Abstract
Objective:
The objective was to determine the proportion of trait (consistency across occasions) and occasion-specific variance in sluggish cognitive tempo (SCT), attention-deficit/hyperactivity disorder (ADHD)-inattention (IN), ADHD-hyperactivity/impulsivity (HI), and oppositional defiant disorder (ODD) symptom ratings.
Methods:
A single trait factor-multiple state factors model was applied to parent ratings of SCT, ADHD-IN, ADHD-HI, and ODD symptoms for 978 children (50% female) across prekindergarten (M= 4.90 years), kindergarten (M = 6.27 years), first (M = 7.42 years), second (M = 8.45 years), and fourth grade (M = 10.45 years) assessments.
Results:
For the prekindergarten assessment, SCT, ADHD-IN, ADHD-HI, and ODD contained more occasion-specific than trait variance (54%, 64%, 56%, and 55% occasion-specific variance, respectively). In contrast, SCT, ADHD-IN, ADHD-HI, and ODD contained more trait than occasion-specific variance for the kindergarten through fourth grade assessments (62% to 72%, 65% to 68%, 71% to 75%, and 60% to 69% trait variance, respectively).
Conclusions:
SCT, ADHD-IN, ADHD-HI, and ODD are slightly to moderately more state-like than trait-like during the prekindergarten developmental period but are more stable traits than fluctuating states from kindergarten to fourth grade. Findings indicate that, particularly after children start formal schooling, these psychopathology dimensions are primarily stable traits; implications for assessment are discussed.
Attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and sluggish cognitive tempo (SCT) are believed to be more trait- than state-like constructs (e.g., Becker, Leopold, et al., 2016; Lahey & Waldman, 2017; Willcutt et al., 2012). One feature of a trait-like construct is stability across measurement occasions. Although moderate to high test-retest correlations for ADHD, ODD, and SCT provide evidence for their trait-like nature, such correlations between observed variables are attenuated due to measurement error. Therefore, they may underestimate the trait-like nature of the constructs.
Latent state-trait (LST) models separate random measurement error from true score variance and allow a direct test of a construct’s trait- or state-like nature (Geiser, Hintz, Burns, & Servera, 2017; Geiser, Hintz, Burns, & Servera, in press; Steyer, Mayer, Geiser, & Cole, 2015). LST models allow one to determine the proportion of the true score variance that is due to trait or occasion-specific variance in symptom ratings across occasions. According to the LST-revised theory (Steyer et al., 2015), trait variance is the portion of true score variance that is purely person-specific and independent of situational and/or person-situation interaction effects at the given measurement occasion. In contrast, occasion-specific variance is the portion of true score variance that can be attributed to situational and/or person-situation interaction effects at the same time point. In other words, trait variance is variance that represents individual differences that remain stable across time, whereas occasion-specific variance refers to variability in true scores due to time-specific events or person-situation interactions. Accordingly, LST models are well-suited for directly examining whether SCT, ADHD-inattention (IN), ADHD-hyperactivity/impulsivity (HI), and ODD symptoms are more trait or occasion-specific constructs. We now review recent applications of LST models to these psychopathology symptoms to provide a framework for the current study.
SCT, ADHD-IN, ADHD-HI, and ODD: Trait- or State-Like Constructs?
Three studies used LST models to determine the amount of trait and occasion-specific variance in SCT, ADHD-IN, ADHD-HI, and ODD with Spanish children (Litson et al., 2016; Preszler, Burns, Litson, Geiser, & Servera, 2017a, Preszler et al., 2017b). SCT, ADHD-IN, ADHD-HI, and ODD constructs were more trait- than state-like constructs from the first to second grade. For mothers’ ratings, the mean trait variance in SCT, ADHD-IN, ADHD-HI, and ODD was 75%, 74%, 78%, and 67%, respectively, with the values for fathers’ ratings being 77%, 74%, 74%, and 65%. Similar estimates were found for teachers’ ratings.
Although these findings indicated that these constructs are more trait- than state-like constructs, two limitations were the short one-year interval and narrow age range. It is unknown if these constructs would be trait- or state-like at younger and older ages, especially at younger ages when development and behaviors are rapidly changing and perhaps more influenced by contextual events. Moreover, there is greater inter-individual variability in early childhood development, and the current DSM largely lacks a careful consideration of developmental manifestations of psychopathology (Frick & Nigg, 2012; Wakschlag, Tolan, & Leventhal, 2010). It is thus important to determine the amount of trait and occasion-specific variance in SCT, ADHD, and ODD across a broader developmental span than the prior studies and especially at younger ages because of implications for assessment and therapy. Furthermore, the trait- and state-like nature of these constructs must be evaluated among children from different cultural backgrounds to assess the generalizability of earlier results.
Objectives
Parent ratings of SCT, ADHD-IN, ADHD-HI, and ODD symptoms across five assessments (prekindergarten to fourth grade) with children from the United States were used to determine if the symptom dimensions represent more trait- or occasion-specific constructs. It was predicted that the constructs would contain more trait than occasion-specific variance for the first through fourth grade assessments given earlier results with Spanish children. It was unknown, however, if the constructs would be more trait- or state-like for the prekindergarten and kindergarten assessments. It was possible for the constructs to be more state-like at the youngest assessments and then more trait-like constructs from the first to four grades. It was also possible for the constructs to be more trait-like even at the earlier assessments.
Method
Participants and Procedures
Participants were 978 children drawn from 224 monozygotic and 265 dizygotic twin pairs (50% female). There were 965, 904, 884, 900, and 891 children present at prekindergarten, kindergarten, first, second, and fourth grade assessments. The twins were first assessed during the summer prior to kindergarten (Mage = 4.90, SD = .19) with the subsequent assessments occurring during the summers after kindergarten (Mage = 6.27, SD = .31), first (Mage = 7.42, SD = .32), second (Mage = 8.45, SD = .31) and fourth grades (Mage = 10.45, SD = .32). There were 13 children without SCT, ADHD, or ODD symptom ratings at the prekindergarten assessment although these children could have symptom ratings at subsequent assessments.
Approximately 85% of mothers and 83% of fathers identified as White with most mothers (61%) and fathers (54%) having completed 13–16 years of education. All participants were part of the Colorado component of the International Longitudinal Twin Study of Early Reading Development (ILTSERD; Christopher et al., 2015). The Colorado twins were recruited from the Colorado Twin Registry, a registry based on birth records that includes information on over 90% of all twin births in Colorado (Christopher et al., 2015).
Measures
Parents (90% to 95% mothers) completed the Disruptive Behavior Rating Scale (DBRS; Barkley & Murphy, 1998) at each of five assessments. The DBRS measures 18 ADHD and 8 ODD symptoms. Parents rate occurrence of the symptoms for the past 6-months on a 4-point rating scale (0=never or rarely, 1=sometimes, 2=often, and 3=very often). At each assessment, seven potential SCT symptoms were also rated by parents. Five of these seven SCT symptoms showed substantial loadings on the SCT factor and higher loadings on the SCT factor than the ADHD-IN factor at each assessment. These five SCT symptoms defined the SCT construct (i.e., [1] sluggish, slow to respond, [2] seems to be in a fog, [3] drowsy or sleepy, [4] easily confused and [5] daydreams, stares into space). Internal consistency values for SCT, ADHD-IN, ADHD-HI, and ODD scores varied from .70 to .80, .89 to .91, .86 to .89, and .89 to .90, respectively, across the five assessments.
Analyses
Analytic Strategy.
Full information robust maximum likelihood estimation was used for analyses (MLR estimator; Mplus statistical software version 8.0, Muthén & Muthén, 1998–2017). This estimator does not delete any participants with missing information and also accounts for any non-normality in ratings. The comparative fit index (CFI, ideal study criterion ≥ .95), root mean square error of approximation (RMSEA, ideal study criterion ≤ .05), and standardized root mean square residual (SRMR, ideal study criterion ≤ .05) were used to evaluate the models. Given twins were nested within families, Mplus type = complex option was used to correct the standard errors.
The invariance analysis across the five occasions used a decrease in CFI to evaluate the invariance of constraints on like-indicator loadings and intercepts. If the decrease in CFI value was less than .01 with the introduction of constraints, then constraints were considered tenable (Little, 2013). Statistical tests, however, were used to evaluate the equality of factor means across the five occasions.
Preliminary analyses on individual symptoms to justify the creation of parcels.
Longitudinal confirmatory factor analyses (CFA) were first performed on individual symptoms to determine if like-symptoms had statistically equivalent loadings across the five assessments for each symptom set. These analyses treated the symptoms as categorical indicators and used the Mplus WLSMV estimator. If constraints on like-symptom loadings hold across the five assessments, then there is one set of unstandardized loadings rather than five unique sets of loadings for each symptom dimension. The single set of symptom loadings for each symptom dimension was then used to assign the individual symptoms to parcels. Parcels were used to reduce model complexity (Little, 2013).
Symptoms were assigned to parcels with a procedure designed to increase the likelihood of homogenous parcels (Little, 2013). For ADHD-IN and ADHD-HI factors, each factor was defined by three parcels with three symptoms in each parcel with each parcel containing the same three symptoms at each measurement occasion. For the ODD factor, the factor was defined by three parcels with three ODD symptoms in the first and second parcels and two ODD symptoms in the third parcel. Because the SCT factor was measured with five SCT symptoms, the first and second parcels contained two SCT symptoms with the third indicator defined by a single SCT symptom. Table S1 shows the symptoms in each parcel.
Psychometric properties of SCT, ADHD-IN, ADHD-HI, and ODD parcels.
CFA was used to determine the invariance of like-indicator loadings and intercepts with parcels across the five assessments for each symptom dimension separately. Measurement invariance of like-indicator loadings and intercepts across occasions is a requirement for LST analyses. Figure 1 shows this model.
Figure 1.
Longitudinal confirmatory factor analytic model for the five measurement occasions with indicator-specific (IS) factors for indicators two and three.
LST Analyses.
Figure 2 shows the single trait factor-multiple state factors model with two indicator-specific factors along with autoregressions between temporally-adjacent state factors (Steyer & Schmitt, 1994). This model was applied separately to SCT, ADHD-IN, ADHD-HI, and ODD to determine the amount of true score variance in the five state factors due to trait consistency and occasion-specificity. The two indicator-specific factors account for systematic variance associated with use of the same indicator across five occasions (one indicator specific factor for indicator two and one indicator-specific factor for indicator three). Figure 2 also includes autoregression effects between state factors. Autoregressive effects allow one to determine if the construct becomes increasingly trait-like across the assessments and also allow for change in factor means more flexible than linear change. Geiser et al. (2017, in press) and Prenoveau (2016) provide a lengthier description of this LST model.
Figure 2.
Single trait factor-multiple state factor model for the five measurement occasions with indicator-specific (IS) factors for indicators two and three and autoregressive effects between adjacent state factors. OC = occasion-specific variance component.
Results
Missing Information
Covariance coverage for SCT, ADHD-IN, ADHD-HI, and ODD indicators varied from .84 to .99, .83 to .98, .84 to .99, and .81 to .85, respectively. As noted above, the robust maximum likelihood estimator does not eliminate any cases with missing information.
Measurement Models
Invariance of individual symptoms.
Longitudinal CFA on individual symptoms indicated a close fit for SCT, ADHD-IN, ADHD-HI, and ODD for the five assessments (CFI range: .969, .994; RMSEA range: .014, .026). Constraints on like-symptom loadings across the five assessments also yielded close fit with no decrease in CFI value greater than .006. The single set of unstandardized symptom-factor loadings for the five assessments for each symptom set was therefore used to assign symptoms to parcels as described earlier.
Invariance of symptom parcels.
Table 1 shows invariance analyses for SCT, ADHD-IN, ADHD-HI, and ODD. Configural models provided a close fit to the data (CFI range: .995, .999; RMSEA range: .008, .031; and SRMR range: .015, .032), and support was also found for the invariance of like-indicator loadings and intercepts (i.e., no decrease in CFI greater than .005).
Table 1.
Goodness of Fit Statistics for Invariance Analyses over Five Assessments for the Four Symptom Dimensions
| Invariance level | df | χ2 | CFI | SRMR | RMSEA (90% CI) |
|---|---|---|---|---|---|
| Sluggish Cognitive Tempo | |||||
| Configural | 69 | 73.55ns | .998 | .026 | .008 (.000, .021) |
| Metric | 77 | 83.72ns | .997 | .031 | .009 (.000, .021) |
| Scalar | 85 | 99.27ns | .994 | .032 | .013 (.000, .023) |
| ADHD-Inattention | |||||
| Configural | 69 | 92.81* | .997 | .015 | .019 (.006, .028) |
| Metric | 77 | 107.91* | .997 | .020 | .020 (.010, .029) |
| Scalar | 85 | 122.95* | .996 | .020 | .021 (.012, .029) |
| ADHD-Hyperactivity/Impulsivity | |||||
| Configural | 69 | 106.99* | .995 | .021 | .024 (.014, .032) |
| Metric | 77 | 122.69* | .994 | .024 | .025 (.016, .033) |
| Scalar | 85 | 165.01* | .989 | .032 | .031 (.024, .038) |
| Oppositional Defiant Disorder | |||||
| Configural | 69 | 82.24* | .999 | .014 | .014 (.000, .024) |
| Metric | 77 | 103.53* | .997 | .019 | .019 (.007, .028) |
| Scalar | 85 | 128.05* | .995 | .019 | .023 (.020, .031) |
Note. Configural invariance = no constraints; Metric invariance = constraints on like-indicator loadings; Metric invariance = constraints on like-indicator loadings and intercepts
= non-significant; CFI = comparative fit index; SRMR = standardized root mean square residual; RMSEA = root mean square error of approximation; CI = confidence interval.
p < .05.
Factor means.
Table 2 shows SCT, ADHD-IN, ADHD-HI, and ODD factor means for the five assessments. Chi-square difference tests indicated that the model with means constrained equal fit worse than the model with like-indicator loadings and intercepts held equal (ps < .002). Cohen’s latent d values (absolute) for ten comparisons among the five SCT means ranged from .04 to .22. The d values for ADHD-IN, ADHD-HI, and ODD mean comparisons ranged from .02 to .24, .09 to .75, and .00 to .16, respectively. Differences among the SCT, ADHD-IN, and ODD means reflected trivial to small effects with these changes also not reflecting a clear systematic decrease or increase across assessments. For the ADHD-HI factor means, nearly all the decrease was between the prekindergarten assessment and the other four assessments (i.e., a moderate decrease from the prekindergarten assessment to the other four assessments, d values from .44 to .75) with the second largest decrease being from the kindergarten to the fourth grade (d value = .30) with d values for the other comparisons between .10 and .20 (see also Leopold et al., 2016). Given that change in the factor means did not reflect a clear linear decrease or increase for SCT, ADHD-IN, ADHD-HI, and ODD, it was expected that the LST model with autoregression effects would account for these trait changes better than a linear change model (i.e., autoregressions allow for a flexible pattern of factor mean changes, see Geiser et al., 2017, in press). This expectation, however, was subjected to formal tests.
Table 2.
SCT, ADHD-IN, ADHD-HI, and ODD Factor Means, Standard Errors, and Standard Deviations for the Five Assessments
| Pre-Kindergarten | Kindergarten | First Grade | Second Grade | Fourth Grade | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Factor | M | SE | SD | M | SE | SD | M | SE | SD | M | SE | SD | M | SE | SD |
| SCT | 0.15 | 0.01 | 0.23 | 0.14 | 0.01 | 0.24 | 0.13 | 0.01 | 0.22 | 0.16 | 0.01 | 0.27 | 0.19 | 0.01 | 0.31 |
| ADHD-IN | 0.67 | 0.02 | 0.47 | 0.56 | 0.02 | 0.44 | 0.57 | 0.02 | 0.49 | 0.59 | 0.02 | 0.48 | 0.64 | 0.02 | 0.53 |
| ADHD-HI | 0.78 | 0.02 | 0.51 | 0.56 | 0.02 | 0.47 | 0.51 | 0.02 | 0.47 | 0.47 | 0.02 | 0.45 | 0.42 | 0.02 | 0.44 |
| ODD | 0.66 | 0.02 | 0.47 | 0.60 | 0.02 | 0.49 | 0.58 | 0.02 | 0.49 | 0.58 | 0.02 | 0.50 | 0.58 | 0.02 | 0.50 |
Note. Ratings occurred on a 0 to 3-point scale (i.e., 0=never or rarely, 1=sometimes, 2=often, and 3=very often). The text provides information on the significance tests on the factor means.
Factor correlations.
Factor correlations among the five measurement occasions for SCT ranged from .50 (prekindergarten to fourth grade) to .77 (first to second grade). ADHD-IN, ADHD-HI, and ODD factor correlations ranged from .45 (prekindergarten to fourth grade) to .75 (second to fourth grades), .55 (prekindergarten to fourth grade) to .77 (second to fourth grade), and .49 (prekindergarten to fourth grade) to .73 (first to second grade), respectively.
Latent State-Trait Analyses
Model fit.
The LST model resulted in a close fit for SCT, ADHD-IN, ADHD-HI, and ODD (CFI range: .983, .992; RMSEA range: .021, .031; and SRMR range: .025, .041).
Indicator state factor loadings and reliabilities.
The standardized indicator state factor loadings for ADHD-IN, ADHD-HI, and ODD for the five occasions were substantial (ADHD-IN range: .80, .91; ADHD-HI range: .83, .92; and ODD range: .82, .94) with values for SCT slightly lower (SCT range: .52 to .88). Table 2 shows the reliabilities for the three indicators for each factor.
Homogeneity of indicators.
Indicators for SCT, ADHD-IN, ADHD-HI, and ODD were mostly homogeneous over the five assessments. Indicator-specific effects accounted for only a small amount of observed score variance for ADHD-IN (8% to 14%), ADHD-HI (8% to 15%), and ODD (7% to 12%). The indicator-specific variance was slightly larger for SCT (i.e., 15% to 26%). These percentages indicate the amount of indicator variance due to indicator-specific effects that was not shared with the first indicator.
Trait and occasion-specific variance in state factors.
SCT, ADHD-IN, ADHD-HI, and ODD state factors had moderate to substantial standardized loadings on their respective trait factors (SCT: range: .49, 71; ADHD-IN: range: .54, 66; ADHD-HI: .66, 76; and ODD: range: .65, .68). The standardized autoregressions between temporally-adjacent state factors were also statistically (ps < .05) significant (SCT: range = .15, .34; ADHD-IN: range = .22, .32; ADHD-HI: range = .10, .20; and ODD: range = .15, .20). The four constructs thus became increasingly trait-like across occasions.
Table 2 shows the trait and occasion-specific variance for SCT, ADHD-IN, ADHD-HI, and ODD. For the prekindergarten assessment, SCT, ADHD-IN, ADHD-HI, and ODD contained a higher percentage of occasion-specific than trait variance. The proportion of occasion-specific variance was approximately 55% for SCT, ADHD-HI, and ODD and 64% for ADHD-IN. In contrast, for kindergarten to fourth grade assessments, SCT, ADHD-IN, ADHD-HI, and ODD contained a higher proportion of trait variance--SCT range: 62%, 72%, ADHD-IN range: 65%, 68%, ADHD-HI range: 71%, 75%, and ODD range: 60%, 69%.1,2
Discussion
Three earlier studies used LST models to determine the amount of trait and occasion-specific variance in ratings of SCT, ADHD-IN, ADHD-HI, and ODD symptoms among Spanish children over a 12-month interval from first to second grade (Litson et al., 2016; Preszler et al., 2017a, b). Although these studies found the constructs to contain substantially more trait than occasion-specific variance, a major limitation was the narrow age range and short longitudinal span. It was thus unknown if the four constructs contain more trait than occasion-specific variance at younger and older ages, as well as among children of a different nationality. It was especially important to determine if the constructs were more trait or occasion-specific at younger ages as such has implications for assessment and diagnosis.
At the prekindergarten assessment, SCT, ADHD-HI, and ODD contained a slightly higher percentage of occasion-specific than trait variance (54% to 56% occasion-specific variance) with ADHD-IN being the most state-like construct at this first assessment (64% occasion-specific variance). In contrast to the prekindergarten assessment, the four constructs contained a substantial amount of trait variance across kindergarten to fourth grade assessments. These findings indicate that SCT, ADHD-IN, ADHD-HI, and ODD are more stable traits than fluctuating states from kindergarten through fourth grade with the constructs being slightly to moderately more state-like at the prekindergarten assessment. The constructs thus transitioned from state- to trait-like between prekindergarten and kindergarten.
Clinical Implications
Given the more occasion-specific nature of the constructs at the prekindergarten assessment, especially for ADHD-IN symptoms, it is important to consider these findings within the context of assessments with young children. Although SCT is not a recognized diagnosis, it can be reliability assessed in young children (Lee, Burns, & Becker, 2017), though more studies are certainly needed to examine the developmental course of SCT, whether certain aspects of SCT are more clinically concerning or developmental normative in early childhood, and whether developmentally-sensitive items would aid in assessment.
Our findings cannot speak to whether ADHD and ODD can be diagnosed in preschool-aged children, and indeed there is consensus that these disorders can be diagnosed in younger children when a careful and thorough assessment is conducted, though there have also been calls for more developmentally-sensitive diagnostic tools and algorithms (Frick & Nigg, 2012; Wakschlag et al., 2010). Our findings do underscore the importance of a careful assessment of childhood psychopathology, certainly in preschool but also across the developmental span as none of the symptom dimensions came close to 100% trait consistency at any of the measurement occasions. More intensive assessments that incorporate interviews and/or observations may be especially important in certain developmental periods such as preschool (Dirks, De Los Reyes, Briggs-Gowan, Cella, & Wakschlag, 2012).
Our study included a single measure of symptom ratings collected from a single informant (typically the mother), with assessments spaced 1–2 years apart. For a clinical evaluation it would certainly be important to gather information from multiple sources in multiple settings (De Los Reyes & Langer, 2018; Steiner et al., 2007), and gathering information across short-intervals (e.g., one or two weeks) might provide a better understanding of the influences of sources, settings, and occasions on the behaviors, thereby affording a more comprehensive assessment and careful case conceptualization and treatment plan. In addition, the quality of the behaviors displayed, rather than their presence, is especially important to attend to when assessing psychopathology in preschool-aged children. For instance, the occurrence of defiance is normative in preschool-aged children, but exaggerated, extreme, and indiscriminate defiance may be an indicator for clinical concern. In considering SCT, although daydreaming itself is not pathological and is beneficial for play, imagination, and creativity, the duration, intensity, and content of daydreams may be especially important for clinical assessment and discrimination. Considered together, our findings provide additional support for the importance of these recommendations to carefully examine the nature and course of behavior and attention problems among preschool-aged children.
Research Implications
LST models offer additional advantages for understanding the construct validity of SCT, ADHD, and ODD. First, separation of true score variance in SCT, ADHD, and ODD symptoms into trait and occasion-specific variance allows for identification of predictors and outcomes of trait variance in the constructs (Becker, Burns, Leopold, Olson, & Willcutt, 2018). Such analyses, for example, could determine the time-invariant predictors of trait variance. Second, with the inclusion of time-varying predictions (i.e., additional measurements at each measurement occasion such as contextual influences), one can determine if such measures predict occasion-specific variance. As one example, if maternal mood was measured simultaneously with symptoms at each assessment, then research could determine if mood predicted individual differences in occasion-specific deviations from trait scores. Single source LST models allow for the determination of the correlates of trait and occasion-specific variance, therefore providing a better understanding of the construct validity of symptom ratings.
Limitations
An important limitation of the study was the use of a single source. With multiple sources, one can determine if the amount of trait and occasion-specific variance replicates across sources. In addition, with multiple source LST models one can determine if trait and occasion-specific variance shows convergent validity across sources. Another limitation is the need to determine if our findings would replicate with clinical samples (Would the percentage of trait variance be greater among clinical samples? see Seijas et al., 2018). An additional limitation was that the number of SCT symptoms was fewer than currently used to measure SCT (Barkley, 2013; Becker et al., 2017; Sáez et al., 2018). Our SCT symptoms, however, are all contained within current SCT symptom sets and include three central aspects of SCT (daydreaming, mental confusion, and drowsiness/slowness). Our SCT findings should thus extend to the larger set of SCT symptoms. In spite of these limitations, this study demonstrated that parent-rated SCT, ADHD-IN, ADHD-HI, and ODD transitioned from state- to trait-like constructs between prekindergarten and kindergarten with the constructs being substantially trait-like from kindergarten through fourth grade.
Supplementary Material
Table 3.
Trait Consistency, Occasion-Specificity, and Reliability Estimates from Latent State-Trait Analyses on the Four Symptom Dimensions
| Assessment | Trait Consistency | Occasion-Specificity | Reliability of Three Indicators | ||
|---|---|---|---|---|---|
| Sluggish Cognitive Tempo | |||||
| Pre-Kindergarten | .46 | .54 | .58 | .44 | .64 |
| Kindergarten | .72 | .28 | .67 | .51 | .67 |
| First Grade | .70 | .30 | .56 | .55 | .66 |
| Second Grade | .71 | .29 | .72 | .56 | .71 |
| Fourth Grade | .62 | .38 | .77 | .64 | .67 |
| ADHD-Inattention | |||||
| Pre-Kindergarten | .36 | .64 | .75 | .79 | .81 |
| Kindergarten | .65 | .35 | .80 | .78 | .80 |
| First Grade | .67 | .33 | .83 | .83 | .82 |
| Second Grade | .68 | .32 | .80 | .80 | .86 |
| Fourth Grade | .67 | .33 | .82 | .82 | .86 |
| ADHD-Hyperactivity/Impulsivity | |||||
| Pre-Kindergarten | .44 | .56 | .71 | .79 | .80 |
| Kindergarten | .72 | .28 | .77 | .83 | .83 |
| First Grade | .71 | .29 | .77 | .85 | .86 |
| Second Grade | .75 | .25 | .74 | .83 | .87 |
| Fourth Grade | .73 | .27 | .84 | .84 | .86 |
| Oppositional Defiant Disorder | |||||
| Pre-Kindergarten | .45 | .55 | .82 | .78 | .77 |
| Kindergarten | .66 | .34 | .87 | .81 | .78 |
| First Grade | .69 | .31 | .84 | .85 | .80 |
| Second Grade | .68 | .32 | .84 | .82 | .83 |
| Fourth Grade | .60 | .40 | .84 | .87 | .82 |
Note. The reliability values are for the three indicators for each factor at each occasion.
Footnotes
Analyses determined if a second order linear latent growth curve (LGC) model would provide a better fit than the LST model (i.e., Would a linear change function describe the change in the factor means better than the autoregressions?). For ADHD-IN, ADHD-HI, and ODD, the BIC value was smaller for the LST model, thus the LST model was the better choice. For SCT, the BIC was smaller for the LGM model (Mslope = 0.008, SEslope = .002, p < .001). This positive linear slope effect represented a small effect and it was mainly due to the increase in the SCT mean at the last occasion of measurement (see also Leopold et al., 2016).
The four LST analyses were repeated by randomly selecting one twin from each twin pair (n = 489). The percentage of trait variance for each occasion was identical for ADHD-IN and within three percentage points for each occasion for ADHD-HI and ODD. For SCT, occasion one was identical, occasion two 7% more trait variance, occasion three 5% more trait variance, occasion four 15% more trait variance, and occasion five 2% less trait variance. Similar percentages of trait variance occurred when the four analyses were repeated on the other twin within the twin pair except the trait variance for SCT was similar to the trait variance for the total sample in Table 3 rather than the slight increase for the first twin.
Contributor Information
G. Leonard Burns, Department of Psychology, Washington State University.
Stephen P. Becker, Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center and Department of Pediatrics, University of Cincinnati College of Medicine
Christian Geiser, Department of Psychology, Utah State University.
Daniel R. Leopold, Department of Psychology and Neuroscience, University of Colorado Boulder
Erik G. Willcutt, Department of Psychology and Neuroscience, University of Colorado Boulder
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