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Tuesday, August 31, 2010
iPost: Computer based cognitive assessment
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Saturday, August 28, 2010
iPost: Psychometrika, Vol. 75, Issue 3 - New Issue Alert
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In this issue:
A General Family of Limited Information Goodness-of-Fit Statistics for Multinomial Data Author(s) Harry Joe & Alberto Maydeu-Olivares DOI 10.1007/s11336-010-9165-5 Online since April 16, 2010 Page 393 - 419
Modeling Noisy Data with Differential Equations Using Observed and Expected Matrices Author(s) Pascal R. Deboeck & Steven M. Boker DOI 10.1007/s11336-010-9168-2 Online since May 27, 2010 Page 420 - 437
Accumulative Equating Error after a Chain of Linear Equatings Author(s) Hongwen Guo DOI 10.1007/s11336-010-9160-x Online since March 31, 2010 Page 438 - 453
Nested Logit Models for Multiple-Choice Item Response Data Author(s) Youngsuk Suh & Daniel M. Bolt DOI 10.1007/s11336-010-9163-7 Online since April 21, 2010 Page 454 - 473
A Markov Chain Monte Carlo Approach to Confirmatory Item Factor Analysis Author(s) Michael C. Edwards DOI 10.1007/s11336-010-9161-9 Online since April 02, 2010 Page 474 - 497
Bayesian Analysis of Multivariate Probit Models with Surrogate Outcome Data Author(s) Wai-Yin Poon & Hai-Bin Wang DOI 10.1007/s11336-010-9164-6 Online since April 22, 2010 Page 498 - 520
A Constrained Linear Estimator for Multiple Regression Author(s) Clintin P. Davis-Stober, Jason Dana & David V. Budescu DOI 10.1007/s11336-010-9162-8 Online since April 01, 2010 Page 521 - 541
New Equating Methods and Their Relationships with Levine Observed Score Linear Equating Under the Kernel Equating Framework Author(s) Haiwen Chen & Paul Holland DOI 10.1007/s11336-010-9171-7 Online since June 09, 2010 Page 542 - 557
A Bayesian Multi-Level Factor Analytic Model of Consumer Price Sensitivities Across Categories Author(s) Sri Devi Duvvuri & Thomas S. Gruca DOI 10.1007/s11336-010-9167-3 Online since May 12, 2010 Page 558 - 578
BOOK REVIEW P. Sprent & N.C. Smeeton (2007). Applied Nonparametric Statistical Methods (4th ed.). Author(s) Laura M. Schultz DOI 10.1007/s11336-010-9166-4 Online since May 07, 2010 Page 579 - 580
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Friday, August 27, 2010
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Thursday, August 26, 2010
iPost: Test your attentional focus: is multi-tasking a good thing?
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Monday, August 23, 2010
iPost: The Flynn Effect and IQ Disparities Among Races, Ethnicities, and Nations: Are There Common Links?
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The Flynn Effect and IQ Disparities Among Races, Ethnicities, and Nations: Are There Common Links: Guest post by S. B. Kaufman
The Flynn Effect and IQ Disparities Among Races, Ethnicities, and Nations: Are There Common Links?
By Scott Barry Kaufman, Ph.D.
Over the years, various ‘social multipliers' (Dickens & Flynn, 2006) have been proposed to account for the Flynn Effect-the dramatic increase in IQ witnessed every decade of the 20th century. Potential environmental effects include increased nutrition, increased test familiarity, heterosis, increased scientific education, video games, TV show complexity, modernization, and more. Surely a combination of factors contributed to the rise. In this post, I want to focus though on a few changes over the course of the past 100 years that have particular implications for understanding race, ethnic, and national disparities in IQ. First let’s look at literacy.
Literacy involves the ability to write, read, and comprehend information of varying levels of complexity. It is estimated that there are 774 million illiterate adults in the world, 65% whom are women (UNESCO Intsistute for Statistics, 2007). In the United States alone, 5% of the adult population is completely nonliterate (Kirsch, Jungeblut, Jenkins, & Kolstad, 1993). Self-reported literacy skills of both White and Black populations of the U.S. have been increasing steadily since 1870, however (National Center for Education Statistics, 1993). One study showed that the IQ and literacy scores of Blacks increased in parallel from 1980 to 2000 (Dickens & Flynn, 2006).
The importance of being able to read for performance on an IQ test cannot be understated. Instead of measuring ‘intelligence' in a nonliterate test-taker, the test is measuring that person's inability to read. While ‘intelligence' may certainly influence an individual's ability to read, society has a lot of influence on how many inhabitants even get the chance to read in the first place. Therefore, reading skills may exert important effects on particular races, ethnicities, and nationalities that have historically been through much discrimination and as a result, received limited opportunities for literacy development.
An enormous body of evidence collected over the past 50 years shows that different ethnicities and races within a country tend to show substantial differences in their average level of IQ. Some researchers argue that this gap is narrowing (Dickens & Flynn, 2006) whereas others argue that the IQ gap has remained stable (Murray, 2006). IQ test score discrepancies are also found between nations. For instance, sub-Saharan African countries have demonstrated statistically significantly lower IQs than other nations (Lynn, 2006, 2008). These findings have led some researchers to propose that such IQ gaps found across ethnicities, races, and nationalities suggests a difference in innate brain capacity (see Lynn & Vanhanen, 2006).
Until recently, the phenomenon of the Flynn Effect, and IQ gaps found between different ethnicities, races, and nationalities have not been tied together. For the first time ever, Psychologist David F. Marks systematically analyzed the association between literacy skills and IQ across time, nationality, and race (Marks, 2010).
If increasing literacy were really explaining a number of seemingly different IQ trends, then you would expect to see a few things. First, within a population you should expect increased education of literacy skills to be associated with an increase in the average IQ of that population. Second, IQ gains should be most pronounced in the lower half of the IQ bell curve since this is the section of the population that prior to the education would have obtained relatively lower scores due to their inability to comprehend the intelligence test's instructions. With increased literacy, you should expect to see a change in the skewness of the IQ distribution from positive to negative as a result of higher rates of literacy in the lower half of the IQ distribution (but very little change in the top half of the distribution). You should also expect to see differences on the particular intelligence test subscales, with increased literacy showing the strongest effects on verbal tests of intelligence and minimal differences on other tests of intelligence. If all these predictions hold up, there would be support for the notion that secular IQ gains and race differences are not different phenomena but have a common origin in literacy.
To test these predictions, Marks looked at samples representative of whole populations (rather than individuals), and used ecological methods to calculate statistical associations between IQ and literacy rates across different countries. Were Marks' findings consistent with the predictions?
Strikingly, yes. He found that the higher the literacy rate of a population, the higher that population's mean IQ, and the higher that population's mean IQ, the higher the literacy rate of that population. When literacy rates declined, mean IQ also declined. Marks also found evidence for unequal improvements across the entire IQ spectrum: the greatest effects of increased literacy rates were on those in the lower half of the IQ distribution. Interestingly, he also found that both the Flynn Effect and racial/national IQ differences showed the largest effects of literacy on verbal tests of intelligence, with the perceptual tests of intelligence showing no consistent pattern.
It must be noted that literacy wasn't the only factor responsible for the Flynn effect. Adopting the Cattell-Horn-Carroll (C-H-C) framework (McGrew, 2005, 2009) Marks found that Visual processing (Gv) and Processing Speed (Gs) also made important contributions.
It should also be noted that Mark's findings only speak to populations (not individuals) and do not say much about causation. The findings can only definitively say that some not-yet-identified variable is causing both literacy and IQ scores to change. To really test for causation, future experimental studies should be conducted to look at the effect of literacy intervention on IQ scores in comparison with a control group not receiving literacy intervention and should also investigate intervening variables that affect both literacy and IQ. Still, the result that population level literacy changes with population IQ is suggestive that increased literacy is causing increased IQ.
Even though there is still much work to be done, Marks’ findings have some very strong implications for our understanding of the Flynn effect, the nature of intelligence, and the origin of race and secular differences in intelligence.
In Hernstein & Murray's 1994 book The bell curve: intelligence and class structure in American life, most of their controversial claims about IQ differences, ethnicity, and social issues came from the United States Department of Labor's National Longitudinal Survey of Youth. This survey includes the Armed Forces Qualifications Test, which was developed by the Department of Defense and measures the ability of potential recruits to learn how to perform military duties. Since many of Hernstein & Murray's conclusions were based on this test, it's important to really examine what that test measures.
Marks did just that by scanning the literature for datasets containing test estimates for populations of groups taking both the Armed Forces Qualifications Test and tests of literacy. One study on nine groups of soldiers differing in job and reading ability found a correlation of .96 between the Armed Forces Qualifications Test and reading achievement (Sticht, Caylor, Kern, & Fox, 1972). Another study looking at the period between 1980 and 1992 found significant improvements among Black and Hispanic populations in their Armed Forces Qualifications Test scores while Whites only showed a slight decrement (Kilburn, Hanser, & Klerman, 1998). Another study obtained reading scores for 17-year olds for those same ethnic groups and dates and found a correlation of .997 between reading scores and Armed Forces Qualifications Test scores (Campbell et al., 2000). This nearly perfect correlation was based on six pairs of data points from six independent population samples evaluated by two separate groups of investigators. As Marks notes,
"On the basis of the studies summarized here, there can be little doubt that the Armed Forces Qualifications Test is a measure of literacy."
The Flynn Effect was intriguing all by itself. Now that researchers have shown common linkages between the Flynn Effect, race, ethnic, and nationality disparities, there are even more questions to be answered and potential research avenues to be explored. The Marks study suggests a crucial environmental factor is literacy. If this is so, then interventions that increase literacy will also narrow the IQ gap found between different races and nationalities.
Literacy intervention can take many forms though. Researchers should consider not just improved access to schooling but also lots of other conditions that may affect literacy rates. For instance, recent research shows the important effects of parasites and pathogens on a nation's intelligence (see recent article in The Economist called Mens sana in corpore sano). Christopher Eppig and colleague's argue in their recent article in Proceedings of the Royal Society that the Flynn effect may be caused in part by the decrease in the intensity of infectious diseases as nations develop. Looking at data from 192 countries and 28 infectious diseases in those countries, they found that the higher the disease burden of that population, the lower that population's mean IQ level, with robust correlations ranging from -0.76 to -0.82. The chance that this correlation came about at random is reported by The Economist to be less than 10,000. Interestingly, when Eppig and colleagues controlled for other contributing variables to national differences in IQ (temperature, distance from Africa, gross domestic product per capita and various measures of education), infectious disease remained the most powerful predictor of average national IQ.
These results suggest that infections and parasites such as intestinal worms, malaria, and perhaps most importantly (according to Eppig and colleagues) bugs that cause diarrhea, can all have important effects on both literacy rates and IQ scores. The good news is that disease interventions such as vaccinations, clean water and proper sewage can have quite outstanding effects on multiple areas of cognition.
This latest research on the environmental effects of nutrition (Colom et al., 2005, but see Flynn, 2009), disease, literacy, and more on both the rise in IQ and ethnic, racial, and national disparities in IQ point to the importance of the environment for developing intelligence as well as the importance for researchers to be very careful when they use intelligence test performance (especially verbal tests) to make inferences about hereditary differences between different ethnic groups and nationalities.
© 2010 by Scott Barry Kaufman
Acknowledgments: Thanks to Louisa Egan for bringing the Economist article to my attention.
For more on the Flynn Effect, see:
Are you smarter than Aristotle? Part I
Are you smarter than Aristotle?: On the Flynn Effect and the Aristotle Paradox
IQ Bashing, Breadancing, The Flynn Effect, and Genes
References
Campbell, J. R., Hombo, C. M., & Mazzeo, J. (2000) Trends in academic progress: three decades of student performance, NCES 2000-469. Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement, National Center for Education Statistics, NAEP 1999.
Colom, R., Lluis-Font, J. M., & Andrés-Pueyo, A. (2005) The generational intelligence gains are caused by decreasing variance in the lower half of the distribution: supporting evidence for the nutrition hypothesis. Intelligence, 33, 83-91.
Dickens, W. T., & Flynn, J. R. (2006) Black Americans reduce the racial IQ gap: evidence from standardization samples. Psychological Science, 17, 913-920.
Eppig, C., Fincher, C.L., & Thornhill, R. (2010). Parasite prevalence and the worldwide distribution of cognitive ability. Proceedings of the Royal Society B, doi: 10.1098/rspb.2010.0973.
Flynn, J. R. (2009) Requiem for nutrition as the cause of IQ gains: Raven's gains in Britain 1938 to 2008. Economics and Human Biology, 7, 18-27.
Herrnstein, R. J., & Murray, C. (1994) The bell curve: Intelligence and class structure in American life. New York: Free Press.
Kilburn, M. R., Hanser, L. M., & Klerman, J. A. (1998) Estimating AFQT scores for National Educational Longitudinal Study(NELS) respondents. Santa Monica, CA: RAND Distribution Services.
Kirsch, I. S., Jungeblut, A., Jenkins, L., & Kolstad, A. (1993) Adult literacy in America: A first look ook at the results of the National Adult Literacy Survey. Princeton, NJ: Educational Testing Service.
Lynn, R. (2006) Race differences in intelligence: an evolutionary analysis. Augusta, GA: Washington Summit.
Lynn, R. (2008) The global bell curve. Augusta, GA: Washington Summit.
Lynn, R., & Vanhanen, T. (2002) IQ and the wealth of nations. Westport, CT: Praeger.
Marks, D.F. (2010). IQ variations across time, race, and nationality: An artifact of differences in literacy skills. Psychological Reports, 106, 3, 643-664.
McGrew, K. S. (2005) The Cattell-Horn-Carroll theory of cognitive abilities: past, present, and future. In D. P. Flanagan & P. L. Harrison (Eds.), Contemporary intellectual assessment: theories, tests, and issues. (2nd ed.) New York: Guilford. Pp. 136-182.
McGrew, K. (2009). Editorial. CHC theory and the human cognitive abilities project. Standing on the shoulders of the giants of psychometric intelligence research, Intelligence, 37, 1-10.
Murray, C. (2006) Changes over time in the Black-White difference on mental tests: evidence from the children of the 1979 cohort of the National Longitudinal Survey of Youth. Intelligence, 34, 527-540.
National Center for Education Statistics. (1993) 120 years of American educ ation: a statistical portrait. (T. Snyder, Ed.) Washington, DC: U.S. Department of Education, Institute of Education Sciences, NCES 1993.
Sticht, T. G., Caylor, J. S., Kern, R. P., & Fox, L. C. (1972) Project REALISTIC: determination of adult functional literacy skill levels. Reading Research Quarterly, 7, 424-465.
Technorati Tags: Psychology, school psychology, developmental psychology, educational psychology, forensic psychology, neuropsychology, special education, intelligence, cognitive abilities, cognition, intelligence theories, CHC theory, CHC, Cattell-Horn-Carroll, intelligence, cognition, IQ, IQ tests, race and IQ, racial differences, Flynn effect, national IQ, Beautiful Minds, Scott Barry Kaufman, literacy and IQ
iPost: Flynn effect for memory could invalidate neuropsychologists' tests
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Sunday, August 22, 2010
iPost: The five dimensions of an autistic brain
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iPost: Advances in Data Analysis and Classification, Vol. 4, Issue 2 - New Issue Alert
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Preface Special Issue on Robust Methods for Classification and Data Analysis Author(s) Marco Riani, Andrea Cerioli & Peter J. Rousseeuw DOI 10.1007/s11634-010-0071-6 Online since August 06, 2010 Page 85 - 87
Regular Article A review of robust clustering methods Author(s) Luis Angel García-Escudero, Alfonso Gordaliza, Carlos Matrán & Agustín Mayo-Iscar DOI 10.1007/s11634-010-0064-5 Online since June 18, 2010 Page 89 - 109
Regular Article A simulation study to compare robust clustering methods based on mixtures Author(s) Pietro Coretto & Christian Hennig DOI 10.1007/s11634-010-0065-4 Online since June 26, 2010 Page 111 - 135
Regular Article The k-step spatial sign covariance matrix Author(s) C. Croux, C. Dehon & A. Yadine DOI 10.1007/s11634-010-0062-7 Online since June 11, 2010 Page 137 - 150
Regular Article Robust kernel principal component analysis and classification Author(s) Michiel Debruyne & Tim Verdonck DOI 10.1007/s11634-010-0068-1 Online since June 24, 2010 Page 151 - 167
Regular Article Optimal robust estimates using the Hellinger distance Author(s) Alfio Marazzi & Victor J. Yohai DOI 10.1007/s11634-010-0061-8 Online since June 04, 2010 Page 169 - 179
Regular Article Inference for robust canonical variate analysis Author(s) Stefan Van Aelst & Gert Willems DOI 10.1007/s11634-010-0063-6 Online since June 08, 2010 Page 181 - 197
Regular Article A review on consistency and robustness properties of support vector machines for heavy-tailed distributions Author(s) Arnout Van Messem & Andreas Christmann DOI 10.1007/s11634-010-0067-2 Online since June 19, 2010 Page 199 - 220
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Saturday, August 21, 2010
iPost: Why working memory matters in the knowledge age: study
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Friday, August 20, 2010
iPost: Auditory selective attention deficits found as problem for some military trainees
Interesting story on potential predictive validity of WJ III Auditory Discrimination test in the military. Story at link below.
[Conflict of interest note: I am a coauthor and have a royalty interest in the WJIII battery. ]
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Thursday, August 19, 2010
iPost: History of Psychology - Volume 13, Issue 3
Focus on Czech, Brazil, Spain and Italy
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Date: August 19, 2010 11:39:16 AM CDT
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Subject: History of Psychology - Volume 13, Issue 3
A new issue is available for the following Educational Publishing Foundation journal:History of Psychology
2010 Volume 13, Issue 3 (Aug)
Historiography of Czech psychology.Page 309-334
An essay by Walter Benjamin.Page 339-340
Tracking down some ancient baboons.Page 340-341
News.Page 341-348
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iPost: Children's hierarchical spatial (Gv) analysis patterns by age
Citation
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Children's spatial analysis of hierarchical patterns: Construction and perception.
Vinter, Annie; Puspitawati, Ira; Witt, Arnaud
Developmental Psychology, Aug 16, 2010, No Pagination Specified. doi: 10.1037/a0020615
Abstract
- Two experiments were reported that aimed at investigating the development of spatial analysis of hierarchical patterns in children between 3 and 9 years of age. A total of 108 children participated in the drawing experiment, and 224 children were tested in a force-choice similarity judgment task. In both tasks, participants were exposed to consistent and inconsistent targets for short (300-ms) and long (3-s) durations. The drawing task showed that 3-year-old children either preferred to draw the local level or reproduced both levels in a nonintegrated manner. Coordination between the 2 processes started to emerge at 4 years of age, and 6-year-old children produced essentially correct integrated responses. The similarity judgment task confirmed that local processing dominated at 3 years of age. Preference for global processing appeared at 5 years of age, and it gained in strength later. Significant effects of stimulus consistency and stimulus duration were also found. In particular, the use of inconsistent patterns in the similarity judgment task revealed a phenomenon of local-to-global interference in the 3-year-olds. (PsycINFO Database Record (c) 2010 APA, all rights reserved)
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Decomposing model fit: Measurement vs. Theory in organizational research using latent variables.
O'Boyle, Ernest H.; Williams, Larry J.
Journal of Applied Psychology, Aug 16, 2010, No Pagination Specified. doi: 10.1037/a0020539
Abstract
- Goodness-of-fit indices have an important role in structural equation model evaluation. However, some studies (e.g., McDonald & Ho, 2002; Mulaik et al., 1989) have raised concerns that overall fit values primarily reflect the fit of the measurement model, and this allows significant misspecification among the latent variables to be masked. Using an approach analogous to Anderson and Gerbing's (1988) 2-step approach that isolates the measurement component of a composite model, we present the rationale and evidence for the root mean square error of approximation of the path component (RMSEA-P), a relatively new fit index that isolates the path component. We reviewed 5 of the top organizational behavior/human resources journals from 2001 to 2008 and identified 43 studies using structural equation modeling in which the overall composite model could be decomposed into its measurement and path components. The RMSEA-P for these studies generally showed unfavorable results, with many values failing to meet commonly accepted standards. Incorporating the RMSEA-P and its confidence interval into James, Mulaik, and Brett's (1982) framework for model testing, we provide evidence that many of the conclusions based upon the goodness of fit of the overall model may be inaccurate. We conclude with recommendations for how researchers can focus more attention on path models and latent variable relations and improve their model evaluation process. (PsycINFO Database Record (c) 2010 APA, all rights reserved)
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Wednesday, August 18, 2010
Research bytes 8-18-2010: Is emotional intelligence (EI) a valid construct distinct from Gc and Gf?
MacCann, C. (2010). Further examination of emotional intelligence as a standard intelligence: A latent variable analysis of fluid intelligence, crystallized intelligence, and emotional intelligence. Personality and Individual Differences, 49(5), 490-496.
Abstract
This study tests whether emotional intelligence (EI) is distinct from existing factors of intelligence after controlling for method factors in EI measurement. The relationship between EI, fluid intelligence (Gf), and crystallized intelligence (Gc) latent factors is examined in a sample of Australian undergraduates (N = 207). EI measures are all multiple-choice so as to control for response format, and the study also examines the effect of consensus scoring on the distinction of EI from Gf and Gc. Results show that EI forms a latent factor distinct from Gf and Gc, though strongly related to Gc, and that consensus scoring has only minor effects on the factor structure. EI and Gc factors show similar relationships with big five personality, relating only to Openness. Females tend to score higher on EI, whereas males tend to score higher on Gf and Gc. It is suggested that EI might be considered a different content domain for acquired knowledge than is typically examined by Gc tests, and may have different motivational pathways to development.
Article Outline
Technorati Tags: Psychology, school psychology, developmental psychology, educational psychology, forensic psychology, neuropsychology, special education, intelligence, cognitive abilities, cognition, intelligence theories, CHC theory, CHC, Cattell-Horn-Carroll, intelligence, cognition, IQ, IQ tests, Gf, Gc, EI, emotional intelligence, factor analysis
Abstract
This study tests whether emotional intelligence (EI) is distinct from existing factors of intelligence after controlling for method factors in EI measurement. The relationship between EI, fluid intelligence (Gf), and crystallized intelligence (Gc) latent factors is examined in a sample of Australian undergraduates (N = 207). EI measures are all multiple-choice so as to control for response format, and the study also examines the effect of consensus scoring on the distinction of EI from Gf and Gc. Results show that EI forms a latent factor distinct from Gf and Gc, though strongly related to Gc, and that consensus scoring has only minor effects on the factor structure. EI and Gc factors show similar relationships with big five personality, relating only to Openness. Females tend to score higher on EI, whereas males tend to score higher on Gf and Gc. It is suggested that EI might be considered a different content domain for acquired knowledge than is typically examined by Gc tests, and may have different motivational pathways to development.
Article Outline
1. Introduction
1.1. Summary of hypotheses
2. Method
2.1. Participants
2.2. Materials
2.2.1. Situational Test of Emotional Understanding – short form (STEU)
2.2.2. Situational Test of Emotion Management – short form (STEM)
2.2.3. Blends and Changes test from the MEIS
2.2.4. Vocabulary test
2.2.5. Esoteric analogies
2.2.6. General knowledge
2.2.7. Letter series
2.2.8. Nonsense syllogisms
2.2.9. Letter counting
2.2.10. Five factor model of personality
3. Results
3.1. Reliability and descriptive statistics
3.2. Structural analyses
3.2.1. EFA
3.2.1.1. Intelligence tests scored dichotomously
3.2.1.2. Intelligence tests scored by consensus
3.2.2. CFA
3.2.2.1. Intelligence tests scored dichotomously
3.2.2.2. Intelligence tests scored by consensus
3.2.3. Hierarchical factor analysis
3.3. Personality correlates of Gf, Gc, and EI factors
4. Discussion
4.1. The effect of consensus scoring on factor structure
4.2. Limitations and future directions
5. General conclusion
Acknowledgements
References
Technorati Tags: Psychology, school psychology, developmental psychology, educational psychology, forensic psychology, neuropsychology, special education, intelligence, cognitive abilities, cognition, intelligence theories, CHC theory, CHC, Cattell-Horn-Carroll, intelligence, cognition, IQ, IQ tests, Gf, Gc, EI, emotional intelligence, factor analysis
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