The Search for a New OPAC: Selecting an Open Source Discovery Layer

Abstract

In early 2011, an Indiana University Libraries task force was charged with selecting an open source discovery layer to serve as the public interface for IU's online catalog, IUCAT. This process included creating a rubric of core functionality and rating two discovery layers based on criteria in four main categories: general features and functionality; authentication and account management; export and share; and search functionality and results display. The article includes information about our rubric and the two discovery layers reviewed, Blacklight and VuFind, as well as a discussion of the priorities of the task force. The article concludes with future steps and anticipated highlights for IUCAT.     

Integrative Families and Systems Treatment: a middle path toward integrating common and specific factors in evidence-based family therapy

A moderate common factors approach is proposed as a synthesis or middle path to integrate common and specific factors in evidence-based approaches to high-risk youth and families. The debate in family therapy between common and specific factors camps is reviewed and followed by suggestions from the literature for synthesis and creative flexibility in manual development. A preliminary integrative model termed Integrative Family and Systems Treatment is offered as one option in developing and testing a moderate common factors approach. Such a model might then be studied in eventual clinical trials with other well-developed evidence-based protocols to further address the common versus specific factor debate. Implications for further research and practice are offered.     

Canonical analysis and optimal criterion validity scaling

This article suggests methods for forming linear composites which will have either optimum (maximum) criterion validity or an optimum ratio of valid to reliable variance. These methods may be of interest to researchers whose data contain substantial systematic but invalid components. These techniques are formally related to canonical correlation analysis, and the output from standard canonical correlation computer programs can be directly used in forming the composites.     

Consequences of Dating for Post‐divorce Maternal Well‐being

Repartnering has been linked to health benefits for mothers, yet few studies have examined relationship quality in this context. According to the divorce–stress–adaptation perspective, relationship quality may influence the relationship between maternal well‐being and dating after divorce. The current study examines the consequences of dating, relationship quality, and dating transitions (breaking up and dating new partners) on maternal well‐being (negative affect and life satisfaction). Using monthly surveys completed by mothers over a 2‐year period after filing for divorce, we examined changes in intercepts and slopes of dating status and transitions for maternal well‐being while also testing the effects of relationship quality. Mothers entering high‐quality relationships were likely to report boosts in well‐being at relationship initiation compared to single mothers and mothers entering low‐quality relationships. Mothers entering lower‐quality relationships were likely to report lower levels of well‐being than single mothers. Dating transitions were associated with increases in well‐being. Implications for maternal adjustment are discussed.     

Savings Account Ownership During the Great Recession

In the aftermath of the Great Recession, a substantial number of families were left financially at risk, especially those with lower income, less wealth, fewer years of education, and unstable employment. This study examined how families responded to the financial stresses of the Great Recession and found that families who were working age, had higher levels of education, and had become non-homeowners were more likely to maintain or open a basic savings account; whereas families who had lost a substantial amount of annual family income or wealth or were Black or Hispanic were more likely to have been without a savings account over the period. Insights from this study will help inform policymakers and others interested in encouraging family financial security and resiliency through basic savings accounts.     

A descriptive analysis of family discussions about everyday problems and decisions

The Analysis of Verbal Behavior - Skinner’s Verbal Behavior (1957) and “An operant analysis of problem solving” (1966) were used to develop a coding system to analyze the...     

A research design for generalizing from multiple case studies

Many evaluations would be more useful to decision makers if their results were both explanatory and generalizable. In view of the limitations of traditional methods in meeting both these goals at the same time, the authors describe a research design explicitly intended for generalizing about the range of applicability of “explanatory patterns” (within a designated “target” population of cases). The paper presents an overview of the main features of a “multiple case study design,” shorthand for a multiple site, structured case study design. It then discusses the nature of explanatory patterns, how case study investigators pursue and recognize valid patterns, and how an analyst can apply the same logic to cross-site analysis in order to make valid inferences about the limiting conditions under which particular explanatory patterns apply to particular populations.     

Fixed Effects and Bias Due to the Incidental Parameters Problem in the Tobit Model

The maximum likelihood estimator (MLE) in nonlinear panel data models with fixed effects is widely understood (with a few exceptions) to be biased and inconsistent when T, the length of the panel, is small and fixed. However, there is surprisingly little theoretical or empirical evidence on the behavior of the estimator on which to base this conclusion. The received studies have focused almost exclusively on coefficient estimation in two binary choice models, the probit and logit models. In this note, we use Monte Carlo methods to examine the behavior of the MLE of the fixed effects tobit model. We find that the estimator's behavior is quite unlike that of the estimators of the binary choice models. Among our findings are that the location coefficients in the tobit model, unlike those in the probit and logit models, are unaffected by the “incidental parameters problem.” But, a surprising result related to the disturbance variance emerges instead - the finite sample bias appears here rather than in the slopes. This has implications for estimation of marginal effects and asymptotic standard errors, which are also examined in this paper. The effects are also examined for the probit and truncated regression models, extending the range of received results in the first of these beyond the widely cited biases in the coefficient estimators.     

SHORT-RUN PRICING STRATEGIES TO INCREASE CORPORATE AVERAGE FUEL ECONOMY

Since 1974 the average fuel economy of new cars has doubled from fourteen to twenty-eight miles per gallon. Shifts in sales to more fuel efficient models within a product line accounted for little of the improvement. This paper explores the use of pricing strategies to shift sales to achieve a legislated fuel economy target. A multinomial logit model is used to compute surcharges and rebates that leave consumer satisfaction unchanged yet increase the sales-weighted average fuel economy. The results suggest pricing strategies are efficient for small improvements in fuel economy, but are expensive for large improvements.