scholarly journals Appendix 4: Summary of Sample Size Procedures for Different Statistical Tests

2014 ◽  
pp. 167-167
Keyword(s):  
Sample Size ◽  
Statistical Tests

scholarly journals Parallelized calculation of permutation tests

2020 ◽  
Author(s):  
Markus Ekvall ◽  
Michael Höhle ◽  
Lukas Käll
Keyword(s):  
Dynamic Programming ◽  
Sample Size ◽  
Permutation Test ◽  
Statistical Tests ◽  
Permutation Tests ◽  
Supplementary Information ◽  
Attractive Alternative ◽  
Test Statistic ◽  
Sample Distribution ◽  
Running Time

Abstract Motivation Permutation tests offer a straightforward framework to assess the significance of differences in sample statistics. A significant advantage of permutation tests are the relatively few assumptions about the distribution of the test statistic are needed, as they rely on the assumption of exchangeability of the group labels. They have great value, as they allow a sensitivity analysis to determine the extent to which the assumed broad sample distribution of the test statistic applies. However, in this situation, permutation tests are rarely applied because the running time of naïve implementations is too slow and grows exponentially with the sample size. Nevertheless, continued development in the 1980s introduced dynamic programming algorithms that compute exact permutation tests in polynomial time. Albeit this significant running time reduction, the exact test has not yet become one of the predominant statistical tests for medium sample size. Here, we propose a computational parallelization of one such dynamic programming-based permutation test, the Green algorithm, which makes the permutation test more attractive. Results Parallelization of the Green algorithm was found possible by non-trivial rearrangement of the structure of the algorithm. A speed-up—by orders of magnitude—is achievable by executing the parallelized algorithm on a GPU. We demonstrate that the execution time essentially becomes a non-issue for sample sizes, even as high as hundreds of samples. This improvement makes our method an attractive alternative to, e.g. the widely used asymptotic Mann-Whitney U-test. Availabilityand implementation In Python 3 code from the GitHub repository https://github.com/statisticalbiotechnology/parallelPermutationTest under an Apache 2.0 license. Supplementary information Supplementary data are available at Bioinformatics online.

Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

1981 ◽  
Vol 18 (1) ◽  
pp. 39-50 ◽  
Author(s):  
Claes Fornell ◽  
David F. Larcker
Keyword(s):  
Measurement Error ◽  
Sample Size ◽  
Structural Equation ◽  
Structural Model ◽  
Structural Equation Models ◽  
Statistical Tests ◽  
Explanatory Power ◽  
Measurement Model ◽  
Measurement Properties ◽  
Chi Square

The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model.

scholarly journals Digitizing Dunhuang Cultural Heritage: A User Evaluation of Mogao Cave Panorama Digital Library

2017 ◽  
Vol 2 (3) ◽  
pp. 49-67 ◽  
Author(s):  
Xiao Hu ◽  
Eric M. Y. Ho ◽  
Chen Qiao
Keyword(s):  
Cultural Heritage ◽  
Sample Size ◽  
Digital Library ◽  
Information Seeking ◽  
Small Sample Size ◽  
Statistical Tests ◽  
Small Sample ◽  
Evaluation Framework ◽  
User Evaluation ◽  
Usability Tests

Abstract Purpose This study is a user evaluation on the usability of the Mogao Cave Panorama Digital Library (DL), aiming to measure its effectiveness from the users’ perspective and to propose suggestions for improvement. Design/methodology/approach Usability tests were conducted based on a framework of evaluation criteria and a set of information seeking tasks designed for the Dunhuang cultural heritage, and interviews were conducted for soliciting in-depth opinions from participants. Findings The results of the usability tests indicate that the DL was more efficient in supporting simple information seeking tasks than those of higher-complexity levels. Statistical tests reveal that there were correlations among dimensions of usability criteria and user effectiveness measures. Moreover, interview discourses exposed specific usability issues of the DL. Research limitations This research is based on a relatively small sample size, resulting in a limited representativeness of user diversity. A larger sample size is needed for a systematic cross group comparison. Practical implications This study evaluated the usability of the Mogao Cave Panorama DL and proposed suggestions for its improvement for better experience. The results also provide a reference to other cultural heritage DLs with panorama functions. Originality/value This study is one of the first evaluating cultural heritage DLs from the perspective of user experience. It provides methodological references for relevant studies: the evaluation framework, the designed information seeking tasks, and the interview questions can be adopted or adapted in evaluating other visually centric DLs of cultural heritage.

The importance of considering effect size and statistical power in research

2015 ◽  
Author(s):  
David Clark-Carter
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
Statistical Power ◽  
Statistical Tests ◽  
Clinical Psychologists ◽  
Future Research

This chapter explores why effect size needs to be taken into account when designing and reporting research. It gives an effect size for each of the standard statistical tests which health and clinical psychologists employ, and looks at the need to consider statistical power when choosing a sample size for a study and how statistical power can help to guide the advice which can be given when discussing future research.

scholarly journals Impact of TV Violence on Aggression of Youth; A Comparative Analysis of Male and Female Students of Lahore

2020 ◽  
Vol 6 (2) ◽  
pp. 549-556
Author(s):  
Muhammad Irfan Qadir ◽  
Shafiq Jullandhry
Keyword(s):  
Comparative Analysis ◽  
Sample Size ◽  
Female Student ◽  
Statistical Tests ◽  
Male Student ◽  
Sampling Technique ◽  
Female Students ◽  
Male And Female ◽  
Significant Difference ◽  
Convenience Sampling

This study focuses on impact of TV violence on aggression young viewers of Lahore. For this study 500 (Male, 250 and Female 250) students are taken as a sample size from different universities of Lahore. Data is collected through stratified and convenience sampling technique from the targeted population. Major results indicate the significant difference in exposure to TV violence and aggression of male and female students. Major results of statistical tests male student exposure and attitude to TV violence has significant impact on aggression but exposure of female student has not significant impact on aggression whereas attitude to TV violence has significant impact on aggression. Further, there is also need to set some sort of filters on media contents which are presenting violence.

scholarly journals A systematic review of the statistical scope of restoration ecology of invaded grassland ecosystems

2014 ◽  
Author(s):  
Amanda R Liczner
Keyword(s):  
Systematic Review ◽  
Impact Factor ◽  
Sample Size ◽  
Restoration Ecology ◽  
Statistical Tests ◽  
Citation Rate ◽  
Statistical Test ◽  
Experimental Designs ◽  
The Impact ◽  
Journal Impact

Restoration ecology is a rapidly growing field of research. The statistical analyses and experimental designs used in this field have likely also expanded. In this review, the statistical scope of the restoration ecology of invaded grasslands will be investigated. A systematic review was conducted on 103 articles to examine the types of statistical tests used and how they changed over time, if assumptions are tested, and how the number of statistical tests and the experimental design influence both the citation rate of articles and the impact factor of journals where these articles are published. ANOVAs have consistently been the dominant test. Statistical test diversity has increased since the year 2000. Most articles did test the assumptions of statistical analyses. The number of tests, and sample size of experiments are both positively correlated with the average citation rate of articles and the impact factor of the journal while the number of factors was negatively correlated. GLMs are recommended as a statistical test to be used more frequently in the future over ANOVAs. There is room for improvement in terms of reporting statistics accurately, including testing assumptions. When possible, sample sizes should be increased to both increase the quality of data, and the citation rate and the journal impact where articles are published. When possible and appropriate, sample sizes and the number of statistical tests should be increased. Adding factors in experimental designs should only be done so without compromising sample size as it has been shown to hinder the citation rate and journal impact.

scholarly journals Pengaruh Kompetensi dan Karakter Dosen Terhadap Metode Pembelajaran Serta Dampaknya Kepada Kinerja Dosen di Universitas Subang

ijd-demos ◽  
2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Taufiq Hidayat Hasibuan
Keyword(s):  
Action Research ◽  
Sample Size ◽  
Research Method ◽  
Statistical Tests ◽  
Research Approach ◽  
Learning Method ◽  
West Java ◽  
Action Research Approach ◽  
Learning Techniques ◽  
The University

Tujuan penelitian ini adalah untuk mengetahui dan menganalisis: (1) Kompetensi Dosen, (2) Karakter Dosen, (3) Metode Pembelajaran, dan (4) Kinerja Dosen, serta (5) Pengaruh Kompetensi Dosen, Karakter Dosen dan Teknik Pembelajaran terhadap Kinerja Dosen di Universitas Subang Kabupaten Subang Jawa Barat. Metode penelitian yang digunakan dalam penelitian adalah metode penelitian assosiatif dengan proses kualitatif dan pendekatan action research. Dan unit analisis dalam penelitian adalah Mahasiswa Universitas Subang Jawa Barat dengan sampel sebanyak 100 orang. Dalam Penelitian ini, ukuran sampel ditentukan oleh bentuk uji statistik dan yang digunakan adalah Analisis Jalur (Path Analisis), dan untuk menentukan ukuran sampel minimal pada koefisien korelasi yang dilakukan secara iterative (perhitungan berulang –ulang).Berdasarkan hasil penelitian, diperoleh bahwa Kompetensi serta Karakter Dosen sangat memberikan pengaruh terhadap Metode Pembelajaran yang akan diberikan oleh Dosen kepada mahasiswa. Dosen-dosen yang sudah ada harus lebih meningkatkan Kompetensinya dengan Karakter yang kuat dalam berhadapan dengan Mahasiswa karena memberikan pengaruh baik secara simultan maupun secara parsial terhadap Metode Pembelajaran yang lebih hidup dan bervariatif yang akan diberikan kepada Mahasiswa. Hal ini berdampak kepada baik atau tidaknya kinerja Dosen di Universitas Subang. The purpose of this research was to determine and analyze: (1) Lecturer Competence, (2) Lecturer Character, (3) Learning Method, and (4) Lecturer Performance, and (5) Effect of Lecturer Competence, Lecturer Character and Learning Techniques on Lecturer Performance at the University of Subang, Subang Regency, West Java. This  research method used in research is an associative research method with a qualitative process and action research approach. And the unit of analysis in this study is the Students of Subang University, West Java with a sample of 100 people. In this study, the sample size is determined by the form of statistical tests and the Path Analysis is used, and to determine the minimum sample size on the correlation coefficient which is done iteratively (repeated calculations). Based on the results of the study, it was found that the Competence and Character of the Lecturer greatly influenced the Learning Method to be provided by the Lecturer to students. Existing lecturers must further enhance their competence with strong character in dealing with students because they provide both simultaneous and partial influence on the more lively and varied Learning Methods that will be given to students. This has an impact on whether or not the performance of Lecturers at the University of Subang.

scholarly journals Parallelized calculation of permutation tests

2020 ◽  
Author(s):  
Markus Ekvall ◽  
Michael Höhle ◽  
Lukas Käll
Keyword(s):  
Dynamic Programming ◽  
Sample Size ◽  
Permutation Test ◽  
Statistical Tests ◽  
Permutation Tests ◽  
Supplementary Information ◽  
Attractive Alternative ◽  
Test Statistic ◽  
Sample Distribution ◽  
Running Time

AbstractMotivationPermutation tests offer a straight forward framework to assess the significance of differences in sample statistics. A significant advantage of permutation tests are the relatively few assumptions about the distribution of the test statistic are needed, as they rely on the assumption of exchangeability of the group labels. They have great value, as they allow a sensitivity analysis to determine the extent to which the assumed broad sample distribution of the test statistic applies. However, in this situation, permutation tests are rarely applied because the running time of naive implementations is too slow and grows exponentially with the sample size. Nevertheless, continued development in the 1980s introduced dynamic programming algorithms that compute exact permutation tests in polynomial time. Albeit this significant running time reduction, the exact test has not yet become one of the predominant statistical tests for medium sample size. Here, we propose a computational parallelization of one such dynamic programming-based permutation test, the Green algorithm, which makes the permutation test more attractive.ResultsParallelization of the Green algorithm was found possible by nontrivial rearrangement of the structure of the algorithm. A speed-up – by orders of magnitude – is achievable by executing the parallelized algorithm on a GPU. We demonstrate that the execution time essentially becomes a non-issue for sample sizes, even as high as hundreds of samples. This improvement makes our method an attractive alternative to, e.g., the widely used asymptotic Mann-Whitney U-test.AvailabilityIn Python 3 code from the GitHub repository https://github.com/statisticalbiotechnology/parallelPermutationTest under an Apache 2.0 [email protected] informationSupplementary data are available at Bioinformatics online.

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