澳洲代写

美国代写:肥胖和瘦的偏见

美国代写:肥胖和瘦的偏见

我所做的IAT测试是为了了解我对肥胖和瘦人群的偏见。我参加测试的网站表明,测试是一个衡量概念之间联系的标准,比如为什么我们认为脂肪更好或更瘦更好。我的IAT得分表明,对胖人和瘦人的偏爱略高。测试结果是“对肥胖人群的自动偏好高于瘦人”。现在我相信这些结果可能是正确的,因为我甚至会把自己归类为一个可能不会有体重问题的人,如果有人被认为比公认的标准稍微胖一些,我仍然认为他们是健康的。我认为这种理解是因为依恋理论。我的主要照顾者是我的母亲,虽然她是一个健康的人,但她也不是很瘦。因此,我的自动偏好可能是在那段时间形成的。然而,我也相信我不会自动将那些稍微胖的人归类为健康的人,我也不赞同任何人胖或瘦。这是他们的个人选择,尽管我的依恋理论,我仍然认为人们保持健康和健康是明智的。这反映在我的分数上,因为我的分数显示出轻微的自动偏好,而不是强烈的自动偏好。因此,这表明尽管存在着影响偏差,我还是能够通过推理来克服偏见。

美国代写:肥胖和瘦的偏见
波尔曼等人(2009)在一项元分析研究中发现,使用IAT来理解行为和偏见有很高的预测效度。作者仍然认为,这不能被理解为IAT在测量内隐偏差上是准确的。准确的评估内隐态度的方法(双方都有研究)以及你如何相信内隐态度会影响行为。以IAT为例,对两个目标概念与属性的差异关联进行了实证研究,结果表明,在接近普遍的评价差异(如花与昆虫)之间存在差异关联。在种族歧视的基础上有一些预期的个体差异,最后,有一些人有意识地否认了基于种族偏见的评估差异(格林沃尔德等人,1998)。由于有如此多的元素对IAT产生影响,可以说测试必须对个人、国家、种族和更多的背景特征进行调节或校准。这在IAT中并没有完全被捕获,因此,测试本身可能是缺乏的。

美国代写:肥胖和瘦的偏见

The IAT test that I took is to understand the bias that I might have with respect to fat versus thin people. The site where I took the test indicates that the test is a measure of the associations between concepts such as why we think fat is better or thin is better. My IAT score indicates a slightly higher preference for fat people versus thin people. The test result reads “Automatic preference for Fat People over Thin People”. Now I believe these results could be right because I would categorize myself even explicitly as someone who might not have an issue with putting on weight and if someone was considered to be slightly fat compared to the accepted norms, I would still consider them as healthy. I think this understanding is because of attachment theory. My primary caregiver was my mother, and although she was a fit person, she was not exactly thin either. Therefore, my automatic preferences could have been formed during that time. However, I also believe that I do not automatically categorize people who are slightly fat as being healthy and nor do I endorse anybody to be fat or thin. It is their personal choice really, and in spite of my attachment theory, I would still think it is wise for people to keep in good health and fitness. This is reflected in my score, as my score indicates slight automatic preference, not a strong automatic preference. Thus, this shows that despite there being an impact bias, I am able to work against the bias by reasoning.

美国代写:肥胖和瘦的偏见
Poehlman et al. (2009) in a Meta analytic study were able to find out that there was a high amount of predictive validity associated with using the IAT to understand behavior and bias. Authors still contend that this cannot be taken to mean IAT is accurate in measuring implicit bias. Accurate way of assessing implicit attitudes (there is research on both sides) AND how you believe implicit attitudes can impact behaviors. An empirical study with IAT where differential association of two target concepts with attributes was measured indicated that there was a differential association in the case of near-universal evaluative differences, such as a flower vs. insect. There were some expected individual differences based on race and finally, there were some consciously disavowed evaluative differences based on racial prejudice (Greenwald et al., 1998). With so many elements having an impact on IAT, it could be said that the test has to be moderated or calibrated for person, country, race and many more background characteristics. This is not thoroughly captured in the IAT and hence, the test by itself could be lacking.