cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13528334
title: Illustrating Randomness in Statistics Courses With Spatial Experiments

description: Understanding the concept of randomness is fundamental for students in introductory statistics courses, but the notion of randomness is deceivingly complex, so it is often emphasized less than the mechanics of probability and inference. The most commonly used classroom tools to assess students’ production or perception of randomness are binary choices, such as coin tosses, and number sequences, such as dice rolls. The field of psychology has a long history of research on random choice, and we have replicated some experiments that support results seen there regarding the collective distribution of individual choices in spatial geometries. The data from these experiments can easily be incorporated into the undergraduate classroom to visually illustrate the concepts of random choice, complete spatial randomness (CSR), and Poisson processes. Furthermore, spatial statistics classes can use this point pattern data in exploring hypothesis tests for CSR along with simulation. To foster student engagement, it is simple to collect additional data from students to assess agreement with existing data or to develop related, unique experiments. All R code and data to duplicate results are provided.
DOI: 10.6084/m9.figshare.13528334.v3
title: Illustrating Randomness in Statistics Courses With Spatial Experiments

description: Understanding the concept of randomness is fundamental for students in introductory statistics courses, but the notion of randomness is deceivingly complex, so it is often emphasized less than the mechanics of probability and inference. The most commonly used classroom tools to assess students’ production or perception of randomness are binary choices, such as coin tosses, and number sequences, such as dice rolls. The field of psychology has a long history of research on random choice, and we have replicated some experiments that support results seen there regarding the collective distribution of individual choices in spatial geometries. The data from these experiments can easily be incorporated into the undergraduate classroom to visually illustrate the concepts of random choice, complete spatial randomness (CSR), and Poisson processes. Furthermore, spatial statistics classes can use this point pattern data in exploring hypothesis tests for CSR along with simulation. To foster student engagement, it is simple to collect additional data from students to assess agreement with existing data or to develop related, unique experiments. All R code and data to duplicate results are provided.
DOI: 10.6084/m9.figshare.13528334.v2
title: Illustrating Randomness in Statistics Courses With Spatial Experiments

description: Understanding the concept of randomness is fundamental for students in introductory statistics courses, but the notion of randomness is deceivingly complex, so it is often emphasized less than the mechanics of probability and inference. The most commonly used classroom tools to assess students’ production or perception of randomness are binary choices, such as coin tosses, and number sequences, such as dice rolls. The field of psychology has a long history of research on random choice, and we have replicated some experiments that support results seen there regarding the collective distribution of individual choices in spatial geometries. The data from these experiments can easily be incorporated into the undergraduate classroom to visually illustrate the concepts of random choice, complete spatial randomness (CSR), and Poisson processes. Furthermore, spatial statistics classes can use this point pattern data in exploring hypothesis tests for CSR along with simulation. To foster student engagement, it is simple to collect additional data from students to assess agreement with existing data or to develop related, unique experiments. All R code and data to duplicate results are provided.
DOI: 10.6084/m9.figshare.11927112
title: Improving Effect Estimates by Limiting the Variability in Inverse Propensity Score Weights

description: This study describes a novel method to reweight a comparison group used for causal inference, so the group is similar to a treatment group on observable characteristics yet avoids highly variable weights that would limit statistical power. The proposed method generalizes the covariate-balancing propensity score (CBPS) methodology developed by Imai and Ratkovic (2014) to enable researchers to effectively prespecify the variance (or higher-order moments) of the matching weight distribution. This lets researchers choose among alternative sets of matching weights, some of which produce better balance and others of which yield higher statistical power. We demonstrate using simulations that our penalized CBPS approach can improve effect estimates over those from other established propensity score estimation approaches, producing lower mean squared error. We discuss applications where the method or extensions of it are especially likely to improve effect estimates and we provide an empirical example from the evaluation of Comprehensive Primary Care Plus, a U.S. health care model that aims to strengthen primary care across roughly 3000 practices. Programming code is available to implement the method in Stata.
DOI: 10.6084/m9.figshare.11927112.v4
title: Improving Effect Estimates by Limiting the Variability in Inverse Propensity Score Weights

description: This study describes a novel method to reweight a comparison group used for causal inference, so the group is similar to a treatment group on observable characteristics yet avoids highly variable weights that would limit statistical power. The proposed method generalizes the covariate-balancing propensity score (CBPS) methodology developed by Imai and Ratkovic (2014) to enable researchers to effectively prespecify the variance (or higher-order moments) of the matching weight distribution. This lets researchers choose among alternative sets of matching weights, some of which produce better balance and others of which yield higher statistical power. We demonstrate using simulations that our penalized CBPS approach can improve effect estimates over those from other established propensity score estimation approaches, producing lower mean squared error. We discuss applications where the method or extensions of it are especially likely to improve effect estimates and we provide an empirical example from the evaluation of Comprehensive Primary Care Plus, a U.S. health care model that aims to strengthen primary care across roughly 3000 practices. Programming code is available to implement the method in Stata.
DOI: 10.6084/m9.figshare.11927112.v3
title: Improving Effect Estimates by Limiting the Variability in Inverse Propensity Score Weights

description: This study describes a novel method to reweight a comparison group used for causal inference, so the group is similar to a treatment group on observable characteristics yet avoids highly variable weights that would limit statistical power. The proposed method generalizes the covariate-balancing propensity score (CBPS) methodology developed by Imai and Ratkovic (2014) to enable researchers to effectively prespecify the variance (or higher-order moments) of the matching weight distribution. This lets researchers choose among alternative sets of matching weights, some of which produce better balance and others of which yield higher statistical power. We demonstrate using simulations that our penalized CBPS approach can improve effect estimates over those from other established propensity score estimation approaches, producing lower mean squared error. We discuss applications where the method or extensions of it are especially likely to improve effect estimates and we provide an empirical example from the evaluation of Comprehensive Primary Care Plus, a U.S. health care model that aims to strengthen primary care across roughly 3000 practices. Programming code is available to implement the method in Stata.
DOI: 10.6084/m9.figshare.13014006
title: From One Environment to Many: The Problem of Replicability of Statistical Inferences

description: Among plausible causes for replicability failure, one that has not received sufficient attention is the environment in which the research is conducted. Consisting of the population, equipment, personnel, and various conditions such as location, time, and weather, the research environment can affect treatments and outcomes, and changes in the research environment that occur when an experiment is redone can affect replicability. We examine the extent to which such changes contribute to replicability failure. Our framework is that of an initial experiment that generates the data and a follow-up experiment that is done the same way except for a change in the research environment. We assume that the initial experiment satisfies the assumptions of the two-sample <i>t</i>-statistic and that the follow-up experiment is described by a mixed model which includes environmental parameters. We derive expressions for the effect that the research environment has on power, sample size selection, <i>p</i>-values, and confidence levels. We measure the size of the environmental effect with the environmental effect ratio (EER) which is the ratio of the standard deviations of environment by treatment interaction and error. By varying EER, it is possible to determine conditions that favor replicability and those that do not.
DOI: 10.6084/m9.figshare.13014006.v4
title: From One Environment to Many: The Problem of Replicability of Statistical Inferences

description: Among plausible causes for replicability failure, one that has not received sufficient attention is the environment in which the research is conducted. Consisting of the population, equipment, personnel, and various conditions such as location, time, and weather, the research environment can affect treatments and outcomes, and changes in the research environment that occur when an experiment is redone can affect replicability. We examine the extent to which such changes contribute to replicability failure. Our framework is that of an initial experiment that generates the data and a follow-up experiment that is done the same way except for a change in the research environment. We assume that the initial experiment satisfies the assumptions of the two-sample <i>t</i>-statistic and that the follow-up experiment is described by a mixed model which includes environmental parameters. We derive expressions for the effect that the research environment has on power, sample size selection, <i>p</i>-values, and confidence levels. We measure the size of the environmental effect with the environmental effect ratio (EER) which is the ratio of the standard deviations of environment by treatment interaction and error. By varying EER, it is possible to determine conditions that favor replicability and those that do not.
DOI: 10.6084/m9.figshare.13014006.v3
title: From One Environment to Many: The Problem of Replicability of Statistical Inferences

description: Among plausible causes for replicability failure, one that has not received sufficient attention is the environment in which the research is conducted. Consisting of the population, equipment, personnel, and various conditions such as location, time, and weather, the research environment can affect treatments and outcomes, and changes in the research environment that occur when an experiment is redone can affect replicability. We examine the extent to which such changes contribute to replicability failure. Our framework is that of an initial experiment that generates the data and a follow-up experiment that is done the same way except for a change in the research environment. We assume that the initial experiment satisfies the assumptions of the two-sample <i>t</i>-statistic and that the follow-up experiment is described by a mixed model which includes environmental parameters. We derive expressions for the effect that the research environment has on power, sample size selection, <i>p</i>-values, and confidence levels. We measure the size of the environmental effect with the environmental effect ratio (EER) which is the ratio of the standard deviations of environment by treatment interaction and error. By varying EER, it is possible to determine conditions that favor replicability and those that do not.
DOI: 10.6084/m9.figshare.12395696
title: Adjusting Published Estimates for Exploratory Biases Using the Truncated Normal Distribution

description: <i>Abstract–</i>Publication bias can occur for many reasons, including the perceived need to present statistically significant results. We propose and compare methods for adjusting a single published estimate for possible publication bias using a truncated normal distribution. We attempt to estimate the mean of the underlying normal sampling distribution using only summary data readily available in most published work, making the results practical for use by a consumer of research. The adjustment methods are investigated via simulation and their results compared in terms of bias, mean squared error, and confidence interval coverage. The methods are also applied to eleven previously published studies. We find the proposed methods improve but do not eliminate biases from the statistical significance filter.
DOI: 10.6084/m9.figshare.12395696.v2
title: Adjusting Published Estimates for Exploratory Biases Using the Truncated Normal Distribution

description: <i>Abstract–</i>Publication bias can occur for many reasons, including the perceived need to present statistically significant results. We propose and compare methods for adjusting a single published estimate for possible publication bias using a truncated normal distribution. We attempt to estimate the mean of the underlying normal sampling distribution using only summary data readily available in most published work, making the results practical for use by a consumer of research. The adjustment methods are investigated via simulation and their results compared in terms of bias, mean squared error, and confidence interval coverage. The methods are also applied to eleven previously published studies. We find the proposed methods improve but do not eliminate biases from the statistical significance filter.
DOI: 10.6084/m9.figshare.12395696.v1
title: Adjusting Published Estimates for Exploratory Biases Using the Truncated Normal Distribution

description: <i>Abstract–</i>Publication bias can occur for many reasons, including the perceived need to present statistically significant results. We propose and compare methods for adjusting a single published estimate for possible publication bias using a truncated normal distribution. We attempt to estimate the mean of the underlying normal sampling distribution using only summary data readily available in most published work, making the results practical for use by a consumer of research. The adjustment methods are investigated via simulation and their results compared in terms of bias, mean squared error, and confidence interval coverage. The methods are also applied to eleven previously published studies. We find the proposed methods improve but do not eliminate biases from the statistical significance filter.
DOI: 10.6084/m9.figshare.11365901.v4
title: Connecting and Contrasting the Bayes Factor and a Modified ROPE Procedure for Testing Interval Null Hypotheses

description: There has been strong recent interest in testing interval null hypotheses for improved scientific inference. For example, Lakens et al. and Lakens and Harms use this approach to study if there is a prespecified meaningful treatment effect in gerontology and clinical trials, instead of a point null hypothesis of any effect. Two popular Bayesian approaches are available for interval null hypothesis testing. One is the standard Bayes factor and the other is the region of practical equivalence (ROPE) procedure championed by Kruschke and others over many years. This article connects key quantities in the two approaches, which in turn allow us to contrast two major differences between the approaches with substantial practical implications. The first is that the Bayes factor depends heavily on the prior specification while a modified ROPE procedure is very robust. The second difference is concerned with the statistical property when data are generated under a neutral parameter value on the common boundary of competing hypotheses. In this case, the Bayes factors can be severely biased whereas the modified ROPE approach gives a reasonable result. Finally, the connection leads to a simple and effective algorithm for computing Bayes factors using draws from posterior distributions generated by standard Bayesian programs such as BUGS, JAGS, and Stan.
DOI: 10.6084/m9.figshare.11365901
title: Connecting and Contrasting the Bayes Factor and a Modified ROPE Procedure for Testing Interval Null Hypotheses

description: There has been strong recent interest in testing interval null hypotheses for improved scientific inference. For example, Lakens et al. and Lakens and Harms use this approach to study if there is a prespecified meaningful treatment effect in gerontology and clinical trials, instead of a point null hypothesis of any effect. Two popular Bayesian approaches are available for interval null hypothesis testing. One is the standard Bayes factor and the other is the region of practical equivalence (ROPE) procedure championed by Kruschke and others over many years. This article connects key quantities in the two approaches, which in turn allow us to contrast two major differences between the approaches with substantial practical implications. The first is that the Bayes factor depends heavily on the prior specification while a modified ROPE procedure is very robust. The second difference is concerned with the statistical property when data are generated under a neutral parameter value on the common boundary of competing hypotheses. In this case, the Bayes factors can be severely biased whereas the modified ROPE approach gives a reasonable result. Finally, the connection leads to a simple and effective algorithm for computing Bayes factors using draws from posterior distributions generated by standard Bayesian programs such as BUGS, JAGS, and Stan.
DOI: 10.6084/m9.figshare.11365901.v3
title: Connecting and Contrasting the Bayes Factor and a Modified ROPE Procedure for Testing Interval Null Hypotheses

description: There has been strong recent interest in testing interval null hypotheses for improved scientific inference. For example, Lakens et al. and Lakens and Harms use this approach to study if there is a prespecified meaningful treatment effect in gerontology and clinical trials, instead of a point null hypothesis of any effect. Two popular Bayesian approaches are available for interval null hypothesis testing. One is the standard Bayes factor and the other is the region of practical equivalence (ROPE) procedure championed by Kruschke and others over many years. This article connects key quantities in the two approaches, which in turn allow us to contrast two major differences between the approaches with substantial practical implications. The first is that the Bayes factor depends heavily on the prior specification while a modified ROPE procedure is very robust. The second difference is concerned with the statistical property when data are generated under a neutral parameter value on the common boundary of competing hypotheses. In this case, the Bayes factors can be severely biased whereas the modified ROPE approach gives a reasonable result. Finally, the connection leads to a simple and effective algorithm for computing Bayes factors using draws from posterior distributions generated by standard Bayesian programs such as BUGS, JAGS, and Stan.
DOI: 10.6084/m9.figshare.16930063
title: Supplementary Material for: The Influence of Amyloid Burden on Cognitive Decline over 2 years in Older Adults with Subjective Cognitive Decline: A Prospective Cohort Study

description: <b><i>Background:</i></b> Subjective cognitive decline (SCD) is a self-perceived cognitive worsening without objective cognitive impairment. Due to its heterogeneity and potential risk of Alzheimer’s disease (AD), baseline biomarkers to predict progression are clinically important. In the present study, cognitive trajectories during a 24-month period were compared between amyloid-positive SCD (A+SCD) and amyloid-negative SCD (A−SCD) subjects, and biomarkers associated with memory decline were investigated. <b><i>Methods:</i></b> Data from a prospective cohort study in Korea between 2016 and 2019 were analyzed. SCD subjects ≥50 years of age were eligible. All participants underwent neuropsychological tests, brain magnetic resonance imaging, and florbetaben positron emission tomography scans. Amyloid burden and regional volumes were measured. Cognitive changes corrected for age were compared between A+SCD and A−SCD groups. Biomarkers associated with memory decline were assessed. <b><i>Results:</i></b> Forty-seven SCD subjects (69.9 ± 6.7 years, mini-mental state examination (MMSE) score 27.5) were enrolled, and 31 completed at least 1 annual follow-up (mean follow-up: 24.7 months). Baseline characteristics except age, hippocampal atrophy, and white matter hyperintensities were similar between A+SCDs (<i>n</i> = 12, 25.6%) and A−SCDs (<i>n</i> = 35). A+SCD subjects showed greater decline in the verbal memory function compared with the A−SCD subjects after adjustment for age. MMSE scores decreased more in the A+SCD (1.1 in the A+SCD; 0.55 in the A−SCD), although it was not statistically significant. Amyloid burden and baseline memory score were associated with memory decline. <b><i>Conclusions:</i></b> Within SCD, A+SCD subjects showed faster memory decline compared with the A−SCD subjects and amyloid burden might be associated with future memory decline in SCD.
DOI: 10.25375/uct.16929382
title: Digital preservation as a way to make content more accessible and usable for the long-term

description: This body of work was presented at the University of Cape Town, UCT Libraries: World Digital Preservation Day 2021 as an opening address. For more information visit: https://arkivum.com/<br>
DOI: 10.25375/uct.16929382.v2
title: Digital preservation as a way to make content more accessible and usable for the long-term

description: This body of work was presented at the University of Cape Town, UCT Libraries: World Digital Preservation Day 2021 as an opening address. For more information visit: https://arkivum.com/<br>
DOI: 10.25375/uct.16929382.v1
title: Digital preservation as a way to make content more accessible and usable for the long-term

description: This body of work was presented at the University of Cape Town, UCT Libraries: World Digital Preservation Day 2021 as an opening address. For more information visit: https://arkivum.com/<br>
DOI: 10.5517/ccdc.csd.cc2619td
title: CCDC 2027053: Experimental Crystal Structure Determination

description: Related Article: Siwen Niu, Dong Liu, Zongze Shao, Jianrong Liu, Aili Fan, Wenhan Lin|2021|Phytochemistry|192|112978|doi:10.1016/j.phytochem.2021.112978
DOI: 10.5517/ccdc.csd.cc2754q9
title: CCDC 2060530: Experimental Crystal Structure Determination

description: Related Article: Vera Seidl, Michelle Sternberg, Frank W. Heinemann, Martin Schmiele, Tobias Unruh, Peter Wasserscheid, Karsten Meyer|2021|Cryst.Growth Des.|21|6276|doi:10.1021/acs.cgd.1c00734
DOI: 10.5517/ccdc.csd.cc2754rb
title: CCDC 2060531: Experimental Crystal Structure Determination

description: Related Article: Vera Seidl, Michelle Sternberg, Frank W. Heinemann, Martin Schmiele, Tobias Unruh, Peter Wasserscheid, Karsten Meyer|2021|Cryst.Growth Des.|21|6276|doi:10.1021/acs.cgd.1c00734
DOI: 10.5517/ccdc.csd.cc2754sc
title: CCDC 2060532: Experimental Crystal Structure Determination

description: Related Article: Vera Seidl, Michelle Sternberg, Frank W. Heinemann, Martin Schmiele, Tobias Unruh, Peter Wasserscheid, Karsten Meyer|2021|Cryst.Growth Des.|21|6276|doi:10.1021/acs.cgd.1c00734