個人特性としての好奇心の領域とタイプについて : 知的好奇心と対人的好奇心

関西大

個人特性としての好奇心の領域とタイプについて―知的好奇心と対人的好奇心― [論文要旨及び審査の要旨]

関西大

On the construct of ego-resiliency : An overview of self-report measures

Ego-resiliency has been received much attention in the field of clinical psychology. However, the dimensionality of the Ego-resiliency scale is not clear enough. In the European and American studies, the Ego-resiliency scale has been reported to have single-factor or two to four factors. It has been pointed out that there is a difference between Resilience and Ego-resiliency overseas; thus the two terms must be distinguished when used (Luthar, 2000). In Japan, the definition of the difference between Resilience and Ego-resiliency remains unclear and research on the scale development of Resilience sololy depend on individual characteristics. In addition to this, the condition that is the premise of Resilience is characterized not only by the "difficult or phenomenal situation", "the risk that is thought to bring serious consequences" or "the serious adversity", but rather by the stress level experienced on a daily basis.Ego-resiliency 尺度の構成概念についての議論は未だ続いており，次元性についても未だ明確となっていない。研究者によりEgo-resiliency の個人差測定に用いられる尺度の構成概念は単一因子， 2因子， 3因子， 4因子と様々な報告がなされている。また，海外においては，Resilience とEgo-resiliency の違いや両者を区別して使用することが指摘されているが (Luthar, 2000)，我が国においては，Resilience と Ego-resiliency の両者の違いの定義が曖昧なままResilience を個人特性として扱いその測定のための尺度開発の研究が進められている現状がある。また，Resilience の前提となるレジリエントな状況の範囲が，『困難あるいは驚異的な状況』，『深刻な結果をもたらすと考えられるリスク』，『重大な逆境』から，日常的に経験しうるストレスレベルに拡大解釈されてきているという特徴もみられる

Attitudes toward possible food radiation contamination following the Fukushima nuclear accident: a nine-year, ten-wave panel survey

被災地産食品回避は不安の低下と批判的思考が減少させる --10回の継続調査からみた福島原発事故によるリスク認知の変化と地域差--. 京都大学プレスリリース. 2023-03-14.Overcoming nuke stigma through critical thinking: Changes in post-Fukushima risk perception show regional differences. 京都大学プレスリリース. 2023-05-25.After the Fukushima nuclear accident, we examined changes in risk perception regarding the radiation contamination of food and information-seeking behavior among residents of three regions progressively more distant from the disaster area, the Tokyo Metropolitan area to the Kansai area. We conducted a ten-wave panel survey and obtained data from 1, 752 citizens six months to nine years after the accident. The results indicate that anxiety related to radioactive contamination, active information-seeking behavior, and avoidance of foods from affected areas decreased with time. Active information-seeking behavior and radiation-related knowledge were higher in the disaster-affected prefectures than in other areas. Conversely, avoidance of foods from affected areas was lower in affected prefectures than in the Kansai area. The credibility of government information increased from a considerably low level but did not reach the midpoint level. Multiple regression analysis, cross-lagged analysis, and structural equation modeling indicated that avoidance of foods from affected areas was promoted by anxiety related to radioactive contamination (experiential thinking/System 1) and inhibited by critical thinking attitudes (analytical thinking/System 2). Finally, we discussed the significance of risk literacy, which integrates risk-related knowledge, scientific literacy, media literacy, and critical thinking

Trait-state distinction model with structured means : Methodologies and applications for longitudinal data sets of multiple occasions

探索的因子分析の文脈で、Cattell (1965)は、「特定化方程式において、特性因子得点以外に状態因子得点を常に付け加えなければならない」と述べている。この方程式について、Cattell (1973)は、観測された変数が特性因子と状態因子に負荷すると定義している。彼の考えを複数機会の縦断データに構造方程式モデリングに適用しながら、われわれは、特性因子がすべての測定機会に対して因子パターン不変であり、一つの測定機会からなる状態因子が異なる測定機会の他の状態因子と不変であるとする特性・状態区分モデルを提案した。われわれのモデルとGeiser (2021)の単一特性・多状態モデルの違いは、特性と状態の因子分散が因子パターンの不変性の下で独立して推定され、特性と状態の因子得点の平均も推定されることである。特性・状態区別モデルは、2回測定の状態特性不安尺度、3回測定の大学での学習観尺度、5回測定のGrit尺度、そして、3回測定のBig Five形容詞30項目の尺度で使用された。これらの心理的変数の特性を明らかにするために、推定された因子の分散を、特性度と状態度の2次元空間にプロットした。因子の分散と因子の平均を組み合わせることの意義などが、特性・状態区分と関連づけて議論された。In the context of exploratory factor analysis, Cattell (1965) noted that "in the specification equation we must always add state factor scores along with trait factor scores." This equation was defined by Cattell (1973) as the observed variables loading on the trait factors and the state factors. Applying his idea to structural equation modeling for longitudinal data of multiple occasions, we proposed the traitstate distinction model, wherein the trait factor was invariant for all measurement occasions and the state factor of one measurement occasion was invariant with the other state factor of different measurement occasion. The differences between our model and the singletrait-multistate model of Geiser (2021) are that the trait factor variances and the state factor variances were estimated independently under the factor pattern invariance, and, the means of the trait and state factor scores were also estimated. The trait-state distinction model in this paper was utilized for the State Trait Anxiety Scale of two occasions, the College Learning Perspective Scales of three occasions, the Grit Scale of five occasions, and the Big Five Adjective Scale of 30 items of three occasions. To characterize these psychological variables, the estimated factors\u27 variances were plotted in a two-dimensional space of trait and state proportions. The implications of combining factor variances and factor means were discussed in relation to trait-state distinction