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. Author manuscript; available in PMC: 2023 Apr 1.
Published in final edited form as: J Exp Child Psychol. 2022 Jan 11;216:105321. doi: 10.1016/j.jecp.2021.105321

Does Preschool Children’s Self-Regulation Moderate the Impacts of Instructional Activities? Evidence from a Randomized Intervention Study

Christopher J Lonigan 1, Eric D Hand 2, Jamie A Spiegel 3, Brittany M Morris 4, Colleen M Jungersen 5, Sarah V Alfonso 6, Beth M Phillips 7,8
PMCID: PMC8905356  NIHMSID: NIHMS1771122  PMID: 35030386

Abstract

There is strong evidence linking children’s self-regulation with their academic and behavioral outcomes. These relations have led to the development of interventions aimed at improving academic outcomes by promoting self-regulation, based, in part, on the idea that self-regulation promotes the development of academic skills. Although a considerable number of studies have examined the degree to which interventions designed to improve aspects of self-regulation have a positive impact on academic outcomes, only a few studies have examined the degree to which children’s self-regulation moderates the effects of academic interventions. The goal of this study was to examine if self-regulation, indexed by a direct assessment of executive function and teacher-rated attention, moderated the uptake of early-literacy interventions for 184 children (average age = 58 months, SD = 3.38; 66% Black/African-American, 28% White; 59% male) at risk for reading difficulties who participated in a randomized controlled trial examining the efficacy of Tier 2 interventions in preschool. Multi-level models were used to examine the degree to which children’s self-regulation moderated the impacts of the interventions. The results of this study provided little evidence that self-regulation moderated the impacts of the interventions and call into question the likelihood of a causal relation between self-regulation and academic achievement.

Keywords: self-regulation, executive function, attention, early literacy, preschool

A considerable amount of research has linked self-regulation with behavioral, social, and academic outcomes in early childhood (Lonigan et al., 2017; Schmitt et al., 2017) and beyond (Duncan et al., 2007; McClelland et al., 2013). Results of cross-sectional studies have demonstrated moderate but consistent associations between self-regulation and academic achievement for preschool and kindergarten children (Allan et al., 2015; Allan & Lonigan, 2011; Becker et al., 2014). Results of longitudinal studies have indicated that self-regulation measured in the fall of preschool contributes unique variance to later measures of academic achievement in preschool (Fuhs et al., 2014; Lonigan et al., 2017; McClelland et al., 2007) and in kindergarten (Welsh et al., 2010; Willoughby et al., 2017). The association between self-regulation and academic achievement has led to the development of interventions intended to improve academic outcomes by promoting self-regulation (e.g., Raver et al., 2011; Sasser et al., 2017; Schmitt et al., 2015). Although researchers have hypothesized that children with higher self-regulation are better able to benefit from academic instruction than are children with lower self-regulation, few studies have examined this possibility directly.

Self-Regulation

Self-regulation encompasses a variety of skills that allow individuals to regulate thoughts and behaviors (Nigg, 2017). The two most commonly studied aspects of self-regulation with young children are attention and executive function (EF; Lonigan et al., 2017; Miller et al., 2014). Attention refers to the cognitive process of selecting or sustaining focus on relevant stimuli. In a classroom setting, attention can be conceptualized as the ability to maintain focus on teacher instructions or learning activities (McClelland et al., 2013; Walcott et al., 2010), and is the behavioral dimension most associated with academic performance (e.g., Burns et al., 2020; Sims & Lonigan, 2013; Willcutt et al., 2007). Attentional systems are hypothesized to organize the top-down control of thoughts, emotions, and behaviors by controlling levels of arousal (Blair & Raver, 2015; Garon et al., 2008).

EF is commonly defined as a domain-general cognitive process that controls, directs, and coordinates other cognitive processes in the service of goal-directed activity (Miyake et al., 2000). EF is conceptualized as encompassing a range of top-down monitoring processes that serve to regulate action (Lee et al., 2013). EF is believed to regulate behavior through the activation and inhibition of other brain regions (Garon et al., 2008). Factor analyses with adolescent and adult samples provide evidence for three distinct EF dimensions: working memory (WM), inhibitory control (IC), and shifting (SH; e.g., Lee et al., 2013; Miyake et al., 2000). WM represents the updating or active use of information held in short-term memory. IC represents the ability to suppress or override a predisposed response, and SH represents the ability to alternate between sets of stimulus-response rules. In contrast, factor analysis with preschool children provides evidence for either a one- (e.g., Allan et al., 2015; Sims et al., 2016) or two-dimensional (i.e., IC and WM; Lerner & Lonigan, 2014; Lonigan et al., 2016) model of EF.

Self-Regulation and Academic Outcomes

Both attention and EF are associated with academic achievement concurrently and longitudinally. Specifically, early childhood attentional capacity has been linked to the pre-mathematics and pre-literacy skills of preschool-age children (Lonigan et al., 2017; Welsh et al., 2010) and to the overall academic achievement (Duncan et al., 2007), literacy (Walcott et al., 2010), and mathematics (Fuhs et al., 2014) of school-age children. Similarly, EF accounts for unique predictive variance in academic outcomes in both preschool- (Fuhs et al., 2015) and school-age (Bull et al., 2011; Fuhs et al., 2014; Viterbori et al., 2015; Willoughby et al., 2017) children.

Although the exact mechanisms by which self-regulation influences academic outcomes are unknown, researchers have hypothesized that children with stronger self-regulation are better able to meet the demands of the classroom and thus develop stronger academic skills (e.g., Blair & Raver, 2015; Lawson & Farah, 2017). Children with higher levels of attention may receive more academic instruction and spend more time engaging in academic endeavors than children with lower levels of attention (e.g., Duncan et al., 2007). Compared to children with lower levels of EF, children with higher levels of EF may be better able to plan and to complete more challenging academic tasks. For instance, WM may increase the ability to store and manipulate symbols and to use multistep procedures (Morgan et al., 2017), both of which are necessary for completing math problems, decoding words, and comprehending text (Geary et al., 2012; Swanson et al., 2008). IC may encourage a more reflective response style (Purpura et al., 2017) that may help children consider their answers rather than responding impulsively, and SH may assist children in adapting to the changing requirements of the classroom (e.g., transitions; task focus; Purpura et al., 2017).

Self-Regulation Instruction and Academic Outcomes

Based on significant associations between self-regulation and academic achievement and the dominant hypothesized direction of this relation (i.e., better self-regulation leads to higher academic achievement), researchers have explored the possibility that academic outcomes could be improved by self-regulation training. To our knowledge, existing studies on stand-alone self-regulation training programs have focused exclusively on WM training. Meta-analytic findings indicate that although WM interventions have immediate effects on the WM measures directly targeted by the interventions, they do not result in significant academic improvements (Melby-Lervåg et al., 2016). Academic programs that promote EF and other aspects of self-regulation may offer an alternative to stand-alone EF interventions as a means of enhancing children’s school readiness and success (e.g., Blair & Diamond, 2008; Diamond, 2010; Ursache et al., 2012).

Most studies designed to enhance younger children’s academic skills via classroom-based self-regulation interventions take one of two forms: (a) a self-regulation curriculum is added to the business-as-usual (BAU) academic curriculum; or (b) a self-regulation curriculum is combined with an enhanced academic curriculum. Studies examining the addition of self-regulation interventions to BAU academic curricula in relation to academic outcomes have yielded mixed results. For example, the Chicago School Readiness Program (CSRP), which adapted the Incredible Years behavior-management curriculum (e.g., Webster-Stratton et al., 2004), resulted in improved academic and behavioral outcomes compared to a BAU control. In contrast, the Head Start CARES study (Morris et al., 2013), which compared three interventions designed to improve children’s self-regulation (i.e., Incredible Years, Promoting Alternative Thinking Strategies [PAThS; Domitrovich et al., 1999], and parts of Tools of the Mind [TOOLS; Bodrova & Leong, 2007]) to BAU control, did not report impacts on children’s language, literacy, or math skills, despite positive impacts on self-regulation in two of three conditions. Similarly, Tominey and McClelland (2011) reported a small effect on letter knowledge but not vocabulary or math for preschool children who participated in their Red Light Purple Light (RLPL) intervention.

Results of studies that combine self-regulation and academic interventions have also been mixed. Positive impacts on academic skills were reported for the REDI preschool curriculum, which combined enhanced academic instruction with the PAThS curriculum (Bierman, Domitrovich et al., 2008), and for the TOOLS curriculum with kindergarten students (Blair & Raver, 2014; Diamond et al., 2019). However, null results for academic outcomes have been reported in at least seven randomized-controlled studies to date that compared combined self-regulation and academic interventions, including studies involving TOOLS (Barnett et al., 2008; Clements et al., 2020; Nesbitt & Farran, 2021; Hammer et al., 2013), RLPL (Duncan et al., 2018), the 4Rs program (Jones et al., 2011), and an adaptation of PAThS (Lonigan et al., 2015).

For studies that report a significant impact of self-regulation interventions on academic outcomes, the hypothesized mechanism is that the direct positive effects of self-regulation interventions on self-regulation skills mediate improvements in academic performance. For instance, Raver et al. (2011) reported that different aspects of children’s self-regulation fully mediated children’s academic-skill gains. Similarly, Bierman, Nix et al. (2008) reported that the two EF measures impacted by the REDI program mediated gains in children’s academic outcomes. However, studies that report positive impacts on self-regulation outcomes but no impacts on academic outcomes (e.g., Barnett et al., 2008; Duncan et al., 2018; Jones et al., 2011; Morris et al., 2013) call into question this hypothesized mechanism. Moreover, the design of most studies evaluating combined self-regulation and academic intervention (e.g., Bierman, Domitrovich et al., 2008; Blair & Raver, 2014) does not allow improvements in academic skills to be unambiguously attributed to the self-regulation interventions because the comparison condition is typical instructional practice that may or may not be effective. No unique advantage of self-regulation intervention on academic outcomes was reported in the studies that allowed the isolation of effects (i.e., Clements et al., 2020; Duncan et al., 2018; Lonigan et al., 2015).

Self-Regulation as a Moderator of Instructional Effectiveness

An alternative explanation for the association between self-regulation and academic skills is that self-regulation moderates the effect of academic instruction. Based on the hypothesis that self-regulation enhances engagement with, reception of, and encoding of academic instruction, which in turn results in improved academic performance, children with higher levels of self-regulation should make greater academic gains than do children with lower levels of self-regulation when receiving similar academic instruction. To our knowledge, however, few studies have examined the possible moderating effects of self-regulation on academic instruction. Connor et al. (2010) reported that children’s initial self-regulation was associated with growth on academic outcomes but did not test whether this relation differed for children in their intervention and control groups. Bierman, Nix et al. (2008) reported that initial self-regulation moderated the impact of the REDI curriculum on children’s print knowledge. However, it was children with lower initial self-regulation who benefitted more. Children with higher initial self-regulation did equally well in REDI and BAU classrooms. Using data from the ECLS-K, Ribner (2020) reported that kindergarten children with higher EF appeared to benefit more from a higher amount of math instruction than did children with lower EF.

Current Study

Although children’s self-regulation has been posited to have a significant influence on the development of children’s academic skills, most studies supporting such a relation are correlational. A relatively large number of studies have examined the degree to which interventions designed to enhance aspects of self-regulation have positive impacts on academic outcomes; however, these studies tend to yield null results. Most theories of how self-regulation is related to academic outcomes include mechanisms by which children with higher levels of self-regulation benefit more from instruction than do children with lower levels of self-regulation; however, few studies have examined the degree to which self-regulation moderates the impact of instructional interventions. In this study, the degree to which preschool children’s EF or attention moderated the impacts of effective early literacy intervention was examined.

Children in this study participated in an intervention study for preschool children who demonstrated significant risk for later reading difficulties in one or more domains of early literacy skills (i.e., oral language, phonological awareness, print knowledge). Results of the primary study revealed statistically significant positive impacts of the small-group interventions in all outcome domains (Lonigan & Phillips, 2016). Prior to the intervention, children’s classroom teachers rated the children’s attention on the Conners Teacher Rating Scale (CTRS; Conners, 1989), and children’s EF was assessed before the intervention using the Head Toes Knees Shoulders (HTKS; McClelland et al., 2007) task. For this study we had two primary research questions. First, we examined whether children’s self-regulation moderated the effects of the early literacy interventions. Based on theoretical models suggesting that children with better self-regulation benefit more from academic instruction (e.g., Blair & Raver, 2015; Duncan et al., 2007; Lawson & Farah, 2017; Morgan et al., 2017), we hypothesized that children with higher initial levels of self-regulation would gain more from the interventions than would children with lower initial levels of self-regulation. Second, we examined whether moderation effects differed based upon the aspect of self-regulation examined (i.e., attention vs EF). Based upon the findings of Lonigan et al. (2017) that inattention but not EF predicted academic growth over time, we hypothesized that teacher-reported inattention would be a stronger moderator of the impacts of the intervention on academic outcomes than would performance-based EF.

Method

Participants in this study were part of a larger study of preschool risk for reading difficulties, which included assessments of children’s early literacy skills at the beginning (i.e., September/October), middle (i.e., January), and end (i.e., April/May) of the preschool year. This study used a child-level randomized design in which 184 children identified as at-risk for reading difficulties were randomly assigned to either an experimental intervention group or a BAU control group. Results of the primary study (i.e., impacts of the intervention) revealed statistically significant positive impacts of the small-group interventions in all outcome domains (Lonigan & Phillips, 2016). The focus of the current study was whether the degree to which children benefitted from the interventions was affected by their self-regulation and, if so, which aspect of self-regulation. Children were identified as at-risk if they scored at or below the 25th percentile on the within-skill-area average of two standardized measures of oral language, phonological awareness, or print knowledge based on the assessments completed at the midyear assessment. The midyear assessment also served as the pretest scores for the interventions. During the fall assessment, children completed a direct assessment of self-regulation, the HTKS, and classroom teachers rated children’s self-regulation in the classroom using the CTRS.

Participants and Participating Schools

All children in this study attended the Title I-funded prekindergarten program in the local school district. Children enrolled in these preschools included those with identified developmental delays in language and those whose family backgrounds (e.g., SES) indicated risk. Some enrolled children had diagnosed developmental disabilities. Exclusion criteria for this study included frank sensory impairment (i.e., children with severely impaired visual or auditory abilities) and children with no or very limited expressive language ability. Children were recruited from 21 of the district’s schools in the fall for the larger project. All classroom teachers were degreed and certified. Of the 277 children recruited for the larger project, 14 were unavailable for midyear assessment and seven attended two schools in which there were too few children consented and qualified for the intervention to allow randomization at the school. From the remaining 256 children, 184 who qualified in at least one skill area (i.e., oral language, phonological awareness, print knowledge) were randomized. The randomized sample included 23 children who qualified on the basis of language skills only, 42 children that qualified on the basis of code-related skills (i.e., phonological awareness, print knowledge) only, and 119 children who qualified on the basis of both language and code-related skills.

The mean age of the sample at the pre-intervention (midyear) assessment was 58.04 months (SD = 3.48, range = 51–64 months). The majority of the children were Black/African American (66%); 28% were White, 1% were Asian; and the race of 8% was not specified. Five percent of the sample identified as Latino/Hispanic. Children’s parents or guardians were asked to complete a questionnaire about their families that included family composition, parental education, and family income. Of the 184 child-participants’ families, 142 (77%) provided some or all of this information. Two-parent homes comprised 66% of respondents, and families reported an average of 2.75 (SD = 1.26) children in the home. Reported annual income ranged from $0 to $150,000, with 44% of families reporting annual income between $15,000 and $30,000. Median maternal education was some college or an AA degree, and median paternal education was a high school diploma or a GED. None of these variables were associated with pre-intervention scores on outcome measures. Although information concerning language in the home was collected, information about children’s primary language was not. English was the only language spoken at home for 78% of reporting families, and only 10 families (7%) reported that a language other than English was spoken in the home 75% or more of the time. Neither the presence nor the frequency of a language other than English spoken in the home was associated with children’s pre-intervention scores.

Interventions

The interventions were designed to include in-depth, teacher-directed, small-group instruction in three early literacy domains (i.e., phonological awareness, print knowledge, oral language). The daily activities for each intervention were described in detailed written lesson plans that included descriptions of instructional activities for each day, duration of the activity, and, when appropriate, scripts for the instructional activities. The scope and sequence for each intervention is provided in the supplemental on-line materials (SOM; see Appendix S-A).

The oral language intervention focused on teaching vocabulary in semantic categories related to basic concepts appropriate for the preschool period (e.g., colors, size attributes, shapes, comparing and contrasting concrete attributes such as size and color, body parts), and these basic concepts were used as the framework for teaching verbs, prepositions, spatial relations, and Wh-questions. The phonological awareness intervention focused on teaching children manipulation of compound words, two-syllable words, and onset-rime, and included both blending and elision activities at each level of linguistic complexity. Instruction included explicit introductory and review activities designed to provide clear models and repeated, scaffolded practice of the task being taught in each lesson. Materials included sets of picture cards and “puzzle cards” that divided pictures to represent the syllable or onset-rime breaks in words (e.g., a card picturing an elephant divided into three pieces to represent the three syllables in the word “elephant”) to make the abstract phonological concepts more concrete. The print knowledge intervention initially focused on the letters in participating children’s names and then letter names and letter sounds of 11 target letters (A, U, B, M, P, T, D, N, F, J, G), which were selected both to represent the range of more and less likely to be known letters and to include two vowels. Materials included letter cards of various sizes, upper- and lower-case magnet letters, tubs containing objects beginning with the letter sound, and sets of alliterative picture cards. Review activities also used bingo cards, a board game, and word-family beach balls.

Interventions were conducted for 11 weeks in small groups of two to four children for four days each week. Fridays were used as a make-up day for children absent one or more days earlier in the week. As described above, children qualified for the intervention based on midyear scores. Children qualified for phonological awareness, print knowledge, and oral language interventions independently, and, if assigned to the intervention group, children received all interventions for which they qualified. Children who qualified for phonological awareness or print knowledge but not both received 15 minutes per day of intervention in that domain. Children who qualified for both phonological awareness and print knowledge received 10 minutes per day of intervention in each domain. Children who qualified for language received 20 minutes per day of language intervention. Consequently, children received between 60 and 160 minutes of intervention per week, depending on whether they qualified for one, two, or all three interventions. Although some participating schools had more than one preschool classroom, children from across classrooms in these schools participated in intervention sessions based on the time of day children were available.

Measures

Preschool Comprehensive Test of Phonological and Print Processing (P-CTOPPP; Lonigan et al., 2002).

Participants were administered the P-CTOPPP, which measures three- to five-year-old children’s phonological awareness, print knowledge, and oral language skills. The P-CTOPPP was the development version of the Test of Preschool Early Literacy (Lonigan et al., 2007). Children’s phonological awareness skills were measured using the Blending and Elision subtests, which include both multiple-choice and free-response items. The Blending subtest consists of 21 items that require children to combine word sounds to form a word. The Elision subtest consists of 18 items that require children to remove a part of a word to form a new word. Items on both subtests increase in complexity from compound words to phonemes, following the developmental continuum of phonological awareness (Anthony & Lonigan, 2004). The Print Knowledge subtest consists of 36 items related to print concepts, letter-name and letter-sound recognition, and letter-name and letter-sound production. The Receptive Vocabulary subtest consists of 40 items for which children are required to point to a picture that represents a word spoken by the examiner. The Definitional Vocabulary subtest contains 40 items that require children to first provide the word that matches a picture and then to provide information about the feature or function of the pictured item. All subtests include at least one practice item, and all items were administered to all children without ceiling criteria. Internal consistency reliability for the subtests of the P-CTOPPP is high (αs = .86 – .96), and the subtests have moderate to high convergent validity correlations with other measures of the same constructs (i.e., rs = .59 – .77).

Test of Early Reading Ability-3rd edition (TERA-3; Reid et al., 2001).

Children completed the three subtests of the TERA-3. The Alphabet subtest assesses children’s knowledge of the alphabet, sound-letter correspondence, and rudimentary word-decoding skills. The Conventions subtest assesses children’s knowledge of concepts about print (i.e., directionality of text, parts of books, basic punctuation). The Meaning subtest assesses children’s beginning understanding of print (e.g., environmental print, signs, simple words). Internal consistency reliabilities of the three subtests are high (αs ≥ .95), and they have good test-retest reliabilities (rs ≥ .92). Each subtest was administered according to established basal and ceiling rules. Raw scores on the three subtests were combined to yield a single score for the measure.

Clinical Evaluation of Language Fundamentals-Preschool (CELF-P; Wiig et al., 1992).

Children completed the three receptive-language subtests (i.e., Linguistic Concepts, Basic Concepts, Sentence Structure) and two of the three expressive-language subtests (i.e., Formulating Labels, Word Structure) of the CELF-P, which were used to create the Receptive- and Expressive-Language scales. All subtests include basal and ceiling rules which were followed. The CELF-P has good internal consistency (i.e., αs =.81-.96), and it has good evidence of validity based on correlations with other measures of oral language (e.g., CELF-R; Preschool Language Scales; Wiig et al., 1992).

Code and Language Intervention Posttest (CLIP).

To assess letter knowledge and oral language concepts that were the targets of intervention, a curriculum-aligned assessment was developed for the study. On the CLIP, children had to name all 26 letter names, and, separately, name all letter sounds, when shown individual cards presenting an uppercase letter. During the assessments, if a child provided the letter name/sound when the other was requested, she or he was prompted with “that is the name/sound of the letter, can you tell me the sound/name?” Internal consistency reliabilities for both the letter-name (α = .96) and letter-sound (α = .95) subtests were high, including the subset of letter-names (α = .92) and letter-sounds (α = .91) specifically targeted by the intervention. For the CLIP expressive vocabulary subtest, 15 items were selected from the vocabulary targets of the intervention (e.g., colors, shapes, body parts, animals), and 12 items comprised the oral language subtest on which all items were receptive (e.g., “point to the building that is taller”). For each item, children were shown a picture and had to either provide a verbal label in response to a verbal prompt (e.g., “What is this?”) for expressive vocabulary items or point to the correct picture in response to a verbal prompt for the oral language items. Internal consistency reliability of the CLIP language measure was somewhat less than adequate (α = .65) and was lower for the two subtests (Vocabulary α = .55, CLIP Language α = .43), likely because of the small number of items on each subtest.

Head-Toes-Knees-Shoulders (McClelland et al., 2007; Ponitz et al., 2009).

Children’s EF was directly assessed by the HTKS. For this task children are instructed to do the opposite of the examiner’s request. For example, if the examiner says “touch your toes” the child must touch their head. Before examination, children are given practice trials to ensure understanding of the instructions. Initially, children are assessed using two commands (i.e., “touch your head,” “touch your toes”) alternating in a fixed order for 10 trials. After the initial set, two additional commands are taught (i.e., “touch your knees,” “touch your shoulders”) with the child instructed to touch their shoulders if asked to “touch their knees” and vice versa. The second 10 trials include all four commands in a fixed alternating order, following a practice trial. Trials are scored using a three-point scale (maximum score = 40): Correct responses (i.e., child immediately did the opposite of request) earn two points, self-corrects (e.g., child initially reaches in the wrong direction but then performs the correct response) earn one point, and incorrect responses earn zero points. Internal consistency reliability for the HTKS was high (α = .93). Scores on the HTKS are strongly correlated with other direct measures of EF (e.g., Allan & Lonigan, 2011; Spiegel & Lonigan, 2018).

Conners Teacher Rating Scale (Conners, 1989).

Teacher ratings of self-regulation were obtained using a 44-item hybrid version of the CTRS. The hybrid version included the 28 items from the CTRS-Restandardized and the non-overlapping items from the original CTRS-Short Form (see Gerhardstein et al., 2003, for details). This version of the CTRS yields three factors when completed for preschool children: Inattention, Hyperactivity/ Impulsivity, and Oppositional Behavior. Teachers rate each item on the CTRS using a four-point scale that ranges from 0 (not at all) to 3 (frequently), with higher scores indicative of poorer self-regulation. Only scores from the 11-item Inattention subscale (i.e., unit sum of all items from the Inattention factor; maximum score 33) were used in this study. Internal consistency reliability for this subscale was high (α = .92).

Procedure

Parents of all participating children provided written informed permission/consent for their children’s participation, and all classroom teachers consented to participate. Recruitment of children occurred in the fall of the preschool year, and, in January, children’s eligibility for the intervention study was determined based on their scores on the standardized measures. Eligible children were grouped based on the domain(s) in which they qualified and their average standard scores within domains to the extent possible and then randomized within group within school to the intervention group or to the BAU condition. Children assigned to the BAU control group received only the instruction provided by their classroom teachers. Children completed all academic outcome measures in the fall, winter (pre-intervention), and end-of-preschool year (post-intervention), except for the CLIP, which was only completed post-intervention. Children completed the HTKS in the fall, and children’s classroom teachers completed the CTRS during the fall assessment period. Assessments were administered to children in a quiet area of their preschools by trained research assistants. Assessments within each time period were completed over three to four 20- to 30-minute sessions within a two-week period. Children were given breaks if requested or if the examiner noticed fatigue or distraction. Order of test administration varied across children.

All small-group intervention sessions were conducted by project teachers who were employed by the project and had education ranging from bachelors to masters degrees. Project teachers participated in six hours of training prior to the start of the intervention. Training included overviews of intervention designs, rationale for the selected target skills, and modeling and practice of the interventions following the written lesson plans. Project teachers received a 2–1/2 hour booster training session midway through the intervention. All project teachers delivered both the code-related and the language-related interventions. Throughout the intervention period, intervention coordinators provided additional support, including feedback on the lessons and advice for differentiating instruction and pacing within the structured lesson format. Details on fidelity of implementation, which was high, are provided in the SOM (see Appendix S-C).

Results

To simplify analyses, analyses were conducted for a code-related-intervention group (i.e., children who qualified on the basis of print knowledge, phonological awareness, or both) and a language-intervention group. Of the 184 children randomized, 170 (92%) completed posttests. For children who qualified for the language intervention, 93% completed posttests (92% control, 94% intervention), and, for children who qualified for the code-related interventions, 92% completed posttests (90% control, 94% intervention). Missing data were minimal for children who completed posttests, and scores on most pre-intervention and postintervention variables were normally distributed (see Tables S6 and S7 in SOM). Children with missing data for a specific analysis were excluded from that analysis but included in all other analyses.

Descriptive statistics for the intervention group and BAU control group on pre-intervention raw scores (adjusted for child age) for the children who completed posttesting and qualified for the language intervention and the children who completed posttesting and qualified for the code-related intervention are shown in Table 1. Table 1 also shows effect sizes for group comparisons. For the language intervention contrast, no difference approached significance (ps > .43). For the code-related intervention contrasts, children in the intervention group scored marginally lower than children in the BAU control group on P-CTOPPP Elision (p = .09), but no other difference approached significance (ps > .50). There also were no differences between children assigned to treatment or control groups for either intervention grouping in terms of child sex (ps > .37), percentage of Black/African-American children (ps > .39), or percentage of White children (ps > .20). For children whose parents completed the family survey, there were no differences between intervention and control groups for two-parent households (ps > .56), number of children in the home (ps > .28), maternal (ps > .29) and paternal (ps > .45) education, and family income (ps > .62). Correlations between measures are included in the SOM (see Tables S4 and S5).

Table 1.

Pre-intervention scores for completer children in control and intervention groups who received language- and code-related interventions

Control Group
 Intervention Group
 Outcome Measure Adj.-M (SD) Adj.-M (SD) ES
Language Intervention Contrast
 Chronological Age (months) 57.93 (3.56) 58.40 (3.24) .14
 Pre-CTOPPP Def. Vocabulary 41.98 (11.18) 41.74 (9.54) −.01
 Pre-CTOPPP Rec. Vocabulary 27.49 (4.94) 27.37 (4.71) −.05
 CELF-P Receptive Language 16.76 (6.03) 16.55 (5.61) −.04
 CELF-P Expressive Language 18.57 (5.52) 18.86 (5.44) .05
 HTKS 8.11 (8.54) 7.31 (8.12) −.10
 CTRS 5.45 (4.30) 5.42 (4.14) −.01
Code-Related Intervention Contrast
 Chronological Age (months) 57.86 (3.48) 58.41 (3.38) .16
 Pre-CTOPPP Elision 7.44 (2.94) 6.76 (2.94) −.23+
 Pre-CTOPPP Blending 12.00 (3.90) 12.09 (3.64) .02
 Pre-CTOPPP Print 15.30 (7.91) 14.87 (8.72) −.05
 TERA-3 Total Score 11.83 (5.91) 12.53 (8.62) .09
 HTKS 7.61 (8.29) 6.96 (8.33) −.08
 CTRS 5.41 (4.39) 5.68 (4.04) .06
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Notes. Adj-M = raw score on measure adjusted for children’s chronological ages; ES = effect size (Hedges g) for contrast between intervention and control groups; CA = Chronological Age; Pre-CTOPPP = Preschool Comprehensive Test of Phonological and Print Processing; CELF-P = Clinical Evaluation of Language Fundamentals-Preschool; Def. Vocabulary = Definitional Vocabulary; Rec. Vocabulary = Receptive Vocabulary; TERA-3 = Test of Early Reading Achievement, third edition; HTKS = Head Toes Knees Shoulders task; CTRS = Inattention subscale of Connors Teacher Rating Scale.

+

p < .10.

Moderation of Impacts of Interventions by Self-Regulation

Mixed (multi-level) models in SPSS were used for the primary analyses. Children were nested within assignment block within schools, and both variables were included as random effects in initial models. Raw scores were used in all analyses. For several outcomes, one or both random effects accounted for no variance in the model. For each outcome, random effects were retained only if they accounted for non-zero variance in the final model (see SOM Appendix S-C). Because children assigned to the intervention group only received the intervention(s) for the early literacy domain(s) in which they qualified for the study, analyses of the six language outcomes were restricted to children who qualified on the basis of language scores pre-intervention (n = 142), and analyses of the six code-related outcomes were restricted to children who qualified on the basis of code-related scores pre-intervention (n = 161). To address Research Question 1, mixed models included intervention-group assignment, one of the self-regulation variables, and the interaction of intervention-group assignment and the self-regulation variable (centered), with the interaction term the critical test for the question. To address Research Question 2, we examined two self-regulation variables: EF as measured by the HTKS and inattention as measured by the CTRS.

All models included child age and the pre-intervention score on the outcome measure as covariates. Preliminary models examined interaction terms for intervention group with child age and the pre-intervention covariate; these interaction terms were not significant and were dropped from the models. Because the CLIP was only administered post-intervention, the P-CTOPPP Definitional Vocabulary subtest was used as the pre-intervention covariate for CLIP Vocabulary and CLIP Language subtests, and the P-CTOPPP Print Knowledge subtest was used as the pre-intervention covariate for CLIP Letter Names and CLIP Letter Sounds subtests. Output of the mixed models was used to compute effect sizes (Hedges-g) and significance levels for each outcome measure at low, average, and high levels of self-regulation by estimating adjusted means at one SD below the mean, at the mean, and one SD above the mean for the measure of self-regulation. Differences between adjusted means (i.e., intervention mean - control mean) were divided by the pooled SD for the outcome measure.

Language outcomes.

Parameter estimates for the language intervention are shown in Table 2. Consistent with the main impact report (Lonigan & Phillips, 2016), the largest effects of the intervention were on the CLIP for the vocabulary and other language outcomes that were specific targets of the intervention. Pre-intervention scores were significantly associated with all post-intervention scores. HTKS scores were significantly associated with post-intervention scores on the P-CTOPPP Definitional Vocabulary subtest and the Expressive scale of the CELF-P. CTRS scores were significantly associated with post-intervention scores on the P-CTOPPP Definitional Vocabulary subtest and the Receptive Language scale of the CELF-P.

Table 2.

Model Parameters and Effect Sizes for Intervention Groups at Different Levels of Self-Regulation for Language Intervention

Model Parameters
 Effect Sizesa for Levels of Self-Regulation
Outcome Variable SR Var Moder ator Intercept Age Pretest SR Var Group SR Var * Group Low Avg High
PCTOPPP Rec Voc HTKS 15.48* −0.01 0.57*** −0.10 0.95 −0.15+ .43* .19 −.04
CTRS 16.93* −0.03 0.55*** 0.06 0.88 −0.27 .40* .18 −.04
PCTOPPP Def Voc HTKS 22.63* −0.05 0.67*** 0.22* −0.25 0.25* −.24 −.03 .19
CTRS 23.35* −0.03 0.63*** 0.52** −0.28 0.17 −.10 −.03 .04
CELF-P Receptive HTKS 1.43 0.07 0.87*** −0.10 0.78 −0.13 .24 .11 −.03
CTRS 2.04 0.07 0.81*** 0.46** 0.62 0.28 −.07 .08 .21
CELF-P Expressive HTKS 16.81* −0.14 0.66*** 0.18* 0.07 0.18+ −.23 .01 .25
CTRS 16.74* −0.14 0.64*** 0.28+ 0.12 0.28 −.17 .02 .21
CLIP Vocabulary HTKS 9.26* −0.04 0.09*** 0.003 1.30*** 0.04 .44* .59*** .73**
CTRS 10.04** −0.05 0.08*** 0.11+ 1.28*** −0.04 .64** 57*** .52*
CLIP Language HTKS 3.05 0.06 0.05* −0.02 0.65* −0.04 .49* .32+ .16
CTRS 3.34 0.06 0.04* 0.10+ 0.65* −0.02 .36+ .33* .29
Open in a new tab

Note.

a

Effect sizes computed as Hedges-g using pooled SDs. SR Var = Self-regulation Variable; HTKS = Head-Toes-Knees-Shoulders task; CTRS = Connors Teacher-Rating Scale, Inattention subscale; PCTOPPP = Preschool Comprehensive Test of Phonological and Print Processing; Rec Voc = Receptive Vocabulary subtest; Def Voc = Definitional Vocabulary subtest; CELF-P = Clinical Evaluation of Language Fundamentals-Preschool; CLIP = Code and Language Intervention Posttest.

+

p < .10

*

p < .05

**

p < .01

***

p < .001.

The parameters of primary interest for Research Question 1 were the HTKS*Intervention Group interactions and the CTRS*Intervention Group interactions. The HTKS*Intervention Group interaction was statistically significant for the P-CTOPPP Definitional Vocabulary subtest, and it was marginally significant for both the P-CTOPPP Receptive Vocabulary subtest and the Expressive Language scale of the CELF-P. No CTRS*Intervention Group interaction was statistically significant or marginally significant for any language outcome.

To address Research Question 2, effect sizes for language outcomes at low, average, and high levels of self-regulation are shown in the rightmost columns of Table 2. For two of the significant or marginally significant HTKS*Intervention Group interactions (i.e., P-CTOPPP Definitional Vocabulary, CELF-P Expressive), the effect represented a cross-over interaction (i.e., positive effects of the intervention for high HTKS scores, near zero effects of the intervention for average HTKS scores, and negative effects of the intervention for low HTKS scores); however, none of these effect sizes was significantly different than zero. In contrast, the marginally significant HTKS*Intervention Group interaction for P-CTOPPP Receptive Vocabulary represented positive effects of the intervention for children with low HTKS scores and near zero effects of the intervention for children with high HTKS scores. This same pattern of results was obtained when the CTRS was used as the measure of self-regulation. For the CLIP outcomes, on which the largest impacts of the intervention were obtained, three of four analyses showed larger effects at lower levels of self-regulation and one effect showed larger effects at higher levels of self-regulation (i.e., HTKS moderation of CLIP Vocabulary); however, none of the HTKS*intervention group or CTRS*intervention group interactions approached significance for these outcomes.

Code-related outcomes.

Parameter estimates for the code-related intervention are shown in Table 3. Consistent with the main impact report, the largest effects of the intervention were for the code-related outcomes, including elision, print knowledge, and the letter names and the letter sounds targeted by the intervention. Pre-intervention scores were significantly associated with post-intervention scores, and age was associated with post-intervention scores for elision and the TERA-3. HTKS scores were not significantly associated with any post-intervention score, but CTRS scores were significantly associated with post-intervention scores on PCTOPPP Elision and Blending subtests and the TERA-3. The parameters of primary interest for Research Question 1 were the HTKS*Intervention Group interactions and the CTRS*Intervention Group interactions. The only significant or marginally significant interaction term was for PCTOPPP Blending outcomes.

Table 3.

Model Parameters and Effect Sizes for Intervention Groups at Different Levels of Self-Regulation for Code-Related Intervention

Model Parameters
 Effect Sizesa for Levels of Self-Regulation
Outcome Variable SR Var Moder ator Intercept Age Pretest SR Var Group SR Var * Group Low Avg High
PCTOPPP Elision HTKS −13.85** 0.34*** 0.44*** −.03 1.64*** −0.08 .57** .45** .32
CTRS −13.29** 0.34*** 0.39*** 0.19* 1.71*** 0.001 .43* .46*** .49*
PCTOPPP Blending HTKS 9.29 0.01 0.32*** −0.04 0.52 −0.16* .43* .13 −.17
CTRS 10.08+ 0.01 0.27** 0.37*** 0.79 0.26+ −.05 .19 .43+
PCTOPPP Print HTKS 8.09 0.02 0.83*** −0.08 1.79* −0.14 .34* .21* .08
CTRS 8.02 0.03 0.79*** 0.20 1.85* −0.12 .27* .22* .16
TERA-3 HTKS −11.99 0.27* 0.87*** 0.05 −0.80 0.05 −.13 −.09 −.06
CTRS −12.58+ 0.29* 0.80*** 0.40** −0.55 0.25 −.18 −.07 .05
CLIP LN HTKS 12.64 −0.13 0.74*** −0.05 1.16 −0.03 .17 .14 .11
CTRS 13.19 −0.13 0.71*** 0.15 1.21 −0.03 .17 .15 .13
CLIP LS HTKS 13.78 −0.22 0.71*** 0.005 1.35 −0.06 .23 .18 .12
CTRS 12.57 −0.19 0.69*** 0.08 1.32 −0.15 .25+ .17 .09
CLIP Targeted LN HTKS 6.96 −0.07 0.30*** −0.02 1.35** −0.02 .40* .36** .32+
CTRS 7.08 −0.06 0.29*** 0.05 1.36** −0.02 .39* .37** .35+
CLIP Targeted LS HTKS 7.87+ −0.11 0.31*** −0.008 1.42** −0.07 .54** .39** .23
CTRS 6.99 −0.09 0.30*** 0.04 1.42** −0.05 .45** .39** .33+
Open in a new tab

Note.

a

Effect sizes computed as Hedges-g using pooled SDs. SR Var = Self-regulation Variable; HTKS = Head-Toes-Knees-Shoulders task; CTRS = Connors Teacher-Rating Scale, Inattention subscale; PCTOPPP = Preschool Comprehensive Test of Phonological and Print Processing; TERA-3 = Test of Early Reading Ability, 3rd ed.; CLIP = Code and Language Intervention Posttest; LN = Letter Name; LS = Letter Sound.

+

p < .10

*

p < .05

**

p < .01

***

p < .001.

To address Research Question 2, effect sizes for code-related outcomes at low, average, and high levels of self-regulation are shown in the rightmost columns of Table 3. For the significant HTKS*Intervention Group interaction on P-CTOPPP Blending, lower HTKS scores (lower self-regulation) were associated with a significant and positive impact of the intervention, but higher HTKS scores were associated with a smaller and non-significant positive impact of the intervention. In contrast, for the marginally significant CTRS*Intervention Group interaction on P-CTOPPP Blending, the positive impact of the intervention was larger for lower CTRS scores (higher self-regulation) than for higher CTRS scores. For the CLIP, the general pattern was for larger positive impacts of the intervention to be associated with lower HTKS scores and higher CTRS scores (i.e., lower levels of self-regulation; see Table 3).

Additional Examination of Potential Moderation Effects

As is often the case with tests of moderation, moderation analyses in this study were statistically underpowered. Post-hoc power analysis using the Power-Up!-Moderator program (Spybrook et al., 2016) indicated that, at power of .80, this study had a minimal detectable effect size for moderation of about 0.60 (95% CI: 0.17 – 1.04). To account for the underpowered nature of these analyses and to increase the likelihood of identifying possible moderation effects, the magnitude of moderation effects was examined in addition to their statistical significance. Using a moderation effect of .15 or higher (i.e., the level at which some moderation effects achieved at least marginal levels of significance in this study), four outcomes were potentially moderated by HTKS and seven outcomes were potentially moderated by CTRS.

Of these 11 cases of potential moderation, there were five instances of cross-over effects, with non-significant effects of intervention in roughly equal and opposite directions for high and low EF (i.e., P-CTOPPP Definitional Vocabulary*, CELF-P Expressive Language* [effects marked with an asterisk achieved at least marginal levels of significance]) and high and low attention groups (i.e., P-CTOPPP Definitional Vocabulary, CELF-P Expressive Language, TERA-3), two instances of positive effects in which the intervention group scored higher than the control group only for children with higher ratings of attention (i.e., CELF-P Receptive Language, P-CTOPPP Blending*), and four instances of negative effects in which the intervention group scored higher than the control group only for children with lower EF (i.e., P-CTOPPP Receptive Vocabulary*, P-CTOPPP Blending*) or lower ratings of attention (i.e., P-CTOPPP Receptive Vocabulary, CLIP Letter Sounds). Hence, no consistent pattern of moderation was revealed even when using a liberal criterion for identifying moderation.

Discussion

The primary question of this study was whether children’s self-regulation moderated the impacts of effective early literacy interventions. Consistent with dominant theories of how self-regulation and academic outcomes are related, we hypothesized that preschool children with higher initial levels of self-regulation would benefit more from the interventions than would children with lower initial levels of self-regulation because these children would be better equipped to manage the demands of instruction, would participate more consistently during instruction, and would have more effective exposure to intervention activities. Contrary to our expectations, however, children’s self-regulation did not have a substantial or consistent influence on the positive impacts of the interventions. The second question was whether moderation effects would differ based upon the aspect of self-regulation examined (i.e., attention vs EF), and we hypothesized that the more behavioral manifestation of self-regulation, attention, would be a stronger moderator. However, there was no clear support for moderation by either aspect of self-regulation. These results further the understanding of the association between self-regulation and academic achievement.

Moderating Effect of Self-Regulation on Early-Literacy Instruction

Overall, the results of this study indicated that self-regulation did not consistently moderate the impacts of effective early literacy interventions. Of the 28 examined relations (i.e., academic outcome with self-regulation pairs), only five achieved at least marginal levels of statistical significance (i.e., p < .10). Of these, two moderation effects were in the positive direction (i.e., higher self-regulation associated with larger impacts) and three moderation effects were in the negative direction (i.e., higher self-regulation associated with smaller impacts). Even when the pattern of effects was examined regardless of a statistically or marginally significant moderation effect, there was no clear pattern of directionality of moderation.

There were five crossover effects that were consistent with the original hypothesis (i.e., more positive treatment effects for children with higher initial levels of self-regulation); however, these crossover effects also indicated that, for children with lower initial levels of self-regulation, participation in the interventions resulted in detrimental treatment effects, albeit ones that were not statistically significant. Given the effectiveness of the interventions reported in the impact study (Lonigan & Phillips, 2016), we can think of no reason why lower initial levels of self-regulation and participation in the interventions would lead to worse outcomes than having not participated in the interventions. Therefore, it seems likely that the moderation crossover effects reflect a statistical artifact within the data rather than a true effect of self-regulation on the children’s uptake of the interventions.

The academic interventions used in this study tended to be effective regardless of children’s self-regulation. Of the seven significant impacts of the interventions on the targeted academic outcomes, only one was moderated by self-regulation, and, for this outcome, children with lower EF showed larger positive treatment effects of the intervention than did children with higher EF. In contrast, for the outcomes on which there were no main effects of interventions, six out of seven (86%) showed evidence of potential moderation by at least one measure of self-regulation. However, the direction of these effects was inconsistent, making it difficult to draw conclusions about the role of children’s self-regulation in the uptake of academic interventions.

This inconsistency of the presence and direction of potential moderation effects, coupled with the lack of evidence of potential moderation of outcomes for which there were statistically significant main effects, casts doubt on the narrative that the relation between self-regulation and academic achievement is explained by the influence of self-regulation on the uptake of academic instruction (e.g., Duncan et al., 2007). Support for this theory would have been offered by positive moderation effects, which, with one exception, were not present in this study. In contrast, several moderation effects were in the opposite direction, such that the intervention group scored higher than the control group for children with lower self-regulation. Our results were consistent with those of Bierman, Nix et al. (2008), who reported that of 15 moderation models examining the relation of self-regulation with academic outcomes, only one (7%) had a significant moderating effect, and the effect was in the opposite direction than was expected (i.e., children with lower self-regulation benefitted more). It is possible that the explicit, focused, and scaffolded nature of the instruction used in this study negated any potential effects of self-regulation; however, such a possibility speaks more to the nature of effective instruction than to the importance of self-regulation for the acquisition of academic skills.

Limitations and Future Directions

Although this study had numerous strengths, there were some limitations. First, like most studies designed to detect main effects, statistical power to detect moderation was not optimal; however, exploration of effect sizes as an indicator of moderation did not suggest that the failure to detect hypothesized moderation effects was the result of low power. Regardless, future studies with larger samples or that combine data across multiple studies should explore the possibility that self-regulation moderates the impact of instruction. Second, all post-intervention data were collected immediately following children’s participation in the intervention. Therefore, we were unable to determine if self-regulation may have yielded the hypothesized effect on academic outcomes over a longer period. Future studies would benefit from longer-term follow-up data to examine possible effects. Finally, it is possible that the distribution of HTKS scores (i.e., some children scoring near floor of measure) could have weakened potential moderation effects. This seems an unlikely explanation, however, because the HTKS and not the CTRS yielded the statistically significant moderation effects, and the CTRS was normally distributed.

Summary and Conclusion

To our knowledge, this study and the study by Bierman, Nix et al. (2008) are the only studies to date that have examined the moderating effect of self-regulation on the impacts of academic instruction in an experimental study. Contrary to our hypothesis, the results of this study provided little to no evidence that self-regulation moderated the impacts of the interventions. Hundreds of studies have reported significant associations between self-regulation and academic outcomes (e.g., see meta-analyses by Allan et al., 2014, Jacob & Parkinson, 2015; Peng et al., 2016, 2018; Spiegel et al., 2021). Whereas the correlational studies are often interpreted as implying that higher self-regulation leads to better academic outcomes, the results of this study, results of interventions on components of EF (Melby-Lervåg et al., 2016), and intervention studies that can isolate the effects of self-regulation intervention from the effects of academic instruction (e.g., Clements et al., 2020; Duncan et al., 2018; Lonigan et al., 2015) do not support a causal role of self-regulation in the development of academic competencies either directly or via an impact on instructional effectiveness. Regardless of its potential causal role in the development of academic skills, self-regulation is an important skill due to its predictive links with other important developmental outcomes, including social competence (e.g., McKown et al. 2009; Ros & Graziano, 2018) and some forms of psychopathology (e.g., Pennington & Ozonoff, 1996), and it can, therefore, be an important indicator of future development.

Supplementary Material

1

Highlights.

  • Preschooler’s self-regulation did not moderate early literacy interventions’ effects.

  • Consistent results with self-regulation assessed as executive function or attention.

  • Effective instruction was effective regardless of children’s self-regulation.

Acknowledgments

This research was supported by a grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (HD052120). The views expressed herein are those of the authors and have not been reviewed or approved by the granting agency.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Contributor Information

Christopher J. Lonigan, Department of Psychology and the Florida Center for Reading Research, Florida State University

Eric D. Hand, Department of Psychology, Florida State University

Jamie A. Spiegel, Department of Psychology, Florida State University

Brittany M. Morris, Department of Psychology, Florida State University

Colleen M. Jungersen, Department of Psychology, Florida State University

Sarah V. Alfonso, Department of Psychology, Florida State University

Beth M. Phillips, Department of Educational Psychology and Learning Systems and the Florida Center for Reading Research, Florida State University Florida State University.

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