Approved for public release; distribution is unlimited. / MBA Professional Report / The purpose of this MBA project is to determine how best to collect empirical data regarding the current state of services acquisition management of the installation level within the Department of Defense and conduct an initial analysis of collected data. The project designed a web-based, self-administered, cross-sectional survey using SurveyMonkey, a web-based survey engine. The survey's pilot test was conducted between mid-October and early November 2007 and obtained a 50 percent response rate. Of the respondents, 60 percent was Army, 20 percent was Marine Corps, and 20 percent Air Force. The pilot test captured valuable data which was analyzed; however improvements to the core survey maay generate a higher response rate and provide a clearer picture of the current state of services acquisition management at the installation level within the Department of Defense. The results of this project will support on-going research in the area of services acquisition management.

This thesis is a comparative study of optimal design-based univariate stratification as applied to highly skewed populations such as those observed in business and agricultural surveys. Optimal stratification is a widely used method for reducing the variance or cost of estimates, and this work considers various optimal stratification algorithms, and in particular optimal boundary algorithms, to support this objective. We first provide a background to the theory of stratification and stratified random sampling, and extend this through the derivation of optimal allocation strategies. We then examine the effect of allocation strategies on the variance and design effect of estimators, and in particular find several issues in applying optimal or Neyman allocation when there is little correlation between the survey population and auxiliary information. We present a derivation of the intractable equations for the construction of optimal stratum boundaries, based on the work of Dalenius (1950), and derive the cumulative square root of frequency approximation of Dalenius & Hodges (1957). We then note a number of issues within the implementation of the cumulative square root of frequency rule surrounding the construction of initial intervals, and find that the placement of boundaries and the variance of estimates can be affected by the number of initial intervals. This then leads us to propose two new extensions to the cumulative square root of frequency algorithm, using linear and spline interpolation, and we find that these result in some improvements in the results for this algorithm. We also present a complete derivation of the Ekman algorithm, and consider the extended approach of Hedlin (2000). We derive several new results relating to the Ekman algorithm, and propose a new kernel density based algorithm. We find all three Ekman based algorithms produce similar results for larger populations, and provide some recommendations on the use of these algorithms depending on the size of the population. We look at the derivation and implementation of the Lavallee-Hidiroglou algorithm, and find that it is often slow to converge or does not converge for Neyman allocation. We therefore adopt a random search model of Kozak (2004), and note that the Lavallee-Hidiroglou algorithm generally produces superior results across all populations used in this thesis. We briefly investigate the optimal number of strata by examining the work of Cochran (1977) and Kozak (2006), and find that there is a diminishing marginal effect from increasing the number of strata and possibly some benefit from constructing more than six strata. However we also acknowledge that the cost of constructing such strata may offset any potential gain in precision from constructing more than five or six strata. Finally we consider the how many of these problems can be developed further, and ultimately find that such problems for deciding the number of strata, construction of stratum boundaries, and the allocation of sample units among the strata may require an approach that takes account of the relationship between the auxiliary variable and the survey information. We therefore suggest investigating these algorithms further within the context of a model-assisted environment in order to help account for the relationship between the auxiliary information and survey population.

Abstract COGNITIVE ORIGINS OF THE “BIMBY” EFFECT: A MIXED METHODS EXPLORATION OF SURVEY RATINGS REGARDING THE QUALITY OF PUBLIC SCHOOLS By James M. Ellis, Jr., Ph.D. A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, School of Education at Virginia Commonwealth University. Virginia Commonwealth University, 2011. Director: James H. McMillan, Ph.D. Professor of Education, School of Education Public education and public opinion are pillars of democracy. In surveys about education, respondents in aggregate almost always rate schools attended by their children highest, schools in their communities moderately, and schools in the nation poorly. This phenomenon holds for many other survey topics. Some call it “BIMBY” for “better in my back yard.” This dissertation used mixed methods to investigate BIMBY. Eight qualitative interviews with nine participants used grounded theory to generate hypotheses about BIMBY’s causes. This research revealed a qualitative “insider” view of school quality used by participants for schools familiar to them, and a more quantitative “outsider” view used for unfamiliar schools. The qualitative research generated four main hypotheses tested in a quantitative survey: xviii 1. An empathy hypothesis, tested by framing “nurturant” and “strict” sets of propositions about public schools. 2. A hypothesis about lack of information, tested by sometimes offering explicit don’t know options for school ratings. 3. A community attachment hypothesis, tested by sometimes offering questions about community activities and the like. 4. A hypothesis about a sense of the “here and now,” tested by sometimes asking respondents the number of times they changed schools. This was a full factorial design using sixteen forms of a brief mail survey. A truncated Dillman protocol was used with a randomly selected sample of 960 residences in the Richmond and Charlottesville areas. There were 208 completed surveys. The empathy experiment increased ratings for schools at all levels. Additional analyses indicated that ratings for both local and national schools were influenced by the empathy experiment and the respondent’s world view (nurturant or strict). Ratings for local schools were also influenced by the type of area in which respondents lived (urban, suburban, etc.) and opinions about their communities. Ratings for schools nationally were also influenced by the experiments regarding explicit don’t know responses and community attachment. Thus, respondents draw on different domains of opinion when rating different schools. Ratings for local schools relate to opinions about the community. Ratings for schools nationally may relate to a general world view and the respondent’s identities within the community and the nation.

Introduction: Referral for CS/HIPEC is variable, and barriers encountered by referring physicians are unknown. Identification of such barriers is useful for the creation of tailored knowledge translation (KT) strategies. Methods: Interviews of 20 medical oncologists and surgeons in the New York (NY) area were completed to identify barrier topics, using the Pathman framework of uptake of innovations (awareness, agreement, adoption, adherence) at the various levels of the individual, practice group, and organization. Barriers were used to structure a survey for evaluation of prevalence at the population level of medical oncologists and surgeons in NY State. Results: Barrier topics of awareness included training at a CS/HIPEC center, and availability of multidisciplinary cancer conferences. Agreement barriers centered mainly on quality of published literature, and the paradigm shift of carcinomatosis as a systemic to locoregional disease process. Adoption barriers included knowledge of outcomes of a CS/HIPEC surgeon, and concerns with morbidity/mortality rates. Adherence barriers included the lack of reflection of CS/HIPEC in current CPGs, financial/resource and logistic concerns of referrals, and lack of quality measures for the procedure. For the survey, 119 responded (12% response rate), including 42 medical oncologist and 77 surgeons. The majority were aware of CS/HIPEC (n=113, 95%). Medical oncologists were less likely than surgeons to agree with CS/HIPEC related to published evidence (76% vs 92 %, p = 0.02). Surgeons were more likely to be aware of where to refer patients for the procedure, and were less likely to have concerns regarding morbidity/mortality, compared with medical oncologists (p = 0.05, p = 0.04). Representation of CS/HIPEC in CPGs and quality measures/outcomes data was felt to result in adherence to a regular referral practice. Discussion: This prospective study of stakeholders for CS/HIPEC is the first to evaluate and characterize barriers to referral for this complex and controversial surgical innovation, with prevalence at the population level. / Thesis / Master of Health Sciences (MSc) / This thesis identifies problems encountered with referring patients to a specialist surgeon for a procedure that involves both surgical removal of tumors and treatment of the abdominal cavity with chemotherapy. These problems are evaluated from interviews with specialists in the field, and then evaluated at a higher level of all practicing referring specialists through a survey. The results will be used to improve patient outcomes in the future.

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In public relations, minority public relations practitioners are feeling left behind by the profession (Ford & Appelbaum, 2005). Where do top executives stand on employment diversity within their organizations? An online survey of 20 top executives of small-sized public relations agencies explored how top executives’ perceptions of and normative beliefs about diversity practices were related to their future engagement in diversity practices at work. Based on the theory of reasoned action, this explanatory study found that executives’ perceptions of peer endorsement of diversity were associated with greater intention of organizational engagement in diversity practices. Neither perceived benefits of nor perceived concerns about diversity were related to future engagement. Recommendations for contacting this hard-to-reach audience, as well as suggestions for promoting diversity practices among top executives, were discussed.

We use survey findings to analyse the effects of the Austrian income tax reform 2015/2016 on private consumption differentiated by income classes. Using survey data, we also estimate the corresponding average marginal propensities to consume and compare them to applied average marginal propensities to consume in economic models used to analyse the previous two income tax reforms in Austria. The estimated average marginal propensity to consume amounts to approximately 0.46, whereby in tendency increasing from the lowest income class (0.42-0.43) to the highest income class (0.48-0.50). Our estimated average marginal propensity to consume across all income classes basically corresponds to those used in economic models to evaluate the income tax reform 2015/2016. However, our estimated marginal propensities to consume by income classes fundamentally differ from those used in the economic models. / Series: Department of Economics Working Paper Series

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As data become more pervasive and computing power increases, the opportunity for transformative use of data grows. Collecting data from individuals can be useful to the individuals (by providing them with personalized predictions) and the data collectors (by providing them with information about populations). However, collecting these data is costly: answering survey items, collecting sensed data, and computing values of interest deplete finite resources of time, battery, life, money, etc. Dynamically ordering the items to be collected, based on already known information (such as previously collected items or paradata), can lower the costs of data collection by tailoring the information-acquisition process to the individual. This thesis presents a framework for an iterative dynamic item ordering process that trades off item utility with item cost at data collection time. The exact metrics for utility and cost are application-dependent, and this frame- work can apply to many domains. The two main scenarios we consider are (1) data collection for personalized predictions and (2) data collection in surveys. We illustrate applications of this framework to multiple problems ranging from personalized prediction to questionnaire scoring to government survey collection. We compare data quality and acquisition costs of our method to fixed order approaches and show that our adaptive process obtains results of similar quality at lower cost. For the personalized prediction setting, the goal of data collection is to make a prediction based on information provided by a respondent. Since it is possible to give a reasonable prediction with only a subset of items, we are not concerned with collecting all items. Instead, we want to order the items so that the user provides information that most increases the prediction quality, while not being too costly to provide. One metric for quality is prediction certainty, which reflects how likely the true value is to coincide with the estimated value. Depending whether the prediction problem is continuous or discrete, we use prediction interval width or predicted class probability to measure the certainty of a prediction. We illustrate the results of our dynamic item ordering framework on tasks of predicting energy costs, student stress levels, and device identification in photographs and show that our adaptive process achieves equivalent error rates as a fixed order baseline with cost savings up to 45%. For the survey setting, the goal of data collection is often to gather information from a population, and it is desired to have complete responses from all samples. In this case, we want to maximize survey completion (and the quality of necessary imputations), and so we focus on ordering items to engage the respondent and collect hopefully all the information we seek, or at least the information that most characterizes the respondent so imputed values will be accurate. One item utility metric for this problem is information gain to get a “representative” set of answers from the respondent. Furthermore, paradata collected during the survey process can inform models of user engagement that can influence either the utility metric ( e.g., likelihood therespondent will continue answering questions) or the cost metric (e.g., likelihood the respondent will break off from the survey). We illustrate the benefit of dynamic item ordering for surveys on two nationwide surveys conducted by the U.S. Census Bureau: the American Community Survey and the Survey of Income and Program Participation.

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