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What to do to Save the Planet

I have recently read two of the scientific articles about the environment protection. The first one, Comparative analysis of environmental impacts of agricultural production systems, agricultural input efficiency, and food choice dealt with comparison of the food choices, with plants being the best, followed by animal produced vegetarian food, poultry and pork, fish and last beef, goat and lamb meat.

But the was the second article that was more interesting. The article researched how many times different environment protecting actions were recommended in high-school textbooks. But the interesting thing was the classification of actions into low-, middle- and high-impact actions. Beside one high-impact (which I found disturbing), I am listing the actions with their impact level below:

Action Impact
Live car free High
Avoid one flight High
Purchase green energy High
Reduce effects of driving High
Eat a plant-based diet High
Action Impact
Home heating/cooling efficiency Middle
Install solar panels/renewables Middle
Use public transportation, bike, walk Middle
Buy energy efficient products Middle
Conserve energy Middle
Reduce food waste Middle
Eat less meat Middle
Reduce consumption Middle
Reuse Middle
Recycle Middle
Eat local Middle
Action Impact
Conserve water Low
Eliminate unnecessary travel Low
Minimize waste Low
Plant a tree Low
Compost Low
Purchase carbon offsets Low
Reduce lawn mowing Low
Ecotourism Low
Keep backyard chickens Low
Buy Ecolabel products Low
Calculate your home’s footprint Low

Some Interesting Facts About Individual (and Team) Differences

Here I am going to write about some of the interesting individual differences, that I have read about in the recent weeks.

The first one if from the article Worth Less?: Why Men (and Women) Devalue Care-Oriented Careers. It seems that women prefer having a HEED (healthcare, early education and domestic) roles to STEM, while men prefer to have a STEM job over HEED job. But as far as valuing the job, both jobs value the STEM jobs about the same, while the women value HEED jobs more than men. The difference in value comes from the difference in communal values. Both would pay STEM more than HEED, but the difference in payment was bigger for men and women.

The second one is from the article Personality Predicts Obedience in a Milgram Paradigm. This article reproduced the Milgram experiment as a test run for a show (now sure, if the show was ever supposed to be run). After excluding the participants, that were aware of the Milgram experiment, the people that were more agreeable and more conscientious people were more likely to administer electric shocks to the learner. Left-leaning and female active in political activism were also less likely to administer electric shock. To me, left-leaning was expected, since right-leaning are usually higher in conscientiousness. But female political activism was not, because women are higher in agreeableness and I don't understand what effect would political activism have.

The third one is from the article Revisiting the Stanford Prison Experiment: Could Participant Self-Selection Have Led to the Cruelty?. They tried to see, if advertisement for participating in the prison experiment would bring in different people than advertisement for a psychological experiment with no mention of setting. The differences with the predictive validity were aggression, narcissism and social dominance. This could potentially explain, how could the Stanford prison experiment ended so badly (if you don't know it, Google it, since it is quite interesting).

The forth one is going to combine the three article, since they talk about similar things. The article The measurement of dominance in pregnant women by use of the simple adjective test showed, that aggressive and dominant women were more likely to bear male children than non-aggressive and non-dominant women. The article Dominance and testosterone in women then showed, that women that score higher in the dominance test, used in the previous article, have a higher level of testosterone in blood. Exposure to testosterone in womb can be measured by the ratio of second and forth digit. And in the last article in this section, The Impact of Prenatal Testosterone on Female Interest in Slash Fiction they used this to show, that women that were exposed to more testosterone in the womb were more likely to read slash fiction. I think I remember reading long ago, who testosterone is also connected to the interest in stuff over people, but I can not remember the article now.

The fifth one is going to be in subject differences. Mostly, how are economists different from other people. The first article is Does Studying Economics Inhibit Cooperation?. In review of some of the already done experiments, they note that economics professors (in comparison to other professors) are less likely to give money to charity, but they are equally likely to volunteer and vote. Then they had people go through the prisoner dilemma in different conditions. The economists were more likely to deflect, when there was no chance to make a promise with a person. They also checked, how likely would a person return strangers 100$ and report a billing error in their favor. They tested it before and after the class. The game-theory class had the most negative effect, the astronomy class the least and the socialist economics were on the middle. The article Business education and erosion of character speculate that this is because of the model used in economics and students making the leap from this is how we model people to this is how people ought to act.

Added 2019-03-11: I found notes from reading another article in differences between economists and psychology. In the article The influence of academic discipline on empathy and psychopathic personality traits in undergraduate students they showed, that student of psychology have a higher level of empathy (no matter if they measured cognitive, affective or general level of empathy) than business students. The article Who volunteers in psychology experiments? An empirical review of prosocial motivation in volunteering instead looked at the more behavioural level. They tried to figure out, who volunteers for participating in research. And the difference in motivation is what explained the difference (psychology students volunteered more than economics students). Among psychology students, there were 57% of people with prosocial motivation, 37% individualistic motivation and 6% of competitive motivation. Among economic students, the picture was different. There were more people with individualistic motivation (with 47%) and competitive motivation (17%), but less people with prosocial motivation (36%), when compared to the students of psychology.

The next one is not from the scientific article, but from the book chapter. It is Suppressing intelligence research: Hurting those we intend to help by Linda Gottfredson. There was a lot packed in that chapter. But I think the most interesting was, how much IQ can affect life and how much politics can stop the science, which could eventually help them, just because they don't like the results. There is a small percent of people, that can not do the simple task of locating the expiry date on their ID. I think the intelligence issue is one of the most important, but neglected issue today, and this is one of the simple ways to dive in.

The last individual one is from the learning perspective. The article Reviews Matter: How Distributed Mentoring Predicts Lexical Diversity on tries to quantify the effect of review on the writing skills. 650 reviews are about the equivalent to the year of improvement in adolescence, when controlling for both fandom and age of the writer. So even a distributive mentoring through comments can be helpful.

The last one is more team oriented. The article Large teams develop and small teams disrupt science and technology researches the scientific impact and the size of the team. The smaller teams were more disruptive, coming up with new ideas, while the larger teams were more developing, so building on an existing ideas. Teams that were funded for the project were also more likely to be developing teams. Nobel prize winners were more likely to be disruptive. And another interesting fact, the disruptive teams were more likely to cite older and/or less popular stuff.

Notes about Statistics in Social Sciences

There are just two notes, that I think are interesting and they are relevant to the social sciences.

The first one comes from the Many Analysts, One Data Set article. This article gave multiple teams the same data and hypotesis to research. There were still a lot of differences in aproaches the teams took, the variables that they used from the dataset and the effect that they come. So they then recomend, that single analysist would need to make as many analysis as possible, and calculate how many of them would need to show an effect, to be confident that there is an effect. Even better would be, if analysis in science would be done in a crowdsourcing matter.

The second one comes from the One Hundred Years of Social Psychology Quantitatively Described. This used all the possible researches, that they could find, to try and see what kind of effect is expcted for each variable. This can be useful when testing hypotesis when using Basiyan statistics or for (at least) some calculations fo power and needed number of participants for that power. The table of effects is below.

Effect Number of Meta Studies Effect Size Standard Deviation
Agression 31 0.24 0.20
Attitudes 32 0.27 0.14
Attribution 36 0.14 0.14
Expectancy effects 16 0.16 0.22
Gender roles 19 0.18 0.13
Group processes 27 0.32 0.15
Health psychology 22 0.17 0.13
Helping behavior 14 0.18 0.16
Intergroup relations 28 0.19 0.18
Law 25 0.17 0.08
Leadership 42 0.25 0.18
Methodology 29 0.21 0.10
Motivation 12 0.15 0.12
Nonverbal communication 29 0.22 0.17
Personality 32 0.21 0.14
Relationships 32 0.22 0.12
Social cognition 22 0.20 0.19
Social influence 26 0.13 0.18
Total 474 0.21 0.15

I am quite interested in the personal differences, so I also copied the who things, where they went a bit more in the peronality difference. The first is the personality vs. situation problem (disclamer: they are about the same):

Effect Number of Studies Effect Size
Personality 16,282 0.19
Situation 17,631 0.22

And then for the sex differences (sans cognitive differences, since these are only variables from social psychology):

Effect Number of Studies Effect Size
Sex differences 83 0.12
Sex targets 0.08
Sex actors 0.13