Assessment, Statistics, and Research

How will we know if opening up the country is safe? My bottom line answer is when people stop dying from Covid-19. But tracking actual deaths can help us know whether we are making progress or not. We can measure progress against our own baseline. The chart provides an estimate of a baseline, which will need to be corrected when additional deaths are recorded. Think of the baseline as a channel. The national US baseline appears to be in the range of 1800 to 2000 with some outliers starting 8 April 2020. I am not using averages because the data are not obviously normalized. I prefer to look at a range of relatively stable values, which can be called a channel. The sharp deviations have to be ignored to get a sense of what is "typical" of a pattern. A new relatively stable range above or below this range should help us determine a new trend. If the numbers are corrected, we will need to revisit the range. I am looking at a move of about 20% either way for evi

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

Lifespan is not the same concept as life expectancy. Lifespan is the maximum period of time a species lives. The human lifespan is measured in years. As of 2020, the documented human lifespan is 122 years ( see also lifespan concept in psychological science). Life expectancy is the average period of time a member of a population with certain characteristics lives. Human life expectancy, measured in years, varies by sex and environment. Human life expectancy varies by the age group. For example, life expectancy of people at birth will be different from a group of people who are alive at age 70. United Nations data are reported by sex and country. Overall, there has been an increase in human life expectancy on a worldwide basis between 1950 (47.0 years) and 2020 (73.2 years;  worldometers ). I have rounded the numbers which were reported up to two decimal places. Examples of recent life expectancy data for wealthy nations reveal marked differences compared to other nations. Da

This creative time and data chart helps readers understand the details that explain why a broad concept does not always make sense. Official government reports tell us price inflation is low, but our experience tells us so many things cost so much more like health insurance and medical expenses. And compared to retired folks, working people earn so much more than retirees used to earn for the same job. (See  Marketwatch Story for the chart and related data) . I think this type of chart would be useful when dimensions of a metaconcept change over time. For example, the process from an offense to forgiveness has multiple dimensions of change like avoidance and thoughts of revenge. If multiple measures are taken at different time points, they may be plotted over months or years to demonstrate increases and decreases. In fact, the idea of the "cost" of forgiveness might be worthy of consideration. After all, the Christian concept of forgiveness is analogous

Scale Name: Group Forgiveness Scale (GFS) The Group Forgiveness Scale (GFS) was developed to measure forgiveness of identity-related offenses. Research supports three factors for the 17 items: Avoidance, Revenge, Decision to Forgive. In the article describing its development, the authors focused on problems of race relations in the United States (see Davis et al., 2015, below). The GFS is an adaptation of the Transgression-Related Interpersonal Motivations Scale (TRIM). According to the 2015 article by Don Davis and his research team, 17-items resulted in factor loadings on three distinct subscales: Avoidance, Revenge, and Decision to Forgive. Sample items for each factor are as follows: Avoidance             I am avoiding them. Revenge             I am going to get even. Decision to Forgive             I have decided to forgive them. Reliability Data Reliability values were strong as measured by Cronbach’s alpha (Study 3: Avoidance .96; Revenge .9