self-esteem英文解释

Self-esteem refers to a person's overall subjective evaluation of their worth, value, and abilities. It involves a person's beliefs and feelings about themselves, including their competence, attractiveness, and likability. High self-esteem is associated with positive mental health outcomes, while low self-esteem can contribute to a range of mental health problems, such as depression and anxiety. Self-esteem can be influenced by a variety of factors, including personal experiences, social interactions, and cultural values.

相关问题

self-esteem

Self-esteem refers to an individual's overall sense of self-worth or value. It is the subjective evaluation of one's abilities, qualities, and achievements. High self-esteem is associated with positive emotions, confidence, and resilience, while low self-esteem is associated with negative emotions, self-doubt, and insecurity. Self-esteem can be influenced by various factors, including one's upbringing, experiences, relationships, and societal messages. It is important to cultivate a healthy level of self-esteem to promote well-being and success in various aspects of life.

mplus中介效应模型的路径分析代码

以下是在Mplus中运行中介效应模型的路径分析代码示例。我们将使用Covariate为性别和年龄，Predictor为自尊度，Mediator为社交支持，Outcome为抑郁症状：

VARIABLES:
NAMES ARE
id sex age social selfesteem depression;
USEVARIABLES ARE
sex age social selfesteem depression;
MISSING ARE ALL (-99);
CATEGORICAL = sex;
GROUPING = sex (0 = male, 1 = female);

MODEL:
! Direct path from self-esteem to depression
depression ON selfesteem;
! Direct path from social support to depression
depression ON social;
! Indirect path from self-esteem to depression through social support
social ON selfesteem;
depression IND social;

ANALYSIS:
TYPE = GENERAL;
BOOTSTRAP = 1000;
ESTIMATOR = ML;
PROCESS = 1;

OUTPUT:
TECH11;
TECH14;
TECH15;
STANDARDIZED;
CINTERVAL(BOOTSTRAP);

在上面的代码中，我们首先指定了变量名和使用的变量，包括性别、年龄、社交支持、自尊度和抑郁症状。然后，我们将性别设置为分类变量，并将样本分组为男性（0）和女性（1）。在MODEL部分，我们定义了一条从自尊度到抑郁症状的直接路径，一条从社交支持到抑郁症状的直接路径，并在社交支持和抑郁症状之间定义了一个中介效应。最后，我们设置了一些输出选项，包括技术报告、标准化参数和自举置信区间。

注意，在这个例子中，我们还使用了自举技术来估计间接效应的置信区间。这对于确认中介效应的存在和强度非常重要。