Considering the Needs of English Language Learner Populations

Abstract

This article synthesizes reading intervention research studies intended for use with struggling or at-risk students to determine which studies adequately address population validity, particularly in regard to the diverse reading needs of English language learners. An extensive search of the professional literature between 2001 and 2010 yielded a total of 67 reading intervention studies targeting at-risk elementary students. Findings revealed that many current research studies fail to adequately describe the sample, including the accessible and target populations, and to disaggregate their findings based on demographic characteristics. When population validity issues are not addressed, researchers cannot generalize findings to other populations of students, and it becomes unclear what intervention strategies work, especially with English language learner student populations. However, 25 studies did specifically recognize and address the needs of English language learners, indicating more researchers are taking into consideration the diverse needs of other struggling student populations.

Keywords

population validity response to intervention English language learners quality indicators reading

Special education researchers are engaged in a focused effort to improve the quality and utility of special education research. This is evidenced by the special issue of Exceptional Children dedicated to quality indicators in different types of research (e.g., for group design studies; Gersten et al., 2005) as well as efforts by key organizations such as the Council for Exceptional Children and the Response to Intervention Center to assess the quality of instructional practices to determine if they are of high enough quality to be considered evidence based. In recent years, various researchers have used scoring rubrics to judge the quality of the evidence in support of specific practices (e.g., Chard, Ketterlin-Geller, Baker, Doabler, & Apichatabutra, 2009, who assessed the research in support of repeated reading, and Jitendra, Burgess, & Gajria, 2010, who evaluated the research on comprehension strategy instruction). Yet these efforts may not be focusing enough on external validity, particularly population validity, when evaluating the effectiveness and appropriateness of practices for English language learner (ELL) populations. The purpose of this examination is to compile and study the research on reading interventions to determine who the participants are and for whom interventions are being generalized. We consider whether or not the results of studies are being generalized as if they apply broadly to unique populations such as ELLs without having being tested with ELLs.

Population Validity

Establishing external validity by creating a research study that can be generalized from a smaller group of respondents within a certain set of conditions to a larger population is paramount for educational researchers (Bracht & Glass, 1968). External validity refers to the degree to which research findings developed in one contextual setting and time with one particular population or culture can be generalized to other contextual settings, times, populations, or cultures (Fyans, 1983; Parker, 1990). Addressing external validity ensures that the success of the reading intervention being studied can be adequately translated from the sample population of participants to different populations of students (see Note 1).

According to Bracht and Glass (1968), population validity grants researchers the ability to gain knowledge about a larger target population by studying a small section of that population. Kempthorne (1961) discussed the need for researchers to carefully examine the characteristics of both the target population and the experimentally accessible population. The target population, as defined by Kempthorne, is the larger group of persons with whom the researcher is interested and to whom the researcher intends to generalize findings. The experimentally accessible population is a smaller and more readily available version of the target population (Kempthorne, 1961). Ensuring population validity allows the researcher to make two leaps in generalizations (Bracht & Glass, 1968). First, the researcher can generalize findings from the sample to the experimentally accessible population from which the sample was drawn. Second, research findings can then be generalized from the experimentally accessible population to the larger target population (Bracht & Glass, 1968). These leaps in generalizations cannot be made until the researcher has gained a thorough knowledge of the demographic characteristics of both the experimentally accessible population and the target population (Bracht & Glass, 1968). Without this level of examination, the results of the experiment can pertain only to the sample population and cannot be generalized to the larger population (Bracht & Glass, 1968).

To ensure threats to population validity are adequately addressed, researchers must clearly describe the characteristics of both the sample and the target populations (Bracht & Glass, 1968; Lysynchuk, Pressley, d’Ailly, Smith, & Cake, 1989; Troia, 1999). This description must include all demographic information such as gender, race/ethnicity, language use, socioeconomic status, and the geographic locale from where the sample was drawn (Troia, 1999). Artiles, Rueda, Salazar, and Higareda (2005) also argued for researchers to describe and disaggregate results for subpopulations of ELLs because of the diversity within ELL populations (e.g., race/ethnicity, language spoken, language proficiency levels) and because the term ELL is often not clearly defined or specifically described in much educational research. Pedraza and Rivera (2005) similarly noted that much research on Latino/a populations emphasizes language proficiency with little regard given to other aspects of diversity (e.g., recent immigrant, long-term American citizen, Mexican, Puerto Rican, Cuban, Central American, South American, Caribbean). There is no doubt that demographic characteristics of target, accessible, and sample populations, the “variables” of population validity noted above, influence the effects of interventions. For example, Charity, Scarborough, and Griffen (2004) examined how racial difference, particularly in terms of familiarity with school English, had an impact on the reading achievement of African American children. E. C. Crowe, Connor, and Petscher (2009) conducted a comparative analysis of evidence-based core reading curricula for children from lower socioeconomic households, noting differences in reading growth based on the variable of socioeconomic status (SES). Rogoff, Paradise, Mejía Arauz, Correa-Chávez, and Angelillo (2003) discovered differences in how children learn through participation with adults across cultures (European American, indigenous communities around the world) with schools in the United States privileging European American participation patterns, Finally, Gutiérrez and colleagues (e.g., Gutiérrez, Asato, Santos, & Gotanda, 2002; Gutiérrez, Baquedano-López, & Tejeda, 1999) have consistently documented the impact of English-only pedagogy on ELLs while calling for more robust research in developing effective classrooms for both mono- and multilingual students. In educational research, it can be challenging to define or describe target and accessible populations. Students in general are the usual target population for educational research, yet demographic characteristics of students vary widely across schools, districts, states, and the nation. Equally problematic for educational researchers is the accessible population. Acquiring an accessible population that is representative of a specific target population may be difficult if the available accessible population has demographic characteristics that differ from those of the intended target population. However, these threats to population validity can be minimized if researchers specifically define for whom they intend the research to be applied and if they purposely search for an accessible population that matches the demographic characteristics of the population of students for whom they are interested.

Calls to focus more on population validity in special education research are not new. As long ago as 1978, Keogh, Major, Reid, Gandara, and Omori suggested that researchers use a system of marker variables to address concerns about the lack of uniformity in the reporting of educational research study samples. A marker variable, as defined by Bell and Hertz (1976, as cited in Keogh et al., 1978), is not the specific focal variable of the study but a background variable that is sufficiently relevant to what is being measured. Without the uniform and consistent use of marker variables in the field, Keogh et al. argued that individual researchers would define their samples differently, creating data that would be inconsistent and problematic to replicate: “Wide variability in children’s characteristics predictably leads to a variability of program effects” (p. 6). When researchers neglect to use descriptive marker variables, such as gender, age, race/ethnicity, SES, and language of the student, the data are not empirically anchored, and it is uncertain which background variables are influencing the focal variables being measured (Keogh et al., 1978). Furthermore, marker variables of importance for ELLs or Latino/a populations must consider diversity within ELL populations and describe how language proficiency is determined (Artiles et al., 2005).

In 1999, Troia noted that a certain degree of methodological rigor is necessary to be able to interpret and generalize the findings of experimental studies. In particular, Troia looked at threats to population validity in terms of participant selection and description, specifically defining the following categories: how participants are selected, age, grade, gender, race, SES, locale of participants, IQ, achievement status, history in special education, and disability criteria. Each category was weighted from 1 (an unlikely threat to external validity) to 3 (a serious flaw and major threat to validity). Findings indicated that researchers’ neglect to address threats to external validity created potentially fatal flaws in the research of instructional interventions, thus limiting what could be understood about what works for struggling students. Troia’s research, however, did not consider language use or proficiency.

More recently, researchers in the field of special education have called for the development of guidelines for conducting and evaluating the quality of research to ensure that instructional practices emerging from research are evidence-based (Odom et al., 2005). Gersten et al. (2005) developed a set of quality indicators for this purpose, proposing the use of weighted criteria for evaluating special education experimental and quasi-experimental research, but they stopped short of creating a rubric for use in evaluating studies. They deemed certain indicators within experimental and quasi-experimental studies as essential for the study to be determined evidence based, whereas they considered other indicators to be desirable but less crucial.

Building on this initial work with quality indicators, several studies designed quality indicator rubrics to apply to reviewing specific special education research interventions. Montague and Dietz (2009) evaluated cognitive strategy use in mathematical problem solving with a rubric noting whether studies conformed to the quality indicators developed by Gersten et al. (2005) or did not. Chard et al. (2009) conducted a review of research on repeated reading, creating a 4-point rubric with three categories under description of participants: information about diagnosis of disability or difficulty, comparability of samples across conditions, and information about comparability of the intervention across conditions. Using the rubric developed in the Chard et al. (2009) study, Baker, Chard, Ketterlin-Geller, Apichatabutra, and Doabler (2009) evaluated the quality of research conducted on self-regulated strategy development in writing. Jitendra et al. (2010) conducted a review of research on cognitive strategies by creating a 3-point rubric with three categories for participants and setting: description of participants (i.e., age, gender, IQ, disability, diagnosis), how participants were selected, and descriptions of the setting. Although the use of quality indicators as a tool for measuring the quality of special education research is relatively new, only the Jitendra et al. (2010) study specifically used ELL status as an important demographic marker for researchers to include.

Historically, researchers have not always described variables that reflect the diversity of students with disabilities. For example, Artiles, Trent, and Kuan (1997) conducted a review of 22 years of empirical research published in special education journals and noted a substantial disregard for diversity issues. They argued that the proportion of published articles that addressed issues of diversity was “alarmingly low,” despite the publication of several special issues on ethnic minority students. They also suggested that many educational researchers draw conclusions about the performance of culturally and linguistically diverse students without attending to external validity or without designing research that is sensitive to cultural differences. The Artiles et al. (1997) study was recently replicated with research conducted from 1995 to 2009, with findings suggesting there has been a slight increase in the number of studies specifically reporting findings on ethnic minority students, yet the majority of studies conducted during this time span continued to fail to disaggregate results for many subpopulations of learners (Vasquez et al., 2011). In analyzing research conducted with ELLs, Bos and Fletcher (1997) and Artiles et al. (2005) found a scarcity of research on within-group diversity among ELLs, noting the neglect of many studies to adequately describe the demographics and language proficiency of ELL populations. Yet relative language proficiency is an important variable that can and does affect treatment outcomes (Ortiz, 1997).

In this article, we focus specifically on the population validity of reading intervention research and how it is being applied to ELLs. We look to see if recent reading research articles include more information about the language proficiency of participants than did articles of the past. We also note whether authors overgeneralize their findings as if they apply to ELLs even when ELLs were not included in their samples. We emphasize that it is not enough to find out “what works” in a general sense; it is crucial to know what reading intervention practices work for whom (Artiles et al., 2005; Gandara & Bial, 2001; Klingner & Edwards, 2006). We argue that the results of reading intervention research studies that do not include ELLs in their sample populations should never be generalized to ELLs.

English Language Learners

ELLs are the fastest growing segment of the U.S. school-age population (National Clearinghouse for English Language Acquisition [NCELA], 2007). About two thirds of ELLs come from low-income families (NCELA, 2007). Although ELLs are often thought to be immigrants, most ELLs are born in the United States and are often second- or third-generation U.S. citizens. Many become long-term ELLs, students who are not reclassified as fluent in English even after 7 years (Menken & Kleyn, 2009). ELLs may have adequate conversational skills in English but lack the vocabulary and academic language needed for success in school. They generally score lower on academic achievement tests than their fluent English peers (Hemphill & Vanneman, 2011). The majority of ELLs speak Spanish as their first language (73%; NCELA, 2011). On the National Assessment of Educational Progress, reading scores have increased significantly for both White and Hispanic students, but the achievement gap between Hispanic and White students has not changed for fourth or eighth graders when comparing 1992 to 2009 data. The gap in 2009 for fourth graders was 25 points, and the gap for eighth graders was 24 points (Hemphill & Vanneman, 2011). Only a very small percentage of eighth-grade ELLs tested as proficient in reading (Aud et al., 2011). Research that is designed to examine reading interventions being used in response to intervention models in schools must be cognizant of these gaps in reading achievement. When interventions that have not been validated with ELLs are implemented broadly in schools, ELLs may be erroneously identified and placed into special education simply because the interventions are not meeting their language and learning needs (Orosco & Klingner, 2010).

Although identification and placement into special education may be beneficial and necessary for children with disabilities, it can be stigmatizing and lead to inequitable educational opportunities, particularly for students who are misidentified. The evidence suggests that culturally and linguistically diverse students are more likely to be overrepresented in certain special education categories compared with their White, English-dominant peers (Donovan & Cross, 2002). Older ELLs who are likely to be long-term ELLs may be those most at risk of overrepresentation in special education (Artiles et al., 2005). Furthermore, culturally and linguistically diverse students are more often placed in separate classrooms from their White, English-dominant special education peers (Fierros & Conroy, 2002; Parrish, 2002), thus missing essential exposure to general education curricula (Donovan & Cross, 2002). The broad application of “evidence-based” research, particularly research that has failed to address threats to population validity, places ELLs at risk of misidentification and overrepresentation into special education categories. Below we outline the response to intervention model, specifically considering the reading needs of second language learners.

Response to Intervention and Reading Interventions for English Language Learners

The response to intervention (RTI) model originated in part from the recommendations of the National Research Council report that questioned the validity of the existing special education classification system and expressed concerns about the disproportionate representation of culturally and linguistically diverse students in special education (Donovan & Cross, 2002; Individuals with Disabilities Education Improvement Act, 2004). Vaughn and Fuchs (2003) described RTI as a three-tier prevention model for identifying and assisting children with possible learning disabilities. The first tier involves evidence-based quality instruction in the general education classroom for all students. The second tier of instruction and assessment is introduced for a student if that student struggles to make gains in the general education curriculum. In the second tier, the struggling student receives more explicit instruction through the use of evidence-based interventions in addition to the quality instruction in the general education classroom. If this student fails to respond to this second layer of intervention, the student moves to the final tier of intervention and assessment. This third tier involves assessment for and placement in special education. When a student fails to respond to intervention at this level, it is a signal of a possibly disability, and he or she is often identified and placed into special education. Furthermore, reading intervention research that provides evidence of success has the potential to influence instructional practices as it is scaled up for use in broader settings, specifically in general education Tier 1 classrooms (Denton, Vaughn, & Fletcher, 2003). Consequently, it is imperative that reading intervention research attend to threats to population validity because of the implications of scaled-up, broadly applied intervention practices on linguistically diverse student populations.

Before identifying and placing an ELL in special education, the student must have received quality, evidence-based reading intervention instruction (Vaughn & Fuchs, 2003). This notion that instruction must be based on scientific evidence, or research, is a central one. Students who fail to respond to evidence-based general education literacy instruction should receive more intensive, explicit, evidence-based reading interventions prior to placement in special education (Torgesen et al., 2001). Placement into special education should come only after the student has received evidence-based reading interventions that have been validated on similar struggling students and it has been demonstrated that students have received an adequate opportunity to learn (Klingner & Edwards, 2006). However, this evidence-based reading intervention must be validated with students similar to those with whom it will be used. Drawing conclusions about the success of a reading intervention for ELLs without carefully validating the practice with similar students is problematic (Artiles et al., 1997). In the next section, we provide an overview of learning to read in English as a second language to highlight why validating reading interventions with ELLs is relevant.

English Language Learners and Learning to Read in English as a Second Language

Although elements of generic good teaching apply to ELLs and learning to read in English as one’s first language in many ways resembles learning to read in English as a second or additional language, there also are important differences that should be considered (August & Shanahan, 2006; Goldenberg, 2008). A common misconception applied to ELLs is that learning to read is simply learning to read, regardless of the language the student speaks predominantly. Reports on reading such as that by the 1998 National Research Council perpetuate this idea (National Institute of Child Health and Human Development [NICHD], 2000). The five major reading components emphasized in the report, phonological awareness, fluency, comprehension, vocabulary, and word study, have been at the center of education policies such as Reading First and No Child Left Behind, suggesting that reading instruction and interventions should rely heavily on these reading components to foster reading development in all children. Although the report notes that it “did not address issues relevant to second language learning” (NICHD, 2000, p. 3) and the panel omitted research with ELLs from their synthesis, recommendations are generalized as if they apply to all populations. More recently, the National Early Literacy Panel (NELP, 2008) report noted the limited number of studies focusing on specific subpopulations, such as ELLs, and recommended making some practices effective with monolingual English-speaking children (i.e., code-focused instruction in phonics) “available to all populations of young children at least until research more directly addresses this question” (p. 120). This is problematic because policy recommendations such as these may be erroneously taken as fact that “what works” in reading instruction and intervention for some works for all. Although the NRC and NELP reports acknowledge the limited knowledge base of effective reading instructional practices for ELLs and also advise that generalizing findings from existing studies to ELLs is recommended only until more definitive research emerges, we believe that there are other options available to practitioners that better take into account what is known about supporting young ELLs’ language and literacy development. Overemphasizing phonics and isolated word reading and underemphasizing other components of literacy, such as vocabulary, comprehension, and oral language, can contribute to a gap between ELLs’ word reading and these other skills (Crosson & Lesaux, 2009; Mancilla-Martinez & Lesaux, 2011a, 2011b).

The National Literacy Panel on Language-Minority Children and Youth (August & Shanahan, 2006) summarized the existing body of research on learning to read in a language other than English and recommended that ELLs receive instruction in the same components of reading as their monolingual peers, but with some key differences. They noted, “Instructional approaches found to be successful with native English speakers do not have as positive a learning impact on language-minority students” (August & Shanahan, 2006, p. 10). This may be particularly true for comprehension instruction. ELLs appear to need more support with oral language and vocabulary than their fluent English peers to benefit from comprehension strategies. Along with other scholars in the field, August and Shanahan (2006) suggest differences necessary for ELLs and recommend more research specifically targeting the needs of ELLs (Goldenberg, 2008; Klingner, Artiles, & Barletta, 2006).

Thus, it is the premise of this examination that before reading interventions can be adopted, scaled up, and broadly used with ELLs, they must clearly demonstrate their success with ELLs, and must also explicitly state to whom the findings can be applied. In particular, reading interventions either need to be found to apply broadly to diverse populations because they have been tested with ELLs and the data have been disaggregated for such populations or must have been specifically developed for ELLs, tested with ELLs, and found to be effective for ELLs. Threats to population validity can be reduced by attending to descriptive marker variables and directing attention to the demographic characteristics of the sample, including the accessible and target populations, and by openly discussing how the research findings can be generalized from the sample population to the target population.

Although many evidence-based reading interventions have been deemed successful generally, a question remains: Do they work as well for subpopulations of students, particularly for ELLs? The purpose of this article is threefold. First, we examine the research conducted on reading interventions since 2001 being used with struggling or at-risk elementary students with the intent of determining how well these research studies describe their sample and target populations to determine if they are adequately addressing threats to population validity. Second, we seek to determine the extent to which researchers are conducting reading interventions research with ELLs as a means toward building a better knowledge base of effective reading for ELLs. And finally, we look specifically to whom researchers are generalizing their findings and whether they appear to be overgeneralizing. This examination of reading intervention research addresses the following main question:

How generalizable are the research findings from reading intervention studies for subpopulations, especially ELLs? In particular, this examination looks at the following subquestions: (a) How are threats to population validity addressed in each study (i.e., target, accessible, and sample populations defined and described)? (b) How many reading intervention studies include ELLs in their samples or are specifically targeting ELLs, and how many disaggregate their findings for ELLs? and (c) To whom are the reported findings being generalized?

Method

Data Collection

For this examination of reading intervention research, we conducted a three-step comprehensive search of the literature from the years 2001 to 2010 (Cooper, 1998). The year 2001 was selected because RTI models emerged in the early part of the decade in response to the problems with using IQ discrepancy as an identification and placement method for special education (Vaughn & Fuchs, 2003). RTI reading interventions differ from earlier reading interventions in terms of being directed toward struggling general education students and not necessarily students already identified and placed into special education settings. This is relevant for our synthesis as we are seeking to understand how interventions are being studied and to whom the findings are being applied. We specifically considered reading interventions that target students who are not making adequate progress within the general education curriculum, seeking to understand to whom these interventions are being generalized. Consequently, we searched for research articles that targeted smaller groups of students as opposed to whole-group, general education instructional approaches.

Initially, the search was broad, with the purpose of locating all possible articles, and then moved to a more selective search of research articles meeting the criteria for inclusion in this examination of reading intervention research. The first step involved a systematic electronic search using the ERIC (Cambridge Scientific Abstracts), PsycINFO, SAGE Journals, MEDLINE, and Wilson Web online databases. The following descriptors and keywords were used to capture any article pertaining to students who are at risk of developing or who have been identified as having a reading disability: reading instruction, RTI, reading intervention, reading remediation, reading disability, struggling reader, and at-risk reader. The term English language learner was also used as a descriptor to seek additional articles specifically addressing the needs of ELLs and not initially found using the above descriptors. Along with relevant articles from the initial search, a list of the 11 most frequently cited journals containing articles matching the descriptors and keywords was compiled. This initial search revealed 2,875 articles matching the search terms. We cast our net wide in this search by not limiting our subject matter to education only.

The second step involved a hand search of the following journals: Annals of Dyslexia, Elementary School Journal, Exceptional Children, Journal of Educational Psychology, Journal of Learning Disabilities, Journal of Special Education, Learning Disability Quarterly, Learning Disabilities Research & Practice, Reading Research Quarterly, Remedial and Special Education, and Scientific Studies of Reading. The hand search consisted of first searching within each journal using EBSCOhost Academic Search Premier’s online tool and the descriptors and keywords and then following up by searching through the table of contents of each issue within the above journals from the years 2001 to 2010 to ensure that no article was overlooked. This second search revealed 580 articles, many of which were already located in the initial search.

The third step involved searching the reference lists and footnotes from relevant reading intervention studies to locate additional research articles that had not previously been located. We were then able to narrow down the relevant articles to 67 articles that met the following criteria (see Table 1).

Table 1.

Studies Synthesized Showing Target Populations, Sample Population Marker Variables Noted, and How Findings Were Reported.

		Sample		Findings Discussion or Conclusion
Study	Target	Ethnicity Provided?	ELLs Provided?	Were Findings Disaggregated for ELLs?	To Whom Authors Generalized Findings	Applicable to ELLs?
Allor & McCathren (2004)	At-risk 1st grade	Yes	No	No—no ELLs in study	At-risk 1st graders	No
Berninger et al. (2006)	2nd grade at risk for RD	Yes	No	No—no ELLs in study	At-risk readers	No
Blachman et al. (2004)	2nd–3rd grade RD	Yes	No^a	No—no ELLs in study	2nd–3rd grade failing readers	No
Calhoon et al. (2006)	1st Hispanic	Partial	Yes	Yes—for Hispanics	At-risk bilingual Hispanic	Yes
Calhoon et al. (2007)	1st ELL	Yes	Yes	Yes—for ELLs	ELLs	Yes
Carlo et al. (2004)	ELL	No	Yes	Yes—for ELLs	ELLs	Yes
Case et al. (2010)	At-risk 1st grade	Yes	No^a	No—no ELLs in study	1st grade at risk	No
Coyne et al. (2004)	1st grade at risk	Yes	No	No—no ELLs in study	Some at-risk students	No
Coyne et al. (2007)	K at risk	Yes	No	No—no ELLs in study	At risk	No
Denton et al. (2004)	Spanish-dominant ELL	Yes	Yes	Yes—for ELLs	Spanish-dominant ELL	Yes
Denton et al. (2006)	Nonresponders to RTI	Yes	No	No—no ELLs in study	Nonresponders to RTI	No
Denton, Nimon, et al. (2010)	1st grade at risk	Yes	Yes	No-have ELLs	At-risk 1st grade	No
Denton, Solari, et al. (2010)	High-poverty urban	Yes	No	No—no ELLs in study	At risk	No
Gerber et al. (2004)	K–1st ELL	Yes	Yes	Yes—for ELLs	ELLs	Yes
Graham et al. (2002)	Poor spellers	Yes	No	No—no ELLs in study	Weaker spellers	No
Greenwood et al. (2001)	ELL	No	Yes	Yes—for ELLs	ELLs	Yes
Gunn et al. (2002)	Hispanic	Partial	Yes	Yes—for Hispanics	Hispanic students	Yes
Gunn et al. (2005)	Hispanic	Partial	Yes	Yes—for Hispanics	Hispanic students	Yes
Guthrie et al. (2009)	Low-achieving readers	Yes	Yes	No-have ELLs	Low-achieving readers	No
Haager & Windmueller (2001)	ELL	Yes	Yes	Yes—for ELLs	ELLs	Yes
Harn et al. (2008)	At-risk 1st grade	No	No	No—no demographics	At risk	No
Kamps et al. (2007)	ELL	No	Yes	Yes—for ELLs	ELLs	Yes
Kamps et al. (2008)	At-risk K	Yes	Yes	No-have ELLs	Students at risk of RD	No
Leafstedt et al. (2004)	At-risk ELL	Yes	Yes	Yes—for ELLs	ELLs	Yes
Linan-Thompson et al. (2003)	2nd-grade ELL	No	Yes	Yes—for ELLs	ELLs	Yes
Linan-Thompson et al. (2005)	At-risk K Spanish	No	Yes	Yes—for ELLs	ELLs	Yes
Linan-Thompson et al. (2006)	At-risk ELL	Yes	Yes	Yes—for ELLs	ELLs	Yes
Lo et al. (2009)	At-risk, urban K	Yes	No	No—no ELLs in study	Urban, at risk	No
Mason (2004)	Struggling readers	Yes	No	No—no ELLs in study	Struggling students	No
Mathes & Babyak (2001)	Lowest performing readers	Yes	No	No—no ELLs in study	Lowest quartile	No
Mathes et al. (2003)	Struggling, 1st grade	Yes	No	No—no ELLs in study	Low performing	No
Mathes et al. (2005)	Struggling readers	Yes	Yes	No-have ELLs	1st grade at risk	No
McCandliss et al. (2003)	Children with poor reading	No	No	No—no demographics	Students poor decoding	No
McIntosh et al. (2007)	1st grade ELL	No	Yes	Yes—for ELLs	ELLs	Yes
McMaster et al. (2005)	Nonresponders	No	No	No—no demographics	Struggling students	No
McMaster et al. (2008)	ELL	Yes	Yes	Yes—for ELLs	ELLs	Yes
Menzies et al. (2008)	1st grade, reading difficulties	Yes	Yes	No-have ELLs	At risk	No^a
Nelson et al. (2005)	K at risk	Yes	No	No—no ELLs in study	K at risk	No
O’Connor et al. (2005)	Struggling readers	Yes	Yes	No-have ELLs	Struggling students	No
O’Connor et al. (2007)	Struggling readers	Yes	Yes	Yes—for ELLs	No results for groups	Yes
O’Connor et al. (2010)	Language difficulties	No	Yes	Yes—for ELLs	Students at risk for LD	Yes
Oudeans (2003)	Low-phoneme awareness	No	No	No—no demographics	At risk	No
Pullen et al. (2010)	At risk for RD	Partial	Yes	No-have ELLs	At risk	No
Reis et al. (2007)	Urban, CLD	Yes	Yes	No-have ELLs	Diverse cultural students	Yes
Saenz et al. (2005)	ELL with LD	Yes	Yes	Yes—for ELLs	ELLs	Yes
Santoro et al. (2006)	Children at risk for RD	Yes	No^a	No—no ELLs in study	K at risk for RD	No
Schwartz (2005)	At-risk 1st grade	Yes	No	No—no ELLs in study	Low performing students	No
Silverman (2007b)	ELL	Yes	Yes	Yes—for ELLs	ELLs	Yes
Simmons et al. (2007)	K at risk for RD	Yes	No^a	No—no ELLs in study	K at risk for RD	No
Solari & Gerber (2008)	Spanish speaking ELL	Yes	Yes	Yes—for ELLs	ELLs	Yes
Tong et al. (2010)	Hispanic ELL	Yes	Yes	Yes—for ELLs	ELLs	Yes
Torgesen et al. (2001)	Severe reading disabilities	Partial	No^a	No—no ELLs in study	Severe RD	No
Torgesen et al. (2010)	At risk for dyslexia	Partial	No	No—no ELLs in study	At risk	No
Tuckwiller et al. (2010)	At-risk K	No	Yes	No-have ELLs	At-risk learners	No
Vadasy et al. (2002)	At risk for RD	Yes	Yes	No-have ELLs	At-risk 1st grade	No
Vadasy et al. (2005)	Lowest quartile	Yes	Yes	No-have ELLs	Lowest quartile	No
Vadasy et al. (2006a)	At risk for reading problems	Partial	Yes	No-have ELLs	High-risk for RD	No
Vadasy et al. (2006b)	At risk for reading problems	Partial	Yes	No-have ELLs	2nd–3rd grade RD	No
Vadasy et al. (2007)	Below grade level	Yes	Yes	No-have ELLs	Low skilled	No
Vadasy et al. (2008)	Lowest quartile	Partial	Yes	No-have ELLs	Bottom quartile	No
Vadasy & Sanders (2008)	Lowest quartile	Yes	Yes	No-have ELLs	Below level word reading	No
Vadasy & Sanders (2009)	2nd–3rd low fluency	Yes	Yes	No-have ELLs	Low fluency	No
Vaughn et al. (2003)	Students with RD	Yes	Yes	Yes—for ELLs	ELLs	Yes
Vaughn, Linan-Thompson, et al. (2006)	1st grade ELL at risk for RD	Yes	Yes	Yes—for ELLs	ELLs	Yes
Vaughn, Mathes, et al. (2006)	1st grade ELL at risk	Yes	Yes	Yes—for ELLs	ELLs	Yes
Vaughn et al. (2009)	Low responders to RTI	Yes	No	No—no ELLs in study	Low responders	No
Wanzek & Vaughn (2008)	Low responders to RTI	Partial	No	No—no ELLs in study	Low responders	No

Note: CLD = culturally and linguistically diverse; ELLs = English language learner; LD = learning disability; RD = reading disability; RTI = response to intervention.

Specifically noted the exclusion of ELLs from their sample.

1. Research studies of reading intervention

The articles included in this synthesis are research studies of a reading instructional intervention specifically targeting the reading needs of students not making growth. Intervention, therefore, pertains to additional reading support to supplement the regular reading curriculum. Studies described as “intervention” but that were applied to whole classrooms of students were excluded (e.g., Silverman, 2007a). Although we started our search with research from 2001, marking the beginning of RTI, we did not exclude intervention studies not referring to RTI. Articles describing the inception of the RTI model, ways to use the model, and merits of or problems with the model were not included in this synthesis (e.g., Hollenbeck, 2007; Kavale, Holdnack, & Mostert, 2006; Vaughn & Fuchs, 2003). Research studies describing predictor variables and/or the identification of children with possible learning disabilities were also not included (e.g., Chard et al., 2008; Fuchs, Fuchs, & Compton, 2004). Essays, literature reviews, meta-analyses, book reviews, and editorials were not included.

2. Studies conducted in the United States

Because our focus in this synthesis is on the population validity of reading intervention research used in RTI models of identification and placement into special education, we specifically selected studies conducted in the United States. Research studies examining reading difficulties in children located in other countries were eliminated (e.g., Iversen, Tunmer, & Chapman, 2005; Jiménez et al., 2003). Studies conducted with ELLs in other countries were also eliminated (e.g., D’Angiulli, Siegel, & Maggi, 2004; Lovett et al., 2008).

3. Participants

Included in this article were reading intervention studies using participants in elementary grades (kindergarten through 5th grade). These grade levels are relevant because students who struggle to develop successful reading skills and strategies in the elementary years have continuing academic problems when they move into secondary grades (NICHD, 2000). Studies involving research that focused on secondary level (6th grade through 12th grade) reading intervention strategies were not included. Because the focus of this synthesis was to determine if the needs of ELLs are being met, studies were not limited to monolingual, English-speaking students. Studies focusing on reading interventions targeting ELLs and/or limited English proficient populations were included in this synthesis.

4. Research design

Articles included experimental and quasi-experimental research studies using treatment/comparison designs or single-group designs. As a research methodology, experimental and quasi-experimental studies are often taken as the “gold standard” in research, and findings from such studies are most often broadly applied to other populations as “what works” (Klingner & Boardman, 2011). Consequently, we were interested in understanding how such studies applied their findings. Small case studies with fewer than 20 participants were not included in this article (e.g., L. K. Crowe, 2005).

Data Analysis

Coding procedures

To answer our research questions, we developed comprehensive coding procedures for organizing relevant information from each study. Our interest was in examining the population validity of reading intervention research particularly for ELLs. We did not synthesize the findings or effect sizes of any of the reading intervention studies examined in this article. Instead, as we read each study, we added information to a coding spreadsheet that included the following two major variables: population information and reported findings.

Within the population information variable, we coded studies based on the demographic data or descriptions given about target, accessible, and sample populations to determine if the studies addressed population validity (Bracht & Glass, 1968) or used marker variables (Keogh et al., 1978). The marker variables of interest to us in this study pertained specifically to population validity and how researchers addressed and described their ELL populations (see Artiles et al., 2005) and are described in more detail below (e.g., race/ethnicity, ELL status, how ELL status is determined by the researchers). In this variable, we coded studies in three steps. First, we determined and coded the target population as noted in the article title, abstract, and problem and purpose statements (e.g., at-risk first grade students, struggling kindergarteners). Next, we coded the accessible population in two ways: (a) if the study provided demographic information on race/ethnicity and (b) if the study provided demographic information on ELLs. Finally, we coded the sample population in the following manner: (a) numbers provided for students by race/ethnicity (i.e., White, Hispanic, Black, Asian, Other), (b) number of ELLs in the study, and (c) how ELLs were described (e.g., languages spoken, how language proficiency was determined).

In the reported findings variable, we looked specifically to whom the studies attempted to apply their findings. We did not attempt to synthesize findings in terms of effect sizes or particular intervention strategies to use for ELLs. For the purpose of our examination, we coded studies in three steps. First, we noted the level of disaggregation of results for their sample populations, which revealed four codes: (a) “no—no demographics” for studies that did not provide any demographic information about their sample populations, (b) “no—no ELLs in study” for studies that did not disaggregate their findings based on the demographic information provided about their sample population and did not note ELLs in their sample, (c) “no—have ELLs” for studies that did not disaggregate their findings based on the demographic information provided for their sample population but did have ELLs noted in their sample, and (d) “yes—for ____” for studies that did disaggregate their findings for a specific population (e.g., ELLs, Hispanic). Next, we extensively searched and coded the discussion sections of each article and recorded the specific wording used in the study to determine to whom (the target population) findings were applied. Finally, we compared the wording used in studies to their stated target populations to determine if the study was applicable to ELLs.

Analysis procedures

Our purpose in this examination of reading intervention research was not to determine quality of research in terms of the work being done by Gersten et al. (2005) and quality indicators nor to synthesize the results or suitability of the studies we found that did consider the needs of ELLs, but instead to determine how studies addressed threats to population validity and to determine how generalizable findings are for ELL populations. Thus, we did not weight variables as we coded. Instead, we tallied each category entered into our coding spreadsheet. For the population information variable we first collapsed the target population descriptions found in studies into two broad themes: general and specific. General target population descriptions consisted of “at risk” or “struggling” and often contained a grade level. Specific target population descriptions included “ELL,” “Hispanic,” and “Native American.” These were then tallied and noted. For the accessible population category, we tallied the number of studies in each category (e.g., included race/ethnicity demographics, included ELLs demographics) to determine how many studies included neither category, only one category, or both categories. Finally, we tallied the number of studies that provided race/ethnicity of their sample populations, the number that provided the number of ELL students in their study and the number of studies providing information about their ELL sample populations in terms of language proficiency and languages spoken.

For the reported findings variable, we tallied the number of studies in each sample disaggregation coding category (e.g., no—no demographics, no—no ELLs in study, no-have ELLs, yes—for ELLs). We then compared wording in the discussion sections (to whom they applied findings) to stated target populations (e.g., at risk, struggling, ELLs) and tallied the number of studies that could be generalized to ELL populations.

Reliability

The accuracy of coding was determined through interrater agreement. All coding was initially done by the first author with the second author randomly selecting 50% of the articles to cross-check to determine if the article met the search criteria and to verify the codes obtained and recorded in the spreadsheet. Interrater agreement of 100% was obtained on coding categories requiring tallying (e.g., number of studies noting geographic locale, providing information on number of ELL participants). Discrepancies that emerged in coding the findings section were the result of lack of clarity in the studies included in this synthesis, and such disagreements were discussed by the authors and resolved to meet interrater agreement of 100%.

Results

We located 67 intervention studies published since 2000 with students considered to be struggling or at-risk readers. We present our findings with our research questions as an organizational framework. First we describe how authors addressed population validity. Then we discuss whether they included ELLs in their studies. We finish by noting to whom authors generalized their findings.

Population Validity

To determine if the findings stated in reading intervention studies are appropriate for larger or different populations of struggling students, particularly ELLs, the studies must have addressed threats to external validity. The first subquestion in this synthesis was as follows: How are threats to population validity addressed in each study (i.e., target, accessible, and sample populations defined and described)? A study that has taken into consideration the target, accessible, and sample populations and described them in detail by stating the number of participants and their gender, SES, and race/ethnicity and described the demographics based on language use of the sample population can more readily be replicated and can generalize findings to other similar populations of students ensuring that other struggling readers can be expected to respond to the intervention in a similar manner as the sample of participants responded.

Target populations

Of the 67 reading intervention studies reviewed in this article, 32 referred to their target populations in general terms, failing to state specifically to whom they intended their findings to be applied. A total of 26 studies described their target populations as “at-risk,” “struggling,” or “low-achieving” elementary students. Six studies described their target populations as “below grade level,” students with “language difficulties,” or students who demonstrated poor spelling, fluency, or phonemic awareness.

The remaining 35 studies described their target populations in more specific terms. Eight described their target populations as students whose reading scores fell in the lowest quartile or students who had not previously responded to interventions. Two studies specifically targeted students who had previously been identified as having reading disabilities. Three studies noted their target populations as urban students. One study specifically targeted Spanish-speaking students (Linan-Thompson, Bryant, Dickson, & Kouzekanani, 2005). Three studies defined their target population as Hispanic students, with all studies drawing their samples from predominantly Hispanic classrooms and intentionally targeting similar populations of students (Calhoon, Otaiba, Greenberg, King, & Avalos, 2006; Gunn, Smolkowski, Biglan, & Black, 2002; Gunn, Smolkowski, Biglan, Black, & Blair, 2005). Finally, 18 studies targeted ELLs struggling after instruction in the elementary grades as their population of interest. Demographic characteristics of the ELL target populations were described for these studies mostly as students whose primary language is a language other than English or students who were learning English as a second language. Table 1 lists the studies synthesized in this article and denotes the populations indicated within each study.

Accessible populations

The experimentally accessible population, as defined by Kempthorne (1961), is a smaller representative version of the target population that is more readily available to the researcher. For each of the studies included in this article, the accessible population generally included at-risk elementary students attending elementary schools in which the studies were conducted. Of the reviewed intervention studies, only 15 provided adequate demographic information about race/ethnicity and ELLs in their accessible populations. These studies can more readily make leaps in generalization from their samples to their accessible populations. In all, 35 studies provided no demographic information about race/ethnicity and no information about ELLs in their accessible populations. Consequently, it is difficult to determine if their sample populations were similar enough to their accessible population to make a leap in generalization from sample to accessible and therefore to target. Of the remaining 17 studies, some provided only race/ethnicity demographics and some provided only numbers of ELLs in their accessible populations, making leaps in generalization from sample to accessible populations problematic. However, several exceptions should be noted (i.e., near the Mexico border: Calhoon, Otaiba, Cihak, King, & Avalos, 2007; predominantly Mexican American and Puerto Rican American: Carlo et al., 2004; primarily Spanish-speaking community: Leafstedt, Richards, & Gerber, 2004).

Sample populations

Of the 67 studies examined, 12 did not use marker variables to note the racial/ethnic demographic characteristics of their sample populations. And 10 studies partially described the racial/ethnic demographics of their populations. Of these studies, 6 did so in vague terms such as “other,” “minority,” or “non-White.” Of the studies reporting partial demographics, 4 specifically targeted Hispanic student populations (as noted above) and described their samples in terms of Hispanic students and “other, non-Hispanic students” (Calhoon et al., 2006; Gunn et al., 2002; Gunn et al., 2005; Wanzek & Vaughn, 2008). Of the 67 studies, 45 did provide demographic information about their sample populations.

Out of the 67 examined, 25 studies either did not denote whom they included or did not include ELLs in their sample populations. Five of these latter studies noted that they intentionally excluded ELLs from their sample populations.

Studies Specifically Attending to the Needs of ELLs

Of the 67 studies included in this examination of reading intervention research, 42 included and specifically noted the ELLs in their sample populations. This represents 63% of the studies we reviewed. Marker variables were used to indicate the language use of sample participants in 22 studies (e.g., Spanish, Hmong, Vietnamese). In all, 8 studies used home language surveys and 14 used oral language proficiency scores (from school or state testing) to determine the ELL status of their sample populations. Of the studies, 22 specifically targeted the reading needs of ELLs (see Table 1).

To Whom Reported Findings Are Being Generalized

The final subquestion for this examination was as follows: To whom are the reported findings applied? To answer this question, we looked at which studies disaggregated their data, to whom authors generalized their findings, and whether or not the findings could be applicable to ELL populations. Differences in the demographic characteristics of students are not necessarily seen by researchers to be as relevant as academic differences of students in term of the importance of conducting reading intervention research. Yet research has consistently indicated that variables of population validity (e.g., race/ethnicity, language use, language proficiency) have an influence on the reading achievement of students (e.g., Charity et al., 2004; E. C. Crowe et al., 2009; Gutiérrez et al., 1999; Gutiérrez et al., 2002; Rogoff et al., 2003). Although the target population of students may be generally considered “at risk,” variability in demographics of “at-risk” populations exists and can have an influence on the outcome of the study. Therefore, providing demographic characteristics of sample populations is necessary, and disaggregating findings informs the field about what works for ELLs.

Out of the 67 studies included in this examination, 4 (coded as “no—no demographics”) did not provide demographic information about the race/ethnicity or ELL status of their sample populations and were consequently not able to disaggregate their findings. These studies attempted to apply their findings in general to “at-risk” students, “struggling students,” or “students with poor decoding.” Yet they did not address threats to population validity, so their findings cannot be replicated and can apply only to the students who participated in their study.

In all, 21 studies in this article did provide adequate demographic information about the race/ethnicity of their sample populations but either did not note the number or presence of ELLs in their sample or simply did not include them. Of these studies, 5 specifically excluded ELLs from their sample. Of these studies, 11 attempted to apply their findings in general terms to “at-risk” students; 5 attempted to apply to “lowest performing” or “struggling” students. The remaining 5 studies attempted to apply their findings to more specific populations, using wording such as “non-responders” or “students with severe reading disabilities.” By providing demographic information about their samples, these studies could be replicated. However, their findings cannot be applied to ELLs because they were either excluded or simply not mentioned.

Of the 67 studies synthesized, 17 noted the inclusion of ELLs in their sample populations but did not disaggregate their findings for ELLs, thus making it problematic to assume that results will be relevant for such populations. Of these studies, 8 attempted to apply their findings to “at-risk” students; 7 tried to generalize their findings to “struggling,” “low-performing,” or “low-responding” students; and 1 noted that their findings could be applied to students with severe reading disabilities. However, 1 study is particularly noteworthy: Reis et al. (2007) specifically targeted urban, culturally and linguistically diverse students, described the race/ethnicity and the language status of their sample populations, and suggested their findings are applicable to other culturally and linguistically diverse, urban populations.

The remaining 25 studies in this article are noteworthy because they did address threats to population validity, used marker variables to describe their sample populations, disaggregated their findings, and applied them to diverse student populations. By doing so, these studies are more readily replicable and their findings can be generalized to other similar populations of students. Three studies noted earlier specifically targeted Hispanic student populations, drew their samples from such populations, and provided and applied findings specifically for those populations (Calhoon et al., 2006; Gunn et al., 2002; Gunn et al., 2005). The remaining 22 of the 67 studies also addressed threats to population validity by either specifically targeting ELLs or by purposely drawing their sample from such populations and providing results for such populations. These 25 studies and the 1 noted in the paragraph above specifically considered the needs of ELLs.

Discussion

In an era of high-stakes testing and accountability for the academic growth of all students, policy makers have raised the bar, calling for instructional and intervention practices that are rigorous, relevant, and “evidence based” (ESRA, 2002). Evidence-based practices that have been deemed successful are then scaled up and applied to broader populations of students through the use of RTI models of identification and placement of struggling students into special education. Researchers testing the reliability and validity of instructional reading interventions prior to their application for broader populations of students must ensure that threats to external validity, more specifically, population validity, have been addressed.

The purpose of this examination of reading intervention research was to take a closer look at reading intervention research studies targeting struggling readers with the intention of determining if the reading intervention strategies being recommended have considered the needs of ELL populations. The literacy needs of ELLs learning to read in English differ from the needs of their monolingual, English-speaking peers and research must take this into consideration. When studies fail to disaggregate their findings based on the subpopulations of their sample, it is difficult to determine how well the intervention works for diverse groups of students. If reading interventions are being scaled up and applied to student populations without having been tested and validated with similar sample populations of students, the needs of struggling students, specifically ELLs, may not be adequately addressed in schools.

It remains significant that 25 reading intervention studies out of 67 in the past 10 years or so attempted to apply their findings to nonspecific at-risk, struggling, low-performing elementary students either without clearly noting who these students are demographically or without disaggregating those results when they did note them. We advocate for the field to know what works for whom before broadly applying intervention approaches. These 25 studies did not provide enough information about their target, accessible, and sample populations to adequately address threats to population validity. Therefore, these studies do not meet the standards of evidence-based research, so their findings can be applied only to their sample populations of students.

Another issue we wish to call attention to is the exclusion of ELLs in a study’s sample population. Among the 67 studies examined, 5 specifically noted that they excluded ELLs from their samples. Of these studies, 4 did not provide a rationale for why ELLs were excluded. The other study noted that a variety of student groups were excluded. The authors stated that such exclusions “were necessary to control for extraneous effects on students’ reading skills once intervention took place” (Case et al., 2010, p. 405). Although it is not our position to argue against such exclusions in educational research, we merely wish to note that when ELLs are excluded from a sample population, the results cannot be applied to other ELLs in learning environments elsewhere.

Of the 67 studies, 17 included ELLs in their samples and could have provided some information on how well the ELLs in their study did in terms of the intervention being studied. Although these studies provided some evidence about reading interventions, this information cannot readily be assumed to work as well with ELLs. By not addressing threats to population validity, findings from these studies are really applicable only to the students on whom the intervention was studied.

Among the 67 studies reviewed in this article, 25 specifically defined and described their target and accessible populations and adequately defined and described their sample populations using marker variables to a point where their findings could be replicable and generalizable to a broader, at-risk population of ELLs. The majority of these studies also heeded the advice of Artiles et al. (2005) to attend to within-group diversity by noting the race/ethnicity, language spoken, and language proficiency of their ELL sample populations.

This article points to a more positive direction in special education research than have past examinations on population validity. As Bracht and Glass (1968), Kempthorne (1961), and Keogh et al. (1978) advised years ago, we seem to be doing a better job considering the merit of defining our target and accessible populations and adequately describing the demographic characteristics of our sample populations. More and more research is taking into consideration the diverse needs of other struggling student populations.

Implications for Practice and for Research

This examination of reading intervention research looked closely at how reading intervention research studies addressed issues of population validity with the purpose of determining how applicable the findings were for other student populations such as ELLs. Issues pertaining to ecological validity, which ensures that the experimental environment that the respondents experience is the environment expected and designed by the researchers (Washington & McLoyd, 1982), were not considered for this article because of space limitations and because our desire was to draw attention to the issue of population validity and how it potentially affects ELL student populations. Future research addressing reading intervention strategies should take into consideration threats to ecological validity as well as threats to population validity.

Although most reading intervention research has not necessarily set the specific goal of targeting ELL student populations, much of the research has searched for ways to help struggling, at-risk elementary readers. Although this is certainly relevant and valuable, it is also imperative that educational researchers recognize and specifically target other, diverse student populations (i.e., based on marker variables such as race, ethnicity, SES, gender) when conducting reading intervention research to help us build a quality, evidence-based foundation of knowledge about what works for different struggling reader populations. And research that does address ELL needs should consider and describe the demographic subpopulations within ELL populations (Artiles et al., 1997).

The introduction of quality indicators into special education research planning and evaluation shows some promise. However, very few of the quality indicators suggested by Gersten et al. (2005) evaluate potential threats to external validity in terms of population validity or applying findings to broader, more diverse populations of students. Their essential quality indictors attend to describing the disabilities of the sample participants and to ensuring that “relative characteristics of participants in the sample were comparable across conditions” (p. 152). The rubric designed by Chard et al. (2009) specifically uses the quality indicators suggested by Gersten et al. Although these are indeed essential to high-quality research, we argue that more specific demographic details about the sample population (i.e., race/ethnicity, language use, languages spoken, oral language proficiency) and more clearly defined and described target and accessible populations will increase the quality of research and propel forward our knowledge about what works for ELL student populations. The rubric designed by Jitendra et al. (2010) accounts more for potential threats to population validity by attending to how research studies describe their populations in terms of race, gender, SES, and ELL status. We suggest that researchers consider ways to address potential threats to population validity by more accurately describing target, accessible, and sample populations through the use of marker variables.

This examination revealed 25 studies that addressed population validity to a point where their findings could be replicable and generalizable to a broader, at-risk population of ELLs. Our purpose was not to synthesize the suitability of the findings in these studies but to draw attention to the ways research addresses threats to population validity, specifically for ELL populations. A future effort should look more closely at the body of research on reading interventions designed specifically for ELLs to determine what seems to be the most effective. Furthermore, a future synthesis of the findings of reading intervention research that does specifically target the needs of ELLs should take into consideration the work of the National Literacy Panel on Language-Minority Children and Youth (August & Shanahan, 2006) to determine the components of reading interventions that are necessary for ELLs.

Finally, consumers of reading intervention research must carefully evaluate how research has been conducted and to which student populations findings can be readily applied. Educators who are savvy consumers of research know to read descriptions of participants and determine which samples seem most like their own. They realize that although some interventions show great potential for the academic success for some struggling readers, differing student populations may not show such gains. When educators of ELLs use standard treatment protocol RTI models or hybrid models that include a menu of acceptable, evidence-based reading interventions from which to choose interventions for particular students or groups of students, they need to make sure that the list includes interventions found to be effective with ELLs like their students.

Conclusion

Threats to population validity can more readily be accounted for when researchers attend to how they describe and define their populations and to whom they apply their findings. Overall, from 2001 to the present, we found several studies that successfully addressed the needs of ELLs, helping build our knowledge base about what works for diverse student populations. We are encouraged that researchers seem to be describing diverse samples in more detail than in the past. However, we remain concerned that in several research studies, the authors did not give adequate consideration to student variation and overgeneralized their findings as applying to students not part of their research populations. It is not enough to ask, “What works?” We must consistently ask, “What works with whom?”

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

References

Artiles

A. J.

Rueda

Salazar

J. J.

Higareda

(2005). Within-group diversity in minority disproportionate representation: English language learners in urban school districts. Exceptional Children, 71(3), 283–300.

Artiles

A. J.

Trent

S. C.

Kuan

(1997). Learning disabilities empirical research on ethnic minority students: An analysis of 22 years of studies published in selected refereed journals. Learning Disabilities Research & Practice, 12(2), 82–91.

Aud

Hussar

Kena

Bianco

Frohlich

Kemp

Tahan

(2011). The condition of education 2011 (NCES 2011-033). U.S. Department of Education, National Center for Education Statistics. Washington, DC: Government Printing Office.

August

Shanahan

(2006). Developing literacy in second-language learners: Report of the National Literacy Panel on Language-Minority Children and Youth. Mahwah, NJ: Lawrence Erlbaum.

Baker

S. K.

Chard

D. J.

Ketterlin-Geller

L. R.

Apichatabutra

Doabler

(2009). Teaching writing to at-risk students: The quality of evidence for self-regulated strategy development. Exceptional Children, 75(3), 303–318.

Bos

C. S.

Fletcher

T. V.

(1997). Sociocultural considerations in learning disabilities inclusion research: Knowledge gaps and future directions. Learning Disabilities Research & Practice, 12, 92–99.

Bracht

G. H.

Glass

G. V.

(1968). The external validity of experiments. American Educational Research Journal, 5(4), 437–474.

Chard

D. J.

Ketterlin-Geller

L. R.

Baker

Doabler

Apichatabutra

(2009). Repeated reading interventions for students with learning disabilities: Status of the evidence. Exceptional Children, 75(3), 263–281.

Chard

D. J.

Stoolmiller

Harn

B. A.

Wanzek

Vaughn

Linan-Thompson

Kame’enui

E. J.

(2008). Predicting reading success in a multilevel schoolwide reading model. Journal of Learning Disabilities, 41(2), 174–188.

10.

Charity

A. H.

Scarborough

H. S.

Griffen

D. M.

(2004). Familiarity with school English in African American children and its relation to early reading achievement. Child Development, 75(5), 1340–1356.

11.

Cooper

H. M.

(1998). Synthesizing research: A guide for literature reviews. Thousand Oaks, CA: Sage.

12.

Crosson

A. C.

Lesaux

N. K.

(2009). Revisiting assumptions about the relationship of fluent reading to comprehension: Spanish-speakers’ text-reading fluency in English. Reading and Writing: An Interdisciplinary Journal, 23, 475–494.

13.

Crowe

E. C.

Connor

C. M.

Petscher

(2009). Examining the core: Relations among reading curricula, poverty, and first through third grade reading achievement. Journal of School Psychology, 47, 187–214.

14.

Crowe

L. K.

(2005). Comparison of two oral reading feedback strategies in improving reading comprehension of school-age children with low reading ability. Remedial and Special Education, 26(1), 32–42.

15.

D’Angiulli

Siegel

L. S.

Maggi

(2004). Literacy instruction, SES, and word-reading achievement in English language learners and children with English as a first language: A longitudinal study. Learning Disabilities Research & Practice, 19(4), 202–213.

16.

Denton

Vaughn

Fletcher

(2003). Bringing research-based practice in reading intervention to scale. Learning Disabilities Research & Practice, 18(3), 201–211.

17.

Donovan

Cross

C. T.

(2002). Minority students in special and gifted education. Washington, DC: National Academy Press.

18.

Education Sciences Reform Act of 2002 (Pub. L. No. 107-279). Retrieved September 18, 2012, from http://www.ed.gov/legislation/EdSciencesRef/

19.

Fierros

E. G.

Conroy

J. W.

(2002). Double jeopardy: An exploration of restrictiveness and race in special education. In Losen

D. J.

Orfield

(Eds.), Racial inequity in special education (pp. 39–70). Cambridge, MA: Harvard Education Press.

20.

Fuchs

L. S.

Compton

D. L.

(2004). Identifying reading disabilities by responsiveness-to-instruction: Specifying measures and criteria. Learning Disability Quarterly, 27, 216–227.

21.

Fyans

L. J.

(1983). Multilevel analysis and cross-level inference of validity using generalizability theory. New Directions for Testing and Measurement, 18, 49–66.

22.

Gandara

Bial

(2001). Paving the way to postsecondary education: K–12 intervention programs for underrepresented youth. Washington, DC: National Center for Education Statistics, Office of Educational Research and Improvement, U.S. Department of Education.

23.

Gersten

Fuchs

L. S.

Compton

Coyne

Greenwood

Innocenti

M. S.

(2005). Quality indicators for group experimental and quasi-experimental research in special education. Exceptional Children, 72(2), 149–164.

24.

Goldenberg

(2008). Teaching English language learners: What the research does, and does not, say. American Educator, 32(2), 8–43.

25.

Gutiérrez

K. D.

Asato

Santos

Gotanda

(2002). Backlash pedagogy: Language and culture and the politics of reform. Review of Education, Pedagogy, and Cultural Studies, 24, 335–351.

26.

Gutiérrez

K. D.

Baquedano-López

Tejeda

(1999). Rethinking diversity: Hybridity and hybrid language practices in the third space. Mind, Culture, and Activity, 6(4), 286–303.

27.

Hemphill

F. C.

Vanneman

(2011). Achievement gaps: How Hispanic and White students in public schools perform in mathematics and reading on the National Assessment of Educational Progress (NCES 2011–459). Washington, DC: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.

28.

Hollenbeck

A. F.

(2007). From IDEA to implementation: A discussion of foundational and future responsiveness-to-intervention research. Learning Disabilities Research & Practice, 22(2), 137–146.

29.

Individuals with Disabilities Education Improvement Act, Pub. L. No. 108-446, 118 Stat. 2647 (2004).

30.

Iversen

Tunmer

W. E.

Chapman

J. W.

(2005). The effects of varying group size on the reading recovery approach to preventive early intervention. Journal of Learning Disabilities, 38(5), 456–472.

31.

Jiménez

J. E.

del Rosario Ortiz

Rodrigo

Hernández-Valle

Ramírez

Estévez

de la Luz Trabaue

(2003). Do the effects of computer-assisted practice differ for children with reading disabilities with and without IQ-achievement discrepancy? Journal of Learning Disabilities, 36(1), 34–47.

32.

Jitendra

A. K.

Burgess

Gajria

(2010, May). Cognitive strategy instruction for improving expository text comprehension of students with learning disabilities: The quality of evidence. Paper presented at the annual meeting of the American Educational Research Association, Denver, CO.

33.

Kavale

K. A.

Holdnack

J. A.

Mostert

M. P.

(2006). Responsiveness to intervention and the identification of specific learning disability: A critique and alternative proposal. Learning Disability Quarterly, 28(1), 2–16.

34.

Kempthorne

(1961). The design and analysis of experiments with some reference to educational research. In Collier

R. O.

Elam

S. M.

(Eds.), Research design and analysis: Second annual Phi Delta Kappa symposium on educational research (pp. 97–133). Bloomington, IN: Phi Delta Kappa.

35.

Keogh

B. K.

Major

S. M.

Reid

H. P.

Gandara

Omori

(1978). Marker variables: A search for comparability and generalizability in the field of learning disabilities. Learning Disability Quarterly, 1(3), 5–11.

36.

Klingner

J. K.

Artiles

A. J.

Barletta

L. M.

(2006). English language learners who struggle with reading: Language acquisition or LD? Journal of Learning Disabilities, 39(2), 108–128.

37.

Klingner

J. K.

Boardman

(2011). Addressing the “research gap” in special education through mixed methods. Learning Disability Quarterly, 34(3), 208–218.

38.

Klingner

J. K.

Edwards

P. A.

(2006). Cultural considerations with response to intervention models. Reading Research Quarterly, 41(1), 108–117.

39.

Lovett

M. W.

De Palma

Frijters

Steinbach

Temple

Benson

Lacerenza

(2008). Interventions for reading difficulties: A comparison of response to intervention by ELL and EFL struggling readers. Journal of Learning Disabilities, 41(4), 333–352.

40.

Lysynchuk

L. M.

Pressley

D’Ailly

Smith

Cake

(1989). A methodological analysis of experimental studies of comprehension strategy instruction. Reading Research Quarterly, 24(4), 458–470.

41.

Menken

Kleyn

(2009). The difficult road for long-term English language learners. Educational Leadership, 66(7). Retrieved from http://www.ascd.org/publications/educational_leadership/apr09/vol66/num07/The_Difficult_Road_for_Long-Term_English_Learners.aspx

42.

Montague

Dietz

(2009). Evaluating the evidence base for cognitive strategy instruction and mathematical problem solving. Exceptional Children, 75(3), 285–302.

43.

Odom

S. L.

Brantlinger

Gersten

Horner

R. H.

Thompson

Harris

K. R.

(2005). Research in special education: Scientific methods and evidence-based practice. Exceptional Children, 71(2), 137–148.

44.

Orosco

Klingner

J. K.

(2010). One school’s implementation of RTI with English language learners: “Referring into RTI.” Journal of Learning Disabilities, 43, 269–288.

45.

Mancilla-Martinez

Lesaux

N. K.

(2011b). Predictors of reading comprehension for struggling readers: The case of Spanish-speaking language minority learners. Journal of Educational Psychology, 102, 701–711.

46.

National Clearinghouse for English Language Acquisition. (2007). The growing numbers of limited English proficient students: 1995/96–2005/06. Washington, DC: Author.

47.

National Clearinghouse for English Language Acquisition. (2011). The growing numbers of English learner students, 1998/99–2008/09. Retrieved from http://www.ncela.gwu.edu/files/uploads/9/growingLEP_0809.pdf

48.

National Early Literacy Panel. (2008). Developing early literacy: Report of the National Early Literacy Panel. Washington, DC: National Institute for Literacy.

49.

National Institute of Child Health and Human Development. (2000). Report of the National Reading Panel. Teaching children to read: An evidence-based assessment of the scientific research literature on reading and its implications for reading instruction (NIH Pub. No. 00–4769). Washington, DC: Government Printing Office.

50.

Ortiz

A. A.

(1997). Learning disabilities occurring concomitantly with linguistic differences. Journal of Learning Disabilities, 30(3), 321–332.

51.

Parker

R. M.

(1990). Power, control, and validity in research. Journal of Learning Disabilities, 23(10), 613–620.

52.

Parrish

(2002). Racial disparities in the identification, funding, and provision of special education. In Losen

D. J.

Orfield

(Eds.), Racial inequity in special education (pp. 15–38). Cambridge, MA: Harvard Education Press.

53.

Pedraza

Rivera

(2005). Latino education: An agenda for community action research. Mahwah, NJ: Lawrence Erlbaum.

54.

Rogoff

Paradise

Mejía Arauz

Correa-Chávez

Angelillo

(2003). Firsthand learning through intent participation. Annual Review of Psychology, 54, 175–203.

55.

Silverman

(2007a). A comparison of three methods of vocabulary instruction during read-alouds in kindergarten. Elementary School Journal, 108(2), 97–113.

56.

Torgesen

J. K.

Alexander

A. W.

Wagner

R. K.

Rashotte

C. A.

Voeller

K. S.

Conway

(2001). Intensive remedial instruction for children with severe reading disabilities: Immediate and long-term outcomes from two instructional approaches. Journal of Learning Disabilities, 34(1), 33–58.

57.

Troia

G. A.

(1999). Phonological awareness intervention research: A critical review of the experimental methodology. Reading Research Quarterly, 34(1), 28–52.

58.

Vasquez

Lopez

Straub

Powell

McKinney

Walker

Bedesem

(2011). Empirical research on ethnic minority students: 1995–2009. Learning Disabilities Research & Practice, 26(2), 84–93.

59.

Vaughn

Fuchs

L. S.

(2003). Redefining learning disabilities as inadequate response to intervention: The promise and potential problems. Learning Disabilities Research & Practice, 18(3), 137–146.

60.

Washington

E. D.

McLoyd

V. C.

(1982). The external validity of research involving American minorities. Human Development, 25(5), 324–339.

61.

Allor

McCathren

(2004). The efficacy of an early literacy tutoring program implemented by college students. Learning Disabilities Research & Practice, 19(2), 116–129.

62.

Berninger

V. W.

Abbott

R. D.

Vermeulen

Fulton

C. M.

(2006). Paths to reading comprehension in at-risk second-grade readers. Journal of Learning Disabilities, 39(4), 334–351.

63.

Blachman

B. A.

Fletcher

J. M.

Clonan

S. M.

Schatschneider

Francis

D. J.

Shaywitz

B. A.

Shaywitz

S. E.

(2004). Effects of intensive reading remediation for second and third graders and a 1–year follow-up. Journal of Educational Psychology, 96(3), 444–461.

64.

Calhoon

M. B.

Otaiba

S. A.

Cihak

King

Avalos

(2007). Effects of a peer-mediated program on reading skill acquisition for two-way bilingual first-grade classrooms. Learning Disability Quarterly, 30, 169–184.

65.

Calhoon

M. B.

Otaiba

S. A.

Greenberg

King

Avalos

(2006). Improving reading skills in predominantly Hispanic Title 1 first grade classrooms: The promise of peer-assisted learning strategies. Learning Disabilities Research & Practice, 21(4), 261–272.

66.

Carlo

M. S.

August

McLaughlin

Snow

C. E.

Dressler

Lippman

D. N.

White

C. E.

(2004). Closing the gap: Addressing the vocabulary needs of English-language learners in bilingual and mainstream classrooms. Reading Research Quarterly, 39(2), 188–215.

67.

Case

L. P.

Speece

D. L.

Silverman

Ritchey

K. D.

Schatschneider

Cooper

D. H.

Jacobs

(2010). Validation of a supplemental reading intervention for first-grade children. Journal of Learning Disabilities, 43(5), 402–417.

68.

Coyne

M. D.

Kame’enui

E. J.

Simmons

D. C.

Harn

B. A.

(2004). Beginning reading intervention as inoculation or insulin: First-grade reading performance of strong responders to kindergarten intervention. Journal of Learning Disabilities, 37(2), 90–104.

69.

Coyne

M. D.

McCouch

D. B.

Kapp

(2007). Vocabulary intervention for kindergarten students: Comparing extended instruction to embedded instruction and incidental exposure. Learning Disability Quarterly, 30, 74–88.

70.

Denton

C. A.

Anthony

J. L.

Parker

Hasbrouck

J. E.

(2004). Effects of two tutoring programs on the English reading development of Spanish-English bilingual students. Elementary School Journal, 104(4), 289–305.

71.

Denton

C. A.

Fletcher

J. M.

Anthony

J. L.

Francis

D. J.

(2006). An evaluation of intensive intervention for students with persistent reading difficulties. Journal of Learning Disabilities, 39(5), 447–466.

72.

Denton

C. A.

Nimon

Mathes

P. G.

Swanson

E. A.

Kethley

Kurz

T. B.

Shih

(2010). Effectiveness of a supplemental early reading intervention scaled up in multiple schools. Exceptional Children, 76(4), 394–416.

73.

Denton

C. A.

Solari

E. J.

Ciancio

D. J.

Hecht

S. A.

Swank

P. R.

(2010). A pilot study of a kindergarten summer school reading program in high-poverty urban schools. Elementary School Journal, 110(4), 423–439.

74.

Gerber

Jimenez

Leafstedt

Villaruz

Richards

English

(2004). English reading effects of small-group intensive intervention in Spanish for K–1 English learners. Learning Disabilities Research & Practice, 19(4), 239–251.

75.

Graham

Harris

K. R.

Chorzempa

B. F.

(2002). Contribution of spelling instruction to the spelling, writing and reading of poor spellers. Journal of Educational Psychology, 94(4), 669–686.

76.

Greenwood

C. R.

Arreaga-Mayer

Utley

C. A.

Gavin

K. M.

Terry

B. J.

(2001). ClassWide Peer Tutoring learning management system. Remedial and Special Education, 22(1), 34–47.

77.

Gunn

Smolkowski

Biglan

Black

(2002). Supplemental instruction in decoding skills for Hispanic and non-Hispanic students in early elementary school: A follow-up. Journal of Special Education, 36(2), 69–79.

78.

Gunn

Smolkowski

Biglan

Black

Blair

(2005). Fostering the development of reading skill through supplemental instruction: Results for Hispanic and non-Hispanic students. Journal of Special Education, 39(2), 66–85.

79.

Guthrie

J. T.

McRae

Coddington

C. S.

Klauda

S. L.

Wigfield

Barbosa

(2009). Impacts of comprehensive reading instruction on diverse outcomes of low- and high-achieving readers. Journal of Learning Disabilities, 42(3), 195–214.

80.

Haager

Windmueller

M. P.

(2001). Early reading intervention for English language learners at-risk for learning disabilities: Student and teacher outcomes in an urban school. Learning Disability Quarterly, 24, 235–250.

81.

Harn

B. A.

Linan-Thompson

Roberts

(2008). Intensifying instruction: Does additional instructional time make a difference for the most at-risk first graders? Journal of Learning Disabilities, 41(2), 115–125.

82.

Kamps

Abbott

Greenwood

Arreaga-Mayer

Wills

Longstaff

Walton

(2007). Use of evidence-based, small-group reading instruction for English language learners in elementary grades: Secondary-tier intervention. Learning Disability Quarterly, 30, 153–168.

83.

Kamps

Abbott

Greenwood

Wills

Veerkamp

Kaufman

(2008). Effects of small-group reading instruction and curriculum differences for students most at risk in kindergarten: Two-year results for secondary- and tertiary-level interventions. Journal of Learning Disabilities, 41(2), 101–114.

84.

Leafstedt

J. M.

Richards

C. R.

Gerber

M. M.

(2004). Effectiveness of explicit phonological-awareness instruction for at-risk English learners. Learning Disabilities Research & Practice, 19(4), 252–261.

85.

Linan-Thompson

Bryant

D. P.

Dickson

S. V.

Kouzekanani

(2005). Spanish literacy instruction for at-risk kindergarten students. Remedial and Special Education, 26(4), 236–244.

86.

Linan-Thompson

Vaughn

Hickman-Davis

Kouzekanani

(2003). Effectiveness of supplemental reading instruction for second-grade English language learners with reading difficulties. Elementary School Journal, 103(3), 221–238.

87.

Linan-Thompson

Vaughn

Prater

Cirino

P. T.

(2006). The response to intervention of English language learners at risk for reading problems. Journal of Learning Disabilities, 39(5), 390–398.

88.

Wang

Haskell

(2009). Examining the impacts of early reading intervention on the growth rates in basic literacy skills of at-risk urban kindergarteners. Journal of Special Education, 43(1), 12–28.

89.

Mancilla-Martinez

Lesaux

N. K.

(2011a). The gap between Spanish speakers’ word reading and word knowledge: A longitudinal study. Child Development, 82(5), 1544–1560.

90.

Mason

L. H.

(2004). Explicit self-regulated strategy development versus reciprocal questioning: Effects on expository reading comprehension among struggling readers. Journal of Educational Psychology, 96(2), 283–296.

91.

Mathes

P. G.

Babyak

A. E.

(2001). The effects of peer-assisted literacy strategies for first-grade with and without additional mini-skills lessons. Learning Disabilities Research & Practice, 16(1), 28–44.

92.

Mathes

P. G.

Denton

C. A.

Fletcher

J. M.

Anthony

J. L.

Francis

D. J.

Schatschneider

(2005). The effects of theoretically different instruction and student characteristics on the skills of struggling readers. Reading Research Quarterly, 41(2), 148–182.

93.

Mathes

P. G.

Torgesen

J. K.

Clancy-Menchetti

Santi

Nicholas

Robinson

Grek

(2003). A comparison of teacher-directed versus peer-assisted instruction to struggling first-grade readers. Elementary School Journal, 103(5), 459–479.

94.

McCandliss

Beck

I. L.

Sandak

Perfetti

(2003). Focusing attention on decoding for children with poor reading skills: Design and preliminary tests of the word building intervention. Scientific Studies of Reading, 71(1), 75–104.

95.

McIntosh

A. S.

Graves

Gersten

(2007). Effects of response to intervention literacy development in multiple language settings. Learning Disability Quarterly, 30, 197–212.

96.

McMaster

K. L.

Fuchs

L. S.

Compton

D. L.

(2005). Responding to nonresponders: An experimental field trial of identification and intervention methods. Exceptional Children, 71(4), 445–463.

97.

McMaster

K. L.

Kung

S. H.

Han

Cao

(2008). Peer-assisted learning strategies: A “tier 1” approach to promoting English learners’ response to intervention. Exceptional Children, 74(2), 194–214.

98.

Menzies

H. M.

Mahdavi

J. N.

Lewis

J. L.

(2008). Early intervention in reading: From research to practice. Remedial and Special Education, 29(2), 67–77.

99.

Nelson

J. R.

Stage

S. A.

Epstein

M. H.

Pierce

C. D.

(2005). Effects of a prereading intervention on the literacy and social skills of children. Exceptional Children, 72(1), 29–45.

100.

O’Connor

R. E.

Bocian

Beebe-Frankenberger

Linklater

D. L.

(2010). Responsiveness of students with language difficulties to early intervention in reading. Journal of Special Education, 43(4), 220–235.

101.

O’Connor

R. E.

Fulmer

Harty

K. R.

Bell

K. M.

(2005). Layers of reading intervention in kindergarten through third grade: Changes in teaching and student outcomes. Journal of Learning Disabilities, 38(5), 440–455.

102.

O’Connor

R. E.

White

Swanson

H. L.

(2007). Repeated reading versus continuous reading: Influences on reading fluency and comprehension. Exceptional Children, 74(1), 31–46.

103.

Oudeans

M. K.

(2003). Integration of letter-sound correspondences and phonological awareness skills of blending and segmenting: A pilot study examining effects of instructional sequence on reading for kindergarten children with low phonological awareness. Learning Disability Quarterly, 26, 258–280.

104.

Pullen

P. C.

Tuckwiller

E. D.

Konold

T. R.

Maynard

K. L.

Coyne

M. D.

(2010). A tiered intervention model for early vocabulary instruction: The effects of tiered instruction for young students at risk for reading disability. Learning Disabilities Research & Practice, 25(3), 110–123.

105.

Reis

S. M.

McCoach

D. B.

Coyne

Schreiber

F. J.

Eckert

R. D.

Gubbins

E. J.

(2007). Using planned enrichment strategies with direct instruction to improve reading fluency, comprehension, and attitude toward reading: An evidence-based study. Elementary School Journal, 108(1), 3–23.

106.

Saenz

L. M.

Fuchs

L. S.

Fuchs

(2005). Peer-assisted learning strategies for English language learners with learning disabilities. Exceptional Children, 71(3), 231–247.

107.

Santoro

L. E.

Coyne

M. D.

Simmons

D. C.

(2006). The reading-spelling connection: Developing and evaluating a beginning spelling intervention for children at risk of reading disability. Learning Disabilities Research & Practice, 21(2), 122–133.

108.

Schwartz

R. M.

(2005). Literacy learning of at-risk first-grade students in the Reading Recovery early intervention. Journal of Educational Psychology, 97(2), 257–267.

109.

Silverman

(2007b). Vocabulary development of English-language learners and English-only learners in kindergarten. Elementary School Journal, 107(4), 365–383.

110.

Simmons

D. C.

Kame’enui

E. J.

Harn

Coyne

M. D.

Stoolmiller

Santoro

L. E.

Kaufman

N. K.

(2007). Attributes of effective and efficient kindergarten reading intervention: An examination of instructional time and design specificity. Journal of Learning Disabilities, 40(4), 331–347.

111.

Solari

E. J.

Gerber

M. M.

(2008). Early comprehension instruction for Spanish-speaking English language learners: Teaching text-level reading skills while maintaining effects on word-level skills. Learning Disabilities Research & Practice, 23(4), 155–168.

112.

Tong

Irby

B. J.

Lara-Alecio

Yoon

Mathes

P. G.

(2010). Hispanic English learners’ responses to longitudinal English instructional intervention and the effect of gender: A multilevel analysis. Elementary School Journal, 110(4), 542–566.

113.

Torgesen

J. K.

Alexander

A. W.

Wagner

R. K.

Rashotte

C. A.

Voeller

K. K. S.

Conway

114.

Torgesen

J. K.

Wagner

R. K.

Rashotte

C. A.

Herron

Lindamood

(2010). Computer-assisted instruction to prevent early reading difficulties in students at risk for dyslexia: Outcomes from two instructional approaches. Annals of Dyslexia, 60, 40–56.

115.

Tuckwiller

E. D.

Pullen

P. C.

Coyne

(2010). Use of regression discontinuity design in tiered intervention research: A pilot study exploring vocabulary instruction for at-risk kindergarteners. Learning Disabilities Research & Practice, 25(3), 137–150.

116.

Vadasy

P. F.

Sanders

E. A.

(2008). Repeated reading intervention: Outcomes and interactions with readers’ skills and classroom instruction. Journal of Educational Psychology, 100(2), 272–290.

117.

Vadasy

P. F.

Sanders

E. A.

(2009). Supplemental fluency intervention and determinants of reading outcomes. Scientific Studies of Reading, 13(5), 383–425.

118.

Vadasy

P. F.

Sanders

E. A.

Abbott

R. D.

(2008). Effects of supplemental early reading intervention at 2–year follow up: Reading skill growth patterns and predictors. Scientific Studies of Reading, 12(1), 51–89.

119.

Vadasy

P. F.

Sanders

E. A.

Peyton

J. A.

(2005). Relative effectiveness of reading practice or word-level instruction in supplemental tutoring: How text matters. Journal of Learning Disabilities, 38(4), 364–380.

120.

Vadasy

P. F.

Sanders

E. A.

Peyton

J. A.

(2006a). Code-oriented instruction for kindergarten students at risk for reading difficulties: A randomized field trial with paraeducator implementers. Journal of Educational Psychology, 98(3), 508–528.

121.

Vadasy

P. F.

Sanders

E. A.

Peyton

J. A.

(2006b). Paraeducator-supplemented instruction in structural analysis with text reading practice for second and third graders at risk for reading problems. Remedial and Special Education, 27(6), 365–378.

122.

Vadasy

P. F.

Sanders

E. A.

Peyton

J. A.

Jenkins

J. R.

(2002). Timing and intensity of tutoring: A closer look at the conditions for effective early literacy tutoring. Learning Disabilities Research & Practice, 17(4), 227–241.

123.

Vadasy

P. F.

Sanders

E. A.

Tudor

(2007). Effectiveness of paraeducator-supplemented individual instruction: Beyond basic decoding skills. Journal of Learning Disabilities, 40(6), 508–525.

124.

Vaughn

Linan-Thompson

Kouzekanani

Bryant

D. P.

Dickson

Blozis

S. A.

(2003). Reading instruction grouping for students with reading difficulties. Remedial and Special Education, 24(5), 301–315.

125.

Vaughn

Linan-Thompson

Mathes

P. G.

Cirino

P. T.

Carlson

C. D.

Pollard-Durodola

S. D.

Francis

D. J.

(2006). Effectiveness of Spanish intervention for first-grade English language learners at risk for reading difficulties. Journal of Learning Disabilities, 39(1), 56–73.

126.

Vaughn

Mathes

Linan-Thompson

Cirino

Carlson

Pollard-Durodola

Francis

(2006). Effectiveness of an English intervention for first-grade English language learners at risk for reading problems. Elementary School Journal, 107(2), 153–180.

127.

Vaughn

Wanzek

Murray

C. S.

Scammacca

Linan-Thompson

Woodruff

A. L.

(2009). Response to early reading intervention: Examining higher and lower responders. Exceptional Children, 72(2), 165–183.

128.

Wanzek

Vaughn

(2008). Response to varying amounts of time in reading intervention for students with low response to intervention. Journal of Learning Disabilities, 41(2), 126–142.