Modeling the role of investigator bias, interrogation techniques, and suspect decision -making on the likelihood of confession

The purpose of the current study was to attempt to model various cognitive and social processes that are believed to lead to false confessions. More specifically, this study manipulated the variables of experimenter expectancy, guilt-innocence of the suspect, and interrogation techniques using the Russano et al. (2005) paradigm. The primary measure of interest was the likelihood of the participant signing the confession statement. By manipulating experimenter expectancy, the current study sought to further explore the social interactions that may occur in the interrogation room. In addition, in past experiments, the interrogator has typically been restricted to the use of one or two interrogation techniques. In the present study, interrogators were permitted to select from 15 different interrogation techniques when attempting to solicit a confession from participants. ^ Consistent with Rusanno et al. (2005), guilty participants (94%) were more likely to confess to the act of cheating than innocent participants (31%). The variable of experimenter expectancy did not effect confessions rates, length of interrogation, or the type of interrogation techniques used. Path analysis revealed feelings of pressure and the weighing of consequences on the part of the participant were associated with the signing of the confession statement. The findings suggest the guilt/innocence of the participant, the participant's perceptions of the interrogation situation, and length of interrogation play a pivotal role in the signing of the confession statement. Further examination of these variables may provide researchers with a better understanding of the relationship between interrogations and confessions. ^

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as ƒ-test is performed during each node's split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

A Case Study of the Ellison Model's Use of Mentoring as an Approach Toward Inclusive Community Building

The Ellison Executive Mentoring Inclusive Community Building (ICB) Model is a paradigm for initiating and implementing projects utilizing executives and professionals from a variety of fields and industries, university students, and pre-college students. The model emphasizes adherence to ethical values and promotes inclusiveness in community development. It is a hierarchical model in which actors in each succeeding level of operation serve as mentors to the next. Through a three-step process--content, process, and product--participants must be trained with this mentoring and apprenticeship paradigm in conflict resolution, and they receive sensitivitiy and diversity training, through an interactive and dramatic exposition. The content phase introduces participants to the model's philosophy, ethics, values and methods of operation. The process used to teach and reinforce its precepts is the mentoring and apprenticeship activities and projects in which the participants engage and whose end product demontrates their knowledge and understanding of the model's concepts. This study sought to ascertain from the participants' perspectives whether the model's mentoring approach is an effective means of fostering inclusiveness, based upon their own experiences in using it. The research utilized a qualitative approach and included data from field observations, individual and group interviews, and written accounts of participants' attitudes. Participants complete ICB projects utilizing the Ellison Model as a method of development and implementation. They generally perceive that the model is a viable tool for dealing with diversity issues whether at work, at school, or at home. The projects are also instructional in that whether participants are mentored or seve as apprentices, they gain useful skills and knowledge about their careers. Since the model is relatively new, there is ample room for research in a variety of areas including organizational studies to dertmine its effectiveness in combating problems related to various kinds of discrimination.

Effect of Performance Feedback on Perceived Knowledge and Likelihood to Pursue Continuing Education

The purpose of this study was to assess the effect of performance feedback on Athletic Trainers’ (ATs) perceived knowledge (PK) and likelihood to pursue continuing education (CE). The investigation was grounded in the theories of “the definition of the situation” (Thomas & Thomas, 1928) and the “illusion of knowing,” (Glenberg, Wilkinson, & Epstein, 1982) suggesting that PK drives behavior. This investigation measured the degree to which knowledge gap predicted CE seeking behavior by providing performance feedback designed to change PK. A pre-test post-test control-group design was used to measure PK and likelihood to pursue CE before and after assessing actual knowledge. ATs (n=103) were randomly sampled and assigned to two groups, with and without performance feedback. Two independent samples t-tests were used to compare groups on the difference scores of the dependent variables. Likelihood to pursue CE was predicted by three variables using multiple linear regression: perceived knowledge, pre-test likelihood to pursue CE, and knowledge gap. There was a 68.4% significant difference (t101= 2.72, p=0.01, ES=0.45) between groups in the change scores for likelihood to pursue CE because of the performance feedback (Experimental group=13.7% increase; Control group= 4.3% increase). The strongest relationship among the dependent variables was between pre-test and post-test measures of likelihood to pursue CE (F2,102=56.80, p<0.01, r=0.73, R2=0.53). The pre- and post-test predictive relationship was enhanced when group was included in the model. In this model [YCEpost=0.76XCEpre-0.34 Xgroup+2.24+E], group accounted for a significant amount of unique variance in predicting CE while the pre-test likelihood to pursue CE variable was held constant (F3,102=40.28, p<0.01,: r=0.74, R2=0.55). Pre-test knowledge gap, regardless of group allocation, was a linear predictor of the likelihood to pursue CE (F1,102=10.90, p=.01, r=.31, R2=.10). In this investigation, performance feedback significantly increased participants’ likelihood to pursue CE. Pre-test knowledge gap was a significant predictor of likelihood to pursue CE, regardless if performance feedback was provided. ATs may have self-assessed and engaged in internal feedback as a result of their test-taking experience. These findings indicate that feedback, both internal and external, may be necessary to trigger CE seeking behavior.

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.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as f-test is performed during each node’s split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.