <ref id="AN0176065278-29"> <title> Footnotes </title> <blist> <bibtext> The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the US Department of Education grant number S206A200007 – 21.</bibtext> </blist> </ref> <aug> <p>By Matthew C. Makel; Scott J. Peters; Lindsay Ellis Lee; Tamra Stambaugh; Matthew T. McBee; D. Betsy McCoach and Kiana R. Johnson</p> <p>Reported by Author; Author; Author; Author; Author; Author; Author</p> <p></p> <p>Matthew C. Makel, PhD, is a Professor and Research Chair in High Abilities Studies in the Werklund School of Education at the University of Calgary. His research focuses on academic talent development and open science research methods.</p> <p>Scott J. Peters, PhD, is a Senior Research Scientist at NWEA. Prior to joining NWEA he served as a Professor of Assessment and Research Methodology at the University of Wisconsin –Whitewater. His research work focuses on educational assessment and data use, gifted and talented student identification, equity within advanced educational opportunities, and educational policy.</p> <p>Lindsay Ellis Lee, PhD, is an Assistant Research Professor in the Department of Pediatrics and a Faculty Research Affiliate with the Center for Excellence in Early Childhood Learning & Development at East Tennessee State University. Dr. Lee's research interests include equitably identifying advanced students, evaluating psychological and educational measurements, talent development across domains, and developing learning environments that encourage growth.</p> <p>Tamra Stambaugh, PhD, is an Associate Professor and the Margo Long Endowed Chair in Gifted Education at Whitworth University. Her research interests include curriculum and instruction, the development of expertise and talent, rural gifted education, poverty, and professional development.</p> <p>Matthew McBee, PhD, is a recovering academic and is now Vice President of Data Science at Service Management Group.</p> <p>D. Betsy McCoach, PhD, is a Professor of Research Methods, Measurement, and Evaluation in the Educational Psychology department at the University of Connecticut. Dr. McCoach's research interests include latent variable modeling, multilevel modeling, longitudinal modeling, instrument design, and gifted education.</p> <p>Kiana R. Johnson, PhD, is an Associate Professor in the Department of Pediatrics, Quillen College of Medicine, East Tennessee State University.</p> </aug> <nolink nlid="nl1" bibid="bib16" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib23" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib13" firstref="ref4"></nolink> <nolink nlid="nl4" bibid="bib20" firstref="ref5"></nolink> <nolink nlid="nl5" bibid="bib15" firstref="ref7"></nolink> <nolink nlid="nl6" bibid="bib22" firstref="ref8"></nolink> <nolink nlid="nl7" bibid="bib21" firstref="ref9"></nolink> <nolink nlid="nl8" bibid="bib10" firstref="ref12"></nolink> <nolink nlid="nl9" bibid="bib11" firstref="ref13"></nolink> <nolink nlid="nl10" bibid="bib14" firstref="ref14"></nolink> <nolink nlid="nl11" bibid="bib12" firstref="ref28"></nolink> <nolink nlid="nl12" bibid="bib17" firstref="ref30"></nolink> <nolink nlid="nl13" bibid="bib18" firstref="ref48"></nolink>
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  Data: Effective Identification through Multiple Criteria
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  Data: <searchLink fieldCode="AR" term="%22Matthew+C%2E+Makel%22">Matthew C. Makel</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-3837-0088">0000-0002-3837-0088</externalLink>)<br /><searchLink fieldCode="AR" term="%22Scott+J%2E+Peters%22">Scott J. Peters</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-2459-3384">0000-0003-2459-3384</externalLink>)<br /><searchLink fieldCode="AR" term="%22Lindsay+Ellis+Lee%22">Lindsay Ellis Lee</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4519-7209">0000-0003-4519-7209</externalLink>)<br /><searchLink fieldCode="AR" term="%22Tamra+Stambaugh%22">Tamra Stambaugh</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5587-1506">0000-0001-5587-1506</externalLink>)<br /><searchLink fieldCode="AR" term="%22Matthew+T%2E+McBee%22">Matthew T. McBee</searchLink><br /><searchLink fieldCode="AR" term="%22D%2E+Betsy+McCoach%22">D. Betsy McCoach</searchLink><br /><searchLink fieldCode="AR" term="%22Kiana+R%2E+Johnson%22">Kiana R. Johnson</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Gifted+Child+Today%22"><i>Gifted Child Today</i></searchLink>. 2024 47(2):108-118.
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  Data: <searchLink fieldCode="DE" term="%22Academically+Gifted%22">Academically Gifted</searchLink><br /><searchLink fieldCode="DE" term="%22Talent+Identification%22">Talent Identification</searchLink><br /><searchLink fieldCode="DE" term="%22Gifted+Education%22">Gifted Education</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+Criteria%22">Evaluation Criteria</searchLink><br /><searchLink fieldCode="DE" term="%22Correlation%22">Correlation</searchLink>
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  Data: 10.1177/10762175231222300
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  Data: Finding all the "gifted" students who would benefit from a gifted and talented service is a perpetual concern. In this article, we focus on how to effectively implement multiple criteria in identification. First, we provide some broad background before introducing three different ways to combine multiple data points (AND, OR, and MEAN) when identifying students for gifted services. Next, we discuss how effective use of combining multiple criteria--including using two-phase identification systems--contributes to schools saving time and money while also better identifying students. To do this, we use newly introduced criteria for evaluating gifted and talented identification systems. Finally, we provide several keys for success that can help schools accomplish their identification goals effectively.
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    Identifiers:
      – Type: doi
        Value: 10.1177/10762175231222300
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 108
    Subjects:
      – SubjectFull: Academically Gifted
        Type: general
      – SubjectFull: Talent Identification
        Type: general
      – SubjectFull: Gifted Education
        Type: general
      – SubjectFull: Evaluation Criteria
        Type: general
      – SubjectFull: Correlation
        Type: general
    Titles:
      – TitleFull: Effective Identification through Multiple Criteria
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Matthew C. Makel
      – PersonEntity:
          Name:
            NameFull: Scott J. Peters
      – PersonEntity:
          Name:
            NameFull: Lindsay Ellis Lee
      – PersonEntity:
          Name:
            NameFull: Tamra Stambaugh
      – PersonEntity:
          Name:
            NameFull: Matthew T. McBee
      – PersonEntity:
          Name:
            NameFull: D. Betsy McCoach
      – PersonEntity:
          Name:
            NameFull: Kiana R. Johnson
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 1076-2175
            – Type: issn-electronic
              Value: 2162-951X
          Numbering:
            – Type: volume
              Value: 47
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Gifted Child Today
              Type: main
ResultId 1