![]() We suggest a method for investigating the effects of sample size on the precision of a population size estimate obtained using multipler methods and respondent-driven sampling. As expected, sample size requirements are higher when the design effect of the survey is assumed to be greater. Random error around the size estimate reflects uncertainty from M and P, particularly when the estimate of P in the respondent-driven sampling survey is low. We describe an application to estimate the number of female sex workers in Harare, Zimbabwe. ![]() We have developed an approach to sample size calculation, interpreting methods to estimate the variance around estimates obtained using multiplier methods in conjunction with research into design effects and respondent-driven sampling. The population size estimate is obtained by dividing the number of individuals receiving a service or the number of unique objects distributed (M) by the proportion of individuals in a representative survey who report receipt of the service or object (P). ![]() To guide the design of multiplier method population size estimation studies using respondent-driven sampling surveys to reduce the random error around the estimate obtained. While guidance exists for obtaining population size estimates using multiplier methods with respondent-driven sampling surveys, we lack specific guidance for making sample size decisions. Sample Size Calculations for Population Size Estimation Studies Using Multiplier Methods With Respondent-Driven Sampling Surveys.įearon, Elizabeth Chabata, Sungai T Thompson, Jennifer A Cowan, Frances M Hargreaves, James R Correlated quadrat abundance estimates based on mark–recapture or distance sampling methods occur. We develop estimators that allow correlated quadrat abundance estimates, even for quadrats in different sampling strata. Ganeyįinite population sampling theory is useful in estimating total population size (abundance) from abundance estimates of each sampled unit (quadrat). It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization.Įstimating population size with correlated sampling unit estimatesĭavid C. RnaSeq SampleSize provides a convenient and powerful way for power and sample size estimation for an RNAseq experiment. A user friendly web graphic interface is provided at SampleSize/. RnaSeq SampleSize is implemented in R language and can be installed from Bioconductor website. Read counts and their dispersions were estimated from the reference's distribution using that information, we estimated and summarized the power and sample size. Datasets from previous, similar experiments such as the Cancer Genome Atlas (TCGA) can be used as a point of reference. To solve these issues, we developed a sample size and power estimation method named RnaSeq SampleSize, based on the distributions of gene average read counts and dispersions estimated from real RNA-seq data. Thus, additional issues should be carefully addressed, including the false discovery rate for multiple statistic tests, widely distributed read counts and dispersions for different genes. However, thousands of genes are quantified and tested for differential expression simultaneously in RNA-Seq experiments. A few negative binomial model-based methods have been developed to estimate sample size based on the parameters of a single gene. One of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. Zhao, Shilin Li, Chung-I Guo, Yan Sheng, Quanhu Shyr, Yu ![]() RnaSeq SampleSize: real data based sample size estimation for RNA sequencing. A few parameters are required to estimate sample sizes and… Although there are many sources available for estimating sample sizes, methods are not often integrated across statistical tests, levels of measurement of variables, or effect sizes. This article presents a simple approach to making quick sample size estimates for basic hypothesis tests. ERIC Educational Resources Information Center ![]()
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