New Directions for Child and Adolescent Development最新文献

Developmental researchers often have research questions about cross-lag effects-the effect of one variable predicting a second variable at a subsequent time point. The cross-lag panel model (CLPM) is often fit to longitudinal panel data to examine cross-lag effects; however, its utility has recently been called into question because of its inability to distinguish between-person effects from within-person effects. This has led to alternative forms of the CLPM to be proposed to address these limitations, including the random-intercept CLPM and the latent curve model with structured residuals. We describe these models focusing on the interpretation of their model parameters, and apply them to examine cross-lag associations between reading and mathematics. The results from the various models suggest reading and mathematics are reciprocally related; however, the strength of these lagged associations was model dependent. We highlight the strengths and limitations of each approach and make recommendations regarding modeling choice.

Developmental scientists are often interested in subgroups of people who share commonalities in aspects of development; these subgroups often cannot be captured directly but instead must be inferred from other information. Mixture models can be used in these situations. Two specific types of mixture models, latent profile transition analyses and growth mixture models, are highly relevant to developmental science because they can identify subgroups of people who are similar in their patterns of change. This guide highlights foundational aspects of these two types of models and is intended for readers who have not previously conducted either an LPTA or a GMM, or perhaps no mixture model analyses at all. It includes four primary sections. The first focuses on understanding mixture models conceptually and applying that knowledge to identifying appropriate research questions. The second section addresses data requirements, including planning for data collection or evaluating the suitability of previously collected data, and data preparation. The third section focuses on conducting analyses, with step-by-step instructions and syntax, and the final section discusses presenting the results. I illustrate these concepts and procedures with an example data set and research questions derived from the Five Cs model of positive youth development.

Longitudinal research is often interested in identifying correlates of heterogeneity in change. This paper compares three approaches for doing so: the mixed-effects model (latent growth curve model), the growth mixture model, and structural equation model trees. Each method is described, with special focus given to how each structures heterogeneity, attributes that heterogeneity to covariates, and the kinds of research questions each can be used to address. Each approach is used to analyze data from the National Longitudinal Survey of Youth to understand the similarities and differences between methods in the context of empirical data. Specifically, changes in weight across adolescence are examined, as well as how differences in these change patterns can be explained by sex, race, and mother's education. Recommendations are provided for how to select which approach is most appropriate for analyzing one's own data.

Developmental researchers commonly utilize multilevel models (MLMs) to describe and predict individual differences in change over time. In such growth model applications, researchers have been widely encouraged to supplement reporting of statistical significance with measures of effect size, such as R-squareds (R² ) that convey variance explained by terms in the model. An integrative framework for computing R-squareds in MLMs with random intercepts and/or slopes was recently introduced by Rights and Sterba and it subsumed pre-existing MLM R-squareds as special cases. However, this work focused on cross-sectional applications, and hence did not address how the computation and interpretation of MLM R-squareds are affected by modeling considerations typically arising in longitudinal settings: (a) alternative centering choices for time (e.g., centering-at-a-constant vs. person-mean-centering), (b) nonlinear effects of predictors such as time, (c) heteroscedastic level-1 errors and/or (d) autocorrelated level-1 errors. This paper addresses these gaps by extending the Rights and Sterba R-squared framework to longitudinal contexts. We: (a) provide a full framework of total and level-specific R-squared measures for MLMs that utilize any type of centering, and contrast these with Rights and Sterba's measures assuming cluster-mean-centering, (b) explain and derive which measures are applicable for MLMs with nonlinear terms, and extend the R-squared computation to accommodate (c) heteroscedastic and/or (d) autocorrelated errors. Additionally, we show how to use differences in R-squared (ΔR² ) measures between growth models (adding, for instance, time-varying covariates as level-1 predictors or time-invariant covariates as level-2 predictors) to obtain effects sizes for individual terms. We provide R software (r2MLMlong) and a running pedagogical example analyzing growth in adolescent self-efficacy to illustrate these methodological developments. With these developments, researchers will have greater ability to consider effect size when analyzing and predicting change using MLMs.

Longitudinal panel studies are widely used in developmental science to address important research questions on human development across the lifespan. These studies, however, are often challenging to implement. They can be costly, time-consuming, and vulnerable to test-retest effects or high attrition over time. Planned missingness designs (PMDs), in which partial data are intentionally collected from all or some of the participants, are viable solutions to these challenges. This article provides an overview of several PMDs with potential utilities in longitudinal studies, including the multi-form designs, multi-method designs, varying lag designs, accelerated longitudinal designs, and efficient designs for analysis of change. For each of the designs, the basic rationale, design considerations, data analysis, advantages, and limitations are discussed. The article is concluded with some general recommendations to developmental researchers and promising directions for future research.

Parental Burnout (PB) is an exhaustion syndrome resulting from exposure to overwhelming parenting stress. The current gold-standard instrument, namely, Parental Burnout Assessment (PBA) was used in the International Investigation of Parental Burnout (IIPB), a 40-country study of the prevalence of PB around the world. The IIPB study has stimulated worldwide interest, but efforts are still needed to validate the PBA in different cultures. This study is the first on PB in a collectivist, predominantly Islamic country. It aims to examine the psychometric properties of the Turkish translation of the PBA. The PBA-Turkish was administered to 452 Turkish parents (60% mothers). The results showed that the PBA is a relevant construct in Turkish culture. We replicated the original four-factor structure of the PBA and tested a second-order factor structure through confirmatory factor analyses. The first- and second-order factor models fit the data well. The Emotional Distancing subscale, however, demonstrated lower reliability than the other subscales. We then attained measurement invariance across genders in a set of nested models with gradually increasing parameter constraints. Finally, non-working and single parents reported higher PB, pointing to the possible role of relationships and support as protective factors for Turkish parents.

A recent initiative known as the International Investigation of Parental Burnout, sought to study the prevalence of parental burnout in over 40 countries globally using the Parental Burnout Assessment (PBA) instrument. Four countries investigated here provide a first insight into parental burnout in Africa, based on a pooled dataset of 738 parents (48.8% mothers) sampled from Burundi (n = 187; 25.3%), Cameroon (n = 208; 28.2%), Rwanda (n = 240; 32.5%), and Togo (n = 103; 14%). As a first step, we tested the content validity of the PBA that was developed and validated in Western countries. Second, we tested the relations between the PBA and several sociodemographic characteristics such as age, gender, and level of education. The results provide evidence that the concept of parental burnout makes sense for African parents, and that the PBA can be considered as a psychometrically sound instrument to measure it. However, the results also point to the need for further exploration of the nature of parental burnout in Africa. As in previous studies, low correlations between parental burnout and the sociodemographic characteristics were found. Finally, the current results suggest the existence of parenting subcultures across the four participating countries that would be interesting to document.

In this commentary we first examine psychometric issues in the ambitious enterprise of cross-cultural application of the Parental Burnout Assessment (PBA). The present reports span a wide range of cultural places. Overall, the PBA presents good face validity and a strong replication of factor structure; future multi-group confirmatory factor analysis will enable quantitative comparisons not currently possible. Content validity is not fully addressed in these reports, so nuanced differences in the nature of parental burnout remain an interesting possibility. Variation the PBA's correlations with other measures, such as education and household type, suggests cultural mediation in the causes and dynamics of parental burnout. In the second part of our commentary, we address more directly whether parental burnout is influenced by the sociocultural context in which it is manifest. We propose that future research will benefit from more precise description of the particular cultural community involved, including the settings, customs, and ethnotheories of parenting. Gaining a global understanding of parental burnout, in other words, rests on building firmer and more differentiated pictures at the local level. The papers in this volume nevertheless present an important step forward in what promises to be an exciting journey of discovery.

This study- the first study on parental burnout in an Arabic speaking country in the Middle East- aimed to examine the psychometric properties of the Lebanese translation of the Parental Burnout Assessment (PBA-Lebanese). The PBA-Lebanese was administered to 200 Lebanese parents (67% mothers). The results showed parental burnout is a valid construct relevant to Lebanese culture. In particular, we replicated the original four-factor structure of the PBA and we tested a second order factor structure through confirmatory factor analysis. The first and second order factor model fitted the data, and reliability indexes were high (α = .97 for exhaustion, .95 for contrast, .92 for feelings of being fed up, .80 for emotional distancing and .98 for the global score). There was no significant gender difference in the prevalence of parental burnout, but mean levels were higher in mothers than in fathers. Both less educated parents and single parents reported higher parental burnout, pointing to the possible role of education and social relations as protective factors for Lebanese parents.