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This study evaluated measurement invariance across males and females on the Neurobehavioral Symptom Inventory (NSI) in U.S. military veterans enrolled in the VA Million Veteran Program. Participants (N = 17,059; males: n = 15,450; females: n = 1,609) included Veterans who took part in the VA Traumatic Brain Injury (TBI) Screening and Evaluation Program and completed the NSI. Multiple-group confirmatory factor analyses investigated measurement invariance of the NSI 4-factor model. The configural (comparative fit index [CFI] = 0.948, root mean square error of approximation [RMSEA] = 0.060) and metric (CFI = 0.948, RMSEA = 0.058) invariance models showed acceptable fit. There was a minor violation of scalar invariance (Δχ2 = 232.50, p <.001); however, the degree of noninvariance was mild (ΔCFI = −0.002, Δ RMSEA = 0. 000). Our results demonstrate measurement invariance across sex, suggesting that the NSI 4-factor model can be used to accurately assess symptoms in males and females following TBI. Findings highlight the importance of considering validity of measurement across study groups to increase confidence that a measure is interpreted similarly by respondents from different subgroups. [ABSTRACT FROM AUTHOR]

As student engagement in science learning continues to garner significant attention from educational researchers, practitioners, and policymakers, the importance of understanding person and context-related factors that impact student engagement is increasingly recognized. In this mixed methods study, middle school students' (N = 1,848) engagement was examined in relation to opportunities to participate in science learning (OtP in science) from 29 schools in the United States. Drawing on the ecological systems and opportunity-propensity frameworks, multilevel structural equation modeling was applied to test the relationship between OtP in science and student engagement, accounting for student (gender, grade, ethnicity) and school (socioeconomic status) factors. Results showed that at the student level, OtP in science was a statistically significant predictor of engagement, and this relationship varied by school. In addition, school SES predicted school-average OtP in science, and there was a statistically significant cross-level interaction for school SES, in that the relationship between OtP in science and engagement was stronger for schools with higher SES. In the qualitative phase, 6 student focus group interviews were analyzed to develop explanatory accounts for the quantitative results. Collaborative hands-on activities, peer-to-peer discourse, and positive teacher and student relationships were identified as social processes that facilitated students' propensity to engage in the science learning opportunities. Factors related to school SES that explain the link between OtP in science and student engagement are also described. Taken together, our findings provide a more complete picture of how students' engagement in science relates to person and context-related factors and hold theoretical and practical implications for the study of engagement in diverse classrooms.

Previous studies have detailed the consequence of ignoring a level of clustering in multilevel models with straightly hierarchical structures and have proposed methods to adjust for the fixed effect standard errors (SEs). However, in behavioral and social science research, there are usually two or more crossed clustering levels, such as when students are cross-classified by schools and neighborhoods, yet it is not uncommon that researchers focus only on one level of clustering. Using the generalized least squares framework, in this study, we derive the bias in the fixed effect SE estimators when one crossed random effect is omitted. We then showed, using data from the Scotland Neighborhood Study, how one can correct for the SEs and obtain corrected statistical inference when a misspecified two-level model was used in a primary study, which is useful when evaluating observational studies or cluster randomized trials that ignored a crossed random effects or when conducting meta-analyses. In addition, our analytic results provide theoretical insights on how one can quantify imbalance with cross-classified data by the strength of association between the two-crossed random effects in a contingency table and how the degree of imbalance relates to the correction factor for the fixed effect SEs.

With clustered data, such as where students are nested within schools or employees are nested within organizations, it is often of interest to estimate and compare associations among variables separately for each level. While researchers routinely estimate between-cluster effects using the sample cluster means of a predictor, previous research has shown that such practice leads to biased estimates of coefficients at the between level, and recent research has recommended the use of latent cluster means with the multilevel structural equation modeling framework. However, the latent cluster mean approach may not always be the best choice as it (a) relies on the assumption that the population cluster sizes are close to infinite, (b) requires a relatively large number of clusters, and (c) is currently only implemented in specialized software such as Mplus. In this paper, we show how using empirical Bayes estimates of the cluster means can also lead to consistent estimates of between-level coefficients, and illustrate how the empirical Bayes estimate can incorporate finite population corrections when information on population cluster sizes is available. Through a series of Monte Carlo simulation studies, we show that the empirical Bayes cluster-mean approach performs similarly to the latent cluster mean approach for estimating the between-cluster coefficients in most conditions when the infinite-population assumption holds, and applying the finite population correction provides reasonable point and interval estimates when the population is finite. The performance of EBM can be further improved with restricted maximum likelihood estimation and likelihood-based confidence intervals. We also provide an R function that implements the empirical Bayes cluster-mean approach, and illustrate it using data from the classic High School and Beyond Study. [ABSTRACT FROM AUTHOR]

Path models with observed composites based on multiple items (e.g., mean or sum score of the items) are commonly used to test interaction effects. Under this practice, researchers generally assume that the observed composites are measured without errors. In this study, we reviewed and evaluated two alternative methods within the structural equation modeling (SEM) framework, namely, the reliability-adjusted product indicator (RAPI) method and the latent moderated structural equations (LMS) method, which can both flexibly take into account measurement errors. Results showed that both these methods generally produced unbiased estimates of the interaction effects. On the other hand, the path model--without considering measurement errors--led to substantial bias and a low confidence interval coverage rate of nonzero interaction effects. Other findings and implications for future studies are discussed.

Measurement invariance holds when a latent construct is measured in the same way across different levels of background variables (continuous or categorical) while controlling for the true value of that construct. Using Monte Carlo simulation, this paper compares the multiple indicators, multiple causes (MIMIC) model and MIMIC-interaction to a novel use of alignment optimization (AO) for detecting measurement noninvariance when the violator is a continuous variable. Results showed that MIMIC and MIMIC-interaction in sequential likelihood ratio tests and Wald tests with a Bonferroni correction provided a good balance between identifying invariant and noninvariant (linear violations) items when n ≥ 500 in terms of classification accuracy (CA). AO (CA ≥ .86) was as competitive as MIMIC and MIMIC-interaction to linear invariance violations but was far better under nonlinear quadratic violations when n ≥ 1,000 (i.e., 100 per group for 10 groups). [ABSTRACT FROM AUTHOR]

Although letter naming fluency (LNF) and letter sound fluency (LSF) measures are widely available to educators for assessing early literacy skills of kindergarten children, better understanding of the contributions of these skills to reading development can help improve the interpretation of LNF and LSF data for instructional decisions. This study investigated the interrelations of growth in LNF and LSF across the kindergarten year and their unique roles in predicting later reading fluency. Piecewise parallel-process growth models indicated that although LNF and LSF were highly correlated at kindergarten entry, fall LNF status was positively predictive of LSF growth across the fall. Bidirectional effects were present, as initial LSF was also a positive predictor of LNF growth across the fall; however, its effects were not as strong as those of initial LNF on LSF growth. More importantly, both initial status and growth in LNF and LSF were uniquely predictive of first-grade reading fluency, indicating the independent effects of each on subsequent text reading skills. Indirect effects were also observed for kindergarten LNF and LSF growth on reading fluency in second and third grades. Implications for kindergarten assessment and instruction are discussed.

Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models (which differ in the location of the clustering effect). With a 3 (design effect) × 5 (cluster size) × 4 (number of clusters) Monte Carlo simulation study we found that the rule should not be applied when researchers: (a) are interested in the effects of higher-level predictors, or (b) have a cluster size less than 10. Implications of the findings and limitations of the study are discussed.

Key Points: Question: Is there an association between sedentary behavior and risk of all-cause dementia in older adults? Findings: In this retrospective study of prospectively collected data of 49 841 adults participating in the UK Biobank, more time spent in sedentary behaviors (determined through a machine learning–based analysis of wrist-worn accelerometer data) was significantly associated with higher risk of incident dementia. Meaning: Among older adults, more time spent in sedentary behaviors was associated with higher risk of incident all-cause dementia. Importance: Sedentary behavior is associated with cardiometabolic disease and mortality, but its association with dementia is unclear. Objective: To investigate whether accelerometer-assessed sedentary behavior is associated with incident dementia. Design, Setting, and Participants: A retrospective study of prospectively collected data from the UK Biobank including 49 841 adults aged 60 years or older without a diagnosis of dementia at the time of wearing the wrist accelerometer and living in England, Scotland, or Wales. Follow-up began at the time of wearing the accelerometer (February 2013 to December 2015) and continued until September 2021 in England, July 2021 in Scotland, and February 2018 in Wales. Exposures: Mean daily sedentary behavior time (included in the primary analysis) and mean daily sedentary bout length, maximum daily sedentary bout length, and mean number of daily sedentary bouts (included in the secondary analyses) were derived from a machine learning–based analysis of 1 week of wrist-worn accelerometer data. Main Outcome and Measures: Incident all-cause dementia diagnosis from inpatient hospital records and death registry data. Cox proportional hazard models with linear and cubic spline terms were used to assess associations. Results: A total of 49 841 older adults (mean age, 67.19 [SD, 4.29] years; 54.7% were female) were followed up for a mean of 6.72 years (SD, 0.95 years). During this time, 414 individuals were diagnosed with incident all-cause dementia. In the fully adjusted models, there was a significant nonlinear association between time spent in sedentary behavior and incident dementia. Relative to a median of 9.27 hours/d for sedentary behavior, the hazard ratios (HRs) for dementia were 1.08 (95% CI, 1.04-1.12, P <.001) for 10 hours/d, 1.63 (95% CI, 1.35-1.97, P <.001) for 12 hours/d, and 3.21 (95% CI, 2.05-5.04, P <.001) for 15 hours/d. The adjusted incidence rate of dementia per 1000 person-years was 7.49 (95% CI, 7.48-7.49) for 9.27 hours/d of sedentary behavior, 8.06 (95% CI, 7.76-8.36) for 10 hours/d, 12.00 (95% CI, 10.00-14.36) for 12 hours/d, and 22.74 (95% CI, 14.92-34.11) for 15 hours/d. Mean daily sedentary bout length (HR, 1.53 [95% CI, 1.03-2.27], P =.04 and 0.65 [95% CI, 0.04-1.57] more dementia cases per 1000 person-years for a 1-hour increase from the mean of 0.48 hours) and maximum daily sedentary bout length (HR, 1.15 [95% CI, 1.02-1.31], P =.02 and 0.19 [95% CI, 0.02-0.38] more dementia cases per 1000 person-years for a 1-hour increase from the mean of 1.95 hours) were significantly associated with higher risk of incident dementia. The number of sedentary bouts per day was not associated with higher risk of incident dementia (HR, 1.00 [95% CI, 0.99-1.01], P =.89). In the sensitivity analyses, after adjustment for time spent in sedentary behavior, the mean daily sedentary bout length and the maximum daily sedentary bout length were no longer significantly associated with incident dementia. Conclusions and Relevance: Among older adults, more time spent in sedentary behaviors was significantly associated with higher incidence of all-cause dementia. Future research is needed to determine whether the association between sedentary behavior and risk of dementia is causal. This study of prospectively collected data from the UK Biobank investigates whether accelerometer-assessed sedentary behavior is associated with incident dementia in adults aged 60 years or older without a diagnosis of dementia at the time of wearing the wrist accelerometer. [ABSTRACT FROM AUTHOR]

Objective The purpose of this study was to explore racial/ethnic differences in neurobehavioral symptom reporting and symptom validity testing among military veterans with a history of traumatic brain injury (TBI). Method Participants of this observational cross-sectional study (N  = 9,646) were post-deployed Iraq-/Afghanistan-era veterans enrolled in the VA's Million Veteran Program with a clinician-confirmed history of TBI on the Comprehensive TBI Evaluation (CTBIE). Racial/ethnic groups included White, Black, Hispanic, Asian, Multiracial, Another Race, American Indian or Alaska Native, and Native Hawaiian or Other Pacific Islander. Dependent variables included neurobehavioral symptom domains and symptom validity assessed via the Neurobehavioral Symptom Inventory (NSI) and Validity-10, respectively. Results Chi-square analyses showed significant racial/ethnic group differences for vestibular, somatic/sensory, and affective symptoms as well as for all Validity-10 cutoff scores examined (≥33, ≥27, ≥26, >22, ≥22, ≥13, and ≥7). Follow-up analyses compared all racial/ethnic groups to one another, adjusting for sociodemographic- and injury-related characteristics. These analyses revealed that the affective symptom domain and the Validity-10 cutoff of ≥13 revealed the greatest number of racial/ethnic differences. Conclusions Results showed significant racial/ethnic group differences on neurobehavioral symptom domains and symptom validity testing among veterans who completed the CTBIE. An enhanced understanding of how symptoms vary by race/ethnicity is vital so that clinical care can be appropriately tailored to the unique needs of all veterans. Results highlight the importance of establishing measurement invariance of the NSI across race/ethnicity and underscore the need for ongoing research to determine the most appropriate Validity-10 cutoff score(s) to use across racially/ethnically diverse veterans. [ABSTRACT FROM AUTHOR]

Measurement invariance research has focused on identifying biases in test indicators measuring a latent trait across two or more groups. However, relatively little attention has been devoted to the practical implications of noninvariance. An important question is whether noninvariance in indicators or items results in differences in observed composite scores across groups. The current study introduces the Bayesian region of measurement equivalence (ROME) as a framework for visualizing and testing the combined impact of partial invariance on the group difference in observed scores. Under the proposed framework, researchers first compute the highest posterior density intervals (HPDIs)-which contain the most plausible values-for the expected group difference in observed test scores over a range of latent trait levels. By comparing the HPDIs with a predetermined range of values that is practically equivalent to zero (i.e., region of measurement equivalence), researchers can determine whether a test instrument is practically invariant. The proposed ROME method can be used for both continuous indicators and ordinal items. We illustrated ROME using five items measuring mathematics-specific self-efficacy from a nationally representative sample of 10th graders. Whereas conventional invariance testing identifies a partial strict invariance model across gender, the statistically significant noninvariant items were found to have a negligible impact on the comparison of the observed scores. This empirical example demonstrates the utility of the ROME method for assessing practical significance when statistically significant item noninvariance is found. [ABSTRACT FROM AUTHOR]

Multilevel modeling techniques are becoming more popular in handling data with multilevel structure in educational and behavioral research. Recently, researchers have paid more attention to cross-classified data structure that naturally arises in educational settings. However, unlike traditional single-level research, methodological studies about multilevel effect size have been rare and those that have recently appeared had an emphasis on strictly hierarchical data structure. This article extends the work on multilevel standardized mean differences from strictly hierarchical structure to both fully and partially cross-classified structures. Analytically derived formulae for calculating effect sizes and the corresponding sampling variances (or standard errors) are presented, verified by simulation results, and illustrated with real data examples. Implications for primary research studies and meta-analyses are discussed.

The authors review the "Advanced Clinical Solutions for WAIS-IV and WMS-IV". The "Advanced Clinical Solutions (ACS) for the Wechsler Adult Intelligence Scale-Fourth Edition" (WAIS-IV; Wechsler, 2008) and the "Wechsler Memory Scale-Fourth Edition" (WMS-IV; Wechsler, 2009) was published by Pearson in 2009. It is a clinical tool for extending the assessment of individuals' cognitive functioning. Generally, the ACS provides supplemental information for the results of the WAIS-IV and WMS-IV; it includes six components that are relatively independent, namely, additional scores, effort measures, demographically adjusted norms, reliable change scores, test of premorbid functioning, and social cognition subtests. These new elements were specially designed for forensic evaluations, readministrations, and neuropsychological evaluations. The age range for the ACS varies for different elements, but for most tests it is 16 to 90, as consistent with the WAIS-IV and WMS-IV. A technician or graduate assistant with appropriate graduate-level training can administer and score the tests under supervision, but as stressed in the Administration and Scoring Manual results should only be interpreted by professionals with extensive training in assessment. In addition, to use the ACS for assessing neuropsychological functioning, examiners must have adequate background in neuropsychological assessment. The components of the ACS for WAIS-IV and WMS-IV are described.

In path analysis, using composite scores without adjustment for measurement unreliability and violations of factorial invariance across groups lead to biased estimates of path coefficients. Although joint modeling of measurement and structural models can theoretically yield consistent structural association estimates, estimating a model with many variables is often impractical in small samples. A viable alternative is two-stage path analysis (2S-PA), where researchers first obtain factor scores and the corresponding individual-specific reliability coefficients, and then use those factor scores to analyze structural associations while accounting for their unreliability. The current paper extends 2S-PA to also account for partial invariance. Two simulation studies show that 2S-PA outperforms joint modeling in terms of model convergence, the efficiency of structural parameter estimation, and confidence interval coverage, especially in small samples and with categorical indicators. We illustrate 2S-PA by reanalyzing data from a multiethnic study that predicts drinking problems using college-related alcohol beliefs. [ABSTRACT FROM AUTHOR]

Daily emotion dynamics provide valuable information about individuals' emotion processes as they go about their lives. Emotion dynamics such as emotion levels (mean), emotion variability (degree of fluctuation), and emotion network density (strength of temporal connections among emotions) are associated with risks for various psychopathology in youth and adults. Prior work has shown that caregivers and friends play crucial socializing roles in adolescent emotional well‐being, but less is known about their roles in daily emotion dynamics. This study examined whether caregiver emotion coaching, caregiver‐adolescent closeness, and friendship quality were associated with adolescents' emotion levels, emotion variability, and emotion network density. Further, we examined whether caregiver‐adolescent closeness moderated the associations between coaching and emotion dynamics. Participants were 150 adolescents (61% girls; Mage = 14.75) and one of their caregivers (95% female; Mage = 43.35) who completed a baseline survey and 21 daily surveys. Results showed that caregiver emotion coaching interacted with caregiver‐adolescent closeness in predicting emotion levels and variability. Specifically, when closeness was higher, emotion coaching was significantly associated with lower sadness and anger levels, higher happiness levels, and lower happiness variability. Caregiver emotion coaching, independent of closeness, was also associated with lower anxiety levels, lower sadness variability, and lower emotion network density. Friendship quality was significantly associated with lower levels of sadness, anxiety, and anger, higher levels of happiness, and lower variability in anxiety and anger. These findings suggest that caregivers and friends are central to everyday emotion levels and variability and a more flexible emotion system in adolescents. [ABSTRACT FROM AUTHOR]

Longitudinal measurement invariance—the consistency of measurement in data collected over time—is a prerequisite for any meaningful inferences of growth patterns. When one or more items measuring the construct of interest show noninvariant measurement properties over time, it leads to biased parameter estimates and inferences on the growth parameters. In this paper, I extend the recently developed alignment-within-confirmatory factor analysis (AwC) technique to adjust for measurement biases for growth models. The proposed AwC method does not require a priori knowledge of noninvariant items and the iterative searching of noninvariant items in typical longitudinal measurement invariance research. Results of a Monte Carlo simulation study comparing AwC with the partial invariance modeling method show that AwC largely reduces biases in growth parameter estimates and gives good control of Type I error rates, especially when the sample size is at least 1,000. It also outperforms the partial invariance method in conditions when all items are noninvariant. However, all methods give biased growth parameter estimates when the proportion of noninvariant parameters is over 25%. Based on the simulation results, I conclude that AO is a viable alternative to the partial invariance method in growth modeling when it is not clear whether longitudinal measurement invariance holds. The current paper also demonstrates AwC in an example modeling neuroticism over three time points using a public data set, which shows how researchers can compute effect size indices for noninvariance in AwC to assess to what degree invariance holds and whether AwC results are trustworthy. [ABSTRACT FROM AUTHOR]

The efforts to promote healthy eating remain ineffective. The inability of these campaigns to counter the marketing resources of the mainstream fast-food chains is among the primary explanations for such failures. However, from the information processing perspective, the message's features may also play a significant role. Cue reactivity research has shown the importance of evaluating visual and verbal cues to avoid undesirable addictive behavioral outcomes. Bridging the evaluative space model (ESM) and the limited capacity model of motivated mediated message processing (LC4MP), the goal of this experiment is to further explore how food-related visual and verbal cues elicit psychophysiological reactivity and how this reactivity in turn affects attitudes toward the promotion. Multilevel modeling results showed that healthy food images paired with encouragement words elicited an uncoupled pattern of the appetitive system activation and received a predominantly positive attitude from audiences (N = 180). Results suggested that thematic congruent messages are preferred in health communication settings. Junk food images, regardless of being paired with encouragement or discouragement verbal cues, should be avoided due to their automatic activation of the appetitive motivational system. [ABSTRACT FROM AUTHOR]

Sedentary behavior (SB) is associated with cardiometabolic disease and mortality, but its association with dementia is currently unclear. This study investigates whether SB is associated with incident dementia regardless of engagement in physical activity (PA). A total of 146,651 participants from the UK Biobank who were 60 years or older and did not have a diagnosis of dementia (mean [SD] age: 64.59 [2.84] years) were included. Selfreported leisure-time SBs were divided into two domains: time spent watching television (TV) or time spent using a computer. A total of 3,507 individuals were diagnosed with all-cause dementia over a mean follow-up of 11.87 (±1.17) years. In models adjusted for a wide range of covariates, including time spent in PA, time spent watching TV was associated with increased risk of incident dementia (HR [95% CI] = 1.24 [1.15 to 1.32]) and time spent using a computer was associated with decreased risk of incident dementia (HR [95% CI] = 0.85 [0.81 to 0.90]). In joint associations with PA, TV time and computer time remained significantly associated with dementia risk at all PA levels. Reducing time spent in cognitively passive SB (i.e., TV time) and increasing time spent in cognitively active SB (i.e., computer time) may be effective behavioral modification targets for reducing risk of dementia regardless of engagement in PA. [ABSTRACT FROM AUTHOR]

When estimating path coefficients among psychological constructs measured with error, structural equation modeling (SEM), which simultaneously estimates the measurement and structural parameters, is generally regarded as the gold standard. In practice, however, researchers usually first compute composite scores or factor scores, and use those as observed variables in a path analysis, for purposes of simplifying the model or avoiding model convergence issues. Whereas recent approaches, such as reliability adjustment methods and factor score regression, has been proposed to mitigate the bias induced by ignoring measurement error in composite/factor scores with continuous indicators, those approaches are not yet applicable to models with categorical indicators. In this article, we introduce the two-stage path analysis (2S-PA) with definition variables as a general framework for path modeling to handle categorical indicators, in which estimation of factor scores and path coefficients are separated. It thus allows for different estimation methods in the measurement and the structural path models and easier diagnoses of violations of model assumptions. We conducted three simulation studies, ranging from latent regression to mediation analysis with categorical indicators, and showed that 2S-PA generally produced similar estimates to those using SEM in large samples, but gave better convergence rates, less standard error bias, and better control of Type I error rates in small samples. We illustrate 2S-PA using data from a national data set, and show how researchers can implement it in Mplus and OpenMx. Possible extensions and future directions of 2S-PA are discussed. [ABSTRACT FROM AUTHOR]

Background: Childhood obesity prevention interventions routinely focus on changing maternal parenting practices. Failure to assess how fathers' weight-related (ie, diet and physical activity) parenting practices contribute to children's energy balance behaviors limits the understanding of their paternal role within the family. Examining the independent and interacting effects of fathers' and mothers' weight-related parenting practices on children's diet and physical activity addresses this important research gap. Objective: This study used ecological momentary assessment (EMA) to investigate the within-subject and between-subject independent and interactive effects of maternal and paternal encouragement to eat and preparation of fruits and vegetables (F/V) and encouragement of and taking their child to be physically active on their child's self-reported F/V intake and physical activity engagement. Methods: Participants included mother-father-child triads (n=22 triads, n=205-213 prompts/occasions) in the Mothers and Their Children's Health Study and the University of Southern California Fathers Study. Simultaneously, mothers and fathers (agesmean 44.2 years, SD 5.6, and 45.2 years, SD 8.1, respectively), and their children (agemean 12.0 years, SD 0.7) completed up to 8 randomly prompted EMA surveys per day on separate smartphones for 7 days. At each prompt, mothers and fathers each reported whether they did the following in the past 2 hours: (1) encouraged their child to eat F/V, (2) prepared F/V for their child, (3) encouraged their child to be physically active, or (4) took their child to be physically active. Children self-reported whether they consumed F/V or were physically active in the past 2 hours. Results: Results from Bayesian multilevel logistic models (all in log-odd units) indicated that at the within-subject level, greater maternal encouragement (ß=2.28, 95% CI 0.08 to 5.68) of eating F/V was associated with greater child report of eating F/V, but paternal encouragement (ß=1.50, 95% CI -0.83 to 4.52) showed no effects above and beyond maternal encouragement. Additionally, greater than usual paternal encouragement (ß=2.28, 95% CI 0.08 to 5.54) and maternal encouragement (ß=2.94, 95% CI 0.36 to 6.69) of physical activity had significant independent effects and were associated with greater child report of physical activity. No other within-subject or between-subject associations nor interactive effects were significant. Conclusions: Findings from this study suggest that fathers play a role in supporting their children's physical activity but not their intake of F/V. Future EMA studies should recruit larger samples to evaluate the independent and interacting roles of mothers' and fathers' weight-related parenting practices on child's obesogenic behaviors. [ABSTRACT FROM AUTHOR]

There has been tremendous growth in research on measurement invariance over the past two decades. However, given that psychological tests are commonly used for making classification decisions such as personnel selections or diagnoses, surprisingly, there has been little research on how noninvariance impacts classification accuracy. Millsap and Kwok previously proposed a selection accuracy framework for that purpose, which has been recently extended to categorical data. Their framework, however, only deals with classification using a unidimensional test. In contrast, classification in practice usually involves multidimensional tests (e.g., personality) or multiple tests, with different weights assigned to each dimension. In the current paper, we extend Millsap and Kwok's framework for examining the impact of noninvariance to a multidimensional test on classification. We also provide an R script for the proposed method and illustrate it with a personnel selection example using data from a published report featuring a five-factor personality inventory. [ABSTRACT FROM AUTHOR]

Although many methodologists and professional organizations have urged applied researchers to compute and report effect size measures accompanying tests of statistical significance, discussions on obtaining confidence intervals (CIs) for effect size with clustered/multilevel data have been scarce. In this paper, I explore the bootstrap as a viable and accessible alternative for obtaining CIs for multilevel standardized mean difference effect size for cluster-randomized trials. A simulation was carried out to compare 17 analytic and bootstrap procedures for constructing CIs for multilevel effect size, in terms of empirical coverage rate and width, for both normal and nonnormal data. Results showed that, overall, the residual bootstrap with studentized CI had the best coverage rates (94.75% on average), whereas the residual bootstrap with basic CI had better coverage in small samples. These two procedures for constructing CIs showed better coverage than using analytic methods for both normal and nonnormal data. In addition, I provide an illustrative example showing how bootstrap CIs for multilevel effect size can be easily obtained using the statistical software R and the R package bootmlm. I strongly encourage applied researchers to report CIs to adequately convey the uncertainty of their effect size estimates. [ABSTRACT FROM AUTHOR]

This study examined how vocabulary, syntactic knowledge, and orthographic knowledge are related to comprehension monitoring and whether comprehension monitoring mediates the relations between these language skills and reading comprehension. Eighty-nine Chinese children were assessed on their vocabulary, syntactic knowledge, orthographic knowledge, and comprehension monitoring in Grade 1. Their reading comprehension skills were assessed in Grade 1 and Grade 3. Results showed that in Grade 1, comprehension monitoring mediated the relations between vocabulary and syntactic knowledge and reading comprehension. For Grade 3 reading comprehension, syntactic knowledge in Grade 1 was the only significant predictor. These findings indicate that multiple language skills make direct and indirect contributions via comprehension monitoring to Chinese reading comprehension, and the relations would change as children's reading skills develop. [ABSTRACT FROM AUTHOR]

We investigated the performance of two single indicator methods: latent moderated structural equation (LMS) and reliability-adjusted product indicator (RAPI) methods, on testing interaction effects with congeneric measures, which vary in factor loadings and error variances under a common factor. Additionally, in the simulation study, we compared the performance of four reliability estimates (Cronbach's alpha, omega composite, Coefficient H, and greatest lower bound [GLB]) to adjust for the exogenous composites' measurement errors. Results from the study showed that: while estimating interaction effects with exogenous composites from congeneric measures, the four reliability estimates performed comparably well. Recommendations on the choice of reliability estimates between the LMS and the RAPI methods under different sample sizes and population reliability conditions are further discussed. [ABSTRACT FROM AUTHOR]

This article shows how the concept of reliability of composite scores, as defined in classical test theory, can be extended to the context of multilevel modeling. In particular, it discusses the contributions and limitations of the various level-specific reliability indices proposed by Geldhof, Preacher, and Zyphur (2014), denoted as ω̃b and ω̃w (and also α̃b and α̃w). One major limitation of those indices is that they are quantities for latent, unobserved level-specific composite scores, and are not suitable for observed composites at different levels. As illustrated using simulated data in this article, ω̃b can drastically overestimate the true reliability of between-level composite scores (i.e., observed cluster means). Another limitation is that the development of those indices did not consider the recent conceptual development on construct meanings in multilevel modeling (Stapleton & Johnson, 2019; Stapleton, Yang, & Hancock, 2016). To address the second limitation, this article defines reliability indices (ω2l, ωb, ωw, α2l, αb, αw) for three types of multilevel observed composite scores measuring various multilevel constructs: individual, configural, shared, and within-cluster. The article also shows how researchers can obtain sample point and interval estimates using the derived formulas and the provided R and Mplus code. In addition, a large-scale national data set was used to illustrate the proposed methods for estimating reliability for the three types of multilevel composite scores, and practical recommendations on when different indices should be reported are provided. [ABSTRACT FROM AUTHOR]

Objective: We investigated whether men's social confidence in an initial, opposite‐sex chatting context can be improved through a video tutorial and the extent to which being perceived as socially confident results in being seen as more romantically desirable and worthy of future contact. Method: Women chatted with men who had received or not received a tutorial on how to handle speed‐dating chats (Study 1: N = 129; Study 2: N = 60) or with male targets selected for having high versus moderate confidence in handling initial, opposite‐sex encounters (Study 3: N = 46). Results: Tutorial‐trained men felt more confident going into the chats and they, as well as male targets selected for their confidence, were perceived by female chat partners to be higher in social confidence, status, and dominance. However, only perceptions of social confidence were further associated with being perceived as more romantically desirable (as a short‐term mate) and worthy of future contact. Conclusions: Findings indicate that social confidence is trainable and that other‐perceived social confidence can impact the outcomes of social interactions. [ABSTRACT FROM AUTHOR]

Path models with observed composites based on multiple items (e.g., mean or sum score of the items) are commonly used to test interaction effects. Under this practice, researchers generally assume that the observed composites are measured without errors. In this study, we reviewed and evaluated two alternative methods within the structural equation modeling (SEM) framework, namely, the reliability-adjusted product indicator (RAPI) method and the latent moderated structural equations (LMS) method, which can both flexibly take into account measurement errors. Results showed that both these methods generally produced unbiased estimates of the interaction effects. On the other hand, the path model—without considering measurement errors—led to substantial bias and a low confidence interval coverage rate of nonzero interaction effects. Other findings and implications for future studies are discussed. [ABSTRACT FROM AUTHOR]

In testing the factorial invariance of a measure across groups, the groups are often of different sizes. Large imbalances in group size might affect the results of factorial invariance studies and lead to incorrect conclusions of invariance because the fit function in multiple-group factor analysis includes a weighting by group sample size. The implication is that violations of invariance might not be detected if the sample sizes of the 2 groups are severely unbalanced. In this study, we examined the effects of group size differences on results of factorial invariance tests, proposed a subsampling method to address unbalanced sample size issue in factorial invariance studies, and evaluated the proposed approach in various simulation conditions. Our findings confirm that violations of invariance might be masked in the case of severely unbalanced group size conditions and support the use of the proposed subsampling method to obtain accurate results for invariance studies. [ABSTRACT FROM PUBLISHER]

Much of the previous literature on partial measurement invariance has focused on (a) statistically detecting noninvariance, and (b) modeling partial invariance to obtain correct inferences for latent mean comparisons across groups in a single research study. However, very little guidance is provided on the practical implications of partial invariance on the instrument itself in the context of selection. In a frequently cited paper, Millsap and Kwok (2004) provided a framework for evaluating the impact of partial invariance by quantifying the magnitude of noninvariance on the efficacy of the test for selection purposes, yet our literature review found that only a few of the citations have fully captured the essence of Millsap and Kwok's method. In this article, we briefly review the selection accuracy analysis for partial invariance and provide a user-friendly R script (also available as a Web application) that takes parameter estimates as input, automatically produces summary statistics for evaluating selection accuracy, and generates a graph for visualizing the results. Hypothetical and real data examples are provided to illustrate the use of the R script. The goal of this article is to help readers understand Millsap and Kwok's framework of evaluating the impact of partial invariance through an accessible computer program and step-by-step demonstrations of the selection accuracy analysis. [ABSTRACT FROM AUTHOR]

Although letter naming fluency (LNF) and letter sound fluency (LSF) measures are widely available to educators for assessing early literacy skills of kindergarten children, better understanding of the contributions of these skills to reading development can help improve the interpretation of LNF and LSF data for instructional decisions. This study investigated the interrelations of growth in LNF and LSF across the kindergarten year and their unique roles in predicting later reading fluency. Piecewise parallel-process growth models indicated that although LNF and LSF were highly correlated at kindergarten entry, fall LNF status was positively predictive of LSF growth across the fall. Bidirectional effects were present, as initial LSF was also a positive predictor of LNF growth across the fall; however, its effects were not as strong as those of initial LNF on LSF growth. More importantly, both initial status and growth in LNF and LSF were uniquely predictive of first-grade reading fluency, indicating the independent effects of each on subsequent text reading skills. Indirect effects were also observed for kindergarten LNF and LSF growth on reading fluency in second and third grades. Implications for kindergarten assessment and instruction are discussed. [ABSTRACT FROM AUTHOR]