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Record ID: 150 [ Page 2 of 16, No. 1 ]
Application of Consecutive Sampling Technique in a Clinical Survey for an Ordered Population: Does it Generate Accurate Statistics?
Authors: Mohamad Adam Bujang, Tg Mohd Ikhwan Tg Abu Bakar Sidik, and Nadiah Sa'at
Abstract:
This study aims to compare the statistical generalizations which are inferred from samples obtained by both systematic sampling and consecutive sampling and then compare both their results with the true population parameters of the target population. This study was conducted using two approaches. The first approach was a comparison between sample statistics and population parameters based on a simulation analysis to estimate the population parameters from three types of statistical distributions (i.e. Normal, Exponential, and Poisson) by using seven sub-samples and 1000 iterations. The second approach was a comparison between sample statistics and population parameters based on real-life data sets which comprise six sub-samples and four parameters. Based on results from the simulation analysis, systematic sampling offers a greater advantage by having a smaller value of mean square error (MSE) in 40 out of 70 comparisons (57.1%) while consecutive sampling has a smaller value of MSE in 29 out of 70 comparisons (41.4%). There is only one MSE comparison that was identical between systematic sampling and consecutive sampling. Based on a validation approach, systematic sampling produced more accurate statistics than consecutive sampling with six out of eight comparisons. In summary, systematic sampling offers a better advantage in terms of accuracy. However, consecutive sampling is still able to generate valid and accurate statistics despite the fact that it is a type of non-probability sampling, especially if a sufficiently large sample size has been obtained for statistical analysis. Therefore, it is recommended that in any situation when it can be difficult to apply a systematic sampling technique for a particular clinical setting, researchers may opt to apply the consecutive technique in the recruitment process as an alternative, with a limitation on making generalizations about the target population.
Keywords: population parameters; sample statistics; systematic sampling.
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Year: 2022 Vol.: 71 No.: 1
Record ID: 149 [ Page 2 of 16, No. 2 ]
On Some Efficient Classes of Estimators Based on Higher Order Moments of an Auxiliary Attribute
Authors: Shashi Bhushan and Anoop Kumar
Abstract:
This paper discusses the problem of estimating the population mean utilizing information on the mean and variance of qualitative characteristics. We introduce some efficient classes of estimators based on higher order moments such as the variance of an auxiliary attribute. The conventional mean estimator, Bhushan and Gupta (2016) estimator, and the traditional regression and ratio estimators proposed by Naik and Gupta (1996) are shown to be the sub-class of the proposed estimators for properly chosen valuations of the described scalars. The effective performance of the suggested estimators has been assessed empirically and theoretically with respect to the contemporary estimators.
Keywords: mean square error, efficiency, qualitative characteristics
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Year: 2022 Vol.: 71 No.: 1
Record ID: 148 [ Page 2 of 16, No. 3 ]
An Application of CATANOVA and Logistic Regression on the Most Prevalent Sexually Transmitted Infection (A Case Study of the University of Nigeria Teaching Hospital)
Authors: Nnaemeka Martin Eze, Oluchukwu Chukwuemeka Asogwa, Samson Offorma Ugwu, Chinonso Michael Eze, Felix Obi Ohanuba, Tobias Ejiofor Ugah
Abstract:
This research focused on the application of CATANOVA and logistic regression on the most prevalent Sexually Transmitted Infection (STI) reported in the University of Nigeria Teaching Hospital from 2010- 2020. A population of 20,704 patients was recorded to have contracted eight(8) selected STIs. Prevalence analysis was computed to determine the most prevalent STI. Two-way CATANOVA cross-classification was computed to ascertain the age group and gender that suffer more from the most prevalent STI. Three-way CATANOVA was computed to ascertain the association among drug prescription, age, and gender of the Gonorrhea patients. A logistic regression model was fitted to predict infertility as an effect of the most prevalent STI. The prevalence analysis showed Gonorrhea infection as the most prevalent STI at 33.08%. A population of 6,850 patients recorded to have contracted Gonorrhea infection from 2010-2020 was employed for the analysis. Two-way CATANOVA cross-classification showed that gender, age, and interaction effects were statistically significant at a 5% significance level. Male (3,752; 54.8%) suffers Gonorrhea infection more than female (3,098;45.2%) and aged 30-39 years (1,946; 28.4%) suffers it more than any other age interval. The interaction effect shows that the rate of contracting Gonorrhea infection by gender differs from one age interval to another. Three-way CATANOVA results showed that drugs prescribed for the treatment of Gonorrhea infection depend on gender and age. Logistic regression results showed that an increase in age, body mass index, blood pressure, blood sugar, bacteria quantity, and Gonorrhea history were associated with an increased likelihood of the Gonorrhea patient being infertile.
Keywords: Chi-square test, Prediction, Prevalence
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Year: 2022 Vol.: 71 No.: 1
Record ID: 147 [ Page 2 of 16, No. 4 ]
Analytic Hierarchy Process with Rasch Measurement in the Construction of a Composite Metric of Student Online Learning Readiness Scale
Authors: Joyce DL. Grajo, James Roldan S. Reyes1, Liza N. Comia, Lara Paul A. Ebal, Jared Jorim O. Mendoza, and Mara Sherlin DP. Talento
Abstract:
This paper developed the Online Learning Readiness Composite Scale (OLRCS), a composite measure of student online learning readiness based on five dimensions, namely (1) computer/internet self-efficacy; (2) self-directed learning; (3) learner control; (4) motivation for learning; and (5) online communication self-efficacy. A single metric of online learning readiness has its advantage over its disaggregated dimensions. For one, it allows a summative description of each student which school administrators can use for an effective student targeting toward flexible learning. Rasch Analysis (RA) was performed to come up with an objective measure for each dimension while Analytic Hierarchy Process (AHP) was applied to aggregate the computed Rasch scores of the five dimensions. Three OLRCS have been constructed using weights generated by (1) teacher participants, (2) student participants, and (3) combined student and teacher participants. Results showed that motivation for learning consistently received the highest weight while online communication self-efficacy and computer/internet selfefficacy got low weights among the three OLRCS. Research findings also showed that student participants gave more importance to learner control than self-directed learning, unlike the teacher participants. The difference in the teacher and student perspectives merits detailed attention to optimize the online learning environment and enable individual support. Nevertheless, using cluster analysis, the distribution of students who are ready, undecided, or not ready for online learning is similar to the three constructed OLRCS.
Keywords: multidimensional latent variable; multi-criteria decision analysis; linear aggregation
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Year: 2022 Vol.: 71 No.: 1
Record ID: 146 [ Page 2 of 16, No. 5 ]
Implementing an Effective Survey Operations for a Research and Development Survey in the Philippines
Authors: Ramoncito G. Cambel, Dalisay S. Maligalig, Maurice C. Borromeo, and Ronald R. Roldan Jr., and Clifford B. Lesmoras
Abstract:
Studies have shown that research and development (R&D) is a good driver of economic growth. Policies and programs that are based on good quality data are expected to produce better results. Hence, to formulate and implement policies and programs in R&D, good quality data is vital. A good data support system is also essential in identifying critical areas that need intervention, formulating viable approaches in addressing these issues, and allocating limited resources. In the Philippines, the Department of Science and Technology (DOST) has been conducting the Survey on Research and Development Expenditures and Personnel (R&D Survey) since 2003 so that R&D data and indicators can be compiled. To ensure that good quality R&D data and indicators are achieved, the DOST granted a research fund to the Institute of Statistics (INSTAT) of the University of the Philippines Los Baños (UPLB) in 2018 to further improve the design, conduct and analysis of the R&D Survey. This paper describes the processes that were developed and implemented through this research grant in relation to the dimensions of data quality, namely, relevance, accuracy, timeliness, accessibility, coherence, and comparability. Based on the evaluation of these processes, the paper also recommends further improvement on the survey operations of future rounds of the R&D Survey.
Keywords: Survey on Research and Development Expenditures and Personnel, data quality
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Year: 2022 Vol.: 71 No.: 1
Record ID: 145 [ Page 2 of 16, No. 6 ]
Analysis of Longitudinal Data with Missing Values in the Response and Covariates Using the Stochastic EM Algorithm
Authors: Ahmed M. Gad and Nesma M. Darwish
Abstract:
Longitudinal data are not uncommon in many disciplines where repeated measurements on a response variable are collected for each subject. Missing values are unavoidable in longitudinal studies. Missing values could be in the response variable, the covariates or in both. Dropout pattern occurs when some subjects leave the study prematurely. When the probability of missingness depends on the missing value, and may be on the observed values, the missing data mechanism is termed as non-random. Ignoring the missing values in this case leads to biased inferences. In this paper we will handle missing values in covariates using multiple imputations (MI) and the selection model to fit longitudinal data in the presence of nonrandom dropout. The stochastic EM (Expectation-Maximization) algorithm is developed to obtain the model parameter estimates. Also, parameter estimates of the dropout model have been obtained. Standard errors of estimates have been calculated using the developed Monte Carlo method. The proposed approach performance is evaluated through a simulation study. Also, the proposed approach is applied to a real data set.
Keywords: Interstitial Cystitis data; missing covariates; dropout missingness; multiple imputation; selection model; the SEM algorithm.
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Year: 2022 Vol.: 71 No.: 1
Record ID: 144 [ Page 2 of 16, No. 7 ]
Two New Tests for Tail Independence in Extreme Value Models
Authors: Mohammad Bolbolian Ghalibaf
Abstract:
This paper proposes two new tests for tail independence in extreme value models. We use the conditional distribution function (df) of X + Y, given that X + Y > c based approach of Falk and Michel to test for tail independence in extreme value models. We recommend using Cramervon Mises and Anderson-Darling tests for tail independence. Simulations show that the two tests are better than the Kolmogorov-Smirnov test which has good results among the proposed tests by Falk and Michel. Finally, by using two real datasets, we illustrate the application of the two proposed tests as well as the traditional tests of Falk and Michel.
Keywords: extreme value model, tail independence, Copula function, Cramer-von Mises test, Anderson-Darling test, Neyman- Pearson test, Kolmogorov-Smirnov test, Fisher’s ? test, Chi-square goodness-of-fit test
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Year: 2021 Vol.: 70 No.: 2
Record ID: 143 [ Page 2 of 16, No. 8 ]
Time Series Prediction of CO2 Emissions in Saudi Arabia Using ARIMA, GM(1,1), and NGBM(1,1) Models
Authors: Z. F. Althobaiti, and A. Shabri
Abstract:
The investigation of economic aspects of gas emissions in terms of its volume and consequences is very important, given the current increasing trend. Therefore, the prediction of carbon dioxide emissions in Saudi Arabia becomes necessary. This study uses annual time series data on CO2 emissions in Saudi Arabia from 1970 to 2016. The study built the prediction model of CO2 emissions in Saudi Arabia by using Autoregressive Integrated Moving Average (ARIMA), Grey System GM and Nonlinear Grey Bernoulli Model (NGBM), and comparing their efficiency and accuracy based on MAPE metric. The results revealed that Nonlinear Grey Bernoulli Model (NGBM) is more accurate than the other prediction models. The results may be useful to Saudi Arabian government in the development of its future economic policies. As a result, five policy recommendations have been proposed, each of which could play a significant role in the development of acceptable Saudi Arabian climate policies.
Keywords: annual time series data, Autoregressive Integrated Moving Average (ARIMA), CO2 emissions, global warming, Grey Model (GM), Nonlinear Grey Bernoulli Model (NGBM), prediction, Saudi Arabia
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Year: 2021 Vol.: 70 No.: 2
Record ID: 142 [ Page 2 of 16, No. 9 ]
Classes of Estimators under New Calibration Schemes using Non-conventional Measures of Dispersion
Authors: A. Audu, R. Singh, S. Khare, N. S. Dauran
Abstract:
In this paper, we proposed two classes of estimators under two new calibration schemes for a heterogeneous population by incorporating auxiliary information of Non-Conventional Measures of dispersion which are robust against the presence of outlier in the data.Theoretical results are supported by simulation studies conducted on six bivariate populations generated using exponential and normal distributions. The biases and percentage relative efficiencies (PRE) of the proposed and other related estimators in the study were computed and results indicated that the estimators proposed under suggested calibration schemes perform on average more efficiently than conventional unbiased estimator, Rao and Khan (2016) and Nidhi et al. (2017).
Keywords: heterogeneous population, Outliers, Estimators, Robust measures, Population mean
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Year: 2021 Vol.: 70 No.: 2
Record ID: 141 [ Page 2 of 16, No. 10 ]
A New Compound Probability Model Applicable to Count Data
Authors: Showkat Ahmad Dar, Anwar Hassan, Peer Bilal Ahmad and Bilal Ahmad Para
Abstract:
In this paper, we obtained a new model for count data by compounding of Poisson distribution with two parameter Pranav distribution. Important mathematical and statistical properties of the distribution have been derived and discussed. Then, parameter estimation is discussed using maximum likelihood method of estimation. Finally, real data set is analyzed to investigate the suitability of the proposed distribution in modeling count data.
Keywords: Poisson distribution, two parameter Pranav distribution, compound distribution, count data, simulation study, maximum likelihood estimation
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Year: 2021 Vol.: 70 No.: 2