Structural equation modelling

The number of participants with complete data on all variables used in analysis on life course SEP and cognition was 25,127, and 30,846 after including missing data with pairwise present in structural equation analysis.

Table 4.4. Zero-order correlations among variables used in structural equation models (based on listwise deletion, n=25,127)

Figures are Pearson correlations for combinations of continuous variables and polyserial correlations for combinations of continuous and categorical (participants’ education) variables, as treated in structural equation models.

Correlations for men and women (shaded) are below and above the diagonal, respectively.

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Zero-order correlations among the variables used in structural equation analysis are shown in Table 4.4. The intercorrelations for the cognitive tests were significant and mostly similar across centres, ranging from 0.28 (p<0.001) for memory and letter search in Czechs to 0.54 (p<0.001) for verbal fluency and memory in Krakow.

Across centres, all SEP variables were significantly correlated with all cognitive functions, with higher SEP values positively covarying with higher cognitive scores. Across centres SEP measures from each stage of the life course were significantly and positively correlated with SEP measures from all other life course stages. Expectedly, age was negatively correlated with all cognitive functions. Inverse correlations were also observed between age and all SEP measures.

Structural equation models were estimated for complete cases (listwise deletion, n=25,127) and after including missing data (pairwise present, n=30,846). The results were very similar;

therefore results only from the latter are reported. Measurement invariance testing supported invariance of factor loadings but not invariance of intercepts across groups (further details are given in Appendix IV, pg. 256). Invariance of factor loadings is sufficient for comparison of structural parameters across groups, the primary focus of this study.

Direct estimates for the model with all structural paths constrained across groups are reported in Figure 4.1. Indirect and total effects are shown in Table 4.5. Unstandardized estimates are preferred for comparing groups because different variances between groups may lead to different standardized estimates even with the same unstandardized solution. For this reason only unstandardized effects are reported for the model with path coefficients constrained

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across groups, and both unstandardized and standardized effects are reported for the unconstrained multiple-group model.

The fully constrained multiple-group model had an adequate fit to the data (χ²=1326.335, df=178; CFI=0.954; TLI=0.949; RMSEA=0.041 [0.039-0.043]). This model revealed that SEP measures from all stages of the life course were significantly (p<0.001) associated with cognition in mid and later life. Only childhood amenities were not substantively associated with cognition (p=0.013) in this model.

Figure 4.1. Estimates from constrained multiple-group structural equation model

The figure shows unstandardized path coefficients with 95% CIs from the multiple-group structural equation model with all structural parameters constrained across groups representing pathways between childhood SEP, educational attainment, adult household assets, and cognitive function (n=30,846; χ2=1326.335, df=178; CFI=0.954; TLI=0.949; RMSEA=0.041 [0.039-0.043]). Educational attainment is entered as a categorical mediator; paths leading to it are probit coefficients. The model is age-adjusted with direct paths from age to cognition, assets and education, and estimated conditional on the covariances (↔) between observed exogenous variables (childhood SEP measures and age) but, for clarity, age and paths associated with it are not shown in the figure.

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Among the life course SEP measures the strongest direct path to cognition was from own education. The indirect path leading from own education to cognition via household asset ownership was very small. A comparably weaker direct path to cognition was from household asset ownership, a measure of current SEP.

Although statistically significant, the direct path from mother’s education to the latent cognitive factor was weak. Additionally, mother’s education showed a significant indirect association with cognition, largely mediated through its effect on participants’ own education. The indirect effect of mother’s education on cognition was greater than its direct effect.

Table 4.5. Estimates of indirect and total effects of life course SEP measures on cognition from constrained multiple-group structural equation model (n=30,846)

Constrained model

b SE p-value

Indirect effects on cognition

Mother’s education → Education 0.51 0.02 <0.001

Mother’s education → Assets 0.03 0.00 <0.001

Mother’s education → Education → Assets 0.04 0.00 <0.001

Total indirect effect Mother's education → Cognition 0.57 0.02 <0.001

Childhood amenities → Education 0.10 0.01 <0.001

Childhood amenities → Household assets 0.02 0.00 <0.001

Childhood amenities → Education → Household assets 0.01 0.00 <0.001

Total indirect effect Childhood amenities → Cognition 0.13 0.01 <0.001

Education → Household assets 0.08 0.01 <0.001

Total effects on cognition

Mother’s education 0.78 0.03 <0.001

Childhood amenities 0.16 0.01 <0.001

Education 1.09 0.02 <0.001

Household assets 0.16 0.01 <0.001

b=path coefficient; SE=standard error

Model fit indices: χ²(178)=1326.335; CFI=0.954; TLI=0.949; RMSEA=0.041 [0.039-0.043]

The total indirect effect of basic childhood amenities on cognition was rather small. Again, it was conveyed primarily via participants' education; the indirect effect conveyed through household assets was negligible. Taking the total effects of SEP measures on cognition into

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consideration, mother's education came second to participants' own education, followed by the total effects of household assets currently owned and basic amenities in childhood, which were of similar magnitude.

The unconstrained model was a significant improvement over the fully constrained model (χ² (94) = 621.824, RMSEA=0.038 [0.035-0.041]; CFI=0.979; TLI=0.955), suggesting that there were some significant group differences. Post-hoc evaluation of model fit identified parameters that differed significantly across groups. Estimates for the unconstrained model are reported in Table 4.6. and Table 4.7. for men and women, respectively.

The path from assets to cognition was stronger in Krakow and, especially, Novosibirsk than in Kaunas and Czech towns. Household asset ownership tended to be more strongly associated with cognition in men than in women; this was especially apparent in Polish and Czech samples. The direct path from childhood amenities to cognition was statistically significant in Russian women and at least as important as the path from mother’s education.

Mother’s education was more strongly associated with cognition in Czechs compared to other study centres. The direct path from education was the weakest in Novosibirsk and strongest in Kaunas, and both were significantly different from the average effect of own education estimated in the fully constrained model.

With respect to total effects on cognition, the number of household assets rather than mother's education was second to own education in Novosibirsk men. Total effect of childhood amenities on cognition was not significant in Kaunas but, relative to other SEP measures and compared to other groups, it was larger in Russian women and in Krakow. In Czech women the indirect effect of own education via household assets was not statistically significant.

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There was also significant variation in the total effect of education on cognition across centres, partly reflecting variation in the direct effect of education.

When structural equation analysis was repeated using father’s education the pattern of results was the same but mother’s education was slightly more strongly associated with cognition in men, especially among Czechs, whereas father's education was slightly more strongly associated with cognition in Krakow (the results are shown in Table V-1 of Appendix V, pg.

257). As for mother’s education, the total effect of father’s education on cognition was significant in all centres, although its magnitude was not substantial. Education of both parents could not be modelled simultaneously because educational homogamy in these cohorts was very high (polychoric correlations for mother's and father's education ranged from 0.92 in Krakow to 0.64 in Czech towns).

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Table 4.6. Results from unconstrained multiple-group structural equation model in men Czech towns

Fit indices: χ² (94) = 621.824, RMSEA=0.038 [0.035-0.041]; CFI=0.979; TLI=0.955 b and Std. denote unstandardized and standardized path coefficients; SE=standard error Paths from mother’s education and childhood amenities to own education are probit coefficients

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Table 4.7. Results from unconstrained multiple-group structural equation model in women Czech towns

Fit indices: χ² (94) = 621.824, RMSEA=0.038 [0.035-0.041]; CFI=0.979; TLI=0.955 b and Std. denote unstandardized and standardized path coefficients; SE=standard error Paths from mother’s education and childhood amenities to own education are probit coefficients

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Results for associations between SEP measures at different stages of life course were noteworthy. Own education was significantly influenced by mother’s education. The effect was largely similar across study centres. The results for father’s education were also very similar (shown in Appendix V). Childhood amenities had a small direct effect on own education. Current household asset ownership received mostly significant but small direct inputs from childhood amenities and mother’s education (but generally not father's education), with little variation across study centres. Own education had a moderate effect on current asset ownership, and the effect was stronger in men than in women. The effect of education on household asset ownership was especially strong in Krakow.

Finally, the structural equation analysis was repeated with each of the three cognitive measures as the outcome, as shown in Table 4.8. The pattern of results was generally the same as that observed for the latent cognitive factor in each respective centre. The strongest path to each cognitive outcome was from education, followed by a weaker path from household assets. Notably, in all centres the path from own education was stronger for verbal fluency than for the other two cognitive tests. For the most part, the association between mother’s education and verbal fluency was also somewhat stronger relative to word recall and letter cancellation. Mother’s education generally had a small significant effect on verbal cognitive measures in women, while it was mostly significantly associated with letter search in men. Childhood amenities had a significant but small direct effect (p<0.001) on verbal memory in Novosibirsk, and, additionally, on letter search in Novosibirsk women but not in any of the other centres.

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Table 4.8. Direct effects from unconstrained multiple-group structural equation models for each cognitive outcome

Czech towns __ Novosibirsk __ Krakow __ Kaunas

b=unstandardized regression coefficient; SE=standard error; Std=standardized regression coefficient

Model fit indices: Word recall: χ² (3) =4.561; RMSEA=0.012; CFI=1.000; TLI=0.996; Verbal fluency: χ² (3) =4.308, RMSEA=0.011; CFI=1.000; TLI=0.996; Letter cancellation: χ² (3) =3.577;

RMSEA=0.007; CFI=1.000; TLI=0.998

Because the models were just-identified some statistically insignificant structural paths, which were not of primary interest, were constrained to zero in all models in order to estimate model fit.

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