Split-Half Methodology

One way to test the reliability of a test is to repeat the test. This is not always possible. Another approach, which is applicable to questionnaires, is to divide the test into even and odd questions and compare the results.

Example 1: 12 students take a test with 50 questions. For each student the total score is recorded along with the sum of the scores for the even questions and the sum of the scores for the odd question as shown in Figure 1. Determine whether the test is reliable by using the split-half methodology.

Split-half methodology

Figure 1 – Split-half methodology for Example 1

The statistical test consists of looking at the correlation coefficient (cell G3 of Figure 1). If it is high then the questionnaire is considered to be reliable.

r = CORREL(C4:C15,D4:D15) = 0.667277

One problem with the split-half reliability coefficient is that since only half the number of items is used the reliability coefficient is reduced.  To get a better estimate of the reliability of the full test, we apply the Spearman-Brown correction, namely:

Spearman-Brown correction

This result shows that the test is quite reliable.

Real Statistics Function: The Real Statistics Resource Pack contains the following supplemental function:

SPLIT_HALF(R1, R2) = split half coefficient (after Spearman-Brown correction) for data in ranges R1 and R2

Observation: This function ignores any empty cells and cells with non-numeric values.

Observation: For Example 1, SPLIT_HALF(C4:C15, D4:D15) = .800439.

One Response to Split-Half Methodology

  1. Jairo says:

    Big help, thank you!

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