极坐标与参数方程公式-浙江农林大学暨阳学院
克隆巴赫（信度）系数（Cronbach's alpha），是心理或教育测验中最常用的信度评估信度工具。其依据一定公式估量测验内部的一致性，作为信度的指标。克
服部分折半法的缺点 ，是目前社会研究最常使用的信度指标，它是测量一组同义
或平行测“总和”的信度。
克隆巴赫系数公式
α ﹦（n n -1）(1-∑S
i
2
S
t
2
)
α 为信度系数，n 为测验题目数，S
2
i
为每题各被试得分的方差，S
2t
为所有被试
所得总分的方差。
一般来说，该系数愈高，即工具的信度愈高。在基础研究中，信度至少应达到 0.80
才可接受，在探索性研究中，信度只要达到 0.70 就可接受，介于 0.70－0.98 均
属高信度,而低于 0.35 则为低信度，必须予以拒绝。
SPSS FAQ
What does Cronbach's alpha mean?
Cronbach's alpha is a measure of internal consistency, that is, how closely
related a set of items are as a group. A
(along with substantive arguments and possibly other statistical measures) as
evidence that the items measure an underlying (or latent) construct. However,
a high alpha does not imply that the measure is unidimensional. If, in addition
to measuring internal consistency, you wish to provide evidence that the scale
in question is unidimensional, additional analyses can be performed.
Exploratory factor analysis is one method of checking dimensionality.
Technically speaking, Cronbach's alpha is not a statistical test - it is a
coefficient of reliability (or consistency).
Cronbach's alpha can be written as a function of the number of test items and
the average inter-correlation among the items. Below, for conceptual
purposes, we show the formula for the standardized Cronbach's alpha:
Here N is equal to the number of items, c-bar is the average inter-item
covariance among the items and v-bar equals the average variance.
One can see from this formula that if you increase the number of items, you
increase Cronbach's alpha. Additionally, if the average inter-item correlation
is low, alpha will be low. As the average inter-item correlation increases,
Cronbach's alpha increases as well (holding the number of items constant).
An example
Let's work through an example of how to compute Cronbach's alpha
using SPSS, and how to check the dimensionality of the scale using factor
analysis. For this example, we will use a dataset that contains four test items
- q1, q2, q3 and q4. You can download the dataset by clicking on .
To compute Cronbach's alpha for all four items - q1, q2, q3, q4 - use
the reliability command:
RELIABILITY
VARIABLES=q1 q2 q3 q4.
Here is the resulting output from the above syntax:
The alpha coefficient for the four items is .839, suggesting that the items have
relatively high internal consistency. (Note that a reliability coefficient of .70 or
higher is considered
situations.)
In addition to computing the alpha coefficient of reliability, we might also want
to investigate the dimensionality of the scale. We can use the factor command
to do this:
FACTOR
VARIABLES q1 q2 q3 q4
FORMAT SORT BLANK(.35).
Here is the resulting output from the above syntax:
Looking at the table labeled Total Variance Explained, we see that the eigen
value for the first factor is quite a bit larger than the eigan value for the next
factor (2.7 vs. 0.54). Additionally, the first factor accounts for 67% of the total
variance. This suggests that the scale items are unidimensional.
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