心理学代写|心理学代写Psychology代考|PSYCH101

2022年10月13日

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心理学是对心灵和行为的科学研究。心理学包括对有意识和无意识现象的研究,包括感情和思想。

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我们提供的心理学Psychology及其相关学科的代写,服务范围广, 其中包括但不限于:

  • Statistical Inference 统计推断
  • Statistical Computing 统计计算
  • Advanced Probability Theory 高等概率论
  • Advanced Mathematical Statistics 高等数理统计学
  • (Generalized) Linear Models 广义线性模型
  • Statistical Machine Learning 统计机器学习
  • Longitudinal Data Analysis 纵向数据分析
  • Foundations of Data Science 数据科学基础
心理学代写|心理学代写Psychology代考|PSYCH101

心理学代写|心理学代写Psychology代考|PERSONALITY TRAITS

In the late seventies and early eighties, William Morgan (1980) suggested there were two camps within the sport psychology community. In one camp lived individuals who believed personality traits could predict sporting performance and success. In the second camp were those people who doubted that traits predicted performance. Today, there are still people in each camp, although the sceptics outnumber the believers. There is evidence that some personality traits are associated with sporting performance and success, such as conscientiousness and neuroticism, however, most professionals acknowledge that limitations in the research have contributed to a lack of definitive answers and an incomplete understanding (Allen et al., 2013). For example, investigators have used different criteria to define and distinguish athletes from non-athletes. A person considered an athlete in one study might be viewed as a non-athlete in a different investigation. As another limitation, much research has been cross-sectional and descriptive. Recall from Box 1.1, if the research has been descriptive and cross-sectional, it is difficult to determine if personality causes sporting success or the other way around. Most practitioners broadly agree that the understanding of the trait and performance relationship is incomplete, and they generally counsel against the use of trait measures for purposes such as team selection, especially without reference to other information such as recent performance or physical conditioning data (Marchant, 2010).

Researchers have examined the relationship between mood and performance. The Profile of Mood States (POMS) questionnaire has been used most often to measure mood in the sporting context. The POMS measures anger, depression, confusion, fatigue, tension, and vigour. Morgan (1980) proposed that successful athletes had an “iceberg profile” relative to the general population, as illustrated in Figure 2.1. When scores from the POMS are adjusted so that the average from the general population equals 50 , then successful athletes score lower on anger, confusion, depression, fatigue, and tension, but higher on vigour. The iceberg profile is also labelled the Mental Health Model.

The iceberg profile has stimulated much research and the POMS is a popular measure among sport psychology practitioners (Vealey et al., 2019). Overall, the POMS questionnaire suggests that mood is not related to ability levels. Athletes at different skill levels report similar mood profiles (Beedie et al., 2000). When used before a competitive event, however, mood states predict performance, albeit weakly (Beedie et al., 2000). Specifically, better performance is associated with vigour, and poorer performance is related to confusion, fatigue, and depression. Anger and tension are sometimes related with better performance, and sometimes with worse performance. The relationships between mood and performance are a little stronger for open skill, individual, and short-duration sports than for closed skill, team, and longer-duration sports. Also, the relationship is stronger for self-reported performance (e.g., coach ratings) than for objective measures (e.g., time, distance, and score). In all cases, however, the strength of the relationship is not strong: mood does not predict performance well.

心理学代写|心理学代写Psychology代考|PARTICIPATION MOTIVATION

To understand the direction aspect of motivation, sport psychology practitioners have studied why people choose to play sport, and once they have started, why they might decide to stop. The reasons people play sport fall within the realm of participation motivation. Understanding the reasons why people start and continue playing sport helps sport psychology practitioners, coaches, and administrators keep these individuals involved and entice more people to participate. Across the Western world most people fail to get enough physical activity to reap health benefits. Governments want to encourage people into physical activities, such as organized sport, to improve physical and mental wellbeing and reduce national healthcare costs. Sport psychology researchers can help because they have examined participation motivation a great deal.

The major motives people have for playing and withdrawing from sport are diverse, and individuals often have multiple reasons (Weiss, 2019). Broad categories of participation motives include competence, friends, fitness, and fun. Regarding competence, many people participate in sport to learn and improve their skills. People can interpret their competence in relation to themselves or in comparison to others. When self-referenced, people feel competent if they believe their skill levels are improving or they are proficient at the activity. When others-referenced, individuals participate for competitive reasons and feel competent when they show they are better than the other people playing. These folks are motivated to win and beat others. For these athletes the desire to win is a major reason for participating, particularly at the elite and professional level, where their livelihoods are influenced by their ability to defeat opponents.

Regarding the friends, fitness, and fun motives, people often play sport for lots of reasons, including to:

  • Make friends
  • Be part of a team
  • Interact with others
  • Improve their health
  • Enhance their appearance
  • Be active
  • Gain enjoyment
  • Relieve stress
  • Experience sensations associated with the sport
    The above list is not exhaustive and there are many other reasons.
心理学代写|心理学代写Psychology代考|PSYCH101

心理学代写

心理学代写|心理学代写Psychology代考|PERSONALITY TRAITS

.人格特征


在70年代末和80年代初,William Morgan(1980)提出在运动心理学社区中有两个阵营。在一个阵营中,住着相信性格特征可以预测运动成绩和成功的人。第二阵营是那些怀疑特质能预测表现的人。如今,尽管持怀疑态度的人多于相信的人,但两种阵营仍各有其人。有证据表明,一些人格特征与运动成绩和成功有关,如尽责性和神经质,然而,大多数专业人士承认,研究的局限性导致了缺乏明确的答案和不完整的理解(Allen et al., 2013)。例如,调查人员使用了不同的标准来定义和区分运动员和非运动员。在一项研究中被认为是运动员的人,在另一项调查中可能被视为非运动员。另一个限制是,许多研究都是横断面和描述性的。回想一下框1.1,如果研究是描述性的和横断面的,就很难确定是性格导致运动成功还是相反。大多数从业人员普遍认为,对特质和绩效关系的理解是不完整的,他们普遍反对将特质测量用于团队选择等目的,特别是在没有参考其他信息,如最近的绩效或身体条件数据的情况下(Marchant, 2010)


研究人员研究了情绪和表现之间的关系。情绪状态概况(POMS)问卷最常用于测量运动情境下的情绪。POMS衡量的是愤怒、抑郁、困惑、疲劳、紧张和活力。Morgan(1980)提出,与一般人群相比,成功的运动员具有“冰山轮廓”,如图2.1所示。当POMS的分数被调整到普通人群的平均值为50时,那么成功的运动员在愤怒、困惑、抑郁、疲劳和紧张方面的分数较低,但在活力方面的分数较高。冰山剖面图也被称为精神健康模型


冰山轮廓刺激了许多研究,POMS是运动心理学从业者中流行的测量方法(Vealey等人,2019年)。总体而言,POMS问卷显示情绪与能力水平无关。不同技术水平的运动员报告了相似的情绪概况(Beedie et al., 2000)。然而,当在竞技赛事之前使用情绪状态时,情绪状态可以预测表现,尽管效果很弱(Beedie et al., 2000)。具体来说,更好的表现与活力有关,而较差的表现与困惑、疲劳和抑郁有关。愤怒和紧张有时与更好的表现有关,有时与更糟糕的表现有关。情绪和成绩之间的关系在开放技能、个人和短时间运动中比在封闭技能、团队和长时间运动中更强一些。此外,自我报告的表现(如教练评分)比客观测量(如时间、距离和分数)的关系更强。然而,在所有情况下,这种关系的强度都不是很强:情绪不能很好地预测表现。

心理学代写|心理学代写Psychology代考|PARTICIPATION MOTIVATION

To understand the direction aspect of motivation, sport psychology practitioners have studied why people choose to play sport, and once they have started, why they might decide to stop. The reasons people play sport fall within the realm of participation motivation. Understanding the reasons why people start and continue playing sport helps sport psychology practitioners, coaches, and administrators keep these individuals involved and entice more people to participate. Across the Western world most people fail to get enough physical activity to reap health benefits. Governments want to encourage people into physical activities, such as organized sport, to improve physical and mental wellbeing and reduce national healthcare costs. Sport psychology researchers can help because they have examined participation motivation a great deal.


人们参加和退出体育运动的主要动机是多种多样的,个人通常有多种原因(Weiss, 2019)。参与动机的大类包括能力、朋友、健身和乐趣。关于能力,许多人参加体育运动是为了学习和提高他们的技能。人们可以通过与自己的关系或与他人的比较来解释自己的能力。当自我参照时,如果人们相信他们的技能水平在提高,或者他们在活动中很熟练,他们就会感到有能力。当玩家参照他人时,他们会出于竞争的原因而参与游戏,并在表现出自己比其他人更好时感到自己有能力。这些人有赢得和击败他人的动力。对于这些运动员来说,获胜的渴望是他们参加比赛的主要原因,特别是在精英和职业水平上,他们的生计受到他们击败对手的能力的影响


关于朋友,健身和娱乐的动机,人们经常出于许多原因进行体育运动,包括

  • 结交朋友
  • 成为团队的一员
  • 与他人互动
  • 改善他们的健康
  • 增强他们的外表
  • 积极活动
  • 获得乐趣
  • 缓解压力
  • 体验与运动相关的感觉
    以上的列表并不详尽,还有许多其他原因

     

心理学代写|心理学代写Psychology代考 请认准statistics-lab™

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金融工程代写

金融工程是使用数学技术来解决金融问题。金融工程使用计算机科学、统计学、经济学和应用数学领域的工具和知识来解决当前的金融问题,以及设计新的和创新的金融产品。

非参数统计代写

非参数统计指的是一种统计方法,其中不假设数据来自于由少数参数决定的规定模型;这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型(GLM)归属统计学领域,是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。

有限元方法代写

有限元方法(FEM)是一种流行的方法,用于数值解决工程和数学建模中出现的微分方程。典型的问题领域包括结构分析、传热、流体流动、质量运输和电磁势等传统领域。

有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。

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随机分析代写


随机微积分是数学的一个分支,对随机过程进行操作。它允许为随机过程的积分定义一个关于随机过程的一致的积分理论。这个领域是由日本数学家伊藤清在第二次世界大战期间创建并开始的。

时间序列分析代写

随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。

回归分析代写

多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。

MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中,其中问题和解决方案以熟悉的数学符号表示。典型用途包括:数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发,包括图形用户界面构建MATLAB 是一个交互式系统,其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题,尤其是那些具有矩阵和向量公式的问题,而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问,这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展,得到了许多用户的投入。在大学环境中,它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域,MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要,工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数(M 文件)的综合集合,可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

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