Ich stell mal den Tweet hier ein, bevor er zu alt wird.
„Small dick energy“ wäre das Gegenstück zur „Big dick energy“
Ein weibliches Äquivalent gibt es in beide Richtungen nicht.
Ich stell mal den Tweet hier ein, bevor er zu alt wird.
„Small dick energy“ wäre das Gegenstück zur „Big dick energy“
Ein weibliches Äquivalent gibt es in beide Richtungen nicht.
Eine interessante Studie zu der Frage, welche Rolle genetische und soziale Faktoren für berufliche Erfolge etc haben:
The paper examines the effects of socioeconomic background (SES) – measured by social class, family income and parental education – cognitive ability, and gender on a variety of key outcomes from a large longitudinal study based on a representative sample of thirteen-year-olds. The data analysed comprised 6216 children who participated in waves 1 to 3 of the Growing Up in Ireland (GUI) longitudinal survey. The outcome measures drawn from wave 3, when respondents were aged about seventeen, were: examination results and several cognitive measures, life difficulties, and quality of relationships. Three regression models were compared with and without, SES measures (occupational class, household income and parental education) and cognitive ability. On academic and cognitive attainments, cognitive ability at age 13 had substantially more explanatory power than the SES measures together. On measures of adolescent difficulties and on family relationships, cognitive ability was important, but gender and to a lesser extent, household income and parental education had some effects. Claims that class background and family income are of central importance for adolescent outcomes are not supported.
Quelle: Are the effects of intelligence on student achievement and well-being largely functions of family income and social class? Evidence from a
longitudinal study of Irish adolescents (Volltext ScihuB)
Aus der Studie zur „Problemlage“:
It has been amply demonstrated that intelligence is associated with a range of educational, labor market, crime, health and other social outcomes (Deary, 2012; Fergusson, Horwood, & Ridder, 2005; Herrnstein & Murray, 1994; Korenman & Winship, 2000). Silver (2019;1) argues that cognitive ability or intelligence is one of the few social science variables “consistently shown to influence a swath of human outcomes”. This has been confirmed for friendship patterns (Boutwell, Meldrum, & Petkovsek, 2017), aggression (Kaukiainen et al., 1999), self-control (Meldrum et al., 2018), as well as in anti-social and criminal behavior (Mears & Cochran, 2013, Silver & Nedelec, 2018, Ttofi et al., 2016). In relation to pro-social and altruistic behavior, Guo et al. (2019) reported a link between IQ and positive outcomes, while Corgnet et al. (2016) found an association between intelligence and trusting behaviours. Wraw et al. (2018) reported that higher IQ in youth in a sample of over 5000 participants in the NLSY-79 (National Longitudinal Survey of Youth) independently predicted health behaviours in middle-age, about three decades later. The complex pathways between intelligence, and physical and mental morbidity as well as mortality, have also been explored (see Deary, 2009) within the new field of cognitive epidemiology.
Cognitive ability is most important in relation to educational outcomes. Walberg (1984, p. 23) computed an average correlation of 0.71 between various IQ measures and academic achievement. Deary, Strand, Smith, and Fernandes’s (2007) large study of over 70,000 children in England estimated correlations around 0.7 between the latent ability trait, g, and total score or best 8 scores in the General Certificate for School Education. Duckworth, Quinn and Tsukayama (2012, p. 443) reported correlations of between 0.7 and 0.8 for IQ measured in grade 4, and grade 5 and 9 achievement tests. For New Zealand, the correlation between IQ at measured at ages 8 and 9 with academic performance at age 13 was 0.83 (Fergusson, Horwood, & Boden, 2008, p. 285). Kaufman, Reynolds, Liu, Kaufman, and McGrew (2012) calculated a mean correlation of 0.8 between latent factors of cognitive ability and student achievement.
Intelligenz hat wenig überraschend einen großen Einfluss auf die akademische Leistung. Natürlich kann man da anführen, dass Intelligenz wiederum auch nur auf Förderung etc beruht und die eben besser ist, wenn man in einer „höheren Schicht“ lebt. Darauf wird im Folgenden eingegangen:
A recurring criticism of such studies is that they neglect the role played by socioeconomic background. A common argument is as follows: since socioeconomic background is the major influence on intelligence, then observed effects of intelligence are simply proxy effects for socioeconomic background. Therefore, if there were a more comprehensive or more accurate measure of socioeconomic background (SES) then the observed association with intelligence would disappear, or at least be substantially reduced (Hauser & Carter, 1995; Heckman, 1996, p. 1113; Korenman & Winship, 2000). This critique is partially correct: socioeconomic background can have some impact on intelligence. Obviously, severe economic deprivation is detrimental to cognitive development (Duncan, Brooks-Gunn, & Klebanov, 1994; Plomin & Deary, 2014). A substantial body of literature claims that there is a causal link between growing up in low income families and a range of negative impacts on children’s lives beyond only academic achievement (e.g. Watson et al., 2012a, Watson et al., 2012b). Low income is thought to have an impact on mental health, and emotional and behavioral outcomes (Duncan, Yeung, Brooks-Gunn, & Smith, 1998). Duncan et al. (1994) found that growing up in low income households was associated with greater levels of fear, anxiety and sadness, as well as bad temper and tantrums. Holzer, Schanzenbach, Duncan, and Ludwig (2008) linked childhood poverty with poorer self-regulation and attentional skills. Other studies conclude that children in low income families are more likely to display behavioral problems, problems in peer relations, as well as anti-social behavior and depression. Conduct problems and hyperactivity are linked to poorer economic circumstances (Richards, Garratt, & Heath, 2016).
Finde ich eine ganz gute Zusammenfassung.
The argument, however, that the effects of intelligence can be explained largely by socioeconomic background rests on several untenable assumptions.
Das wäre dann die Einleitung zur Gegenargumentation, die Gründe abseits der Sozioökonomie annimmt:
The first is that socioeconomic background is the major influence on intelligence. Two metastudies published in 1981 and 2016 indicate declining correlations between family socioeconomic status (SES) and offspring’s intelligence from 0.33 to 0.22 (Harwell, Maeda, Bishop, & Xie, 2017, p. 208; White, 1982, p. 469). Proponents of the argument that intelligence effects are mere proxy effects for socioeconomic background disregard the significant correlation – ranging from 0.4 and 0.6 – between parents‘ abilities, and those of their biological children (Anger, 2012; Black, Devereux, & Salvanes, 2009; Grönqvist, Öckert, & Vlachos, 2017; Plomin, DeFries, Knopik, & Neiderhiser, 2013, p. 195). Furthermore, maternal ability is a more powerful predictor of children’s test scores than SES (Carlson & Corcoran, 2001, p. 789). Anger and Heineck (2010) found that controlling for parental educational attainment and family background, there remained a ‘very robust’ link between the cognitive abilities of children and their parents, consistent with an “average correlation of 0.5 between parents and their offspring”. (p. 1269).
In der Tat ist eines der Probleme bei der Bewertung, dass sozioökomomische Faktoren wiederum selbst einen großen Bezug zu bestimmten Fähigkeiten haben. Es wäre auch nicht verwunderlich, wenn die Korrelationen zwischen SES und Intelligenz abnehmen, wenn Gesellschaften bessere Schulen haben und damit Bildung unabhängiger von SES wird, sich also Intelligenz mehr auswirken kann. Wenn Eltern zB begabt in einem bestimmten Bereich sind und ihre Kinder auch dann muss das nicht unbedingt nur an sozialen Faktoren liegen
The second untenable assumption is that genetics is not relevant in relation to intelligence. It is well-established that the heritability of intelligence is around 0.5 during childhood, increasing during adolescence (Bouchard Jr., 2013; Plomin & Deary, 2014). This finding is based on decades of twin and kinship studies. Genome-wide association tests (GWAS) have found genetic effects – identified by single-nucleotide polymorphisms (SNPs) – on intelligence, educational attainment and student achievement. (Allegrini et al., 2019; Lee et al., 2018). Hill et al. (2019) used multi-trait analysis of GWAS on a very large British sample to show that “the genes linked to differences in income are predominantly those that have previously been linked with intelligence, and that intelligence is one of the likely causal factors leading to differences in income” (p. 1).
Wenn Intelligenz vererbt wird und Intelligenz einem erlaubt einen guten Job zu erreichen, dann ist es auch nicht überraschend, dass Eltern mit einem hohen Bildungsniveau (=als Anzeichen von Intelligenz) gute Jobs haben und wiederum mit höherer Wahrscheinlichkeit auch Kinder bekommen, die eine höhere Intelligenz haben und dann auch wieder gute Jobs bekommen.
This is linked to the final untenable assumption – that the effects of socioeconomic background are causal. They are likely, at least in part, to reflect the effects of parents‘ abilities. Strenze’s (2007) meta-analysis found that an individual’s intelligence measured during childhood or adolescence correlated with their later attainment in education (r = 0.56), occupational status (0.45) and family income (0.23). Rindermann and Ceci (2018) found that across 7 countries, and 19 sub-samples, that parental education was far more important than family wealth in predicting children’s measured intelligence. Lemos, Almeida, and Colom (2011) conclude that the observed relationship between parents‘ education and intelligence is more likely to reflect the genetic transmission of intelligence rather than social processes typically associated with parent’s education such as, more frequent reading to children, more books in the home, better parenting, more positive attitudes to education, etc.
Also ein deutlicher Zusammenhang zwischen Intelligenz und Bildung sowie beruflichen Status und ein schwächerer mit Familieneinkommen. Natürlich ist auch nicht jeder Beruf, der sich aus einem Studium ergibt, geeignet sehr viel Geld zu verdienen (etwa: Sozialpädagogik etc).
There is a large body of prominent research and social commentary on student achievement that overlooks cognitive ability, and focuses on family income and socioeconomic background (Chmielewski, 2019; Chmielewski & Reardon, 2016; OECD, 2019; Reardon, 2011). The influential Programme for International Student Assessment (PISA) administered by the OECD (2016), relies heavily on a composite SES measure, Economic and Social and Cultural Status (ESCS) comprising parents‘ occupation and education, and many indicators of material, cultural and educational resources. In the paradigm of PISA, ESCS is seen as a powerful independent predictor of student achievement. Students who outdid their ESCS forecast – i.e. who overcame disadvantaged socio-economic origins by scoring well in PISA tests – are defined as ‘resilient’ students.
Es ist immer gefährlich mit falschen oder nicht vollständigen Kriterien zu prüfen und nicht zu bemerken, dass noch gänzlich andere Faktoren hineinspielen können, um so mehr, wenn diese in einem gewissen Zusammenhang mit den anderen Faktoren ergeben.
Dazu hatte ich auch bereits ein paar Artikel:
Es ist natürlich ebenso gefährlich NUR Biologie zu betrachten und soziales völlig auszublenden. Aber gerade weil vieles in der Biologie im Vergleich zur Soziologie noch sehr neu ist droht glaube ich in dem Bereich der Soziologie die Gefahr eher.
In the UK, politicians and senior civil servants maintain that “the primary determinant of how well (or badly) you do in life is class, not your talent or effort” (Saunders, 2019, pp. 3–19,14). The Children’s Society (UK) links childhood poverty to academic underachievement, poor mental health, the experience of bullying, and adult unemployment. Wilkinson and Pickett’s (2009) book linking greater household income inequality to a series of negative outcomes with data from several countries with children’s educational levels, physical and mental health, social and family relations was a clear statement of exogenous influences bearing down on children’s lives.
In Ireland, the prevailing view among politicians, academics and journalists is that SES inequalities pervade educational outcomes. In a newspaper interview in 2016, a leading educational sociologist in Ireland, and associate of the Economic and Social Research Institute (ESRI) linked poor academic ability and challenging classroom behaviours among children to their parents‘ lower income, and the parental inability to purchase educationally stimulating materials for the home. Harsher parenting, by economically stressed parents, was also linked to economic insecurity (quoted in June 13th, 2016 in The Irish Examiner, ‘Poverty impacting children’s ability to learn’). An ongoing Irish government initiative since 2005, DEIS, Delivering Equality of Opportunity in Schools, linked lower scores in reading and mathematics primarily to economic deprivation, and sought to address the problem by directing additional resources to schools in deprived areas. A 2019 parliamentary report on educational inequality and disadvantage in Ireland, twice made the claim that the association between social inequality/social class and educational outcome was causal: “Social class further impacts on children’s educational attainment. At the end of primary school, children from higher professional backgrounds had a mean literacy score of 43 (out of a possible 50), those from semi- or un-skilled manual backgrounds had a score of 28, and those in households where neither parent was employed had a mean score of 25.” (Houses of the Oireachtas, 2019; 5 and Appendix 3, section 3; 2).
Es ist wahrscheinlich auch eine politisch besser vertretbare These, Übertrieben formuliert „Ihr seid halt im Schnitt arm, weil ihr im Schnitt dumm seid und eure Kinder werden es auch nicht besser haben, weil sie auch dumm sind“ kommt wahrscheinlich nicht gut an. Die Hoffnung, bestimmte Hürden zu beseitigen und jedem die gleiche Chance zu ermöglichen klingt da schon besser. Und natürlich sollte das auch ein Anliegen sein: Alle Fördern, die die Möglichkeiten haben.
This study examines the effects of SES measured by social class, household income and parental education vis-à-vis cognitive ability for a range of important educational and social outcomes measured several years later.
There are several advantages of this study compared to previous studies.
Hier also die Ausführung dazu, was sie besser machen als die anderen Studien:
First, the measure of cognitive ability is a standard cognitive ability test, the Drumcondra Reasoning Test (DRT). The widely relied-upon AFQT has been criticized as being a measure, not of intelligence, but of school achievement (Fischer et al., 1996, p. 56). Currie and Thomas (1999) suggest that AFQT scores are a better measure of family background than intelligence. Second, unlike the AFQT measure, DRT is measured at a single point in the educational career. A common criticism of Herrnstein and Murray’s (1994) analyses is that AFQT score correlates highly (r = 0.54) with years of education at the time of testing (Fischer et al., 1996, p. 60), since it was collected from adolescents aged 16 to 22. Finally, in contrast to most studies of adolescents, the Irish dataset includes an accurate and household-size adjusted measure of family income. The overall aim of this study is to assess the veracity of the widespread belief that the educational and social outcomes of Irish adolescents can be attributed largely to SES, indicated here by social class, family income, and parental education.
Mal sehen, was dabei rauskommt.
The correlation matrix for the continuous measures (non-imputed) is presented in Table 1 above. Cognitive ability is seen to be strongly associated with intellectual attainment, and the Pearson’s r score for association to the national state exam score was 0.53. The correlations of vocabulary and numeracy scores with cognitive ability were higher still, (0.61, 0.58) but lower with verbal fluency lower (0.35). The correlations of the cognitive ability score to household income (logged) was 0.28, and to parental education was 0.29. Cognitive ability was moderately correlated with the overall SDQ score, (− 0.26), and SDQ-hyperactivity difficulties subscale (− 0.27), i.e. students with higher cognitive ability were reported to have less difficulties, particularly in relation to hyperactivity. Cognitive ability was only modestly linked to higher levels of ‘admiration for mother’ sub-scale, trust in people, and satisfaction with life, and very modestly negatively associated with stress levels among the primary caregiver. Household income (logged) was positively related to exam and intellectual attainment, but at levels lower than cognitive ability (exams = 0.29, verbal = 0.16, vocabulary = 0.22, numerical = 0.21). Income was also negatively related to adolescent difficulties, with the five measures all close to the very modest − 0.1 association.
Weaker still were the correlates of income with relationship measures, though income was still significantly associated with more positive outcomes – more admiration for mother, less caregiver stress, more trust in people, more satisfaction in life. The correlates for parental education largely shadowed those of household income, with modest positive associations to attainments (exams = 0.29, verbal = 0.17, vocabulary = 0.24, and numerical = 0.22), very modest relationships to negative adolescent difficulties, i.e. less difficulties where parents had higher levels of education, but not significant for two of the relationship measures (mother admiration, and caregiver stress).
The multivariate results are presented in three regression tables. Table 2 presents the regression coefficients for the analyses of examinations performance, and verbal, vocabulary and numeracy attainment. These models used the following combinations as independent variables in a linear multiple regression.
Model 1 (M1): social class (three dummy-coded variables), logged mean household income, gender, and parental education.
Model 2 (M2): cognitive ability, captured by the DRT, and gender.
Model 3 (M3): social class, logged household income, gender, parental education, and cognitive ability.
In Table 2, the first dependent measure was performance on a national examination taken two to three years subsequent to the DRT measure. Estimates for strength of individual variable associations were based on the t value of their coefficident, pooled from ten imputed iterations. Positive t values indicate better exam performance is associated with higher levels of the independent measure. The data in model 1 show that exam performance was higher among children from professional, managerial and white collar workers compared to children of manual workers. Exam performance increased with household income and parental education, and was higher among girls compared to boys. Combined, these measures explained 16.3% of variance. However, cognitive ability and gender explained almost 32.7% of the variance (model 2). Including all the independent variable measures in model 3,the variance explained increases to 36.8%. The addition of social class, family income and parental education only increased variance explained by 4%. Cognitive ability at age 13 had a very strong effect with a t value for its co-efficient of 39.86. The effects of SES were much smaller. The beta for parental education was 7.15 in model 3 compared to 12.06 in model 1. The pattern was similar for the three other dependent measures in Table 1. In each case, cognitive ability and gender together accounted for more variance than the SES variables and gender together. The beta coefficients of family income and parental education were far smaller than for cognitive ability. There were sizable gender differences. For the national examination, girls exhibited higher scores and for numeracy, boys had higher scores.
In Table 3, the dependent measures were the SDQ measures; with higher scores meaning greater difficulties. According to model three for the first dependent measure – SDQ-emotional difficulties – children from professional and managerial backgrounds had less emotional difficulties than children from manual backgrounds. Children from manual backgrounds had somewhat less difficulties, on average, than children from white-collar backgrounds. Greater emotional difficulties were reported among girls; and associated with lower parental education and lower cognitive ability. Overall, across the five SDQ measures, cognitive ability tended to explain more variance than the SES variables. Gender was important for hyperactivity, boys being more problematic. For the analyses in Table 3, the common pattern is that cognitive ability and gender accounted for more variation in the dependent variable than the SES variables plus gender, and the effects of cognitive decline only marginally with the addition of the SES variables. In contrast, the addition of cognitive ability (model 3) reduces the effects of the SES measures more substantially. The exception was ‘peer problems’ where the effects of household income and the difference between children from manager and manual households only marginally declined with the addition of cognitive ability.
Table 4 included four dependent measures. Higher scores indicated greater admiration of the child’s mother, more stress of the primary caregiver, greater trust in people, and more satisfaction in life. Although overall, the adjusted R squared values were low, the consistently most powerful variable was cognitive ability which was significantly associated with more positive outcomes. Of the SES variables, only family income had significant, albeit small effects. For two of the four variables (mother admiration, satisfaction with life), model 2 – gender and cognitive ability – explained as much or almost as much variance, as model 3, in which all the social class measures are added. In other words, the addition of the SES measures did not increase the variance explained by cognitive ability and gender. Gender was important to Table mother admiration (girls more admiring of their mothers, than boys),
Hier sieht man also in den Ergebnissen, dass Intelligenz mehr erklärt (aber eben auch nicht alles erklärt) als der sozioökonomische Hintergrund.
The analysis focused on the effects of SES measured by social class, household income and parental education vis-a-vis ` cognitive ability on a range of adolescent outcomes in Ireland. Cognitive ability measured at age 13 had strong associations with educational, cognitive, life difficulties, and relationship outcomes. On the other hand, SES factors– family social class, household income, and parents’ educational attainment–had much weaker effects with outcomes often considered strongly linked to SES.