Assessment of Agility in Elderly Population of Islamabad

Background: Agility is considered as one of the important components of physical fitness. In older adults, it is of utmost importance in response to any stimulus. The objective of this study was to determine normative values of agility in elderly population of Islamabad. Methods: A Cross-Sectional Survey was conducted in community settings of Islamabad from February–July 2019 after approval by the Ethical Review Committee of Foundation University Islamabad. The calculated sample size was found to be 267, but due to missing data, analysis was done on 250(100 females and 150 males). Participants were selected by convenient sampling. Physically independent participants were included and diseased population (severe musculoskeletal, neurological, and cardiopulmonary disorders), decreased functional status affecting hearing, vision, memory recall was excluded from the study. PAR-Q was utilized in uncovering any possible health risks linked to exercise. For evaluation of agility, the American Alliance for Health, Physical Education, Recreation, and Dance (AAHPERD) Agility Test was performed. Agility scoring is based on time in seconds, with higher score representing less agility, and agility score of less than 62 represents good agility. Data were analyzed through SPSS version 21. Results: The mean age, BMI and agility score of participants were 60.7±5.81 years, 26±4.30 kg/m² and 22.42±5.2 respectively. There was significant difference (P<0.001) in agility between males and females, with the mean agility score higher in females as compared to males. Similarly, Agility score was significantly high in females as compared to males in BMI range of 18.5 to >30 and all age categories. Conclusion: Elderly population of Pakistan has good agility score

Three Sample Estimates of Fraction of Missing Information From Full Information Maximum Likelihood

In missing data analysis, the reporting of missing rates is insufficient for the readers to determine the impact of missing data on the efficiency of parameter estimates. A more diagnostic measure, the fraction of missing information (FMI), shows how the standard errors of parameter estimates increase from the information loss due to ignorable missing data. FMI is well-known in the multiple imputation literature (Rubin, 1987), but it has only been more recently developed for full information maximum likelihood (Savalei and Rhemtulla, 2012). Sample FMI estimates using this approach have since then been made accessible as part of the lavaan package (Rosseel, 2012) in the R statistical programming language. However, the properties of FMI estimates at finite sample sizes have not been the subject of comprehensive investigation. In this paper, we present a simulation study on the properties of three sample FMI estimates from FIML in two common models in psychology, regression and two-factor analysis. We summarize the performance of these FMI estimates and make recommendations on their application.

MISSING DATA SAMPLES: SYSTEMATIZATION AND CONDUCTING METHODS-A REVIEW

This paper investigates the phenomenon of the incomplete data samples by analyzing their structure and also resolves the necessary procedures regularly used in missing data analysis. The research gives a crucial perceptive of the techniques and mechanisms needed in dealing with missing data issues in general. The motivation for writing this brief overview of the topic lies in the fact that statistical researchers inevitably meet missing data in their analysis. The authors examine the applicability of regular approaches for handling the missing data situations. Based on several previously published results, the authors provide an example of the incomplete data sample model that can be implemented when confronting with specific missing data patterns.

Social Avoidance and stigma among healthcare workers who deal with Coronavirus COVID-19 Patients in Saudi Arabia

<p>An online questionnaire was distributed to the target population (<i>N </i>= ~2000); 226 completed forms were received from respondents Missing values in all variables did not exceed 6% of cases. Missing data analysis was then followed with Little’s (1988) missing completely at random test. The results were not significant, χ² (59) = 73.340, <i>p</i> = .099, suggesting that the values were missing entirely by chance. Thus, the missing values in the dataset were estimated with the expectation–maximization algorithm. To examine outliers among cases, data were evaluated for univariate and multivariate outliers by examining Mahalanobis distance for each participant. An outlier was defined as a Mahalanobis score that was over than Mahal. Critical score cv = 55.32; univariate or multivariate outliers were 31 cases with 13% (Tabachnik & Fidell, 2013, McLachlan GJ. (1999).</p>

Social Avoidance and stigma among healthcare workers who deal with Coronavirus COVID-19 Patients in Saudi Arabia

<p>An online questionnaire was distributed to the target population (<i>N </i>= ~2000); 226 completed forms were received from respondents Missing values in all variables did not exceed 6% of cases. Missing data analysis was then followed with Little’s (1988) missing completely at random test. The results were not significant, χ² (59) = 73.340, <i>p</i> = .099, suggesting that the values were missing entirely by chance. Thus, the missing values in the dataset were estimated with the expectation–maximization algorithm. To examine outliers among cases, data were evaluated for univariate and multivariate outliers by examining Mahalanobis distance for each participant. An outlier was defined as a Mahalanobis score that was over than Mahal. Critical score cv = 55.32; univariate or multivariate outliers were 31 cases with 13% (Tabachnik & Fidell, 2013, McLachlan GJ. (1999).</p>