Deutsches Institut für Internationale Pädagogische Forschung (DIPF)
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- age differences (1)
- autobiographical reasoning (1)
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- emotionality (1)
- ergodic subspace analysis (1)
- ergodicity (1)
- imageability (1)
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Objective: Although meaning making and specifically autobiographical reasoning are expected to relate to well‐being, findings tend to be mixed. Attempts at meaning making do not always lead to meaning made. We aimed to disentangle these complex relationships and also explore the role of level of education.
Method: Ninety participants (mean age 36.73 years, SD = 7.27; 74.4% women, 25.6% men) who had experienced the loss of a parent through death, going missing, or Alzheimer's disease narrated this loss, a sad, a turning point, and a self‐defining memory, and completed questionnaires assessing depression, trauma symptoms, and protracted grief. Three aspects of autobiographical reasoning (quantity, valence, and change‐relatedness of self‐event connections) were related to meaning made (sophistication of meaning making) and symptom level.
Results: Years of education correlated both with positive implications of autobiographical reasoning and with meaning made. The quantity, positivity, and change‐relatedness of attempts at meaning making (self‐event connections) predicted accomplished meaning made, and positivity alone predicted less prolonged grief.
Conclusions: Adapting the life story after a loss such that change of the self is acknowledged and positive change can be constructed helps finding meaning and lowering protracted grief. These changes in narrative identity are supported by more years of education.
Imageability and emotionality ratings for 2592 German nouns (3–10 letters, one to three phonological syllables) were obtained from younger adults (21–31 years) and older adults (70–86 years). Valid ratings were obtained on average from 20 younger and 23 older adults per word for imageability, and from 18 younger and 19 older adults per word for emotionality. The internal consistency (Cronbach’s α) and retest rank-order stability of the ratings were high for both age groups (α and r ≥ .97). Also, the validity of our ratings was found to be high, as compared to previously published ratings (r ≥ .86). The ratings showed substantial rank-order stability across younger and older adults (imageability, r = .94; emotionality, r = .85). At the same time, systematic differences between age groups were found in the mean levels of ratings (imageability, d = 0.38; emotionality, d = 0.20) and in the extent to which the rating scales were used (imageability, SD = 24 vs. 19, scale of 0 to 100; emotionality, SD = 26 vs. 31, scale of −100 to 100). At the descriptive level, our data hint at systematically different evaluations of semantic categories regarding imageability and emotionality across younger and older adults. Given that imageability and emotionality have been reported, for instance, as important determinants for the recognition and recall of words, our findings highlight the importance of considering age-specific information in age-comparative cognitive (neuroscience) experimental studies using word materials. The age-specific imageability and emotionality ratings for the 2592 German nouns can be found in the electronic supplementary material...
Ergodic subspace analysis
(2020)
Properties of psychological variables at the mean or variance level can differ between persons and within persons across multiple time points. For example, cross-sectional findings between persons of different ages do not necessarily reflect the development of a single person over time. Recently, there has been an increased interest in the difference between covariance structures, expressed by covariance matrices, that evolve between persons and within a single person over multiple time points. If these structures are identical at the population level, the structure is called ergodic. However, recent data confirms that ergodicity is not generally given, particularly not for cognitive variables. For example, the <i>g</i> factor that is dominant for cognitive abilities between persons seems to explain far less variance when concentrating on a single person’s data. However, other subdimensions of cognitive abilities seem to appear both between and within persons; that is, there seems to be a lower-dimensional subspace of cognitive abilities in which cognitive abilities are in fact ergodic. In this article, we present ergodic subspace analysis (ESA), a mathematical method to identify, for a given set of variables, which subspace is most important within persons, which is most important between person, and which is ergodic. Similar to the common spatial patterns method, the ESA method first whitens a joint distribution from both the between and the within variance structure and then performs a principle component analysis (PCA) on the between distribution, which then automatically acts as an inverse PCA on the within distribution. The difference of the eigenvalues allows a separation of the rotated dimensions into the three subspaces corresponding to within, between, and ergodic substructures. We apply the method to simulated data and to data from the COGITO study to exemplify its usage.