The effects of faking on non-cognitive predictors of academic performance in University students

This study examined the extent to which students could fake responses on personality and approaches to studying questionnaires, and the effects of such responding on the validity of non-cognitive measures for predicting academic performance (AP). University students produced a profile of an ‘ideal’ student using the Big-Five personality taxonomy, which yielded a stereotype with low scores for Neuroticism, and high scores for the other four traits. A sub-set of participants were allocated to a condition in which they were instructed to fake their responses as University applicants, portraying themselves as positively as possible. Scores for these participants revealed higher scores than those in a control condition on measures of deep and strategic approaches to studying, but lower scores on the surface approach variable. Conscientiousness was a significant predictor of AP in both groups, but the predictive effect of approaches to studying variables and Openness to Experience identified in the control group was lower in the group who faked their responses. Non-cognitive psychometric measures can be valid predictors of AP, but scores on these measures can be affected by instructional set. Further implications for psychometric measurement in educational settings are discussed.

Some aspects of drill performance

High speed twist drills are probably the most common of all metal cutting tools and also the least efficient. In this study, detailed research was undertaken into aspects of drill performance and ways in which drilling could be improved in short hole depths of up to two diameters. The work included an evaluation of twist drill geometry and grinding parameters. It was established that errors in point grinding lead to increased hole oversize and reduced drill life. A fundamental analysis was made to establish predictive equations for the drill torque and thrust using modified orthogonal cutting equations and empirical data. A good correlation was obtained between actual and predicted results. Two new techniques for extending twist drill life by the use of coolant feeding holes and also the application of titanium nitride coatings were evaluated. Both methods were found to have potential for improving drill performance. A completely new design of carbide tipped drill was designed and developed. The new design was tested and it compared favourably with two commercially available carbide tipped drills. In further work an entirely different type of drill point geometry was developed for the drill screw. A new design was produced which enabled the drilling time to be minimised for the low thrust forces that were likely to be used with hand held power tools.

An advanced real-time predictive maintenance framework for large scale machine systems

This thesis introduces and develops a novel real-time predictive maintenance system to estimate the machine system parameters using the motion current signature. Recently, motion current signature analysis has been addressed as an alternative to the use of sensors for monitoring internal faults of a motor. A maintenance system based upon the analysis of motion current signature avoids the need for the implementation and maintenance of expensive motion sensing technology. By developing nonlinear dynamical analysis for motion current signature, the research described in this thesis implements a novel real-time predictive maintenance system for current and future manufacturing machine systems. A crucial concept underpinning this project is that the motion current signature contains infor­mation relating to the machine system parameters and that this information can be extracted using nonlinear mapping techniques, such as neural networks. Towards this end, a proof of con­cept procedure is performed, which substantiates this concept. A simulation model, TuneLearn, is developed to simulate the large amount of training data required by the neural network ap­proach. Statistical validation and verification of the model is performed to ascertain confidence in the simulated motion current signature. Validation experiment concludes that, although, the simulation model generates a good macro-dynamical mapping of the motion current signature, it fails to accurately map the micro-dynamical structure due to the lack of knowledge regarding performance of higher order and nonlinear factors, such as backlash and compliance. Failure of the simulation model to determine the micro-dynamical structure suggests the pres­ence of nonlinearity in the motion current signature. This motivated us to perform surrogate data testing for nonlinearity in the motion current signature. Results confirm the presence of nonlinearity in the motion current signature, thereby, motivating the use of nonlinear tech­niques for further analysis. Outcomes of the experiment show that nonlinear noise reduction combined with the linear reverse algorithm offers precise machine system parameter estimation using the motion current signature for the implementation of the real-time predictive maintenance system. Finally, a linear reverse algorithm, BJEST, is developed and applied to the motion current signature to estimate the machine system parameters.

Leadership propensity and sales performance among sales personnel and managers in a speciality retail store setting

We propose that specialty store managers, as well as outside sales personnel attached to the store, have selling responsibilities. In addition, we propose that sales personnel, as well as store managers, should have a propensity for leadership, which reflects an individual's enduring disposition to exhibit leadership within the context of his or her organizational roles. In two studies, we develop a new individual difference measure of propensity to lead and investigate its nomological validity within a specialty retail store environment. As predicted, leadership propensity was predictive of self-rated sales performance and a proclivity to identify prospects through cold calls to close sales, to reveal customer orientation, and to exhibit organizational citizenship behavior. We found that propensity to lead did not differ between salespeople and retail store managers, but we found that the respondent's role moderated the relationship between propensity to lead and supervisor performance ratings. Study limitations and managerial implications of this heretofore unidentified trait of salespeople are discussed.

Satisfaction as a predictor of future performance:a replication

Since the introduction of the Net Promoter concept there has been a vivid and ongoing debate among academics and practitioners about the performance of the Net Promoter Score (NPS) in comparison to other customer metrics, such as customer satisfaction, to predict company growth rates. We report results from a study using data from customers and firms in the Netherlands on the relationship between different satisfaction and loyalty metrics as well as the NPS with sales revenue growth, gross margins and net operating cash flows. We find that all metrics perform equally well in predicting current gross margins and current sales revenue growth and equally poor for predicting future sales growth and gross margins as well as current and future net cash flows. The NPS is neither superior nor inferior to other metrics. Taken together, our study suggests that the predictive capability of customer metrics, such as NPS, for future company growth rates is limited. © 2013 Elsevier B.V.

Evaluating the performance of a EuroDivisia index using artificial intelligence techniques

We compare two methods in order to predict inflation rates in Europe. One method uses a standard back propagation neural network and the other uses an evolutionary approach, where the network weights and the network architecture is evolved. Results indicate that back propagation produces superior results. However, the evolving network still produces reasonable results with the advantage that the experimental set-up is minimal. Also of interest is the fact that the Divisia measure of money is superior as a predictive tool over simple sum.

Evaluating the performance of a EuroDivisia index using artificial intelligence techniques

This paper compares two methods to predict in°ation rates in Europe. One method uses a standard back propagation neural network and the other uses an evolutionary approach, where the network weights and the network architecture are evolved. Results indicate that back propagation produces superior results. However, the evolving network still produces reasonable results with the advantage that the experimental set-up is minimal. Also of interest is the fact that the Divisia measure of money is superior as a predictive tool over simple sum.

Optimising the diagnostic performance of the Geriatric Depression Scale

The aim of this work is to empirically generate a shortened version of the Geriatric Depression Scale (GDS), with the intention of maximising the diagnostic performance in the detection of depression compared with previously GDS validated versions, while optimizing the size of the instrument. A total of 233 individuals (128 from a Day Hospital, 105 randomly selected from the community) aged 60 or over completed the GDS and other measures. The 30 GDS items were entered in the Day Hospital sample as independent variables in a stepwise logistic regression analysis predicting diagnosis of Major Depression. A final solution of 10 items was retained, which correctly classified 97.4% of cases. The diagnostic performance of these 10 GDS items was analysed in the random sample with a receiver operating characteristic (ROC) curve. Sensitivity (100%), specificity (97.2%), positive (81.8%) and negative (100%) predictive power, and the area under the curve (0.994) were comparable with values for GDS-30 and higher compared with GDS-15, GDS-10 and GDS-5. In addition, the new scale proposed had excellent fit when testing its unidimensionality with CFA for categorical outcomes (e.g., CFI=0.99). The 10-item version of the GDS proposed here, the GDS-R, seems to retain the diagnostic performance for detecting depression in older adults of the GDS-30 items, while increasing the sensitivity and predictive values relative to other shortened versions.

Növelhető-e a csőd-előrejelző modellek előre jelző képessége az új klasszifikációs módszerek nélkül? (Can the predictive capacity of bankruptcy forecasting models be increased without new classification methods?)

A Bázel–2. tőkeegyezmény bevezetését követően a bankok és hitelintézetek Magyarországon is megkezdték saját belső minősítő rendszereik felépítését, melyek karbantartása és fejlesztése folyamatos feladat. A szerző arra a kérdésre keres választ, hogy lehetséges-e a csőd-előrejelző modellek előre jelző képességét növelni a hagyományos matematikai-statisztikai módszerek alkalmazásával oly módon, hogy a modellekbe a pénzügyi mutatószámok időbeli változásának mértékét is beépítjük. Az empirikus kutatási eredmények arra engednek következtetni, hogy a hazai vállalkozások pénzügyi mutatószámainak időbeli alakulása fontos információt hordoz a vállalkozás jövőbeli fizetőképességéről, mivel azok felhasználása jelentősen növeli a csődmodellek előre jelző képességét. A szerző azt is megvizsgálja, hogy javítja-e a megfigyelések szélsőségesen magas vagy alacsony értékeinek modellezés előtti korrekciója a modellek klasszifikációs teljesítményét. ______ Banks and lenders in Hungary also began, after the introduction of the Basel 2 capital agreement, to build up their internal rating systems, whose maintenance and development are a continuing task. The author explores whether it is possible to increase the predictive capacity of business-failure forecasting models by traditional mathematical-cum-statistical means in such a way that they incorporate the measure of change in the financial indicators over time. Empirical findings suggest that the temporal development of the financial indicators of firms in Hungary carries important information about future ability to pay, since the predictive capacity of bankruptcy forecasting models is greatly increased by using such indicators. The author also examines whether the classification performance of the models can be improved by correcting for extremely high or low values before modelling.

Pavement performance prediction using adaptive logic networks

Pavement performance is one of the most important components of the pavement management system. Prediction of the future performance of a pavement section is important in programming maintenance and rehabilitation needs. Models for predicting pavement performance have been developed on the basis of traffic and age. The purpose of this research is to extend the use of a relatively new approach to performance prediction in pavement performance modeling using adaptive logic networks (ALN). Adaptive logic networks have recently emerged as an effective alternative to artificial neural networks for machine learning tasks. ^ The ALN predictive methodology is applicable to a wide variety of contexts including prediction of roughness based indices, composite rating indices and/or individual pavement distresses. The ALN program requires key information about a pavement section, including the current distress indexes, pavement age, climate region, traffic and other variables to predict yearly performance values into the future. ^ This research investigates the effect of different learning rates of the ALN in pavement performance modeling. It can be used at both the network and project level for predicting the long term performance of a road network. Results indicate that the ALN approach is well suited for pavement performance prediction modeling and shows a significant improvement over the results obtained from other artificial intelligence approaches. ^

The predictive power of homework assignments on student achievement in grade three

Homework has been a controversial issue in education for the past century. Research has been scarce and has yielded results at both ends of the spectrum. This study examined the relationship between homework performance (percent of homework completed and percent of homework correct), student characteristics (SAT-9 score, gender, ethnicity, and socio-economic status), perceptions, and challenges and academic achievement determined by the students' average score on weekly tests and their score on the FCAT NRT mathematics assessment. ^ The subjects for this study consisted of 143 students enrolled in Grade 3 at a suburban elementary school in Miami, Florida. Pearson's correlations were used to examine the associations of the predictor variables with average test scores and FCAT NRT scores. Additionally, simultaneous regression analyses were carried out to examine the influence of the predictor variables on each of the criterion variables. Hierarchical regression analyses were performed on the criterion variables from the predictor variables. ^ Homework performance was significantly correlated with average test score. Controlling for the other variables homework performance was highly related to average test score and FCAT NRT score. ^ This study lends support to the view that homework completion is highly related to student academic achievement at the lower elementary level. It is suggested that at the elementary level more consideration be given to the amount of homework completed by students and to utilize the information in formulating intervention strategies for student who may not be achieving at the appropriate levels. ^

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.

Does management capacity increase organizational performance? An empirical analysis of public housing agencies

Since the 1990s, scholars have paid special attention to public management’s role in theory and research under the assumption that effective management is one of the primary means for achieving superior performance. To some extent, this was influenced by popular business writings of the 1980s as well as the reinventing literature of the 1990s. A number of case studies but limited quantitative research papers have been published showing that management matters in the performance of public organizations. ^ My study examined whether or not management capacity increased organizational performance using quantitative techniques. The specific research problem analyzed was whether significant differences existed between high and average performing public housing agencies on select criteria identified in the Government Performance Project (GPP) management capacity model, and whether this model could predict outcome performance measures in a statistically significant manner, while controlling for exogenous influences. My model included two of four GPP management subsystems (human resources and information technology), integration and alignment of subsystems, and an overall managing for results framework. It also included environmental and client control variables that were hypothesized to affect performance independent of management action. ^ Descriptive results of survey responses showed high performing agencies with better scores on most high performance dimensions of individual criteria, suggesting support for the model; however, quantitative analysis found limited statistically significant differences between high and average performers and limited predictive power of the model. My analysis led to the following major conclusions: past performance was the strongest predictor of present performance; high unionization hurt performance; and budget related criterion mattered more for high performance than other model factors. As to the specific research question, management capacity may be necessary but it is not sufficient to increase performance. ^ The research suggested managers may benefit by implementing best practices identified through the GPP model. The usefulness of the model could be improved by adding direct service delivery to the model, which may also improve its predictive power. Finally, there are abundant tested concepts and tools designed to improve system performance that are available for practitioners designed to improve management subsystem support of direct service delivery.^

The Predictive Power of Homework Assignments on Student Achievement in Mathematics

This study examined the relationship between homework performance (percent of homework completed and percent of homework correct), student characteristics (Stanford Achievement Test score, gender, ethnicity, and socio-economic status), perceptions, and challenges and academic achievement determined by the students’ average score on weekly tests and their score on the Florida Comprehensive Assessment Test (FCAT) Norm Reference Test (NRT) mathematics assessment.

A retrospective-longitudinal examination of the relationship between apportionment of seat time in community-college algebra courses and student academic performance

During the past decade, there has been a dramatic increase by postsecondary institutions in providing academic programs and course offerings in a multitude of formats and venues (Biemiller, 2009; Kucsera & Zimmaro, 2010; Lang, 2009; Mangan, 2008). Strategies pertaining to reapportionment of course-delivery seat time have been a major facet of these institutional initiatives; most notably, within many open-door 2-year colleges. Often, these enrollment-management decisions are driven by the desire to increase market-share, optimize the usage of finite facility capacity, and contain costs, especially during these economically turbulent times. So, while enrollments have surged to the point where nearly one in three 18-to-24 year-old U.S. undergraduates are community college students (Pew Research Center, 2009), graduation rates, on average, still remain distressingly low (Complete College America, 2011). Among the learning-theory constructs related to seat-time reapportionment efforts is the cognitive phenomenon commonly referred to as the spacing effect, the degree to which learning is enhanced by a series of shorter, separated sessions as opposed to fewer, more massed episodes. This ex post facto study explored whether seat time in a postsecondary developmental-level algebra course is significantly related to: course success; course-enrollment persistence; and, longitudinally, the time to successfully complete a general-education-level mathematics course. Hierarchical logistic regression and discrete-time survival analysis were used to perform a multi-level, multivariable analysis of a student cohort (N = 3,284) enrolled at a large, multi-campus, urban community college. The subjects were retrospectively tracked over a 2-year longitudinal period. The study found that students in long seat-time classes tended to withdraw earlier and more often than did their peers in short seat-time classes (p < .05). Additionally, a model comprised of nine statistically significant covariates (all with p-values less than .01) was constructed. However, no longitudinal seat-time group differences were detected nor was there sufficient statistical evidence to conclude that seat time was predictive of developmental-level course success. A principal aim of this study was to demonstrate—to educational leaders, researchers, and institutional-research/business-intelligence professionals—the advantages and computational practicability of survival analysis, an underused but more powerful way to investigate changes in students over time.