Last Updated on February 20, 2022
The purpose of this post is to cite predictors for academic achievement. I will focus on merely listing effect sizes rather than synthesizing the research. Note that when I say “predictor”, I’m referring to variables that are statistically associated with academic achievement. So this post does not establish that any of the variables below have a causal influence on academic achievement. Undoubtedly, some of the variables below do have a causal influence on academic achievement, but many of the variables do not. In order to determine whether a given variable has a causal influence, more analysis must be performed to determine if the association between that variable and academic achievement persists after controlling for confounding (as I’ve explained in this post).
The post will be split into sections. The first section will report correlation coefficients between various predictors and academic achievement (e.g., the correlation between intelligence and grades). The second section (TBD) will report standardized mean differences in achievement between pairs of groups (e.g., difference in mean grades between students with ADHD vs students without ADHD). The last section will cite sources which do not belong in the prior sections which provide useful data on predictors of academic achievement. I’ll try to focus on meta-analyses or very large samples when possible, though sometimes smaller samples will be provided if I cannot find a better sample reporting on the variables of interest.
The effect sizes will be reported in tables where each row contains the variable(s) in question, followed by the average effect size (r or d), the number of independent effect sizes (k), the number of subjects involved (N), and the source. A “Notes” column will sometimes be included indicating where to find the effect size in the source and any other useful information.
Correlation coefficients
This section will list correlation coefficients between academic achievement and various predictors. Some background on correlation coefficients is given here. Note that some of these correlation coefficients have been corrected for range restriction. Range restriction corrections are required when the sample is restricted to limited range of values for the predictor variable in question (e.g., the range of SAT scores for college students is much narrower than the range in the general population). When this occurs, I will list the corrected correlation in the r column and I will list the uncorrected correlation in the “Notes” column.
Academic achievement (primary school only)
This table lists predictors of academic achievement in primary school. Primary school corresponds to elementary school (grades 1 to 5) in the United States. All of the effect sizes are reported using grades as the measure of academic achievement. The correlation between grades and intelligence was corrected for range restriction (RR). The best predictor is intelligence, with all of the personality traits having moderate to strong predictive power as well.
| Variable | r | k | N | Source | Notes |
|---|---|---|---|---|---|
| Intelligence | 0.45 | 71 | 18,584 | Roth et al. (2015) | Table 1; uncorrected r = 0.40 |
| Agreeableness | 0.30 | 8 | 3,196 | Poropat (2009) | Table 2 |
| Conscientiousness | 0.28 | 8 | 3,196 | Poropat (2009) | Table 2 |
| Openness to new experience | 0.24 | 8 | 3,196 | Poropat (2009) | Table 2 |
| Parental SES | 0.23 | 46 | Harwell et al. (2016) | Table 2 | |
| Emotional Stability | 0.20 | 8 | 3,196 | Poropat (2009) | Table 2 |
| Extraversion | 0.18 | 8 | 3,196 | Poropat (2009) | Table 2 |
Academic achievement (secondary school only)
The table lists predictors of academic achievement in secondary school. This corresponds to middle and high school (grades 6 to 12) in the United States. Some studies report effect sizes specifically for middle school (MS) and/or high school (HS). The studies use a variety of measures of academic achievement, including grades (Roth et al. 2015, Poropat 2009, Westrick et al. 2015) or combinations of grades and standardized achievement tests (Harwell et al. 2016). Again, the best predictor is intelligence, followed conscientiousness and various measures of parental SES. The predictive validity for all of the personality measures decreased significantly compared to the primary school data.
| Variable | r | k | N | Source | Notes |
|---|---|---|---|---|---|
| Intelligence (HS) | 0.58 | 71 | 15,427 | Roth et al. (2015) | Table 1; uncorrected r = 0.46 |
| Intelligence (MS) | 0.54 | 75 | 49,771 | Roth et al. (2015) | Table 1; uncorrected r = 0.46 |
| Conscientiousness | 0.21 | 35 | 31,980 | Poropat (2009) | Table 2 |
| Parental income (HS) | 0.20 | 1 | 5,606,273 | Westrick et al. (2015) | Table 3; sample = ACT-test-taking population |
| Parental SES (HS) | 0.19 | 41 | Sackett et al. (2009) | Table 8; uncorrected r = -0.01 | |
| Parental SES (HS) | 0.16 | 46 | Harwell et al. (2016) | Table 2 | |
| Parental SES (MS) | 0.16 | 104 | Harwell et al. (2016) | Table 2 | |
| Openness to new experience | 0.12 | 25 | 25,909 | Poropat (2009) | Table 2 |
| Agreeableness | 0.05 | 24 | 25,488 | Poropat (2009) | Table 2 |
| Extraversion | -0.03 | 25 | 25,648 | Poropat (2009) | Table 2 |
| Emotional Stability | 0.01 | 24 | 25,495 | Poropat (2009) | Table 2 |
Academic achievement (K-12)
The table lists predictors of academic achievement in pre-college schooling (grades K-12). The studies used a variety of measures of academic achievement, including grades (Roth et al. 2015) or a combination of grades and standardized tests (Harwell et al. 2016, Castro et al. 2015, Wickersham et al. 2021, and Bücker et al. 2018).
| Variable | r | k | N | Source | Notes |
|---|---|---|---|---|---|
| Intelligence | 0.54 | 240 | 105,185 | Roth et al. (2015) | Table 1; uncorrected r = 0.44 |
| Parental income | 0.26 | 17 | Harwell et al. (2016) | Table 2 | |
| Parental SES (Composite) | 0.23 | 113 | Harwell et al. (2016) | Table 2 | |
| Parental expectations | 0.22 | 8 | Castro (2015) | Table 2 | |
| Parental occupation | 0.21 | 50 | Harwell et al. (2016) | Table 2 | |
| Parental SES (white students only) | 0.21 | 75 | Harwell et al. (2016) | page 12 | |
| Depression | -0.19 | 22 | Wickersham et al. (2021) | ||
| Parental education | 0.17 | 48 | Harwell et al. (2016) | Table 2 | |
| Reading with children | 0.17 | 4 | Castro (2015) | Table 2 | |
| General parental participation | 0.17 | 37 | Castro (2015) | Table 2 | |
| Subjective well-being | 0.16 | 64 | Bücker et al. (2018) | Table 2 | |
| Parental home resources | 0.16 | 84 | Harwell et al. (2016) | Table 2 | |
| Parental style | 0.13 | 14 | Castro (2015) | Table 2 | |
| Parental SES (black students only) | 0.12 | 75 | Harwell et al. (2016) | page 12 | |
| Teacher efficacy | 0.10 | 98 | 14,215 | Kim et al. (2018) | Table 1 |
| Parental supervision of schoolwork | 0.02 | 18 | Castro (2015) | Table 2 | |
| Parental participation in school activities | 0.01 | 21 | Castro (2015) | Table 2 |
College admissions tests
This table lists predictors of scores on college admissions tests. Because there are multiple college admissions tests (the SAT and ACT), the first column will list both the predictor and the specific test or subtest. Unsurprisingly, the best predictors of scores for a given test are the scores from other tests. The next best predictors of test scores are measures of cognitive ability, measured as either g or Raven’s Advanced Matrices. High school GPA and parental SES are also strong predictors of test scores. Finally, aside from openness to new experience and verbal test scores, personality measures seem to be weak predictors of test scores, although this may be due to range restriction due to the elite sample (UC-Davis undergraduates).
| Variable | r | k | N | Source | Notes |
|---|---|---|---|---|---|
| SAT and ACT | 0.92 | 1 | 103,525 | Dorans (1999) | page 2 |
| ACT math and SAT math | 0.89 | 1 | 589,753 | The College Board, ACT (2018) | page 7 |
| ACT Reading/English and SAT Reading/Writing | 0.88 | 1 | 589,753 | The College Board, ACT (2018) | page 7 |
| g and SAT | 0.82 | 1 | 917 | Frey and Detterman (2004) | Table 2 |
| g and ACT | 0.77 | 1 | 1,075 | Koenig et al. (2008) | Table 2 |
| Raven’s Advanced Matrices and ACT | 0.75 | 1 | 149 | Koenig et al. (2008) | page 158; uncorrected r = 0.61 |
| Raven’s Advanced Matrices and SAT | 0.72 | 1 | 103 | Frey and Detterman (2004) | page 376; uncorrected r = 0.48 |
| HSGPA and ACT | 0.58 | 1 | 6,625,660 | Westrick et al. (2015) | Table 3 |
| HSGPA and SAT | 0.53 | 171 | 223,858 | Westrick et al. (2019) | Table 5 |
| HSGPA and SAT | 0.53 | 110 | 151,316 | Kobrin et al. (2008) | Table 4 |
| Parental SES and SAT | 0.42 | 41 | Sackett et al. (2009) | uncorrected r = 0.22 | |
| Parental income and ACT | 0.34 | 1 | 6,257,643 | Westrick et al. (2015) | Table 4 |
| Openness to new experience and SAT verbal | 0.20 | 1 | 10,497 | Noftle and Robins (2007) | sample = UC-Davis undergraduates |
| Conscientiousness and SAT math | -0.07 | 1 | 10,497 | Noftle and Robins (2007) | ^ |
| Neuroticism and SAT math | -0.07 | 1 | 10,497 | Noftle and Robins (2007) | ^ |
| Agreeableness and SAT math | -0.06 | 1 | 10,497 | Noftle and Robins (2007) | ^ |
| Extraversion and SAT math | -0.06 | 1 | 10,497 | Noftle and Robins (2007) | ^ |
| Neuroticism and SAT verbal | -0.05 | 1 | 10,497 | Noftle and Robins (2007) | ^ |
| Openness to new experience and SAT math | 0.05 | 1 | 10,497 | Noftle and Robins (2007) | ^ |
| Agreeableness and SAT verbal | -0.03 | 1 | 10,497 | Noftle and Robins (2007) | ^ |
| Extraversion and SAT verbal | 0.02 | 1 | 10,497 | Noftle and Robins (2007) | ^ |
| Conscientiousness and SAT verbal | -0.01 | 1 | 10,497 | Noftle and Robins (2007) | ^ |
College GPA
The following table lists correlations between college GPA and various predictors. All values are from Richardson et al. (2012). Note that many of these correlations are likely underestimates due to no controls for range restriction.
| Variable | r | k | N |
|---|---|---|---|
| Grade goal | 0.49 | 13 | 2,670 |
| High School GPA | 0.41 | 46 | 34,724 |
| ACT | 0.40 | 21 | 31,971 |
| Effort regulation | 0.35 | 19 | 8,862 |
| SAT | 0.33 | 29 | 22,289 |
| A level points | 0.31 | 4 | 933 |
| Procrastination | -0.25 | 10 | 1,866 |
| Conscientiousness | 0.23 | 69 | 27,875 |
| Intelligence | 0.21 | 35 | 7,820 |
| Test anxiety | -0.21 | 29 | 13,497 |
| Emotional intelligence | 0.17 | 14 | 5,024 |
| Academic intrinsic motivation | 0.16 | 22 | 7,414 |
| Locus of control | 0.15 | 13 | 2,126 |
| Socioeconomic status | 0.15 | 21 | 75,000 |
| Academic integration | 0.13 | 11 | 13,755 |
| Self-esteem | 0.12 | 21 | 4,795 |
| Openness to new experience | 0.09 | 52 | 23,096 |
| Social support | 0.09 | 14 | 5,840 |
| Agreeableness | 0.06 | 47 | 21,734 |
| Sex (female) | 0.04 | 21 | 6,176 |
| Extraversion | -0.03 | 58 | 23,730 |
| Age | 0.03 | 17 | 42,989 |
| Depression | 0.03 | 17 | 6,335 |
| Social integration | 0.03 | 15 | 19,028 |
| Institutional integration | 0.03 | 18 | 19,773 |
| Neuroticism | 0.01 | 58 | 23,659 |
| Academic extrinsic motivation | 0.00 | 10 | 2,339 |
The following table also lists correlations between college GPA and various predictors. However, unlikely the previous table, this displays correlations after correcting for range restriction. The best predictors are combinations of standardized test scores and high school GPA (HSGPA), followed by test scores or HSGPA individually. Parental SES and letters of recommendation are comparatively weak predictors. Some studies focus on first-year GPA (FYGPA) whereas others use fourth-year cumulative GPA (Cum GPA).
| Variable | r | k | N | Source | Notes |
|---|---|---|---|---|---|
| SAT + HSGPA | 0.64 | 55 | 56,939 | Mattern and Patterson (2006) | Table 4; Cum GPA; uncorrected r = 0.46 |
| SAT + HSGPA | 0.61 | 171 | 223,858 | Westrick et al. (2019) | Table 5; FYGPA; uncorrected r = 0.42 |
| HSGPA | 0.58 | 50 | 150,305 | Westrick et al. (2015) | Table 5; FYGPA; uncorrected r = 0.47 |
| SAT | 0.56 | 55 | 56,939 | Mattern and Patterson (2006) | Table 4; Cum GPA; uncorrected r = 0.37 |
| HSGPA | 0.56 | 55 | 56,939 | Mattern and Patterson (2006) | Table 4; Cum GPA; uncorrected r = 0.36 |
| HSGPA | 0.53 | 171 | 223,858 | Westrick et al. (2019) | Table 5; FYGPA; uncorrected r = 0.33 |
| ACT | 0.51 | 50 | 169,818 | Westrick et al. (2015) | Table 5; FYGPA; uncorrected r = 0.38 |
| SAT | 0.51 | 171 | 223,858 | Westrick et al. (2019) | Table 5; FYGPA; uncorrected r = 0.32 |
| Letters of recommendation | 0.28 | 6 | 5,155 | Kuncel et al. (2014) | Table 1 |
| Parental income | 0.24 | 50 | 139,354 | Westrick et al. (2015) | Table 5; FYGPA; uncorrected r = 0.12 |
| Parental SES | 0.22 | 41 | Sackett et al. (2009) | Table 8; FYGPA; uncorrected r = 0.12 |
Grad School GPA
The following table lists correlations between various predictors and GPA in graduate school. The best predictors are various test scores followed by undergraduate GPA.
| Variable | r | k | N | Source | Notes |
|---|---|---|---|---|---|
| MCAT | 0.46 | 28 | 4,706 | Kuncel et al. (2007) | Table 1; uncorrected r = .32 |
| GRE-Subject | 0.41 | 22 | 2,413 | Kuncel et al. (2007) | Table 1; uncorrected r = .24 |
| LSAT | 0.40 | 142 | 22,218 | Kuncel et al. (2007) | Table 1 |
| MAT | 0.39 | 70 | 11,368 | Kuncel et al. (2007) | Table 1; uncorrected r = .27 |
| GRE-Total | 0.37 | 103 | 14,291 | Kuncel et al. (2007) | Table 1; uncorrected r = .25 |
| GMAT | 0.35 | 28 | 5,538 | Kuncel et al. (2007) | Table 1; uncorrected r = .25 |
| Undergraduate GPA | 0.30 | 58 | 9,748 | Kuncel et al. (2001) | Table 2; uncorrected r = .28 |
| Letters of recommendation | 0.13 | 7 | 489 | Kuncel et al. (2014) | Table 1 |
School variables
The following table lists correlations between academic achievement and various school-level predictors. Some studies focused on standardized achievement tests (Armor et al. 2018, Bankston and Caldas 1998), whereas others used combinations of tests and grades (Holzberger et al. 2020).
| Variable | r | k | N | Source | Notes |
|---|---|---|---|---|---|
| Percentage of female-headed | -0.35 | 1 | 18,310 | Bankston and Caldas (1998) | Table 2; sample = Louisiana 10th graders |
| Percentage of black | -0.30 | 1 | 18,310 | Bankston and Caldas (1998) | ^ |
| SES composition | 0.30 | 69 | Holzberger et al. (2020) | Table 3; International sample | |
| Mean level of school poverty | -0.29 | 1 | 18,310 | Bankston and Caldas (1998) | ^ |
| School SES | 0.27 | 1 | 500,000 | Armor et al. (2018) | page 6; North Carolina 3rd to 8th graders |
| Mean level of parental education | 0.22 | 1 | 18,310 | Bankston and Caldas (1998) | ^ |
| Out-of-school activities | 0.18 | 11 | Holzberger et al. (2020) | ^ | |
| Academic pressure | 0.14 | 28 | Holzberger et al. (2020) | ^ | |
| Classroom climate | 0.13 | 23 | Holzberger et al. (2020) | ^ | |
| Instructional practices | 0.13 | 19 | Holzberger et al. (2020) | ^ | |
| School climate | 0.08 | 46 | Holzberger et al. (2020) | ^ | |
| Material resources | 0.08 | 52 | Holzberger et al. (2020) | ^ | |
| Personnel resources | 0.03 | 37 | Holzberger et al. (2020) | ^ |
The indicators for the variables from Holzberger et al. (2020) are as follows (note different studies may have relied on different indicators):
- SES composition: Several indicators were used, parental educational and occupational background, SES index (e.g. International Socio-Economic Index of Occupational Status [ISEI], Index of Economic, Social and Cultural Status [ESCS]), free or reduced lunch eligibility, and ethnic minority.
- Out-of-school activities: offering school activities (e.g. science clubs, excursions, competitions) or programs in science and math.
- Academic pressure: academic achievement expectations and emphasis on homework.
- Classroom climate: learning climate in class (e.g. student-teacher or student-student relationships, and disciplinary climate) and limitations on instruction (e.g. academic abilities, backgrounds, or needs too diverse; disruptive or uninterested students).
- Instructional practices: contrasted student-centered techniques with teacher-centered techniques.
- School climate: problem behaviors (e.g. attendance, crime, violence, suspension), quality of relationships (e.g. student-teacher, teacher-teacher), school learning climate, and safety.
- Material resources: a school’s material infrastructure at the school – in particular, the availability or shortage of adequate resources and information and communication technology (ICT) resources.
- Personnel resources: the degree to which teachers were certified, whether they had a teaching degree or a Master’s in education, teacher experience, the number of teachers, and teachers’ readiness to teach.
State-level reading/mathematics scores
from McDaniel (2006).
| Variable | r | k | N |
|---|---|---|---|
| State health | 0.75 | 1 | 50 |
| Percent low birth weight | -0.71 | 1 | 50 |
| Percent whites not in public school | -0.63 | 1 | 50 |
| Violent crime | -0.58 | 1 | 50 |
| Percent no prenatal care | -0.58 | 1 | 50 |
| Percent black | -0.51 | 1 | 50 |
| State expenditure per student | 0.39 | 1 | 50 |
| Public/teacher ratio | -0.38 | 1 | 50 |
| Percent Hispanic | -0.34 | 1 | 50 |
| Gross state product | 0.28 | 1 | 50 |
| Percent Asian | -0.27 | 1 | 50 |
PISA reading scores
Predictors of PISA reading scores are all taken from Lee et al. (2019) which reported data on nearly half a million students in 65 countries. I used data specifically for the 2012 dataset. Here are the within-country correlations, which are obtained by calculating the correlations within each country and then averaging the correlations from each country:
| Variable | r | k | N |
|---|---|---|---|
| Economic, social and cultural status (ESCS) | 0.35 | 1 | 473,648 |
| Parental occupational status (HISEI) | 0.31 | 1 | 450,621 |
| Home possessions (HOMEPOS) | 0.26 | 1 | 479,807 |
| Parental education in years (PARED) | 0.24 | 1 | 473,091 |
| Cultural possessions (CULTPOS) | 0.24 | 1 | 471,357 |
| Home educational resources (HEDRES) | 0.24 | 1 | 477,772 |
| Wealth proxy | 0.13 | 1 | 479,597 |
Here are the between-country correlations, which are obtained by calculating the correlation between country-level scores and country-level predictors.
| Variable | r | k | N |
|---|---|---|---|
| Home possessions (HOMEPOS) | 0.54 | 1 | 65 |
| Home educational resources (HEDRES) | 0.53 | 1 | 65 |
| Wealth proxy | 0.47 | 1 | 65 |
| Economic, social and cultural status (ESCS) | 0.45 | 1 | 65 |
| Parental occupational status (HISEI) | 0.32 | 1 | 65 |
| Parental education in years (PARED) | 0.26 | 1 | 65 |
| Cultural possessions (CULTPOS) | 0.07 | 1 | 65 |
Notes on the variables used:
- Parental occupational status (HISEI): Highest parental occupation
- Parental education in years (PARED): Highest parental education
- Wealth proxy: constructed based on students’ responses (yes/no) on the items of whether they had: a room of their own, a link to the Internet, a dishwasher, and a (DVD or some other types of) player. In addition, there were three items that would represent family wealth of a particular country as the country-specific wealth-related items, which also had the yes/no response options. Lastly, students were asked whether they had the following items at home: cellular phones, televisions, computers, cars and the number of rooms with a bath or shower, which were assessed on four-categories of “none, one, two, and three or more”.
- Home educational resources (HEDRES): based on the students’ responses (yes/no) on the following seven items: a desk to study, a quiet place to study, a computer that students can use for school work, educational software, books to help with school work, technical reference books, and a dictionary.
- Cultural possessions (CULTPOS): based on students’ responses (yes/no) on the following three items: classic literature, books of poetry, and works of art.
- Home possessions (HOMEPOS): Number of books at home, CULTPOS, HEDRES, and Wealth
- Economic, social and cultural status (ESCS): HOMEPOS, PARED, and HISEI
Other sources
This section lists some other sources that are useful in providing information on predictors of academic achievement. They are listed here instead of above either because they do not use an effect size used above (e.g., they report regression coefficients, etc.) or because I have not read through them to determine that they belong above.
- Greenwald et al. (1996). “The Effect of School Resources on Student Achievement“.
- Hanushek (1997). “Assessing the Effects of School Resources on Student Performance: An Update“.
- Rumberger and Palardy (2000). “Does Segregation Still Matter? The Impact of Student Composition on Academic Achievement in High School“.
- Hanushek et al. (2002). “New Evidence about Brown v. Board of Education: The Complex Effects of School Racial Composition on Achievement“.
- Frazier et al. (2007). “ADHD and Achievement: Meta-Analysis of the Child, Adolescent, and Adult Literatures and a Concomitant Study With College Students”.
- Jeynes (2007). “A Meta-Analysis of the Relationship Between Phonics Instruction and Minority Elementary School Student Academic Achievement“.
- Shin and Chung (2009). “Class size and student achievement in the United States: A meta-analysis“.
- Hattie (2009). “A synthesis of over 800 meta-analyses relating to achievement“.
- Ewijk and Sleegers (2010). “Peer Ethnicity and Achievement: a Meta-analysis Into the Compositional Effect“.
- Ewijk and Sleegers (2010). “The effect of peer socioeconomic status on student achievement: A meta-analysis“.
- Shulruf (2011). “Do extra-curricular activities in schools improve educational outcomes? A critical review and meta-analysis of the literature”.
- Abrams and Kong (2012). “The Variables Most Closely Associated With Academic Achievement: A Review of the Research Literature“.
- Mickelson et al. (2013). “Effects of School Racial Composition on K–12 Mathematics Outcomes: A Metaregression Analysis“.
- Duncan et al. (2015). “School Composition and the Black–White Achievement Gap“.
- Hedges (2016). “The Question of School Resources and Student Achievement: A History and Reconsideration“.
- Berkowitz et al. (2016). “A Research Synthesis of the Associations Between Socioeconomic Background, Inequality, School Climate, and Academic Achievement“.
- Hanushek (2016). “What Matters for Student Achievement“.
- Reardon (2016). “School District Socioeconomic Status, Race, and Academic Achievement“.
- Uttl et al. (2017). “Meta-analysis of faculty’s teaching effectiveness: Student evaluation of teaching ratings and student learning are not related“.
- Kraft et al. (2018). “The Effect of Teacher Coaching on Instruction and Achievement: A Meta-Analysis of the Causal Evidence“.
- Reardon et al. (2019). “The Geography of Racial/Ethnic Test Score Gaps“.
- Conway-Turner et al. (2020). “Does Diversity Matter? School Racial Composition and Academic Achievement of Students in a Diverse Sample“.
- Mickelson et al. (2020). “A Metaregression Analysis of the Effects of School Racial and Ethnic Composition on K–12 Reading, Language Arts, and English Outcomes“.
- Holzberger et al. (2020). “A meta-analysis on the relationship between school characteristics and student outcomes in science and maths – evidence from large-scale studies“.
- Li et al. (2020). “Relationship Between SES and Academic Achievement of Junior High School Students in China: The Mediating Effect of Self-Concept“.
For the college GPA correlates, are you aware of any studies examining whether chosen major/courses would affect these results? It seems Richardson et al. (2012) mostly relied on cumulative GPA, and from a very brief look at the references, I gather some studies in their meta-analysis focused on students from select majors and some didn’t.
For instance, for intelligence, if assessing overall student body and if it happens that more intelligent students pick more difficult majors/courses, then that would distort the variance in GPA explained by intelligence, no?
That could very well be an issue, but my guess is it wouldn’t distort findings too much since there aren’t large IQ differences by college major from what I’ve seen (though some of the data that I’ve seen on IQ by major is questionable/outdated). Also, there isn’t much variation in IQ in general among college students due to range restriction.
Anyway, I’m not aware of any studies that take into account major when analyzing IQ-GPA correlations. The [closest study](https://files.eric.ed.gov/fulltext/ED563124.pdf) that I know of looks at SAT-GPA correlations by college majors. If you look at the SAT-GPA correlation within a specific major, they deviate a bit from the overall SAT-GPA correlation (overall correlation is 0.57, but the within-major correlation hovers between 0.42 and 0.63 depending on the major). If you wanted, you could calculate the weighted average of the within-major correlations to determine the SAT-GPA correlation after controlling for major, but I doubt it would differ too much from the overall correlation. Of course, this is SAT rather than IQ so there could be different findings if you did a similar analysis for IQ.
Thanks a lot for the response. I’ll admit I’m coming at this from an IQ-skeptic perspective, which is why I find the Richardson et al. (2012) results intriguing.
As an aside, did you catch that the purported IQ-Job Performance relationship looks like a total bust (https://ink.library.smu.edu.sg/lkcsb_research/7188/). Seems like IQ experts had themselves fooled for more than half a century. Vindicates Richardson & Norgate (2015)(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4557354/). Very brief summary by Russell Warne here(https://pbs.twimg.com/media/F2DqqqTXwAAjMhx?format=jpg&name=medium).
I haven’t looked into this, but I have similar papers bookmarked for later. Quickly skimming through the Sackett paper, it seems like the current estimates of the meta-analytic IQ-performance correlations are lower than previously estimated due to (1) improvements over the conventional range restrictions corrections which turned out to be overcorrections (these improved techniques result in lower validities for most predictors, not just IQ), and (2) more recent data samples which show lower IQ-performance correlations than the older data samples (which the authors speculate are due to the decreasing role of manufacturing jobs and the increasing role of team structures).
Regarding (1), I don’t have the technical expertise or domain knowledge to personally audit the credibility of their claims at the moment. The authors do refer to a series of authors (Oh et al.) who are critical of their argument (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4308528). It seems like there have been a few exchanges back and forth by Sackett et al. vs Oh et al. and Ones et al. (see papers in the sidebar here https://www.cambridge.org/core/journals/industrial-and-organizational-psychology/article/abs/response-to-speculations-about-concurrent-validities-in-selection-implications-for-cognitive-ability/5C25EFD82F603039E111FFAF299B1B0F). The latest paper in this debate was published just a few weeks ago. So if I were really wanted to get a full understanding of these criticisms, I would just wait for the debate to settle and read the exchanges from each party. More likely, I’ll just wait a few months/years and see where the expert consensus settles instead of trying to personally audit what seems like a controversy over technical matters (side point about my general epistemology: as a layman, I think the only reliable way to acquire knowledge of a technical field is to start by understanding the facts for which there is no controversy. Learn as many of these facts as you can and then maybe you can become qualified to start picking sides on contentious topics. Until then, I’ll just remain agnostic on the controversies).
Regarding (2), they cite the following source as evidence of lower IQ-performance correlations in the 21st century: “A contemporary Look at the Relationship Between Cognitive Ability and Job Performance”. However, in the references, this is cited as a “poster” and I can’t find the source anywhere online. It would be interesting to see if the lower validities are due to real changes in the workplace vs better methodology, data collection, etc.
Anyway, these factors imply that the more appropriate meta-analytic estimate of the IQ-performance correlation is 0.31 for the older data samples and 0.23 for more recent data samples. I’m not sure what you mean by “total bust”, but worse-case this just means that the IQ-performance correlation was large in the 20th century but merely modest in the 21st century (based on the differential psychology guidelines reported by https://www.researchgate.net/publication/305699288_Effect_size_guidelines_for_individual_differences_researchers which argue that 0.1, 0.2, 0.3 should be considered small, medium, and large correlations). Regardless, I’m going to withhold judgment for the reasons I gave earlier. Ideally, I’ll wait to see where the consensus shifts. Even if there is no consensus, the debate appears to be still ongoing, which means I need to wait for future replies even if I wanted to personally audit each party’s arguments in detail.
Fair enough. Yea, I’m being a little polemical with “total bust”. I had in mind the longstanding declarations of “single best predictor” and so on.