Find definitions and interpretation guidance for every statistic and graph that is provided with item analysis.

The correlation matrix shows the Pearson correlation values, which measure the degree of linear relationship between each pair of items or variables. The correlation values can fall between -1 and +1. However, in practice, items generally have positive correlations. If the two items tend to increase and decrease together, the correlation value is positive.

Use the correlation matrix to assess the strength and direction of the relationship between two items or variables. A high, positive correlation values indicates that the items measure the same skill or characteristic. If the items are not highly correlated, then the items may measure different characteristics or may not be clearly defined.

Often, variables with correlation values greater than 0.7 are considered highly correlated. However, the appropriate benchmark value to use also depends on the standards in your subject area and the number of items in the analysis.

The covariance matrix displays the covariance values, which measure the linear relationship for each pair of items or variables. Positive covariance values indicate that above average values of one variable are associated with above average values of the other variable, and that below average values of one variable are associated with below average values of another variable. Negative covariance values indicate that above average values of one variable are associated with below average values of the other variable.

Unlike the correlation coefficient, the covariance is not standardized. Therefore, covariance values can range from negative infinity to positive infinity and can be difficult to interpret. To more easily interpret the linear relationship between each pair of items or variables, use the correlation matrix.

The mean is the sum of the observations divided by the number of observations. Minitab calculates the mean of each item and the total mean. The item mean is the sum of all the scores for one item divided by the number of scores for that item. The total mean is the sum of all the item means.

Use the item mean to describe the scores for each item or variable with a single value that represents the center of the data.

The standard deviation (StDev) is a measure of dispersion, or how spread out the data are about the mean. Minitab calculates the standard deviation of the scores for each item or variable, as well as the total of the standard deviations.

Use the standard deviation for the items to determine how spread out the scores are from the mean for each item.

Cronbach's alpha is a measure of internal consistency that is calculated using sample variance, total scores, and number of items.

Use Cronbach's alpha to assess how consistently multiple items in a survey or test assess the same skill or characteristic. Higher values of Cronbach's alpha suggest higher internal consistency. A benchmark value of 0.7 is commonly used. Generally, if Cronbach's alpha is greater than 0.7, then you have evidence that the survey or test items measure the same skill or characteristic. If Cronbach's alpha is below 0.7, then the items may not reliably measure a single skill or characteristic to conclude that at least some of the items measure the same characteristic. However, the appropriate benchmark value to use also depends on the standards in your subject area and the number of items in the analysis.

The adjusted total mean is the sum of all the item means, excluding the mean of the omitted item.

Use the adjusted total mean to see how the value of the total mean changes when an item is removed from the analysis.

The adjusted total standard deviation is the standard deviation of the total score after omitting an item.

Use the adjusted total standard deviation to see how the value of the total standard deviation changes when an item is removed from the analysis.

The item-adjusted total correlation is the correlation between the scores of one omitted item and the total scores of all other items. Although values of item-adjusted total correlation can theoretically range from -1 to 1, in practice, values usually range between 0 and 1.

Use item-adjusted total correlation to assess whether removing an item from the analysis improves the internal consistency. A higher value of item-adjusted correlation (closer to 1) suggests that the omitted item measures the same characteristic as the other items. If an omitted item has a low item-adjusted correlation value, a low squared multiple correlation value, and a substantially higher Cronbach's alpha value, then you might consider removing the item from the survey or test to improve its internal consistency.

The squared multiple correlation is the coefficient of determination (R^{2}) when the omitted item is regressed on the remaining items. Values range between 0 and 1.

Use the squared multiple correlation values to assess whether removing an item from the analysis improves the internal consistency. A higher value of squared multiple correlation (closer to 1) suggests that the omitted item measures the same characteristic as the other items. If an omitted item has a low squared multiple correlation value, a low item-adjusted total correlation value, and a substantially higher Cronbach's alpha value, then you might consider removing the item from the survey or test to improve its internal consistency.

Cronbach's alpha for an omitted item is the value of Cronbach's alpha for the remaining items after one item is removed from the analysis. Cronbach's alpha is a measure of internal consistency that is calculated using sample variance, total scores, and number of items.

Use Cronbach's alpha for omitted items to assess whether removing an item from the analysis improves the internal consistency. Fairly consistent values of Cronbach's alpha for all omitted items suggest that all items measure the same characteristic. A substantially higher Cronbach's alpha for a specific omitted item suggests that it may not measure the same characteristic as the other items. If an omitted item has a low squared multiple correlation value, a low item-adjusted total correlation value, and a substantially higher Cronbach's alpha value, then you might consider removing the item from the survey or test to improve its internal consistency.

The matrix plot is an array of scatterplots. Each scatterplot in the matrix graphs the scores for a pair of items on the x and y axes.

Use the plot to visually assess the relationship between every combination of items or variables.