scholarly journals Coevolution of brain size and longevity in parrots

2021 ◽  
Author(s):  
Simeon Q Smeele ◽  
Dalia A Conde ◽  
Annette Baudisch ◽  
Simon Bruslund ◽  
Andrew Iwaniuk ◽  
...  
Keyword(s):  
Life Expectancy ◽  
Cognitive Complexity ◽  
Structural Equation ◽  
Large Scale ◽  
Missing Values ◽  
Structural Equation Models ◽  
Brain Size ◽  
Developmental Time ◽  
Data Uncertainty ◽  
Relative Brain Size

Parrots are well-known for their exceptionally long lives and cognitive complexity. While previous studies have demonstrated a correlation between longevity and brain size in a variety of taxa, little research has been devoted to understanding this link in parrots. Here we employed a large-scale comparative analysis that investigated the influence of brain size and life history variables on patterns of longevity. Specifically, we addressed two hypotheses for evolutionary drivers of longevity: the Cognitive Buffer Hypothesis, which proposes that increased cognitive abilities enable longer life spans, and the Expensive Brain Hypothesis, which holds that the increase in life span is caused by prolonged developmental time of and increased parental investment in, large brained offspring. We estimated life expectancy from detailed zoo records for 133,818 individuals across 244 parrot species. Using Bayesian structural equation models, we found a consistent correlation between relative brain size and life expectancy in parrots. This correlation was best explained by a direct effect of relative brain size. Notably, we found no effects of developmental time, clutch size, or age at first reproduction. Our results provide support for the Cognitive Buffer Hypothesis, and demonstrate a principled Bayesian approach that addresses data uncertainty and imputation of missing values.

Structural equation modelling with complex sampling designs and non-random attrition: A tutorial using lavaan and Mplus

2021 ◽  
Author(s):  
Aja Louise Murray ◽  
Anastasia Ushakova ◽  
Helen Wright ◽  
Tom Booth ◽  
Peter Lynn
Keyword(s):  
Structural Equation ◽  
Large Scale ◽  
Structural Equation Models ◽  
Cluster Sampling ◽  
Complex Sampling ◽  
Latent Trajectory ◽  
Adequate Sampling ◽  
Complex Sampling Designs ◽  
Selection Probabilities ◽  
Pseudo Maximum Likelihood

Complex sampling designs involving features such as stratification, cluster sampling, and unequal selection probabilities are often used in large-scale longitudinal surveys to improve cost-effectiveness and ensure adequate sampling of small or under-represented groups. However, complex sampling designs create challenges when there is a need to account for non-random attrition; a near inevitability in social science longitudinal studies. In this article we discuss these challenges and demonstrate the application of weighting approaches to simultaneously account for non-random attrition and complex design in a large UK-population representative survey. Using an auto-regressive latent trajectory model with structured residuals (ALT-SR) to model the relations between relationship satisfaction and mental health in the Understanding Society study as an example, we provide guidance on implementation of this approach in both R and Mplus is provided. Two standard error estimation approaches are illustrated: pseudo-maximum likelihood robust estimation and Bootstrap resampling. A comparison of unadjusted and design-adjusted results also highlights that ignoring the complex survey designs when fitting structural equation models can result in misleading conclusions.

scholarly journals A Practical Guide for Analyzing Large-Scale Assessment Data Using Mplus: A Case Demonstration Using the Program for International Assessment of Adult Competencies Data

2020 ◽  
pp. 107699862097855
Author(s):  
Takashi Yamashita ◽  
Thomas J. Smith ◽  
Phyllis A. Cummins
Keyword(s):  
Structural Equation ◽  
Large Scale ◽  
Structural Equation Models ◽  
Preparation Process ◽  
Data Preparation ◽  
Assessment Data ◽  
Statistical Software ◽  
Large Scale Assessment ◽  
Scale Assessment ◽  
International Assessment

In order to promote the use of increasingly available large-scale assessment data in education and expand the scope of analytic capabilities among applied researchers, this study provides step-by-step guidance, and practical examples of syntax and data analysis using Mplus. Concise overview and key unique aspects of large-scale assessment data from the 2012/2014 Program for International Assessment of Adult Competencies (PIAAC) are described. Using commonly-used statistical software including SAS and R, a simple macro program and syntax are developed to streamline the data preparation process. Then, two examples of structural equation models are demonstrated using Mplus. The suggested data preparation and analytic approaches can be immediately applicable to existing large-scale assessment data.

scholarly journals Science Self-Concept – More Than the Sum of its Parts? A Comparison of Different Assessment and Modeling Approaches

2019 ◽  
Author(s):  
Ulrich Schroeders ◽  
Malte Jansen
Keyword(s):  
Structural Equation ◽  
Large Scale ◽  
Structural Equation Models ◽  
Factor Model ◽  
Self Concept ◽  
General Science ◽  
Theoretical Status ◽  
Subject Specific ◽  
Mean Structure ◽  
The Subject

Academic self-concept is understood as a multidimensional, hierarchical construct. Multidimensionality refers to the subject-specific differentiation of academic self-concepts, whereas hierarchy refers to the aggregation of more specific facets of self-concepts into more general ones. Previous research demonstrated that students distinguish between their self-concepts in biology, chemistry, and physics if taught as separate school subjects, as is done in Germany. However, large-scale international educational studies, such as PISA, often use a monolithic science self-concept measure. It is yet unclear whether an aggregate of subject-specific self-concepts is equivalent to a directly measured science self-concept. We assessed the subject-specific and and a general science self-concept of 1,232 German grade 10 students. A higher-order factor model and a bifactor model demonstrated a very high correlation between the “inferred” and the explicitly assessed general science self-concept. Despite the high empirical overlap, we argue for a more nuanced view of the science self-concept, because statistical unity is not to be confused with causal unity. Moreover, from a methodological perspective, we used multi-group confirmatory factor analysis to examine the mean structure and local weighted structural equation models to study measurement invariance across science ability. Implications for the theoretical status of self-concept as a hierarchical construct are discussed.

scholarly journals Hydraulic diversity stabilizes productivity in a large-scale subtropical tree biodiversity experiment

2021 ◽  
Author(s):  
Florian Schnabel ◽  
Xiaojuan Liu ◽  
Matthias Kunz ◽  
Kathryn E. Barry ◽  
Franca J. Bongers ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Richness ◽  
Tree Species ◽  
Structural Equation ◽  
Large Scale ◽  
Structural Equation Models ◽  
Forest Productivity ◽  
Climate Mitigation ◽  
Climate Change Effects ◽  
Tree Species Richness

AbstractExtreme climatic events threaten forests and their climate mitigation potential globally. Understanding the drivers promoting ecosystems stability is therefore considered crucial to mitigate adverse climate change effects on forests. Here, we use structural equation models to explain how tree species richness, asynchronous species dynamics and diversity in hydraulic traits affect the stability of forest productivity along an experimentally manipulated biodiversity gradient ranging from 1 to 24 tree species. Tree species richness improved stability by increasing species asynchrony. That is at higher species richness, inter-annual variation in productivity among tree species buffered the community against stress-related productivity declines. This effect was mediated by the diversity of species’ hydraulic traits in relation to drought tolerance and stomatal control, but not the community-weighted means of these traits. Our results demonstrate important mechanisms by which tree species richness stabilizes forest productivity, thus emphasizing the importance of hydraulically diverse, mixed-species forests to adapt to climate change.

scholarly journals Model-Based Manifest and Latent Composite Scores in Structural Equation Models

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Norman Rose ◽  
Wolfgang Wagner ◽  
Axel Mayer ◽  
Benjamin Nagengast
Keyword(s):  
Latent Variables ◽  
Structural Equation ◽  
Missing Values ◽  
Structural Equation Models ◽  
Likelihood Estimation ◽  
Average Score ◽  
Estimation Model ◽  
Model Based ◽  
Sum Score ◽  
Composite Scores

Composite scores are commonly used in the social sciences as dependent and independent variables in statistical models. Typically, composite scores are computed prior to statistical analyses. In this paper, we demonstrate the construction of model-based composite scores that may serve as outcomes or predictors in structural equation models (SEMs). Model-based composite scores of manifest variables are useful in the presence of ignorable missing data, as full-information maximum likelihood estimation can be used for parameter estimation. Model-based composite scores of latent variables account for measurement error in the aggregated variables. We introduce the pseudo-indicator model (PIM) for the construction of three composite scores: (a) the sum score, (b) the weighted sum score, and (c) the average score of manifest and latent variables in SEM. The utility of manifest model-based composite scores in the case of missing values is shown by a simulation study. The use of multiple manifest and latent model-based composite scores in SEM is illustrated with data from motivation research.

Disease, War, Hunger, and Deprivation: A Cross-National Investigation of the Determinants of Life Expectancy in Less-Developed and Sub-Saharan African Nations

2012 ◽  
Vol 55 (3) ◽  
pp. 421-447 ◽  
Author(s):  
Kelly F. Austin ◽  
Laura A. McKinney
Keyword(s):  
Life Expectancy ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Positive Influence ◽  
Sub Saharan Africa ◽  
National Life ◽  
Sub Saharan ◽  
Developed Nations ◽  
African Sample ◽  
Cross National

Researchers note a recent trend of increasing inequality in cross-national life expectancy rates, largely due to conditions in the poorest of nations. Threats to life expectancy in less-developed nations include poverty, warfare, intense hunger, and disease, particularly AIDS/HIV. This article utilizes structural equation models for a sample of less-developed nations and a subsample of Sub-Saharan African nations to test interrelationships among predictors. Findings indicate modernization to be the most robust predictor of life expectancy across less-developed nations and HIV to be the strongest determinant of life expectancy in Sub-Saharan African nations. Somewhat surprisingly, warfare and hunger do not have direct impacts on life expectancy among less-developed nations; however, important linkages among warfare, hunger, and disease are evidenced in the Sub-Saharan African sample, along with a notable positive influence of modernization on HIV rates. The findings demonstrate the significance of HIV on cross-national life expectancy scores and illuminate unique dynamics in Sub-Saharan Africa.

scholarly journals Environmental harshness is associated with lower investment in collective actions

2019 ◽  
Author(s):  
N. Lettinga ◽  
P.O. Jacquet ◽  
J-B. André ◽  
N. Baumard ◽  
C. Chevallier
Keyword(s):  
Life History ◽  
Structural Equation ◽  
Large Scale ◽  
Structural Equation Models ◽  
Life History Strategy ◽  
World Values Survey ◽  
Collective Actions ◽  
The World ◽  
European Values ◽  
European Values Study

AbstractAlthough humans cooperate universally, there is variability across individuals, times and cultures in the amount of resources people invest in cooperative activities. The origins of such variability are not known but recent work highlights that variations in environmental harshness may play a key role. A growing body of experimental work in evolutionary psychology suggests that humans adapt to their specific environment by calibrating their life-history strategy. In this paper, we apply structural equation models to test the association between current and childhood environmental harshness, life-history strategy and adult cooperation in two large-scale datasets (the World Values Survey and the European Values Study). The present study replicates existing research linking a harsher environment (both in adulthood and in childhood) with a modulated reproduction-maintenance trade-off and extends these findings to the domain of collective actions. Specifically, we find that a harsher environment (both in adulthood and in childhood) is associated with decreased involvement in collective action and that this association is mediated by individuals’ life-history strategy.

scholarly journals Cognitive Abilities Explain Wording Effects in the Rosenberg Self-Esteem Scale

Assessment ◽  
2017 ◽  
Vol 27 (2) ◽  
pp. 404-418 ◽  
Author(s):  
Timo Gnambs ◽  
Ulrich Schroeders
Keyword(s):  
Factor Structure ◽  
Cognitive Abilities ◽  
Latent Variable ◽  
Structural Equation ◽  
Large Scale ◽  
Structural Equation Models ◽  
Common Factor ◽  
Self Esteem ◽  
Specific Factor ◽  
Measurement Models

There is consensus that the 10 items of the Rosenberg Self-Esteem Scale (RSES) reflect wording effects resulting from positively and negatively keyed items. The present study examined the effects of cognitive abilities on the factor structure of the RSES with a novel, nonparametric latent variable technique called local structural equation models. In a nationally representative German large-scale assessment including 12,437 students competing measurement models for the RSES were compared: a bifactor model with a common factor and a specific factor for all negatively worded items had an optimal fit. Local structural equation models showed that the unidimensionality of the scale increased with higher levels of reading competence and reasoning, while the proportion of variance attributed to the negatively keyed items declined. Wording effects on the factor structure of the RSES seem to represent a response style artifact associated with cognitive abilities.

The Dimensionality of Reading Self-Concept: Examining Its Stability Using Local Structural Equation Models

Assessment ◽  
2022 ◽  
pp. 107319112110696
Author(s):  
Geetanjali Basarkod ◽  
Herbert W. Marsh ◽  
Baljinder K. Sahdra ◽  
Philip D. Parker ◽  
Jiesi Guo ◽  
...  
Keyword(s):  
Reading Achievement ◽  
Structural Equation ◽  
Large Scale ◽  
Structural Equation Models ◽  
Student Assessment ◽  
Cognitive Testing ◽  
Self Concept ◽  
International Student Assessment ◽  
High Competence ◽  
Achievement Levels

For results from large-scale surveys to inform policy and practice appropriately, all participants must interpret and respond to items similarly. While organizers of surveys assessing student outcomes often ensure this for achievement measures, doing so for psychological questionnaires is also critical. We demonstrate this by examining the dimensionality of reading self-concept—a crucial psychological construct for several outcomes—across reading achievement levels. We use Programme for International Student Assessment 2018 data ( N = 529,966) and local structural equation models (LSEMs) to do so. Results reveal that reading self-concept dimensions (assessed through reading competence and difficulty) vary across reading achievement levels. Students with low reading achievement show differentiated responses to the two item sets (high competence–high difficulty). In contrast, students with high reading achievement have reconciled responses (high competence–low difficulty). Our results highlight the value of LSEMs in examining factor structure generalizability of constructs in large-scale surveys and call for greater cognitive testing during item development.

A Comparison of Joint Model and Fully Conditional Specification Imputation for Multilevel Missing Data

2017 ◽  
Vol 42 (4) ◽  
pp. 432-466 ◽  
Author(s):  
Stephen A. Mistler ◽  
Craig K. Enders
Keyword(s):  
Multiple Imputation ◽  
Structural Equation ◽  
Missing Values ◽  
Structural Equation Models ◽  
Real Data ◽  
Joint Model ◽  
Analysis Model ◽  
Analytic Work ◽  
Fully Conditional Specification ◽  
Conditional Specification

Multiple imputation methods can generally be divided into two broad frameworks: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution, whereas FCS imputes variables one at a time from a series of univariate conditional distributions. In single-level multivariate normal data, these two approaches have been shown to be equivalent, but less is known about their similarities and differences with multilevel data. This study examined four multilevel multiple imputation approaches: JM approaches proposed by Schafer and Yucel and Asparouhov and Muthén and FCS methods described by van Buuren and Carpenter and Kenward. Analytic work and computer simulations showed that Asparouhov and Muthén and Carpenter and Kenward methods are most flexible, as they produce imputations that preserve distinct within- and between-cluster covariance structures. As such, these approaches are applicable to random intercept models that posit level-specific relations among variables (e.g., contextual effects analyses, multilevel structural equation models). In contrast, methods from Schafer and Yucel and van Buuren are more restrictive and impose implicit equality constraints on functions of the within- and between-cluster covariance matrices. The analytic work and simulations underscore the conclusion that researchers should not expect to obtain the same results from alternative imputation routines. Rather, it is important to choose an imputation method that partitions variation in a manner that is consistent with the analysis model of interest. A real data analysis example illustrates the various approaches.

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