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大学生生涯决策自我效能、生涯发展障碍与应对策略的现状与相关研究

The Current Situation and Relevant Study on the Career Decision-making Self-efficacy, Career Barriers and Coping Strategies for Graduate Students

【作者】 章小波

【导师】 樊琪;

【作者基本信息】 苏州大学 , 应用心理学, 2005, 硕士

【摘要】 近年来,随着“供需见面、双向选择、择优录用”就业制度的确立,大学生择业的自主性越来越大。因此,如何根据市场的需求和个人的特点与能力,找到适合自己的生涯发展方向,并做出正确的生涯选择,是每个大学生在职业生涯发展过程中都不可回避的现实问题。而作为学校,不仅肩负着对大学生知识、技能的传授与培养的责任,同时也应担当其对大学生生涯辅导的重任,以帮助大学生提高生涯选择时的理性决策能力与合理生涯规划能力。 那么,在生涯辅导过程中,如何指导大学生做出令自己满意的职业生涯决策呢?学校只有对大学生生涯发展的现状有所了解,并在此基础上,进一步分析大学生在求学阶段或未来毕业后就业时可能遇到的阻碍其生涯顺利发展的各种障碍因素,从而才能确定其在目前的生涯发展中有哪些地方是需要改善或加强的,进而为大学生提供有效的、合适的生涯辅导策略。因此,本研究试图通过对我国大学生生涯决策自我效能、生涯发展障碍与应对策略现状及相关性的研究,为大学生生涯辅导提供测量和诊断手段,这对于更好地做好大学生的生涯辅导工作以及改变大学生生涯决策的效果都具有重要的指导意义和实践意义。 本研究主要通过问卷调查收集资料,采用因素分析、信度分析、描述性分析、T检验、皮尔逊积差相关分析和多元回归分析等,对收集到的资料进行分析并检验各项假设。研究结果如下: 一、不同性别、专业类别(文科和理工科)、学校性质(公办和民办)、工作经验、生涯辅导(有与无)的大学生,在生涯决策自我效能方面有显著的不同;不同专业类别(文科和理工科)、学校性质(公办和民办)的大学生,在生涯发展障碍方面有显著的不同;不同学校性质(公办和民办)、工作经验、生涯辅导(有与无)的大学生,在生涯应对策略方面有显著的不同。 二、大学生生涯决策自我效能总量表及各分量表,均与生涯发展障碍总量表及各分量表间,达到显著的负相关;大学生生涯决策自我效能总量表及各分量表,与生涯应对策略总量表及各分量表间,均达到显著的正相关;大学生生涯发展障碍总量表及各分量表,与生涯应对策略总量表及除“寻求支持”之外的其他四个分量表间,达到显著的正相关。 三、大学生生涯决策自我效能量表中的“问题解决”、“信息收集”、“自我评估”与“计划制定”,及生涯发展障碍量表中的“方向选择”、“个人特质”等六个变量能有效预测大学生生涯应对策略的选择,可解释43.4%的变异量。其中生涯决策自我效能的“问题解决”为主要的预测变量,可解释28.9%的变异量。

【Abstract】 Recently, with the establishment of the open flexible employment system, the independence of university students’ in their career choice grow in a notable pace. In view of this situation, all graduates will face this inevitable question: how to choose a career suitable for oneself since the demands of the employment market are varied and the capability of each student is different. So different from the past, universities should not only teach knowledge and learning ability to their students, but also give some guidance in their career-decision making in order to enhance their ability in making rational decision.The problem is how to help them make satisfactory decision. The authorities should have some ideas about the current situation of their vocational development, and basing on this understanding, go on to analyze all the possible barriers they will be confronted with in future. Only in this way, the authorities could make sure what can be improved or enforced. So in career counseling, one of the major tasks of the authorities is to develop and increase student’s ability of career-decision making. Holding this belief, this paper makes some research on the career decision-making self-efficacy, career barriers and coping strategies to find out some testing and diagnostic methods. The author thinks this kind of research has important theoretical and practical sense.This research collects the materials by making questionnaire. Those materials are analyzed from the following aspects: factors analysis, reliability analysis, descriptive distribution, T-test analysis, Pearson’s correlation and multiple regression analysis. The conclusions derived are set as follows:1 .Gender, major (Arts or Science and Engineering), the nature of the university( public or private), working experience and career tutoring experience, those elements have dominant influence on Career decision-making self-efficacy. Among those elements, major and the nature of the university also have notable influence on career barriers, and the last three elements have notable influence on coping strategies.2.The gross scale and the sub-scale of the career decision-making self-efficacy increase as those two scales of career barriers decrease while the relationship with the scales of coping strategies is on the contrary. The scales of career barriers increase with the increase of the scales of coping strategies (with the exception of the element seeking support).3.Four elements in the career decision-making self-efficacy scale, that is, problem resolution, of information collection, self evaluation and plan making, together with two elements in the career barriers scale, that is, the career direction and personality, can efficiently forecast the variation instudent’s career decision-making. Those six elements together can predict 43.4% variation, while the element "problem resolution" itself can predict 28.9%, which shows that it is the main element in making prediction.

  • 【网络出版投稿人】 苏州大学
  • 【网络出版年期】2006年 05期
  • 【分类号】B844.2;G647.38
  • 【被引频次】50
  • 【下载频次】1822
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