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 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 Suicidal Ideation during the COVID-19 Pandemic among A Large-Scale Iranian Sample: The Roles of Generalized Trust, Insomnia, and Fear of COVID-19

Healthcare ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 93
Author(s):  
Chung-Ying Lin ◽  
Zainab Alimoradi ◽  
Narges Ehsani ◽  
Maurice M. Ohayon ◽  
Shun-Hua Chen ◽  
...  
Keyword(s):  
Suicidal Ideation ◽  
Structural Equation ◽  
Large Scale ◽  
Healthcare Providers ◽  
Age Groups ◽  
Equation Modeling ◽  
Cluster Sampling ◽  
Generalized Trust ◽  
Trained Research Assistant ◽  
Gender Groups

The novel 2019 coronavirus disease (COVID-19) is still not under control globally. The pandemic has caused mental health issues among many different cohorts and suicidal ideation in relation to COVID-19 has been reported in a number of recent studies. Therefore, the present study proposed a model to explain the associations between generalized trust, fear of COVID-19, insomnia, and suicidal ideation during the COVID-19 pandemic among a large-scale Iranian sample. Utilizing cluster sampling with multistage stratification, residents from Qazvin province in Iran were invited to participate in the present study. Adults aged over 18 years (n = 10,843; 6751 [62.3%] females) completed ‘paper–and-pencil’ questionnaires with the assistance of a trained research assistant. Structural equation modeling (SEM) was applied to understand the associations between generalized trust, fear of COVID-19, insomnia, and suicidal ideation. Slightly over one-fifth of the participants (n = 2252; 20.8%) reported suicidal ideation. Moreover, the SEM results indicated that generalized trust was indirectly associated with suicidal ideation via fear of COVID-19 and insomnia. Furthermore, generalized trust was not directly associated with suicidal ideation. The proposed model was invariant across gender groups, age groups, and participants residing in different areas (i.e., urban vs. rural). Generalized trust might reduce individuals’ suicidal ideation during the COVID-19 pandemic period via reduced levels of fear of COVID-19 and insomnia. Healthcare providers and policymakers may want to assist individuals in developing their generalized trust, reducing fear of COVID-19, and improving insomnia problems to avoid possible suicidal behaviors.

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.

120 Analytical Strategies for Survey Data in Pet Nutrition and Management

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 63-63
Author(s):  
Sandra L Rodriguez-Zas
Keyword(s):  
Survey Data ◽  
Large Scale ◽  
Companion Animal ◽  
Data Sets ◽  
Complex Sampling ◽  
Analytical Strategies ◽  
Complex Sampling Designs ◽  
Survey Responses ◽  
Survey Methodologies ◽  
Analytical Approaches

Abstract Companion animal researchers have been at the forefront of using survey methodologies to study dogs’ and cats’ dietary and health patterns in the general population. The reporting of survey results has increased in recent years, facilitated by the rise in internet access, the modest cost of conducting web surveys, and the capability to target surveys to pet owners through address lists collected by services and social media. Data from population surveys have the potential to garner unique and comprehensive information that complements the understanding offered by designed experiments. Recent developments in survey methodologies and the availability of user-friendly survey tools enable the collection of large-scale or even Big Data sets, not only in the number of survey responses but also in the number and type of variables measured. Irrespective of the sample size, the study of survey data necessitates the consideration of complex sampling designs and analysis approaches that reflect the nature of this data. An overview of the characteristics of complex sampling designs typical of survey data with applications to companion animal nutrition is presented. The fundamentals of the analytical approaches that are suitable for survey data are demonstrated, and procedures available to accommodate clustering, stratification, underrepresentation, and nonresponse are reviewed. Examples of survey data visualization and analysis strategies are presented.

PENGARUH REWARD TERHADAP KNOWLEDGE SHARING PERANGKAT DESA BERDAMPAK PENINGKATAN PARTISIPASI MASYARAKAT

2021 ◽  
Vol 3 (2) ◽  
pp. 104-118
Author(s):  
Faisal Amri ◽  
Ida Yusnita ◽  
Ayu Esteka Sari
Keyword(s):  
Knowledge Sharing ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Cluster Sampling ◽  
Extrinsic Rewards ◽  
Intrinsic Rewards

Penelitian ini memiliki tujuan mendapatkan hasil dari pengaruh reward terhadap knowledge sharing perangkat desa berdampak terhadap peningkatan partisipasi masyarakat. Reward pada penelitian ini terbagi atas Extrinsic Rewards dan Intrinsic Rewards. Penelitian ini dilaksanakan di Kabupaten Kerinci dengan Perangkat desa sebagai subjek penelitian. Penelitian ini dilaksanakan pada Bulan Juni 2020 - September 2020. Perangkat Desa di Kabupaten Kerinci merupakan populasi dalam penelitian ini dengan menggunakan metode penarikan sampel adalah Cluster Sampling dengan mengelompokkan sampel didasari wilayah dengan jumlah sampel adalah 108 responden. Sumber data didapatkan dari wawancara (interview) serta daftar pertanyaan (questionnaire). Pada penelitian ini menggunakan analisis data Structural Equation Models (SEM) serta menggunakan AMOS sebagai alat analisis. Hasil penelitian didapatkan koefisien determinasi besar pengaruh knowledge sharing yang dapat dijelaskan oleh variabel extrinsic rewards dan intrinsic rewards sebesar 17%. Sedangkan koefisien determinasi persamaan Partisipasi Masyarakat sebesar 20,2%.  Hasil dari penelitian didapatkan dari pengujian hipotesis bahwa extrinsic rewards dan intrinsic rewards memiliki pengaruh yang positif dan signifikan terhadap knowledge sharing, knowledge sharing dan intrinsic rewards berpengaruh positif dan signifikan terhadap partisipasi masyarakat sedangkan extrinsic rewards berpengaruh tidak signifikan terhadap partisipasi masyarakat. Knowledge Sharing dalam penelitian ini bukan merupakan variabel intervening karena pengaruh langsung extrinsic rewards terhadap partisipasi masyarakat lebih besar dari pada pengaruh tidak langsung melalui knowledge sharing dan pengaruh langsung intrinsic rewards terhadap partisipasi masyarakat juga lebih besar dari pengaruh tidak langsung terhadap partisipasi masyarakat melalui knowledge sharing. Hasil dari penelitian ini memberikan bukti empiris sebagai panduan bagi pemerintahan dan perangkat desa untuk menetapkan strategi yang tepat dalam knowledge sharing dan meningkatkan partisipasi masyarakat termasuk dampak terhadap pembangunan daerah

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.

Sign in / Sign up

Export Citation Format

Share Document