If there are multiple factors then you need to determine which questions are testing which factors. ), then you need to split the questionnaire/test into three tests, one containing the questions testing factor 1, one with the questions testing factor 2 and the third with questions testing factor 3.You then calculate Cronbach’s alpha for each of the three tests.
Example 2: Calculate Cronbach’s alpha for the survey in Example 1, where any one question is removed.As you can see from Figure 5, Cronbach’s alpha is .73802, the same value calculated in Figure 1.Observation: Alternatively, we could use the Real Statistics Two Factor ANOVA data analysis tool, setting the Number of Rows per Sample to 1.In particular, it can be used for testing with partial credit and for questionnaires using a Likert scale.Definition 1: Given variable as the measurement error values. Observation: Cronbach’s alpha provides a useful lower bound on reliability (as seen in Property 1).One problem with the split-half method is that the reliability estimate obtained using any random split of the items is likely to differ from that obtained using another.One solution to this problem is to compute the Spearman-Brown corrected split-half reliability coefficient for every one of the possible split-halves and then find the mean of those coefficients. Cronbach’s alpha is superior to Kuder and Richardson Formula 20 since it can be used with continuous and non-dichotomous data.Cronbach’s alpha will generally increase when the correlations between the items increase.For this reason the coefficient measures the internal consistency of the test.The goal in designing a reliable instrument is for scores on similar items to be related (internally consistent), but for each to contribute some unique information as well.Observation: There are an number reasons why Cronbach’s alpha could be low or even negative even for a perfectly valid test.