Elise Tipton
Biographic Data
| ID | 1009433 |
|---|---|
| NAME | Elise Tipton |
| GIVEN NAMES | Elise |
| FAMILY NAME | Tipton |
| SIGNATURE | TIPTON E |
| AFFILIATIONS | Northwestern University |
| ORCID | 0000-0001-5608-1282 |
| VERIFIED | Yes |
| TOTAL WORKS | 21 |
| TOTAL CITATIONS | 82 |
| AUTHOR COUNT | 21 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1993 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 4 |
The Value of Variation: Embracing Heterogeneity in Intervention Research
If our goal in education research is to produce knowledge that is useful and used, we need to design impact evaluations that value understanding variation as much as understanding average impact. The context in which an intervention is implemented and the characteristics of participants are integral components of the intervention, and we need to study those components just as seriously as we do the intervention itself. Better understanding variat…
Why meta-analyses of growth mindset and other interventions should follow best practices for examining heterogeneity: Commentary on Macnamara and Burgoyne (2023) and Burnette et al. (2023)
Meta-analysts often ask a yes-or-no question: Is there an intervention effect or not? This traditional, all-or-nothing thinking stands in contrast with current best practice in meta-analysis, which calls for a heterogeneity-attuned approach (i.e., focused on the extent to which effects vary across procedures, participant groups, or contexts). This heterogeneity-attuned approach allows researchers to understand where effects are weaker or stronger…
Meta-analysis with Robust Variance Estimation: Expanding the Range of Working Models
Sample Selection in Randomized Trials With Multiple Target Populations
Practitioners and policymakers often want estimates of the effect of an intervention for their local community, e.g., region, state, county. In the ideal, these multiple population average treatment effect (ATE) estimates will be considered in the design of a single randomized trial. Methods for sample selection for generalizing the sample ATE to date, however, focus only on the case of a single target population. In this paper, I provide a frame…
Toward a System of Evidence for All: Current Practices and Future Opportunities in 37 Randomized Trials
As a result of the evidence-based decision-making movement, the number of randomized trials evaluating educational programs and curricula has increased dramatically over the past 20 years. Policy makers and practitioners are encouraged to use the results of these trials to inform their decision making in schools and school districts. At the same time, however, little is known about the schools taking part in these randomized trials, both regardin…
Why Facilitation
Behavioural science is unlikely to change the world without a heterogeneity revolution
A national experiment reveals where a growth mindset improves achievement
A global priority for the behavioural sciences is to develop cost-effective, scalable interventions that could improve the academic outcomes of adolescents at a population level, but no such interventions have so far been evaluated in a population-generalizable sample. Here we show that a short (less than one hour), online growth mindset intervention—which teaches that intellectual abilities can be developed—improved grades among lower-achieving …
Improved Generalizability Through Improved Recruitment: Lessons Learned From a Large-Scale Randomized Trial
Randomized control trials (RCTs) have long been considered the “gold standard” for evaluating the impacts of interventions. However, in most education RCTs, the sample of schools included is recruited based on convenience, potentially compromising a study’s ability to generalize to an intended population. An alternative approach is to recruit schools using a stratified recruitment method developed by Tipton. Until now, however, there has been lim…
Small-Sample Methods for Cluster-Robust Variance Estimation and Hypothesis Testing in Fixed Effects Models
In panel data models and other regressions with unobserved effects, fixed effects estimation is often paired with cluster-robust variance estimation (CRVE) to account for heteroscedasticity and un-modeled dependence among the errors. Although asymptotically consistent, CRVE can be biased downward when the number of clusters is small, leading to hypothesis tests with rejection rates that are too high. More accurate tests can be constructed using b…
A Review of Statistical Methods for Generalizing From Evaluations of Educational Interventions
School-based evaluations of interventions are increasingly common in education research. Ideally, the results of these evaluations are used to make evidence-based policy decisions for students. However, it is difficult to make generalizations from these evaluations because the types of schools included in the studies are typically not selected randomly from a target population. This paper provides an overview of statistical methods for improving …
Meta-analysis of action video game impact on perceptual, attentional, and cognitive skills
The ubiquity of video games in today's society has led to significant interest in their impact on the brain and behavior and in the possibility of harnessing games for good.The present meta-analyses focus on one specific game genre that has been of particular interest to the scientific community-action video games, and cover the period 2000 -2015.To assess the long-lasting impact of action video game play on various domains of cognition, we first…
Handling Complex Meta-analytic Data Structures Using Robust Variance Estimates: A Tutorial in R
Small sample adjustments for robust variance estimation with meta-regression.
Although primary studies often report multiple outcomes, the covariances between these outcomes are rarely reported. This leads to difficulties when combining studies in a meta-analysis. This problem was recently addressed with the introduction of robust variance estimation. This new method enables the estimation of meta-regression models with dependent effect sizes, even when the dependence structure is unknown. Although robust variance estimati…
Small-Sample Adjustments for Tests of Moderators and Model Fit Using Robust Variance Estimation in Meta-Regression
Meta-analyses often include studies that report multiple effect sizes based on a common pool of subjects or that report effect sizes from several samples that were treated with very similar research protocols. The inclusion of such studies introduces dependence among the effect size estimates. When the number of studies is large, robust variance estimation (RVE) provides a method for pooling dependent effects, even when information on the exact d…
Robust variance estimation with dependent effect sizes: practical considerations including a software tutorial in Stata and spss: Robust variance estimation
Methodologists have recently proposed robust variance estimation as one way to handle dependent effect sizes in meta-analysis. Software macros for robust variance estimation in meta-analysis are currently available for Stata (StataCorp LP, College Station, TX, USA) and spss (IBM, Armonk, NY, USA), yet there is little guidance for authors regarding the practical application and implementation of those macros. This paper provides a brief tutorial o…
The malleability of spatial skills: A meta-analysis of training studies
Robust variance estimation in meta‐regression with dependent effect size estimates
Conventional meta‐analytic techniques rely on the assumption that effect size estimates from different studies are independent and have sampling distributions with known conditional variances. The independence assumption is violated when studies produce several estimates based on the same individuals or there are clusters of studies that are not independent (such as those carried out by the same investigator or laboratory). This paper provides an…
Modern Japan: A Social and Political History
This thoroughly updated second edition of Modern Japan provides a concise and fascinating introduction to the social, cultural and political history of modern Japan. Ranging from the Tokugawa period to the present day, the book charts the country's evolution into a modernized, economic and political world power. Dealing with a broad and stimulating range of topics in an engaging style that will appeal to university students and the general reader…
Ishimoto shizue: The margaret sanger of Japan
Post-1945 Japan is known for its remarkably low birth rate and a heavy reliance on abortion for birth control purposes. What may be less well known is the extensive use of contraceptive methods as well to limit or regulate births and the role played by the prewar birth control movement in making the concept of birth control socially acceptable. The prewar movement owed its origins and much of its success in changing attitudes toward birth control…
Janus-Faced Justice: Political Criminals in Imperial Japan
Behavioural science is unlikely to change the world without a heterogeneity revolution
The malleability of spatial skills: A meta-analysis of training studies
Meta-analysis of action video game impact on perceptual, attentional, and cognitive skills
The ubiquity of video games in today's society has led to significant interest in their impact on the brain and behavior and in the possibility of harnessing games for good.The present meta-analyses focus on one specific game genre that has been of particular interest to the scientific community-action video games, and cover the period 2000 -2015.To assess the long-lasting impact of action video game play on various domains of cognition, we first…
Why meta-analyses of growth mindset and other interventions should follow best practices for examining heterogeneity: Commentary on Macnamara and Burgoyne (2023) and Burnette et al. (2023)
Meta-analysts often ask a yes-or-no question: Is there an intervention effect or not? This traditional, all-or-nothing thinking stands in contrast with current best practice in meta-analysis, which calls for a heterogeneity-attuned approach (i.e., focused on the extent to which effects vary across procedures, participant groups, or contexts). This heterogeneity-attuned approach allows researchers to understand where effects are weaker or stronger…
Why Facilitation
Improved Generalizability Through Improved Recruitment: Lessons Learned From a Large-Scale Randomized Trial
Randomized control trials (RCTs) have long been considered the “gold standard” for evaluating the impacts of interventions. However, in most education RCTs, the sample of schools included is recruited based on convenience, potentially compromising a study’s ability to generalize to an intended population. An alternative approach is to recruit schools using a stratified recruitment method developed by Tipton. Until now, however, there has been lim…
Janus-Faced Justice: Political Criminals in Imperial Japan
Ishimoto shizue: The margaret sanger of Japan
Post-1945 Japan is known for its remarkably low birth rate and a heavy reliance on abortion for birth control purposes. What may be less well known is the extensive use of contraceptive methods as well to limit or regulate births and the role played by the prewar birth control movement in making the concept of birth control socially acceptable. The prewar movement owed its origins and much of its success in changing attitudes toward birth control…
Modern Japan: A Social and Political History
This thoroughly updated second edition of Modern Japan provides a concise and fascinating introduction to the social, cultural and political history of modern Japan. Ranging from the Tokugawa period to the present day, the book charts the country's evolution into a modernized, economic and political world power. Dealing with a broad and stimulating range of topics in an engaging style that will appeal to university students and the general reader…
Robust variance estimation in meta‐regression with dependent effect size estimates
Conventional meta‐analytic techniques rely on the assumption that effect size estimates from different studies are independent and have sampling distributions with known conditional variances. The independence assumption is violated when studies produce several estimates based on the same individuals or there are clusters of studies that are not independent (such as those carried out by the same investigator or laboratory). This paper provides an…
The malleability of spatial skills: A meta-analysis of training studies
Robust variance estimation with dependent effect sizes: practical considerations including a software tutorial in Stata and spss: Robust variance estimation
Methodologists have recently proposed robust variance estimation as one way to handle dependent effect sizes in meta-analysis. Software macros for robust variance estimation in meta-analysis are currently available for Stata (StataCorp LP, College Station, TX, USA) and spss (IBM, Armonk, NY, USA), yet there is little guidance for authors regarding the practical application and implementation of those macros. This paper provides a brief tutorial o…
Small sample adjustments for robust variance estimation with meta-regression.
Although primary studies often report multiple outcomes, the covariances between these outcomes are rarely reported. This leads to difficulties when combining studies in a meta-analysis. This problem was recently addressed with the introduction of robust variance estimation. This new method enables the estimation of meta-regression models with dependent effect sizes, even when the dependence structure is unknown. Although robust variance estimati…
Small-Sample Adjustments for Tests of Moderators and Model Fit Using Robust Variance Estimation in Meta-Regression
Meta-analyses often include studies that report multiple effect sizes based on a common pool of subjects or that report effect sizes from several samples that were treated with very similar research protocols. The inclusion of such studies introduces dependence among the effect size estimates. When the number of studies is large, robust variance estimation (RVE) provides a method for pooling dependent effects, even when information on the exact d…
Handling Complex Meta-analytic Data Structures Using Robust Variance Estimates: A Tutorial in R
Small-Sample Methods for Cluster-Robust Variance Estimation and Hypothesis Testing in Fixed Effects Models
In panel data models and other regressions with unobserved effects, fixed effects estimation is often paired with cluster-robust variance estimation (CRVE) to account for heteroscedasticity and un-modeled dependence among the errors. Although asymptotically consistent, CRVE can be biased downward when the number of clusters is small, leading to hypothesis tests with rejection rates that are too high. More accurate tests can be constructed using b…
A Review of Statistical Methods for Generalizing From Evaluations of Educational Interventions
School-based evaluations of interventions are increasingly common in education research. Ideally, the results of these evaluations are used to make evidence-based policy decisions for students. However, it is difficult to make generalizations from these evaluations because the types of schools included in the studies are typically not selected randomly from a target population. This paper provides an overview of statistical methods for improving …
Meta-analysis of action video game impact on perceptual, attentional, and cognitive skills
The ubiquity of video games in today's society has led to significant interest in their impact on the brain and behavior and in the possibility of harnessing games for good.The present meta-analyses focus on one specific game genre that has been of particular interest to the scientific community-action video games, and cover the period 2000 -2015.To assess the long-lasting impact of action video game play on various domains of cognition, we first…
A national experiment reveals where a growth mindset improves achievement
A global priority for the behavioural sciences is to develop cost-effective, scalable interventions that could improve the academic outcomes of adolescents at a population level, but no such interventions have so far been evaluated in a population-generalizable sample. Here we show that a short (less than one hour), online growth mindset intervention—which teaches that intellectual abilities can be developed—improved grades among lower-achieving …
Improved Generalizability Through Improved Recruitment: Lessons Learned From a Large-Scale Randomized Trial
Randomized control trials (RCTs) have long been considered the “gold standard” for evaluating the impacts of interventions. However, in most education RCTs, the sample of schools included is recruited based on convenience, potentially compromising a study’s ability to generalize to an intended population. An alternative approach is to recruit schools using a stratified recruitment method developed by Tipton. Until now, however, there has been lim…
Toward a System of Evidence for All: Current Practices and Future Opportunities in 37 Randomized Trials
As a result of the evidence-based decision-making movement, the number of randomized trials evaluating educational programs and curricula has increased dramatically over the past 20 years. Policy makers and practitioners are encouraged to use the results of these trials to inform their decision making in schools and school districts. At the same time, however, little is known about the schools taking part in these randomized trials, both regardin…
Why Facilitation
Behavioural science is unlikely to change the world without a heterogeneity revolution
Meta-analysis with Robust Variance Estimation: Expanding the Range of Working Models
Sample Selection in Randomized Trials With Multiple Target Populations
Practitioners and policymakers often want estimates of the effect of an intervention for their local community, e.g., region, state, county. In the ideal, these multiple population average treatment effect (ATE) estimates will be considered in the design of a single randomized trial. Methods for sample selection for generalizing the sample ATE to date, however, focus only on the case of a single target population. In this paper, I provide a frame…
Why meta-analyses of growth mindset and other interventions should follow best practices for examining heterogeneity: Commentary on Macnamara and Burgoyne (2023) and Burnette et al. (2023)
Meta-analysts often ask a yes-or-no question: Is there an intervention effect or not? This traditional, all-or-nothing thinking stands in contrast with current best practice in meta-analysis, which calls for a heterogeneity-attuned approach (i.e., focused on the extent to which effects vary across procedures, participant groups, or contexts). This heterogeneity-attuned approach allows researchers to understand where effects are weaker or stronger…
The Value of Variation: Embracing Heterogeneity in Intervention Research
If our goal in education research is to produce knowledge that is useful and used, we need to design impact evaluations that value understanding variation as much as understanding average impact. The context in which an intervention is implemented and the characteristics of participants are integral components of the intervention, and we need to study those components just as seriously as we do the intervention itself. Better understanding variat…
Computer Science (13 works) · Mathematics (13 works) · Statistics (11 works) · Psychology (10 works) · Econometrics (7 works) · Medicine (7 works) · Economic and Environmental Valuation (6 works) · Sample size determination (6 works) · Population (5 works) · Statistical Methods and Bayesian Inference (5 works)