Methods For Sample Size Determination Research Articles

Review of sample size determination methods for the intraclass correlation coefficient in the one-way analysis of variance model.

Reliability of measurement instruments providing quantitative outcomes is usually assessed by an intraclass correlation coefficient. When participants are repeatedly measured by a single rater or device, or, are each rated by a different group of raters, the intraclass correlation coefficient is based on a one-way analysis of variance model. When planning a reliability study, it is essential to determine the number of participants and measurements per participant (i.e. number of raters or number of repeated measurements). Three different sample size determination approaches under the one-way analysis of variance model were identified in the literature, all based on a confidence interval for the intraclass correlation coefficient. Although eight different confidence interval methods can be identified, Wald confidence interval with Fisher's large sample variance approximation remains most commonly used despite its well-known poor statistical properties. Therefore, a first objective of this work is comparing the statistical properties of all identified confidence interval methods-including those overlooked in previous studies. A second objective is developing a general procedure to determine the sample size using all approaches since a closed-form formula is not always available. This procedure is implemented in an R Shiny app. Finally, we provide advice for choosing an appropriate sample size determination method when planning a reliability study.

Role of Forensic Accounting in Uncovering Accounting Fraud

This study concentrated on the Role of Forensic Accounting in Uncovering Accounting Fraud with reference to Guaranty Trust Bank Enugu Metropolis. The study amongst others examines the role, scope and tools of forensic accounting in detecting accounting fraud. For the objective to be achieved, questionnaire was used as a major source of data. 81 respondents were used as total population while 67 was used as the sample size of the study. Taro-Yamane statistical tool was applied as a method for sample size determination. Table and five point likert style was used for presentation and analysis of data. chi-square was used for test of hypothesis. The findings indicates that the roles of Forensic accounting in detecting accounting fraud is identification of fraud such as fake teller valuations, suspicious bankruptcy valuations, and analysis of financial documents in fraud schemes, the scope of forensic accounting in detecting accounting fraud are litigation support and investigative accounting services and the forensic accounting tools in detecting accounting fraud are training, mindset, risk assessment, systems analysis, mathematical modeling and exception reporting. The study therefore recommended that different organizations should always advocate forensic accounting as a means of uncovering accounting fraud because it will always help for quick identification of fake teller/receipt, suspicious bankruptcy and analysis of financial documents through valuation method, management of business entities both private and public should try all possible means in focusing on the main scope of forensic accounting such as litigation and investigation services for it help for proper detection of accounting fraud at the right time in a way it will not affect the profitability of business and training, mindset mathematical modeling and others as tools of forensic accounting should be employ by organizations an

Developing an Employee Retention Strategy for Improving Organizational Performance in Zimbabwe: Case of Mashonaland Tobacco Company

This study was aimed at an employee retention strategy for improving organizational performance in Zimbabwe: Case of Mas honaland Tobacco Company. This study was guided by objectives such as to establish the relationship between employee retention strategies and worker performance, to determine factors that influence employee retention, to identify challenges associated with the implementation of the retention strategies and to develop an employee retention model aimed to improve organizational performance at MTC. This study adopted pragmatism research philosophy which assisted the researcher in making decisions in regards to what worked best in finding answers for the research objectives mentioned. A sample size of 218 was determined using Taro Yamane sample size determination method. In this study it was established that the organization have been offering paid time off, incentives, basic salary, career advancement, commissions, rewards and holidays to their employees as a retention strategy. It has been noted that employee retention improves productivity, employee retention increase employee morale and employee retention motivates employees thereby improves their participation. Through the use of employee retention an organization attracts qualified employees. Compensation affects employee retention, organizational culture and structure, ineffective leadership and ineffective communication influence employee retention. Whenever implementing employee retention strategies organizations always face increased resistant to change. Their operations are affected by limited financial resources and budget constraints. Insufficient communication and coordination affect the implementation of employee retention strategy. It has been noted that an organization should pay for performance, have team solidarity in an organization and prioritize recognition of top performers.

Nested Maximum Entropy Designs for Computer Experiments

Presently, computer experiments with multiple levels of accuracy are widely applied in science and engineering. This paper introduces a class of nested maximum entropy designs for such computer experiments. A multi-layer DETMAX algorithm is proposed to construct nested maximum entropy designs. Based on nested maximum entropy designs, we also propose an integer-programming procedure to specify the sample sizes in multi-fidelity computer experiments. Simulated annealing techniques are used to tackle complex optimization problems in the proposed methods. Illustrative examples show that the proposed nested entropy designs can yield better prediction results than nested Latin hypercube designs in the literature and that the proposed sample-size determination method is effective.

Sample Size Determination for Food Sampling

Industry and public health agencies sample and test food products for various purposes related to food safety and quality. Methods of sample selection and sample size determination are important in designing an optimal sampling plan. The appropriate sample size of a sampling plan depends on the objective. We examine the methods of sample size calculation for the following four objectives commonly associated with food sampling: (1) estimate prevalence (e.g., of contaminated products), (2) detect presence (e.g., of contaminated products), (3) estimate maximum prevalence, and (4) compare estimated prevalence with a specified value (e.g., a previous estimate or a threshold value). We illustrate these methods using examples and provide a web-based application (https://simple-sample.galaxytrakr.org/) written in R, using the shiny package, to help users with the application of each method.

Analysis of sample size for friction test of short fiber reinforced polymer composites

AbstractAccording to the recommendations of literature and standards, the minimum sample size for the friction test of thermoplastics is usually 3–5. While this size is valid for the friction test of neat polymer resins with homogeneous structures, its applicability to short fiber reinforced polymer composites with inhomogeneous structures remains unclear because the uneven distribution of discontinuous fibers or fillers in the matrix leads to the performance of the composites being dispersed. In this work, as a typical example, 100 friction samples made of short carbon fiber reinforced polyetherimide (SCF/PEI) composite with a typical content of 10 vol% SCFs are prepared, and their friction coefficients are measured. The maximum likelihood estimation and goodness of fit methods are employed to assess the fitting degree between the overall friction coefficient results and five mathematical distributions. The results show that the overall friction coefficient data distribution is in good agreement with the Lognormal, Generalized extreme value (GEV), and Burr distributions, respectively. Based on the fitting result, the minimum sample size is analyzed by Monte Carlo Simulation. It shows that the minimum sample size strongly depends on the property dispersion parameter (COV: coefficient of variation). Based on this parameter, the minimum sample size is given. In a practical test, the performance dispersion degree of a composite cannot be known in advance, hence a sample size determination method is proposed to yield a reasonable performance description parameter by applying the results of this work, and this method is validated by the overall test data.Highlights Proposed a sample size determination method for practical friction tests. The minimum sample size for the friction test is yielded statistically. The minimum sample size strongly depends on a property dispersion parameter.

A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement Learning

Computational models that formalize complex human behaviors enable study and understanding of such behaviors. However, collecting behavior data required to estimate the parameters of such models is often tedious and resource intensive. Thus, estimating dataset size as part of data collection planning (also known as Sample Size Determination) is important to reduce the time and effort of behavior data collection while maintaining an accurate estimate of model parameters. In this article, we present a sample size determination method based on Uncertainty Quantification (UQ) for a specific Inverse Reinforcement Learning (IRL) model of human behavior, in two cases: (1)pre-hocexperiment design—conducted in the planning stage before any data is collected, to guide the estimation of how many samples to collect; and (2)post-hocdataset analysis—performed after data is collected, to decide if the existing dataset has sufficient samples and whether more data is needed. We validate our approach in experiments with a realistic model of behaviors of people with Multiple Sclerosis (MS) and illustrate how to pick a reasonable sample size target. Our work enables model designers to perform a deeper, principled investigation of the effects of dataset size on IRL model parameters.

Comparison of 7 methods for sample size determination based on confidence interval estimation for a single proportion

To compare different methods for calculating sample size based on confidence interval estimation for a single proportion with different event incidences and precisions. We compared 7 methods, namely Wald, AgrestiCoull add z2, Agresti-Coull add 4, Wilson Score, Clopper-Pearson, Mid-p, and Jefferys, for confidence interval estimation for a single proportion. The sample size was calculated using the search method with different parameter settings (proportion of specified events and half width of the confidence interval [ω=0.05, 0.1]). With Monte Carlo simulation, the estimated sample size was used to simulate and compare the width of the confidence interval, the coverage of the confidence interval and the ratio of the noncoverage probability. For a high accuracy requirement (ω =0.05), the Mid-p method and Clopper Pearson method performed better when the incidence of events was low (P < 0.15). In other settings, the performance of the 7 methods did not differ significantly except for a poor symmetry of the Wald method. In the setting of ω=0.1 with a very low p (0.01-0.05), failure of iteration occurred with nearly all the methods except for the Clopper-Pearson method. Different sample size determination methods based on confidence interval estimation should be selected for single proportions with different parameter settings.

COMMERCIAL LAYING HEN PRODUCTION AND EGG VALUE CHAIN MAPPING IN NORTHERN ETH

&lt;p&gt;&lt;strong&gt;Background:&lt;/strong&gt; In Ethiopia, commercial farms have increased in farm size, and the majority of the large-scale poultry farms are concentrated around Addis Ababa the capital city of Ethiopia. &lt;strong&gt;Objective:&lt;/strong&gt; To assess poultry production, map value chains and identify major production challenges in Northern Ethiopia. &lt;strong&gt;Methodology:&lt;/strong&gt; A multistage sampling technique along with a proportionate sample size determination method was used to fix the sample size. Accordingly, 147, 109 and 64 poultry producers were selected from semi-intensive, small-scale, and medium-scale farms, respectively. Nine focus group discussions comprising relevant stakeholders were conducted. &lt;strong&gt;Result:&lt;/strong&gt; The earlier age at first egg-laying in weeks was reported as 16.7 ± 0.93 in the medium-scale production system. A high laying percentage of 85.47 &lt;strong&gt;± &lt;/strong&gt;5.01 was recorded in the medium-scale and the highest mortality 23.5 &lt;strong&gt;± &lt;/strong&gt;8.8 was in semi-intensive. The price of eggs varied by about +38% in November 2019/2020 and negatively changed price by -22% in May 2019/2020. In a ranking exercise, feed cost, feed quality, product marketing, disease, predator, and lack of information and training were ranked top five. Further risk factors were ranked, accordingly, lack of vaccine, poor biosecurity measures, and management were ranked as major causes. &lt;strong&gt;Implication: &lt;/strong&gt;The government and private sector should promote improving the feeding habit of the community and improve the management practice. &lt;strong&gt;Conclusion:&lt;/strong&gt; The high price of feed ingredients, limited access to veterinary services, poor market linkages in the value chain, poor feeding habits, and poor extension services aggravated by a lack of awareness of producers were the most important challenges affecting commercial production.&lt;/p&gt;

Investigating the mediating role of rumination between parental control and shyness in adolescent girls

rumination between parental control and shyness in adolescent girls. Method: The current research method is descriptive-survey of correlation type. The purpose of the research is applied and cross-sectional in terms of time. The statistical population includes all female secondary school students of Bojnord city who were engaged in the analysis in the academic year of 2014-2016. In order to estimate the sufficient sample size, Klein's (2005) sample size determination method was used, and 567 people were selected as the sample size. The sampling method is a single-stage cluster. In order to collect the desired information and measure the research variables, Hoeksma and Maro (1991) Rumination Questionnaire, Leslie et al.'s (1986) Parental Control (PLOC) and Stanford Shyness Questionnaire were used. SPSS and AMOS software were used to analyze the data. Results: According to the results, the causal model of students' shyness based on parental control with the mediating role of rumination has a suitable fit. Conclusion: parental control has a direct effect on self-criticism. Parental control has a direct effect on shyness. Parental control has a direct effect on rumination. Rumination has a direct effect on modesty. Rumination plays a mediating role between parental control and shyness.

Sample size variation in single-time post-dose assessment vs multi-time post-dose assessment

Background: Many randomized trials measure a continuous outcome simultaneously at baseline and after taking the drug. For a single continuous post-treatment outcome, the sample size calculation is simple, but if there are assessments at multiple time points post-treatment then this longitudinal data may give more insights by analyzing the data using the repeated measures method. Also, if the sample size is calculated using the single time-point method for longitudinal data, it may lead to a larger than required sample size, increasing the cost and time. Methods: In this research, an effort is made to determine the size of the sample for repeated measures case and then compared with the single post-baseline case. The sample sizes were examined under different scenarios for the continuous type of response variable. Under Mean contrast and Diff contrast the sample sizes were calculated with different correlations. These two scenarios were again examined under compound symmetry as well as Auto regressive of order 1 type of correlation structure in longitudinal data. The graphical presentation is given for better visualization of the scenarios. Results: Sample size required for highly correlated longitudinal data using multi timepoint sample size derivation method led to much smaller sample size requirement as compared to single timepoint sample size calculation method. Conclusions: This study will help researchers to make better decisions in choosing the right method for sample size determination which may reduce the time and cost of carrying out the experiment. Also, we must carefully assess which method to go with when the correlation is weak. More complex correlation structures are not studied in this article but can be studied in the same fashion.

Corporate Governance Characteristics and Firm Value of Deposit Money Banks in Nigeria

The degree of corporate and market procedures, lack transparency, distortions and poor corporate practices which results in corporate failures and abysmal corporate financial performance negatively influence corporate objectives. Hence, this study investigated the effects of corporate governance mechanisms on the value of deposit money banks in Nigeria from 2010 to 2020. The specific objectives include investigating the relationship between board size and Tobin q; evaluating the relationship between board independence and Tobin q; determining the relationship between board ownership and Tobin q; investigating the effect of gender diversity on Tobin q and evaluating the relationship between board meetings and Tobin q. The study population consisted of all deposit money banks and the Taro Yamene method of sample size determination was applied. The secondary data for the study was from the published financial statements of sampled banks for the period after the validity and reliability test of data. The data obtained were tested using univariate, bivariate and multivariate analysis. The results from the multiple regression results disclosed that board independence, the board size, ownership structure, gender diversity and board meetings positively and significantly influence the value of deposit money banks in Nigeria. The study concluded that corporate governance attributes positively and significantly affect the value of deposit money banks in Nigeria. The study made several recommendations amongst others that board sizes should be enhanced as this allows for the appropriate combination of directors. A large board increases the chance of directors having appropriate knowledge, skill and networks. The knowledge, skill and networks of directors may increase the financial performance of an organisation. Also, deposit money banks in Nigeria should have non-executive directors who act as professional advisers to ensure that competition among insiders encourages measures consistent with the maximisation of shareholder value. Hence, the implication of this study provides that the implementation of corporate governance characteristics enhances the value of firms in deposit money banks in Nigeria.

Distribution-Free Approach to the Design and Analysis of Randomized Stroke Trials With the Modified Rankin Scale.

Many methods have been suggested for analyzing the modified Rankin Scale (mRS). However, there lacks a unified approach to analysis and sample size determination that properly uses the ordinal nature of the data. We propose a simple method for CI estimation and corresponding sample size determination. We quantify treatment effect by the win probability (WinP) that a randomly selected patient in the treatment group has an equal or a better mRS score than a patient in the control group. Thus, a win probability of 0.5 means no effect, likened to a draw in competitive sports. We estimate the win probability and its SE based on the ranks of mRS scores, where tied scores are handled by average ranks. Corresponding methods for hypothesis testing, CI estimation, and sample size determination are derived. The methods are evaluated with a simulation study based on real data from 10 randomized stroke trials that used mRS as the outcome measure. Simulation results demonstrated that the methods performed very well in terms of CI coverage, tail errors, and assurance to achieving the prespecified precision. Because the methods are very simple, we implemented them in an Excel spreadsheet, requiring only user inputs on frequencies of mRS scores in 2 comparison groups. Sound statistical methods are important for the success of randomized stroke trials. The proposed methods and associated spreadsheet should prove useful for stroke researchers in the planning and analysis of randomized trials. Meta-analysis has also been made easy for trials with ordinal scores.

Optimal design of cyclic-stress accelerated life tests for lognormal lifetime distribution

ABSTRACT Accelerated life tests (ALTs) have frequently been used in industry to assess the reliability of products within an affordable amount of time and cost. Most of the previous works on designing ALT plans have dealt with the case of constant-, step- or progressive-stress loading, which may not be adequate for products operating under cyclic-stress loading at the use condition. In this paper, optimal and compromise ALT plans are developed under the assumptions of cyclic-stress loading, lognormal lifetime distribution, log-linear relationship between the scale parameter of the lifetime distribution and stress variable and the cumulative exposure model for the effect of changing stress levels. In particular, the common floor level and the proportion of units assigned to two (for optimal plans) or three (for compromise plans) cyclic-stress conditions are determined such that the asymptotic variance of the maximum likelihood estimator of the q-th quantile of the lifetime distribution at the use condition is minimized. In addition, a sample size determination method is developed, and sensitivity analysis procedures are presented to evaluate the robustness of the proposed ALT plans in the face of uncertainty in the pre-estimation of a model parameter. The whole process is illustrated with an example.

Optimal sample size determination for single-arm trials in pediatric and rare populations with Bayesian borrowing

ABSTRACT In many therapeutic areas with unmet medical needs, such as pediatric oncology and rare diseases, one of the deterrent factors for clinical trial interpretability is the limited sample size with less-than-ideal operating characteristics. Single arm is usually the only viable design due to feasibility and ethical concerns. For the trial results to be more interpretable and conclusive, the evaluation of operating characteristics, such as type I error rate and power, and the appropriate utilization of prior information for study design, shall be prespecified and fully investigated during the trial planning phase. So far, very few existing literature addressed optimal sample size determination issues for the planning of pediatric and rare population trials, with majority of research focusing on analysis perspective with focus on Bayesian borrowing. In practice, when a single-arm trial is designed for rare population, it is not uncommon that the only information available is from an earlier trial and/or a few clinical publications based on observational studies, often constituting mixed or uncertain conclusions. In light of this, an optimal Bayesian sample size determination method for single-arm trial with binary or continuous endpoint is proposed, where conflicting prior beliefs can be readily incorporated. Prior effective sample size can be calculated to assess the robustness as well as the prior information borrowed. Moreover, due to the lack of closed-form posterior distributions in general, an alternative approach for calculating Bayesian power is described. Simulation studies are provided to demonstrate the utility of the proposed methods. In addition, a case study in pediatric patients with leukemia is included to illustrate the proposed method with the existing approaches.

Stepped Wedge Cluster Randomized Trials: A Methodological Overview

Stepped wedge cluster randomized trials enable rigorous evaluations of health intervention programs in pragmatic settings. In the present study, we aimed to update neurosurgeon scientists on the design of stepped wedge randomized trials. We have presented an overview of recent methodological developments for stepped wedge designs and included an update on the newer associated methodological tools to aid with future study designs. We defined the stepped wedge trial design and reviewed the indications for the design in depth. In addition, key considerations, including mainstream methods of analysis and sample size determination, were discussed. Stepped wedge designs can be attractive for study intervention programs aiming to improve the delivery of patient care, especially when examining a small number of heterogeneous clusters.

Sample size determination with a pilot study.

We analyse standard confidence intervals for the mean of a finite population, with a view to sample size determination. We first consider a standard method for sample size determination based on the assumption of knowledge of the population variance parameter. We then consider the more realistic case where the population variance is treated as an unknown, and we derive a new method of sample size determination that accounts for the uncertainty in this parameter. We develop a sample size calculation method based on a preliminary sample of data which allows us to increase the accuracy of our inference by some specified amount with a specified level of confidence. We compare this method to the standard method.

Impact of Computerized Information System on Tax Collection: A Case Study on Ethiopian Revenue and Custom Authority Adama Branch

A computerized information system involves the use of computer hardware and software to perform sorting, storing and retrieving functions that would otherwise have been done manually by a staff of a bank or an owner of a business. This study was conduct on the title impact of CIS on tax collection system of ERCA Adama branch. It addresses four basic research questions designed to assess impact of CIS on tax collection system of the office. For the purpose of the study primary data were used, in which more information was obtained by questionnaires and unstructured interviews. The researchers used both open and close-ended questionnaires to employees of the office. The researchers used a census method of sample size determination and descriptive design of data analysis. After the data has gathered, it was analyzed and presented. The data analysis has been carried out based on tabulation and percentage method. Careful interpretation of analyzed information would carry out to arrive at reasonable generalization. Ultimately, the research is expected to provide reasonable results that could help the management ERCA Adama branch to make reasonable decisions for their future activity. The major finding of the study states that CIS system in one or another way is important for government revenue collection in ERCA Adama branch office.

Investigating the mediating role of self-criticism between emotional self-regulation and students' shyness

Background and Aim: The purpose of this research is to investigate the mediating role of self-criticism between emotional self-regulation and shyness of students. Methods: The current research method is descriptive-survey of correlation type. The purpose of the research is applied and cross-sectional in terms of time. The statistical population includes all female secondary school students of Bojnord city who were engaged in the analysis in the academic year of 2014-2016. In order to estimate the sufficient sample size, Klein's (2005) sample size determination method was used, and 567 people were selected as the sample size. The sampling method is a single-stage cluster. The data collection method is library and field studies, and the information collection tools were Thompson and Zorov's self-criticism levels questionnaire (2004), Stanford's shyness questionnaire (1990) and emotional self-regulation questionnaire from Hoffman and Kashdan's questionnaire (2010). Results: The findings showed that the causal model of students' shyness based on emotional self-regulation with the mediating role of self-criticism has a suitable fit. Also, emotional self-regulation has a direct effect on self-criticism, and emotional self-regulation has a direct effect on shyness. Another is that self-criticism has a direct effect on shyness, and self-criticism plays a mediating role between emotional self-regulation and shyness. Conclusion: It can be concluded that self-criticism plays a mediating role between students' emotional self-regulation and shyness. Therefore, the planners of the educational system with the help of psychologists and school counselors can include the necessary measures in the curriculum to remove the shyness of students.

Calculating the Sample Size in Quantitative Studies

The determination of the required sample size is always an important step during the planning of the quality of research for a quantitative study. The researchers need to have sound prerequisite knowledge of the inferential statistics concept for determining a minimum sample size requirement and its estimation because an insufficient sample size may not be able to answer the research questions and a large sample is wastage of resources, may add to the complexity, and take more time for the study. Both situations are ethically unacceptable for the researchers. Therefore, this study provides researchers with appropriate sample size determination methods for quantitative study. The Google Scholar, PubMed, and Library Genesis were used to find out the required information for the review of this paper. This study presents the sample size calculation formula in a simplified manner with relevant examples so that researchers may effectively use them in their research.