Transitions from Telephone Surveys to Self-Administered and Mixed-Mode Surveys: AAPOR Task Force Report

Telephone surveys have been a ubiquitous method of collecting survey data, but the environment for telephone surveys is changing. Many surveys are transitioning from telephone to self-administration or combinations of modes for both recruitment and survey administration. Survey organizations are conducting these transitions from telephone to mixed modes with only limited guidance from existing empirical literature and best practices. This article summarizes findings by an AAPOR Task Force on how these transitions have occurred for surveys and research organizations in general. We find that transitions from a telephone to a selfadministered or mixed-mode survey are motivated by a desire to control costs, to maintain or improve data quality, or both. The most common mode to recruit respondents when transitioning is mail, but recent mixedmode studies use only web or mail and web together as survey administration modes. Although early studies found that telephone response rates met or exceeded response rates to the self-administered or mixed modes, after about 2013, response rates to the self-administered or mixed modes tended to exceed those for the telephone mode, largely because of a decline in the telephone mode response rates. Transitioning offers opportunities related to improved frame coverage and geographic targeting, delivery of incentives, visual design of an instrument, and cost savings, but challenges exist related to selecting a respondent within a household, length of a questionnaire, differences across modes in use of computerization to facilitate skip patterns and other questionnaire design features, and lack of an interviewer for respondent motivation and clarification. Other challenges related to surveying youth, conducting surveys in multiple languages, collecting nonsurvey data such as biomeasures or consent to link to administrative data, and estimation with multiple modes are also prominent

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

An Innovative Approach to the Design of a National Probability Sample of Sexual Minority Adults

Purpose: Sampling lesbian, gay, and bisexual (LGB) people to recruit a national probability sample is challenging for many reasons, including the low base rate of LGB people in the population. To address this challenge, researchers have relied on diverse approaches to sampling LGB people. We aimed to test an innovative method to assemble a U.S. national probability sample of non-transgender sexual minority adults. Methods: Our approach used two phases. In Phase 1, we identified LGBT respondents in a probability general population sample. These respondents were then queried about their sexual orientation and gender identity using short screening questions to identify non-transgender sexual minority respondents. In Phase 2, the identified sexual minority respondents completed the targeted survey online or on a mailed questionnaire. Results: In Phase 1, using random-digit dialing, a nationally representative sample of 366,644 respondents were screened in a brief telephone interview. Of them, 3.5% (n = 12,837) identified as LGB or transgender. In Phase 2, eligible respondents were asked to participate in a self-administered survey questionnaire. Eligibility was based on gender identity, age, race and ethnicity, and educational restrictions. Of the 3525 who were eligible, 81% (n = 2840) agreed to participate in the study (78% agreed to use the web version and 22% the mailed questionnaire), and 49% of web surveys and 46% of mailed surveys were completed. The final sample included 1331 respondents. Conclusion: The benefits of this approach include the ability to assess sexual minority-specific content in a national probability sample; challenges include high cost and low base rates for Asian and American Indian or Alaska Native individuals in the United States

Who's Tweeting About the President? What Big Survey Data Can Tell Us About Digital Traces?

Researchers hoping to make inferences about social phenomena using social media data need to answer two critical questions: What is it that a given social media metric tells us? And who does it tell us about? Drawing from prior work on these questions, we examine whether Twitter sentiment about Barack Obama tells us about Americans' attitudes toward the president, the attitudes of particular subsets of individuals, or something else entirely. Specifically, using large-scale survey data, this study assesses how patterns of approval among population subgroups compare to tweets about the president. The findings paint a complex picture of the utility of digital traces. Although attention to subgroups improves the extent to which survey and Twitter data can yield similar conclusions, the results also indicate that sentiment surrounding tweets about the president is no proxy for presidential approval. Instead, after adjusting for demographics, these two metrics tell similar macroscale, long-term stories about presidential approval but very different stories at a more granular level and over shorter time periods

Within-household selection and dual-frame telephone surveys: a comparative experiment of eleven different selection methods

Numerous within-household selection methods have been tested in general population surveys since the advent of telephone interviewing. However, very few selection studies, if any, have been conducted with a dual frame (landline and cell phone) sample. Landline and cell phone frames are known to represent demographically different groups of respondents, and selection methods that may result in more representative demographics in a landline frame may actually skew the results when combined with the cell phone frame. This study tested 11 different within-household selection methods with approximately 11,000 landline respondents. A parallel cell phone sample was also collected with 1,000 respondents, and the frames were combined for analysis. The selection methods tested included one probability-based method, four quasi-probability methods and six nonprobability methods. The methods were evaluated on four criteria: response rates, accuracy, demographic representation and substantive results. The demographic representativeness of each method was examined for the landline frame only and for the dual (landline and cell phone) frame combination. The probability method had the lowest response rate, while the nonprobability at-home methods had the highest. Accuracy rates were lowest for the quasi-probability birthday methods. There were few demographic differences between selection methods, and no substantive differences, when combined with the cell phone sample

Effect of local skin blood flow during light and medium activities on local skin temperature predictions

The quality of local skin temperature prediction by thermophysiological models depends on the local skin blood flow (SBF) control functions. These equations were derived for low activity levels (0.8−1met) and mostly in sitting or supine position. This study validates and discusses the prediction of foot SBF during activities of 1−3met in male and females, and the effect on the foot skin temperature prediction (ΔTskin,foot) using the thermophysiological simulation model ThermoSEM. The SBF at the foot was measured for ten male and ten female human subjects at baseline and during three activities (sitting, walking at 1km/h, preferred walking around 3km/h). Additional measurements included the energy expenditure, local skin temperatures (Tskin,loc), environmental conditions and body composition. Measured, normalized foot SBF is 2-8 times higher than the simulated SBF during walking sessions. Also, SBF increases are significantly higher in females vs. males (preferred walking: 4.8±1.5 versus 2.7±1.4, P < 0.05). The quality of ΔTskin,foot using the simulated foot SBF is poor (median deviation is −4.8°C, maximumumdeviationis−6°C). Using the measured SBF in ThermoSEM results in an improved local skin temperature prediction (new maximum deviation is −3.3°C). From these data a new SBF model was developed that includes the walking activity level and gender, and improves SBF prediction and ΔTskin,foot of the thermophysiological model. Accurate SBF and local skin temperature predictions are beneficial in optimizing thermal comfort simulations in the built environment, and might also be applied in sport science or patient's temperature management

Effect of local skin blood flow during light and medium activities on local skin temperature predictions

\u3cp\u3eThe quality of local skin temperature prediction by thermophysiological models depends on the local skin blood flow (SBF) control functions. These equations were derived for low activity levels (0.8−1met) and mostly in sitting or supine position. This study validates and discusses the prediction of foot SBF during activities of 1−3met in male and females, and the effect on the foot skin temperature prediction (ΔT\u3csub\u3eskin,foot\u3c/sub\u3e) using the thermophysiological simulation model ThermoSEM. The SBF at the foot was measured for ten male and ten female human subjects at baseline and during three activities (sitting, walking at 1km/h, preferred walking around 3km/h). Additional measurements included the energy expenditure, local skin temperatures (T\u3csub\u3eskin,loc\u3c/sub\u3e), environmental conditions and body composition. Measured, normalized foot SBF is 2-8 times higher than the simulated SBF during walking sessions. Also, SBF increases are significantly higher in females vs. males (preferred walking: 4.8±1.5 versus 2.7±1.4, P &lt; 0.05). The quality of ΔT\u3csub\u3eskin,foot\u3c/sub\u3e using the simulated foot SBF is poor (median deviation is −4.8°C, maximumumdeviationis−6°C). Using the measured SBF in ThermoSEM results in an improved local skin temperature prediction (new maximum deviation is −3.3°C). From these data a new SBF model was developed that includes the walking activity level and gender, and improves SBF prediction and ΔT\u3csub\u3eskin,foot\u3c/sub\u3e of the thermophysiological model. Accurate SBF and local skin temperature predictions are beneficial in optimizing thermal comfort simulations in the built environment, and might also be applied in sport science or patient's temperature management.\u3c/p\u3