China
Africa
Explore chapters and articles related to this topic
Assessing the relationship between exercise and employee mental health: methodological concerns
Published in John Kerr, Amanda Griffiths, Tom Cox, Workplace Health, Employee Fitness and Exercise, 2020
Steve M. Jex, Deanne A. Heinisch
Statistical power is the extent to which a study is capable of detecting true effects when they exist. According to Cohen (1969), statistical power is primarily a function of the size of the effect that a researcher is trying to detect, measurement reliability and sample size. Assuming that a researcher is using reliable measures, relatively small sample sizes are sufficient to detect large effects. Conversely, large sample sizes are needed to detect small effects. According to Cohen (1992), researchers in many areas of psychology have neglected the issue of statistical power. Furthermore, such neglect on the part of researchers comes with the increased danger of drawing erroneous conclusions from research results. For example, a researcher may conduct an experiment and conclude that the experimental treatment has no effect. The real problem, however, may be that the effect is small and statistical power was lacking.
Involving older adults in design research
Published in Sara J. Czaja, Walter R. Boot, Neil Charness, Wendy A. Rogers, Designing for Older Adults, 2019
Sara J. Czaja, Walter R. Boot, Neil Charness, Wendy A. Rogers
In larger efficacy or effectiveness trials, statistical power is a critical issue as it impacts the confidence that can be placed in the findings of the study. Statistical power is the extent to which the study can detect the difference between two groups and is a function of three factors: the criterion established for statistical significance (alpha level, typically set at .05), the difference that exists between the groups (effect size), and the sample size. Various algorithms and software programs are available to help calculate statistical power and derive needed sample size. Calculation of the appropriate sample size must occur prior to the beginning of the study for planning purposes. In addition to statistical power, the number of participants that will be required impacts the recruitment strategy, staffing requirements, budget, and timeline.
Research Methodology
Published in Zakari Mustapha, Clinton Aigbavboa, Wellington Thwala, Contractor Health and Safety Compliance for Small to Medium-Sized Construction Companies, 2017
Zakari Mustapha, Clinton Aigbavboa, Wellington Thwala
Sample size is the number of observations or replicates to be included in a statistical sample. The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample, just like the current study. The sample size used in the study was determined based on the expanse of data collection, and the need to have sufficient statistical power to validate the conceptual model. The sample size for the current study was not based on the entire population of the selected SME contractors in the major cities of Ghana; therefore, the sample size was not equal to the population size. The question of how large a sample should be, depends on the following: the kind of data analysis the researcher plans to use; how accurate the sample has to be for the researcher’s purposes and the population characteristics (Neuman, 2006).
How to regulate users’ disorderly parking behavior of free-floating bike sharing? An empirical study based on social psychology
Published in Transportation Letters, 2022
Meng Wang, Xi-Xi Zhang, Yong Liu
This study aims to examine the roles of psychological invulnerability, danger invulnerability, interpersonal invulnerability, and past behavior in determining users’ disorderly parking intentions. To achieve this goal, the target population of this study was identified as all users who had used the FFBS system. Determining an appropriate sample size is very important for ensuring the quality and rigor of any study. According to Sarstedt, Ringle, and Hair (2017) and Thompson, Barclay, and Higgins (1995), in the structural model, there is a 10-fold rule that states that the minimum sample size should be 10 times the maximum number of structural paths pointing to a particular construct. The structural model of this study includes five constructs (i.e., three independent variables, one moderator variable, and one dependent variable) and six structural paths; according to the 10-times rule criterion, the minimum sample size should be 60 respondents.
Time series forecasting for port throughput using recurrent neural network algorithm
Published in Journal of International Maritime Safety, Environmental Affairs, and Shipping, 2021
Nguyen Duy Tan, Hwang Chan Yu, Le Ngoc Bao Long, Sam-Sang You
This paper deals with forecasting port throughput for decision support using the Grey model and ESN algorithm. Two prediction methods include the regression-based method and machine learning scheme. Comparative case study has been presented for container throughput analysis and assessing the proposed predictive techniques. In fact, the case study employs both qualitative and quantitative approaches to formulate generalizations that extend across multiple cases. With a small sample size, the traditional GM method has proved its high accuracy on forecasting. Most importantly, a large sample size is more representative of the population. When performing throughput data analysis by increasing sample size, the ESN approach will provide the higher level of accuracy and superiority over other strategies. Simulation results show that ESN algorithm can be a potential scheme to build prediction models for container throughput, despite the limited simple sizes and observations. Based on the obtained results, machine learning approach is useful to train the forecasting models, but the nature of the problems or the characteristics of the sample might have an impact on the forecasting performance of the approaches.
Determinants of users’ perceived taxi service quality in the context of a developing country
Published in Transportation Letters, 2021
Sajad Askari, Farideddin Peiravian, Nebiyou Tilahun, Maryam Yousefi Baseri
It is very crucial to choose the right sample size in order to ensure the quality and reliability of any study. For PLS-SEM analyzes, Hair et al. (2017) have proposed that the minimum sample size should be ‘10 times the largest number of structural paths directed at a particular construct in structural model’. As shown in Figure 4, 16 indicators were used in this study, and the number of constructs were to be even less than that. According to the ‘10 times rule’, therefore, the minimum sample size required was 160. As explained in the 'Data collection' section, this study used a sample size of 400, much higher than the required value for PLS-SEM. In order to employ the two-stage approach, SPSS Statistical software v. 24 and SmartPLS 3 (Ringle, Wende, and Becker 2015) were used to analyze the data in the above-mentioned two-step process. The overall process of survey design and analysis, as explained earlier, is demonstrated in Figure 6.