Example Evaluation Plan for a Quasi-Experimental Design
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When Does Blocking Help?
page 1 When Does Blocking Help? Teacher Notes, Part I The purpose of blocking is frequently described as “reducing variability.” However, this phrase carries little meaning to most beginning students of statistics. This activity, consisting of three rounds of simulation, is designed to illustrate what reducing variability really means in this context. In fact, students should see that a better description than “reducing variability” might be “attributing variability”, or “reducing unexplained variability”. The activity can be completed in a single 90-minute class or two classes of at least 45 minutes. For shorter classes you may wish to extend the simulations over two days. It is important that students understand not only what to do but also why they do what they do. Background Here is the specific problem that will be addressed in this activity: A set of 24 dogs (6 of each of four breeds; 6 from each of four veterinary clinics) has been randomly selected from a population of dogs older than eight years of age whose owners have permitted their inclusion in a study. Each dog will be assigned to exactly one of three treatment groups. Group “Ca” will receive a dietary supplement of calcium, Group “Ex” will receive a dietary supplement of calcium and a daily exercise regimen, and Group “Co” will be a control group that receives no supplement to the ordinary diet and no additional exercise. All dogs will have a bone density evaluation at the beginning and end of the one-year study. (The bone density is measured in Houndsfield units by using a CT scan.) The goals of the study are to determine (i) whether there are different changes in bone density over the year of the study for the dogs in the three treatment groups; and if so, (ii) how much each treatment influences that change in bone density. -
Lec 9: Blocking and Confounding for 2K Factorial Design
Lec 9: Blocking and Confounding for 2k Factorial Design Ying Li December 2, 2011 Ying Li Lec 9: Blocking and Confounding for 2k Factorial Design 2k factorial design Special case of the general factorial design; k factors, all at two levels The two levels are usually called low and high (they could be either quantitative or qualitative) Very widely used in industrial experimentation Ying Li Lec 9: Blocking and Confounding for 2k Factorial Design Example Consider an investigation into the effect of the concentration of the reactant and the amount of the catalyst on the conversion in a chemical process. A: reactant concentration, 2 levels B: catalyst, 2 levels 3 replicates, 12 runs in total Ying Li Lec 9: Blocking and Confounding for 2k Factorial Design 1 A B A = f[ab − b] + [a − (1)]g − − (1) = 28 + 25 + 27 = 80 2n + − a = 36 + 32 + 32 = 100 1 B = f[ab − a] + [b − (1)]g − + b = 18 + 19 + 23 = 60 2n + + ab = 31 + 30 + 29 = 90 1 AB = f[ab − b] − [a − (1)]g 2n Ying Li Lec 9: Blocking and Confounding for 2k Factorial Design Manual Calculation 1 A = f[ab − b] + [a − (1)]g 2n ContrastA = ab + a − b − (1) Contrast SS = A A 4n Ying Li Lec 9: Blocking and Confounding for 2k Factorial Design Regression Model For 22 × 1 experiment Ying Li Lec 9: Blocking and Confounding for 2k Factorial Design Regression Model The least square estimates: The regression coefficient estimates are exactly half of the \usual" effect estimates Ying Li Lec 9: Blocking and Confounding for 2k Factorial Design Analysis Procedure for a Factorial Design Estimate factor effects. -
Chapter 7 Blocking and Confounding Systems for Two-Level Factorials
Chapter 7 Blocking and Confounding Systems for Two-Level Factorials &5² Design and Analysis of Experiments (Douglas C. Montgomery) hsuhl (NUK) DAE Chap. 7 1 / 28 Introduction Sometimes, it is impossible to perform all 2k factorial experiments under homogeneous condition. I a batch of raw material: not large enough for the required runs Blocking technique: making the treatments are equally effective across many situation hsuhl (NUK) DAE Chap. 7 2 / 28 Blocking a Replicated 2k Factorial Design 2k factorial design, n replicates Example 7.1: chemical process experiment 22 factorial design: A-concentration; B-catalyst 4 trials; 3 replicates hsuhl (NUK) DAE Chap. 7 3 / 28 Blocking a Replicated 2k Factorial Design (cont.) n replicates a block: each set of nonhomogeneous conditions each replicate is run in one of the blocks 3 2 2 X Bi y··· SSBlocks= − (2 d:f :) 4 12 i=1 = 6:50 The block effect is small. hsuhl (NUK) DAE Chap. 7 4 / 28 Confounding Confounding(干W;混雜;ø絡) the block size is smaller than the number of treatment combinations impossible to perform a complete replicate of a factorial design in one block confounding: a design technique for arranging a complete factorial experiment in blocks causes information about certain treatment effects(high-order interactions) to be indistinguishable(p|辨½的) from, or confounded with blocks hsuhl (NUK) DAE Chap. 7 5 / 28 Confounding the 2k Factorial Design in Two Blocks a single replicate of 22 design two batches of raw material are required 2 factors with 2 blocks hsuhl (NUK) DAE Chap. 7 6 / 28 Confounding the 2k Factorial Design in Two Blocks (cont.) 1 A = 2 [ab + a − b−(1)] 1 (any difference between block 1 and 2 will cancel out) B = 2 [ab + b − a−(1)] 1 AB = [ab+(1) − a − b] 2 (block effect and AB interaction are identical; confounded with blocks) hsuhl (NUK) DAE Chap. -
Introduction to Biostatistics
Introduction to Biostatistics Jie Yang, Ph.D. Associate Professor Department of Family, Population and Preventive Medicine Director Biostatistical Consulting Core 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 biostatistics 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… 푿 , 풔, 풑 ,… 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 Design with Blocking – dealing with nuisance factor which has some effect on the response, but of no interest to the experimenter; Without blocking, large unexplained error leads to less detection power. 1. Randomized Complete Block Design (RCBD) - One single blocking factor 2. Latin Square 3. Cross over Design Design (two (each subject=blocking factor) 4. Balanced Incomplete blocking factor) Block Design EXPERIMENT DESIGN Factorial Design: similar to randomized block design, but allowing to test the interaction between two treatment effects. -
Third Year Preceptor Evaluation Form
3rd Year Preceptor Evaluation Please select student’s home campus: Auburn Carolinas Virginia Printed Student Name: Start Date: End Date: Printed Preceptor Name and Degree: Discipline: The below performance ratings are designed to evaluate a student engaged in their 3rd year of clinical rotations which corresponds to their first year of full time clinical training. Unacceptable – performs below the expected standards for the first year of clinical training (OMS3) despite feedback and direction Below expectations – performs below expectations for the first year of clinical training (OMS3). Responds to feedback and direction but still requires maximal supervision, and continual prompting and direction to achieve tasks Meets expectations – performs at the expected level of training (OMS3); able to perform basic tasks with some prompting and direction Above expectations – performs above expectations for their first year of clinical training (OMS3); requires minimal prompting and direction to perform required tasks Exceptional – performs well above peers; able to model tasks for peers or juniors, of medical students at the OMS3 level Place a check in the appropriate column to indicate your rating for the student in that particular area. Clinical skills and Procedure Log Documentation Preceptor has reviewed and discussed the CREDO LOG (Clinical Experience) for this Yes rotation. This is mandatory for passing the rotation for OMS 3. No Area of Evaluation - Communication Question Unacceptable Below Meets Above Exceptional N/A Expectations Expectations Expectations 1. Effectively listen to patients, family, peers, & healthcare team. 2. Demonstrates compassion and respect in patient communications. 3. Effectively collects chief complaint and history. 4. Considers whole patient: social, spiritual & cultural concerns. -
The Fire and Smoke Model Evaluation Experiment—A Plan for Integrated, Large Fire–Atmosphere Field Campaigns
atmosphere Review The Fire and Smoke Model Evaluation Experiment—A Plan for Integrated, Large Fire–Atmosphere Field Campaigns Susan Prichard 1,*, N. Sim Larkin 2, Roger Ottmar 2, Nancy H.F. French 3 , Kirk Baker 4, Tim Brown 5, Craig Clements 6 , Matt Dickinson 7, Andrew Hudak 8 , Adam Kochanski 9, Rod Linn 10, Yongqiang Liu 11, Brian Potter 2, William Mell 2 , Danielle Tanzer 3, Shawn Urbanski 12 and Adam Watts 5 1 University of Washington School of Environmental and Forest Sciences, Box 352100, Seattle, WA 98195-2100, USA 2 US Forest Service Pacific Northwest Research Station, Pacific Wildland Fire Sciences Laboratory, Suite 201, Seattle, WA 98103, USA; [email protected] (N.S.L.); [email protected] (R.O.); [email protected] (B.P.); [email protected] (W.M.) 3 Michigan Technological University, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USA; [email protected] (N.H.F.F.); [email protected] (D.T.) 4 US Environmental Protection Agency, 109 T.W. Alexander Drive, Durham, NC 27709, USA; [email protected] 5 Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512, USA; [email protected] (T.B.); [email protected] (A.W.) 6 San José State University Department of Meteorology and Climate Science, One Washington Square, San Jose, CA 95192-0104, USA; [email protected] 7 US Forest Service Northern Research Station, 359 Main Rd., Delaware, OH 43015, USA; [email protected] 8 US Forest Service Rocky Mountain Research Station Moscow Forestry Sciences Laboratory, 1221 S Main St., Moscow, ID 83843, USA; [email protected] 9 Department of Atmospheric Sciences, University of Utah, 135 S 1460 East, Salt Lake City, UT 84112-0110, USA; [email protected] 10 Los Alamos National Laboratory, P.O. -
Journals in Assessment, Evaluation, Measurement, Psychometrics and Statistics
Leadership Awards Publications Resources Membership About Journals in Assessment, Evaluation, Measurement, Psychometrics and Statistics Compiled by (mailto:[email protected]) Lihshing Leigh Wang (mailto:[email protected]) , Chair of APA Division 5 Public and International Affairs Committee. Journal Association/ Scope and Aims (Adapted from journals' mission Impact Publisher statements) Factor* American Journal of Evaluation (http://www.sagepub.com/journalsProdDesc.nav? American Explores decisions and challenges related to prodId=Journal201729) Evaluation conceptualizing, designing and conducting 1.16 Association/Sage evaluations. Offers original articles about the methods, theory, ethics, politics, and practice of evaluation. Features broad, multidisciplinary perspectives on issues in evaluation relevant to education, public administration, behavioral sciences, human services, health sciences, sociology, criminology and other disciplines and professional practice fields. The American Statistician (http://amstat.tandfonline.com/loi/tas#.Vk-A2dKrRpg) American Publishes general-interest articles about current 0.98^ Statistical national and international statistical problems and Association programs, interesting and fun articles of a general nature about statistics and its applications, and the teaching of statistics. Applied Measurement in Education (http://www.tandf.co.uk/journals/titles/08957347.asp) Taylor & Francis Because interaction between the domains of 0.33 research and application is critical to the evaluation and improvement of -
Research Designs for Program Evaluations
03-McDavid-4724.qxd 5/26/2005 7:16 PM Page 79 CHAPTER 3 RESEARCH DESIGNS FOR PROGRAM EVALUATIONS Introduction 81 What Is Research Design? 83 The Origins of Experimental Design 84 Why Pay Attention to Experimental Designs? 89 Using Experimental Designs to Evaluate Programs: The Elmira Nurse Home Visitation Program 91 The Elmira Nurse Home Visitation Program 92 Random Assignment Procedures 92 The Findings 93 Policy Implications of the Home Visitation Research Program 94 Establishing Validity in Research Designs 95 Defining and Working With the Four Kinds of Validity 96 Statistical Conclusions Validity 97 Working with Internal Validity 98 Threats to Internal Validity 98 Introducing Quasi-Experimental Designs: The Connecticut Crackdown on Speeding and the Neighborhood Watch Evaluation in York, Pennsylvania 100 79 03-McDavid-4724.qxd 5/26/2005 7:16 PM Page 80 80–●–PROGRAM EVALUATION AND PERFORMANCE MEASUREMENT The Connecticut Crackdown on Speeding 101 The York Neighborhood Watch Program 104 Findings and Conclusions From the Neighborhood Watch Evaluation 105 Construct Validity 109 External Validity 112 Testing the Causal Linkages in Program Logic Models 114 Research Designs and Performance Measurement 118 Summary 120 Discussion Questions 122 References 125 03-McDavid-4724.qxd 5/26/2005 7:16 PM Page 81 Research Designs for Program Evaluations–●–81 INTRODUCTION Over the last 30 years, the field of evaluation has become increasingly diverse in terms of what is viewed as proper methods and practice. During the 1960s and into the 1970s most evaluators would have agreed that a good program evaluation should emulate social science research and, more specifically, methods should come as close to randomized experiments as possible. -
An Outline of Critical Thinking
AN OUTLINE OF CRITICAL THINKING LEVELS OF INQUIRY 1. Information: correct understanding of basic information. 2. Understanding basic ideas: correct understanding of the basic meaning of key ideas. 3. Probing: deeper analysis into ideas, bases, support, implications, looking for complexity. 4. Critiquing: wrestling with tensions, contradictions, suspect support, problematic implications. This leads to further probing and then further critique, & it involves a recognition of the limitations of your own view. 5. Assessment: final evaluation, acknowledging the relative strengths & limitations of all sides. 6. Constructive: an articulation of your own view, recognizing its limits and areas for further inquiry. EMPHASES Issues! Reading: Know the issues an author is responding to. Writing: Animate and organize your paper around issues. Complexity! Reading: assume that there is more to an idea than is immediately obvious; assume that a key term can be used in various ways and clarify the meaning used in the article; assume that there are different possible interpretations of a text, various implications of ideals, and divergent tendencies within a single tradition, etc. Writing: Examine ideas, values, and traditions in their complexity: multiple aspects of the ideas, different possible interpretations of a text, various implications of ideals, different meanings of terms, divergent tendencies within a single tradition, etc. Support! Reading: Highlight the kind and degree of support: evidence, argument, authority Writing: Support your views with evidence, argument, and/or authority Basis! (ideas, definitions, categories, and assumptions) Reading: Highlight the key ideas, terms, categories, and assumptions on which the author is basing his views. Writing: Be aware of the ideas that give rise to your interpretation; be conscious of the definitions you are using for key terms; recognize the categories you are applying; critically examine your own assumptions. -
Design of Engineering Experiments Blocking & Confounding in the 2K
Design of Engineering Experiments Blocking & Confounding in the 2 k • Text reference, Chapter 7 • Blocking is a technique for dealing with controllable nuisance variables • Two cases are considered – Replicated designs – Unreplicated designs Chapter 7 Design & Analysis of Experiments 1 8E 2012 Montgomery Chapter 7 Design & Analysis of Experiments 2 8E 2012 Montgomery Blocking a Replicated Design • This is the same scenario discussed previously in Chapter 5 • If there are n replicates of the design, then each replicate is a block • Each replicate is run in one of the blocks (time periods, batches of raw material, etc.) • Runs within the block are randomized Chapter 7 Design & Analysis of Experiments 3 8E 2012 Montgomery Blocking a Replicated Design Consider the example from Section 6-2 (next slide); k = 2 factors, n = 3 replicates This is the “usual” method for calculating a block 3 B2 y 2 sum of squares =i − ... SS Blocks ∑ i=1 4 12 = 6.50 Chapter 7 Design & Analysis of Experiments 4 8E 2012 Montgomery 6-2: The Simplest Case: The 22 Chemical Process Example (1) (a) (b) (ab) A = reactant concentration, B = catalyst amount, y = recovery ANOVA for the Blocked Design Page 305 Chapter 7 Design & Analysis of Experiments 6 8E 2012 Montgomery Confounding in Blocks • Confounding is a design technique for arranging a complete factorial experiment in blocks, where the block size is smaller than the number of treatment combinations in one replicate. • Now consider the unreplicated case • Clearly the previous discussion does not apply, since there -
Project Quality Evaluation – an Essential Component of Project Success
Mathematics and Computers in Biology, Business and Acoustics Project Quality Evaluation – An Essential Component of Project Success DUICU SIMONA1, DUMITRASCU ADELA-ELIZA2, LEPADATESCU BADEA2 Descriptive Geometry and Technical Drawing Department1, Manufacturing Engineering Department2, Faculty of Manufacturing Engineering and Industrial Management “Transilvania” University of Braşov 500036 Eroilor Street, No. 29, Brasov ROMANIA [email protected]; [email protected]; [email protected]; Abstract: - In this article are presented aspects, elements and indices regarding project quality management as a potential source of sustainable competitive advantages. The success of a project depends on implementation of the project quality processes: quality planning, quality assurance, quality control and continuous process improvement drive total quality management success. Key-Words: - project management, project quality management, project quality improvement. 1 Introduction negative impact on the possibilities of planning and Project management is the art — because it requires the carrying out project activities); skills, tact and finesse to manage people, and science — • supply chain problems; because it demands an indepth knowledge of an • lack of resources (funds or trained personnel); assortment of technical tools, of managing relatively • organizational inefficiency. short-term efforts, having finite beginning and ending - external factors: points, usually with a specific budget, and with customer • natural factors (natural disasters); specified performance criteria. “Short term” in the • external economic influences (adverse change in context of project duration is dependent on the industry. currency exchange rate used in the project); The longer and more complex a project is, the more it • reaction of people affected by the project; will benefit from the application of project management • implementation of economic or social policy tools. -
Biostatistics and Experimental Design Spring 2014
Bio 206 Biostatistics and Experimental Design Spring 2014 COURSE DESCRIPTION Statistics is a science that involves collecting, organizing, summarizing, analyzing, and presenting numerical data. Scientists use statistics to discern patterns in natural systems and to predict how those systems will react in different situations. This course is designed to encourage an understanding and appreciation of the role of experimentation, hypothesis testing, and data analysis in the sciences. It will emphasize principles of experimental design, methods of data collection, exploratory data analysis, and the use of graphical and statistical tools commonly used by scientists to analyze data. The primary goals of this course are to help students understand how and why scientists use statistics, to provide students with the knowledge necessary to critically evaluate statistical claims, and to develop skills that students need to utilize statistical methods in their own studies. INSTRUCTOR Dr. Ann Throckmorton, Professor of Biology Office: 311 Hoyt Science Center Phone: 724-946-7209 e-mail: [email protected] Home Page: www.westminster.edu/staff/athrock Office hours: Monday 11:30 - 12:30 Wednesday 9:20 - 10:20 Thursday 12:40 - 2:00 or by appointment LECTURE 11:00 – 12:30, Tuesday/Thursday Patterson Computer Lab Attendance in lecture is expected but you will not be graded on attendance except indirectly through your grades for participation, exams, quizzes, and assignments. Because your success in this course is strongly dependent on your presence in class and your participation you should make an effort to be present at all class sessions. If you know ahead of time that you will be absent you may be able to make arrangements to attend the other section of the course.