Repeatability can be tested within observers (that is, the same observer performing the measurement on two separate occasions) and also between observers (comparing measurements made by different observers on the same subject or specimen).
#Point measure vs measure of validity full#
Measurements of disease in life are often incapable of full validation.
The pathologist can describe changes at necropsy, but these may say little about the patient’s symptoms or functional state. The validity of a questionnaire for diagnosing angina cannot be fully known: clinical opinion varies among experts, and even coronary arteriograms may be normal in true cases or abnormal in symptomless people. More often, however, there is no sure reference standard. For example, a sphygmomanometer’s validity can be measured by comparing its readings with intraarterial pressures, and the validity of a mammographic diagnosis of breast cancer can be tested (if the woman agrees) by biopsy. Sometimes a reliable standard is available against which the validity of a survey method can be assessed. An ideal survey technique is valid (that is, it measures accurately what it purports to measure). Information biasĪs indicated above, errors in measuring exposure or disease can be an important source of bias in epidemiological studies In conducting studies, therefore, it is important to assess the quality of measurements. The problems of incomplete response to surveys are considered further in. Furthermore, when responses are incomplete, the scope for bias must be assessed. The possibility of selection bias should always be considered when defining a study sample. Both of these deficiencies are potential sources of selection bias.
Also, not all of the subjects selected for study will necessarily complete and return questionnaires, and non-responders may have different drinking habits from those who take the trouble to reply. These excluded subjects might have different patterns of drinking from those included in the study. With this design, one source of error would be the exclusion from the study sample of those residents not registered with a doctor. He might try to do this by selecting a random sample from all the adults registered with local general practitioners, and sending them a postal questionnaire about their drinking habits. Suppose that an investigator wishes to estimate the prevalence of heavy alcohol consumption (more than 21 units a week) in adult residents of a city. Selection bias occurs when the subjects studied are not representative of the target population about which conclusions are to be drawn.
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