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Positive and negative valences of identities: Longitudinal associations of identity content valences with adaptive and maladaptive functioning among Japanese young adults

Published online by Cambridge University Press:  26 March 2021

Shogo Hihara*
Affiliation:
Department of Psychology, Graduate School of Humanities and Social Sciences, Hiroshima University, Hiroshima, Japan
Kazumi Sugimura
Affiliation:
Department of Psychology, Graduate School of Humanities and Social Sciences, Hiroshima University, Hiroshima, Japan
Tomotaka Umemura
Affiliation:
Department of Psychology, Graduate School of Humanities and Social Sciences, Hiroshima University, Hiroshima, Japan
Yasuhiro Iwasa
Affiliation:
Department of Psychology, Graduate School of Education, Hiroshima University, Hiroshima, Japan
Moin Syed
Affiliation:
Department of Psychology, University of Minnesota, Minneapolis, MN, USA
*
Author for Correspondence: Shogo Hihara, Graduate School of Humanities and Social Sciences, Hiroshima University, 1-1-1 Kagamiyama, Higashihiroshima, Hiroshima 739-8524, Japan; E-mail: hiharashogo@gmail.com
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Abstract

Erikson's psychosocial developmental theory assumes that valence of one's identity (i.e., identity content valence) defined by positive and negative identity elements is important for understanding patterns of (mal)adaptation. However, previous empirical research on identity and (mal)adaptation has focused on how individuals deal with identity issues (e.g., exploration and commitment), while neglecting identity content valences. In contrast, this study assessed identity content valences in terms of positive and negative identity elements. Theoretically, identity content valences affect (mal)adaptation, whereas individuals’ (mal)adaptation influences their identities. Consequently, this study examined reciprocal relationships between identity content valences and adaptation (i.e., prosocial behaviors) and maladaptation (e.g., externalizing symptoms) in a sample of Japanese young adults, including socioculturally relevant indicators of maladaptation (i.e., hikikomori symptoms and suicidal ideation). This study includes 2,313 Japanese young adults who participated in a three-wave longitudinal study. The cross-lagged panel model and random intercept cross-lagged panel model revealed reciprocal relationships between identity content valences and (mal)adaptation at the between-person level. Negative identity elements positively predicted suicidal ideation. Meanwhile, prosocial behaviors positively predicted positive identity elements, whereas hikikomori symptoms and suicidal ideation positively predicted negative identity elements. These relationships were significant only for socioculturally relevant indicators, suggesting the importance of considering sociocultural contexts.

Information

Type
Regular Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press
Figure 0

Table 1. Coding system for the Twenty Statements Test and the proportion of responses in each category

Figure 1

Figure 1. Conceptual model of the cross-lagged panel analysis linking between identity content valences and adaptive and maladaptive functioning. For the sake of clarity, within-time correlations are presented in dotted line, and paths from age and gender are not displayed.

Figure 2

Figure 2. Conceptual model of the random intercept cross-lagged panel analysis linking between identity content valences and adaptive and maladaptive functioning. For the sake of clarity, within-time correlations are presented in dotted line, and paths from age and gender are not displayed.

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Table 2. Descriptive statistics for the study variables

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Table 3. Within-time correlations among demographic characteristics and the study variables

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Table 4. Fit indices and model comparisons for cross-lagged panel models and random intercept cross-lagged panel models

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Table 5. Cross-lagged and stability paths for the cross-lagged panel analysis and random intercept cross-lagged panel analysis

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Table 6. Within-time correlations for the cross-lagged panel analysis and random intercept cross-lagged panel analysis

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Table 7. Correlations between random intercepts in the random intercept cross-lagged panel analysis