scholarly journals Can education be personalised using pupils’ genetic data?

2019 ◽  
Author(s):  
Tim T Morris ◽  
Neil M Davies ◽  
George Davey Smith
Keyword(s):  
Predictive Power ◽  
Predictive Accuracy ◽  
Educational Performance ◽  
Phenotypic Data ◽  
Individual Level ◽  
Polygenic Scores ◽  
Prior Achievement ◽  
Using Data ◽  
The Individual ◽  
Potential Tool

AbstractThe increasing predictive power of polygenic scores for education has led to their promotion by some as a potential tool for genetically informed policy. How well polygenic scores predict educational performance conditional on other phenotypic data is however not well understood. Using data from a UK cohort study, we investigated how well polygenic scores for education predicted pupils’ realised achievement over and above phenotypic data that are available to schools. Across our sample, prediction of educational outcomes from polygenic scores were inferior to those from parental socioeconomic factors. There was high overlap between the polygenic score and achievement distributions, leading to weak predictive accuracy at the individual level. Furthermore, conditional on prior achievement polygenic scores were not predictive of later achievement. Our results suggest that while polygenic scores can be informative for identifying group level differences, they currently have limited use for predicting individual educational performance or for personalised education.

scholarly journals Can education be personalised using pupils’ genetic data?

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Tim T Morris ◽  
Neil M Davies ◽  
George Davey Smith
Keyword(s):  
Predictive Power ◽  
Predictive Accuracy ◽  
Educational Performance ◽  
Phenotypic Data ◽  
Individual Level ◽  
Polygenic Scores ◽  
Prior Achievement ◽  
Using Data ◽  
The Individual ◽  
Potential Tool

The increasing predictive power of polygenic scores for education has led to their promotion by some as a potential tool for genetically informed policy. How accurately polygenic scores predict an individual pupil's educational performance conditional on other phenotypic data is however not well understood. Using data from a UK cohort study with data linkage to national schooling records, we investigated how accurately polygenic scores for education predicted pupils’ test score achievement. We also assessed the performance of polygenic scores over and above phenotypic data that are available to schools. Across our sample, there was high overlap between the polygenic score and achievement distributions, leading to poor predictive accuracy at the individual level. Prediction of educational outcomes from polygenic scores were inferior to those from parental socioeconomic factors. Conditional on prior achievement, polygenic scores failed to accurately predict later achievement. Our results suggest that while polygenic scores can be informative for identifying group level differences, they currently have limited use for accurately predicting individual educational performance or for personalised education.

A Critical Review of Theories and Models of Technology Adoption and Acceptance in Information System Research

2015 ◽  
Vol 6 (4) ◽  
pp. 58-77 ◽  
Author(s):  
Ali Tarhini ◽  
Nalin Asanka Gamagedara Arachchilage ◽  
Ra'ed Masa'deh ◽  
Muhammad Sharif Abbasi
Keyword(s):  
Information System ◽  
Technology Adoption ◽  
Environmental Factors ◽  
Critical Review ◽  
Predictive Power ◽  
Theoretical Models ◽  
System Research ◽  
Information System Research ◽  
Individual Level ◽  
The Individual

Previous research shows that selecting an appropriate theory or model has always remained a critical task for IS researchers. To the best of the authors' knowledge, there are few papers that review and compare the acceptance theories and models at the individual level. Hence, this article aims to overcome this problem by providing a critical review of eight of the most influential theories that have been used to predict and explain human behaviour towards adoption of various technologies at the individual level. This article also summarizes their evolution; highlight the key constructs, extensions, strengths, and criticisms from a selective list of published articles appeared in the literature related to IS. This review provides a holistic picture for future researchers in selecting appropriate single/multiple theoretical models/constructs based on their strengths and weaknesses and in terms of predictive power and path significance. It is concluded that a well-established theory should consider the personal, social, cultural, technological, organizational and environmental factors

scholarly journals On “Nationology”: The Gravitational Field of National Culture

2021 ◽  
pp. 002202212110447
Author(s):  
Plamen Akaliyski ◽  
Christian Welzel ◽  
Michael Harris Bond ◽  
Michael Minkov
Keyword(s):  
Gravitational Field ◽  
Cultural Values ◽  
National Culture ◽  
Random Noise ◽  
Religious Groups ◽  
Individual Values ◽  
Individual Level ◽  
Collective Phenomenon ◽  
Using Data ◽  
The Individual

Nations have been questioned as meaningful units for analyzing culture due to their allegedly limited variance-capturing power and large internal heterogeneity. Against this skepticism, we argue that culture is by definition a collective phenomenon and focusing on individual differences contradicts the very concept of culture. Through the “miracle of aggregation,” we can eliminate random noise and arbitrary variation at the individual level in order to distill the central cultural tendencies of nations. Accordingly, we depict national culture as a gravitational field that socializes individuals into the orbit of a nation’s central cultural tendency. Even though individuals are also exposed to other gravitational forces, subcultures in turn gravitate within the limited orbit of their national culture. Using data from the World Values Survey, we show that individual values cluster in concentric circles around their nation’s cultural gravity center. We reveal the miracle of aggregation by demonstrating that nations capture the bulk of the variation in the individuals’ cultural values once they are aggregated into lower-level territorial units such as towns and sub-national regions. We visualize the gravitational force of national cultures by plotting various intra-national groups from five large countries that form distinct national clusters. Contrary to many scholars’ intuitions, alternative social aggregates, such as ethnic, linguistic, and religious groups, as well as diverse socio-demographic categories, add negligible explained variance to that already captured by nations.

Levels of Aggregation in Conjoint Analysis: An Empirical Comparison

1980 ◽  
Vol 17 (4) ◽  
pp. 516-523 ◽  
Author(s):  
William L. Moore
Keyword(s):  
Conjoint Analysis ◽  
Predictive Power ◽  
Aggregate Level ◽  
Segmentation Method ◽  
Empirical Comparison ◽  
Individual Level ◽  
Aggregate Analysis ◽  
The Individual ◽  
Level Analysis

Two segmented methods of performing conjoint anal/sis, clustered and componential segmentation, are compared with each other as well as with individual level and totally aggregate level analyses. The two segmented methods provide insights to the data that (1) are not obtainable at the aggregate level and (2) are in a form that is more easily communicated than the information from the individual level analysis. The predictive power of the clustered segmentation method is higher than that of componential segmentation, and both are superior to the aggregate analysis but inferior to individual level analysis.

Disentangling Differences in Homicide Incarceration Rates by Immigration Status: A Comparison in Texas

2020 ◽  
pp. 001112872094096
Author(s):  
Erin A. Orrick ◽  
Alexander H. Updegrove ◽  
Alex R. Piquero ◽  
Tomislav Kovandzic
Keyword(s):  
Undocumented Immigrants ◽  
Immigrant Status ◽  
Immigration Status ◽  
Incarceration Rate ◽  
Individual Level ◽  
Crime And Punishment ◽  
Using Data ◽  
The Individual ◽  
Almost All ◽  
Immigration And Crime

Research addressing the purported relationship between immigration and crime remains popular, but some gaps remain under-explored. One important gap involves disentangling differences in crime and punishment by immigrant status, as measured across different definitions of immigration status and in relation to U.S. natives, at the individual level. Using data from Texas, results show that native-born U.S. citizens are incarcerated for homicide at higher rates than almost all immigrant groups. While the incarceration rate for undocumented immigrants was 24% greater than the rate for all foreign-citizens, this rate was significantly less than that for U.S. citizens. Among the immigrant status classifications available in this study, all were associated with lower incarceration rates for homicide than that of U.S. citizens.

scholarly journals Unravelling ecological analysis

2006 ◽  
Vol 2006 ◽  
pp. 1-18 ◽  
Author(s):  
D. G. Steel ◽  
M. Tranmer ◽  
D. Holt
Keyword(s):  
Linear Regression Analysis ◽  
Aggregate Data ◽  
Auxiliary Variables ◽  
Ecological Analysis ◽  
Individual Level ◽  
Ecological Regression ◽  
Australian Census ◽  
Using Data ◽  
The Individual

Ecological analysis involves analysing aggregate data for groups of individuals to make inferences about relationships at the individual level. Often the results of such analyses give badly biased estimates. This paper will consider the sources of bias in linear regression analysis using aggregate data. The role of variation of the individual level relationships between groups and the consequent within-group correlations and how these are related to auxiliary variables that characterise the differences between groups is considered. A method of adjusting ecological regression for the effects of auxiliary variables is described and evaluated using data from the 1991 Australian Census.

The Average Response Method of Scaling

1990 ◽  
Vol 15 (1) ◽  
pp. 9-38 ◽  
Author(s):  
Albert E. Beaton ◽  
Eugene G. Johnson
Keyword(s):  
Linear Models ◽  
Predictive Distribution ◽  
Average Response ◽  
Individual Level ◽  
Multiple Imputations ◽  
Plausible Values ◽  
Scale Scores ◽  
Using Data ◽  
The Individual ◽  
Response Method

The average response method (ARM) of scaling nonbinary data was developed to scale the data from the assessments of writing conducted by the National Assessment of Educational Progress (NAEP). The ARM applies linear models and multiple imputations technologies to characterize the predictive distribution of the person-level average of ratings over a pool of exercises when each person has responded to only a few of the exercises. The derivations of “plausible values” from the individual-level distributions of potential scale scores are given. Conditions are provided for the unbiasedness of estimates based on the plausible values, and the potential magnitude of the bias when the conditions are not met is indicated. Also discussed is how the plausible values allow for an accounting of the uncertainties due to the sampling of individuals and to the incomplete information on each sampled individual. The technique is illustrated using data from the assessment of writing.

Redistribution under the Social Security benefit formula at the individual and household levels, 1992 and 2004

2012 ◽  
Vol 12 (1) ◽  
pp. 1-27 ◽  
Author(s):  
ALAN L. GUSTMAN ◽  
THOMAS L. STEINMEIER ◽  
NAHID TABATABAI
Keyword(s):  
Social Security ◽  
Labor Force Participation ◽  
Social Security Benefit ◽  
Household Level ◽  
Individual Level ◽  
The Social ◽  
High Earning ◽  
Using Data ◽  
The Individual ◽  
Over Time

AbstractStudies using data from the early 1990s suggested that while the progressive Social Security benefit formula succeeded in redistributing benefits from individuals with high earnings to individuals with low earnings, it was much less successful in redistributing benefits from households with high earnings to households with low earnings. Wives often earned much less than their husbands. As a result, much of the redistribution at the individual level was effectively from high earning husbands to their own lower earning wives. In addition, spouse and survivor benefits accrue disproportionately to women from high income households. Both factors mitigate redistribution at the household level. It has been argued that with the increase in the labor force participation and earnings of women, Social Security now should do a better job of redistributing benefits at the household level. To be sure, when we compare outcomes for a cohort with a household member age 51 to 56 in 1992 with those from a cohort born twelve years later, redistribution at the household level has increased over time. Nevertheless, as of 2004 there still is substantially less redistribution of benefits from high to low earning households than from high to low earning individuals.

scholarly journals The power of pathway-based polygenic risk scores

2021 ◽  
Author(s):  
Paul O’Reilly ◽  
Shing Choi ◽  
Judit Garcia-Gonzalez ◽  
Yunfeng Ruan ◽  
Hei Man Wu ◽  
...  
Keyword(s):  
Complex Disease ◽  
Predictive Accuracy ◽  
Risk Scores ◽  
Distinct Advantage ◽  
Genetic Profile ◽  
Genetic Liability ◽  
Polygenic Risk ◽  
Individual Level ◽  
Genome Wide ◽  
Polygenic Scores

Abstract Polygenic risk scores (PRSs) have been among the leading advances in biomedicine in recent years. As a proxy of genetic liability, PRSs are utilised across multiple fields and applications. While numerous statistical and machine learning methods have been developed to optimise their predictive accuracy, all of these distil genetic liability to a single number based on aggregation of an individual’s genome-wide alleles. This results in a key loss of information about an individual’s genetic profile, which could be critical given the functional sub-structure of the genome and the heterogeneity of complex disease. Here we evaluate the performance of pathway-based PRSs, in which polygenic scores are calculated across genomic pathways for each individual, and we introduce a software, PRSet, for computing and analysing pathway PRSs. We find that pathway PRSs have similar power for evaluating pathway enrichment of GWAS signal as the leading methods, with the distinct advantage of providing estimates of pathway genetic liability at the individual-level. Exemplifying their utility, we demonstrate that pathway PRSs can stratify diseases into subtypes in the UK Biobank with substantially greater power than genome-wide PRSs. Compared to genome-wide PRSs, we expect pathway-based PRSs to offer greater insights into the heterogeneity of complex disease and treatment response, generate more biologically tractable therapeutic targets, and provide a more powerful path to precision medicine.

scholarly journals Predicting Violence in Merseyside: a Network-Based Approach Using No Demographic Information

2020 ◽  
Vol 4 (3-4) ◽  
pp. 89-102
Author(s):  
Paolo Campana ◽  
Andrea Giovannetti
Keyword(s):  
Regression Models ◽  
Predictive Power ◽  
Demographic Information ◽  
Data Set ◽  
Logistic Regression Models ◽  
Individual Level ◽  
The Individual ◽  
High Level ◽  
Very High ◽  
Individual Information

Abstract Purpose We explore how we can best predict violent attacks with injury using a limited set of information on (a) previous violence, (b) previous knife and weapon carrying, and (c) violence-related behaviour of known associates, without analysing any demographic characteristics. Data Our initial data set consists of 63,022 individuals involved in 375,599 events that police recorded in Merseyside (UK) from 1 January 2015 to 18 October 2018. Methods We split our data into two periods: T1 (initial 2 years) and T2 (the remaining period). We predict “violence with injury” at time T2 as defined by Merseyside Police using the following individual-level predictors at time T1: violence with injury; involvement in a knife incident and involvement in a weapon incident. Furthermore, we relied on social network analysis to reconstruct the network of associates at time T1 (co-offending network) for those individuals who have committed violence at T2, and built three additional network-based predictors (associates’ violence; associates’ knife incident; associates’ weapon incident). Finally, we tackled the issue of predicting violence (a) through a series of robust logistic regression models using a bootstrapping method and (b) through a specificity/sensitivity analysis. Findings We found that 7720 individuals committed violence with injury at T2. Of those, 2004 were also present at T1 (27.7%) and co-offended with a total of 7202 individuals. Regression models suggest that previous violence at time T1 is the strongest predictor of future violence (with an increase in odds never smaller than 123%), knife incidents and weapon incidents at the individual level have some predictive power (but only when no information on previous violence is considered), and the behaviour of one’s associates matters. Prior association with a violent individual and prior association with a knife-flagged individual were the two strongest network predictors, with a slightly stronger effect for knife flags. The best performing regressors are (a) individual past violence (36% of future violence cases correctly identified); (b) associates’ past violence (25%); and (c) associates’ knife involvement (14%). All regressors are characterised by a very high level of specificity in predicting who will not commit violence (80% or more). Conclusions Network-based indicators add to the explanation of future violence, especially prior association with a knife-flagged individual and association with a violent individual. Information about the knife involvement of associates appears to be more informative than a subject’s own prior knife involvement.

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