(Essay found in Nesselroade & Grimm, 2019; pg. 548) A common activity for many academic psychologists is the construction of measuring tools. There are literally hundreds of different psychological traits, tendencies, and abilities that psychologists are interested in measuring; from commonly used concepts like extroversion and neuroticism to less frequently-referenced concepts like humility (e.g., Rowatt et al., 2006) and right-wing authoritarianism (e.g., Mirels & Dean, 2006). The scales used to measure these attributes, however, need to be created. They do not appear out of thin air. Scale development is usually an extensive process.  First, the concept is carefully defined, with

(Essay found in Nesselroade & Grimm, 2019; pgs. 209- 210) Programs found on the internet allow us actually to see how changing the sample size, mean, and the standard deviation of the population of raw scores change the resulting sampling distribution. Some of the ones recently found online include the StatKey Sampling Distribution for a Proportion program (www.lock5stat.com/StatKey/sampling_1_cat/sampling_1_cat.html), the Rice Virtual Lab in Statistics (onlinestatbook.com/stat_sim/sampling_dist/) and the Rossman/Chance Applet Collection (www.rossmanchance.com/applets/OneSample.html). There are others. A program that is quite flexible, however, is one created by Dr. Patrick Wessa (www.wessa.net/rwasp_samplingdistributionmean.wasp). In this program, we can input the number of replications we

(Essay found in Nesselroade & Grimm, 2019; pg. 138) Is he taller than he is heavy? This question, at first glance, seems to be nonsensical; like comparing apples with oranges. The reason the question appears to be unanswerable is that height and weight are different variables and measured in different units. How can we say that 6 ft 2 in. is more or less than 145 lb? However, in the world of statistics we can compare the relative position of scores in different distributions by using standardized scores. The z score transformation will convert original scores, from different scales, to

(Essay found in Nesselroade & Grimm, 2019; pgs. 79 – 80) In Chapter 2 readers were introduced to a critical difference between ordinal scales and interval or ratio scales; the nature of the relationship between numerical values. Ordinal scales are a quantitatively organized series of categories, and as such, make no assumptions about the quantitative distance between these categories. Interval and ratio scales hold the intervals constant throughout the measure. In this chapter we learned that the concept of a deviation score is necessary to find a mean. A mean is defined as the point where the deviation scores sum

(Essay found in Nesselroade & Grimm, 2019; pgs. 46 – 47) Although ancient civilizations like the Egyptians and Chinese used tabulation and other simple statistics to keep track of tax collections, government expenditures, and the availability of soldiers, the modern use of statistics arguably began with the Englishman John Graunt (1620–1674). Graunt tabulated information on death rates in his home town of London and noted that the frequency of certain diseases, suicides, and accidents occurred with remarkable regularity from year-to-year. This realization, by the way, helped to develop the establishment of insurance companies. Graunt also found the occurrence of greater

(Essay found in Nesselroade & Grimm, 2019; pg. 23) Remember the research on mass versus distributed practice by Baddeley and Longman (1978) that was mentioned earlier in the chapter? The finding that ‘distributed practice is more effective than mass practice’ is actually a very old one, first uncovered by one of the pioneers of psychology, Hermann Ebbinghaus (1885), when conducting his famous studies on memory. It is also one of the more robust findings of psychology; shown to apply to the acquisition of a variety of both neuro-muscular skills like archery (Lashley, 1915) as well as cognitive abilities like face

(Essay found in Nesselroade & Grimm, 2019; pgs. 705 – 706) Throughout this book we have periodically stopped to look more closely at some commonly discussed problems in the world of science; in particular, we have tried to understand better the current reproducibility crisis that is afflicting the social sciences. Let us finish this series by stepping back to look a bit more philosophically at the scientific endeavor as a whole. What can we hope to accomplish with the help of science, and what, if anything, lies on the outside? This has sometimes been referred to as science’s demarcation problem;

(Essay found in Nesselroade & Grimm, 2019; pg. 355) Using the 1960 volume of the Journal of Abnormal and Social Psychology, Cohen (1962) conducted an interesting study. Although the authors of the articles in that volume did not use power analyses, Cohen computed the power of the statistical tests used in each of the studies. According to Cohen’s early guidelines, a small effect size is about 0.20; a medium effect size is around 0.50; and a large effect size is around 0.80. Assuming that the researchers would want to detect a medium treatment-effect size, the average power of the tests

(Essay found in Nesselroade & Grimm, 2019; pg. 324) This is another box in the series exploring the various reasons for the current reproducibility crisis in the social, behavioral, and medical sciences. Fellow researchers sometimes wonder if the use of one-tailed tests in the literature occurs because it is the only way to reject the null hypothesis. The following study may be a case in point. Buttery and White (1978) were interested in the relationship between affective states (feelings) and biorhythms. According to biorhythm theory, people experience a 28-day emotional cycle. At the peak of the cycle, people are expected

(Essay found in Nesselroade & Grimm, 2019; pgs. 236 – 237) If there is a “ground zero” for the current reproducibility crisis in the social, behavioral, and medical sciences, it may be found in the personhood of John Ioannidis, Professor of Medicine and of Health Research and Policy at the Stanford University School of Medicine. In 2005, he published an article in PLoS Medicine entitled, “Why Most Published Research Findings are False.” As one might imagine, this article created a firestorm of controversy as well as an avalanche of articles reacting to this claim; some supporting (e.g., Freedman, 2010), some