Essays on corporate reputation: Antecedents and consequences

This dissertation studied the determinants and consequences of corporate reputation. It explored how firm-, industry-, and country-level factors influence the general public’s assessment of a firm’s reputation and how this reputation assessment impacted the firm’s strategic actions and organizational outcomes. The three empirical essays are grounded on separate theoretical paradigms in strategy, organizational theory, and corporate governance. The first essay used signaling theory to investigate firm-, industry-, and country-level determinants of individual-level corporate reputation assessments. Using a hierarchical linear model, it tested the theory based on individual evaluations of the largest companies across countries. Results indicated that variables at multiple analysis levels simultaneously impact individual level reputation assessments. Interactions were also found between industry- and country-level factors. Results confirmed the multi-level nature of signaling influences on reputation assessments. Building on a stakeholder-power approach to corporate governance, the second essay studied how differences in the power and preferences of three stakeholder groups—shareholders, creditors, and workers—across countries influence the general public’s reputation assessments of corporations. Examining the largest companies across countries, the study found that while the influence of stock market return is stronger in societies where shareholders have more power, social performance has a more significant role in shaping reputation evaluations in societies with stronger labor rights. Unexpectedly, when creditors have greater power, the influence of financial stability on reputation assessment becomes weaker. Exploring the consequences of reputation, the third essay investigated the specific effects of intangible assets on strategic actions and organizational outcomes. Particularly, it individually studied the impacts of acquirer acquisition experience, corporate reputation, and approach toward social responsibilities as well as their combined effect on market reactions to acquisition announcements. Using an event study of acquisition announcements, it confirmed the significant impacts of both action-specific (acquisition experience) and general (reputation and social performance) intangible assets on market expectations of acquisition outcomes. Moreover, the analysis demonstrated that reputation magnifies the impact of acquisition experience on market response to acquisition announcements. In conclusion, this dissertation tried to advance and extend the application of management and organizational theories by explaining the mechanisms underlying antecedents and consequences of corporate reputation.

Factors associated with prompt difficulty in automated essay scoring

This study explores factors related to the prompt difficulty in Automated Essay Scoring. The sample was composed of 6,924 students. For each student, there were 1-4 essays, across 20 different writing prompts, for a total of 20,243 essays. E-rater® v.2 essay scoring engine developed by the Educational Testing Service was used to score the essays. The scoring engine employs a statistical model that incorporates 10 predictors associated with writing characteristics of which 8 were used. The Rasch partial credit analysis was applied to the scores to determine the difficulty levels of prompts. In addition, the scores were used as outcomes in the series of hierarchical linear models (HLM) in which students and prompts constituted the cross-classification levels. This methodology was used to explore the partitioning of the essay score variance.^ The results indicated significant differences in prompt difficulty levels due to genre. Descriptive prompts, as a group, were found to be more difficult than the persuasive prompts. In addition, the essay score variance was partitioned between students and prompts. The amount of the essay score variance that lies between prompts was found to be relatively small (4 to 7 percent). When the essay-level, student-level-and prompt-level predictors were included in the model, it was able to explain almost all variance that lies between prompts. Since in most high-stakes writing assessments only 1-2 prompts per students are used, the essay score variance that lies between prompts represents an undesirable or "noise" variation. Identifying factors associated with this "noise" variance may prove to be important for prompt writing and for constructing Automated Essay Scoring mechanisms for weighting prompt difficulty when assigning essay score.^

Predictors of adolescent sexual intentions and behavior: Attitudes, parenting, and neighborhood risk

The current study was a cross-sectional examination of data collected during an HIV risk reduction intervention in south Florida. The purpose of the study was to explore the relationships between neighborhood stress, parenting, attitudes, and adolescent sexual intentions and behavior. The Theory of Planned Behavior was used as a model to guide variable selection and propose an interaction pathway between predictors and outcomes. Potential predictor variables measured for adolescents ages 13–18 (n=196) included communication about sex, parent-family connectedness, parental presence, parent-adolescent activity participation, attitudes about sex and condom use, neighborhood disorder, and exposure to violence. Outcomes were behavioral intentions and sexual behavior for the previous eight months. Neighborhood data was supplemented with ZIP Code level data from regional sources and included median household income, percentage of minority and Hispanic residents, and number of foreclosures. Statistical tests included t-tests, Pearson's correlations, and hierarchical linear regressions. Results showed that males and older adolescents reported less positive behavioral intentions than females and adolescents younger than 16. Intentions were associated with condom attitudes, sexual attitudes, and parental presence; unprotected sexual behavior was associated with parental presence. The best fit model for intentions included gender, sexual attitudes, condom attitudes, parental presence, and neighborhood disorder. The unsafe sexual behavior model included whether the participant lived with both natural parents in the previous year, and the percent of Hispanic residents in the neighborhood. Study findings indicate that more research on adolescent sexual behavior is warranted, specifically examining the differentials between variables that affect intentions and those that affect behavior. A focus on gender and age differences during intervention development may allow for better targeting and more efficacious interventions. Adding peer and media influences to the framework of attitudes, parenting, and neighborhood may offer more insight into patterns of adolescent sexual behavior risk.

Site specific growth of metal catalyzed silica nanowires for biological and chemical sensing

In this research the integration of nanostructures and micro-scale devices was investigated using silica nanowires to develop a simple yet robust nanomanufacturing technique for improving the detection parameters of chemical and biological sensors. This has been achieved with the use of a dielectric barrier layer, to restrict nanowire growth to site-specific locations which has removed the need for post growth processing, by making it possible to place nanostructures on pre-pattern substrates. Nanowires were synthesized using the Vapor-Liquid-Solid growth method. Process parameters (temperature and time) and manufacturing aspects (structural integrity and biocompatibility) were investigated. Silica nanowires were observed experimentally to determine how their physical and chemical properties could be tuned for integration into existing sensing structures. Growth kinetic experiments performed using gold and palladium catalysts at 1050°C for 60 minutes in an open-tube furnace yielded dense and consistent silica nanowire growth. This consistent growth led to the development of growth model fitting, through use of the Maximum Likelihood Estimation (MLE) and Bayesian hierarchical modeling. Transmission electron microscopy studies revealed the nanowires to be amorphous and X-ray diffraction confirmed the composition to be SiO2 . Silica nanowires were monitored in epithelial breast cancer media using Impedance spectroscopy, to test biocompatibility, due to potential in vivo use as a diagnostic aid. It was found that palladium catalyzed silica nanowires were toxic to breast cancer cells, however, nanowires were inert at 1μg/mL concentrations. Additionally a method for direct nanowire integration was developed that allowed for silica nanowires to be grown directly into interdigitated sensing structures. This technique eliminates the need for physical nanowire transfer thus preserving nanowire structure and performance integrity and further reduces fabrication cost. Successful nanowire integration was physically verified using Scanning electron microscopy and confirmed electrically using Electrochemical Impedance Spectroscopy of immobilized Prostate Specific Antigens (PSA). The experiments performed above serve as a guideline to addressing the metallurgic challenges in nanoscale integration of materials with varying composition and to understanding the effects of nanomaterials on biological structures that come in contact with the human body.

Influence of gender and life stressors on longitudinal depression treatment outcomes among older primary care patients

Purpose: Depression in older females is a significant and growing problem. Females who experience life stressors across the life span are at higher risk for developing problems with depression than their male counterparts. The primary aim of this study was (a) to examine gender-specific differences in the correlates of depression in older primary care patients based on baseline and longitudinal analyses; and (b) to examine the longitudinal effect of biopsychosocial risk factors on depression treatment outcomes in different models of behavioral healthcare (i.e., integrated care and enhanced referral). Method: This study used a quantitative secondary data analysis with longitudinal data from the Primary Care Research in Substance Abuse and Mental Health for Elderly (PRISM-E) study. A linear mixed model approach to hierarchical linear modeling was used for analysis using baseline assessment, and follow-up from three-month and six-month. Results: For participants diagnosed with major depressive disorder female gender was associated with increased depression severity at six-month compared to males at six-month. Further, the interaction between gender and life stressors found that females who reported loss of family and friends, family issues, money issues, medical illness was related to higher depression severity compared to males whereas lack of activities was related to lower depression severity among females compared to males. Conclusion: These findings suggest that gender moderated the relationship between specific life stressors and depression severity similar to how a protective factor can impact a person's response to a problem and reduce the negative impact of a risk factor on a problem outcome. Therefore, life stressors may be a reliable predictor of depression for both females and males in either behavioral health treatment model. This study concluded that life stressors influence males basic comfort, stability, and survival whereas life stressors influence females' development, personal growth, and happiness; therefore, life stressors may be a useful component to include in gender-based screening and assessment tools for depression. ^

Predictors of Adolescent Sexual Intentions and Behavior: Attitudes, Parenting, and Neighborhood Risk

The current study was a cross-sectional examination of data collected during an HIV risk reduction intervention in south Florida. The purpose of the study was to explore the relationships between neighborhood stress, parenting, attitudes, and adolescent sexual intentions and behavior. The Theory of Planned Behavior was used as a model to guide variable selection and propose an interaction pathway between predictors and outcomes. Potential predictor variables measured for adolescents ages 13-18 (n=196) included communication about sex, parent-family connectedness, parental presence, parent-adolescent activity participation, attitudes about sex and condom use, neighborhood disorder, and exposure to violence. Outcomes were behavioral intentions and sexual behavior for the previous eight months. Neighborhood data was supplemented with ZIP Code level data from regional sources and included median household income, percentage of minority and Hispanic residents, and number of foreclosures. Statistical tests included t-tests, Pearson’s correlations, and hierarchical linear regressions. Results showed that males and older adolescents reported less positive behavioral intentions than females and adolescents younger than 16. Intentions were associated with condom attitudes, sexual attitudes, and parental presence; unprotected sexual behavior was associated with parental presence. The best fit model for intentions included gender, sexual attitudes, condom attitudes, parental presence, and neighborhood disorder. The unsafe sexual behavior model included whether the participant lived with both natural parents in the previous year, and the percent of Hispanic residents in the neighborhood. Study findings indicate that more research on adolescent sexual behavior is warranted, specifically examining the differentials between variables that affect intentions and those that affect behavior. A focus on gender and age differences during intervention development may allow for better targeting and more efficacious interventions. Adding peer and media influences to the framework of attitudes, parenting, and neighborhood may offer more insight into patterns of adolescent sexual behavior risk.

Site Specifc Growth of Metal Catalyzed Silica Nanowires for Biological and Chemical Sensing

In this research the integration of nanostructures and micro-scale devices was investigated using silica nanowires to develop a simple yet robust nanomanufacturing technique for improving the detection parameters of chemical and biological sensors. This has been achieved with the use of a dielectric barrier layer, to restrict nanowire growth to site-specific locations which has removed the need for post growth processing, by making it possible to place nanostructures on pre-pattern substrates. Nanowires were synthesized using the Vapor-Liquid-Solid growth method. Process parameters (temperature and time) and manufacturing aspects (structural integrity and biocompatibility) were investigated. Silica nanowires were observed experimentally to determine how their physical and chemical properties could be tuned for integration into existing sensing structures. Growth kinetic experiments performed using gold and palladium catalysts at 1050 ˚C for 60 minutes in an open-tube furnace yielded dense and consistent silica nanowire growth. This consistent growth led to the development of growth model fitting, through use of the Maximum Likelihood Estimation (MLE) and Bayesian hierarchical modeling. Transmission electron microscopy studies revealed the nanowires to be amorphous and X-ray diffraction confirmed the composition to be SiO2 . Silica nanowires were monitored in epithelial breast cancer media using Impedance spectroscopy, to test biocompatibility, due to potential in vivo use as a diagnostic aid. It was found that palladium catalyzed silica nanowires were toxic to breast cancer cells, however, nanowires were inert at 1µg/mL concentrations. Additionally a method for direct nanowire integration was developed that allowed for silica nanowires to be grown directly into interdigitated sensing structures. This technique eliminates the need for physical nanowire transfer thus preserving nanowire structure and performance integrity and further reduces fabrication cost. Successful nanowire integration was physically verified using Scanning electron microscopy and confirmed electrically using Electrochemical Impedance Spectroscopy of immobilized Prostate Specific Antigens (PSA). The experiments performed above serve as a guideline to addressing the metallurgic challenges in nanoscale integration of materials with varying composition and to understanding the effects of nanomaterials on biological structures that come in contact with the human body.

Principal component and second generation wavelet analysis of Treasury yield curve evolution

Prices of U.S. Treasury securities vary over time and across maturities. When the market in Treasurys is sufficiently complete and frictionless, these prices may be modeled by a function time and maturity. A cross-section of this function for time held fixed is called the yield curve; the aggregate of these sections is the evolution of the yield curve. This dissertation studies aspects of this evolution. ^ There are two complementary approaches to the study of yield curve evolution here. The first is principal components analysis; the second is wavelet analysis. In both approaches both the time and maturity variables are discretized. In principal components analysis the vectors of yield curve shifts are viewed as observations of a multivariate normal distribution. The resulting covariance matrix is diagonalized; the resulting eigenvalues and eigenvectors (the principal components) are used to draw inferences about the yield curve evolution. ^ In wavelet analysis, the vectors of shifts are resolved into hierarchies of localized fundamental shifts (wavelets) that leave specified global properties invariant (average change and duration change). The hierarchies relate to the degree of localization with movements restricted to a single maturity at the base and general movements at the apex. Second generation wavelet techniques allow better adaptation of the model to economic observables. Statistically, the wavelet approach is inherently nonparametric while the wavelets themselves are better adapted to describing a complete market. ^ Principal components analysis provides information on the dimension of the yield curve process. While there is no clear demarkation between operative factors and noise, the top six principal components pick up 99% of total interest rate variation 95% of the time. An economically justified basis of this process is hard to find; for example a simple linear model will not suffice for the first principal component and the shape of this component is nonstationary. ^ Wavelet analysis works more directly with yield curve observations than principal components analysis. In fact the complete process from bond data to multiresolution is presented, including the dedicated Perl programs and the details of the portfolio metrics and specially adapted wavelet construction. The result is more robust statistics which provide balance to the more fragile principal components analysis. ^

Essays in environmental economic valuation and decision making in the presence of an environmental disaster

The first essay developed a respondent model of Bayesian updating for a double-bound dichotomous choice (DB-DC) contingent valuation methodology. I demonstrated by way of data simulations that current DB-DC identifications of true willingness-to-pay (WTP) may often fail given this respondent Bayesian updating context. Further simulations demonstrated that a simple extension of current DB-DC identifications derived explicitly from the Bayesian updating behavioral model can correct for much of the WTP bias. Additional results provided caution to viewing respondents as acting strategically toward the second bid. Finally, an empirical application confirmed the simulation outcomes. The second essay applied a hedonic property value model to a unique water quality (WQ) dataset for a year-round, urban, and coastal housing market in South Florida, and found evidence that various WQ measures affect waterfront housing prices in this setting. However, the results indicated that this relationship is not consistent across any of the six particular WQ variables used, and is furthermore dependent upon the specific descriptive statistic employed to represent the WQ measure in the empirical analysis. These results continue to underscore the need to better understand both the WQ measure and its statistical form homebuyers use in making their purchase decision. The third essay addressed a limitation to existing hurricane evacuation modeling aspects by developing a dynamic model of hurricane evacuation behavior. A household's evacuation decision was framed as an optimal stopping problem where every potential evacuation time period prior to the actual hurricane landfall, the household's optimal choice is to either evacuate, or to wait one more time period for a revised hurricane forecast. A hypothetical two-period model of evacuation and a realistic multi-period model of evacuation that incorporates actual forecast and evacuation cost data for my designated Gulf of Mexico region were developed for the dynamic analysis. Results from the multi-period model were calibrated with existing evacuation timing data from a number of hurricanes. Given the calibrated dynamic framework, a number of policy questions that plausibly affect the timing of household evacuations were analyzed, and a deeper understanding of existing empirical outcomes in regard to the timing of the evacuation decision was achieved.

Explaining the effects of class size on faculty and students

This study explored the effects of class size on faculty and students. Specifically, it examined the relationship of class size and students' participation in class, faculty interactive styles, and academic environment and how these behaviors affected student achievement (percentage of students passing). The sample was composed of 629 students in 30 sections of Algebra I at a large, urban community college. A survey was administered to the students to solicit their perceptions on their participation in class, their faculty interaction style, and the academic environment in their classes. Selected classes were observed to triangulate the findings. The relationship of class size to student participation, faculty interactive styles, and academic environment was determined by using hierarchical linear modeling (HLM). A significant difference was found on the participation of students related to class size. Students in smaller classes participated more and were more engaged than students in larger classes. Regression analysis using the same variables in small and large classes showed that faculty interactive styles significantly predicted student achievement. Stepwise regression analyses of student and faculty background variables showed that (a) students' estimate of GPA was significantly related to their achievement (r = .63); (b) older students reported more participation than did younger ones, (c) students in classes taught by female, Hispanic faculty earned higher passing grades, and (d) students' participation was greater with adjunct professors. Class observations corroborated these findings. The analysis and observational data provided sufficient evidence to warrant the conclusion that small classes were not always most effective in promoting achievement. It was found that small classes may be an artifact of ineffectual teaching, actual or by reputation. While students in small classes participate and are more engaged than students in larger classes, the class-size effect is essentially due to what happens in instruction to promote learning. The interaction of the faculty with students significantly predicted students' achievement regardless of class size. Since college students select their own classes, students do not register for classes taught by faculty with poor teaching reputation, thereby leading to small classes. Further studies are suggested to determine reasons why classes differ in size.

DIMUSE: An integrated framework for distributed multimedia system with database management and security support

With the recent explosion in the complexity and amount of digital multimedia data, there has been a huge impact on the operations of various organizations in distinct areas, such as government services, education, medical care, business, entertainment, etc. To satisfy the growing demand of multimedia data management systems, an integrated framework called DIMUSE is proposed and deployed for distributed multimedia applications to offer a full scope of multimedia related tools and provide appealing experiences for the users. This research mainly focuses on video database modeling and retrieval by addressing a set of core challenges. First, a comprehensive multimedia database modeling mechanism called Hierarchical Markov Model Mediator (HMMM) is proposed to model high dimensional media data including video objects, low-level visual/audio features, as well as historical access patterns and frequencies. The associated retrieval and ranking algorithms are designed to support not only the general queries, but also the complicated temporal event pattern queries. Second, system training and learning methodologies are incorporated such that user interests are mined efficiently to improve the retrieval performance. Third, video clustering techniques are proposed to continuously increase the searching speed and accuracy by architecting a more efficient multimedia database structure. A distributed video management and retrieval system is designed and implemented to demonstrate the overall performance. The proposed approach is further customized for a mobile-based video retrieval system to solve the perception subjectivity issue by considering individual user's profile. Moreover, to deal with security and privacy issues and concerns in distributed multimedia applications, DIMUSE also incorporates a practical framework called SMARXO, which supports multilevel multimedia security control. SMARXO efficiently combines role-based access control (RBAC), XML and object-relational database management system (ORDBMS) to achieve the target of proficient security control. A distributed multimedia management system named DMMManager (Distributed MultiMedia Manager) is developed with the proposed framework DEMUR; to support multimedia capturing, analysis, retrieval, authoring and presentation in one single framework.

Resources, strategies, location determinants, and host country location choice by emerging market firms

The extant literature had studied the determinants of the firms’ location decisions with help of host country characteristics and distances between home and host countries. Firm resources and its internationalization strategies had found limited attention in this literature. To address this gap, the research question in this dissertation was whether and how firms’ resources and internationalization strategies impacted the international location decisions of emerging market firms. ^ To explore the research question, data were hand-collected from Indian software firms on their location decisions taken between April 2000 and March 2009. To analyze the multi-level longitudinal dataset, hierarchical linear modeling was used. The results showed that the internationalization strategies, namely market-seeking or labor-seeking had direct impact on firms’ location decision. This direct relationship was moderated by firm resource which, in case of Indian software firms, was the appraisal at CMMI level-5. Indian software firms located in developed countries with a market-seeking strategy and in emerging markets with a labor-seeking strategy. However, software firms with resource such as CMMI level-5 appraisal, when in a labor-seeking mode, were more likely to locate in a developed country over emerging market than firms without the appraisal. Software firms with CMMI level-5 appraisal, when in market-seeking mode, were more likely to locate in a developed country over an emerging market than firms without the appraisal. ^ It was concluded that the internationalization strategies and resources of companies predicted their location choices, over and above the variables studied in the theoretical field of location determinants.^

A Q factor analysis of college undergraduate students' study behaviors

The purpose of this study was to better understand the study behaviors and habits of university undergraduate students. It was designed to determine whether undergraduate students could be grouped based on their self-reported study behaviors and if any grouping system could be determined, whether group membership was related to students’ academic achievement. A total of 152 undergraduate students voluntarily participated in the current study by completing the Study Behavior Inventory instrument. All participants were enrolled in fall semester of 2010 at Florida International University. The Q factor analysis technique using principal components extraction and a varimax rotation was used in order to examine the participants in relation to each other and to detect a pattern of intercorrelations among participants based on their self-reported study behaviors. The Q factor analysis yielded a two factor structure representing two distinct student types among participants regarding their study behaviors. The first student type (i.e., Factor 1) describes proactive learners who organize both their study materials and study time well. Type 1 students are labeled “Proactive Learners with Well-Organized Study Behaviors”. The second type (i.e., Factor 2) represents students who are poorly organized as well as being very likely to procrastinate. Type 2 students are labeled Disorganized Procrastinators. Hierarchical linear regression was employed to examine the relationship between student type and academic achievement as measured by current grade point averages (GPAs). The results showed significant differences in GPAs between Type 1 and Type 2 students at the .05 significance level. Furthermore, student type was found to be a significant predictor of academic achievement beyond and above students’ attribute variables including sex, age, major, and enrollment status. The study has several implications for educational researchers, practitioners, and policy makers in terms of improving college students' learning behaviors and outcomes.

Investigating the outcomes of two chronic disease self-management programs and understanding the correlates of completion for each program

Chronic disease affects 80% of adults over the age of 65 and is expected to increase in prevalence. To address the burden of chronic disease, self-management programs have been developed to increase self-efficacy and improve quality of life by reducing or halting disease symptoms. Two programs that have been developed to address chronic disease are the Chronic Disease Self-Management Program (CDSMP) and Tomando Control de su Salud (TCDS). CDSMP and TCDS both focus on improving participant self-efficacy, but use different curricula, as TCDS is culturally tailored for the Hispanic population. Few studies have evaluated the effectiveness of CDSMP and TCDS when translated to community settings. In addition, little is known about the correlation between demographic, baseline health status, and psychosocial factors and completion of either CDSMP or TCDS. This study used secondary data collected by agencies of the Healthy Aging Regional Collaborative from 10/01/2008–12/31/2010. The aims of this study were to examine six week differences in self-efficacy, time spent performing physical activity, and social/role activity limitations, and to identify correlates of program completion using baseline demographic and psychosocial factors. To examine if differences existed a general linear model was used. Additionally, logistic regression was used to examine correlates of program completion. Study findings show that all measures showed improvement at week six. For CDSMP, self-efficacy to manage disease (p = .001), self-efficacy to manage emotions (p = .026), social/role activities limitations (p = .001), and time spent walking (p = .008) were statistically significant. For TCDS, self-efficacy to manage disease (p = .006), social/role activities limitations (p = .001), and time spent walking (p = .016) and performing other aerobic activity (p = .005) were significant. For CDSMP, no correlates predicting program completion were found to be significant. For TCDS, participants who were male (OR=2.3, 95%CI: 1.15–4.66), from Broward County (OR=2.3, 95%CI: 1.27–4.25), or living alone (OR=2.0, 95%CI: 1.29-–3.08) were more likely to complete the program. CDSMP and TCDS, when implemented through a collaborative effort, can result in improvements for participants. Effective chronic disease management can improve health, quality of life, and reduce health care expenditures among older adults.

Reliability analysis of encapsulation methods for lead -based paint through an accelerated life testing

The adverse health effects of long-term exposure to lead are well established, with major uptake into the human body occurring mainly through oral ingestion by young children. Lead-based paint was frequently used in homes built before 1978, particularly in inner-city areas. Minority populations experience the effects of lead poisoning disproportionately. ^ Lead-based paint abatement is costly. In the United States, residents of about 400,000 homes, occupied by 900,000 young children, lack the means to correct lead-based paint hazards. The magnitude of this problem demands research on affordable methods of hazard control. One method is encapsulation, defined as any covering or coating that acts as a permanent barrier between the lead-based paint surface and the environment. ^ Two encapsulants were tested for reliability and effective life span through an accelerated lifetime experiment that applied stresses exceeding those encountered under normal use conditions. The resulting time-to-failure data were used to extrapolate the failure time under conditions of normal use. Statistical analysis and models of the test data allow forecasting of long-term reliability relative to the 20-year encapsulation requirement. Typical housing material specimens simulating walls and doors coated with lead-based paint were overstressed before encapsulation. A second, un-aged set was also tested. Specimens were monitored after the stress test with a surface chemical testing pad to identify the presence of lead breaking through the encapsulant. ^ Graphical analysis proposed by Shapiro and Meeker and the general log-linear model developed by Cox were used to obtain results. Findings for the 80% reliability time to failure varied, with close to 21 years of life under normal use conditions for encapsulant A. The application of product A on the aged gypsum and aged wood substrates yielded slightly lower times. Encapsulant B had an 80% reliable life of 19.78 years. ^ This study reveals that encapsulation technologies can offer safe and effective control of lead-based paint hazards and may be less expensive than other options. The U.S. Department of Health and Human Services and the CDC are committed to eliminating childhood lead poisoning by 2010. This ambitious target is feasible, provided there is an efficient application of innovative technology, a goal to which this study aims to contribute. ^