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＜電子ブック＞
Determining Sample Size and Power in Research Studies : A Manual for Researchers / by J. P. Verma, Priyam Verma

版	1st ed. 2020.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2020
大きさ	XIII, 127 p. 61 illus., 43 illus. in color : online resource
著者標目	*Verma, J. P author Verma, Priyam author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Sociology—Methodology LCSH:Research—Methodology FREE:Statistical Theory and Methods FREE:Sociological Methods FREE:Applied Statistics FREE:Research Skills
一般注記	Chapter 1: Introduction To Sample Size Determination -- Chapter 2: Understanding Statistical Inference -- Chapter 3: Understanding Concepts In Estimating Sample Size In Survey Studies -- Chapter 3: Chapter 4: Understanding Concepts In Estimating Sample Size In Hypothesis Testing Experiments Chapter 5: Use Of G*Power Software -- Chapter 6: Determining Sample Size In Experimental Studies -- Chapter 7: Determining Sample Size In General Linear Models This book addresses sample size and power in the context of research, offering valuable insights for graduate and doctoral students as well as researchers in any discipline where data is generated to investigate research questions. It explains how to enhance the authenticity of research by estimating the sample size and reporting the power of the tests used. Further, it discusses the issue of sample size determination in survey studies as well as in hypothesis testing experiments so that readers can grasp the concept of statistical errors, minimum detectable difference, effect size, one-tail and two-tail tests and the power of the test. The book also highlights the importance of fixing these boundary conditions in enhancing the authenticity of research findings and improving the chances of research papers being accepted by respected journals. Further, it explores the significance of sample size by showing the power achieved in selected doctoral studies. Procedure has been discussed to fix power in the hypothesis testing experiment. One should usually have power at least 0.8 in the study because having power less than this will have the issue of practical significance of findings. If the power in any study is less than 0.5 then it would be better to test the hypothesis by tossing a coin instead of organizing the experiment. It also discusses determining sample size and power using the freeware G*Power software, based on twenty-one examples using different analyses, like t-test, parametric and non-parametric correlations, multivariate regression, logistic regression, independent and repeated measures ANOVA, mixed design, MANOVA and chi-square HTTP:URL=https://doi.org/10.1007/978-981-15-5204-5

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