Cross-Sectional and Longitudinal Predictability of Mental Health Using Sense of Coherence: Estimation Using Linear Regression and Generalized Additive Models

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  • Kase Takayoshi
    College of Contemporary Psychology, Rikkyo University
  • Ueno Yuki
    Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo

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  • Sense of Coherenceによる精神的健康の横断的・縦断的予測可能性の検討――線形回帰モデルと一般化加法モデルによる推定
  • Sense of Coherence ニ ヨル セイシンテキ ケンコウ ノ オウダンテキ ・ ジュウダンテキ ヨソク カノウセイ ノ ケントウ : センケイ カイキ モデル ト イッパンカ カホウ モデル ニ ヨル スイテイ

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<p>The present study investigated the cross-sectional and longitudinal predictability of mental health using Sense of Coherence (SOC) through a linear regression model (LRM) and generalized additive model (GAM). The estimation using LRM and GAM showed that SOC predicted mental health in both cross-sectional and longitudinal data. Moreover, the model fit index and analysis of deviance showed that the GAM fitted better with both data compared to LRM. These results suggest that SOC can be used as a predictor of the current and future states of mental health as well as the continuous and gradual changes in mental health.</p>

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