Using Machine Learning to Uncover Hidden Heterogeneities in Survey Data

Survey responses in public health surveys are heterogeneous. The quality of a respondent’s answers depends on many factors, including cognitive abilities, interview context, and whether the interview is in person or self-administered. A largely unexplored issue is how the language used for public health survey interviews is associated with the survey response. We introduce a machine learning approach, Fuzzy Forests, which we use for model selection. We use the 2013 California Health Interview Survey (CHIS) as our training sample and the 2014 CHIS as the test sample. We found that non-English language survey responses differ substantially from English responses in reported health outcomes. We also found heterogeneity among the Asian languages suggesting that caution should be used when interpreting results that compare across these languages. The 2013 Fuzzy Forests model also correctly predicted 86% of good health outcomes using 2014 data as the test set. We show that the Fuzzy Forests methodology is potentially useful for screening for and understanding other types of survey response heterogeneity. This is especially true in high-dimensional and complex surveys

Using Machine Learning to Uncover Hidden Heterogeneities in Survey Data

Survey responses in public health surveys are heterogeneous. The quality of a respondent’s answers depends on many factors, including cognitive abilities, interview context, and whether the interview is in person or self-administered. A largely unexplored issue is how the language used for public health survey interviews is associated with the survey response. We introduce a machine learning approach, Fuzzy Forests, which we use for model selection. We use the 2013 California Health Interview Survey (CHIS) as our training sample and the 2014 CHIS as the test sample. We found that non-English language survey responses differ substantially from English responses in reported health outcomes. We also found heterogeneity among the Asian languages suggesting that caution should be used when interpreting results that compare across these languages. The 2013 Fuzzy Forests model also correctly predicted 86% of good health outcomes using 2014 data as the test set. We show that the Fuzzy Forests methodology is potentially useful for screening for and understanding other types of survey response heterogeneity. This is especially true in high-dimensional and complex surveys

Who Voted in 2016? Using Fuzzy Forests to Understand Voter Turnout

Objective: What can machine learning tell us about who voted in 2016? There are numerous competing voter turnout theories, and a large number of covariates are required to assess which theory best explains turnout. This article is a proof of concept that machine learning can help overcome this curse of dimensionality and reveal important insights in studies of political phenomena. Methods: We use fuzzy forests, an extension of random forests, to screen variables for a parsimonious but accurate prediction. Fuzzy forests achieve accurate variable importance measures in the face of high‐dimensional and highly correlated data. The data that we use are from the 2016 Cooperative Congressional Election Study. Results: Fuzzy forests chose only a small number of covariates as major correlates of 2016 turnout and still boasted high predictive performance. Conclusion: Our analysis provides three important conclusions about turnout in 2016: registration and voting procedures were important, political issues were important (especially Obamacare, climate change, and fiscal policy), but few demographic variables other than age were strongly associated with turnout. We conclude that fuzzy forests is an important methodology for studying overdetermined questions in social sciences

Who Voted in 2016? Using Fuzzy Forests to Understand Voter Turnout

Objective: What can machine learning tell us about who voted in 2016? There are numerous competing voter turnout theories, and a large number of covariates are required to assess which theory best explains turnout. This article is a proof of concept that machine learning can help overcome this curse of dimensionality and reveal important insights in studies of political phenomena. Methods: We use fuzzy forests, an extension of random forests, to screen variables for a parsimonious but accurate prediction. Fuzzy forests achieve accurate variable importance measures in the face of high‐dimensional and highly correlated data. The data that we use are from the 2016 Cooperative Congressional Election Study. Results: Fuzzy forests chose only a small number of covariates as major correlates of 2016 turnout and still boasted high predictive performance. Conclusion: Our analysis provides three important conclusions about turnout in 2016: registration and voting procedures were important, political issues were important (especially Obamacare, climate change, and fiscal policy), but few demographic variables other than age were strongly associated with turnout. We conclude that fuzzy forests is an important methodology for studying overdetermined questions in social sciences

Matching method and exact solvability of discrete PT-symmetric square wells

Discrete PT-symmetric square wells are studied. Their wave functions are found proportional to classical Tshebyshev polynomials of complex argument. The compact secular equations for energies are derived giving the real spectra in certain intervals of non-Hermiticity strengths Z. It is amusing to notice that although the known square well re-emerges in the usual continuum limit, a twice as rich, upside-down symmetric spectrum is exhibited by all its present discretized predecessors.Comment: 25 pp, 3 figure

Joint effect of physical activity and sedentary behaviour on cardiovascular risk factors in Chilean adults

Background: To investigate the associations between combined categories of moderate-to-vigorous physical activity (MVPA) and sedentary behaviour (SB) with markers of adiposity and cardiovascular risk in adults. Methods: Overall, 5040 participants (mean age 46.4 years and 59.3% women) from the cross-sectional Chilean National Health Survey 2009–2010 were included in this study. MVPA and SB were measured using the Global Physical Activity questionnaire. Four categories were computed using MVPA- and SB-specific cut-offs (‘High-SB & Active’, ‘Low-SB & Active’, ‘High-SB & Inactive’ and ‘Low-SB & Inactive’). Results: Compared to the reference group (‘High-SB & Inactive’), those in ‘High-SB & Active’ and ‘Low-SB & Active’ were less likely to have an obese BMI (OR: 0.67 [0.54; 0.85], P = 0.0001 and 0.74 [0.59; 0.92] P = 0.0007, respectively) and less likely to have metabolic syndrome (OR: 0.63 [0.49; 0.82], P < 0.0001 and 0.72 [0.57; 0.91], P = 0.007), central obesity (OR: 0.79 [0.65; 0.96], P = 0.016 and 0.71 [0.59; 0.84], P < 0.0001), diabetes (OR: 0.45 [0.35; 0.59], P < 0.0001 and 0.44 [0.34; 0.56], P < 0.0001) and hypertension (OR: 0.52 [0.43; 0.63], P < 0.0001 and 0.60 [0.50; 0.72], P < 0.0001), respectively. Conclusions: Being physically active and spending less time in SBs was associated with lower adiposity and improvements in cardiovascular risk factors

Gamma-Ray Bursts in Circumstellar Shells: A Possible Explanation for Flares

It is now generally accepted that long-duration gamma ray bursts (GRBs) are due to the collapse of massive rotating stars. The precise collapse process itself, however, is not yet fully understood. Strong winds, outbursts, and intense ionizing UV radiation from single stars or strongly interacting binaries are expected to destroy the molecular cloud cores that give birth to them and create highly complex circumburst environments for the explosion. Such environments might imprint features on GRB light curves that uniquely identify the nature of the progenitor and its collapse. We have performed numerical simulations of realistic environments for a variety of long-duration GRB progenitors with ZEUS-MP, and have developed an analytical method for calculating GRB light curves in these profiles. Though a full, three-dimensional, relativistic magnetohydrodynamical computational model is required to precisely describe the light curve from a GRB in complex environments, our method can provide a qualitative understanding of these phenomena. We find that, in the context of the standard afterglow model, massive shells around GRBs produce strong signatures in their light curves, and that this can distinguish them from those occurring in uniform media or steady winds. These features can constrain the mass of the shell and the properties of the wind before and after the ejection. Moreover, the interaction of the GRB with the circumburst shell is seen to produce features that are consistent with observed X-ray flares that are often attributed to delayed energy injection by the central engine. Our algorithm for computing light curves is also applicable to GRBs in a variety of environments such as those in high-redshift cosmological halos or protogalaxies, both of which will soon be targets of future surveys such as JANUS or Lobster.Comment: 12 pages, 5 figures, Accepted by Ap

Effects of Backpacks on Ground Reaction Forces in Children of Different Ages When Walking, Running, and Jumping

Backpacks for transporting school loads are heavily utilized by children, and their mechanical advantages have been allowing children to transport heavy loads. These heavy loads may increase ground reaction forces (GRFs), which can have a negative effect on joints and bone health. The aim of this study was to investigate the effect of backpacks on the GRFs generated by children during walking, running, and jumping. Twenty-one children from the fifth (G-5, n = 9) and ninth (G-9, n = 12) grades walked, ran, and jumped over a force plate. When walking, the G-5 had GRF increments in the first (17.3%; p 0.05), unlike the G-5 (p = 0.001). When running, total stance time increased 15% (p < 0.001) and 8.5% (p < 0.001) proportionally to the relative load carried, in the G-5 and G-9, respectively. Peak GRF did not increase in any group when running or landing from a jump over an obstacle. It was found that GRF was affected by the backpack load when walking and running. However, when landing from a jump with the backpack, schoolchildren smoothed the landing by prolonging the reception time and thus avoiding GRF peak magnitudes.info:eu-repo/semantics/publishedVersio