Evaluation of quality assurance instruments in higher education institutions: A case of Oman

The use of a variety of instruments for quality assurance, management, and enhancement in higher education is well recognized. This article investigated the instruClose Panelments used by Higher Education Institutions (HEIs) in Oman to measure, control, and manage the quality of their services in alignment with the standards set by Oman Academic Accreditation Authority (OAAA). Quality Assurance Managers (QAMs) from five HEIs were interviewed to identify the instruments used by them to fulfil the requirements of each standard and the way they make use of the data gathered by using these instruments. Findings from the study reveal that questionnaires and meetings are the most common instruments used by these institutions to measure, control and assure the efficacy of their current quality activities. In addition, HEIs use summary statistics to analyse data and then present them in meetings or through reports. On the other hand, it was found that substantial efforts are made to collect data but the efficient usage of data is missing. The QAMs reported a lack of awareness among the staff on the importance of collecting data since the staff members believe that these data are collected for documentation purposes only. This study emphasizes the importance of using the data gathered from different instruments in decision making and enhancing the quality of HEIs

Prevalence of Nocturnal Enuresis among Schoolchildren in Sana’a City, Yemen

Objective: To estimate the prevalence, frequency and time of nocturnal enuresis (NE) among primary schoolchildren in Sana’a city, Yemen. Methods: This was a cross-sectional study was conducted among 2689 schoolchildren in the primary schools of four randomly selected districts in Sana’a city in the period from September 2012 to December 2013. Data about sociodemographic characteristics, frequency, time, psycho-social effects and the factors possibly associated with NE among children were collected using a pre-designed questionnaire and analyzed using appropriate statistical tests. Results: The overall prevalence of NE was 11.2%, which was significantly higher among males than females (13.0% vs. 10.0%, respectively; P = 0.044) and decreased significantly with increasing age (P <0.001). More than half of children (55.3%) in Sana’a city had the habits of drinking excess fluids and tea at night and/or deep sleeping. Of physical and health disorders, difficulty in breathing and urinary tract infections were the two most frequent conditions among children with NE, being observed among 29.6% and 23.9% of cases, respectively. However, urogenital anomalies and mental retardation were the least frequent conditions in children with NE, being observed among 5.8% and 1.3% of cases, respectively. On the other hand, marital problems (24.8%) and arrival of a new baby (17.9%) were the most frequently observed social conditions among children with NE, while death in the family (8.5%) and parental separation (6.0%) were the least frequently observed conditions. Conclusions: NE is prevalent among 11.2% of schoolchildren in Sana’a city with a significantly higher, though slight, rate among males. This rate is lower than the rates reported from Aden and Mukalla cities in the country and from Saudi Arabia and Turkey. However, it is higher than those reported from Iran and Malaysia. About a third of children experience nightly NE, whereas the lowest proportion of children experience NE twice a month. The habits of drinking excess fluid and tea at night and/or deep sleeping, the disorders of difficulty in breathing and urinary tract infections and the social conditions of marital problems and arrival of a new baby are the most frequent observations among children with NE in Sana’a city

Identification of High Leverage Points in Linear Functional Relationship Model

In a standard linear regression model the explanatory variables, , are considered to be fixed and hence assumed to be free from errors. But in reality, they are variables and consequently can be subjected to errors. In the regression literature there is a clear distinction between outlier in the - space or errors and the outlier in the X-space. The later one is popularly known as high leverage points. If the explanatory variables are subjected to gross error or any unusual pattern we call these observations as outliers in the - space or high leverage points. High leverage points often exert too much influence and consequently become responsible for misleading conclusion about the fitting of a regression model, causing multicollinearity problems, masking and/or swamping of outliers etc. Although a good number of works has been done on the identification of high leverage points in linear regression model, this is still a new and unsolved problem in linear functional relationship model. In this paper, we suggest a procedure for the identification of high leverage points based on deletion of a group of observations. The usefulness of the proposed method for the detection of multiple high leverage points is studied by some well-known data set and Monte Carlo simulations