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The current longitudinal study measured 279 kindergartners' (M[subscript age] = 5.76 years; SD = 0.55; 135 females) place value understanding using both approximate and syntactic measures based on previous evidence that early variation in performance on approximate measures was associated with subsequent syntactic place value understanding. In the present study, we used the same dataset but traced kindergarten variation on both approximate and syntactic measures to children's multidigit calculation skill in second grade. Path analyses indicated that latent approximate place value knowledge predicted later multidigit calculation performance but more precise understanding of counts and base-ten units did not. However, among the individual task predictors, Base-Ten Counting was the strongest predictor followed by a task asking for specific count of units (e.g. 'Which number has ["two tens"]?') and reading and writing number names (Transcoding). Thus, multidigit calculation skills appear to develop from an emerging, but imprecise understanding of count + unit syntax.

Place value concepts were measured longitudinally from kindergarten (2017) to first grade (2018) in a diverse sample (n = 279; M[subscript age] = 5.76 years, SD = 0.55; 135 females; 41% Black, 38% White, 8% Asian, 12% Latino). Children completed three syntactic tasks that required an explicit understanding of base-10 symbols and three approximate tasks that could be completed without this explicit understanding. Approximate performance was significantly better in both age groups. A factor analysis confirmed that syntactic and approximate tasks tapped separate latent variables in kindergarten, but not in first grade. Path analyses indicated that only kindergarten approximate performance predicted overall first-grade place value understanding. These findings suggest that explicit understanding of base-10 principles develops from implicit, partial knowledge of multidigit numbers.

Historically it has been reported that deaf students do not achieve age-appropriate outcomes in reading, with this performance often being characterized in terms of a fourth grade ceiling. However, given the shifts in the field during the past 20 years (e.g., widespread implementation of newborn hearing screening, advances in hearing technologies), it would be timely to question whether this continues to serve as a meaningful benchmark. To this end, the purpose of this study was to investigate reading outcomes of a Canadian cohort of school-aged deaf learners (N = 70) who all used listening and spoken language as the primary mode of communication. Specifically, the goal was to establish whether their achievement approached that of their hearing age peers and to identify demographic factors influencing performance (i.e., gender, unilateral/bilateral hearing loss, personal amplification, level of auditory functioning, grade placement, additional disabilities, home language). Results indicate that participants obtained standard scores in the average range on both the Basic Reading and Reading Comprehension clusters of the "Woodcock Johnson III-Diagnostic Reading Battery" (Woodcock et al., 2004), surpassing the fourth grade reading achievement ceiling often reported for this population.

One of the most vexing challenges facing developmental researchers today is the statistical modeling of two or more behaviors as they unfold jointly over time. Although quantitative methodologists have studied these issues for more than half a century, no widely agreed-upon principled strategy exists to empirically analyze codevelopmental processes. Indeed, the plethora of available options makes selecting a specific analytic approach both confusing and overwhelming. In this article, we argue that a key step in adjudicating among alternative modeling strategies is to embrace the concept of within- and between-person components of change over time. First, we define the disaggregation of effects in grouped data, and then we extend these concepts to repeated measures. Then we review several available modeling strategies that capture these effects to varying degrees and raise three issues that can help to guide practice.

The simple view of reading describes reading as the product of decoding (D) and listening comprehension (LC). However, the simple view of reading has been challenged, and evidence has proved it to be too simple to explain the complexities of reading comprehension in the elementary school years. Hypotheses have been advanced that there are cognitive-linguistic factors that underlie the common variance between D and LC, which are malleable, although there is no clarity at this point regarding what these are. We propose that one such group of malleable cognitive factors is executive function (EF) skills. Further, we posit that EF skills play equally strong roles in explaining reading comprehension variance in emergent bilinguals and English monolinguals. We used multigroup structural equation modeling to determine the contribution of these constructs (D, LC, and EF) to reading comprehension in 425 emergent bilinguals and 302 English monolinguals in grades 2-4. The shared variance between D and LC was explained by direct and indirect effects in the models tested, with strong indirect effects for the EFs of cognitive flexibility and working memory through D and LC, respectively, for both language groups. The indirect effect of cognitive flexibility through LC on reading comprehension was considerably larger for emergent bilinguals than for English monolinguals. Considerations for a more nuanced view of the simple view of reading and its implications for practice are discussed.

Having one's funding cut in the course of conducting a longitudinal study has become an increasingly real challenge faced by developmental researchers. The main purpose of the current work is to propose "post hoc" planned missing (PHPM) data designs as a promising solution in such difficult situations. This study discusses general guidelines that can be followed to search for viable PHPM designs within a given budget restriction. Illustrative examples across different longitudinal research contexts are provided, each showing how PHPM data designs can help salvage longitudinal studies when an unexpected funding cut occurs mid-study. With the illustrative examples, the article also shows how developmental researchers can conveniently identify viable designs using the R package "simPM."

Background and Objectives In this study, we investigated the efficacy of a self-administered, online Social Intelligence Training (SIT) program aimed at enhancing psychological and relational well-being among a nationwide U.S. sample of custodial grandmothers. Research Design and Methods A two-arm randomized clinical trial (RCT) was conducted, where 349 grandmothers raising grandchildren aged 11–18 years were assigned to either SIT or an attention control condition (ACC). Participants self-completed online surveys at baseline and immediately postintervention, in addition to follow-ups at 3-, 6-, and 9-month postintervention. First-order latent difference score models were used to compare SIT to ACC, across all times of measurement, along key indicators of psychological and relational well-being on an intent-to-treat basis. Results Although SIT was largely superior to ACC at yielding positive results, it appears that it attenuated longitudinal declines that occurred among ACC participants. SIT also exerted stronger effects on relational than psychological outcomes, with perceived relations with grandchildren being the most positively affected. Discussion and Implications Given that the historical time of this RCT unpredictably corresponded with the coronavirus disease 2019 pandemic, we suspect that SIT helped offset declines in psychological and relational well-being that are widely documented to have resulted from the pandemic. Our overall positive findings support future use of the inexpensive and easily delivered SIT program under normal environmental conditions, with the vulnerable and geographically disperse population of custodial grandmothers. Clinical Trials Registration Number NCT03239977 [ABSTRACT FROM AUTHOR]

Although there is a long tradition of research analyzing the grammatical complexity of texts (in both linguistics and applied linguistics), there is surprisingly little consensus on the nature of complexity. Many studies have disregarded syntactic (and structural) distinctions in their analyses of grammatical text complexity, treating it instead as if it were a single unified construct. However, other corpus-based studies indicate that different grammatical complexity features pattern in fundamentally different ways. The present study employs methods that are informed by structural equation modeling to test the goodness-of-fit of four models that can be motivated from previous research and linguistic theory: a model treating all complexity features as a single dimension, a model distinguishing among three major structural types of complexity features, a model distinguishing among three major syntactic functions of complexity features, and a model distinguishing among nine combinations of structural type and syntactic functions. The findings show that text complexity is clearly a multi-dimensional construct. Both structural and syntactic distinctions are important. Syntactic distinctions are actually more important than structural distinctions, although the combination of the two best accounts for the ways in which complexity features pattern in texts from different registers. [ABSTRACT FROM AUTHOR]

This report investigates the prediction of labor force status using observed variables, such as gender, age, and immigrant status, and more importantly, measured skill variables, including literacy proficiency and a categorical rating of educational attainment based on the 1994 International Adult Literacy Survey (IALS), the 2003 Adult Literacy and Life Skills Survey (ALL), and the 2011 Programme for the International Assessment of Adult Competencies (PIAAC) projects. We explored the regression relations in the past two decades for six trend countries and subnational regions that provide data for all assessments: the United States, Norway, the Netherlands, Italy, Canada's English-speaking region, and Canada's French-speaking region. Probit regression models with latent predictors were used in this cross-sectional study to investigate how those variables are structurally related to labor market outcomes. Results show the importance of literacy proficiency and education in determining individuals' labor force status across countries/regions, but with key differences among these countries/regions.

As an alternative to Cronbach's [alpha] for estimating scale reliability, McDonald's [omega] has attracted increased attention within the methodological community for its less stringent measurement assumptions. Notwithstanding, [omega] is still seldom used by practitioners, likely due to its unavailability in popular software packages (e.g., SPSS) and the need for confirmatory factor analysis (CFA) parameter estimates for its computation. This study proposes and evaluates an easily-calculated [omega] estimate that does not rely on factor analysis, which is shown to be highly comparable to CFA-based [omega] estimates and thus can be used as a more accessible computational alternative for practitioners, especially if programmed into popular software packages.

Fit indices are descriptive measures that can help evaluate how well a confirmatory factor analysis (CFA) model fits a researcher's data. In multigroup models, before between-group comparisons are made, fit indices may be used to evaluate measurement invariance by assessing the degree to which multiple groups' data are consistent with increasingly constrained nested models. One such fit index is an adaptation of the root mean square error of approximation (RMSEA) called RMSEAD. This index embeds the chi-square and degree-of-freedom differences into a modified RMSEA formula. The present study comprehensively compared RMSEAD to ΔRMSEA, the difference between two RMSEA values associated with a comparison of nested models. The comparison consisted of both derivations as well as a population analysis using one-factor CFA models with features common to those found in practical research. The findings demonstrated that for the same model, RMSEAD will always have increased sensitivity relative to ΔRMSEA with an increasing number of indicator variables. The study also indicated that RMSEAD had increased ability to detect noninvariance relative to ΔRMSEA in one-factor models. For these reasons, when evaluating measurement invariance, RMSEAD is recommended instead of ΔRMSEA. [ABSTRACT FROM AUTHOR]

Temperament growth has been examined in infancy, but the spectrum of reactive and regulatory dimensions was not previously considered. We evaluated linear and nonlinear growth trajectories for overarching factors and fine-grained indicators of infant temperament obtained via parent report (N = 143) at 4, 6, 8, 10, and 12 months of age. Contributions of infant sex, family socioeconomic status, maternal stress, depression, and anxiety to trajectory parameters were also considered. Results indicated nonlinear trajectories as best fitting for negative emotionality (quadratic model) and regulatory capacity/orienting (piecewise), with a linear model deemed most optimal for positive affectivity/surgency. However, models of best fit associated with the overarching temperament factors were not consistently representative of the underlying fine-grained dimensions. Results indicate primarily nonlinear growth of infant temperament across the first year of life and support the importance of fine-grained level analyses. Effects of infant sex, socioeconomic status, maternal stress, anxiety and depression symptoms were generally consistent with hypotheses.

Drawing upon recent developments in structural equation modeling, the current study presents an analytical framework for addressing research questions in which, rather than focusing on means, it is intraindividual (or intragroup) variability that is of direct research interest. Beyond merely serving as an alternative to existing multilevel modeling approaches, this framework allows for extensions to accommodate a variety of complex research scenarios by parameterizing variability as a latent variable that can in turn be embedded within a broader covariance and mean structure involving other observed and/or latent variables. The estimation procedures and parameter interpretation for the latent random variability models are discussed. The versatility of the proposed methods is demonstrated through four empirical examples. The Mplus, BUGS, and Stan model syntax for the illustrative examples are supplied to facilitate the application of the methods. In many research and applied settings across the social, behavioral, and health sciences, it is variability, rather than averages, that is of key interest. Examples include consistency/stability of an individual over multiple measurements (intraindividual variability), and cohesiveness among members within a group or team (intragroup variability). Drawing upon recent developments in structural equation modeling, the current study presents an analytical framework for addressing research questions that focus on intraindividual, or intragroup, variability. Beyond merely serving as an alternative to existing multilevel modeling approaches, this framework allows for extensions to accommodate a variety of complex research scenarios by parameterizing variability as a latent variable, which can be studied as the outcome, predictor, and/or mediators simultaneously in relation to other observed and/or latent variables. This study delineates the latent random variability models and offers a discussion of model estimation as well as parameter interpretation. To demonstrate the versatility of the proposed methods, the latent random variability models are fit to empirical data and parameter estimates are obtained via Bayesian estimation. The Mplus, BUGS, and Stan model syntax for the illustrative examples are supplied for applied researchers' reference. [ABSTRACT FROM AUTHOR]

Adult influenza vaccination rates remain suboptimal, particularly among African Americans. Social norms may influence vaccination behavior, but little research has focused on influenza vaccine and almost no research has focused on racially-specific norms. This mixed methods investigation utilizes qualitative interviews and focus groups (n = 118) and national survey results (n = 1643) to assess both descriptive and subjective norms surrounding influenza vaccination. Qualitative results suggest a perceived descriptive norm that "about half" of the population gets vaccinated. Participants describe differing norms by race and vaccine behavior. Quantitative results confirm a perceived descriptive norm that 40-60% of the population gets vaccinated. Both African Americans and Whites accurately identified race-specific vaccination rates relative to the general population. Individuals who report that a majority of people around them want them to be vaccinated were significantly more likely to be vaccinated, suggesting subjective norms are influential for both White and African American adults. While perceived descriptive norms are somewhat accurate (mirroring the actual influenza vaccination rate), emphasizing a suboptimal vaccination rate may not be beneficial. Health promotion efforts, particularly those targeting African Americans, may benefit from focusing on subjective norms and encouraging friends and family members to talk about the benefits of influenza vaccination.

Structured means analysis is a very useful approach for testing hypotheses about population means on latent constructs. In such models, a z test is most commonly used for testing the statistical significance of the relevant parameter estimates or of the differences between parameter estimates, where a z value is computed based on the asymptotic standard error estimate associated with the parameter of interest. In the current article, a series of population analyses demonstrate that the z tests for latent mean structure parameters or, more directly, the standard error estimates upon which those z tests are based are, not invariant to how factors are scaled. As such, circumstances exist in which latent mean inference is compromised solely as a result of scaling decisions. This problem is illustrated in the context of between-subjects (i.e., multisample) latent means models and within-subjects latent means models. Recommendations for practice are also offered.

For the two-way factorial design in analysis of variance, the current article explicates and compares three methods for controlling the Type I error rate for all possible simple interaction contrasts following a statistically significant interaction, including a proposed modification to the Bonferroni procedure that increases the power of statistical tests for deconstructing interaction effects when they are of primary substantive interest. Results indicate the general superiority of the modified Bonferroni procedure over Scheffé and Roy-type procedures, where the Bonferroni and Scheffé procedures have been modified to accommodate the logical implications of a false omnibus interaction null hypothesis. An applied example is provided and considerations for applied researchers are offered.

We present types of constructs, individual- and cluster-level, and their confirmatory factor analytic validation models when data are from individuals nested within clusters. When a construct is theoretically individual level, spurious construct-irrelevant dependency in the data may appear to signal cluster-level dependency; in such cases, however, and consistent with theory, a single-level analysis with a correction for dependency may be appropriate. Regarding cluster-level constructs, we discuss two types--shared and configural--and present appropriate validation models. Illustrative validation analyses with individual, shared, and configural constructs are provided using empirical data as well as simple simulations demonstrating the spurious effects that can occur with nested data. The article concludes with future directions to be examined in construct validation in multilevel settings.

Although differences in goodness-of-fit indices (?GOFs) have been advocated for assessing measurement invariance, studies that advanced recommended differential cutoffs for adjudicating invariance actually utilized a very limited range of values representing the quality of indicator variables (i.e., magnitude of loadings). Because quality of measurement has been found to be relevant in the context of assessing data-model fit in single-group models, this study used simulation and population analysis methods to examine the extent to which quality of measurement affects ?GOFs for tests of invariance in multiple group models. Results show that ?McDonald's NCI is minimally affected by loading magnitude and sample size when testing invariance in the measurement model, while differences in comparative fit index varies widely when testing both measurement and structural variance as measurement quality changes, making it difficult to pinpoint a common value that suggests reasonable invariance.