If the data doesn’t measure what you say it is measuring, your data cannot be trusted to provide a clear picture of what you sought out to study. In other words, validity asks the question, “Is the data measuring what it purports to be measuring - the right population, characteristic, category, or phenomenon?” Further, validity refers to whether the sample of people you are looking at mirrors the entire population of people who are trying to learn about, in terms of gender, ideology, race/ethnicity, education, income, etc. Validity, as I mentioned, refers to whether the data accurately reflects the real world. Let’s discuss reliability and validity in more detail to help you understand how they help present a more truthful (complete and accurate) picture of the data. When dealing with statistics, consider whether the data you are looking at accurately reflects reality (is valid) and is collected in a consistent manner (is reliable). Reliability concerns whether the data is consistent for the same respondent across surveys and consistently entered each analyst and across analysts. Validity concerns whether the data is an accurate reflection of reality. If numbers and graphs - in other words, the data - is the bread and butter of social statistics, validity and reliability of that data are the measuring cup and oven. Photo by Dan Meyers on Unsplash Numbers You Can Count On
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |