Do Individual Factors Affect the Relationship between Faculty Intrapreneurship and the Entrepreneurial Orientation of Their Organizations?

Several colleges and universities in the Middle East have been undertaking significant initiatives to forge and foster corporate entrepreneurship. The viability and success of those initiatives rest upon the input of faculty, possessing to various degrees an entrepreneurial orientation that revolves around innovativeness, risk-taking, and proactivity. This study investigates the extent to which individual-level factors moderate the influence of faculty entrepreneurial behavior on the entrepreneurial orientation of higher education institutions in Kuwait. These factors include gender, academic qualifications, teaching experience, school affiliation, scientific productivity, industrial experience, and professional certification. Data were collected using questionnaires filled by 291 faculty members, and the model was analyzed using structural equation modelling. The differences for each faculty characteristic in the structural path coefficients were tested using the Z-score statistics. The eight hypotheses that were partially validated as the most notable findings indicate that entrepreneurial orientation among male or business faculty has a greater impact on their institutions’ organizational, entrepreneurial orientation. In contrast, the differences for the rest of the moderating characteristics were insignificant. The originality of this study pertains to the fact that the scope of faculty intrapreneurship does not seem to be strongly affected by any individual-level characteristic.

Housing Affordability among Civil Servants in Ekiti State, Nigeria

Housing has been acknowledged generally as a key human necessity. One of its problems may be claimed that it is not affordable for the ordinary Nigerian worker, whose earnings and wages are now strongly depressed and unable to fulfill their fundamental necessities. From the perspective of the above, this study studied the affordability of government workers in the State of Ekiti, Nigeria. Purposive method of sampling was used to sample two government agencies and parastatals. A total of One Hundred and Twenty Six (126) government officials were picked. 94 questionnaires were retrieved. The questionnaire was used to collect the information required and analysed by descriptive and medium item score statistics. The findings indicated that government employees in Ekiti State could, on average, afford to pay rental housing since most employees spend less than 30 per cent of their yearly salary on rentals, especially in the medium and high income categories. In the study, public and private engagements were proposed, leading to affordable and sustainable state housing delivery. Keywords: Housing, Civil Servant, Affordable, Rent, Ekiti State.

Home sweet home: Quantifying home court advantages for NCAA basketball statistics

Box score statistics are the baseline measures of performance for National Collegiate Athletic Association (NCAA) basketball. Between the 2011-2012 and 2015-2016 seasons, NCAA teams performed better at home compared to on the road in nearly all box score statistics across both genders and all three divisions. Using box score data from over 100,000 games spanning the three divisions for both women and men, we examine the factors underlying this discrepancy. The prevalence of neutral location games in the NCAA provides an additional angle through which to examine the gaps in box score statistic performance, which we believe has been underutilized in existing literature. We also estimate a regression model to quantify the home court advantages for box score statistics after controlling for other factors such as number of possessions, and team strength. Additionally, we examine the biases of scorekeepers and referees. We present evidence that scorekeepers tend to have greater home team biases when observing men compared to women, higher divisions compared to lower divisions, and stronger teams compared to weaker teams. Finally, we present statistically significant results indicating referee decisions are impacted by attendance, with larger crowds resulting in greater bias in favor of the home team.

A Bayesian adjusted plus-minus analysis for the esport Dota 2

Analytics and professional sports have become linked over the past several years, but little attention has been paid to the growing field of esports within the sports analytics community. We seek to apply an Adjusted Plus Minus (APM) model, an accepted analytic approach used in traditional sports like hockey and basketball, to one particular esports game: Defense of the Ancients 2 (Dota 2). As with traditional sports, we show how APM metrics developed with Bayesian hierarchical regression can be used to quantify individual player contributions to their teams and, ultimately, use this player-level information to predict game outcomes. In particular, we first provide evidence that gold can be used as a continuous proxy for wins to evaluate a team’s performance, and then use a Bayesian APM model to estimate how players contribute to their team’s gold differential. We demonstrate that this APM model outperforms models based on common team-level statistics (often referred to as “box score statistics”). Beyond the specifics of our modeling approach, this paper serves as an example of the potential utility of applying analytical methodologies from traditional sports analytics to esports.

Discussion of Mental Illness and Mental Health By NBA Players on Twitter

In 2019, the National Basketball Association (NBA) expanded it’s mental health rules to include mandating that each team have at least one mental health professional on their full-time staff and to retain a licensed psychiatrist to assist when needed. In this work, we investigate the NBA players’ discussion of mental health using historical data from players’ public Twitter accounts. All current and former NBA players with Twitter accounts were identified, and each of their last 800 tweets were scraped, yielding 920,000 tweets. A list of search terms derived from the DSM5 diagnoses was then created and used to search all of the nearly one million tweets. In this work, we present the most common search terms used to identify tweets about mental health, present the change in month-by-month tweets about mental health, and identify the impact of players discussing their own mental health struggles on their box score statistics before and after their first tweet discussing their own mental health struggles.

An extended regularized adjusted plus-minus analysis for lineup management in basketball using play-by-play data

In this work we analyse basketball play-by-play data in order to evaluate the efficiency of different five-man lineups employed by teams. Starting from the adjusted plus-minus framework, we present a model-based strategy for the analysis of the result of partial match outcomes, extending the current literature in two main directions. The first extension replaces the classical response variable (scored points) with a comprehensive score that combines a set of box score statistics. This allows various aspects of the game to be separated. The second extension focuses on entire lineups rather than individual players, using a suitable extended model specification. The model fitting procedure is Bayesian and provides the necessary regularization. An advantage of this approach is the use of posterior distributions to rank players and lineups, providing an effective tool for team managers. For the empirical analysis, we use the 2018/2019 regular season of the Turkish Airlines Euroleague Championship, with play-by-play and box scores for 240 matches, which are made available by the league website. The results of the model fitting can be used for several investigations as, for instance, the comparative analysis of the effects of single players and the estimation of total and synergic effects of lineups monitoring. Moreover, the behaviour of players and lineups during the season, updating the estimation results after each gameday, can represent a rather useful tool.

Interval estimation of proportion ratios for stratified bilateral correlated binary data

Confidence interval (CI) methods for the ratio of two proportions in the presence of correlated bilateral binary data are constructed for comparative clinical trials with stratified design. Simulations are conducted to evaluate the performance of the presented CIs with respect to mean coverage probability (MCP), mean interval width (MIW), and the ratio of mesial non-coverage probability to the distal non-coverage probability (RMNCP). Based on the empirical results, we suggest the use of the proposed CI method based on the complete score statistics (CS) for general applications. An example from a rheumatology study is used to demonstrate the proposed methodologies.

Understanding artificial mouse-microbiome heterogeneity and six actionable themes to increase study power

ABSTRACTThe negative effects of data clustering due to (intra-class/spatial) correlations are well-known in statistics to interfere with interpretation and study power. Therefore, it is unclear why housing many laboratory mice (≥4), instead of one-or-two per cage, with the improper use/reporting of clustered-data statistics, abound in the literature. Among other sources of ‘artificial’ confounding, including cyclical oscillations of the ‘cage microbiome’, we quantified the heterogeneity of modern husbandry practices/perceptions. The objective was to identify actionable themes to re-launch emerging protocols and intuitive statistical strategies to increase study power. Amenable for interventions, ‘cost-vs-science’ discordance was a major aspect explaining heterogeneity and the reluctance to change. Combined, four sources of information (scoping-reviews, professional-surveys, expert-opinion, and ‘implementability-score-statistics’) indicate that a six-actionable-theme framework could minimize ‘artificial’ heterogeneity. With a ‘Housing Density Cost Simulator’ in Excel and fully annotated statistical examples, this framework could reignite the use of ‘study power’ to monitor the success/reproducibility of mouse-microbiome studies.

Improved Score Statistics for Meta-analysis in Single-variant and Gene-level Association Studies

Meta-analysis is now an essential tool for genetic association studies, allowing these to combine large studies and greatly accelerating the pace of genetic discovery. Although the standard meta-analysis methods perform equivalently as the more cumbersome joint analysis under ideal settings, they result in substantial power loss under unbalanced settings with various case-control ratios. Here, we investigate why the standard meta-analysis methods lose power under unbalanced settings, and further propose a novel meta-analysis method that performs as efficiently as joint analysis under general settings. Our proposed method can accurately approximate the score statistics obtainable by joint analysis, for both linear and logistic regression models, with and without covariates. In addition, we propose a novel approach to adjust for population stratification by correcting for known population structures through minor allele frequencies (MAFs). In the simulated gene-level association studies under unbalanced settings, our method recovered up to 85% power loss caused by the standard method. We further showed the power gain of our method in gene-level association studies with 26 unbalanced real studies of Age-related Macular Degeneration (AMD). In addition, we took the meta-analysis of three studies of type 2 diabetes (T2D) as an example to discuss the challenges of meta-analyzing multi-ethnic samples. In summary, we propose improved single-variant score statistics in meta-analysis, requiring “accurate” population-specific MAFs for multi-ethnic studies. These improved score statistics can be used to construct both single-variant and gene-level association studies, providing a useful framework for ensuring well-powered, convenient, cross-study analyses.