Assessment, Statistics, and Research

In testing, measurement error usually refers to the fact that the same people can obtain different scores on the same test at different times. In a broad sense, measurement error can also refer to the degree of accuracy of a test to correctly identify a condition, which is discussed as test validity. Recall that test score reliability is a necessary but insufficient condition for test score validity. Many tests in psychology, medicine, and education are useful. The reliability of the scores will vary depending on such factors as the properties of the test itself as well as how well the user follows standard procedures in administering the test, environmental factors that can affect the scores, and factors within the person taking the test. The scores on many tests conform to the pattern called the normal curve or bell curve. In classical test theory, the scores people obtain on tests are simply called obtained scores (symbol X). Statisticians consider the variation in scores t

Statistical models are needed to guide government leaders and health service providers concerned with maximizing the number of infected people who survive and providing high quality care to those in need. All models have multiple assumptions. In the midst of a pandemic such as Covid-19, new data are constantly being processed. Thus, parameters will need to be changed as new data change the models. Multiple outcomes must be considered without biased interpretations favoring either lower or higher estimates. NYT 13 March 2020 reported by Sheri Fink This report refers to four scenarios and is early than the ones further down the page. "Between 160 million and 214 million people in the United States could be infected over the course of the epidemic, according to a projection that encompasses the range of the four scenarios. That could last months or even over a year, with infections concentrated in shorter periods, staggered across time in different communities

Some highly intelligent scientists have used available data to plot trends related to the spread of Covid-19 such as number of identified infections, recoveries, and deaths. Others plotted ideas rather than data such as flattening the curve. Some rely on "common sense" and others rely on data. Given my experience in teaching graduate research methods and statistics and talking with physicians and psychologists about statistics, I think it worth reminding any readers of this post that a lot of very intelligent people have difficulty with statistical models. There are many PhDs younger than I who know more methods of sophisticated data analyses than I learned during my PhD or since; however, not everyone stops to consider their assumptions when modeling data. That's my point here--we need to review and challenge the assumptions about the data, the way the data are plotted, and how people interpret those data. Missing data are very important to developing an accurat