Validity refers to the extent to which the data measures what it claims to measure. Internal validity refers to whether there is a causal relationship between the phenomenon being studied and the factors that we think causes it. For example, a study may seek to establish a causal relationship between women’s fertility and women’s level of education. The study is internally valid if the researcher can control for the effects of other possible factors, such as access to contraception. External validity refers to whether the results of a study can be generalized to other settings (ecological validity), other people (population validity) and over time (historical validity). For example, if the fertility-education study is conducted in a particular country in a particular year, an externally valid study may yield similar results for a different country, with a different group of respondents, a decade later.
Data is considered reliable if it produces consistent results. For example, a survey question intended to measure a woman’s highest level of education should produce an accurate measure of her educational attainment, if repeated over time.
Granularity refers to the level of detail in a particular data set. For example, if data can be sub-divided by groupings such as sex, geographic region, income level, education level, disability status etc., this improves its level of granularity.
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