Chapter 11. Experimental Design: One-Way Independent Samples Design
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Introduction to Biostatistics
Introduction to Biostatistics Jie Yang, Ph.D. Associate Professor Department of Family, Population and Preventive Medicine Director Biostatistical Consulting Core Director Biostatistics and Bioinformatics Shared Resource, Stony Brook Cancer Center In collaboration with Clinical Translational Science Center (CTSC) and the Biostatistics and Bioinformatics Shared Resource (BB-SR), Stony Brook Cancer Center (SBCC). OUTLINE What is Biostatistics What does a biostatistician do • Experiment design, clinical trial design • Descriptive and Inferential analysis • Result interpretation What you should bring while consulting with a biostatistician WHAT IS BIOSTATISTICS • The science of (bio)statistics encompasses the design of biological/clinical experiments the collection, summarization, and analysis of data from those experiments the interpretation of, and inference from, the results How to Lie with Statistics (1954) by Darrell Huff. http://www.youtube.com/watch?v=PbODigCZqL8 GOAL OF STATISTICS Sampling POPULATION Probability SAMPLE Theory Descriptive Descriptive Statistics Statistics Inference Population Sample Parameters: Inferential Statistics Statistics: 흁, 흈, 흅… 푿ഥ , 풔, 풑ෝ,… PROPERTIES OF A “GOOD” SAMPLE • Adequate sample size (statistical power) • Random selection (representative) Sampling Techniques: 1.Simple random sampling 2.Stratified sampling 3.Systematic sampling 4.Cluster sampling 5.Convenience sampling STUDY DESIGN EXPERIEMENT DESIGN Completely Randomized Design (CRD) - Randomly assign the experiment units to the treatments -
Experimentation Science: a Process Approach for the Complete Design of an Experiment
Kansas State University Libraries New Prairie Press Conference on Applied Statistics in Agriculture 1996 - 8th Annual Conference Proceedings EXPERIMENTATION SCIENCE: A PROCESS APPROACH FOR THE COMPLETE DESIGN OF AN EXPERIMENT D. D. Kratzer K. A. Ash Follow this and additional works at: https://newprairiepress.org/agstatconference Part of the Agriculture Commons, and the Applied Statistics Commons This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License. Recommended Citation Kratzer, D. D. and Ash, K. A. (1996). "EXPERIMENTATION SCIENCE: A PROCESS APPROACH FOR THE COMPLETE DESIGN OF AN EXPERIMENT," Conference on Applied Statistics in Agriculture. https://doi.org/ 10.4148/2475-7772.1322 This is brought to you for free and open access by the Conferences at New Prairie Press. It has been accepted for inclusion in Conference on Applied Statistics in Agriculture by an authorized administrator of New Prairie Press. For more information, please contact [email protected]. Conference on Applied Statistics in Agriculture Kansas State University Applied Statistics in Agriculture 109 EXPERIMENTATION SCIENCE: A PROCESS APPROACH FOR THE COMPLETE DESIGN OF AN EXPERIMENT. D. D. Kratzer Ph.D., Pharmacia and Upjohn Inc., Kalamazoo MI, and K. A. Ash D.V.M., Ph.D., Town and Country Animal Hospital, Charlotte MI ABSTRACT Experimentation Science is introduced as a process through which the necessary steps of experimental design are all sufficiently addressed. Experimentation Science is defined as a nearly linear process of objective formulation, selection of experimentation unit and decision variable(s), deciding treatment, design and error structure, defining the randomization, statistical analyses and decision procedures, outlining quality control procedures for data collection, and finally analysis, presentation and interpretation of results. -
Those Missing Values in Questionnaires
Those Missing Values in Questionnaires John R. Gerlach, Maxim Group, Plymouth Meeting, PA Cindy Garra, IMS HEALTH; Plymouth Meeting, PA Abstract alternative values are not bona fide SAS missing values; consequently, a SAS procedure, expectedly, will include Questionnaires are notorious for containing these alternative values, thereby producing bogus results. responses that later become missing values during processing and analyses. Contrary to a non-response that Typically, you can re-code the original data, so results in a typical, bona fide, missing value, questions that the missing values become ordinary missing values, might allow several alternative responses, necessarily thus allowing SAS to process only appropriate values. Of outside the usual range of appropriate responses. course, there's a loss of information since the original Whether a question represents continuous or categorical missing values did not constitute a non-response. Also, data, a good questionnaire offers meaningful alternatives, such pre-processing might include a myriad of IF THEN / such as: "Refused to Answer" and, of course, the ELSE statements, which can be very tedious and time- quintessential "Don't Know." Traditionally, these consuming to write, and difficult to maintain. Thus, given alternative responses have numeric values such as 97, a questionnaire that has over a hundred variables with 998, or 9999 and, therefore, pose problems when trying varying levels of missing values, the task of re-coding to distinguish them from normal responses, especially these variables becomes very time consuming at best. when multiple missing values exist. This paper discusses Even worse, the permanent re-coding of alternative missing values in SAS and techniques that facilitate the responses to ordinary missing numeric values in SAS process of dealing with multi-valued, meaningful missing precludes categorical analysis that requires the inclusion values often found in questionnaires. -
10 Questions Opinion Polls
Questions you might have on 10opinion polls 1. What is an opinion poll? An opinion poll is a survey carried out to measure views on a certain topic within a specific group of people. For example, the topic may relate to who Kenyans support in the presidential race, in which case, the group of people interviewed will be registered voters. 2. How are interviewees for an opinion poll selected? The group of people interviewed for an opinion poll is called a sample. As the name suggests, a sample is a group of people that represents the total population whose opinion is being surveyed. In a scientific opinion poll, everyone has an equal chance of being interviewed. 3. So how come I have never been interviewed for an opinion poll? You have the same chance of being polled as anyone else living in Kenya. However, chances of this are very small and are estimated at about 1 in 14,000. This is because there are approximately 14 million registered voters in Kenya and, for practical and cost reasons, usually only between 1,000 and 2,000 people are interviewed for each survey carried out. 4. How can such a small group be representative of the entire population? In order to ensure that the sample/survey group is representative of the population, the surveyors must ensure that the group reflects the characteristics of the whole. For instance, to get a general idea of who might win the Kenyan presidential election, only the views of registered voters in Kenya will be surveyed as these are the people who will be able to influence the election. -
Handbook of Recommended Practices for Questionnaire Development and Testing in the European Statistical System
Handbook of Recommended Practices for Questionnaire Development and Testing in the European Statistical System Release year: 2006 Authors: G. Brancato, S. Macchia, M. Murgia, M. Signore, G. Simeoni - Italian National Institute of Statistics, ISTAT K. Blanke, T. Körner, A. Nimmergut - Federal Statistical Office Germany, FSO P. Lima, R. Paulino - National Statistical Institute of Portugal, INE J.H.P. Hoffmeyer-Zlotnik - German Center for Survey Research and Methodology, ZUMA Version 1 Acknowledgements We are grateful to the experts from the network countries who supported us in all relevant stages of the work: Anja Ahola, Dirkjan Beukenhorst, Trine Dale, Gustav Haraldsen. We also thank all colleagues from European and overseas NSIs who helped us in understanding the current practices and in the review of the draft version of the handbook. Executive summary Executive Summary Questionnaires constitute the basis of every survey-based statistical measurement. They are by far the most important measurement instruments statisticians use to grasp the phenomena to be measured. Errors due to an insufficient questionnaire can hardly be compensated at later stages of the data collection process. Therefore, having systematic questionnaire design and testing procedures in place is vital for data quality, particularly for a minimisation of the measurement error. Against this background, the Directors General of the members of the European Statistical System (ESS) stressed the importance of questionnaire design and testing in the European Statistics Code of Practice, endorsed in February 2005. Principle 8 of the Code states that “appropriate statistical procedures, implemented from data collection to data validation, must underpin quality statistics.” One of the indicators referring to this principle requires that “questionnaires are systematically tested prior to the data collection.” Taking the Code of Practice as a starting point, this Recommended Practice Manual aims at further specifying the requirements of the Code of Practice. -
Meta-Analysis of the Relationships Between Different Leadership Practices and Organizational, Teaming, Leader, and Employee Outcomes*
Journal of International Education and Leadership Volume 8 Issue 2 Fall 2018 http://www.jielusa.org/ ISSN: 2161-7252 Meta-Analysis of the Relationships Between Different Leadership Practices and Organizational, Teaming, Leader, and Employee Outcomes* Carl J. Dunst Orelena Hawks Puckett Institute Mary Beth Bruder University of Connecticut Health Center Deborah W. Hamby, Robin Howse, and Helen Wilkie Orelena Hawks Puckett Institute * This study was supported, in part, by funding from the U.S. Department of Education, Office of Special Education Programs (No. 325B120004) for the Early Childhood Personnel Center, University of Connecticut Health Center. The contents and opinions expressed, however, are those of the authors and do not necessarily reflect the policy or official position of either the Department or Office and no endorsement should be inferred or implied. The authors report no conflicts of interest. The meta-analysis described in this paper evaluated the relationships between 11 types of leadership practices and 7 organizational, teaming, leader, and employee outcomes. A main focus of analysis was whether the leadership practices were differentially related to the study outcomes. Studies were eligible for inclusion if the correlations between leadership subscale measures (rather than global measures of leadership) and outcomes of interest were reported. The random effects weighted average correlations between the independent and dependent measures were used as the sizes of effects for evaluating the influences of the leadership practices on the outcome measures. One hundred and twelve studies met the inclusion criteria and included 39,433 participants. The studies were conducted in 31 countries in different kinds of programs, organizations, companies, and businesses. -
Uses for Census Data in Market Research
Uses for Census Data in Market Research MRS Presentation 4 July 2011 Andrew Zelin, Director, Sampling & RMC, Ipsos MORI Why Census Data is so critical to us Use of Census Data underlies almost all of our quantitative work activities and projects Needed to ensure that: – Samples are balanced and representative – ie Sampling; – Results derived from samples reflect that of target population – ie Weighting; – Understanding how demographic and geographic factors affect the findings from surveys – ie Analysis. How would we do our jobs without the use of Census Data? Our work withoutCensusData Our work Regional Assembly What I will cover Introduction, overview and why Census Data is so critical; Census data for sampling; Census data for survey weighting; Census data for statistical analysis; Use of SARs; The future (eg use of “hypercubes”); Our work without Census Data / use of alternatives; Conclusions Census Data for Sampling For Random (pre-selected) Samples: – Need to define stratum sample sizes and sampling fractions; – Need to define cluster size, esp. if PPS – (Need to define stratum / cluster) membership; For any type of Sample: – To balance samples across demographics that cannot be included as quota controls or stratification variables – To determine number of sample points by geography – To create accurate booster samples (eg young people / ethnic groups) Census Data for Sampling For Quota (non-probability) Samples: – To set appropriate quotas (based on demographics); Data are used here at very localised level – ie Census Outputs Census Data for Sampling Census Data for Survey Weighting To ensure that the sample profile balances the population profile Census Data are needed to tell you what the population profile is ….and hence provide accurate weights ….and hence provide an accurate measure of the design effect and the true level of statistical reliability / confidence intervals on your survey measures But also pre-weighting, for interviewer field dept. -
Survey Experiments
IU Workshop in Methods – 2019 Survey Experiments Testing Causality in Diverse Samples Trenton D. Mize Department of Sociology & Advanced Methodologies (AMAP) Purdue University Survey Experiments Page 1 Survey Experiments Page 2 Contents INTRODUCTION ............................................................................................................................................................................ 8 Overview .............................................................................................................................................................................. 8 What is a survey experiment? .................................................................................................................................... 9 What is an experiment?.............................................................................................................................................. 10 Independent and dependent variables ................................................................................................................. 11 Experimental Conditions ............................................................................................................................................. 12 WHY CONDUCT A SURVEY EXPERIMENT? ........................................................................................................................... 13 Internal, external, and construct validity .......................................................................................................... -
CORRELATION COEFFICIENTS Ice Cream and Crimedistribute Difficulty Scale ☺ ☺ (Moderately Hard)Or
5 COMPUTING CORRELATION COEFFICIENTS Ice Cream and Crimedistribute Difficulty Scale ☺ ☺ (moderately hard)or WHAT YOU WILLpost, LEARN IN THIS CHAPTER • Understanding what correlations are and how they work • Computing a simple correlation coefficient • Interpretingcopy, the value of the correlation coefficient • Understanding what other types of correlations exist and when they notshould be used Do WHAT ARE CORRELATIONS ALL ABOUT? Measures of central tendency and measures of variability are not the only descrip- tive statistics that we are interested in using to get a picture of what a set of scores 76 Copyright ©2020 by SAGE Publications, Inc. This work may not be reproduced or distributed in any form or by any means without express written permission of the publisher. Chapter 5 ■ Computing Correlation Coefficients 77 looks like. You have already learned that knowing the values of the one most repre- sentative score (central tendency) and a measure of spread or dispersion (variability) is critical for describing the characteristics of a distribution. However, sometimes we are as interested in the relationship between variables—or, to be more precise, how the value of one variable changes when the value of another variable changes. The way we express this interest is through the computation of a simple correlation coefficient. For example, what’s the relationship between age and strength? Income and years of education? Memory skills and amount of drug use? Your political attitudes and the attitudes of your parents? A correlation coefficient is a numerical index that reflects the relationship or asso- ciation between two variables. The value of this descriptive statistic ranges between −1.00 and +1.00. -
Analysis of Variance and Analysis of Variance and Design of Experiments of Experiments-I
Analysis of Variance and Design of Experimentseriments--II MODULE ––IVIV LECTURE - 19 EXPERIMENTAL DESIGNS AND THEIR ANALYSIS Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur 2 Design of experiment means how to design an experiment in the sense that how the observations or measurements should be obtained to answer a qqyuery inavalid, efficient and economical way. The desigggning of experiment and the analysis of obtained data are inseparable. If the experiment is designed properly keeping in mind the question, then the data generated is valid and proper analysis of data provides the valid statistical inferences. If the experiment is not well designed, the validity of the statistical inferences is questionable and may be invalid. It is important to understand first the basic terminologies used in the experimental design. Experimental unit For conducting an experiment, the experimental material is divided into smaller parts and each part is referred to as experimental unit. The experimental unit is randomly assigned to a treatment. The phrase “randomly assigned” is very important in this definition. Experiment A way of getting an answer to a question which the experimenter wants to know. Treatment Different objects or procedures which are to be compared in an experiment are called treatments. Sampling unit The object that is measured in an experiment is called the sampling unit. This may be different from the experimental unit. 3 Factor A factor is a variable defining a categorization. A factor can be fixed or random in nature. • A factor is termed as fixed factor if all the levels of interest are included in the experiment. -
The Politics of Random Assignment: Implementing Studies and Impacting Policy
The Politics of Random Assignment: Implementing Studies and Impacting Policy Judith M. Gueron Manpower Demonstration Research Corporation (MDRC) As the only nonacademic presenting a paper at this conference, I see it as my charge to focus on the challenge of implementing random assignment in the field. I will not spend time arguing for the methodological strengths of social experiments or advocating for more such field trials. Others have done so eloquently.1 But I will make my biases clear. For 25 years, I and many of my MDRC colleagues have fought to implement random assignment in diverse arenas and to show that this approach is feasible, ethical, uniquely convincing, and superior for answering certain questions. Our organization is widely credited with being one of the pioneers of this approach, and through its use producing results that are trusted across the political spectrum and that have made a powerful difference in social policy and research practice. So, I am a believer, but not, I hope, a blind one. I do not think that random assignment is a panacea or that it can address all the critical policy questions, or substitute for other types of analysis, or is always appropriate. But I do believe that it offers unique power in answering the “Does it make a difference?” question. With random assignment, you can know something with much greater certainty and, as a result, can more confidently separate fact from advocacy. This paper focuses on implementing experiments. In laying out the ingredients of success, I argue that creative and flexible research design skills are essential, but that just as important are operational and political skills, applied both to marketing the experiment in the first place and to helping interpret and promote its findings down the line. -
Double Blind Trials Workshop
Double Blind Trials Workshop Introduction These activities demonstrate how double blind trials are run, explaining what a placebo is and how the placebo effect works, how bias is removed as far as possible and how participants and trial medicines are randomised. Curriculum Links KS3: Science SQA Access, Intermediate and KS4: Biology Higher: Biology Keywords Double-blind trials randomisation observer bias clinical trials placebo effect designing a fair trial placebo Contents Activities Materials Activity 1 Placebo Effect Activity Activity 2 Observer Bias Activity 3 Double Blind Trial Role Cards for the Double Blind Trial Activity Testing Layout Background Information Medicines undergo a number of trials before they are declared fit for use (see classroom activity on Clinical Research for details). In the trial in the second activity, pupils compare two potential new sunscreens. This type of trial is done with healthy volunteers to see if the there are any side effects and to provide data to suggest the dosage needed. If there were no current best treatment then this sort of trial would also be done with patients to test for the effectiveness of the new medicine. How do scientists make sure that medicines are tested fairly? One thing they need to do is to find out if their tests are free of bias. Are the medicines really working, or do they just appear to be working? One difficulty in designing fair tests for medicines is the placebo effect. When patients are prescribed a treatment, especially by a doctor or expert they trust, the patient’s own belief in the treatment can cause the patient to produce a response.