Journal of Abnormal Child Psychology https://doi.org/10.1007/s10802-018-0451-5

Inattentiveness and Language Abilities in Preschoolers: A Latent Profile Analysis Sherine R. Tambyraja 1

&

A. Rhoad-Drogalis 1 & K. S. Khan 1 & L. M. Justice 1 & B. E. Sawyer 2

# Springer Science+Business Media, LLC, part of Springer Nature 2018

Abstract Growing evidence suggests that early symptoms of inattentiveness may affect the language development and academic success of young children. In the present study, we examined the extent to which profiles of inattentiveness and language could be discerned within a heterogeneous group of preschoolers attending early childhood special education programs (n = 461). Based on parent-reported observations of children’s symptoms of inattentiveness and direct assessments of children’s language skills (grammar, vocabulary, and narrative ability), three distinct profiles were identified. The three groups, representing levels of severity (at risk, almost average, above average), differed not only by their end of year performance, but also with respect to which their abilities changed over the course of the academic year. Children in the poorest performing profile had poorer mean scores in the spring of their preschool year on all measures, but exhibited patterns of gain that exceeded or equaled their peers in higher-performing groups, in the domains of vocabulary and grammar. Examination of subsequent kindergarten reading skills suggested that profile differences remained consistent. Findings underscore the associations between early symptoms of inattentiveness and language difficulties, and further indicate that these relations extend to the acquisition of early reading skills. Future research is needed to corroborate these findings with more robust measures of attention, and to understand the long-term associations between inattentiveness, language and literacy, and potential effects on these associations from early intervention. Keywords Inattentiveness . Language impairment . Preschoolers . Latent profile analysis . Special education classrooms Attention deficit/hyperactivity disorder (ADHD) is one of the most commonly diagnosed pediatric neurodevelopmental disorders, and affects approximately 7.4% of children between the ages of 4 and 17 (Centers for Disease Control and Prevention [CDC] 2017). In addition to addressing the behavioral manifestations of the disorder, the identification and treatment of attention difficulties in young children is also of great import due to the large body of research suggesting relations between poor attention skills and academic difficulties (e.g., Birchwood and Daley 2012; McClelland et al. 2013; Washbrook et al. 2013). Children with attention difficulties exhibit symptoms and behaviors that generally align with one of three subtypes: hyperactivity, inattentiveness, or a combination of both. Some

* Sherine R. Tambyraja [email protected] 1

Crane Center for Early Childhood Research and Policy, The Ohio State University, Columbus, OH 43201, USA

2

Lehigh University, Bethlehem, PA, USA

researchers posit that the symptoms of inattentiveness are more obscure than symptoms of hyperactivity in very young children because the activities of preschoolers do not necessarily require extended periods of focus and attention (Bauermeister et al. 2005). Nonetheless, a growing body of research suggests that children demonstrating symptoms of inattentiveness are at greater risk for subsequent academic difficulties, compared to children whose symptoms primarily relate to hyperactivity (Skounti et al. 2010; Sims and Lonigan 2013; Warner-Rogers et al. 2000), even if the number and nature of the symptoms of inattentiveness are subthreshold to a clinical diagnosis (Dittman 2016; Fergusson et al. 1997). To date, much of this work has focused on schoolage children whose diagnoses are conferred, perhaps because developmentally appropriate expectations for preschoolers, with respect to attentiveness in the classroom or at home, are minimal compared to expectations for school-aged children. However, emerging evidence demonstrating associations between inattentiveness in the preschool years and poorer language and literacy outcomes (e.g., Sims and Lonigan 2013) underscores the need for further research regarding preschool inattentiveness. As such, in the present study,

J Abnorm Child Psychol

we focus on the extent to which preschool children exhibit variability with respect to their inattentiveness and language abilities, and the relations between inattentiveness, language, and early reading skills.

Relations between Inattentiveness and Language Children exhibiting symptoms of inattentiveness are at risk for a range of academic problems; one of the most frequently reported correlates of poor attention pertains to language difficulties (e.g., Cohen et al. 1993). Theoretically, one possible explanation for the co-occurrence of inattentiveness and poor language is a shared underlying deficit in executive functioning (EF). Numerous studies suggest that children with attention difficulties perform significantly poorer on a range of EF skills, such as working memory, planning and organizational abilities (e.g., Bernstein and Waber 2007; Semrud-Clikeman et al. 2010). These skills are considered fundamental to many languagerelated competencies, such as sentence comprehension and formulation (Caplan and Waters 1999; Gathercole and Baddeley 1993). Indeed, research suggests that children with language impairment (LI) also perform more poorly on EF measures, compared to typically developing peers (e.g., Henry et al. 2012; Marton 2008). Although a deficit in EF may not be the singular underlying cause of either inattentiveness or LI, it may similarly compromise language learning processes and attentional control. Several studies that have examined children with different types of attention problems (i.e., inattentiveness, hyperactivity, or both), have identified a significant overlap with a concomitant diagnosis of language impairment (LI) (e.g., Cohen et al. 2000). In Cohen et al.’s study that involved 105 children between the ages of 7–14 with ADHD, 69 children also met criteria for LI. Studies of children with symptoms primarily associated with inattentiveness specifically suggest similar levels of language deficits. Results of a chart review study of school-age children with the inattentiveness and combined subtypes showed that children with inattentiveness were twice as likely to have had a history of speech and/or language difficulties compared to those with the combined subtype (Weiss et al. 2003). The intersection of language deficits and inattentiveness is similarly evident in studies examining children with LI, with some studies reporting a higher incidence of attention difficulties in this population compared to typically developing children (e.g., Beitchman et al. 1986; McGrath et al. 2008). Although few studies have specifically focused on determining which language domains are most affected by poor attention, some research does suggest that narrative abilities are likely to be impaired (e.g., Purvis and Tannock 1997). One reason that this skill might be particularly affected is because narrative tasks require the coordination of working memory, listening comprehension, and executive control skills (Montgomery, Polunenko and Marinellie, 2009) – three skills

with which children with attention problems exhibit specific difficulty (Barkley 1997; McInnes et al. 2003). In addition, narrative development may be hindered in children with poor attention, as their ability to learn from and attend to verbal scaffolding opportunities may be rather limited. In addition to compromised narrative abilities, recent evidence from a large, community sample of children without confirmed diagnoses also suggests relations between symptoms of inattentiveness and other oral language skills, such as vocabulary and grammatical knowledge. Lonigan, Wagner, Torgesen, & Rashotte (2007) examined the extent to which a teacher-reported scale of children’s symptoms of inattentiveness related to their concurrent language skills and language growth. Children in their study were predominantly typically developing, with a large proportion from low socioeconomic status backgrounds. Results showed that children’s scores on a inattentiveness scale were significantly associated with concurrent language skills (i.e., receptive and expressive vocabulary, composite measures of receptive and expressive language). In addition, the teacher’s evaluation of inattentiveness significantly predicted children’s growth in these language skills. Taken together, the aforementioned studies indicate a potentially inverse relation between inattentiveness and the language domains of narrative, grammar, and vocabulary; however the extent to which inattentiveness might be more strongly associated with one domain of language over others remains unclear.

Relations among Language, Inattentiveness, and Reading As reviewed above, symptoms of inattentiveness and language difficulties often co-occur, even among preschoolers. One academic skill that is likely to be subsequently impacted by deficits in language and attention is reading (Rabiner & Coie 2000; Tomblin et al. 2000). Theoretical accounts of reading development converge on the notion that reading is a language-based skill (Hoover and Gough 1990; Storch and Whitehurst 2002), requiring both code-based skills such as phonological awareness and alphabet knowledge to decode written words, and meaning-based skills such as grammar and vocabulary for reading comprehension. Indeed, there is a large and consistent body of work suggesting strong correlations between preschool language ability and school-age reading proficiency (Dickinson and Porche 2011; Lonigan et al. 2017; Nation and Snowling 2004; Storch and Whitehurst 2002). Given the frequent concomitant deficits in attention and language, it is not surprising that growing evidence suggests associations between inattentiveness and reading difficulties (Dally 2006; Martinussen, Grimbos, & Ferrari, 2014) and that these associations are evident even at preschool age (e.g.,

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Sims and Lonigan 2013). Although most studies examining reading abilities in children with poor attention have described direct relations between these two skills, very recent evidence suggests that language skills mediate the effects of inattentiveness on reading ability (O’Neill et al. 2016). In other words, children with poor attention skills who have stronger language abilities have better reading outcomes than children with poor attention and similarly poor language skills. These data underscore an interplay between inattentiveness and language that may yield differences in reading outcomes for subgroups of children whose language and attention skills relate variably. To that end, the present study explores longitudinal relations between language, inattentiveness and reading ability in a large heterogeneous sample of preschoolers.

Profiles of Children with Attention and Language Difficulties The studies discussed above have utilized variable-centered approaches (means comparisons, correlation) to enhance our understanding of the multidimensionality of, and relations between, inattentiveness and language difficulties. One limitation of variable-centered approaches is that the interpretation of the results assumes homogeneity among the participants. In other words, these approaches assume that the variables of interest correspond to each other similarly across all participants, and therefore do not account for individual differences. Alternatively, a person-centered approach provides a complementary perspective to variable-centered approaches, and can offer additional information about the ways in which these skills intersect in young children. In the present study, we extend previous work by utilizing a person-centered approach, specifically, latent profile analysis (LPA) to identify possible patterns of how language and inattentiveness relate within a diverse preschool population, specifically, children attending early childhood special education (ECSE) classrooms that serve both typically developing children and those with disabilities. With this approach, we assume heterogeneity among participants, and expect that there may be subgroups of children, irrespective of a clinical diagnosis, who perform variably on measures of language and observed inattentiveness. This approach is particularly advantageous for studying language skills and inattentiveness, as research suggests both of these constructs are multi-dimensional, with children exhibiting a range of associated symptoms and severity (Beitchman et al. 1996; Hill et al. 2006; Tambyraja et al. 2015). To date, little work has determined the extent to which these skills might manifest in discernible patterns within young children. Previous studies incorporating LPA to examine subgroups of children with poor attention skills indeed suggest uniquely identifiable patterns of performance. Ostrander et al. (2008)

conducted an LPA of children ages 6–11 (n = 271) with a diagnosis of ADHD to determine patterns of co-occurring behavioral or psychosocial difficulties. They found six distinct profiles, and only one profile, representing 17% of the participants, exhibited symptoms of only inattentiveness. The largest profile consisted of those classified as exhibiting mild ADHD (38%), and the remaining four profiles exhibited a range of comorbid symptoms representing internal inattentiveness (i.e., inattentiveness and anxiety), moderate disruptive behavior, severe mixed pathology, and internal-disruptive symptomatology (i.e., anxiety and disruptive behaviors). Another LPA of children with ADHD (Martel, Goth-Owens, Martinez-Torteya, & Nigg, 2010) sought to identify profiles according to personality characteristics; four clinical profiles were determined, representing children exhibiting ADHD and a specific personality trait (i.e., introvert, obsessive). Studies examining profiles of children with language difficulties have yielded mixed results with some suggesting that distinct subgroups can be identified according to strengths and weaknesses across language domain (e.g., Conti-Ramsden, Crutchley, & Botting, 1997) and others finding that profiles of language ability are primarily represented levels of severity, rather than differing language domains (i.e., grammar, vocabulary) (Tambyraja et al. 2015). In the present study, we examine the extent to which patterns of performance across language skills and inattentiveness can be uniquely distinguished among a heterogeneous group of preschoolers with varying language abilities. It is possible, for example, that there may be subgroups of children who exhibit levels of inattentiveness that are greater than many of their peers in preschool, and yet have age-appropriate language skills. Alternatively, as indicated by some previous research (e.g., Purvis and Tannock 1997), there may be subgroups of children who exhibit many symptoms of inattentiveness and poor narrative skills, yet reasonably intact vocabulary and grammar abilities. Examining the variable ways that these skills and abilities manifest not only refines our theoretical understanding of the associations between inattentiveness and specific language abilities, but can also inform the development and delivery of interventions for children with poor attention and/or LI. In addition to identifying potential subgroups within a large population of preschoolers with and without disabilities according to attention and language skills, the present study also seeks to examine the extent to which profile membership is associated with language gain over the academic year, and subsequent early reading skills in kindergarten. Although profile analyses can inform our knowledge about the ways in which these characteristics cluster together for specific subgroups of children, understanding the academic development of these subgroups is also important. Previous work examining long-term outcomes of language profiles (e.g., Beitchman et al. 1996; Conti-Ramsden et al., 1997) generally suggest that children with more severe difficulties tend to continue to

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perform at lower levels compared to peers with better language ability. However, no previous study has included both measures of inattentiveness and language in the profile analyses; thus, it is unclear whether the long-term outcomes of children in specific profiles will follow a similar trajectory. In the present study, we examine profiles of children who attend special education preschool classrooms, in which approximately half of children exhibit language difficulties. Although it is likely that children who exhibit patterns of severe inattentiveness and language difficulties will experience slower rates of growth and poorer subsequent reading abilities than others, it is also possible that the ways in which inattentiveness, language gain, and reading intersect might differ for certain subgroups of children.

Purpose of the Current Study Growing evidence suggests that higher levels of inattentiveness in the preschool years may negatively affect children’s language and reading skill development. The extant literature has primarily sought to understand how these variables relate and interact within specific samples of children (i.e., those with ADHD, at risk for reading disabilities). In the present study, we examine patterns of inattentiveness and language difficulties within a large heterogeneous sample of children with and without disabilities who were attending ECSE classrooms. This analytic approach allows us to explore whether increased levels of inattentiveness are associated with deficits across grammar, vocabulary, and narrative domains, or domain-specific deficits. We further examined the extent to which children in unique profiles experience differing levels of language gain throughout the school year as well as differences in subsequent kindergarten early reading skills. The present study extends previous work and addresses the following research questions: a) to what extent can unique profiles among a heterogeneous group of preschool children be identified according to measures of inattentiveness and language ability?, b) to what extent do those profiles differ in their year-long gains in grammar, vocabulary, and narrative?, and c) to what extent do profiles differ in their kindergarten early reading skills?

Method Participants The present study included 461 children who participated in a multi-site, multi-cohort longitudinal study designed to examine the impact of a supplemental literacy curriculum in ECSE classrooms that served both children with and without disabilities. The Institutional Review Boards at The Ohio State

University and Lehigh University provided ethics approval for all study procedures prior to recruitment and data collection activities. Only cases that included data on at least one of the measures of interest in the present study were included in analyses. Approximately 30% (n = 136) of the sample had missing data on one or more LPA measures. The amount of missing data on each measure ranged from 0 to 28%. Preschool teachers across Pennsylvania and Ohio were recruited to participate in the study if they met the following eligibility criteria: a) they were willing to attend professional development and implement the intervention, and b) had at least three eligible children in their classroom whose caregivers provided consent for their child’s participation. Inclusion criteria for children participating in the study required that they: a) were at least three years old by September 1 of that year, b) were enrolled in the participating teacher’s classroom, c) demonstrated a mean length of utterance (MLU) greater than 2, d) communicated proficiently in English; at minimum, understood most of what is said in order to complete study assessments, and e) did not exhibit any severe cognitive or sensory disability that would prevent them from completing study assessments. Because the study was conducted in both inclusive classrooms (i.e., both typically developing and children with disabilities) and self-contained classrooms (i.e., only children with disabilities), eligible participants could be either typically-developing or children with disabilities, as long as they met the inclusion criteria listed above. Disability status was determined by whether or not the child had an Individualized Education Plan (IEP), and the proportion of the sample with an IEP in the present study was 42.5% (n = 196). In the present study, we were particularly interested in the proportion of participating children in these ECSE classrooms who met criteria for LI. LI status was determined by whether or not the child had an Individualized Education Plan (IEP) in addition to scoring below 85 on a standardized comprehensive language assessment. The proportion of the sample with LI in the present study was 27% (n = 125). At the start of the study, in the fall of the preschool year, participating children had a mean age of 54 months (SD = 6.5 months) and 56% were male. The majority of children were White/Caucasian (75%), 13% were AfricanAmerican, 10% were multiracial/other races and 2% identified as Asian, Native Hawaiian, or Other Pacific Islander. Fifteen percent were Hispanic or Latino. Mothers’ highest degrees earned included less than high school diploma (6%), high school diploma (23%), some college (23%), associate degree or technical certificate (14%), bachelor’s degree (16%), master’s degree (15%), or doctoral degree (2%). Thirty-four percent of children’s caregivers reported annual household incomes less than $25,000; 48% reported incomes of $25,000 to $75,000, and 18% reported incomes greater than $75,000.

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Procedures Data collection for the measures of interest in the present study occurred at two time points in the preschool year, one at the beginning of the preschool year (fall) and one at the end of the school year (spring). An additional data collection period occurred in the subsequent fall of the child’s kindergarten year. Children were assessed individually by trained field assessors in a quiet location in or near their classroom. Field assessor training involved completing a self-guided PowerPoint training followed by a quiz on each assessment as well as a fidelity observation in the field. Any departures from the assessment protocol observed were discussed and rectified. Additional fidelity observations were conducted, if necessary. A second, different assessor verified all data collected in the field to ensure accuracy regarding adherence to discontinuation rules. Between assessment time points, assessors were also required to complete a Brefresher^ training by reviewing the selfguided PowerPoint training as well as an additional fidelity observation. At each time point, field assessors administered to children the assessment battery that included multiple language and literacy measures; in most cases, testing was completed on one session, but two were scheduled if additional time was needed. Parents also completed a family background questionnaire at the first time point, which included a rating scale of their child’s attention and impulse control.

Measures Three types of measures were used to address study aims. First, in the fall of the academic year, measures of children’s inattentiveness (via parent report), grammar, vocabulary, and narrative abilities were used to identify language and attentiveness profiles. Second, scores from spring assessments of the three language constructs (grammar, vocabulary, narrative) were included to examine associations between profile membership and language gain. Third, a measure of early reading was administered in kindergarten and used to investigate associations between profile membership and subsequent early reading ability. Inattentiveness Children’s inattentiveness was measured using the Inattentive subscale of the Strengths and Weaknesses of ADHD-symptoms and Normal-Behavior (SWAN; Swanson et al. 2006) scale. The first nine items on the SWAN constitute the Inattentive subscale and are rated by the parent on a seven-point scale ranging from Bfar below average^ (1) to Bfar above average^ (7), with relative strengths/weaknesses also included on the scale such as Bslightly above/below average.^ A sample item on this subscale that a parent is asked to rate their child on compared to other children is as follows: BGives close attention to detail and avoids careless mistakes.^ Other examples include

BEngage in tasks that require continued mental effort^ and BIgnore unrelated activities.^ Subscale scores are on the SWAN are calculated by summing the scores on the items in the specific subset and diving by number of items to determine the average rating-per-item. A lower score on the Inattentive scale indicates greater inattentiveness, with a maximum possible score of seven. Young et al. (2009) computed the internal reliability for the Inattentive subtest as 0.94. Reliability for the current sample was adequate with α = .93. Although only a parent-reported measure of inattentiveness is included in this study, previous research suggests that parents can provide valid and reliable accounts of children’s attentiveness (Glass et al. 2014). Grammar Children’s grammatical skills were assessed with the Word Structure subtest of the Clinical Evaluation of Language Fundamentals Preschool – Second Edition (CELF-P2; Semel et al. 2003). The Word Structure subtest measures children’s abilities to use increasingly complex syntactic constructions to complete sentences, including plurals and verb tenses. Raw scores are calculated by summing the number of correct responses, and converted to scaled scores using the CELF: P2 Examiner’s Manual. The Word Structure subtest has an average coefficient alpha = .90 across ages 3 to 6. Vocabulary Children’s vocabulary skills were measured with the Definitional Vocabulary subtest of the Test of Preschool Early Literacy (TOPEL; Lonigan, Wagner, Torgesen, & Rashotte 2007). This measure of expressive vocabulary assesses children’s abilities to both identify and describe pictured items. For example, a child is shown a picture of a bed and asked the following questions: BWhat is this? What is it for?^ Each response is scored on a 0 or 1 scale, with 1 indicating a correct response. The total possible raw score on this test is 70 points. Raw scores were converted to standard scores. According to the TOPEL manual, this subtest has shown high internal consistency (.94) and test-retest reliability (.89). Narrative Ability Children’s narrative abilities were measured using the Renfrew Bus Story task (Glasgow and Cowley 1994). To administer this test of narrative retelling skill, assessors first reads a 168-word story aloud while the child follows along by viewing a series of 12 corresponding pictures. Then, the child is asked to retell the story using the pictures as an aid. The narrative recall is audio-taped and later transcribed so that the generated narratives can be analyzed for story content. In the present study, the information score was used to represent narrative ability. To score for information, children’s narratives are awarded credit for appropriately referencing story events in the correct sequential order. Possible raw scores on the information index range from 0 to 53 points. Inter-

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rater reliability for coding narrative information scores within the present study was very high (.97).

Table 1 Descriptive analysis and bivariate correlations among indicators of inattentiveness and language Variable

Early Reading Children’s early reading skills were assessed in the fall of their kindergarten year with the Woodcock Johnson III Test of Achievement: Letter-Word Identification (LWI) subtest (WJ-III; Woodcock et al. 2007). The LWI subtest measures a child’s ability to recognize letters and sight words. Initial items require that children verbally identify individual letters. Subsequent items require children to read and verbally identify words of increasing difficulty. Standard scores are available for children ages 5 and up. According to the WJIII manual, median test reliability is high (.94).

Analytic Approach The first research question of the present study utilized a twostep analytic approach. First, latent profiles analysis (LPA) was used to group children into most likely profiles based on their performance on observed reports of attentiveness and direct assessments on language (Samuelson and Dayton 2010). Missing data were estimated using maximum likelihood with robust standard errors. Four variables, namely Inattentiveness, Grammar, Vocabulary, and Narrative, were used as indicators. Scores on these variables were z-scored and used in the analyses. For the second and third research questions, the BCH Method in Mplus was applied to estimate a distal outcome model (Asparouhov and Muthén 2014) and examine the extent to which profile membership was associated with differential gains in language over the preschool academic year and early reading skills in kindergarten, respectively. Gain scores for each measure were calculated by subtracting the fall raw scores from the spring raw scores.

Results Profiles of Inattentiveness and Language in Preschoolers As a preliminary step, descriptive analyses and bivariate correlations among the study variables were assessed (Table 1). Bivariate correlations, ranging from .34 to .78 indicated significant associations among all variables. To address the first research question, standardized scores for the indicators of inattentiveness and language were included in five LPA models in Mplus (Muthén and Muthén 2006) and Mplus LCA helper (Uanhoro and Logan 2017). Models with two to six profiles were evaluated using several fit indices and the three-profile model emerged as the ideal solution (see Table 2 for model fit indices). Bayesian Information Criteria (BIC; Kaplan 2000) values were plotted, with the slope of the curve

N

Mean (SD)

Range

In Gr –

Inattentiveness (In) 461 3.26 (0.99)

0–6

Grammar (Gr)

454 7.49 (3.59)

1–18

Vocabulary (Voc) Narrative (Nar)

452 92.73 (17.71) 54–125 334 88.10 (15.4) 54–145

Voc Nar

.40* .44* .34* .76* .67* .59* –

*p < .001

substantially decreasing after the three-profile model, supporting the three-group solution as the ideal model. The three-profile model resulted in an entropy of .812, suggesting good separation of classes for these models. Entropy values of less than .80 indicate poor separation of classes (Celeux and Soromenho 1996). The model with four profiles had entropy values less than .80. The Lo– Mendell–Rubin test (LMR; Tech 11) and Bootstrap Likelihood Ratio Tests (BLRT; Tech 14) revealed significant p-values (p ≤ .01 for both tests), indicating that the three-profile model produced a significantly better fit than the two-group model (Lo et al. 2001; McLachlan and Peel 2000). Additionally, five- and six-profile models yielded groups consisting of very few children and were not conceptually meaningful. The average z-scores for indicators for each profile are displayed in Fig. 1. Demographic characteristics of children in profiles are described below. Means and standard deviations of the inattentiveness scale and language scores by profiles are found in Table 3.

Profile 1: At Risk (Prevalence = 19%; n = 88) Children in this profile had the lowest scores, on average, across all inattentiveness and language indicators relative to their peers, and as such, were considered to be BAt Risk^ for continued poor outcomes in language and reading. A large proportion of children in this profile had disabilities (n = 78, 88%), most of whom were classified as having LI (n = 73, 83%). Children in this profile were on average 54 months of age at the beginning of the preschool year, and a majority were male (64%). This profile represented approximately one-fifth of the children in the sample. Of those reporting highest maternal education information (98%), 1% of mothers had less than a high school diploma, 6% earned a high school education with diploma or GED, 20% earned a high school diploma or GED plus technical training certificate, 10% completed some college but no degree, 33% had a AA/AS 2-year degree, 16% held a Bachelor’s degree, and 14% had a Master’s or Doctoral degree. Fifty-eight percent of children’s caregivers reported a household income of $25,000 or less, 33% reported earning $25,000 to $75,000, and 9% earned greater than $75,000.

J Abnorm Child Psychol Table 2 Model fit indices for inattentiveness and language

Classes

-2LL

df

AIC

BIC

Entropy

Tech 11

Tech 14

2

−2105.63

13

4237.25

4290.99

.852

0

0

3

−2026.79

18

4089.587

4163.988

0.812

0.0136

0

4 5

−1978.43 −1954.96

23 28

4002.859 3965.926

4097.927 4081.661

0.789 0.823

0.0006 0.0072

0 0

6

−1940.80

33

3947.599

4084.001

0.804

0.0651

0

Bold indicates the selected model. –2LL, −2 log likelihood; df, degrees of freedom; AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; Tech 11, Lo-Mendall-Rubin Likelihood Ratio Test; Tech 14, Parametric Bootstrap Likelihood Ratio Test

Profile 2: Almost Average (Prevalence = 31%; n = 144) The Almost Average profile included the largest number of children with scores indicating close to average inattention and language skills, relative to their peers. Just over half of children in this profile had disabilities (n = 80, 55%), with approximately one-third of children meeting criteria for LI (n = 49, 34%). Two-thirds of the children in this profile were male (66.7%). The average age of children in the fall of the preschool year was 55 months. Maternal education information was reported for 96.5% of children in this profile and household income for 93.5%. Of those reporting highest levels of maternal education, 10% had less than a high school diploma, 18% earned a high school education with diploma or GED, 9% had a high school diploma or GED plus technical training certificate, 27% completed some college but no degree, 13% had a AA/AS 2-year degree, 12% held a Bachelor’s degree, and 11% had a Master’s or Doctoral degree. Thirty-eight percent of children’s caregivers reported a household income of $25,000 or less, 42% reported income levels between $25,000 and $75,000, and 20% reported earning greater than $75,000.

Profile 3: Above Average (Prevalence = 50%; n = 229) The Above Average profile consisted of children with the highest scores, relative to the entire sample, across all

indicators and included the largest number of children. Few children in this profile had disabilities (n = 38, 17%), and very few children in this profile met criteria for LI (n = 3; 1.3%). Most were female (53%), and the average age of children in the Above Average profile was 55 months. Maternal education information was available for 98% and household income for 97% of children. Of those reporting maternal education information, 4% had mothers with less than a high school diploma, 12% high school education with diploma or GED, 16% high school diploma or GED plus technical training certificate, 16% some college but no degree, 13% AA/AS 2-year degree, 22% Bachelor’s degree, and 24% Master’s or Doctoral degree. Twenty-three percent of children’s caregivers reported a household income of $25,000 or less, 37% reported earning between $25,000 and $75,000, and 40% reported earning greater than $75,000.

Profile Membership and Language Gains Our second research aim sought to examine the extent to which profile membership was associated with differential gains in grammar, vocabulary, and narrative abilities throughout the academic year. Means for spring scores and Hedges’ g effect sizes for standard score gains are presented in Table 3. To account for the measurement error of the latent class variable, we applied the BCH method in Mplus to estimate a

1.00

Fig. 1 Inattention and language profile membership. Fall Latent Profiles for Children in ECSE classrooms (N = 461). Profile 1: At Risk (19%); Profile 2: Almost Average (31%); Profile 3: Above Average (50%)

0.50 0.00 -0.50 -1.00 -1.50 -2.00

Inaenveness

Grammar

Narrave

At Risk, n = 88

-0.79

-0.91

-1.46

Vocabulary -1.42

Almost Average, n = 144

-0.20

-0.40

-0.08

-0.22

Above Average, n = 229

0.43

0.74

0.85

0.85

J Abnorm Child Psychol Table 3 Children’s scores in the fall and spring and effect size (g) of gains from fall to spring by profile membership Profile

Fall

Spring

g

At Risk Inattentiveness

2.48 (.94)

Grammar Vocabulary

2.65 (2.17) 63.64 (8.00)

3.93 (2.70) 76.32 (14.31)

.52 1.12

Narrative

72.06 (8.29)

71.30 (12.80)

−.07

Almost Average Inattentiveness

3.06 (.88)

Grammar Vocabulary

6.14 (2.18) 88.82 (8.12)

7.08 (2.63) 91.48 (12.99)

.39 .25

Narrative

79.60 (8.78)

83.47 (12.20)

.36

Inattentiveness Grammar Vocabulary

3.69 (.85) 10.16 (1.96) 106.31 (6.82)

10.81 (2.43) 105.78 (8.14)

.30 −.07

Narrative

97.29 (13.40)

103.01 (15.91)

.39

Above Average

Values in parentheses are standard deviations. Fall and spring scores are standard scores

distal outcome model and compared means across latent classes using chi-square analyses (Asparouhov and Muthén 2014). There were significant differences by profile membership for spring grammar, [χ2(2, N = 461) =, p < .001], vocabulary, [χ2(2, N = 461) = 344.58, p < .001], and narrative, [χ2(2, N = 461) = 303.39, p < .001]. For spring grammar, children in the Above Average profile scored significantly h i g h e r t h a n t h e A l m o s t Av e r a g e p r o f i l e , [ χ 2 ( 2 , N = 461) = 158.08, p < .001]. Children in the Almost Average, [χ2(2, N = 461) = 46.86, p < .001], and Above Average profiles, [χ2(2, N = 461) = 364.89, p < .001], scored significantly higher on grammar than children in the At Risk profile. For spring vocabulary, mean scores of children in the Above Average profile were significantly higher than mean scores for children in the Almost Average profile [χ2(2, N = 461) = 109.78, p < .001]. Children in the At Risk profile had significantly lower scores compared to children in the Almost Average, [χ2(2, N = 461) = 40.43, p < .001], and Above Average profiles, [χ2(2, N = 461) = 256.68, p < .001]. The same pattern of results emerged from analyses of narrative scores. On average, the At Risk profile had the lowest spring scores on the narrative measures compared to their peers in the Almost Average, χ2(2, N = 461) = 23.09, p < .001], and Above Average profiles, [χ 2(2, N = 461) = 247.48, p < .001]. Children in the Above Average profile had significantly higher scores on the narrative measure than their peers in the Almost Average profile, [χ2(2, N = 461) = 147.44, p < .001]. The second step in these analyses was to examine the extent to which the profiles differed with respect to their language gain throughout the academic year. Results suggested

significant profile differences for gains in grammar, [χ2(2, N = 4 6 1 ) = 1 0 . 3 3 , p = . 0 0 6 ] , v o c a b u l a r y, [ χ 2 ( 2 , N = 461) = 76.10, p < .001], and narrative, [χ 2 (2, N = 461) = 11.58, p = .003]. For grammar, the At Risk profile made similar gains to the Average profile, [χ 2 (2, N = 461) = 0.34, p = .56], and the Above Average profiles, [χ2(2, N = 461) = 3.83, p = .05]. The Almost Average profile also displayed greater gains than the Above Average profile, [χ2(2, N = 461) = 8.19, p = .004]. For vocabulary, the At Risk profile demonstrated significantly greater gain over the course of the school year relative to the Almost Average profile [χ2(2, N = 461) = 18.52, p < .001] and Above Average profile [χ2(2, N = 461) = 67.02, p = .001]. Children in the Almost Average profile demonstrated significantly greater gain compared to the Above Average profile [χ2(2, N = 461) = 11.06, p = < .001]. With respect to gains in narrative, children in the Above Average and Almost Average profiles exhibited similar levels of gain [χ2(2, N = 461 = 1.67, p = .20]. Additionally, children in the Almost Average and At Risk profiles exhibited similar levels of gain [χ2(2, N = 461) = 3.21, p = .07]. However, children in the Above Average profile displayed greater gains compared to the At Risk profile, [χ 2 (2, N = 461) = 11.58, p < .001]. Results presented in Table 3 include Hedges g effect sizes, which can be used to interpret the magnitude of change from fall to spring as small (.02), medium (.05) or large (.08). In general, results suggest that gain scores for the At Risk profile were medium to large for Grammar and Vocabulary; however, nearly all gains scores for the Almost Average and Above Average groups were small to moderate.

Profile Membership and Early Reading Skills Our final research question focused on the extent to which profile differences were evident in children’s kindergarten early reading skills. Chi-square results indicated differences by profile membership, χ2(2, N = 461) = 27.85, p < .001]. Specifically, the Above Average profile (M = 108.64, SD = 12.75, range = 73–155) scored significantly higher than the At Risk (M = 95.76, SD = 20.28, range = 46–140, [χ2(2, N = 461) = 19.62, p < .001)] and Almost Average profiles (M = 102.31, SD = 14.72, range = 66–158, [χ 2 (2, N = 461) = 11.27, p < .001)]. Generally, children in the At Risk profile demonstrated similar early reading skills compared to children from the Almost Average profile, [χ2(2, N = 461) = 3.42, p < .06)]. As an extension to this third research question, we also considered the proportion of children in each profile who would be considered as having a reading disability (RD) in the fall of their kindergarten year. Following procedures used in previous studies (e.g., Catts et al. 2002), RD status was determined by a standard score of below 85. Due to attrition in this longitudinal study, early reading scores were only available for 325 children. Of the 55 children

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remaining in the At Risk profile, 24% (n = 13) might be considered as having a RD. Of the 101 children remaining in the Almost Average profile and 169 children remaining in the Above Average profile, 8% (n = 8) and 3% (n = 5) would be considered as having a RD, respectively.

Discussion The present study sought to advance our understanding of the relations between inattentiveness and language ability in preschool children. This work extends previous knowledge in two specific and important ways. First, participants in this study were attending ECSE classrooms, included both children with disabilities and typically developing children, and represented a wide range of ability and skill levels. Previous studies examining inattentiveness and language have primarily included either children with confirmed diagnoses of either ADHD or LI, limiting the generalizability of findings with respect to relations between inattentiveness and language. As such, results from this study may be more broadly applicable to common educational settings. Second, this research utilized a person-centered approach, rather than variablecentered analyses, to understand the patterns of performance on the selected variables. This approach is appropriate for heterogeneous samples, as it does not assume that each individual would perform similarly across attention and language domains and affords a complementary perspective to the extant literature concerning relations between inattentiveness and language abilities. We acknowledge that the only measure of attentiveness was based on parent report, albeit a measure that has strong reliability and validity. As such, these results are interpreted as offering initial and important information regarding potential profiles of preschoolers with and without disabilities, for which future work can further validate with additional robust measures. Results from the present study yielded several findings that warrant further discussion. First, the LPA generated a three-profile solution that broadly related to levels of severity across the indicators of inattentiveness and language. In other words, children with a greater number of observed symptoms of inattentiveness also exhibited poor language skills; children with almost average attention skills exhibited average or just below average language skills, relative to their peers. This finding aligns with research suggesting children’s language and attention abilities are often correlated (Cohen et al. 1993; Weiss et al. 2003), and that a greater proportion of children with LI exhibit inattentiveness, compared to children with typical language skills (e.g., McGrath et al. 2008). Results also converge with research suggesting that children with poor attention skills struggle with narrative tasks (Purvis and Tannock 1997); specifically,

children in the At Risk profile performed particularly poorly on the measure of narrative ability, relative to their peers. Results from the present study are particularly compelling, as measures included both parent-reported inattentiveness and direct language assessments. These results may serve to support the important role of parents’ observations of their child’s behaviors and abilities. Indeed, parent’s concerns about their child’s early behavior are warranted, as these may converge with their child’s language and reading development. Future work can further corroborate these findings by including both direct measures and teacher-reported observations of children’s attentiveness. The three profiles were largely characterized by the proportion of children with LI represented in each subgroup. Not surprisingly, the At Risk profile primarily consisted of children with LI whereas only three children in the Above Average profile met criteria for LI. However, it should be noted that even within these profiles, a considerable degree of heterogeneity was evident. Over one-half of children in the Almost Average profile had an IEP and one-third also exhibited clinically depressed language skills. Conversely, just over 15% of children in the At Risk profile had not been identified as having LI. This pattern suggests that a diagnosis alone is not enough to understand areas of strengths and weaknesses in young children. These results also highlight the value of utilizing a person-centered approach when evaluating multiple variables among a heterogeneous population. In the present study that included both children with and without disabilities across numerous ECSE classrooms, the largest group was actually the Babove average^ subgroup, with less than onefifth being considered Bat risk^, relative to their peers. This approach permitted a complementary perspective of children’s capabilities that were not constrained by a priori diagnostic subgroupings. As such, results showed that some children with LI are on par with their peers, whereas others who are considered typically developing might be struggling to keep up, even at the start of their preschool year. One possible reason that some children with disabilities (i.e., LI) who fell in to the Almost Average group had been receiving intervention for a longer period of time than children in the At Risk profile. Information relating to length of time in intervention was not available for children in this study, but should be considered in future research. Descriptively, the LPA results suggested that the three profiles were also differentiated by socio-demographic factors. Indeed, the lowest-performing group of children not only exhibited lower scores on all measures, but also were more likely to live in poverty, and less likely to have mothers with college degrees compared to those in the Above Average profile. The compounding of risk factors that reflects both demographic and cognitive characteristics aligns with some research suggests a correlation between SES and LI (e.g., Stanton-Chapman et al. 2002).

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The present study suggests these difficulties are associated with a greater degree of inattentiveness as well. Finally, results pertaining to the characteristics of the identified profiles largely align with and support theoretical accounts for the common co-occurrence of attention and language deficits (Beitchman et al. 1986; Cohen et al. 2000; McGrath et al. 2008). These results do suggest a rather linear relationship between these two skills, for children at this age, and this was observed even among a very heterogeneous population (i.e., children with and without disabilities). Of note is that children in the At Risk profile had particularly low scores on the measures of narrative and vocabulary. With respect to narrative abilities, these findings corroborate previous studies suggesting that the skills involving narrative tasks may be most heavily affected in children with attention problems (Purvis and Tannock 1997). It is not entirely clear why the vocabulary measure would be similarly challenging. However, the administration of this task, and the degree of verbal responses required to complete it may have been difficult for children with poor attention. Future studies should also include measures of executive functioning, to determine the extent to which those skills might similarly correlate according to profiles. Beyond the support for theoretical linkages between inattentiveness and language, these results have practical implications for serving children with disabilities. Educators and clinicians must consider a child’s attentiveness, particularly children with language difficulties, as poor attention may exacerbate a child’s inability to learn from many classroom activities that involve language processing (i.e., narrative) and word learning. Our second research question sought to understand the associations between profile membership and language gain over the academic year. Somewhat unexpectedly, results indicated that although the At Risk profile had significantly poorer mean scores on language measures in both the fall and spring, compared to the other two profiles, their gain scores for both grammar and vocabulary were either equivalent to or significantly greater than the gain evidenced by children in the higher performing profiles. This suggests that although their peers in other profiles consistently achieved higher scores at both time points, the rate of change over the year was stronger for children whose scores were lowest at the beginning of the year. This finding contrasts some previous work that has suggested that children with poor attention skills and poor language skills also struggle to maintain a comparable rate of growth to children with superior attention and typical language abilities (Lonigan et al. 2017). Specifically, data from the present study suggest that within the domains of grammar and vocabulary, this may not be the case. One possible reason for these differences in gain scores could be that children in the At Risk profile simply had more room to grow, given their significantly lower starting point. Results from Lonigan et al. (2017) study of preschoolers were

similar in that children who started the preschool year with higher scores gained less throughout the year compared to those with lower initial skill levels. As discussed above, children in this profile experienced multiple risk factors. Evidence suggests that for children from low-SES backgrounds, a year of preschool provides substantial support to facilitate language learning and development (Loeb et al. 2004). Some studies have suggested there are significant relations between levels of maternal education and a child’s early vocabulary knowledge (Dollaghan et al. 1999; Hoff 2003). Therefore, it is possible that children with compounded risk factors began the year with very limited ability, yet showed significantly greater benefit from increased exposure to language learning opportunities in the preschool classroom. It is notable, however, that the remarkable rate of gain exhibited by the At Risk profile in the domains of grammar and vocabulary was not the same in the domain of narrative skill. In fact, children in the At Risk profile exhibited no gain over time on this particular measure and performed particularly poorly on this measure in the fall, compared to their peers. This finding suggests that the measure of narrative information continued to be challenging for children who started the year with poorer attention and lower language skills, despite gains in other language domains, and converges with some previous research that narrative tasks may be particularly difficult for children with attention difficulties (Purvis and Tannock 1997). One possible explanation for the domainspecific difference is that conveying narrative information in a recall task is a much more cognitively demanding and complex skill than single-word responses to questions about morphosyntax and vocabulary. Therefore, it might be the case that gains in narrative ability require a broader array, and coordination, of competencies requiring both language-based skills and attentiveness. Moreover, it is possible that a strong foundation of grammar and vocabulary, which children in the At Risk profile were still in the process of acquiring, are prerequisites for development in narrative ability (Khan et al. in review). Some previous work suggests that narrative is a skill that may take a longer period to develop, particularly for children with language-related difficulties. Paul et al. (1997), for example, found that a group of children considered Blate talkers^ who continues to have language difficulties at school entry, did not demonstrate ageappropriate narrative abilities until second grade. Other work has suggested that for children with LI, narrative skills continue to be poorer compared to typically developing children even through fourth grade (Fey et al. 2004). An important implication from this work is that even when children start their preschool year facing both environmental and cognitive risk factors, positive changes in language development are possible. The extent to which this rate of gain continues in subsequent years is beyond the scope of the present study. However, these results substantiate the need for

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future studies examining the variables that support the development of children with LI and inattentiveness. These results also support the need for further research examining relations between narrative development and inattentiveness. A final goal of the present study was to examine the extent to which profile differences in attention and language related to subsequent early reading ability one year later in the fall of these children’s kindergarten year. Overall, the three profiles, though significantly different in terms of mean scores, all demonstrated relatively competent early reading abilities, with the At Risk profile averaging standard scores just below the standardized mean. A large body of research suggests linkages between language ability and reading (e.g., Catts et al. 1999; Dickinson and Porche 2011; Storch and Whitehurst 2002), as well as studies suggesting children with poor attention experience more difficulty with reading (Greven et al. 2012; Rabiner & Coie 2000). Therefore, the overall average early reading skills of the At Risk group are a particularly promising outcome, given the high proportion of children with disabilities, predominantly language learning disabilities, in this study. One possible reason that these early reading scores were relatively strong could be related to the measure. The Letter-Word Identification subtest is a valid and widely-used assessment of children’s knowledge of letters and sight words, but differs from measures of decoding, which assesses children’s abilities to apply their literacy knowledge towards the reading process. Therefore, it is possible that a larger discrepancy between profiles would have been evident on a measure of word decoding. However, it remains an important finding that despite some evidence for gains in vocabulary and grammar throughout the preceding preschool year by the At Risk profile, their early reading skills were still significantly poorer in kindergarten. In an effort to understand some potential implications from these profile differences beyond group means, we determined the proportion of children in each profile that might potentially be identified as having a reading disability, based on their standard score. The percentages of children with RD was much lower than the percentage of children with LI in the At Risk profile, with only 25% of those children scoring 1 standard deviation or lower. This research contributes to a growing body of research highlighting the connections between poor attention, language and reading. This work further supports the need to identify children with symptoms of inattentiveness in addition to poor language skills, as these children may struggle in their acquisition of early reading skills upon formal school entry.

work should include additional measures of attention, including teacher-reported as well as a direct assessment. Second, the measure of attention was only obtained in the fall of the children’s preschool year. Future studies that measure children’s progress over time should include subsequent assessments of attention to understand the extent to which children may experience change in this domain as well. It is possible that the children in the At Risk profile had poor attention in the beginning of the year, but improved over the school year, which contributed to their language gains. The extent to which changes in attentiveness relate to gains in language is an important area for future research. If multiple timepoints of both inattentiveness and language are measured, future studies could seek to confirm the subgroups identified in the present study, but also determine the extent to which children shift into different profiles over time.

Conclusions Despite the limitations discussed above, the present study makes a significant contribution to our knowledge about relations between inattentiveness and language abilities in preschool children. The profile analysis of a very large and heterogeneous group of preschoolers suggested that these children can be adequately grouped according to their general language and attention skills. The profiles were validated by demographic variables that further classified subgroups into At Risk, Almost Average, or Above Average groupings. Perhaps most notable, however, was that children in the poorest performing group demonstrated significant gains over the year in two important language domains, and had nearly average scores on a kindergarten measure of early reading. This outcome suggests that children facing multiple risk factors can learn and grow at a rate that may even exceed their peers’ levels of growth, even if their starting point is well below that of children with typical language skills. Results from this work strongly substantiate the need for interventions that consider the potential overlap of language and attention difficulties. For example, some work has found that computer assisted instruction can be successful at improving young children’s attention skills by incorporating consistent and positive feedback within learning sessions (Rabiner, Murray, Skinner, & Malone 2010). Future studies should examine the efficacy of similar instructional supports for children with varying levels of inattentiveness. Finally, data from this study may serve as a foundation for future studies aimed at identifying the environmental and cognitive variables that augment the language and attention development for children with varying levels of ability.

Limitations & Future Directions There are some limitations of the present work that we acknowledge but might be addressed in future studies. First, the present study included only one measure of attention, which was based on parent report. Although clear associations were evident between this measure and the direct measures of language, future

Acknowledgements Removed for author anonymity. The authors thank the research team and all the participating teachers and families without whom this research would not be possible. Funding This research was supported by the National Center for Special Education Research, Institute for Education Sciences, through Grant R324A130066 awarded to The Ohio State University (Justice, Piasta,

J Abnorm Child Psychol and O’Connell) and Lehigh University (Sawyer). The opinions expressed are those of the authors and do not represent views of the Institute or National Center for Education Research.

Compliance with Ethical Standards Conflict of Interest The authors declare that they have no conflict of interest. Ethical Approval Removed for author anonymity. Informed Consent Informed consent was obtained from all individual participants included in the study. Human and Animal Rights All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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