A validation study of the construct "jealousy" in eleven- to fourteen-month-old infants: Evidence for its divergence from "fear" and "anger"

There has been increasing interest in expanding the scope of the study of the "basic" emotions and their development in infancy to include more of the so-called "complex" emotions like jealousy. This dissertation investigated evidence for the divergence of jealousy in infants from both fear and anger, two of the basic emotions said to be precursors and contributors to the emergence of jealousy in the later part of the first year of life. Participants judged how well eight emotion-denoting terms (including jealousy, anger and fear) described infants' emotionality in fear-, anger-, and jealousy-provoking situations in which the social context of the emotion episodes was either included or excluded. Differences within and between participants' judgments of the eight terms in the two context conditions were examined across the three emotion-provoking conditions. Results suggested that infants' emotional behavior denoting jealousy was not judged differently from behavior denoting anger or fear in the absence of contextual information and, that when contextual information was provided, attributions of infant jealousy, anger, and fear were made "correctly" for their respective target emotion conditions. ^

Validation study of the Colorado Learning Attitudes about Science Survey at a Hispanic-serving institution

The Colorado Learning Attitudes about Science Survey (CLASS) has been widely acknowledged as a useful measure of student cognitive attitudes about science and learning. The initial University of Colorado validation study included only 20% non-Caucasian student populations. In this Brief Report we extend their validation to include a predominately under-represented minority population. We validated the CLASS instrument at Florida International University, a Hispanic-serving institution, by interviewing students in introductory physics classes using a semistructured protocol, examining students’ responses on the CLASS item statements, and comparing them to the items’ intended meaning. We find that in our predominately Hispanic population, 94% of the students’ interview responses indicate that the students interpret the CLASS items correctly, and thus the CLASS is a valid instrument. We also identify one potentially problematic item in the instrument which one third of the students interviewed consistently misinterpreted.

The *validation of an instrument for the assessment of study behaviors of high school students

The purpose of this study is to validate the Study Behavior Inventory-High School, an instrument designed to measure study behaviors of high school students and to determine the stability of its scores across populations and across grades. Numerous studies have shown the relationship between the level of students' use of appropriate study behaviors and their levels of academic achievement and this is particularly relevant at a time of increasing demands for school and teacher accountability for their students' academic achievement. ^ The instrument was administered to 3,336 students in grades 9 through 12 in four high schools in diverse parts of the United States. Factor analysis yielded a four factor structure for the instrument and evidence for its construct validity was obtained using convergent and discriminant methods. The four factors include academic self-perception and feelings of low self-efficacy, academic preparation behaviors, time management, and the social nature of taking tests and studying. Internal consistency reliability for the scores on each of the four factors was calculated and found to range between .68 and .87. Finally, confirmatory factor analysis indicated acceptable levels of fit between the factor structure obtained in this study and an earlier one obtained during a pilot study using 800 participants. ^ The SBI-HS appears to be a valid and reliable instrument for the measure of study behaviors in high school students. ^

Development and validation of an instrument to predict online student success using faculty perceptions

Distance learning is growing and transforming educational institutions. The increasing use of distance learning by higher education institutions and particularly community colleges coupled with the higher level of student attrition in online courses than in traditional classrooms suggests that increased attention should be paid to factors that affect online student course completion. The purpose of the study was to develop and validate an instrument to predict community college online student course completion based on faculty perceptions, yielding a prediction model of online course completion rates. Social Presence and Media Richness theories were used to develop a theoretically-driven measure of online course completion. This research study involved surveying 311 community college faculty who taught at least one online course in the past 2 years. Email addresses of participating faculty were provided by two south Florida community colleges. Each participant was contacted through email, and a link to an Internet survey was given. The survey response rate was 63% (192 out of 303 available questionnaires). Data were analyzed through factor analysis, alpha reliability, and multiple regression. The exploratory factor analysis using principal component analysis with varimax rotation yielded a four-factor solution that accounted for 48.8% of the variance. Consistent with Social Presence theory, the factors with their percent of variance in parentheses were: immediacy (21.2%), technological immediacy (11.0%), online communication and interactivity (10.3%), and intimacy (6.3%). Internal consistency of the four factors was calculated using Cronbach's alpha (1951) with reliability coefficients ranging between .680 and .828. Multiple regression analysis yielded a model that significantly predicted 11% of the variance of the dependent variable, the percentage of student who completed the online course. As indicated in the literature (Johnson & Keil, 2002; Newberry, 2002), Media Richness theory appears to be closely related to Social Presence theory. However, elements from this theory did not emerge in the factor analysis.

Mitigating inconsistencies by coupling data cleaning, filtering, and contextual data validation in wireless sensor networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Social determinants of fruit and vegetable intake and validation of Pro-Children Eating Habits Questionnaire for 3rd and 5th grade children

BACKGROUND: The Pro Children Eating Habits Questionnaire has been evaluated as a valid and reliable tool in Europe to measure determinants of fruit and vegetable intake for children; however, it has not been validation for United States populations. The purpose of this study was to (1) assess the reliability and discrimination validity of fruit and vegetable correlates for the Pro Children Eating Habits Questionnaire; (2) investigate the predictive validity of determinants of fruit and vegetable consumption for multi-ethnic elementary school children; and, (3) to assess the association of social determinants with fruit and vegetable consumption. METHODS: One hundred and thirty elementary school students from the 3rd and 5th grades completed this cross-sectional study. RESULTS: Fruit and vegetable determinants, had satisfactory internal consistencies. No differences were found between the test and the retest for the individual questions with the exception of the question for mean perceived vegetable intake. The discriminatory validity indicated the questionnaire could show differences across grade and gender levels for barriers of fruit and vegetables but not for other factors. Grade together with gender explained barriers to eating fruit and vegetables. Greater availability of fruit in the home and school was associated with higher frequency of consumption. CONCLUSIONS: The results of this study indicate the Pro-Children Eating Habits Questionnaire may be a reliable and valid tool for assessing fruit and vegetable consumption of children in the United States.

Mitigating Inconsistencies by Coupling Data Cleaning, Filtering, and Contextual Data Validation in Wireless Sensor Networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Validation of an Analytical Method for the Identification of SmartWater CSI Forensic Marking Technology

SmartWater is a chemical taggant used as a crime deterrent. The chemical taggant is a colorless liquid that fluoresces yellow under ultra-violet (UV) light and contains distinctive, identifiable and traceable elemental composition. For instance, upon a break and entry scenario, the burglar is sprayed with a solution that has an elemental signature custom-made to a specific location. The residues of this taggant persist on skin and other objects and can be easily recovered for further analysis. The product has been effectively used in Europe as a crime deterrent and has been recently introduced in South Florida. In 2014, Fourt Lauderdale Police Department reported the use of SmartWater products with a reduction in burglaries of 14% [1]. The International Forensic Research Institute (IFRI) at FIU validated the scientific foundation of the methods of recovery and analysis of these chemical tagging systems using LA-ICP-MS. Analytical figures of merit of the method such as precision, accuracy, limits of detection, linearity and selectivity are reported in this study. Moreover, blind samples were analyzed by LA-ICP-MS to compare the chemical signatures to the company’s database and evaluate error rates and the accuracy of the method. This study demonstrated that LA-ICP-MS could be used to effectively detect these traceable taggants to assist law enforcement agencies in the United States with cases involving transfer of these forensic coding systems.

The study of thermal stresses in a single long elastic fiber embedded in an infinite matrix

This work considered the micro-mechanical behavior of a long fiber embedded in an infinite matrix. Using the theory of elasticity, the idea of boundary layer and some simplifying assumptions, an approximate analytical solution was obtained for the normal and shear stresses along the fiber. The analytical solution to the problem was found for the case when the length of the embedded fiber is much greater than its radius, and the Young's modulus of the matrix was much less than that of the fiber. The analytical solution was then compared with a numerical solution based on Finite Element Analysis (FEA) using ANSYS. The numerical results showed the same qualitative behavior of the analytical solution, serving as a validation tool against lack of experimental results. In general this work provides a simple method to determine the thermal stresses along the fiber embedded in a matrix, which is the foundation for a better understanding of the interaction between the fiber and matrix in the case of the classical problem of thermal-stresses.