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“Can ‘It’ – a Great Depression – happen again? And if ‘It’ can happen why didn’t ‘It’ occur in the years since World War II? These are questions that naturally follow from both the historical record and the comparative success of the past thirty-five years. To answer these questions it is necessary to have an economic theory which makes great depressions one of the possible states in which our type of capitalist economy can find itself.”
― Martin Wolf, The Shifts and the Shocks: What we've learned – and have still to learn – from the financial crisis

“In Depth
Types of Effect Size Indicators
Researchers use several different statistics to indicate effect size depending on the nature of their data. Roughly
speaking, these effect size statistics fall into three broad categories. Some effect size indices, sometimes called dbased effect sizes, are based on the size of the difference between the means of two groups, such as the difference between the average scores of men and women on some measure or the differences in the average scores
that participants obtained in two experimental conditions. The larger the difference between the means, relative
to the total variability of the data, the stronger the effect and the larger the effect size statistic.
The r-based effect size indices are based on the size of the correlation between two variables. The larger the
correlation, the more strongly two variables are related and the more of the total variance in one variable is systematic variance related to the other variable.
A third category of effect sizes index involves the odds-ratio, which tells us the ratio of the odds of an
event occurring in one group to the odds of the event occurring in another group. If the event is equally likely in
both groups, the odds ratio is 1.0. An odds ratio greater than 1.0 shows that the odds of the event is greater in
one group than in another, and the larger the odds ratio, the stronger the effect. The odds ratio is used when the
variable being measured has only two levels. For example, imagine doing research in which first-year students in
college are either assigned to attend a special course on how to study or not assigned to attend the study skills
course, and we wish to know whether the course reduces the likelihood that students will drop out of college.
We could use the odds ratio to see how much of an effect the course had on the odds of students dropping out.
You do not need to understand the statistical differences among these effect size indices, but you will
find it useful in reading journal articles to know what some of the most commonly used effect sizes are called.
These are all ways of expressing how strongly variables are related to one another—that is, the effect size.
Symbol Name
d Cohen’s d
g Hedge’s g
h
2 eta squared
v
2
omega squared
r or r
2 correlation effect size
OR odds ratio
The strength of the relationships between
variables varies a great deal across studies. In some
studies, as little as 1% of the total variance may be
systematic variance, whereas in other contexts,
the proportion of the total variance that is systematic
variance may be quite large,”
― Mark R. Leary, Introduction to Behavioral Research Methods

“I smile and start to count on my fingers: One, people are good. Two, every conflict can be removed. Three, every situation, no matter how complex it initially looks, is exceedingly simple. Four, every situation can be substantially improved; even the sky is not the limit. Five, every person can reach a full life. Six, there is always a win-win solution. Shall I continue to count?”
― Eliyahu M. Goldratt, The Goal: A Process of Ongoing Improvement

“all started at the Temple of Apollo In Delphi. One of his friends approached the oracle with the question: “Is anyone wiser than Socrates?” the answer was “No.” Socrates was profoundly puzzled by this episode. He claimed to know”
― Plato, The Socratic Dialogues