Assess the stability of a survey outcome across time
Phenomena that affect test-retest reliability
1. The length of time between administrations cannot be too short, nor too long. Base decisions on the time difference within the context of your research question. What amount of time is feasible for purposes of testing stability in this context? Most researchers use between one and six weeks of time between administrations. Time lapses of six months are considered too excessive to obtain a stable measure of effect.
2. The sample of individuals should be relatively homogenous in regards to demographic, confounding, clinical, and prognostic factors. People change differently according to their physiological and psychological development.
3. Due to both systematic and unsystematic error in measurement and the general variance and diversity in populations, some measures and constructs will be more unstable across time than others.
The steps for conducting test-retest reliability in SPSS
2. Click Analyze.
3. Drag the cursor over the Correlate drop-down menu.
4. Click on Bivariate.
5. Click on the baseline observation, pre-test administration, or survey score to highlight it.
6. Click on the arrow to move the variable into the Variables: box.
7. Click on the second observation, post-test administration, or survey score to highlight it.
8. Click on the arrow to move the variable into the Variables: box.
9. Click OK.
The steps for interpreting the SPSS output for test-retest reliability
If the p-value is LESS THAN .05, and the Pearson correlation coefficient is above 0.7, then researchers have evidence of test-retest reliability.
If the p-value is MORE THAN .05, or the Pearson correlation coefficient is below 0.7, then researchers do not have evidence of test-retest reliability.
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