Supporting Online Material for
|
|
- Basil Welch
- 8 years ago
- Views:
Transcription
1 Supporting Online Material for Application of Bloom s Taxonomy Debunks the MCAT Myth Alex Y. Zheng, Janessa K. Lawhorn, Thomas Lumley, Scott Freeman* *To whom correspondence should be addressed. srf991@u.washington.edu This PDF file includes Materials and Methods SOM Text Tables S1 to S4 References Published 25 January 2008, Science 319, 414 (2008) DOI: /science
2 Supporting Online Material Additional data To test the hypothesis that essay and short-answer questions test at a higher level than multiple-choice questions, we compared the two categories of questions from exams in our sample that contained both question types: AP Biology and undergraduate exams (Table S1). Table S1: Differences in multiple-choice vs. written answer questions from the same exams. All measurements reflect mean +/- SE. (In all cases, multiple-choice questions are lower.) Weighted proportion Weighted of higher-order questions ratings AP Biology / / Undergraduate / / To evaluate how multiple-choice questions compare among the five sources of exams, we compared the weighted proportion of higher-order questions and weighted Bloom s ratings for multiple-choice questions only. Values from the GRE, MCAT, and Medical School exams are given in Table 1; values from the AP Biology and Undergraduate exams are provided in Table S2. Table S2: Bloom s ratings from AP biology and undergraduate exams, multiple choice questions only. All measurements reflect mean +/- SE. Weighted proportion Weighted n of higher-order questions ratings AP Biology / / Undergraduate / /- 0.11
3 Materials and Methods The exam questions we analyzed came from five types of sources. AP Biology: We obtained permission to use the 1999 and 2002 exams the most recent exams available from the College Board. Introductory biology courses for undergraduate majors: We obtained recent midterm or final exam questions from instructors at Brigham Young University (BYU), Montana State University (MSU), and the University of Washington (UW). The BYU questions were from one of the two courses in their yearlong introductory course; the MSU questions were from both courses in their yearlong introductory course; and the UW questions were from one of the three courses in their yearlong introductory course. We chose these schools because they are among the top ten feeder schools to the University of Washington School of Medicine (UWSOM). Because UWSOM has been rated the top medical school for training primary care physicians in the U.S. for over 10 consecutive years, admission is extremely selective. Thus, it is logical to expect that undergraduate exams from its top feeder schools may be more rigorous than average undergraduate exams. MCAT Biology Portion: We obtained permission from the American Association of Medical Colleges to use the biology portions of the 2005 and 2006 practice exams. The Association does not make its actual exams available, but maintains that its practice exams are accurate representations of actual exam questions. Biology GRE: We were granted permission to use the 2002 Biology GRE, which was the most recent exam available from the Educational Testing Service. 1 st -year Medical School Courses: We obtained recent midterm or final exam questions from five instructors in five first-year courses at the UWSOM. We randomly chose approximately equal numbers of test questions from each of the five categories for analysis. There was one exception to the random sampling: we sampled the essay questions from each of the AP exams with certainty. This was necessary because the essay questions represented just 4 of the 124 total questions on each exam, but represent 40% of the point value. We kept multi-part or sequential questions together during the sampling and rating process. For example, the MCAT and GRE contain numerous question series clustered with the same case history or graph. We kept these question series together in the original sequence for the experts to consider. Multi-part essay questions were treated in the same way. These types of questions represented one draw during the random question-selection process, but were rated as multiple questions. It was not possible to analyze exactly equal numbers from each source because some questions were clustered, and because the raters were not able to complete all of the questions we originally sampled.
4 We re-formatted all questions to a common font and style, randomized their order in a hard copy document, and presented them to a panel of three educational experts who categorized the questions based on Bloom s Taxonomy of learning. The experts did not know the nature of the sources. They were also unaware of the nature of the study. Applying Bloom s Taxonomy for Biology-related Questions Each level of assessment from Bloom s Taxonomy was assigned a numerical value between 1 and 6 (1 = knowledge of terms and recall of information; 2 = comprehension, or conceptual understanding; 3 = application of information and concepts to new situations; 4 = analysis, including the ability to identify patterns and the relationships among underlying components; 5 = synthesis, or the ability to connect disparate and/or new sources of information; and 6 = evaluation of ideas, evidence, and logic). The rating process began with three sessions where the experts scored questions independently, then discussed each question until they were able to come to a consensus rating. These sessions were designed to clarify criteria for assigning questions to a particular level on Bloom s taxonomy and foster agreement among raters. In the course of these initial discussions, the experts devised an extensive chart for evaluating biology exam questions at each level of Bloom s Taxonomy, similar to a previously published chart and descriptions for biology-related questions (S1). The raters are experienced teachers. When rating questions, they assumed that questions were being answered by students with a suitable level of knowledge at the introductory biology level. Although this assumption is appropriate for standardized exams, it may have created a bias towards rating the questions from the courses in our sample at too high of a level. This bias is unavoidable, because it is not possible for raters to know which pieces of information were explicitly stated during class. For example, if a multiple-choice question asked the student to choose an appropriate control for a given experimental design, the raters assumed the student had been taught the requirements for appropriate controls, but were not specifically told which design would be correct for the situation presented on the exam. Thus, a question of this type would be rated at the Application level not the Knowledge (recall) level. Evaluating Inter-rater Reliability To evaluate the agreement between raters, we calculated a weighted Kappa and intraclass correlation coefficient for all questions. The Kappa statistic is used to evaluate the level of agreement between raters for ratings that fit into discrete categories (S2); the weighted Kappa statistic is appropriate for quantifying between-rater agreement regarding discrete, ordinal categories, such as the levels in Blooms Taxonomy (S3). The
5 intra-class correlation coefficient is asymptotically equivalent to weighted Kappa if disagreements between categories are assumed to be proportional to the square of the distance between the categories. Values were calculated with the R statistical computation environment (S4). The average of three pair-wise linear weighted Kappa calculations yielded a result of 0.53, while the intra-class correlation coefficient calculation yielded a result of 0.68 with a 95% confidence interval of 0.64 < ICC < These values indicate a moderately high level of agreement between judges. Although Kappa values as high as we initially observed may be considered justification for only collecting data from a single rater, we took a conservative approach and had all three experts rate the other 477 questions in the study, for a total of 593 questions with ratings. To evaluate whether any of the three experts consistently rated questions differently from the other two, we calculated a disagreement or deviation value for the each of the raters. The equation we used was: Average deviation = n i= 1 (average rating of 3 raters) - (individual rater's rating) n This calculation allowed us to evaluate how much each rater deviated from the average rating of the three raters, over a sample of questions. The deviation values for each judge indicate that none of the judges consistently deviated strongly from the others in terms of ratings (Table S3). Table S3: Average Deviation in Bloom s Taxonomy Scale from Average Rating Judge 1 Judge 2 Judge 3 Average Average Deviation
6 Assigning Final Ratings For the 116 questions that the three raters discussed, the final ratings used in data analysis consisted of the consensus value that emerged. For the remaining 477 questions in the study, the final rating had to be assigned without discussion among the experts. We were able to make these final assignments based on patterns in how the experts resolved conflicts and came to a consensus on the 116 discussed questions. These patterns allowed us to construct conflict resolution rules and assign final ratings to the 477 nondiscussed questions, as follows: When all raters agreed, there was no conflict. This situation occurred in 41 of the 116 discussed questions and 170 of the 477 non-discussed questions. For these questions, which represent 35.6% of the total, the common value was considered the final rating. When two raters agreed and one disagreed, the discussion resulted in the third rater agreeing with the other two raters (n = 60). A total of 269 of the 477 (56.4%) nondiscussed questions were in this category. For these types of non-discussed questions, the rating common to two experts was taken as the final rating. Note that in 92% of cases, at least 2 of the 3 raters agreed on the rating of a non-discussed question. When all three raters disagreed and ratings formed a sequential run of ratings for example 1, 2, and 3 the consensus rating from discussion ended up being the intermediate value (n = 14). Just 33 of the 477 non-discussed questions (6.9%) were in this category. For this category of non-discussed question, we assigned the middle rating of the three as the final rating. When all three raters disagreed and ratings differed by more than one level on Bloom s taxonomy, discussion resulted in a consensus for the middle value (n = 1). Only five of the 477 non-discussed questions (1%) were in this category. For these five questions, an average of the three values was taken as the final rating. Questions that did not fall into one of these categories were dealt with by averaging or were thrown out. Only 5 questions of the 477 were in this category. To assess the validity of these conflict resolution rules, we compared the actual agreements made by judges during their discussions to ratings that were 1) predicted by the rules, 2) calculated by taking straight averages, and 3) determined by using a single judge as a sole decision-maker. Table S4 shows the average error of each of the methods, and indicates that using the decision rules allowed us to assign final ratings that were much closer to approximating the judges decision-making during discussions than straight averages or using any of the judges as a sole decision-maker.
7 Table S4: Average Errors for Various Methods of Approximating Judge Decision- Making Method used Average error Direct Average Conflict Resolution Judge 1 decisionmaker Judge 2 decisionmaker Judge 3 decisionmaker Weighting Test Questions In cases where questions on the same exam were awarded different point values, we weighted each question to reflect its importance on the exam in question, relative to other questions from the same exam, and the probability that it was sampled. For example, we sampled 88 questions from the 1999 AP exam 79 multiple-choice questions and four essay questions, each of which had one to three parts. Each multiple-choice question was worth 0.5% of the total exam while each of the four essay questions was worth 10% of the total exam. (For essay questions with multiple parts, we assumed that the 10% total was divided equally among parts.) To create weights for each question in our sample, we multiplied its point value as a fraction of the total possible on the exam by the reciprocal of the probability that the question was sampled. For example, each multiple-choice question sampled from the 1999 AP exam received a weight of * (120/79) = Because they were sampled with certainty, each essay question on that exam received a weight equal to its relative value on the exam (e.g. each part of the three-part questions received a weight of 0.033, while the one-part question received a weight of 0.10). This weighting scheme allowed us to evaluate questions in the context of each exam in effect, to analyze what proportion of the total assessment from each exam required students to demonstrate mastery at each level on Bloom s taxonomy. To generate the histograms in Figure 1, we summed the weights for questions sampled at each level on Bloom s taxonomy from each exam source, and converted each sum to a proportion of the total. Data Analysis Because some of the exams that we analyzed contained clustered questions, our sampling scheme was analogous to surveys where respondents are clustered by geographic area or other categories that can induce correlations among data points (S5). We thus approached the data as a stratified cluster sample, with sets of dependent questions sampled as
8 clusters. Weights were then post-stratified to give each question a weight proportional to the points available for that question. Because the goal was to generalize to the processes of question selection rather than to the specific finite populations of questions, we did not use a finite-population correction to the variance. Data were analyzed using linear regression in Survey for R (S4, S6, S7); logistic regressions gave qualitatively identical results. Tests comparing multiple groups were design-based F-tests of the null hypothesis that all groups have the same mean; pairwise tests used Hommel s correction for multiple comparisons (S8, S9). Analyzing Exams within Categories Before comparing ratings between types of exams, we asked whether there were significant differences among exams from each of our five sources. Linear regressions, with weights assigned as described above, indicated that there were no significant differences in weighted proportion of higher-order questions or weighted ratings between the 1999 and 2002 AP biology exams, or between the 2005 and 2006 MCAT biology practice exams. For the final data analysis, then, we combined questions from the two years of each exam type. Linear regressions with weighted data showed that there were significant differences among the exams from the three undergraduate institutions in weighted proportion of higher-order question (F = 3.62, df 2, 108, p = 0.03), but not in average ratings (F = 1.70, df 2, 108 p = 0.18). There were also significant differences in the five UWSOM courses (F = 10.03, df 4, 96, p < ). We combined the data within each of these two sources for the final analyses, however, because they represent a sample of a distinct set of student experiences specifically, a sampling of exams at the introductory undergraduate level and the introductory level in medical school. References and notes S1. D. Allen, K.Tanner, Cell Bio. Ed. 1, 63 (2002). S2. C. Schuster, Ed. Psych. Meas. 64, 243, (2004). S3. D. O Connell, A. Dobson, Biometrics 40, 973 (1984). S4. R Development Core Team, ISBN , (accessed 1 October 2007). S5. B.I. Graubard, E.L. Korn, J. Natl. Cancer Inst. 91, 1005 (1999). S6. T. Lumley, J. Stat. Software 9, 1 (2004). S7. T. Lumley, R package version S8. G. Hommel, Biometrika 75, 383 (1988). S9. S.P. Wright, Biometrics 48, 1005 (1992).
Business Statistics. Successful completion of Introductory and/or Intermediate Algebra courses is recommended before taking Business Statistics.
Business Course Text Bowerman, Bruce L., Richard T. O'Connell, J. B. Orris, and Dawn C. Porter. Essentials of Business, 2nd edition, McGraw-Hill/Irwin, 2008, ISBN: 978-0-07-331988-9. Required Computing
More informationCourse Text. Required Computing Software. Course Description. Course Objectives. StraighterLine. Business Statistics
Course Text Business Statistics Lind, Douglas A., Marchal, William A. and Samuel A. Wathen. Basic Statistics for Business and Economics, 7th edition, McGraw-Hill/Irwin, 2010, ISBN: 9780077384470 [This
More informationLAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE
LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE MAT 119 STATISTICS AND ELEMENTARY ALGEBRA 5 Lecture Hours, 2 Lab Hours, 3 Credits Pre-
More informationPELLISSIPPI STATE COMMUNITY COLLEGE MASTER SYLLABUS INTRODUCTION TO STATISTICS MATH 2050
PELLISSIPPI STATE COMMUNITY COLLEGE MASTER SYLLABUS INTRODUCTION TO STATISTICS MATH 2050 Class Hours: 2.0 Credit Hours: 3.0 Laboratory Hours: 2.0 Date Revised: Fall 2013 Catalog Course Description: Descriptive
More informationTHE UNIVERSITY OF TEXAS AT TYLER COLLEGE OF NURSING COURSE SYLLABUS NURS 5317 STATISTICS FOR HEALTH PROVIDERS. Fall 2013
THE UNIVERSITY OF TEXAS AT TYLER COLLEGE OF NURSING 1 COURSE SYLLABUS NURS 5317 STATISTICS FOR HEALTH PROVIDERS Fall 2013 & Danice B. Greer, Ph.D., RN, BC dgreer@uttyler.edu Office BRB 1115 (903) 565-5766
More informationNorthumberland Knowledge
Northumberland Knowledge Know Guide How to Analyse Data - November 2012 - This page has been left blank 2 About this guide The Know Guides are a suite of documents that provide useful information about
More informationEvidence-based teaching in introductory biology. Scott Freeman, Department of Biology University of Washington srf991@u.washington.
Evidence-based teaching in introductory biology Scott Freeman, Department of Biology University of Washington srf991@u.washington.edu Why are we still lecturing? But first: The goal (of higher education)
More informationThe importance of graphing the data: Anscombe s regression examples
The importance of graphing the data: Anscombe s regression examples Bruce Weaver Northern Health Research Conference Nipissing University, North Bay May 30-31, 2008 B. Weaver, NHRC 2008 1 The Objective
More information430 Statistics and Financial Mathematics for Business
Prescription: 430 Statistics and Financial Mathematics for Business Elective prescription Level 4 Credit 20 Version 2 Aim Students will be able to summarise, analyse, interpret and present data, make predictions
More informationElementary Statistics Sample Exam #3
Elementary Statistics Sample Exam #3 Instructions. No books or telephones. Only the supplied calculators are allowed. The exam is worth 100 points. 1. A chi square goodness of fit test is considered to
More information11. Analysis of Case-control Studies Logistic Regression
Research methods II 113 11. Analysis of Case-control Studies Logistic Regression This chapter builds upon and further develops the concepts and strategies described in Ch.6 of Mother and Child Health:
More informationStudents' Opinion about Universities: The Faculty of Economics and Political Science (Case Study)
Cairo University Faculty of Economics and Political Science Statistics Department English Section Students' Opinion about Universities: The Faculty of Economics and Political Science (Case Study) Prepared
More informationSection Format Day Begin End Building Rm# Instructor. 001 Lecture Tue 6:45 PM 8:40 PM Silver 401 Ballerini
NEW YORK UNIVERSITY ROBERT F. WAGNER GRADUATE SCHOOL OF PUBLIC SERVICE Course Syllabus Spring 2016 Statistical Methods for Public, Nonprofit, and Health Management Section Format Day Begin End Building
More informationSTA-201-TE. 5. Measures of relationship: correlation (5%) Correlation coefficient; Pearson r; correlation and causation; proportion of common variance
Principles of Statistics STA-201-TE This TECEP is an introduction to descriptive and inferential statistics. Topics include: measures of central tendency, variability, correlation, regression, hypothesis
More informationService courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics.
Course Catalog In order to be assured that all prerequisites are met, students must acquire a permission number from the education coordinator prior to enrolling in any Biostatistics course. Courses are
More information2. Simple Linear Regression
Research methods - II 3 2. Simple Linear Regression Simple linear regression is a technique in parametric statistics that is commonly used for analyzing mean response of a variable Y which changes according
More information" Y. Notation and Equations for Regression Lecture 11/4. Notation:
Notation: Notation and Equations for Regression Lecture 11/4 m: The number of predictor variables in a regression Xi: One of multiple predictor variables. The subscript i represents any number from 1 through
More informationIntroduction to Regression and Data Analysis
Statlab Workshop Introduction to Regression and Data Analysis with Dan Campbell and Sherlock Campbell October 28, 2008 I. The basics A. Types of variables Your variables may take several forms, and it
More informationX X X a) perfect linear correlation b) no correlation c) positive correlation (r = 1) (r = 0) (0 < r < 1)
CORRELATION AND REGRESSION / 47 CHAPTER EIGHT CORRELATION AND REGRESSION Correlation and regression are statistical methods that are commonly used in the medical literature to compare two or more variables.
More informationFairfield Public Schools
Mathematics Fairfield Public Schools AP Statistics AP Statistics BOE Approved 04/08/2014 1 AP STATISTICS Critical Areas of Focus AP Statistics is a rigorous course that offers advanced students an opportunity
More informationAlgebra 1 Course Information
Course Information Course Description: Students will study patterns, relations, and functions, and focus on the use of mathematical models to understand and analyze quantitative relationships. Through
More informationRARITAN VALLEY COMMUNITY COLLEGE ACADEMIC COURSE OUTLINE MATH 111H STATISTICS II HONORS
RARITAN VALLEY COMMUNITY COLLEGE ACADEMIC COURSE OUTLINE MATH 111H STATISTICS II HONORS I. Basic Course Information A. Course Number and Title: MATH 111H Statistics II Honors B. New or Modified Course:
More informationDescription. Textbook. Grading. Objective
EC151.02 Statistics for Business and Economics (MWF 8:00-8:50) Instructor: Chiu Yu Ko Office: 462D, 21 Campenalla Way Phone: 2-6093 Email: kocb@bc.edu Office Hours: by appointment Description This course
More informationNormality Testing in Excel
Normality Testing in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. mark@excelmasterseries.com
More informationUNIVERSITY OF TORONTO SCARBOROUGH Department of Computer and Mathematical Sciences Midterm Test March 2014
UNIVERSITY OF TORONTO SCARBOROUGH Department of Computer and Mathematical Sciences Midterm Test March 2014 STAB22H3 Statistics I Duration: 1 hour and 45 minutes Last Name: First Name: Student number: Aids
More informationCALCULATIONS & STATISTICS
CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 1-5 scale to 0-100 scores When you look at your report, you will notice that the scores are reported on a 0-100 scale, even though respondents
More informationMTH 140 Statistics Videos
MTH 140 Statistics Videos Chapter 1 Picturing Distributions with Graphs Individuals and Variables Categorical Variables: Pie Charts and Bar Graphs Categorical Variables: Pie Charts and Bar Graphs Quantitative
More informationUNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences Fall 2010
UNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences Fall 2010 COURSE: POM 500 Statistical Analysis, ONLINE EDITION, Fall 2010 Prerequisite: Finite Math
More informationChapter 7: Simple linear regression Learning Objectives
Chapter 7: Simple linear regression Learning Objectives Reading: Section 7.1 of OpenIntro Statistics Video: Correlation vs. causation, YouTube (2:19) Video: Intro to Linear Regression, YouTube (5:18) -
More informationStudy Guide for the Final Exam
Study Guide for the Final Exam When studying, remember that the computational portion of the exam will only involve new material (covered after the second midterm), that material from Exam 1 will make
More informationStatistics 151 Practice Midterm 1 Mike Kowalski
Statistics 151 Practice Midterm 1 Mike Kowalski Statistics 151 Practice Midterm 1 Multiple Choice (50 minutes) Instructions: 1. This is a closed book exam. 2. You may use the STAT 151 formula sheets and
More informationHow To Check For Differences In The One Way Anova
MINITAB ASSISTANT WHITE PAPER This paper explains the research conducted by Minitab statisticians to develop the methods and data checks used in the Assistant in Minitab 17 Statistical Software. One-Way
More informationDepartment/Academic Unit: Public Health Sciences Degree Program: Biostatistics Collaborative Program
Department/Academic Unit: Public Health Sciences Degree Program: Biostatistics Collaborative Program Department of Mathematics and Statistics Degree Level Expectations, Learning Outcomes, Indicators of
More informationMULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Final Exam Review MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) A researcher for an airline interviews all of the passengers on five randomly
More information2013 MBA Jump Start Program. Statistics Module Part 3
2013 MBA Jump Start Program Module 1: Statistics Thomas Gilbert Part 3 Statistics Module Part 3 Hypothesis Testing (Inference) Regressions 2 1 Making an Investment Decision A researcher in your firm just
More informationCleveland State University NAL/PAD/PDD/UST 504 Section 51 Levin College of Urban Affairs Fall, 2009 W 6 to 9:50 pm UR 108
Cleveland State University NAL/PAD/PDD/UST 504 Section 51 Levin College of Urban Affairs Fall, 2009 W 6 to 9:50 pm UR 108 Department of Urban Studies Email: w.weizer @csuohio.edu Instructor: Winifred Weizer
More informationBill Burton Albert Einstein College of Medicine william.burton@einstein.yu.edu April 28, 2014 EERS: Managing the Tension Between Rigor and Resources 1
Bill Burton Albert Einstein College of Medicine william.burton@einstein.yu.edu April 28, 2014 EERS: Managing the Tension Between Rigor and Resources 1 Calculate counts, means, and standard deviations Produce
More informationChapter 13 Introduction to Linear Regression and Correlation Analysis
Chapter 3 Student Lecture Notes 3- Chapter 3 Introduction to Linear Regression and Correlation Analsis Fall 2006 Fundamentals of Business Statistics Chapter Goals To understand the methods for displaing
More informationComparing Alternate Designs For A Multi-Domain Cluster Sample
Comparing Alternate Designs For A Multi-Domain Cluster Sample Pedro J. Saavedra, Mareena McKinley Wright and Joseph P. Riley Mareena McKinley Wright, ORC Macro, 11785 Beltsville Dr., Calverton, MD 20705
More informationUSING THE ETS MAJOR FIELD TEST IN BUSINESS TO COMPARE ONLINE AND CLASSROOM STUDENT LEARNING
USING THE ETS MAJOR FIELD TEST IN BUSINESS TO COMPARE ONLINE AND CLASSROOM STUDENT LEARNING Andrew Tiger, Southeastern Oklahoma State University, atiger@se.edu Jimmy Speers, Southeastern Oklahoma State
More informationMULTIPLE LINEAR REGRESSION ANALYSIS USING MICROSOFT EXCEL. by Michael L. Orlov Chemistry Department, Oregon State University (1996)
MULTIPLE LINEAR REGRESSION ANALYSIS USING MICROSOFT EXCEL by Michael L. Orlov Chemistry Department, Oregon State University (1996) INTRODUCTION In modern science, regression analysis is a necessary part
More informationGeneral Method: Difference of Means. 3. Calculate df: either Welch-Satterthwaite formula or simpler df = min(n 1, n 2 ) 1.
General Method: Difference of Means 1. Calculate x 1, x 2, SE 1, SE 2. 2. Combined SE = SE1 2 + SE2 2. ASSUMES INDEPENDENT SAMPLES. 3. Calculate df: either Welch-Satterthwaite formula or simpler df = min(n
More informationUNIT 1: COLLECTING DATA
Core Probability and Statistics Probability and Statistics provides a curriculum focused on understanding key data analysis and probabilistic concepts, calculations, and relevance to real-world applications.
More informationSoftware Solutions - 375 - Appendix B. B.1 The R Software
Appendix B Software Solutions This appendix provides a brief discussion of the software solutions available to researchers for computing inter-rater reliability coefficients. The list of software packages
More informationData Mining Techniques Chapter 5: The Lure of Statistics: Data Mining Using Familiar Tools
Data Mining Techniques Chapter 5: The Lure of Statistics: Data Mining Using Familiar Tools Occam s razor.......................................................... 2 A look at data I.........................................................
More informationDATA INTERPRETATION AND STATISTICS
PholC60 September 001 DATA INTERPRETATION AND STATISTICS Books A easy and systematic introductory text is Essentials of Medical Statistics by Betty Kirkwood, published by Blackwell at about 14. DESCRIPTIVE
More informationSimple Linear Regression Inference
Simple Linear Regression Inference 1 Inference requirements The Normality assumption of the stochastic term e is needed for inference even if it is not a OLS requirement. Therefore we have: Interpretation
More informationCHAPTER 13 SIMPLE LINEAR REGRESSION. Opening Example. Simple Regression. Linear Regression
Opening Example CHAPTER 13 SIMPLE LINEAR REGREION SIMPLE LINEAR REGREION! Simple Regression! Linear Regression Simple Regression Definition A regression model is a mathematical equation that descries the
More informationAnalyzing Research Articles: A Guide for Readers and Writers 1. Sam Mathews, Ph.D. Department of Psychology The University of West Florida
Analyzing Research Articles: A Guide for Readers and Writers 1 Sam Mathews, Ph.D. Department of Psychology The University of West Florida The critical reader of a research report expects the writer to
More informationSAS Certificate Applied Statistics and SAS Programming
SAS Certificate Applied Statistics and SAS Programming SAS Certificate Applied Statistics and Advanced SAS Programming Brigham Young University Department of Statistics offers an Applied Statistics and
More informationAdditional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm
Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm
More informationA Correlation of. to the. South Carolina Data Analysis and Probability Standards
A Correlation of to the South Carolina Data Analysis and Probability Standards INTRODUCTION This document demonstrates how Stats in Your World 2012 meets the indicators of the South Carolina Academic Standards
More informationECON 523 Applied Econometrics I /Masters Level American University, Spring 2008. Description of the course
ECON 523 Applied Econometrics I /Masters Level American University, Spring 2008 Instructor: Maria Heracleous Lectures: M 8:10-10:40 p.m. WARD 202 Office: 221 Roper Phone: 202-885-3758 Office Hours: M W
More informationWhy Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization. Learning Goals. GENOME 560, Spring 2012
Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization GENOME 560, Spring 2012 Data are interesting because they help us understand the world Genomics: Massive Amounts
More informationDo Supplemental Online Recorded Lectures Help Students Learn Microeconomics?*
Do Supplemental Online Recorded Lectures Help Students Learn Microeconomics?* Jennjou Chen and Tsui-Fang Lin Abstract With the increasing popularity of information technology in higher education, it has
More informationI ~ 14J... <r ku...6l &J&!J--=-O--
City College of San Francisco Technology-Mediated Course Proposal Course Outline Addendum I. GENERAL DESCRIPTION A. Date B. Department C. Course Identifier D. Course Title E. Addendum Preparer F. Chairperson
More informationMath Review. for the Quantitative Reasoning Measure of the GRE revised General Test
Math Review for the Quantitative Reasoning Measure of the GRE revised General Test www.ets.org Overview This Math Review will familiarize you with the mathematical skills and concepts that are important
More informationAP Statistics: Syllabus 1
AP Statistics: Syllabus 1 Scoring Components SC1 The course provides instruction in exploring data. 4 SC2 The course provides instruction in sampling. 5 SC3 The course provides instruction in experimentation.
More informationDOCTOR OF PHILOSOPHY DEGREE. Educational Leadership Doctor of Philosophy Degree Major Course Requirements. EDU721 (3.
DOCTOR OF PHILOSOPHY DEGREE Educational Leadership Doctor of Philosophy Degree Major Course Requirements EDU710 (3.0 credit hours) Ethical and Legal Issues in Education/Leadership This course is an intensive
More informationUsing Repeated Measures Techniques To Analyze Cluster-correlated Survey Responses
Using Repeated Measures Techniques To Analyze Cluster-correlated Survey Responses G. Gordon Brown, Celia R. Eicheldinger, and James R. Chromy RTI International, Research Triangle Park, NC 27709 Abstract
More informationA Comparison of Training & Scoring in Distributed & Regional Contexts Writing
A Comparison of Training & Scoring in Distributed & Regional Contexts Writing Edward W. Wolfe Staci Matthews Daisy Vickers Pearson July 2009 Abstract This study examined the influence of rater training
More informationIncreasing Student Success Using Online Quizzing in Introductory (Majors) Biology
CBE Life Sciences Education Vol. 12, 509 514, Fall 2013 Article Increasing Student Success Using Online Quizzing in Introductory (Majors) Biology Rebecca Orr and Shellene Foster Department of Mathematics
More informationSTAT 2300: BUSINESS STATISTICS Section 002, Summer Semester 2009
STAT 2300: BUSINESS STATISTICS Section 002, Summer Semester 2009 Instructor: Bill Welbourn Office: Lund 117 Email: bill.welbourn@aggiemail.usu.edu Lectures: MWF 7:30AM 9:40AM in ENGR 104 Office Hours:
More informationPCHS ALGEBRA PLACEMENT TEST
MATHEMATICS Students must pass all math courses with a C or better to advance to the next math level. Only classes passed with a C or better will count towards meeting college entrance requirements. If
More informationApproaches for Analyzing Survey Data: a Discussion
Approaches for Analyzing Survey Data: a Discussion David Binder 1, Georgia Roberts 1 Statistics Canada 1 Abstract In recent years, an increasing number of researchers have been able to access survey microdata
More informationAnalysing Questionnaires using Minitab (for SPSS queries contact -) Graham.Currell@uwe.ac.uk
Analysing Questionnaires using Minitab (for SPSS queries contact -) Graham.Currell@uwe.ac.uk Structure As a starting point it is useful to consider a basic questionnaire as containing three main sections:
More informationPolicy Capture for Setting End-of-Course and Kentucky Performance Rating for Educational Progress (K-PREP) Cut Scores
2013 No. 007 Policy Capture for Setting End-of-Course and Kentucky Performance Rating for Educational Progress (K-PREP) Cut Scores Prepared for: Authors: Kentucky Department of Education Capital Plaza
More informationMATH. ALGEBRA I HONORS 9 th Grade 12003200 ALGEBRA I HONORS
* Students who scored a Level 3 or above on the Florida Assessment Test Math Florida Standards (FSA-MAFS) are strongly encouraged to make Advanced Placement and/or dual enrollment courses their first choices
More information1. What is the critical value for this 95% confidence interval? CV = z.025 = invnorm(0.025) = 1.96
1 Final Review 2 Review 2.1 CI 1-propZint Scenario 1 A TV manufacturer claims in its warranty brochure that in the past not more than 10 percent of its TV sets needed any repair during the first two years
More informationMULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS
MULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS MSR = Mean Regression Sum of Squares MSE = Mean Squared Error RSS = Regression Sum of Squares SSE = Sum of Squared Errors/Residuals α = Level of Significance
More informationInstitute of Actuaries of India Subject CT3 Probability and Mathematical Statistics
Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics For 2015 Examinations Aim The aim of the Probability and Mathematical Statistics subject is to provide a grounding in
More informationDescriptive Statistics
Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize
More informationUnderstanding Confidence Intervals and Hypothesis Testing Using Excel Data Table Simulation
Understanding Confidence Intervals and Hypothesis Testing Using Excel Data Table Simulation Leslie Chandrakantha lchandra@jjay.cuny.edu Department of Mathematics & Computer Science John Jay College of
More information1/27/2013. PSY 512: Advanced Statistics for Psychological and Behavioral Research 2
PSY 512: Advanced Statistics for Psychological and Behavioral Research 2 Introduce moderated multiple regression Continuous predictor continuous predictor Continuous predictor categorical predictor Understand
More informationUnit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression
Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression Objectives: To perform a hypothesis test concerning the slope of a least squares line To recognize that testing for a
More informationCase Study in Data Analysis Does a drug prevent cardiomegaly in heart failure?
Case Study in Data Analysis Does a drug prevent cardiomegaly in heart failure? Harvey Motulsky hmotulsky@graphpad.com This is the first case in what I expect will be a series of case studies. While I mention
More informationIntroduction to. Hypothesis Testing CHAPTER LEARNING OBJECTIVES. 1 Identify the four steps of hypothesis testing.
Introduction to Hypothesis Testing CHAPTER 8 LEARNING OBJECTIVES After reading this chapter, you should be able to: 1 Identify the four steps of hypothesis testing. 2 Define null hypothesis, alternative
More informationStatistics Review PSY379
Statistics Review PSY379 Basic concepts Measurement scales Populations vs. samples Continuous vs. discrete variable Independent vs. dependent variable Descriptive vs. inferential stats Common analyses
More informationNCSS Statistical Software Principal Components Regression. In ordinary least squares, the regression coefficients are estimated using the formula ( )
Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates
More informationISCC 207 Risk Management. Risk Management ISCC 11-03-15 V 2.3-EU
ISCC 207 Risk Management Risk Management ISCC 11-03-15 V 2.3-EU Copyright notice ISCC 2011 This ISCC document is protected by copyright. It is freely available from the ISCC website or upon request. No
More informationPreparation of Two-Year College Mathematics Instructors to Teach Statistics with GAISE Session on Assessment
Preparation of Two-Year College Mathematics Instructors to Teach Statistics with GAISE Session on Assessment - 1 - American Association for Higher Education (AAHE) 9 Principles of Good Practice for Assessing
More informationFinal Exam Performance. 50 OLI Accel Trad Control Trad All. Figure 1. Final exam performance of accelerated OLI-Statistics compared to traditional
IN SEARCH OF THE PERFECT BLEND BETWEEN AN INSTRUCTOR AND AN ONLINE COURSE FOR TEACHING INTRODUCTORY STATISTICS Marsha Lovett, Oded Meyer and Candace Thille Carnegie Mellon University, United States of
More informationCHAPTER 13. Experimental Design and Analysis of Variance
CHAPTER 13 Experimental Design and Analysis of Variance CONTENTS STATISTICS IN PRACTICE: BURKE MARKETING SERVICES, INC. 13.1 AN INTRODUCTION TO EXPERIMENTAL DESIGN AND ANALYSIS OF VARIANCE Data Collection
More informationAnalysis of the Effectiveness of Traditional Versus Hybrid Student Performance for an Elementary Statistics Course
International Journal for the Scholarship of Teaching and Learning Volume 6 Number 2 Article 25 7-2012 Analysis of the Effectiveness of Traditional Versus Hybrid Student Performance for an Elementary Statistics
More informationAnalysis of Variance. MINITAB User s Guide 2 3-1
3 Analysis of Variance Analysis of Variance Overview, 3-2 One-Way Analysis of Variance, 3-5 Two-Way Analysis of Variance, 3-11 Analysis of Means, 3-13 Overview of Balanced ANOVA and GLM, 3-18 Balanced
More informationSTAT 360 Probability and Statistics. Fall 2012
STAT 360 Probability and Statistics Fall 2012 1) General information: Crosslisted course offered as STAT 360, MATH 360 Semester: Fall 2012, Aug 20--Dec 07 Course name: Probability and Statistics Number
More informationEconomic Statistics (ECON2006), Statistics and Research Design in Psychology (PSYC2010), Survey Design and Analysis (SOCI2007)
COURSE DESCRIPTION Title Code Level Semester Credits 3 Prerequisites Post requisites Introduction to Statistics ECON1005 (EC160) I I None Economic Statistics (ECON2006), Statistics and Research Design
More informationCONSISTENT VISUAL ANALYSES OF INTRASUBJECT DATA SUNGWOO KAHNG KYONG-MEE CHUNG KATHARINE GUTSHALL STEVEN C. PITTS
JOURNAL OF APPLIED BEHAVIOR ANALYSIS 2010, 43, 35 45 NUMBER 1(SPRING 2010) CONSISTENT VISUAL ANALYSES OF INTRASUBJECT DATA SUNGWOO KAHNG KENNEDY KRIEGER INSTITUTE AND THE JOHNS HOPKINS UNIVERSITY SCHOOL
More informationCalculating P-Values. Parkland College. Isela Guerra Parkland College. Recommended Citation
Parkland College A with Honors Projects Honors Program 2014 Calculating P-Values Isela Guerra Parkland College Recommended Citation Guerra, Isela, "Calculating P-Values" (2014). A with Honors Projects.
More informationInternational College of Economics and Finance Syllabus Probability Theory and Introductory Statistics
International College of Economics and Finance Syllabus Probability Theory and Introductory Statistics Lecturer: Mikhail Zhitlukhin. 1. Course description Probability Theory and Introductory Statistics
More informationA C T R esearcli R e p o rt S eries 2 0 0 5. Using ACT Assessment Scores to Set Benchmarks for College Readiness. IJeff Allen.
A C T R esearcli R e p o rt S eries 2 0 0 5 Using ACT Assessment Scores to Set Benchmarks for College Readiness IJeff Allen Jim Sconing ACT August 2005 For additional copies write: ACT Research Report
More informationStatistics I for QBIC. Contents and Objectives. Chapters 1 7. Revised: August 2013
Statistics I for QBIC Text Book: Biostatistics, 10 th edition, by Daniel & Cross Contents and Objectives Chapters 1 7 Revised: August 2013 Chapter 1: Nature of Statistics (sections 1.1-1.6) Objectives
More informationImplications of Big Data for Statistics Instruction 17 Nov 2013
Implications of Big Data for Statistics Instruction 17 Nov 2013 Implications of Big Data for Statistics Instruction Mark L. Berenson Montclair State University MSMESB Mini Conference DSI Baltimore November
More informationProblem Solving and Data Analysis
Chapter 20 Problem Solving and Data Analysis The Problem Solving and Data Analysis section of the SAT Math Test assesses your ability to use your math understanding and skills to solve problems set in
More information1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number
1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number A. 3(x - x) B. x 3 x C. 3x - x D. x - 3x 2) Write the following as an algebraic expression
More informationStreet Address: 1111 Franklin Street Oakland, CA 94607. Mailing Address: 1111 Franklin Street Oakland, CA 94607
Contacts University of California Curriculum Integration (UCCI) Institute Sarah Fidelibus, UCCI Program Manager Street Address: 1111 Franklin Street Oakland, CA 94607 1. Program Information Mailing Address:
More informationMeasurement Information Model
mcgarry02.qxd 9/7/01 1:27 PM Page 13 2 Information Model This chapter describes one of the fundamental measurement concepts of Practical Software, the Information Model. The Information Model provides
More informationElements of statistics (MATH0487-1)
Elements of statistics (MATH0487-1) Prof. Dr. Dr. K. Van Steen University of Liège, Belgium December 10, 2012 Introduction to Statistics Basic Probability Revisited Sampling Exploratory Data Analysis -
More information