Carla J. Thompson

IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics

Background We live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data analytics procedures. Traditionally, data analytics required a substantial background in statistics and computer science. In 2015, International Business Machines Corporation (IBM) released the IBM Watson Analytics (IBMWA) software that delivered advanced statistical procedures based on the Statistical Package for the Social Sciences (SPSS). The latest entry of Watson Analytics into the field of analytical software products provides users with enhanced functions that are not available in many existing programs. For example, Watson Analytics automatically analyzes datasets, examines data quality, and determines the optimal statistical approach. Users can request exploratory, predictive, and visual analytics. Using natural language processing (NLP), users are able to submit additional questions for analyses in a quick response format. This analytical package is available free to academic institutions (faculty and students) that plan to use the tools for noncommercial purposes. Objective To report the features of IBMWA and discuss how this software subjectively and objectively compares to other data mining programs. Methods The salient features of the IBMWA program were examined and compared with other common analytical platforms, using validated health datasets. Results Using a validated dataset, IBMWA delivered similar predictions compared with several commercial and open source data mining software applications. The visual analytics generated by IBMWA were similar to results from programs such as Microsoft Excel and Tableau Software. In addition, assistance with data preprocessing and data exploration was an inherent component of the IBMWA application. Sensitivity and specificity were not included in the IBMWA predictive analytics results, nor were odds ratios, confidence intervals, or a confusion matrix. Conclusions IBMWA is a new alternative for data analytics software that automates descriptive, predictive, and visual analytics. This program is very user-friendly but requires data preprocessing, statistical conceptual understanding, and domain expertise.

Tempest in a Therapeutic Community: Implementation and Evaluation Issues for Faith-Based Programming

The therapeutic community (TC) is an increasingly utilized intervention model in corrections settings. Rarely do these TCs include faith-based curriculum other than that included in Alcoholics Anonymous or Narcotics Anonymous programs as does the faith-based TC that serves as the basis for this article. Borrowing from the successful TC model, the program discussed here incorporates additional faith-based modules in a jail or work release program setting. Obstacles associated with program design and implementation, funding, data collection, efficacy, and outcomes are described with recommendations for ongoing and future stakeholders, policy-makers, and service providers.

Quantitative approaches in life cycle assessment—part 1—descriptive statistics and factor analysis

PurposeThis study examines the utility of descriptive statistics and factor analysis in the interpretation of an LCA data set. It is believed that the use of descriptive statistics and factor analysis provides a comprehensive summary of the data, and insight on internal validity, which improves interpretation of results. The current study also provides a pretext for subsequent analyses with inferential statistics.MethodsNine building envelope combinations were analyzed according to five service life models (N = 45, 45 data points). Three environmental indicators were used to characterize the data: global warming potential (GWP), atmospheric eco-toxicity (ECO), and atmospheric acidification (ACID) from the Tool for the Reduction and Assessment of Chemical and Other Environmental Impacts (TRACI) assessment method. Descriptive statistics measured minimum, maximum, mean, standard deviation, skewness, and kurtosis. For the factor analysis, data were disaggregated according to nine variables, including cumulative life cycle impact, major replacement, major replacement (frequency), minor replacement, major repairs, minor repairs, inspections 1 and 2, and total transportation (N = 45, 405 data points). All statistical operations were performed using SPSS software.Results and discussionDescriptive statistics show a relatively normal data distribution for GWP. Data distributions for ECO and ACID deviated slightly from normal, which was confirmed through Shapiro-Wilk and Kolmogorov-Smirnov tests. Factor analysis resulted in three factors for GWP and ECO, and two factors for ACID. In each analysis, cumulative life cycle impact, major replacement, and major replacement (frequency) aligned as variables in a common factor. This alignment corroborates previous findings. Other variables in the factor analysis were less clearly or inconsistently aligned.ConclusionsThe descriptive statistics provide important information about the distribution of data in the study. The data are determined to be suitable for further analysis using inferential univariate and multivariate statistical tests, a topic that is examined in part 2 of this study as reported by Grant et al. (Int J Life Cycle Assess, 2015). The factor analysis used in the study corroborates the conclusions of previous studies and provides a basis for future statistical analyses.

Quantitative approaches in life cycle assessment—part 2—multivariate correlation and regression analysis

PurposeThis study examines the use of inferential statistics, specifically multivariate correlation and regression, as a means of interpreting LCA data. It is believed that these methods provide additional context in understanding data and results, and may serve as a way to present the uncertain results that are inherent to LCA.MethodsNine building envelope combinations were analyzed according to five service life models (N = 45). Three environmental indicators were used: global warming potential, atmospheric ecotoxicity, and atmospheric acidification from the Tool for the Reduction and Assessment of Chemical and Other Environmental Impacts assessment method. Multivariate correlation was performed using nine variables, including cumulative life cycle impact, major replacement, major replacement (frequency), minor replacement, major repairs, minor repairs, inspections 1 and 2, and total transportation (N = 45, 405 data points). The same data set was used for the regression analysis, although the variables were limited to major replacement, minor replacement, major repair, and minor repair (N = 45, 225 data points). SPSS software was used for all statistical calculations.Results and discussionMultivariate correlation analysis showed strong, statistically significant correlations between cumulative life cycle impact and major replacement across all environmental indicators. Similarly, the regression analysis showed strong R2 values between cumulative life cycle impact and major replacement, such that the influence of all other variables was considerably diminished.ConclusionsThe use of inferential statistics provides useful information with respect to the strength and statistical significance of correlations between variables as in multivariate correlation, and allows for predictive capacity of impact, as demonstrated through regression analysis. Further studies should be conducted to confirm the added value of these analytical tools.

Infusing Research Integrity Principles and Practices into a Regional Master's Degree–Intensive University: A Case Study

The need for institutions of higher education to advance research and scholarly integrity across graduate degree programs involves a strong commitment from each institution to develop a comprehensive approach for promoting responsible conduct of research (RCR). In response to this need, one masters-intensive regional university implemented a three-tier model project (focusing, developing, and evaluating/sustaining) for infusing research integrity principles and practices while promoting RCR. Components of the model and implementation strategies are presented as a case study analysis. Implications of the model for promoting the RCR are projected relative to graduate programs focused on applied research.

High-Impact Practices in Social Work Education: A Short-Term Study-Abroad Service-Learning Trip to Guatemala.

ABSTRACT Twenty-first-century universities must contend with demands from two constituencies: students wishing to compete in a progressively globalized employment marketplace and employers seeking savvy, prepared, and competent graduates who can “hit the ground running.” One response that holds great promise and has been supported by research is the implementation of high-impact educational practices (HIPs). This article begins with a description of HIPs as elucidated by Kuh (2008) and describes an initiative at one university to integrate them into the curriculum. The article finishes with a case study of one particular high-impact social work class, a short-term study abroad/service-learning course in Guatemala, and presents both quantitative and qualitative data that suggests that the implementation of high-impact practices positively influences the kind of transformative learning social work programs strive to deliver.

Homeless Aging Veterans in Transition: A Life-Span Perspective

The need for counseling and career/educational services for homeless veterans has captured political and economic venues for more than 25 years. Veterans are three times more likely to become homeless than the general population if veterans live in poverty or are minority veterans. This mixed methods study emphasized a life-span perspective approach for exploring factors influencing normative aging and life-quality of 39 homeless veterans in Alabama and Florida. Seven descriptive quantitative and qualitative research questions framed the investigation. Study participants completed a quantitative survey reflecting their preferences and needs with a subset of the sample (N = 12) also participating in individual qualitative interview sessions. Thirty-two service providers and stakeholders completed quantitative surveys. Empirical and qualitative data with appropriate triangulation procedures provided interpretive information relative to a life-span development perspective. Study findings provide evidence of the need for future research efforts to address strategies that focus on the health and economic challenges of veterans before they are threatened with the possibility of homelessness. Implications of the study findings provide important information associated with the premise that human development occurs throughout life with specific characteristics influencing the individuals passage. Implications for aging/homelessness research are grounded in late-life transitioning and human development intervention considerations.