中人网

标题: 关于李克特量表问项的等距性问题 [打印本页]

作者: ddliurp 时间: 2010-9-14 21:58
标题: 关于李克特量表问项的等距性问题

kenneth:

有个学数学的同学问了一个关于李克特量表的问题如下：

李克特量表的5点问项，是从1完全不赞同，2不赞同，3不赞同也不反对，4赞同，5完全赞同，但是1和2,2和3之间很可能是不等距的，也就是说完全不赞同到不赞同，不赞同到不赞同也不反对之间的态度是不等距的。

想请教kenneth,非常感谢。

作者: jkliang 时间: 2010-9-15 09:16
dear ddliurp
若以試題反應理論(IRT)角度視之，Likert scale各點之間的確是不等距的，且各點之間是一個distribution，若對於此有興趣，可尋找partial credit model or rating scale model。
但我自己認為，這個問題應該不會對於探索問題與理論建構產生障礙，不會說用傳統測量理論與試題反應理論所量測出的構念，再進一步探討構念間的相關時，會出現不一致的情形。以上為我的觀點，請參考並指教。

作者: Kenneth 时间: 2010-9-16 13:53
回复 1楼 ddliurp 的帖子

我个人片面的看法是，如果连「假设」它们是等距都不接受的话，Pearson Correlation 在问卷调查研究中就几乎没有地位了。我们就要退到 rank-order correlation 这个层次了。我自己担心的反而是有时候我们会把数据当成是 ratio data （不止是 interval data 这么简单）。不过我有一个很弱的感觉，是有人曾经在早期做过这方面的研究的。有人知道吗？

作者: jkliang 时间: 2010-9-16 21:11
我找到以下資訊，不知有沒有幫助：
authors : Alan Nevill and Andrew Lane, School of Sport, Performing Arts and Leisure, University of Wolverhampton

Sport and exercise psychologists typically use self-report data with assessments being made on likert type scales. The participant is asked to rate a feeling, or estimate an attitude on an arbitrary scale on which numbers are designed to relate to the strength of feelings. For example, participants report feelings on the Brunel Mood Scale (BRUMS: Terry, Lane, Lane, & Keohane, 1999) from ‘not at all’ (0) to ‘very much so’ (4). To illustrate the arbitrary relationship between the number and the statement it describes, the item ‘Nervous’ is on the BRUMS and the Positive and Negative Affect Schedule (PANAS: Watson, Clark, & Tellegen, 1988). On the PANAS, participants rate Nervous from ‘not at all’ to ‘very much so’ on a 1 to 5 scale. A participant who does not feel nervous could report a value of either 0 or 1 to the item Nervous depending on the scale being administered. Clearly, most of these self-report ‘likert’ scales are recorded on an interval rather than a ratio scale. As most authors are well aware, interval data has no fixed “zero” point, i.e. the scale could go, for example, from –2 to +2, from 0 to 4 or from 1 to 5 (as described above). All three scales will result in identical conclusions when analysed using parametric or non-parametric statistical tests. However, if the authors try to take logs of the scale –2 to +2, or 0 to 4, they will not get the same conclusions (compared with 1 to 5). Indeed the authors will have great difficulty in log-transforming zero or negative values on their measurement scales. It is because likert-type data, being interval and not ratio scale data, that the log transformation is totally inappropriate (only appropriate for true ratio scale data).

A second issue with interval data is whether the values ascribed to an attribute accurately reflect the numerical difference. Arguably, the scale is theoretically a continuous construct and values between data points are equal. Evidence indicates that this is not always the case. Lane and Terry (2000) observed that the difference between reporting not at all (0) and ‘A little’ (1) to depressed mood items in the BRUMS (Terry et al., 1999) is much larger than the difference between reporting a value of somewhat (2), moderately (3) or very much so (4). Lane and Terry have given considerable scrutiny to how participants report depressed mood items on the BRUMS (see Lane, 2004). Caution must be placed on whether Likert scale estimates represent true differences between values.

Based on the above observations, we suggest that researchers adopting, analysing and reporting self-report Likert style data, should take great caution when analysing their data using parametric methods. Adopting non-parametric methods are more likely to accommodate the rank style differences between discrete values of a Likert scale, i.e., Likert scale data should be treated as “interval” scale with great caution but certainly, not as ratio scale data, when parametric methods such as logarithmic transformations would be totally inappropriate.

作者: Kenneth 时间: 2010-9-16 22:33
回复 4楼 jkliang 的帖子

是的，这正是我要说的问题。不过，我想除了 log transformation 外，只要我们把一个变量除（或者乘）另一个变量，用interval data 都可能有问题的。

作者: jkliang 时间: 2010-9-17 12:14
若如Kenny所言，使用Likert Scale + SEM的研究是否都將遭受到挑戰！？想想真令人不寒而慄!

欢迎光临中人网 (http://bbs.chinahrd.net/)