Reliability
Argue why the results are reliable, so if gathered again would give similar findings
Good data should be as reliable as possible, i. e. change little when measured at different times or by different people. Reliable measures are stable, without errors caused by the measuring itself. For example, some questions are not reliable because different people interpret them differently. Qualitative studies use standard methods, keep detailed journals and analyze self-consistency to get reliability. Quantitative studies use
Test-retest reliability checks how a measurement changes if repeated, e. g. give a test then give it again a day later to see how much change their is.
Split-half reliability checks if the measure is internally consistent with itself, e. g. compare the first half with the second half of a test, to see if people who score high on the first half also score high on the second half.
Reliability coefficients like Cronbach's alpha are generally accepted if they are 0.85 or higher. One way to ensure a measure is reliable is to use one that someone else has already tested and found reliable.
Example(s)
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