[FOR HIRE] Commissions are open!! RPG Characters | Portraits | Illustrations ~ More info in the comments :)

2021.09.28 23:55 guschch [FOR HIRE] Commissions are open!! RPG Characters | Portraits | Illustrations ~ More info in the comments :)

[FOR HIRE] Commissions are open!! RPG Characters | Portraits | Illustrations ~ More info in the comments :) submitted by guschch to Artistsforhire [link] [comments]


2021.09.28 23:55 wallpapersdance I'm trying to forget about my stress/worries that I will fail to achieve my dreams by burying myself in work/activities. Its hard to prevent the mind from wandering to thoughts about worrying about the future though. What are some good strategies when the mind wanders to these thoughts?

I get a few hours of reprieve from the worries and stress when I get absorbed in a task. But certain times like when my body is tired and I am resting in bed, my mind wanders to stressful thoughts about the future. And fears I will fail to achieve my dreams.
I am trying to develop strategies to deal with this when I am tired and in bed and I cannot just bury my mind in an activity to keep it occupied
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2021.09.28 23:55 AtlasMA It’s always Florida, dude is obviously an Alpha

It’s always Florida, dude is obviously an Alpha submitted by AtlasMA to memes [link] [comments]


2021.09.28 23:55 prawnbiryani 💗☁🍦🌸🧁🤍🦩

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2021.09.28 23:55 rjltrevisan Building Technology To Power A Parallel Christian Society

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2021.09.28 23:55 Nosduhcraft We managed to backtrack a bit, get a sparrow through the vents at the start of Arms Dealer, and get that same sparrow all the way to the end

We managed to backtrack a bit, get a sparrow through the vents at the start of Arms Dealer, and get that same sparrow all the way to the end submitted by Nosduhcraft to destiny2 [link] [comments]


2021.09.28 23:55 Javierham93 A tomahawk and a t bone steak

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2021.09.28 23:55 SuspiciousJelly1926 Summer ms&t internship at merck

Hi everyone, I am a first-year engineering graduate student studying biopharmaceutical processing. I am applying for a manufacturing science and technology internship at Merck and was wondering if anyone here works at Merck or has worked for them in past. How is/was your experience. Do you have any tips for online application and interviews?
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2021.09.28 23:55 3RR00R I read the entirety of the imposter mod and found this…

I read the entirety of the imposter mod and found this… submitted by 3RR00R to FridayNightFunkin [link] [comments]


2021.09.28 23:55 Ok-Ice-7990 H: red asylum W: fc js

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2021.09.28 23:55 jay520 The predictive validity of cognitive ability for important life outcomes

There is overwhelming evidence showing the predictive validity of cognitive ability for important life outcomes. Cognitive ability measured as early as age 6 has a strong association with one’s future success in a number of important outcomes, including academic achievement, occupational performance, income, educational attainment, occupational prestige, criminality, self-control, and health. The associations are typically large, often making cognitive ability the best predictor for such outcomes. In this post, I will cite research showing the evidence of these associations.
Note: this is a shortened version of a blog post here. Due to reddit length limitations, I cannot post the full post. For more data/details, checkout the site. Also, the goal of this post is simply to show that cognitive ability is predictive of the important outcomes that I mentioned above. My goal for this post is not to show that cognitive ability is causal. Now, I think the evidence is equally strong that cognitive ability is causal, but showing that will have to require a separate post.
Background Before citing data showing the predictive validity of cognitive ability, I'll cover some relevant background in this section that is necessary in order to interpret the data.
Correlation coefficients Much of the data presented here use correlation coefficients to show the predictive validity of cognitive ability. I recommend readers check out this post where I provide empirical data on the distributions of correlation coefficients within the field and I also list the correlation coefficients between many commonly understood variables. I'll mention a quick summary of the information there so that readers will have necessary context.
The correlation coefficient always takes on values from 1 to −1, with positive correlations indicating a positive relationship (i.e. as the value for one of the variable increases, the value for the other variable also increases) and negative correlations indicating an inverse relationship. Coefficients with greater absolute values indicate stronger associations.
Gignac and Szodorai (2016) collected a large sample of meta-analytically derived correlations published in the field of individual differences. Researchers gathered a total of 708 observed correlations from a sample of 87 meta-analyses. They found that the 25th, 50th, and 75th percentiles corresponded to correlations of 0.11, 0.19, and 0.29, respectively. Only about 10% of the correlations exceeded 0.40 (Table 1), and only about 2.7% of correlations exceeded 0.50 (page 75). Because of these findings, the authors recommended that the normative guidelines for small, medium, and large correlations should be 0.10, 0.20, and 0.30, respectively. Similar results were reported in Lovakov and Agadullina (2021).
On the basis of these results, I treat low, medium, and large correlations as correlation coefficients in the ranges of r <.15, .15 < r < .30, and r > .30, respectively. The precise boundaries of the ranges are somewhat arbitrary but I think the guidelines are roughly accurate enough to aid in quickly interpreting the magnitude of a given correlation coefficient in social science. These ranges are also the guidelines proposed by Hemphill et al. (2003) which found that these guidelines represented the bottom, middle, and upper third (respectively) of correlation coefficients reported in a couple of meta-analyses in psychological assessment and treatment.
Definitions When I say “cognitive ability”, I’m referring to the definition of “intelligence” given by Gottfredson (1997) [archived]:

Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings-“catching on,” “ making sense” of things, or “figuring out” what to do. (page 13)
For a more precise working definition, it may be useful to frame my definition from the perspective of different theories of intelligence. My working definition of “cognitive ability” corresponds to the visual-spatial, linguistic-verbal, and logical-mathematical forms of intelligence stipulated by Howard Gardner’s Theory of multiple intelligences. It also corresponds to the “analytic” component of intelligence stipulated by Robert Sternberg’s Triarchic theory of intelligence.
I want to emphasize that my working definition of cognitive ability is not an endorsement of any particular theory of intelligence. I make no claims whatsoever about whether creativity, bodily-kinesthetic intelligence, musical intelligence, etc. are “real” forms of intelligence.
IQ scores My working definition of cognitive ability is measured by IQ tests fairly accurately. It is important to understand IQ because, as Nisbett et al. (2012) [archived] notes, IQ is the measure of intelligence for which “the bulk of evidence pertinent to intelligence exists” (page 131). To start, one should understand how IQ scores are distributed.
IQ scores are normed for a given population to produce a mean score of 100 and a standard deviation (SD) of 15 points. Because IQ scores are normally distributed, 32% of the population has an IQ score of more than a standard deviation away from the mean. In other words, about 68% of the population has scores between 85 and 115. About 5% of the population has an IQ score of more than two standard deviations (30 points) from the mean. In other words, about 95% of the population has scores between 70 and 130 (Neisser et al. 1996, page 78).
Now, for some context on specific IQ ranges, consider that the DSM-5 [archived] defines intellectual disability as an IQ score of about 70 or below. “Giftedness” is not a well-defined term but, when defined using IQ scores, it is often defined as possessing an IQ of around 130 or higher (Gottfredson (1997), page 13).
Stability An important point to note about cognitive ability is its reliability or stability across an individual’s lifetime. Neisser et al. (1996) report that “Intelligence test scores are fairly stable during development” (page 81). They note that an individual’s age 17-18 IQ correlates at r=0.86 with their age 5-7 IQ, and correlates at r=0.96 with their age 11-13 IQ. Similar points are made by Sternberg et al. (2001) who makes two observations on IQ correlations between ages from age 3 to age 12: “First, the best predictor of IQ in a given year is the IQ from the previous year. Second, the predictive power of IQ in every subsequent year increases with the child’s age” (page 15). The stability of cognitive ability has also been verified in many recent studies. For example, in a literature review on the stability of intelligence over time, Schneider (2014) notes that there is “broad agreement that the stability of cognitive ability varies as a function of the age of the sample but is rather high from school age on” (page 3). For other studies showing the stability of cognitive ability, see Larsen et al. (2008), Deary et al. (2004), and Yu et al. (2018).
Expert Consensus Gottfredson (1997) [archived] was a very brief 3-page statement that outlines conclusions regarded as mainstream by over 50 experts in intelligence and allied fields. One of the conclusions reached was as follows (page 14):
IQ is strongly related, probably more so than any other single measurable human trait, to many important educational, occupational, economic, and social outcomes. Its relation to the welfare and performance of individuals is very strong in some arenas in life (education, military training), moderate but robust in others (social competence), and modest but consistent in others (law-abidingness). Whatever IQ tests measure, it is of great practical and social importance.
Rindermann, Becker, and Coyle (2020) surveyed the opinions of over 100 experts in the field of intelligence about a variety of questions. One of the questions in the survey was “to what degree is the average socioeconomic status (SES) in Western societies determined by his or her IQ?” They survey found that “Experts believed 45% of SES variance was explained by intelligence and 55% by non-IQ factors (Table 3). 51% of experts believed that the contribution of intelligence (to SES) was below 50%, 38% above 50%, and 12% had a 50–50 opinion.” That is, experts believe that roughly half of the variance in socioeconomic status in Western societies is due to intelligence.
Academic Achievement Standardized Testing Koenig et al. (2008) [archived] reported the correlation between cognitive ability and performance on standardized tests from two different studies.
These large correlations led the authors to conclude that “the ACT is an acceptable measure of general intelligence” (page 158). They ended with the following discussion:
The analyses presented above demonstrate a significant relationship between measures of cognitive ability and ACT scores. Based upon correlations with conventional intelligence tests and the first factor of the ASVAB, it appears that that ACT is a measure of general intelligence. Indeed, based on the correlations among the tests in Study 1, the ACT is indistinguishable from other tests that are identified as intelligence tests. In addition, the ACT shows a high correlation with the SAT, itself considered to be a measure of intelligence (Frey & Detterman, 2004). The jackknife analysis confirms the stability of these results.
This study replicated the findings of Frey and Detterman (2004) which found similarly large correlations between g and SAT scores (r = .72 or r = .86 after correction, depending on the sample), concluding that “the SAT is an adequate measure of general intelligence” (page 377).
To appreciate the magnitude of the IQ-SAT/ACT correlations, note that these correlations are on par with, and sometimes greater than, the correlation between different subtests of the SAT and ACT. For example, Koenig et al. (2008) report correlations between SAT Math and SAT verbal scores (r = .75), ACT math and ACT verbal scores (r = .67), SAT math and ACT math scores (r = .86), SAT verbal and ACT verbal scores (r = .74), and SAT total and ACT total scores (r = .87) (Table 2). Recall that the correlation between IQ (assessed using either g or Raven's Progressive matrices) and SAT/ACT scores ranges from about .72 to .86 after correcting for range restriction. This suggests that IQ scores correlate with SAT/ACT scores about as well as the different SAT/ACT subtests correlate with themselves!
Grades A meta-analysis by Roth et al. (2015) [archived] reports the average correlation between general mental ability and school grades among 240 independent samples and 105,185 total participants. After correcting for measurement error and range restriction, the correlation between general mental ability and grades was ρ = .54 (page 123). Moderator analyses (page 123) revealed greater correlations for mathematics and science (ρ = .49) than for languages (ρ = .44), social sciences (ρ = .43), fine art and music (ρ = .31), and sports (ρ = .09). Furthermore, correlation between cognitive ability and grades correlation was largest for high school students (ρ = .58), followed by middle school students (ρ = .54) and elementary school students (ρ = .45). The meta-analysis concluded with the following (page 126):
The results of our study clearly show that intelligence has substantial influence on school grades and thus can be regarded as one of the most (if not the most) influential variables in this context. Although intelligence turned out to be a significant predictor on all moderator levels, we were able to identify some scenarios in which even higher validities can be obtained. First of all, the population correlation was highest for tests relying on both verbal and nonverbal materials, indicating that a broad measure of intelligence or g respectively is the best predictor of school grades. Furthermore, the importance of intelligence increases throughout grade levels. This leads us to the conclusion that intelligence has special importance in educational contexts which deal with content that is more complex and thus can be mastered fully only with an appropriate cognitive ability level.
For more concrete examples of the association between cognitive ability and high school grades, see Cucina et al. (2016). In one of their reported studies, the authors used the 1997 cohort of the National Longitudinal Survey of Youth (NLSY97) to examine the relationship between high school grades and general cognitive ability (g) extracted from ASVAB scores. Consistent with previous literature, a large correlation was observed between g and high school GPA (r = .44, table 6). To illustrate the association more clearly, the authors also reported the distribution of GPA scores by g quartile (figure 2).
g quartile A average B average C average D average or lower
Quartile 4 (highest) 19.1% 63.9% 16.6% 0.4%
Quartile 3 7.1% 61.2% 29.4% 2.3%
Quartile 2 2.3% 52.4% 40.9% 4.3%
Quartile 1 (lowest) 0.7% 39.5% 50.8% 9.1%
Compared to students with g scores in the bottom quartile (IQ < 90), students with g scores in the top quartile (IQ > 110) were nearly 30 times as likely to earn an average letter grade of an A (19.1% vs 0.7%) and over twice as likely to earn a B or higher (83% vs 40%), whereas students in the bottom quartile were over 20 times as likely to earn a D or lower (9.1% vs 0.4%).
The above analyses included samples reported by cross-sectional studies, i.e. studies that measure the intelligence and academic performance of students at the same time. It may be more interesting to examine the correlations reported specifically by longitudinal studies, i.e. studies that measure the correlation between intelligence at one time and academic achievement measured at a later time. One such longitudinal study is Deary et al. (2006) [archived], which examined a 5-year prospective longitudinal survey of a representative sample of over 70,000 children in England. Researchers measured the relationship between the general factor of intelligence (g) measured at age 11 and GSCE test points at age 16. The results were as follows:
Occupational Performance Schmidt and Hunter (1998) [archived] is a highly cited paper that summarized 85 years of research on the predictive validity of dozens of variables for job performance and job training programs in the United States. The study considered job experience, years of education, interests, employment interviews, conscientiousness tests, work sample tests (hands-on simulations of the job to be performed by the applicant), GMA tests (general mental ability tests), peer ratings of performance, job knowledge tests, behavioral consistency procedures (applicants describe their past achievements to illustrate their ability), and job tryout procedures (applicants are hired with minimal screening and their performance is evaluated within a limited duration, e.g. several months). The correlation between job performance and some of the predictor variables were as follows (Table 1):
Personnel measures Validity (r)
Work Sample tests .54
General Mental Ability tests .51
Employment interviews (structured) .51
Peer ratings .49
Job knowledge .48
Job experience .18
Years of education .10
The correlation between job training and various predictor variables were as follows (Table 2):
Personnel measures Validity (r)
General Mental Ability tests .56
Integrity Tests .38
Peer Ratings .36
Employment interviews .35
Conscientiousness tests .30
Reference checks .23
Years of Education .20
Job Experience .01
An updated meta-analysis was reported in Schmidt et al. (2016), which found largely similar results. Similar results were reported by meta-analyses in other countries (Bertua et al. 2005, Hülsheger et al. 2007, Salgado et al. 2003).
One criticism of the previous studies is that they often rely on supervisory ratings, which may be subject to arbitrary bias. To address this issue, we can use work sample tests instead of supervisory ratings. Roth et al. (2005) meta-analyzed 43 independent samples of 17,563 total subjects to analyze the association between cognitive ability tests and work sample tests. Work sample tests are defined as tests “in which the applicant performs a selected set of actual tasks that are physically and/or psychologically similar to those performed on the job” (page 1010). The mean correlation between cognitive ability tests and work sample tests was r = .32, which increased to r = .38 after correcting for unreliability in work sample (Table 4). This correlation was somewhat reduced because military jobs were included in the meta-analysis, which suffer from significant range restriction (the military uses measures of cognitive ability in its selection process). Among non-military jobs only (K = 16, N = 5,039), the correlation between cognitive ability tests and work sample tests was r = .37, which increased to r = .44 after correcting for unreliability in work sample.
Cognitive ability has predictive validity for nearly every aspect of occupational success. Strenze (2015) [archived] cites several meta-analyses showing the correlation between IQ and a variety of measures of occupational success (Table 25.1). There are large correlations between IQ and job performance (r = .53 for supervisory rating and r = .38 for work samples), skill acquisition in work training (r = .38), group productivity (r = .33), and promotions at work (r = .28). Consistent with the prior meta-analyses, he also notes that cognitive ability is a better predictor of success for cognitively demanding jobs. He states “IQ tests are very useful in selecting good engineers, architects, or dentists…IQ tests are less useful for selecting good dishwashers, weavers, or garbage collectors, although, even among dishwashers, it is obvious that an intelligent worker is better than a less intelligent one” (page 407).
Socioeconomic Success Longitudinal Studies For more concrete examples of the association between adolescent cognitive ability and socioeconomic outcomes, see Murray (1998) [archived]. Murray used the NLSY79 to measure the predictive power of cognitive ability on a variety of socioeconomic outcomes. He separated subjects from the NLSY79 into 5 different “cognitive classes”: those who scored in the 90th+ AFQT percentile (classified as “very bright”), those who scored in the 75th-89th AFQT percentile (“bright”), those who scored in the 25th-75th AFQT percentile (“normal”), those who scored in the 10th-24th AFQT percentile (“dull”), and those who scored below the 10th percentile (“very dull”). He then reported the average levels of socioeconomic success for each cognitive class. As expected, those from the higher cognitive classes attained far higher levels of success than those in the lower cognitive classes (tables 6-1 through 6-3):
Cognitive Class (percentile range) Mean Years of Education (1994) Percentage obtaining a B.A. (1994) Mean Weeks Worked (1993) Median Earned Income (1993)
Very Bright (90th+) 16.5 77% 45.4 $36,000
Bright (75th – 89th) 15.0 50% 45.2 $27,000
Normal (25th – 74th) 13.2 16% 41.8 $21,000
Dull (10th – 24th) 11.9 3% 36.5 $13,000
Very Dull (10th-) 10.9 1% 30.7 $7,500
Murray also performed a similar analysis after restricting the sample to what he calls the “Utopian Sample”. This includes only subjects who grew up with both biological parents married from birth at least until age seven and who had parents with income above 25th percentile. The result was a sample that “has virtually no illegitimacy, divorce, or poverty” (page 33). When the analysis is restricted to the utopian sample, the same association between cognitive ability and socioeconomic outcomes appeared (tables 6-1 through 6-3):
Cognitive Class (percentile range) Mean Years of Education (1994) Percentage obtaining a B.A. (1994) Mean Weeks Worked (1993) Median Earned Income (1993)
Very Bright (90th+) 16.5 80% 45.6 $38,000
Bright (75th – 89th) 15.2 57% 45.1 $27,000
Normal (25th – 74th) 13.4 19% 43.0 $23,000
Dull (10th – 24th) 12.3 4% 39.0 $16,000
Very Dull (10th-) 11.4 1% 35.8 $11,500
These findings on the IQ-income correlations corroborated by Zagorsky (2007) [archived]. He also used the National Longitudinal Survey of Youth 1979 to examine the association between youth IQ and income and net worth measured between the ages of 33 and 41 (page 491). The benefit of this study over the Murray data is that this study was able to report on outcomes at a later stages in life. The study reported medium-large correlations between IQ and income (r = 0.30) and small-medium correlations between IQ and net worth (r = .16) (Table 2). The median incomes and net worth at different IQ points were as follows:
IQ test score Median income (2021 dollars) Median net worth (2021 dollars)
120 $48,681 ($78,587) $127,500 ($184,875)
110 $40,884 ($59,282) $71,445 ($103,595)
100 $36,826 ($53,398) $57,550 ($83,448)
90 $30,881 ($44,777) $37,500 ($54,375)
80 $18,467 ($26,777) $10,500 ($15,225)
Overall $35,918 ($52,081) $55,250 ($80,112)
The raw values are the figures in 2004 dollars, taken directly from the study. The values in parenthesis are in 2021 dollars, by multiplying the raw values by 1.45.
The association between early cognitive ability and later socioeconomic success is a consistent finding that has been replicated in numerous other countries, such as New Zealand (Fergusson et al. 2005), Denmark (Hegelunda et al. 2018), Britain (Bukodi et al. 2013, tables 3-4; Von Stumm 2009), Scotland (Von Stumm et al. 2010, Deary et al. 2005), Sweden (Bergman et al. 2014, Sorjonen et al. 2012), Ireland (O’Connell and Marks 2021), and Germany (Becker et al. 2019).
Alternative Predictors A meta-analysis by Strenze (2007) [archived] shows that intelligence (measured by IQ scores) is a great predictor of future socioeconomic success. Socioeconomic success was measured as educational level, occupational status, and income. The analysis found that IQ measured before age 19 is a powerful predictor of socioeconomic success after age 29, even more powerful than measures parental SES:
Variable Educational Attainment Occupational Prestige Income
Intelligence (best studies) .56 .45 .23
SES index .55 .38 .18
Parental income .39 .27 .20
Father's education .50 .31 .17
Mother's education .48 .27 .13
Father's occupation .42 .35 .19
Academic Performance .53 .37 .09
  • "Best studies" are studies where intelligence is tested before the age of 19, and socioeconomic success is measured after the age of 29.
For comparisons between youth cognitive ability and alternative predictors from within the same sample, see Spengler et al. (2018) [archived]. Researchers in this study used data from Project Talent to compare the validity of various predictors for socioeconomic outcomes long after high school. Project Talent is a longitudinal sample of over 81,000 participants followed from high school to late adulthood. The dataset contains information about each participant’s parental SES, personality traits, academic achievement, and IQ while they were in high school. Parental SES was a composite score consisting of home value, family income, parental education, father’s job status, number of books, number of appliances, number of electronics, and whether the child had a private room. The study reported information on the socioeconomic outcomes of the participants at two points during adulthood, one that was 11 years after the initial sampling and another that was 50 years after the initial sampling. The results of the 50-year follow-up are consistent with the data shown thus far, which is that cognitive ability predicts socioeconomic outcomes better than alternative predictors (Table 2):
Variable Educational Attainment Occupational Prestige Income
IQ .50 .35 .35
Parental SES .40 .27 .28
Interest in school .22 .13 .14
Reading skills .26 .18 .21
Writing skills .25 .17 .16
The authors also conducted regression analyses to measure the association between the predictive variables while controlling for all other variables (see tables 3 to 8). After introducing these controls, the magnitude of the regression coefficients for IQ and parental SES changed slightly, but the basic pattern remained: IQ is a powerful predictor of socioeconomic outcomes, even more powerful than parental SES, even after holding these other covariates constant.
Anti-social Behavior In The Bell Curve, Herrnstein and Murray (1994) reported a number of anti-social behaviors that are (negatively) associated with cognitive ability. Recall that the authors separated subjects from the NLSY79 into 5 different “cognitive classes”: those who scored in the 90th+ AFQT percentile (classified as “very bright”), those who scored in the 75th-89th AFQT percentile (“bright”), those who scored in the 25th-75th AFQT percentile (“normal”), those who scored in the 10th-24th AFQT percentile (“dull”), and those who scored below the 10th percentile (“very dull”). Also recall that these associations are reported specifically for non-Hispanic whites to avoid confounding due to race. Some of the anti-social behaviors negatively associated with cognitive ability include welfare usage, illegitimacy, and incarceration. The following table shows the percentage of subjects at each cognitive class who were incarcerated, were convicted, were unemployed, used welfare usage, or who had illegitimate children.
Cognitive Class (percentile range) Criminal Offending Unemployment Welfare Usage Illegitimacy
Very Bright (90th+) 3% 2% 1% 7%
Bright (75th – 89th) 7% 7% 4% 7%
Normal (25th – 74th) 15% 7% 12% 13%
Dull (10th – 24th) 21% 10% 21% 23%
Very Dull (10th-) 14% 12% 55% 42%
Overall 9% 7% 12% 14%
  • “Criminal Conviction” records the percentage of men who reported being convicted for an offense (page 247). “Unemployment” records the percentage of men who spent one month or more in 1989 (page 163). “Welfare usage” records the percentage of women who went on AFDC (Aid to Families with Dependent Children) within a year of first birth (page 194). “Illegitimacy” records the percentage of first births among women that were illegitimate (page 181).
Another longitudinal study showing the association between early cognitive ability and later criminal offending was conducted by Loeber et al. (2012) [archived]. Researchers used data from the Pittsburgh Youth Study to examine the relationship between IQ measured at about age 12 with criminal history at age 28 in a sample of 422 males. IQ was measured using the Wechsler Intelligence Scale for Children–Revised (WISC-R) test. The results showed that IQ was significantly associated with arrest probability for any charge, particularly during adolescence. For example, the probability of arrest for 17-year-old males with low IQs (60-65%) was about three times the probability for those with high IQs (20-25%) (Figure 1). Low-IQ and high-IQ males were those with IQs one standard deviation below and above the mean, respectively. Note also that this difference was the difference after controlling for race, socioeconomic status, and age.
A meta-analysis by Ttofihi et al. (2016) [archived] investigated the extent to which intelligence may function as a protective factor against delinquency, violence, and crime. The authors investigated 15 longitudinal studies based in Europe (8), the United States (5) and New Zealand (2). The studies reported the impact of intelligence on the likelihood of offending among both high-risk and low-risk groups. “High-risk” groups includes individuals who were exposed to risk factors (other than low intelligence) for offending. These risk factors varied from study to study. Some risk factors included poor child rearing, teacher- and parent-ratings of antisocial behavior, poor concentration, marital disturbance, imprisoned father, physical abuse, etc. (table 1). The authors found that, among the high-risk group, non-offenders were about 2.32 times as likely to have a high intelligence level as offenders (page 13). Some studies also investigated the effect of intelligence on offending among low-risk groups. For this group, non-offenders were only about 1.3 times as likely to have a high intelligence level, a non-significant result (page 12). The meta-analysis concludes that “intelligence can function as a protective factor for offending”. In other words, the impact of risk factors for offending is reduced among individuals of high intelligence; or, conversely, low intelligent individuals are particularly vulnerable to be negatively impacted by the risk factors for offending.
For other studies on the relationship between cognitive ability and crime, see Beaver (2013), Schwartz et al. (2015), Levine (2011). Studies also show that cognitive ability is significantly associated with self-control (Petkovsek and Boutwell 2014, Boisvert et al. 2013).
Conclusion Again, this is only a snippet of the full post due to reddit length limitations. You can click here to view the full post.
The above studies show that cognitive ability is an excellent predictor of many important life outcomes, including academic achievement, occupational performance, socioeconomic outcomes, anti-social behavior, and health. Now, these studies only show that cognitive ability is predictive, so it’s still an open question as to whether cognitive ability is causal. For example, one might say that the correlation between cognitive ability and these outcomes is the result of confounding with a common cause such as family background or personality. In a later post, I show evidence that cognitive ability also predicts important life outcomes after controlling for common confounders, indicating that cognitive ability is actually causal.
Even without showing evidence that cognitive ability is causal, cognitive ability is still important because of it’s predictive powers. For example, imagine that cognitive ability has no causal impact on any of the above outcomes, but is instead only correlated with the outcomes because of shared association with family background, personality, self-control, and a host of other confounders that we may or may not be able to reliably measure. Even if this is the case, cognitive ability would still be an excellent measurable index of a person’s expected future success. It would still be useful to measure the cognitive ability of children in order to reasonably know whether they are on the right track to success. That being said, I believe the evidence shows that cognitive ability is in fact causal, which I demonstrate in my next post.
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2021.09.28 23:55 FTWStoic How prayer works

How prayer works submitted by FTWStoic to exmormon [link] [comments]


2021.09.28 23:55 No-Cheesecake848 Love her new look

Love her new look submitted by No-Cheesecake848 to LivMorgan [link] [comments]


2021.09.28 23:55 Upstairs-Birthday720 💸 AdaCash is launching now, 8% ADA Rewards & Huge Marketing Plans! 🔴

💸 AdaCash aims to be the fastest-growing cryptocurrency of its kind. AdaCash will reward holders in a unique way. Our tokenomics include a monthly increase of the Buying Fee tax regarding the ADA rewards. This means that holders will receive larger and larger awards as time goes by. 💸
Inspired by many ADA reward tokens, we saw a huge potential on this market, aiming to be the biggest one in this market.
🔴 We are planning to launch tomorrow, there will be a fair launch, with no pre-salers. 🔴
🌕 ADA reflections will increase 0.5% every month, starting from 8% and going up to 12% in rewards.
Sell fees will stay the same.
-Our Marketing Plan
As soon as we launch, we will apply on CoinGecko, launch poocoin advertisements 24/7, pay 2 Twitter influencers with more than 100k followers, and one tiktok influencer. Within a few days, our marketing wallet will grow, and we will use those funds for even more aggressive marketing on different platforms, ie facebook. We also plan to use the marketing funds to buy votes on CoinHunters & CoinSniper to get to the top #1 crypto on these platforms. After we reach 1,000 holders, and we get some volume, we will apply to CoinMarketCap.
-Our Tokenomics
🔴 Total Supply: 1,000,000,000
♨️ Fee: 8% from all transactions goes as a reward to holders, 2% goes to the marketing wallet.
⚡️ Redistribution automatic every 1 hours.
🌒 Noone can hold more than 2.5% wallet(Anti-Whale).
✅ Early investors will have the true opportunity to return their investment in a few days, as long as we get a good volume flowing.
✅ Team doxxed + Audit will be completed by DessertSwap.finance and Techrate, the companies who Audited the biggest BSC projects in 2021.
Links related to AdaCash
Contract: 0x1fff53ec2336a8c7eabffa5a88453c4d0199d552
Buy Here: https://pancakeswap.finance/swap?outputCurrency=0x1fff53ec2336a8c7eabffa5a88453c4d0199d552
LP Locked: https://deeplock.io/lock/0x017b26279E54B5ACA04C2dC9c86a4380D7d1Ecc8
Renounced Ownership: https://bscscan.com/token/0x1fff53ec2336a8c7eabffa5a88453c4d0199d552#readContract
submitted by Upstairs-Birthday720 to CryptoMoonCoins [link] [comments]


2021.09.28 23:55 Culpersr The person who just died was your wife!

The person who just died was your wife! submitted by Culpersr to IASIP [link] [comments]


2021.09.28 23:55 Eagle_0909 46 [M4F] - Lost Smile..Help Me Find Again

Here i am posting yet again to find that elusive connection that STAYS and chats DAILY lol. Where we get excited to see next notifications and silly selfie wars. We can vent encourage or just chat. Not single so yeah...there's that..long story.Always post..no replies .. maybe you change that.
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2021.09.28 23:55 FemaleMMA She Knocked Her Out - Girl Fight

She Knocked Her Out - Girl Fight submitted by FemaleMMA to Knockout [link] [comments]


2021.09.28 23:55 zeek1999 A list of all known swear words and racial slurs version 1.1

This is the second version of a list of all the known swear words and racial slurs If I miss any please comment them below and I'll add them to version 1.2
Ass- Bitch- Cunt- Whore- Fuck- Dick- Pussy- Damn Piss- Bastard- Choad- Wanker- Twat- Chink- Beaner- Bollox- Chode- Clit- Cock- Coochie- Coon- Cracker- Cum- Cooter- Dago- Dike - Faggot- Douchebag- Dyke- Gooch- Gook- Gringo- Heeb- Hell- Hoe- Homo- Honkey- Kike- Prick- Puto- Queef- Queer- Retard- Sped- Slut- Nut- Spick- Spook- Tits- Twats- Wetback- Malparido- Maricon- Perra- Nigger- Nigga- Abo-
Again if I'm missing any that you know of comment below, only single word swear words are allowed so no double words like "shitskin" is allowed
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2021.09.28 23:55 bzrkfayz Do you make characters silly looking or serious looking?

Title
View Poll
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2021.09.28 23:55 XxEmaloliscoolxX umop apisndn

umop apisndn submitted by XxEmaloliscoolxX to ihadastroke [link] [comments]


2021.09.28 23:55 Ceeseema 80 bags for $200 Bet

80 bags for $200 Bet submitted by Ceeseema to FentanylFriends [link] [comments]


2021.09.28 23:55 Huge_Instruction_234 🚕Lambo Sports🚕 Fair Launch | ⌛️ | 💩Big Marketing budgets | Doxxed Team Before launch | Charity and Lottery Token |💸 Lottery Lines Starts Already 🚕

🚕Welcome to Lambo Sports🚕
Fair Launch🚕
Lambo Sports utility can be split into 3 sections: 💰EARN,
♻️CHARITY and🔥 BURN.
Lambo Sports implements automatically a fee of 13% for all
transactions. 6% of this fee is automatically
distributed back to the community in BNB. This
system incentivizes our community of holders who in
turn support Lambo Sports to make it a sustainable
movement.
EARN
CHARITY♻️
6% of the fee is send to the contract address where the
auto swap function is implemented. This function auto
swaps the tokens to BNB which then will be send to our
Lambo Sports POOL. It also functions as an anti dump
measurement, a lot of projects have to sell their tokens
to be able to use the funds which results in a dip. This
feature prevents that by auto swapping small amounts
upon each transaction.
50% of the BNB in the Lambo Sports Pool will be used to
support charities globally. What’s unique about
Lambo sports’s ecosystem is that both incoming and outgoing
transactions will contribute to this core value.
BURN🔥
There are cases wherein the burn process increases
the price floor.Lambo Sports follows the same system for
the best interest of the community. 1% of each
transaction will be stored inside the contract and the
community decide when to execute the Buy-Back
and Burn feature. Once those tokens are burned, it is
like adding free BNB to the pool as there are no
tokens to sell in the future.
Prize Fund♻️
The remaining BNB stored in the Lambo Sports Pool will be
used for Prize Fund.
Supply Details :
10,000,000,000
initial Burn:🔥
350,000,000 (35%)
Pancakeswap Liquidity:
600,000,000 (60%)
Marketing Wallet:
20,000,000 (2%)
Shilling Giveaways:
30,000,000 (3%)
Our goals:
📢 Give back to the community as soon as possible!
🙋‍♂️Doxxed Developer
🐳 Anti Whale Features
🔒 Locked Liquidity for 5 Years
💸 Giving away Lambo on 25000 Holders
🏈 Inviting members to the Big running projects
⚠️ ⚡SOCIAL MEDIA ⚡⚠️
🚕Contract: 0x358d9e58722b8795f92b5ee05d4ee4ebd154fae2
🚕Buy Here:https://pancakeswap.finance/swap?outputCurrency=0x358d9e58722b8795f92b5ee05d4ee4ebd154fae2
🚕Renounced Ownership: https://bscscan.com/token/0x358d9e58722b8795f92b5ee05d4ee4ebd154fae2#readContract
🏈Twitter https://Twitter.com/lambo_sports
🔒 Locked Liquidity for 5 Years
submitted by Huge_Instruction_234 to SatoshiBets [link] [comments]


2021.09.28 23:55 vzbzbs Sand or No Sand

Whats the difference between courts with 'Silica Sand' and courts without?
We're currently looking for courts to purchase, all manufacturers we're talking to say that the courts require Silica sand, but most of the videos that keep popping up on social media have courts without the sand.
Please help
submitted by vzbzbs to padel [link] [comments]


2021.09.28 23:55 PACREG86 5' ft: A friend helped me pick out this outfit from my closet and felt pretty and comfortable... wondering if the cardigan is too big...

5' ft: A friend helped me pick out this outfit from my closet and felt pretty and comfortable... wondering if the cardigan is too big... submitted by PACREG86 to PetiteFashionAdvice [link] [comments]


http://snegir-t.ru