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An application of Bayesian measurement invariance to modelling cognition over time in the English Longitudinal Study of Ageing.

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Title: An application of Bayesian measurement invariance to modelling cognition over time in the English Longitudinal Study of Ageing.
Authors: Williams, Benjamin David, Chandola, Tarani, Pendleton, Neil
Source: International Journal of Methods in Psychiatric Research. Dec2018, Vol. 27 Issue 4, pN.PAG-N.PAG. 1p. 4 Charts.
Subjects: Bayesian analysis, Cognition, Attention, Memory, Mathematical symmetry
Abstract: Objectives: Recommended cut‐off criteria for testing measurement invariance (MI) using the comparative fit index (CFI) vary between −0.002 and −0.01. We compared CFI results with those obtained using Bayesian approximate MI for cognitive function. Methods: We used cognitive function data from Waves 1–5 of the English Longitudinal Study of Ageing (ELSA; Wave 1 n = 11,951), a nationally representative sample of English adults aged ≥50. We tested for longitudinal invariance using CFI and approximate MI (prior for a difference between intercepts/loadings ~N(0,0.01)) in an attention factor (orientation to date, day, week, and month) and a memory factor (immediate and delayed recall, verbal fluency, and a prospective memory task). Results: Conventional CFI criteria found strong invariance for the attention factor (CFI + 0.002) but either weak or strong invariance for the memory factor (CFI −0.004). The approximate MI results also supported strong MI for attention but found 9/20 intercepts or thresholds were noninvariant for the memory factor. This supports weak rather than strong invariance. Conclusions: Within ELSA, the attention factor is suitable for longitudinal analysis but not the memory factor. More generally, in situations where the appropriate CFI criteria for invariance are unclear, Bayesian approximate MI could alternatively be used. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Methods in Psychiatric Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Label: Title
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  Data: An application of Bayesian measurement invariance to modelling cognition over time in the English Longitudinal Study of Ageing.
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  Data: <searchLink fieldCode="AR" term="%22Williams%2C+Benjamin+David%22">Williams, Benjamin David</searchLink><br /><searchLink fieldCode="AR" term="%22Chandola%2C+Tarani%22">Chandola, Tarani</searchLink><br /><searchLink fieldCode="AR" term="%22Pendleton%2C+Neil%22">Pendleton, Neil</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Methods+in+Psychiatric+Research%22">International Journal of Methods in Psychiatric Research</searchLink>. Dec2018, Vol. 27 Issue 4, pN.PAG-N.PAG. 1p. 4 Charts.
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  Data: <searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Cognition%22">Cognition</searchLink><br /><searchLink fieldCode="DE" term="%22Attention%22">Attention</searchLink><br /><searchLink fieldCode="DE" term="%22Memory%22">Memory</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+symmetry%22">Mathematical symmetry</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Objectives: Recommended cut‐off criteria for testing measurement invariance (MI) using the comparative fit index (CFI) vary between −0.002 and −0.01. We compared CFI results with those obtained using Bayesian approximate MI for cognitive function. Methods: We used cognitive function data from Waves 1–5 of the English Longitudinal Study of Ageing (ELSA; Wave 1 n = 11,951), a nationally representative sample of English adults aged ≥50. We tested for longitudinal invariance using CFI and approximate MI (prior for a difference between intercepts/loadings ~N(0,0.01)) in an attention factor (orientation to date, day, week, and month) and a memory factor (immediate and delayed recall, verbal fluency, and a prospective memory task). Results: Conventional CFI criteria found strong invariance for the attention factor (CFI + 0.002) but either weak or strong invariance for the memory factor (CFI −0.004). The approximate MI results also supported strong MI for attention but found 9/20 intercepts or thresholds were noninvariant for the memory factor. This supports weak rather than strong invariance. Conclusions: Within ELSA, the attention factor is suitable for longitudinal analysis but not the memory factor. More generally, in situations where the appropriate CFI criteria for invariance are unclear, Bayesian approximate MI could alternatively be used. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Methods in Psychiatric Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/mpr.1749
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        Text: English
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      – SubjectFull: Bayesian analysis
        Type: general
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      – SubjectFull: Attention
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      – SubjectFull: Memory
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      – SubjectFull: Mathematical symmetry
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      – TitleFull: An application of Bayesian measurement invariance to modelling cognition over time in the English Longitudinal Study of Ageing.
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            – D: 01
              M: 12
              Text: Dec2018
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              Y: 2018
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