PPT-Effect Size & Power Analysis G*Power

Office of Methodological amp Data Sciences November 13 2015 Sarah Schwartz wwwcehsusueduresearchomds Quantitative Research Research Question Clear focused amp concise question that drives the study. Jed Friedman, World . Bank. SIEF Regional Impact Evaluation Workshop. Beijing, China. July 2009. Adapted from slides by Esther . Duflo. , J-PAL. Planning Sample Size for . Randomized Evaluations. General question: . PSY505. Spring term, 2012. April . 11, . 2012. Today’s Class. Power Analysis. Statistical Power. Power Analysis. A set of methods for determining. The probability that you will obtain a statistically significant result, assuming a true effect size and sample size of a certain magnitude. Effect Size & Statistical Power. 1. 1. Effect Size. How “meaningful” is the significant difference?. 1. KNR 445. Statistics. Effect sizes. Slide . 3. Significance vs. meaningfulness. As sample size increases, likelihood of significant difference increases. Chapter 8. Hyde, Fennema & Lamon (1990). Mean gender differences in mathematical reasoning were very small.. When extreme tails of the distributions were removed, differences were smaller and reversed direction (favored girls). Laura . Chioda. World Bank. 1. Outline. Ingredients of a general recipe called “Statistical Sampling Theory” . Key message. : a successful design involves some guess work. It is important to have a general rule, but then needs discussion depending on the case at hand. Dr. .. Richy Hetherington. and Dr. . Kim Pearce. Introductions . Today’s Session. Run a live Experiment . Discussion of considerations when setting up experiments. Analyse the results of our experiments with thoughts on what to look out . Not. an Afterthought!. Blair T. Johnson. CHIP | Psychology | UConn. 1. Wow! Methods and Statistical Analysis Sections!!. After lunch??. Really???!!. 2. My background. Assistant-Associate Professor of Psychology, Syracuse University (1988-1999). Cal State Northridge. . 320. Andrew Ainsworth PhD. 2. Major Points. Review. What is power?. What controls power?. Effect size. Power for one sample . t. Power for related-samples. t. Power for two sample . November 17, . 2015. Allen Kunselman, MA. Division of Biostatistics and Bioinformatics. Department of Public . H. ealth Sciences. Quotes. An approximate answer to the right question is worth a good deal more than an exact answer to the wrong question.. General Sampling Issues. Thinking of the steps in sampling (from theoretical population to respondents)—what are some biases that can come in at each point?. What is the proximity similarity model? What are issues with that model?. Pam Davis-Kean. Methods Hour. January 20, 2017. I collected my data, I ran my analyses, and nothing is significant. . What . do I do?. Common . Question. Recommendations for Strong and Rigorous Research. Hao. . Zhou & David . Dueber. February 6, 2017. Applied Psychometric Strategies Lab. Applied Quantitative and Psychometric Series. Outline. Fake real life research scenario. Significance testing and statistical errors. ‘statistically significant’ does not mean ‘important’ . IQ’s of UW undergraduates. Suppose we measured the IQ’s of 10,000 UW undergraduates and found a mean IQ of 100.3. If we were to conduct a one-tailed z-test to determine if this mean is . Power Is. The conditional probability. that one will reject the null hypothesis. given that the null is really false. by a specified amount. and given certain other specifications such as sample size and the criterion of statistical significance (alpha). .