Internal Consistency > Split-Half Reliability
What is Split-Half Reliability?In split-half reliability, a test for a single knowledge area is split into two parts and then both parts given to one group of students at the same time. The scores from both parts of the test are correlated. A reliable test will have high correlation, indicating that a student would perform equally well (or as poorly) on both halves of the test.
Split-half testing is a measure of internal consistency — how well the test components contribute to the construct that’s being measured. It is most commonly used for multiple choice tests you can theoretically use it for any type of test — even tests with essay questions.
- Administer the test to a large group students (ideally, over about 30).
- Randomly divide the test questions into two parts. For example, separate even questions from odd questions.
- Score each half of the test for each student.
- Find the correlation coefficient for the two halves. See: Find Pearson’s Correlation Coefficient for steps.
One drawback with this method — it only works for a large set of questions (a 100 point test is recommended) which all measure the same construct/area of knowledge. For example, this personality inventory test measures introversion, extroversion, depression and a variety of other personality traits. This is not a good candidate for split-half testing.
Difference with Parallel Forms
Split half-reliability is similar to parallel forms reliability, which uses one set of questions divided into two equivalent sets. The sets are given to the same students, usually within a short time frame, like one set of test questions on Monday and another set on Friday. With split-half reliability, the two tests are given to one group of students who sit the test at the same time. Another difference: the two tests in parallel forms reliability are equivalent and are independent of each other. This is not true with split-half reliability; the two sets do not have to be equivalent (“parallel”).------------------------------------------------------------------------------
If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.Comments are now closed for this post. Need help or want to post a correction? Please post a comment on our Facebook page and I'll do my best to help!