Cross-validation is a popular validation strategy in qualitative research. In triangulation, multiple data sources are analyzed to form a final understanding and interpretation of a study’s results.
Through analysis of methods, sources and a variety of research theories, cross-validation can make a powerful contribution to support existing research, hypotheses and researcher hunches by presenting interpretations from multiple perspectives.
Researchers chose which type of instrument, or instruments, to use based on the research question.
This is when data, analytic categories, interpretations and conclusions are tested with members of those groups from whom the data were originally obtained.
This can be done both formally and informally as opportunities for member checks may arise during the normal course of observation and conversation.
Was information obtained over the phone or in person? Each method of collection must be confirmed and validated for any bias, unforeseen impediments or errors.
Consider how the sources were obtained, such as surveys, for example — were they obtained legally and ethically from a population sample that represents the study at hand, or was the population of a protected class, such as children under the age of 18?
It is rare, if nearly impossible, that an instrument be 100% valid, so validity is generally measured in degrees.