Finding Respondents in the Forest: A Comparison of Logistic Regression and Random Forest Models for Response Propensity Weighting and Stratification

Survey response rates for modern surveys using many different modes are trending downward leaving the potential for nonresponse biases in estimates derived from using only the respondents. The reasons for nonresponse may be complex functions of known auxiliary variables or unknown latent variables not measured by practitioners. The degree to which the propensity to respond is associated with survey outcomes casts light on the overall potential for nonresponse biases for estimates of means and totals. The most common method for nonresponse adjustments to compensate for the potential bias in estimates has been logistic and probit regression models. However, for more complex nonresponse mechanisms that may be nonlinear or involve many interaction effects, these methods may fail to converge and thus fail to generate nonresponse adjustments for the sampling weights. In this paper we compare these traditional techniques to a relatively new data mining technique- random forests – under a simple and complex nonresponse propensity population model using both direct and propensity stratification nonresponse adjustments. Random forests appear to offer marginal improvements for the complex response model over logistic regression in direct propensity adjustment, but have some surprising results for propensity stratification across both response models

Are Sliders Too Slick for Surveys? An Experiment Comparing Slider and Radio Button Scales for Smartphone, Tablet and Computer Based Surveys

The continued rise in smartphone penetration globally afford survey researchers with an unprecedented portal into personal survey data collection from respondents who could complete surveys from virtually any place at any time. While the basic research into optimizing the survey experience and data collection on mobile devices has continued to develop, there are still fundamental gaps in our knowledge of how to optimize certain types of questions in the mobile setting. In fact, survey researchers are still trying to understand which online design principles directly translate into presentation on mobile devices and which principles have to be modified to incorporate separate methods for these devices. One such area involves the use of input styles such as sliding scales that lend themselves to more touch centric input devices such as smartphones or tablets. Operationalizing these types of scales begs the question of an optimal starting position and whether these touch centric input styles are equally preferred by respondents using less touch capable devices. While an outside starting position seems optimal for slider questions completed via computer, this solution may not be optimal for completion via mobile devices as these devices are subjected to far more space and layout constraints compared to computers. This experiment moves the mixed device survey literature forward by directly comparing outcomes from respondents who completed a collection of survey scales using their smartphone, tablet or computer. Within each device, respondents were randomly assigned to complete one of 20 possible versions of scale items determined by a combination of three experimental factors including input style, length and number formatting. Results from this study suggest more weaknesses than strengths for using slider scales to collect survey data using mobile devices and also suggest that preference for these touch centric input styles varies across devices and may not be as high as the preference for the more traditional radio button style

Are sliders too slick for surveys? An experiment comparing slider and radio button scales for smartphone, tablet and computer based surveys

"The continued rise in smartphone penetration globally afford survey researchers with an unprecedented portal into personal survey data collection from respondents who could complete surveys from virtually any place at any time. While the basic research into optimizing the survey experience and data collection on mobile devices has continued to develop, there are still fundamental gaps in our knowledge of how to optimize certain types of questions in the mobile setting. In fact, survey researchers are still trying to understand which online design principles directly translate into presentation on mobile devices and which principles have to be modified to incorporate separate methods for these devices. One such area involves the use of input styles such as sliding scales that lend themselves to more touch centric input devices such as smartphones or tablets. Operationalizing these types of scales begs the question of an optimal starting position and whether these touch centric input styles are equally preferred by respondents using less touch capable devices. While an outside starting position seems optimal for slider questions completed via computer, this solution may not be optimal for completion via mobile devices as these devices are subjected to far more space and layout constraints compared to computers. This experiment moves the mixed device survey literature forward by directly comparing outcomes from respondents who completed a collection of survey scales using their smartphone, tablet or computer. Within each device, respondents were randomly assigned to complete one of 20 possible versions of scale items determined by a combination of three experimental factors including input style, length and number formatting. Results from this study suggest more weaknesses than strengths for using slider scales to collect survey data using mobile devices and also suggest that preference for these touch centric input styles varies across devices and may not be as high as the preference for the more traditional radio button style." (author's abstract

Background Checks for Firearm Transfers, 2015 - Statistical Tables

Describes background checks for firearms transfers conducted in 2015 and presents estimates of firearm applications received and denied annually since the effective date of the Brady Act in 1994 through 2015. Statistical tables provide data on the number of firearm applications processed by the FBI and state and local agencies, number of applications denied, reasons for denial, and estimates of applications by jurisdiction and by each type of approval system. Findings are based on data from BJS's Firearm Inquiry Statistics program, which annually surveys state and local background checking agencies to collect information on firearm background check activity and combines this information with the FBI's National Instant Criminal Background Check System transaction data to generate national estimates.Highlights:Since the effective date of the Brady Act on February 28, 1994, through December 31, 2015, nearly 197 million applications for firearm transfers or permits were subject to background checks and more than 3 million applications (1.5%) were denied.Nearly 17 million applications for firearm transfers were received in 2015, up from an estimated 15 million in 2014.About 1.4% of the nearly 17 million applications for firearm transfers or permits in 2015 were denied: about 107,000 by the FBI and about 119,000 by state and local agencies.Among state agency reporters, denial rates in 2015 were 3.0% for purchase permits, 1.5% for instant checks, 1.1% for other approval checks, and 1.1% for exempt carry permits.Local agencies denied 4.2% of applications for purchase permit checks and 1.1% of applications for exempt carry permits

Nuclear cGMP-Dependent Kinase Regulates Gene Expression via Activity-Dependent Recruitment of a Conserved Histone Deacetylase Complex

Elevation of the second messenger cGMP by nitric oxide (NO) activates the cGMP-dependent protein kinase PKG, which is key in regulating cardiovascular, intestinal, and neuronal functions in mammals. The NO-cGMP-PKG signaling pathway is also a major therapeutic target for cardiovascular and male reproductive diseases. Despite widespread effects of PKG activation, few molecular targets of PKG are known. We study how EGL-4, the Caenorhabditis elegans PKG ortholog, modulates foraging behavior and egg-laying and seeks the downstream effectors of EGL-4 activity. Using a combination of unbiased forward genetic screen and proteomic analysis, we have identified a conserved SAEG-1/SAEG-2/HDA-2 histone deacetylase complex that is specifically recruited by activated nuclear EGL-4. Gene expression profiling by microarrays revealed >40 genes that are sensitive to EGL-4 activity in a SAEG-1–dependent manner. We present evidence that EGL-4 controls egg laying via one of these genes, Y45F10C.2, which encodes a novel protein that is expressed exclusively in the uterine epithelium. Our results indicate that, in addition to cytoplasmic functions, active EGL-4/PKG acts in the nucleus via a conserved Class I histone deacetylase complex to regulate gene expression pertinent to behavioral and physiological responses to cGMP. We also identify transcriptional targets of EGL-4 that carry out discrete components of the physiological response

Star Library: Regression on the Rebound

This activity is an advanced version of the "Keep your eyes on the ball" activity by Bereska, et al. (1999). Students should gain experience with differentiating between independent and dependent variables, using linear regression to describe the relationship between these variables, and drawing inference about the parameters of the population regression line. Each group of students collects data on the rebound heights of a ball dropped multiple times from each of several different heights. By plotting the data, students quickly recognize the linear relationship. After obtaining the least squares estimate of the population regression line, students can set confidence intervals or test hypotheses on the parameters. Predictions of rebound length can be made for new values of the drop height as well. Data from different groups can be used to test for equality of the intercepts and slopes. By focusing on a particular drop height and multiple types of balls, one can also introduce the concept of analysis of variance. Key words: Linear regression, independent variable, dependent variables, analysis of varianc

Assessing Community-Based Injury Prevention Services in U.S. Children's Hospitals

Objective: Not-for-profit hospitals are required to meet federal reporting requirements detailing their community benefit activities, which support their tax-exempt status. Children's hospitals have long provided community injury prevention (IP) programming and thus can inform public health outreach work in other areas. This work describes IP programming as a community service offered by children's hospitals in the U.S. Methods: The IP specialist at 232 US-based member institutions of the Children's Hospital Association were invited to complete an assessment of their hospital's IP outreach programming. Results: 47.7 percent of hospitals request financial data from IP programming for tax reporting purposes. Almost all offer injury prevention (IP) services; the majority are in the community (60.3%) and 34.5% are hospital-based. Most IP units are independent (60.3%) and 71.8% are responsible for their own budgets. Conclusions: By integrating dissemination and implementation sciences and community health needs assessments, these findings can help advance community services provided by hospitals to impact public health