Streamlining Missing Data Analysis by Aggregating Multiple Imputations at the Data Level: A Monte Carlo Simulation to Assess the Tenability of the SuperMatrix Approach

A Monte Carlo Simulation Study was conducted to assess the tenability of a novel treatment of missing data. Through aggregating multiply-imputed data sets prior to model estimation, the proposed technique allows researchers to reap the benefits of a principled missing data tool (i.e., multiple imputation), while maintaining the simplicity of complete case analysis. In terms of the accuracy of model fit indices derived from confirmatory factor analyses, the proposed technique was found to perform universally better than a naive ad hoc technique consisting of averaging the multiple estimates of model fit derived from a traditionally conceived implementation of multiple imputation. However, the proposed technique performed considerably worse in this task than did full information maximum likelihood (FIML) estimation. Absolute fit indices and residual based fit indices derived from the proposed technique demonstrated an unacceptable degree of bias in assessing direct model fit, but incremental fit indices led to acceptable conclusions regarding model fit. Chi-squared difference values derived from the proposed technique were unbiased across all study conditions (except for those with very poor parameterizations) and were consistently more accurate than such values derived from the ad hoc comparison condition. It was also found that Chi-squared difference values derived from FIML-based models were negatively biased to an unacceptable degree in any conditions with greater than 10% missing. Implications, limitations and future directions of the current work are discussed

MIBEN: Robust Multiple Imputation with the Bayesian Elastic Net

Correctly specifying the imputation model when conducting multiple imputation remains one of the most significant challenges in missing data analysis. This dissertation introduces a robust multiple imputation technique, Multiple Imputation with the Bayesian Elastic Net (MIBEN), as a remedy for this difficulty. A Monte Carlo simulation study was conducted to assess the performance of the MIBEN technique and compare it to several state-of-the-art multiple imputation methods

Supervised dimensionality reduction for multiple imputation by chained equations

Multivariate imputation by chained equations (MICE) is one of the most popular approaches to address missing values in a data set. This approach requires specifying a univariate imputation model for every variable under imputation. The specification of which predictors should be included in these univariate imputation models can be a daunting task. Principal component analysis (PCA) can simplify this process by replacing all of the potential imputation model predictors with a few components summarizing their variance. In this article, we extend the use of PCA with MICE to include a supervised aspect whereby information from the variables under imputation is incorporated into the principal component estimation. We conducted an extensive simulation study to assess the statistical properties of MICE with different versions of supervised dimensionality reduction and we compared them with the use of classical unsupervised PCA as a simpler dimensionality reduction technique

Solving the "many variables" problem in MICE with principal component regression

Multiple Imputation (MI) is one of the most popular approaches to addressing missing values in questionnaires and surveys. MI with multivariate imputation by chained equations (MICE) allows flexible imputation of many types of data. In MICE, for each variable under imputation, the imputer needs to specify which variables should act as predictors in the imputation model. The selection of these predictors is a difficult, but fundamental, step in the MI procedure, especially when there are many variables in a data set. In this project, we explore the use of principal component regression (PCR) as a univariate imputation method in the MICE algorithm to automatically address the "many variables" problem that arises when imputing large social science data. We compare different implementations of PCR-based MICE with a correlation-thresholding strategy by means of a Monte Carlo simulation study and a case study. We find the use of PCR on a variable-by-variable basis to perform best and that it can perform closely to expertly designed imputation procedures

High-dimensional Imputation for the Social Sciences: a Comparison of State-of-the-art Methods

Including a large number of predictors in the imputation model underlying a multiple imputation (MI) procedure is one of the most challenging tasks imputers face. A variety of high-dimensional MI techniques can help, but there has been limited research on their relative performance. In this study, we investigated a wide range of extant high-dimensional MI techniques that can handle a large number of predictors in the imputation models and general missing data patterns. We assessed the relative performance of seven high-dimensional MI methods with a Monte Carlo simulation study and a resampling study based on real survey data. The performance of the methods was defined by the degree to which they facilitate unbiased and confidencevalid estimates of the parameters of complete data analysis models. We found that using lasso penalty or forward selection to select the predictors used in the MI model and using principal component analysis to reduce the dimensionality of auxiliary data produce the best results

A Novel Item-Allocation Procedure for the Three-Form Planned Missing Data Design

We propose a new method of constructing questionnaire forms in the three-form planned missing data design (PMDD). The random item allocation (RIA) procedure that we propose promises to dramatically simplify the process of implementing three-form PMDDs without compromising statistical performance. Our method is a stochastic approximation to the currently recommended approach of deterministically spreading a scale\u27s items across the X-, A-, B-, and C-blocks when allocating the items in a three-form design. Direct empirical support for the performance of our method is only available for scales containing at least 12 items, so we also propose a modified approach for use with scales containing fewer than 12 items. We also discuss the limitations of our procedure and several nuances for researchers to consider when implementing three-form PMDDs using our method. The RIA procedure allows researchers to implement statistically sound three-form planned missing data designs without the need for expert knowledge or results from prior statistical modeling. The RIA procedure can be used to construct both “paper-and-pencil” questionnaires and questionnaires administered through online survey software. The RIA procedure is a simple framework to aid in designing three-form PMDDs; implementing the RIA method does not require any specialized software or technical expertise

The Effects of Caffeine on Jumping Performance and Maximal Strength in Female Collegiate Athletes

Introduction: Caffeine has long been used to enhance athletic performance. The research regarding caffeine’s effects on strength and power performance is lacking, especially in female athletes. Purpose: To analyze the acute effects of caffeine on jumping performance and maximal strength in female collegiate athletes. Methods: Eight female collegiate athletes performed two testing sessions separated by one week. Using a double-blind approach, athletes randomly received 6 mg/kg of body mass of caffeine (CAF) or a placebo (PLA). Following 60min of quiet sitting and a standardized warm-up, athletes were tested on measures of squat jump height (SJH) and peak power (SJPP), countermovement jump height (CMJH) and peak power (CMJPP), and isometric mid-thigh pull peak force (IPF) and rate of force development (RFD) on force platforms. Heart rate, systolic blood pressure, diastolic blood pressure, and tympanic temperature were measured at three time points across the testing session. A paired samples t-test with Hedge’s g effect size was used to compare performance results between conditions. A 2 x 3 (condition x time) repeated measures ANOVA was used to analyze changes in physiological measures between and within conditions. Alpha level for all analyses was set at pResults: There were small to moderate differences in SJH (p=0.08, g=0.26), SJPP (p=0.03, g=0.63), CMJH (p=0.004, g=0.27), CMJPP (p=0.18, g=0.20), IPF (p=0.08, g=0.25), and RFD (p=0.22, g=0.40) in favor of caffeine over placebo. Physiological measurements increased similarly across time for both conditions with the exception of SBP, which was greater following caffeine 3 administration compared to placebo (p=0.02). Conclusions: Caffeine ingestion produced small to moderate improvements in jumping performance; however, caffeine failed to significantly affect maximal strength when compared with the placebo. Nonetheless, there was a small increase in IPF and RFD compared to placebo. Therefore, caffeine appears to be an effective ergogenic aid when used to enhance jumping performance and possibly maximal strength in female collegiate athletes

On the Joys of Missing Data

We provide conceptual introductions to missingness mechanisms—missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR)—and state-of-the-art methods of handling missing data—full-information maximum likelihood (FIML) and multiple imputation (MI)—followed by a discussion of planned missing designs: multiform questionnaire protocols, two-method measurement models, and wave-missing longitudinal designs. We reviewed 80 articles of empirical studies published in the 2012 issues of the Journal of Pediatric Psychology to present a picture of how adequately missing data are currently handled in this field. To illustrate the benefits of utilizing MI or FIML and incorporating planned missingness into study designs, we provide example analyses of empirical data gathered using a three-form planned missing design

Mimesis stories: composing new nature music for the shakuhachi

Nature is a widespread theme in much new music for the shakuhachi (Japanese bamboo flute). This article explores the significance of such music within the contemporary shakuhachi scene, as the instrument travels internationally and so becomes rooted in landscapes outside Japan, taking on the voices of new creatures and natural phenomena. The article tells the stories of five compositions and one arrangement by non-Japanese composers, first to credit composers’ varied and personal responses to this common concern and, second, to discern broad, culturally syncretic traditions of nature mimesis and other, more abstract, ideas about the naturalness of sounds and creative processes (which I call musical naturalism). Setting these personal stories and longer histories side by side reveals that composition creates composers (as much as the other way around). Thus it hints at much broader terrain: the refashioning of human nature at the confluence between cosmopolitan cultural circulations and contemporary encounters with the more-than-human world

An updated re-entry analysis of the Hubble Space Telescope

The Hubble Space Telescope (HST), launched in 1990, has without question given us a better understanding of the Universe [1]. The storied spacecraft has far exceeded its design life and, in spite of four repair missions, is nearing the end of its useful lifespan. Originally designed to be returned by the Space Shuttle, the HST has no on-board propulsion system. A 2012 study estimated that without intervention, the HST will re-enter the atmosphere in approximately 2027 with a 1:240 risk of fatality [2]. This study updates that analysis with more recent de-orbit technologies and updated trajectory information. We propose a design solution to safely perform a targeted de-orbit, assuming a worst-case scenario (a non-functional, tumbling spacecraft). Multiple de-orbit options are assessed to actively capture the satellite. Results frame an approach that could be accomplished with proven technologies at reasonable cost to improve the fatality risk as required by US Government regulation [3]. Moreover, delayed action would significantly increase mission cost and complexity so we recommend a project start in the near future