a measurement of your IQ a month from now should, within a reasonable margin of error, generate a similar score, assuming it was administered under standardized conditions both times. If not, we might doubt the test's reliability. The Pearson correlation coefficient is commonly used to evaluate test–retest reliability, where a higher‐than‐not coefficient between testings is desirable. In addition to test–retest, we often would like a measure of what is known as the internal consistency of a measure, which, though having potentially several competing meanings (e.g., see Tang et al., 2014), can be considered to assess how well items on a scale “hang together,” which is informal language for whether or not items on a test are interrelated (Schmitt, 1996). For this assessment, we can compute Cronbach's alpha, which we will now briefly demonstrate in SPSS.
As a very small‐scale example, suppose we have a test having only five items (items 1 through 5 in the SPSS data view), and would like to assess the internal consistency of the measure using Cronbach's alpha. Suppose the scores on the items are as follows:
| Item_1 | Item_2 | Item_3 | Item_4 | Item_5 | |
| 1 | 10.00 | 12.00 | 15.00 | 11.00 | 12.00 |
| 2 | 12.00 | 18.00 | 12.00 | 12.00 | 1.00 |
| 3 | 8.00 | 16.00 | 14.00 | 14.00 | 4.00 |
| 4 | 6.00 | 8.00 | 16.00 | 8.00 | 6.00 |
| 5 | 4.00 | 7.00 | 8.00 | 7.00 | 5.00 |
| 6 | 6.00 | 6.00 | 3.00 | 7.00 | 3.00 |
| 7 | 3.00 | 4.00 | 6.00 | 5.00 | 8.00 |
| 8 | 7.00 | 3.00 | 7.00 | 9.00 | 9.00 |
| 9 | 8.00 | 9.00 | 4.00 | 10.00 | 10.00 |
| 10 | 9.00 | 5.00 | 6.00 | 11.00 | 12.00 |
To compute a Cronbach's alpha, and obtain a handful of statistics useful for conducting an item analysis, we code in SPSS:
RELIABILITY /VARIABLES=Item_1 Item_2 Item_3 Item_4 Item_5 /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=DESCRIPTIVE SCALE CORR /SUMMARY=TOTAL.
The MODEL = ALPHA statement requests SPSS to compute a Cronbach's alpha. Select output now follows:
| Reliability Statistics | ||
|---|---|---|
| Cronbach's Alpha | Cronbach's Alpha Based on Standardized Items | No of Items |
| 0.633 | 0.691 | 5 |
| Item Statistics | |||
|---|---|---|---|
| Mean | Std. Deviation | N | |
| Item_1 | 7.3000 | 2.71006 | 10 |
| Item_2 | 8.8000 | 5.05085 | 10 |
| Item_3 | 9.1000 | 4.74810 | 10 |
| Item_4 | 9.4000 | 2.71621 | 10 |
| Item_5 | 7.0000 | 3.80058 | 10 |
| Inter‐Item Correlation Matrix | |||||
|---|---|---|---|---|---|
| Item_1 | Item_2 | Item_3 | Item_4 | Item_5 | |
| Item_1 | 1.000 | 0.679 | 0.351 | 0.827 | 0.022 |
| Item_2 | 0.679 | 1.000 | 0.612 | 0.743 | −0.463 |
| Item_3 | 0.351 | 0.612 | 1.000 | 0.462 | −0.129 |
| Item_4 | 0.827 | 0.743 | 0.462 | 1.000 | −0.011 |
| Item_5 | 0.022 | −0.463 | −0.129 | −0.011 | 1.000 |
We can see that SPSS reports a raw reliability coefficient of 0.633 and 0.691 based on standardized items. SPSS also reports item statistics, which include the mean and standard deviation of each item, as well as the inter‐item correlation
