Paul T Von Hippel
Biographic Data
| ID | 267203 |
|---|---|
| NAME | Paul T Von Hippel |
| GIVEN NAMES | Paul T |
| FAMILY NAME | Von Hippel |
| SIGNATURE | VON HIPPEL P T |
| AFFILIATIONS | The University of Texas at Austin |
| ORCID | 0000-0003-4498-4374 |
| VERIFIED | Yes |
| TOTAL WORKS | 30 |
| TOTAL CITATIONS | 1068 |
| AUTHOR COUNT | 30 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2004 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 10 |
Was Nixon Doomed Before the Smoking Gun? Simulating the Impeachment and Trial of the Only US President to Resign From Office
Richard Nixon resigned the Presidency 3 days after the August 5, 1974 release of the “smoking gun” tape, which recorded Nixon conspiring to obstruct the Watergate investigation. While historians agree that Congress would have removed Nixon from office after the smoking gun, his chances beforehand are still debated. We simulate Nixon's chances by multiply imputing House and Senate votes on three articles of impeachment, building our imputation mod…
The effect of smaller classes on infection-related school absence
Small classes reduced absence, but the reduction was not larger when disease prevalence was high, so the reduction in absence was not necessarily achieved by reducing infection. Small classes, by themselves, may not suffice to reduce the spread of respiratory viruses
Did child obesity decline after 2016 food regulations in Chile
We estimated trends in the prevalence of obesity and overweight among Chilean primary and secondary students before and after Chile’s 2016 regulations on the marketing and availability of foods high in energy, total sugars, sodium, or saturated fat. We used data from Chile’s Survey of Nutrition, which measured the body mass index (BMI) of students in government-funded schools. Using BMI thresholds defined by the World Health Organization, we calc…
The Effects of the Covid-19 Pandemic on Educational Attainment
We study the effects of the COVID pandemic on educational attainment. By 2022, entry into two-year colleges was 21 percent lower than before the pandemic, with larger declines in Black- and Hispanic-majority colleges. Four-year college entry declined by only 6 percent and then rebounded 4 percent. High school graduation reached an all-time high in 2022. To explain these patterns, we undertook an interrupted time series design. Lower numbers of CO…
Improve academic search engines to reduce scholars' biases
Is Psychological Science Self-Correcting? Citations Before and After Successful and Failed Replications
In principle, successful replications should enhance the credibility of scientific findings, and failed replications should reduce credibility. Yet it is unknown how replication typically affects the influence of research. We analyzed the citation history of 98 articles. Each was published by a selective psychology journal in 2008 and subjected to a replication attempt published in 2015. Relative to successful replications, failed replications re…
Is Kindergarten Ability Group Placement Biased? New Data, New Methods, New Answers
Half of kindergarten teachers split children into higher and lower ability groups for reading or math. In national data, we predicted kindergarten ability group placement using linear and ordinal logistic regression with classroom fixed effects. In fall, test scores were the best predictors of group placement, but there was bias favoring girls, high-SES (socioeconomic status) children, and Asian Americans, who received higher placements than thei…
The data revolution comes to higher education
Enrolment in higher education has risen dramatically in Latin America, especially in Chile. Yet graduation and persistence rates remain low. One way to improve graduation and persistence is to use data and analytics to identify students at risk of dropout, target interventions, and evaluate interventions' effectiveness at improving student success. We illustrate the potential of this approach using data from eight Chilean universities. Results sh…
Tonal and "Anti-Tonal" Cognitive Structure in Viennese Twelve-Tone Rows
We show that the twelve-tone rows of Arnold Schoenberg and Anton Webern are "anti-tonal"—that is, structured to avoid or undermine listener's tonal schemata. Compared to randomly generated rows, segments from Schoenberg's and Webern's rows have significantly lower fit to major and minor key profiles. The anti-tonal structure of Schoenberg's and Webern's rows is still evident when we statistically controlled for their preference for other row feat…
How Many Imputations Do You Need? A Two-stage Calculation Using a Quadratic Rule
When using multiple imputation, users often want to know how many imputations they need. An old answer is that 2-10 imputations usually suffice, but this recommendation only addresses the efficiency of point estimates. You may need more imputations if, in addition to efficient point estimates, you also want standard error ( SE) estimates that would not change (much) if you imputed the data again. For replicable SE estimates, the required number o…
Does Achievement Rise Fastest with School Choice, School Resources, or Family Resources? Chile from 2002 to 2013
Debates in education policy draw on different theories about how to raise children's achievement. The school competition theory holds that achievement rises when students can choose among competing schools. The school resources theory holds that achievement rises with schools' resources per student. The family resources theory holds that achievement rises as parents become more educated and earn higher incomes. We test all three theories in Chile…
Do Test Score Gaps Grow before, during, or between the School Years? Measurement Artifacts and What We Can Know in Spite of Them
Do test score gaps between advantaged and disadvantaged children originate inside or outside schools? One approach to this classic question is to ask (1) How large are gaps when children enter school? (2) How much do gaps grow later on? (3) Do gaps grow faster during school or during summer? Confusingly, past research has given discrepant answers to these basic questions. We show that many results about gap growth have been distorted by measureme…
Does more education cause lower BMI, or do lower-BMI individuals become more educated? Evidence from the National Longitudinal Survey of Youth 1979
More educated adults have lower average body mass index (BMI). This may be due to selection, if adolescents with lower BMI attain higher levels of education, or it may be due to causation, if higher educational attainment reduces BMI gain in adulthood. We test for selection and causation in the National Longitudinal Survey of Youth 1979, which has followed a representative US cohort from age 14-22 in 1979 through age 47-55 in 2012. Using ordinal …
Understanding differences between summer vs. school obesogenic behaviors of children
BACKGROUND: Although the scientific community has acknowledged modest improvements can be made to weight status and obesogenic behaviors (i.e., physical activity, sedentary/screen time, diet, and sleep) during the school year, studies suggests improvements are erased as elementary-age children are released to summer vacation. Emerging evidence shows children return to school after summer vacation displaying accelerated weight gain compared to the…
Better Estimates from Binned Income Data
Researchers often estimate income statistics from summaries that report the number of incomes in bins such as $0 to 10,000, $10,001 to 20,000, ..., $200,000+. Some analysts assign incomes to bin midpoints, but this treats income as discrete. Other analysts fit a continuous parametric distribution, but the distribution may not fit well. We fit nonparametric continuous distributions that reproduce the bin counts perfectly by interpolating the cumul…
Gender and Weight among Thoroughbred Jockeys
Discrimination can damage health by limiting an occupation to persons who are physically unsuited to it. In thoroughbred racing, male jockeys damage their health to reach racing weights, while women who could reach those weights more easily are excluded. The authors estimate the number of U.S. men and women who are light enough to work as jockeys and contrast them with the number of male and female jockeys. The authors contrast the distribution o…
Response to commentary on “An education gradient in health, a health gradient in education, or a confounded gradient in both?”
An education gradient in health, a health gradient in education, or a confounded gradient in both
Robust Estimation of Inequality from Binned Incomes
Researchers often estimate income inequality by using data that give only the number of cases (e.g., families or households) whose incomes fall in “bins” such as $ 0 to $9,999, $10,000 to $14,999, . . . , ≥$200,000. We find that popular methods for estimating inequality from binned incomes are not robust in small samples, where popular methods can produce infinite, undefined, or arbitrarily large estimates. To solve these and other problems, we d…
Von Hippel and Benson Respond
von Hippel and Benson Respond Affiliation Paul T. von Hippel PhD, and Rebecca Benson MPHThe authors are with the LBJ School of Public Affairs, University of Texas, Austin.CopyRightCorrespondence should be sent to Paul von Hippel, LBJ School of Public Affairs, University of Texas, Austin, 2315 Red River, Box Y, Austin, TX 78712 (paulvonhippel.[email protected]com). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link.Cont…
Why are educated adults slim-Causation or selection
The Bias and Efficiency of Incomplete-Data Estimators in Small Univariate Normal Samples
Widely used methods for analyzing missing data can be biased in small samples. To understand these biases, we evaluate in detail the situation where a small univariate normal sample, with values missing at random, is analyzed using either observed-data maximum likelihood (ML) or multiple imputation (MI). We evaluate two types of MI: the usual Bayesian approach, which we call posterior draw (PD) imputation, and a little used alternative, which we …
Should a Normal Imputation Model be Modified to Impute Skewed Variables
Researchers often impute continuous variables under an assumption of normality–yet many incomplete variables are skewed. We find that imputing skewed continuous variables under a normal model can lead to bias. The bias is usually mild for popular estimands such as means, standard deviations, and linear regression coefficients, but the bias can be severe for more shape-dependent estimands such as percentiles or the coefficient of skewness. We test…
How to Impute Interactions, Squares, and other Transformed Variables
Researchers often carry out regression analysis using data that have missing values. Missing values can be filled in using multiple imputation, but imputation is tricky if the regression includes interactions, squares, or other transformations of the regressors. In this paper, we examine different approaches to imputing transformed variables; and we find one simple method that works well across a variety of circumstances. Our recommendation is to…
Are 'Failing' Schools Really Failing? Using Seasonal Comparison to Evaluate School Effectiveness
To many, it seems obvious which schools are failing-schools whose students perform poorly on achievement tests. But since evaluating schools on achievement mixes the effects of school and nonschool influences, achievement-based evaluation likely underestimates the effectiveness of schools that serve disadvantaged populations. In this article, the authors discuss school-evaluation methods that more effectively separate school effects from nonschoo…
Regression with Missing Ys
When fitting a generalized linear model—such as linear regression, logistic regression, or hierarchical linear modeling—analysts often wonder how to handle missing values of the dependent variable Y. If missing values have been filled in using multiple imputation, the usual advice is to use the imputed Y values in analysis. We show, however, that using imputed Ys can add needless noise to the estimates. Better estimates can usually be obtained us…
Are Schools the Great Equalizer? Cognitive Inequality during the Summer Months and the School Year
How does schooling affect inequality in cognitive skills? Reproductionist theorists have argued that schooling plays an important role in reproducing and even exacerbating existing disparities. But seasonal comparison research has shown that gaps in reading and math skills grow primarily during summer vacation, suggesting that non-school factors (e.g., family and neighborhood) are the main source of inequality. Using the Early Childhood Longitudi…
How to Impute Interactions, Squares, and other Transformed Variables
Researchers often carry out regression analysis using data that have missing values. Missing values can be filled in using multiple imputation, but imputation is tricky if the regression includes interactions, squares, or other transformations of the regressors. In this paper, we examine different approaches to imputing transformed variables; and we find one simple method that works well across a variety of circumstances. Our recommendation is to…
How Many Imputations Do You Need? A Two-stage Calculation Using a Quadratic Rule
When using multiple imputation, users often want to know how many imputations they need. An old answer is that 2-10 imputations usually suffice, but this recommendation only addresses the efficiency of point estimates. You may need more imputations if, in addition to efficient point estimates, you also want standard error ( SE) estimates that would not change (much) if you imputed the data again. For replicable SE estimates, the required number o…
Are 'Failing' Schools Really Failing? Using Seasonal Comparison to Evaluate School Effectiveness
To many, it seems obvious which schools are failing-schools whose students perform poorly on achievement tests. But since evaluating schools on achievement mixes the effects of school and nonschool influences, achievement-based evaluation likely underestimates the effectiveness of schools that serve disadvantaged populations. In this article, the authors discuss school-evaluation methods that more effectively separate school effects from nonschoo…
Do Test Score Gaps Grow before, during, or between the School Years? Measurement Artifacts and What We Can Know in Spite of Them
Do test score gaps between advantaged and disadvantaged children originate inside or outside schools? One approach to this classic question is to ask (1) How large are gaps when children enter school? (2) How much do gaps grow later on? (3) Do gaps grow faster during school or during summer? Confusingly, past research has given discrepant answers to these basic questions. We show that many results about gap growth have been distorted by measureme…
An education gradient in health, a health gradient in education, or a confounded gradient in both
Robust Estimation of Inequality from Binned Incomes
Researchers often estimate income inequality by using data that give only the number of cases (e.g., families or households) whose incomes fall in “bins” such as $ 0 to $9,999, $10,000 to $14,999, . . . , ≥$200,000. We find that popular methods for estimating inequality from binned incomes are not robust in small samples, where popular methods can produce infinite, undefined, or arbitrarily large estimates. To solve these and other problems, we d…
The Effect of School on Overweight in Childhood
Objectives. To determine whether school or nonschool environments contribute more to childhood overweight, we compared children’s gains in body mass index (BMI) when school is in session (during the kindergarten and first-grade school years) with their gains in BMI when school is out (during summer vacation). Methods. The BMIs of 5380 children in 310 schools were measured as part of the Early Childhood Longitudinal Study, Kindergarten Cohort. We …
Why are educated adults slim-Causation or selection
Better Estimates from Binned Income Data
Researchers often estimate income statistics from summaries that report the number of incomes in bins such as $0 to 10,000, $10,001 to 20,000, ..., $200,000+. Some analysts assign incomes to bin midpoints, but this treats income as discrete. Other analysts fit a continuous parametric distribution, but the distribution may not fit well. We fit nonparametric continuous distributions that reproduce the bin counts perfectly by interpolating the cumul…
The data revolution comes to higher education
Enrolment in higher education has risen dramatically in Latin America, especially in Chile. Yet graduation and persistence rates remain low. One way to improve graduation and persistence is to use data and analytics to identify students at risk of dropout, target interventions, and evaluate interventions' effectiveness at improving student success. We illustrate the potential of this approach using data from eight Chilean universities. Results sh…
Does Achievement Rise Fastest with School Choice, School Resources, or Family Resources? Chile from 2002 to 2013
Debates in education policy draw on different theories about how to raise children's achievement. The school competition theory holds that achievement rises when students can choose among competing schools. The school resources theory holds that achievement rises with schools' resources per student. The family resources theory holds that achievement rises as parents become more educated and earn higher incomes. We test all three theories in Chile…
Does more education cause lower BMI, or do lower-BMI individuals become more educated? Evidence from the National Longitudinal Survey of Youth 1979
More educated adults have lower average body mass index (BMI). This may be due to selection, if adolescents with lower BMI attain higher levels of education, or it may be due to causation, if higher educational attainment reduces BMI gain in adulthood. We test for selection and causation in the National Longitudinal Survey of Youth 1979, which has followed a representative US cohort from age 14-22 in 1979 through age 47-55 in 2012. Using ordinal …
Should a Normal Imputation Model be Modified to Impute Skewed Variables
Researchers often impute continuous variables under an assumption of normality–yet many incomplete variables are skewed. We find that imputing skewed continuous variables under a normal model can lead to bias. The bias is usually mild for popular estimands such as means, standard deviations, and linear regression coefficients, but the bias can be severe for more shape-dependent estimands such as percentiles or the coefficient of skewness. We test…
The Effects of the Covid-19 Pandemic on Educational Attainment
We study the effects of the COVID pandemic on educational attainment. By 2022, entry into two-year colleges was 21 percent lower than before the pandemic, with larger declines in Black- and Hispanic-majority colleges. Four-year college entry declined by only 6 percent and then rebounded 4 percent. High school graduation reached an all-time high in 2022. To explain these patterns, we undertook an interrupted time series design. Lower numbers of CO…
Is Kindergarten Ability Group Placement Biased? New Data, New Methods, New Answers
Half of kindergarten teachers split children into higher and lower ability groups for reading or math. In national data, we predicted kindergarten ability group placement using linear and ordinal logistic regression with classroom fixed effects. In fall, test scores were the best predictors of group placement, but there was bias favoring girls, high-SES (socioeconomic status) children, and Asian Americans, who received higher placements than thei…
Gender and Weight among Thoroughbred Jockeys
Discrimination can damage health by limiting an occupation to persons who are physically unsuited to it. In thoroughbred racing, male jockeys damage their health to reach racing weights, while women who could reach those weights more easily are excluded. The authors estimate the number of U.S. men and women who are light enough to work as jockeys and contrast them with the number of male and female jockeys. The authors contrast the distribution o…
The Bias and Efficiency of Incomplete-Data Estimators in Small Univariate Normal Samples
Widely used methods for analyzing missing data can be biased in small samples. To understand these biases, we evaluate in detail the situation where a small univariate normal sample, with values missing at random, is analyzed using either observed-data maximum likelihood (ML) or multiple imputation (MI). We evaluate two types of MI: the usual Bayesian approach, which we call posterior draw (PD) imputation, and a little used alternative, which we …
Rules for Chord Doubling (and Spacing)
Music theory instructors teach a number of rules for chord tone doubling. These rules fall under two general headings: rules for doubling certain triad members (e.g., double the root of a major root position triad) and rules for doubling certain scale degrees (e.g., avoid doubling the leading tone). The present study tests these doubling rules against large samples of triads by Bach, Mozart, and Haydn. Unlike previous analyses, the study controls…
Rules for Chord Doubling (and Spacing)
In MTO 10.2, we systematically tested common-practice “Rules for Chord Doubling (and Spacing).” We concluded that, while most of the commonly taught rules are not particularly helpful, two are definitely worth teaching: students should be taught to avoid doubling tendency tones, and to favor wide spacing in the bass. In the current issue of MTO, Wibberley expresses an ambivalent attitude toward rules, and questions our methods and results. Wibber…
Are Schools the Great Equalizer? Cognitive Inequality during the Summer Months and the School Year
How does schooling affect inequality in cognitive skills? Reproductionist theorists have argued that schooling plays an important role in reproducing and even exacerbating existing disparities. But seasonal comparison research has shown that gaps in reading and math skills grow primarily during summer vacation, suggesting that non-school factors (e.g., family and neighborhood) are the main source of inequality. Using the Early Childhood Longitudi…
The Effect of School on Overweight in Childhood
Objectives. To determine whether school or nonschool environments contribute more to childhood overweight, we compared children’s gains in body mass index (BMI) when school is in session (during the kindergarten and first-grade school years) with their gains in BMI when school is out (during summer vacation). Methods. The BMIs of 5380 children in 310 schools were measured as part of the Early Childhood Longitudinal Study, Kindergarten Cohort. We …
Regression with Missing Ys
When fitting a generalized linear model—such as linear regression, logistic regression, or hierarchical linear modeling—analysts often wonder how to handle missing values of the dependent variable Y. If missing values have been filled in using multiple imputation, the usual advice is to use the imputed Y values in analysis. We show, however, that using imputed Ys can add needless noise to the estimates. Better estimates can usually be obtained us…
Are 'Failing' Schools Really Failing? Using Seasonal Comparison to Evaluate School Effectiveness
To many, it seems obvious which schools are failing-schools whose students perform poorly on achievement tests. But since evaluating schools on achievement mixes the effects of school and nonschool influences, achievement-based evaluation likely underestimates the effectiveness of schools that serve disadvantaged populations. In this article, the authors discuss school-evaluation methods that more effectively separate school effects from nonschoo…
How to Impute Interactions, Squares, and other Transformed Variables
Researchers often carry out regression analysis using data that have missing values. Missing values can be filled in using multiple imputation, but imputation is tricky if the regression includes interactions, squares, or other transformations of the regressors. In this paper, we examine different approaches to imputing transformed variables; and we find one simple method that works well across a variety of circumstances. Our recommendation is to…
The Bias and Efficiency of Incomplete-Data Estimators in Small Univariate Normal Samples
Widely used methods for analyzing missing data can be biased in small samples. To understand these biases, we evaluate in detail the situation where a small univariate normal sample, with values missing at random, is analyzed using either observed-data maximum likelihood (ML) or multiple imputation (MI). We evaluate two types of MI: the usual Bayesian approach, which we call posterior draw (PD) imputation, and a little used alternative, which we …
Should a Normal Imputation Model be Modified to Impute Skewed Variables
Researchers often impute continuous variables under an assumption of normality–yet many incomplete variables are skewed. We find that imputing skewed continuous variables under a normal model can lead to bias. The bias is usually mild for popular estimands such as means, standard deviations, and linear regression coefficients, but the bias can be severe for more shape-dependent estimands such as percentiles or the coefficient of skewness. We test…
Von Hippel and Benson Respond
von Hippel and Benson Respond Affiliation Paul T. von Hippel PhD, and Rebecca Benson MPHThe authors are with the LBJ School of Public Affairs, University of Texas, Austin.CopyRightCorrespondence should be sent to Paul von Hippel, LBJ School of Public Affairs, University of Texas, Austin, 2315 Red River, Box Y, Austin, TX 78712 (paulvonhippel.[email protected]com). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link.Cont…
Why are educated adults slim-Causation or selection
Robust Estimation of Inequality from Binned Incomes
Researchers often estimate income inequality by using data that give only the number of cases (e.g., families or households) whose incomes fall in “bins” such as $ 0 to $9,999, $10,000 to $14,999, . . . , ≥$200,000. We find that popular methods for estimating inequality from binned incomes are not robust in small samples, where popular methods can produce infinite, undefined, or arbitrarily large estimates. To solve these and other problems, we d…
Response to commentary on “An education gradient in health, a health gradient in education, or a confounded gradient in both?”
An education gradient in health, a health gradient in education, or a confounded gradient in both
Understanding differences between summer vs. school obesogenic behaviors of children
BACKGROUND: Although the scientific community has acknowledged modest improvements can be made to weight status and obesogenic behaviors (i.e., physical activity, sedentary/screen time, diet, and sleep) during the school year, studies suggests improvements are erased as elementary-age children are released to summer vacation. Emerging evidence shows children return to school after summer vacation displaying accelerated weight gain compared to the…
Better Estimates from Binned Income Data
Researchers often estimate income statistics from summaries that report the number of incomes in bins such as $0 to 10,000, $10,001 to 20,000, ..., $200,000+. Some analysts assign incomes to bin midpoints, but this treats income as discrete. Other analysts fit a continuous parametric distribution, but the distribution may not fit well. We fit nonparametric continuous distributions that reproduce the bin counts perfectly by interpolating the cumul…
Gender and Weight among Thoroughbred Jockeys
Discrimination can damage health by limiting an occupation to persons who are physically unsuited to it. In thoroughbred racing, male jockeys damage their health to reach racing weights, while women who could reach those weights more easily are excluded. The authors estimate the number of U.S. men and women who are light enough to work as jockeys and contrast them with the number of male and female jockeys. The authors contrast the distribution o…
Does more education cause lower BMI, or do lower-BMI individuals become more educated? Evidence from the National Longitudinal Survey of Youth 1979
More educated adults have lower average body mass index (BMI). This may be due to selection, if adolescents with lower BMI attain higher levels of education, or it may be due to causation, if higher educational attainment reduces BMI gain in adulthood. We test for selection and causation in the National Longitudinal Survey of Youth 1979, which has followed a representative US cohort from age 14-22 in 1979 through age 47-55 in 2012. Using ordinal …
Do Test Score Gaps Grow before, during, or between the School Years? Measurement Artifacts and What We Can Know in Spite of Them
Do test score gaps between advantaged and disadvantaged children originate inside or outside schools? One approach to this classic question is to ask (1) How large are gaps when children enter school? (2) How much do gaps grow later on? (3) Do gaps grow faster during school or during summer? Confusingly, past research has given discrepant answers to these basic questions. We show that many results about gap growth have been distorted by measureme…
Tonal and "Anti-Tonal" Cognitive Structure in Viennese Twelve-Tone Rows
We show that the twelve-tone rows of Arnold Schoenberg and Anton Webern are "anti-tonal"—that is, structured to avoid or undermine listener's tonal schemata. Compared to randomly generated rows, segments from Schoenberg's and Webern's rows have significantly lower fit to major and minor key profiles. The anti-tonal structure of Schoenberg's and Webern's rows is still evident when we statistically controlled for their preference for other row feat…
How Many Imputations Do You Need? A Two-stage Calculation Using a Quadratic Rule
When using multiple imputation, users often want to know how many imputations they need. An old answer is that 2-10 imputations usually suffice, but this recommendation only addresses the efficiency of point estimates. You may need more imputations if, in addition to efficient point estimates, you also want standard error ( SE) estimates that would not change (much) if you imputed the data again. For replicable SE estimates, the required number o…
Does Achievement Rise Fastest with School Choice, School Resources, or Family Resources? Chile from 2002 to 2013
Debates in education policy draw on different theories about how to raise children's achievement. The school competition theory holds that achievement rises when students can choose among competing schools. The school resources theory holds that achievement rises with schools' resources per student. The family resources theory holds that achievement rises as parents become more educated and earn higher incomes. We test all three theories in Chile…
The data revolution comes to higher education
Enrolment in higher education has risen dramatically in Latin America, especially in Chile. Yet graduation and persistence rates remain low. One way to improve graduation and persistence is to use data and analytics to identify students at risk of dropout, target interventions, and evaluate interventions' effectiveness at improving student success. We illustrate the potential of this approach using data from eight Chilean universities. Results sh…
Is Psychological Science Self-Correcting? Citations Before and After Successful and Failed Replications
In principle, successful replications should enhance the credibility of scientific findings, and failed replications should reduce credibility. Yet it is unknown how replication typically affects the influence of research. We analyzed the citation history of 98 articles. Each was published by a selective psychology journal in 2008 and subjected to a replication attempt published in 2015. Relative to successful replications, failed replications re…
Is Kindergarten Ability Group Placement Biased? New Data, New Methods, New Answers
Half of kindergarten teachers split children into higher and lower ability groups for reading or math. In national data, we predicted kindergarten ability group placement using linear and ordinal logistic regression with classroom fixed effects. In fall, test scores were the best predictors of group placement, but there was bias favoring girls, high-SES (socioeconomic status) children, and Asian Americans, who received higher placements than thei…
Psychology (18 works) · Computer Science (12 works) · Medicine (12 works) · Mathematics (11 works) · Demography (10 works) · Demography (7 works) · Statistics (7 works) · Developmental psychology (6 works) · Economics (6 works) · Body mass index (5 works)