Elements in Research Methods for Developmental Science – serie

Parceling is pre-modeling strategy to create fewer and more reliable indicators of constructs for use with latent variable models. Parceling is particularly useful for developmental scientists because longitudinal models can become quite complex and even intractable when measurement models of items are fit. In this Element the authors provide a detailed account of the advantages of using parcels, their potential pitfalls, as well as the techniques for creating them for conducting latent variable structural equation modeling (SEM) in the context of the developmental sciences. They finish with a review of the recent use of parcels in developmental journals. Although they focus on developmental applications of parceling, parceling is also highly applicable to any discipline that uses latent variable SEM.

The increased international legislation emphasising children's participation agenda heightened the need for high-quality research in early childhood. Listening to young children asserts their participation, agency and voices in research, an approach commonly associated with qualitative research methods. This Element provides a novel perspective to listening to children's voices by focusing on research methods in early childhood studies that are broadly categorised as quantitative, qualitative, and mixed methods. Locating these research methods from a children's rights perspective, this Element is based on values that young children have the right to be involved in research irrespective of culture and context. Each section discusses how the different methodologies and approaches used in early childhood research align with the principles of children's participation and agency, as well as their right to express their views on matters that affect them. The Element concludes with a roadmap for future early childhood research and its ethical dissemination.

Latent variable models are a powerful tool for measuring many of the phenomena in which developmental psychologists are often interested. If these phenomena are not measured equally well among all participants, this would result in biased inferences about how they unfold throughout development. In the absence of such biases, measurement invariance is achieved; if this bias is present, differential item functioning (DIF) would occur. This Element introduces the testing of measurement invariance/DIF through nonlinear factor analysis. After introducing models which are used to study these questions, the Element uses them to formulate different definitions of measurement invariance and DIF. It also focuses on different procedures for locating and quantifying these effects. The Element finally provides recommendations for researchers about how to navigate these options to make valid inferences about measurement in their own data.

This Element demonstrates how and why the alignment method can advance measurement fairness in developmental science. It explains its application to multi-category items in an accessible way, offering sample code and demonstrating an R package that facilitates interpretation of such items' multiple thresholds. It features the implications for group mean differences when differences in the thresholds between categories are ignored because items are treated as continuous, using an example of intersectional groups defined by assigned sex and race/ethnicity. It demonstrates the interpretation of item-level partial non-invariance results and their implications for group-level differences and encourages substantive theorizing regarding measurement fairness.

Latent variable models are a powerful tool for measuring many of the phenomena in which developmental psychologists are often interested. If these phenomena are not measured equally well among all participants, this would result in biased inferences about how they unfold throughout development. In the absence of such biases, measurement invariance is achieved; if this bias is present, differential item functioning (DIF) would occur. This Element introduces the testing of measurement invariance/DIF through nonlinear factor analysis. After introducing models which are used to study these questions, the Element uses them to formulate different definitions of measurement invariance and DIF. It also focuses on different procedures for locating and quantifying these effects. The Element finally provides recommendations for researchers about how to navigate these options to make valid inferences about measurement in their own data.

The increased international legislation emphasising children's participation agenda heightened the need for high-quality research in early childhood. Listening to young children asserts their participation, agency and voices in research, an approach commonly associated with qualitative research methods. This Element provides a novel perspective to listening to children's voices by focusing on research methods in early childhood studies that are broadly categorised as quantitative, qualitative, and mixed methods. Locating these research methods from a children's rights perspective, this Element is based on values that young children have the right to be involved in research irrespective of culture and context. Each section discusses how the different methodologies and approaches used in early childhood research align with the principles of children's participation and agency, as well as their right to express their views on matters that affect them. The Element concludes with a roadmap for future early childhood research and its ethical dissemination.

Historically, infant-parent synchrony has been measured using methods that provide a global assessment of interpersonal synchrony, representing the quality of dyadic interactions. These approaches have illuminated much about synchrony as a broad construct but lack granular details on the temporal dynamics of these interactions. This Element introduces technologically advanced methods for assessing brain and behavior that can offer detailed insights into the dynamic temporal structure of infant-parent social exchanges. These advancements will significantly enhance our understanding of the bidirectional processes that underpin early emerging dyadic exchanges and how these vary across time and context.

Historically, infant-parent synchrony has been measured using methods that provide a global assessment of interpersonal synchrony, representing the quality of dyadic interactions. These approaches have illuminated much about synchrony as a broad construct but lack granular details on the temporal dynamics of these interactions. This Element introduces technologically advanced methods for assessing brain and behavior that can offer detailed insights into the dynamic temporal structure of infant-parent social exchanges. These advancements will significantly enhance our understanding of the bidirectional processes that underpin early emerging dyadic exchanges and how these vary across time and context.

Psychological network analysis (PNA) has emerged as a powerful tool for understanding the complex interplay of constructs in developmental and educational sciences. Unlike traditional models that assume relationships among variables arise from latent factors, PNA conceptualizes them as dynamic systems of interacting components. This tutorial introduces PNA's theoretical foundations, key concepts (e.g., nodes, edges, network structures), and its methodological applications using cross-sectional, longitudinal, intensive, and cohort data. Through step-by-step guidance and real-world examples, we illustrate how PNA can capture developmental changes, reveal causal structures using directed acyclic graphs, and support developmental and educational research. Special emphasis is given to practical implementation using R, including network estimation, accuracy testing, and visualization. By equipping researchers with the necessary tools to construct and interpret psychological networks, this Element provides a comprehensive framework for leveraging PNA to explore the multifaceted relationships shaping learning, motivation, and social-emotional development.

Psychological network analysis (PNA) has emerged as a powerful tool for understanding the complex interplay of constructs in developmental and educational sciences. Unlike traditional models that assume relationships among variables arise from latent factors, PNA conceptualizes them as dynamic systems of interacting components. This tutorial introduces PNA's theoretical foundations, key concepts (e.g., nodes, edges, network structures), and its methodological applications using cross-sectional, longitudinal, intensive, and cohort data. Through step-by-step guidance and real-world examples, we illustrate how PNA can capture developmental changes, reveal causal structures using directed acyclic graphs, and support developmental and educational research. Special emphasis is given to practical implementation using R, including network estimation, accuracy testing, and visualization. By equipping researchers with the necessary tools to construct and interpret psychological networks, this Element provides a comprehensive framework for leveraging PNA to explore the multifaceted relationships shaping learning, motivation, and social-emotional development.

This Element has two main purposes. Firstly, it discusses purposes, advantages, and disadvantages as well as the challenges of different formats of language assessment, concluding with a focus on educator-administered language assessment in early childhood and education programs. It addresses the selection of assessment domains, the trade-off between brevity and precision, the challenge of assessing bilinguals, and accommodating the requirements of funders (e.g., government agencies) and users (e.g., educators and schools). It draws on lessons learned from developing two instruments for a national Danish-language and preliteracy assessment program. Secondly, it introduces those two educator-administered instruments-Language Assessment 3-6 (LA 3-6) and Language Assessment 2-year-olds (LA 2)-with respect to content, norming, gender and socioeconomic influences as well as psychometric qualities. The intention is that this experience can help enable the extension of the educator-based approach to other languages and contexts, while simultaneously acknowledging that linguistic and cultural adaptations are crucial.