Model Fit Estimation for Multilevel Structural Equation Models

Rappaport, Lance M.; Amstadter, Ananda B.; Neale, Michael C.

doi:10.1080/10705511.2019.1620109

Cited by 59 publications

(44 citation statements)

References 32 publications

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“…In line with our hypotheses, a PA and NA structure fit the data better than a single valence factor or two orthogonal factors in all cases, thereby offering support for previous findings from both diary and ESM data (Rappaport et al, 2019;Rush & Hofer, 2014;Viechtbauer et al, 2020). The high correlation of PA and NA at the WP level of the short questionnaire indicated a high degree of redundancy between the two factors.…”

Section: Discussionsupporting

confidence: 90%

“…Studies that were able to distinguish the WP and BP structure of affect have come to varying conclusions. Generally, a two-factor structure with correlated PA and NA showed adequate fit (Brose et al, 2015;Merz & Roesch, 2011), and fit better than a model with a single valence factor (Rappaport et al, 2019;Rush & Hofer, 2014;Viechtbauer et al, 2020) at both levels. Several studies also observed different correlations between PA and NA at the different levels of analysis (Bleidorn & Peters, 2011;Rush & Hofer, 2014;Schmukle et al, 2002).…”

Section: Affective Structure Across Esm Protocolsmentioning

confidence: 93%

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Affective structure, measurement invariance, and reliability across different experience sampling protocols

Eisele¹,

Lafit²,

Vachon³

et al. 2020

Preprint

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While measures of affect are often included in studies using experience sampling methodology (ESM), the affective structure at the between- and within-person level has not been thoroughly investigated. Additionally, it is currently unclear to what extent this structure is influenced by variations in sampling protocols. Our first aim was to investigate the structure of affect at the between- and within-person level when measured with 8 or 18 affect items. Second, we investigated the invariance of this structure across different sampling frequencies and questionnaire lengths. Further, the reliability of positive and negative affect (PA and NA, respectively) was calculated. Participants (N = 163) were randomly assigned to receive either a 30 or 60 item questionnaire three, six, or nine times per day over 14 days. Momentary affect was assessed with 8 or 18 items. At both levels, a two-factor structure with correlated PA and NA showed the best fit compared to an orthogonal and a unidimensional model. However, the CFAs and additional EFAs indicated that items shared additional variance that could be accounted for by freeing residual correlations or adding more nuanced affect factors to achieve acceptable fit. A structure accounting for these additional relationships was invariant across different sampling protocols at the within-person level and resulted in high reliability. We observed differences between levels in the strength of the loadings, the correlation between PA and NA, and the number of factors suggested in EFAs. Taken together, these structural differences indicate a more discrete affective structure within than between persons

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Section: Discussionsupporting

confidence: 90%

Section: Affective Structure Across Esm Protocolsmentioning

confidence: 93%

Affective structure, measurement invariance, and reliability across different experience sampling protocols

Eisele¹,

Lafit²,

Vachon³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

Section: Affective Structure Across Esm Protocolsmentioning

confidence: 99%

Affective structure, measurement invariance, and reliability across different experience sampling protocols

Eisele

Lafit

Vachon

et al. 2021

Journal of Research in Personality

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While measures of affect are often included in studies using experience sampling methodology (ESM), the affective structure at the between-and within-person level has not been thoroughly investigated. Additionally, it is currently unclear to what extent this structure is influenced by variations in sampling protocols. Our first aim was to investigate the structure of affect at the between-and within-person level when measured with 8 or 18 affect items.Second, we investigated the invariance of this structure across different sampling frequencies and questionnaire lengths. Further, the reliability of positive and negative affect (PA and NA, respectively) was calculated. Participants (N = 163) were randomly assigned to receive either a 30 or 60 item questionnaire three, six, or nine times per day over 14 days. Momentary affect was assessed with 8 or 18 items. At both levels, a two-factor structure with correlated PA and NA showed the best fit compared to an orthogonal and a unidimensional model. However, the CFAs and additional EFAs indicated that items shared additional variance that could be accounted for by freeing residual correlations or adding more nuanced affect factors to achieve acceptable fit. A structure accounting for these additional relationships was invariant across different sampling protocols at the within-person level and resulted in high reliability.We observed differences between levels in the strength of the loadings, the correlation between PA and NA, and the number of factors suggested in EFAs. Taken together, these structural differences indicate a more discrete affective structure within than between persons.

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“…Agricultural carbon emission mainly comes from farmland utilization, paddy field, livestock intestinal fermentation and manure management. Among them, agricultural land use carbon emissions (farmland ecosystem carbon emissions), accounted for 34.29% of the total agricultural carbon emissions [ 10 ]. In order to explore the path of agricultural sustainable development, scholars have done a lot of research on the influencing factors of agricultural carbon emissions, it involves different countries and regions.…”

Section: Introductionmentioning

confidence: 99%

“…The commonly decomposition model of carbon emission factors includes LMDI decomposition method, Kaya identities and STRIPAT model [ 1 , 9 , 10 ]. Most of study found that, GDP of planting industry, GDP of agriculture, regional GDP, total regional population and total rural population, agricultural production efficiency, agricultural industrial structure, industrial structure, regional economic development level, urbanization all of the factors have reflected the influencing factors of agricultural carbon emissions [ 4 , 11 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

The influencing factors and spillover effects of interprovincial agricultural carbon emissions in China

Cao

Peng

2020

PLoS ONE

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Agricultural carbon emissions have become the constraints of agricultural low-carbon and circular economy development in China. China’s agricultural production faces severe pressures and challenges in agricultural carbon reduction. In this paper, we take observation for the 31 provinces in china from 1997 to 2017, to explore the influencing factors and spatial spillover effects of agricultural by estimating spatial panel data models. The results show that China’s agricultural carbon emissions will continue to increase in the future, because the growth of per capita gross domestic product (GDP) is the main driving force to accelerate the growth of agricultural carbon emissions, but the agricultural input factors will help to reduce the growth of carbon emissions. Moreover, it is proved that economic factors and agricultural input factors have direct effects and spatial spillover effects on agricultural carbon emissions except for agricultural environmental factors. In the short term, strengthening environmental protection may bring some pressure to the economic development of some places, but to achieve high-quality development, we must fundamentally solve the problem of environmental pollution. The conclusion provides important enlightenment and scientific basis for formulating effective policies to curb the growth of CO 2 emissions in China.

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Model Fit Estimation for Multilevel Structural Equation Models

Cited by 59 publications

References 32 publications

Affective structure, measurement invariance, and reliability across different experience sampling protocols

Affective structure, measurement invariance, and reliability across different experience sampling protocols

Affective structure, measurement invariance, and reliability across different experience sampling protocols

The influencing factors and spillover effects of interprovincial agricultural carbon emissions in China

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