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Item Analysis Help

The Item Analysis procedure is a method of testing how reliable the questions are on a survey. The program uses Cronbach’s Alpha, the Pearson correlation matrix, and the scatter plot matrix to perform the item analysis. An example of the Item Analysis procedure is given below.


You have a four question survey that measures customer satisfaction. You want to know if the questions are reliable – do they all measure the same thing? The questions use the Likert Scale rating of 1 to 5, with 5 representing the highest satisfaction. You would expect a customer who is satisfied to rate the four questions similarly. The results from 25 customers are shown in the table below.

Item Analysis Output

There is at least one worksheet added to your workbook. This one gives the numerical results. If you selected the Scatter Plot Matrix option, a second sheet with the scatter plot matrix results is added.

Numerical Results

The output for the example data are shown in the table below. 

Item Statistics

This portion of the worksheet provides the summary statistics for each item (question in this example). The number (count), the mean, the standard deviation and variance are given. Cronbach’s alpha for all the results is then given. This single number is an indication of how well a set of items (or questions) measure a certain characteristic (like customer satisfaction). Cronbach’s alpha varies between 0 and 1 (although negative values are possible but have no meaning). Values of 0.7 are considered acceptable. In this example, the overall Cronbach alpha is 0.793 – which appears acceptable but you need to look at the Omitted Item Statistics.

Omitted Item Statistics

This portion of the worksheet gives the results when each item is removed from the data set. The “adjusted” mean, standard deviation and variance are the results when that item is not in the data. There are three additional calculations in this portion of the worksheet:

Correlation Matrix

This portion of the worksheet measures the inter-item correlation coefficient (R). The items should be highly correlated if they are measuring the same characteristic. Values close to 1 represent highly correlated values. The probability of the correlation being significant is also given. If the probability is less than 0.05, the probability is in red. This means that there is statistically significant correlation between the two items. As a rule of thumb, if the probability is greater than 0.20, there is not a correlation between the two items. Values between 0.05 and 0.20 need more data before a conclusion can be reached. Question 4 does not correlate with any other results; again this indicates that question 4 is not reliable.

If the scatter plot matrix option was selected, the scatter plots for each pair of items is generated. This provides a visual picture of the results in the correlation table. The results for this example are shown below. Each scatter chart contains the best fit line and value of squared. You can see that the best fit lines for those charts involving Question 4 are not as steep as the others – indicating a lack of correlation.

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