C-1: Variables which are measured on a continuous scale are described in terms of three key characteristics central tendency, variability, and shape.
|
|
- Abner Hines
- 5 years ago
- Views:
Transcription
1 MODULE 02: DESCRIBING DT SECTION C: KEY POINTS C-1: Variables which are measured on a continuous scale are described in terms of three key characteristics central tendency, variability, and shape. C-2: One of the most common quantities used to describe a set of data is its center. In the example above, your GP (Grade Point verage) was used to describe the "center" or "middle" of your scores or grades in individual courses. In statistics, the term central tendency refers to the middle or center of the data on any given variable. There are many descriptive statistics which are use to identify the "center" of a group of scores, and the appropriate one to use depends on the variable and the way it was measured. C-3: The most familiar of the descriptive statistics used to convey information about a variable's central tendency is the arithmetic average or mean. The mean is computed by adding together all of the individual scores on the variable, and dividing the total by the number of scores. Using your grade point average as an example, let's assume that you have taken 5 courses and have earned 3 's, 1 B, and 1 C in these courses. To compute your grade point average, we would add 4 points for each earned, 3 points for each B earned, and 2 points for each C earned. Thus, your total points earned would be computed as TOTL POINTS = =17. To compute your GP, we take the total points earned and divide by the number of courses. In this example, you have taken 5 courses, so your GP is 17/5= 3.4. Since your grade point average is the B range (assuming that a B is anywhere from 3.0 to 3.5) you would be carrying a "B" average, be described as a "B" student, and be expected to earn a "B" in most of the courses you take. However, in looking at your individual scores, you have actually earned more 's than any other grade! Does it seem right that you would be described as a "B" student? Part of the problem comes from the fact that your course grade is really not a continuous measure of your performance. warding students an, B, C, D, or F in a course reduces their performance to a more ordinal measure. In doing this, we lose the difference between a "High " and "Low." Because of this problem, the mean is usually only used as a measure of central tendency for variables which are measured on a continuous scale. nother problem comes from the fact that your course grades are kind of lopsided - almost skewed in their shape. The mean is not a good descriptive statistic to use when the distribution of the variable is not symmetrical. In order to describe variables which are discrete, or which are nonnormal in shape, we need to use a different descriptive statistic. The final problem with the mean is that it is really affected by extreme scores. Considering our example of your GP, one F in one class will really pull your GP down! The mean is very sensitive to extreme scores, whether they are 's or F's. It is only a good measure of central tendency if the data set does not include extreme values (high or low). Page 1 of 7
2 C-4: the median is the numeric value which divides your list of scores in half, with lower scores in the bottom half and higher scores in the top half. To find the median, we arrange of the scores in order, from lowest to highest, and then find the value in the middle. This value is the median. If we have an odd number of scores (i.e. our sample consists of an odd number of patients) then finding the middle number is easy. For example, if we take a body weight measurement of a sample of 7 different patients, and arrange their weights in order from lowest to highest, our resulting data set would look like this: 122 lbs 137 lbs Median = 137 lbs Mean = (122 lbs lbs )/7 = 10836/7 = lbs The median of this data set is 137 lbs, because that is the middle weight, with 3 lower weights below it, and 3 higher weights above it. In this case there is no computation required at all! However, if we have an even number of patients (e.g. n = 6) we might have to do a bit of computation to get the median. Take the same list of body weights as shown above, but remove the lowest weight (122 lbs). The median of the resulting data set is computed as the sum of the middle two values (137 lbs + = 285 lbs) divided by 2 or lbs as shown below: 137 lbs Median = (137lbs + )/2 = lbs The median is a better descriptive statistic to use than the mean when the variable is measured on an ordinal scale because it is based on the order of the scores, not on their actual values. Let's go back to your hypothetical GP. If we arrange your scores from low to high we get: C B In this case, the grade is the median, because it divides the grades into two groups, with two lower grades below it, and two higher grades above it. Since you actually scored more 's than any other score, this seems a bit more accurate when describing your grades, doesn't it? The best option here would be to report both the mean (3.4) and the median (). Using both descriptive statistics, we have characterized your academic performance a bit more accurately than we could have done with either statistic alone. Page 2 of 7
3 It is can also be a helpful companion to the mean when we are describing a continuous variable which is skewed or when there are extreme values in the data set. In either case, the median is not "pulled" or influenced as the mean, and so it may be a better descriptor of the center of the distribution. Let's take our patient weights, only this time let's add a very heavy patient to the original 7 scores. 122 lbs 137 lbs Median = (137 lbs + 148lbs)/2 = 285/2 = lbs 420 lbs Mean = (122 lbs lbs lbs)/8 = 1503/8 = lbs With the new heavier patient, the median moves a bit from 137lbs to 142.5lbs. But the mean is really affected by the extreme weight, moving from 154.7lbs to187.9lbs! For this reason, when scores are skewed or where there are outliers present, the median should be used along with the mean to describe the central tendency. C-5: The mode is the simplest descriptive statistic which is used to measure central tendency. The mode is defined as the most frequently occurring score in a data set. Therefore, to compute the mode we simply need to arrange the scores on our variable in order and select the value which occurs most often. The mode is typically only used to describe scores on variables which are measured on a categorical scale. Let's begin with your GP this time. If we arrange the scores in order, we see that the most frequently occurring grade in the data set is an. Thus the mode is defined as. C B There are 3 's, but only 1 B and only 1 C, so is the mode because it occurs most frequently. Page 3 of 7
4 With variables measured on the ordinal or nominal scale, the mode works very well. However, with continuous variables it may not work at all. Taking our 7 patients and their body weights as an example, we quickly see that this data set has no mode - or since all values occur exactly once, perhaps it actually has 7 modes! 122 lbs 137 lbs In the case of continuous variables like body weight, the mode is often not a very helpful descriptive statistic when we are interested in central tendency. In this case, we are better off using the mean and/or median to describe the middle of the data set. C-6: While the measures of central tendency provide information about what individuals have in common (e.g. the average of their test scores), the measures of variability quantify the degree to which they differ. If not all values of data are the same, they differ and variability exists. The descriptive statistics used for variability describe how "spread out" individual scores are. The larger the statistic, the more "spread out" the individual scores are. The terms variability, spread and dispersion all mean the same thing. C-7: If we know the central tendency of our set of scores that is a good start. However, it is not enough to fully describe our data. descriptive statistic for variability is necessary because different groups, or different samples, vary in different ways. For example, suppose that a teacher gave the same test to two different classes and obtained the following scores. Each class has 5 students as follows: Class 1: 80%, 80%, 80%, 80%, 80% Class 2: 60%, 70%, 80%, 90%, 100% If you calculate the mean for both sets of scores, you will get the same answer: 80%! However, it is obvious that the two sets of scores are not the same. The scores in Class 2 obviously vary more than the scores in the other class. We must use a measure of variability to describe this difference, and to distinguish between these two classes. In this case, the mean is just not enough. Page 4 of 7
5 It is important to describe both the central tendency and the variability of a variable if we want to be able to communicate or compare variables or samples. It may be important to know how two different groups compare on their "center" and on their "spread." For example, if we want to compare the body weight of male and female patients, it would be helpful to know if the average age for males was higher or lower than that for females. It might also be helpful to know if the weights of the male patients varied more than the weights of the female patients. C-8: The easiest and most familiar measure of variability is called the range. The range is simply the lowest value in our data set subtracted from the highest value in our data set. If we used the body weights of our 7 patients as an example again, the range would be computed as RNGE=-122 lbs = 98 lbs. 122 lbs lowest score 137 lbs highest score Range = highest score - lowest score = lbs = 98 lbs The range usually works best in describing continuous variables. But like the mean, the range is really affected by extreme values. If we add our 420 lb patient to the data set, the range increases from 98 lbs to 420 lbs- 122 lbs = 298 lbs! That much of a change just because one score has changed makes our statistic very unstable. It also means that all of the score in between the lowest and the highest don't even matter when we think about how spread out our scores are. That makes the range a very imprecise measure, because all of the scores are not included when we calculate it. Page 5 of 7
6 C-9: The variance of a set of scores is defined as the "average squared distance" of the scores from the mean. To compute the variance, we must first measure how far each data point is from the mean. That is pretty easy - we just take each observed score and subtract the mean. For our 7 patient weights, that would look something like this: 122 lbs lbs = lbs lbs = lbs lbs = lbs 137 lbs lbs = lbs lbs = -6.7 lbs lbs = 40.3 lbs lbs = 65.3 lbs fter I find the difference between each observed score and the mean, I square each difference like this: 122 lbs lbs = lbs (-32.7 lbs)(-32.7 lbs) = lbs lbs = lbs (-29.7 lbs)(-29.7 lbs) = lbs lbs = lbs (-18.7 lbs)(-18.7 lbs) = lbs lbs lbs = lbs (-17.7 lbs)(-17.7 lbs) = lbs lbs = -6.7 lbs (-6.7 lbs)(-6.7 lbs) = 44.9 lbs lbs = 40.3 lbs (40.3 lbs)(40.3 lbs) = lbs lbs = 65.3 lbs (65.3 lbs)(65.3 lbs) = lbs 2 Finally, I add up all of the squared differences, and divide by the total number of patients -1. In this case, the denominator is 7-1 = 6. var = (1069.3lbs lbs lbs lbs lbs lbs lbs 2 )/(7-1) = 8,547.5 lbs 2 / 6 = lbs 2 The variance, usually symbolized as s-squared, is equal to lbs 2. This means that the average squared distance of the weights in the data set from the mean is lbs 2. The larger the variance, the greater the variability in the scores. This estimate of the variability of the scores is more precise than the range, because it includes each of the scores in the data set in the Page 6 of 7
7 computation. But what is a squared pound? We will need a measure of variability which is more understandable, and not in squared units. C-10: the standard deviation, defined as the average distance of the scores in the data set from the mean is the most frequently used measure of variability for continuous variables. It is computed pretty simply by taking the square root of the variance. Based on our weight example, that means: standard deviation (s) = lbs 2 = 37.7 lbs The standard deviation, usually symbolized as s, has the advantage of being reported in the original units! No square pounds are necessary, so it is much easier to understand. s we saw with the variance, the larger the actual value, the greater the variability in the scores. C-11: So far we haven't really talked about a measure of variability that works well with categorical variables. Using our GP example, we would want to express the variability in the following grades: C B We can accurately say that the scores vary from "C" to "." We could even assign point values like we did before, giving all of the 's a 4, B's a 3, and C's a 2. But we still only really have data measured on an ordinal scale. Even using the range by subtracting the lowest from the highest score (4-2 = 2) gives us the number 2. In our example 2 is equivalent to a "C" - so does that mean we have a range of "C?" Here, whether or not we assign numeric variables to the categories of our ordinal variable, they are only categories. There is not much to be gained by trying to describe the variability in the scores with a descriptive statistic. Our verbal description is really the best. Page 7 of 7
CHAPTER 3 DATA ANALYSIS: DESCRIBING DATA
Data Analysis: Describing Data CHAPTER 3 DATA ANALYSIS: DESCRIBING DATA In the analysis process, the researcher tries to evaluate the data collected both from written documents and from other sources such
More informationDescriptive Statistics Lecture
Definitions: Lecture Psychology 280 Orange Coast College 2/1/2006 Statistics have been defined as a collection of methods for planning experiments, obtaining data, and then analyzing, interpreting and
More informationMBA 605 Business Analytics Don Conant, PhD. GETTING TO THE STANDARD NORMAL DISTRIBUTION
MBA 605 Business Analytics Don Conant, PhD. GETTING TO THE STANDARD NORMAL DISTRIBUTION Variables In the social sciences data are the observed and/or measured characteristics of individuals and groups
More informationLesson 8 Descriptive Statistics: Measures of Central Tendency and Dispersion
Lesson 8 Descriptive Statistics: Measures of Central Tendency and Dispersion Learning Objectives All students will define key terms and calculate different descriptive statistics. All students should be
More informationPsychology Research Process
Psychology Research Process Logical Processes Induction Observation/Association/Using Correlation Trying to assess, through observation of a large group/sample, what is associated with what? Examples:
More informationQuantitative Data and Measurement. POLI 205 Doing Research in Politics. Fall 2015
Quantitative Fall 2015 Theory and We need to test our theories with empirical data Inference : Systematic observation and representation of concepts Quantitative: measures are numeric Qualitative: measures
More informationMath Workshop On-Line Tutorial Judi Manola Paul Catalano. Slide 1. Slide 3
Kinds of Numbers and Data First we re going to think about the kinds of numbers you will use in the problems you will encounter in your studies. Then we will expand a bit and think about kinds of data.
More informationVariability. After reading this chapter, you should be able to do the following:
LEARIG OBJECTIVES C H A P T E R 3 Variability After reading this chapter, you should be able to do the following: Explain what the standard deviation measures Compute the variance and the standard deviation
More informationMath Workshop On-Line Tutorial Judi Manola Paul Catalano
Math Workshop On-Line Tutorial Judi Manola Paul Catalano 1 Session 1 Kinds of Numbers and Data, Fractions, Negative Numbers, Rounding, Averaging, Properties of Real Numbers, Exponents and Square Roots,
More informationPsychology Research Process
Psychology Research Process Logical Processes Induction Observation/Association/Using Correlation Trying to assess, through observation of a large group/sample, what is associated with what? Examples:
More informationCHAPTER 2. MEASURING AND DESCRIBING VARIABLES
4 Chapter 2 CHAPTER 2. MEASURING AND DESCRIBING VARIABLES 1. A. Age: name/interval; military dictatorship: value/nominal; strongly oppose: value/ ordinal; election year: name/interval; 62 percent: value/interval;
More informationStatistical Techniques. Masoud Mansoury and Anas Abulfaraj
Statistical Techniques Masoud Mansoury and Anas Abulfaraj What is Statistics? https://www.youtube.com/watch?v=lmmzj7599pw The definition of Statistics The practice or science of collecting and analyzing
More informationAP Psych - Stat 1 Name Period Date. MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
AP Psych - Stat 1 Name Period Date MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) In a set of incomes in which most people are in the $15,000
More informationAP Psych - Stat 2 Name Period Date. MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
AP Psych - Stat 2 Name Period Date MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) In a set of incomes in which most people are in the $15,000
More informationChapter 2--Norms and Basic Statistics for Testing
Chapter 2--Norms and Basic Statistics for Testing Student: 1. Statistical procedures that summarize and describe a series of observations are called A. inferential statistics. B. descriptive statistics.
More informationBusiness Statistics Probability
Business Statistics The following was provided by Dr. Suzanne Delaney, and is a comprehensive review of Business Statistics. The workshop instructor will provide relevant examples during the Skills Assessment
More informationResults & Statistics: Description and Correlation. I. Scales of Measurement A Review
Results & Statistics: Description and Correlation The description and presentation of results involves a number of topics. These include scales of measurement, descriptive statistics used to summarize
More informationStill important ideas
Readings: OpenStax - Chapters 1 11 + 13 & Appendix D & E (online) Plous - Chapters 2, 3, and 4 Chapter 2: Cognitive Dissonance, Chapter 3: Memory and Hindsight Bias, Chapter 4: Context Dependence Still
More informationOn the purpose of testing:
Why Evaluation & Assessment is Important Feedback to students Feedback to teachers Information to parents Information for selection and certification Information for accountability Incentives to increase
More informationBasic Statistics 01. Describing Data. Special Program: Pre-training 1
Basic Statistics 01 Describing Data Special Program: Pre-training 1 Describing Data 1. Numerical Measures Measures of Location Measures of Dispersion Correlation Analysis 2. Frequency Distributions (Relative)
More informationSection 3.2 Least-Squares Regression
Section 3.2 Least-Squares Regression Linear relationships between two quantitative variables are pretty common and easy to understand. Correlation measures the direction and strength of these relationships.
More informationStill important ideas
Readings: OpenStax - Chapters 1 13 & Appendix D & E (online) Plous Chapters 17 & 18 - Chapter 17: Social Influences - Chapter 18: Group Judgments and Decisions Still important ideas Contrast the measurement
More informationDescribe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo
Please note the page numbers listed for the Lind book may vary by a page or two depending on which version of the textbook you have. Readings: Lind 1 11 (with emphasis on chapters 10, 11) Please note chapter
More informationChapter 7: Descriptive Statistics
Chapter Overview Chapter 7 provides an introduction to basic strategies for describing groups statistically. Statistical concepts around normal distributions are discussed. The statistical procedures of
More informationChapter 2 Norms and Basic Statistics for Testing MULTIPLE CHOICE
Chapter 2 Norms and Basic Statistics for Testing MULTIPLE CHOICE 1. When you assert that it is improbable that the mean intelligence test score of a particular group is 100, you are using. a. descriptive
More informationOutcome Measure Considerations for Clinical Trials Reporting on ClinicalTrials.gov
Outcome Measure Considerations for Clinical Trials Reporting on ClinicalTrials.gov What is an Outcome Measure? An outcome measure is the result of a treatment or intervention that is used to objectively
More informationChapter 20: Test Administration and Interpretation
Chapter 20: Test Administration and Interpretation Thought Questions Why should a needs analysis consider both the individual and the demands of the sport? Should test scores be shared with a team, or
More informationDistributions and Samples. Clicker Question. Review
Distributions and Samples Clicker Question The major difference between an observational study and an experiment is that A. An experiment manipulates features of the situation B. An experiment does not
More information3 CONCEPTUAL FOUNDATIONS OF STATISTICS
3 CONCEPTUAL FOUNDATIONS OF STATISTICS In this chapter, we examine the conceptual foundations of statistics. The goal is to give you an appreciation and conceptual understanding of some basic statistical
More informationChoosing the Correct Statistical Test
Choosing the Correct Statistical Test T racie O. Afifi, PhD Departments of Community Health Sciences & Psychiatry University of Manitoba Department of Community Health Sciences COLLEGE OF MEDICINE, FACULTY
More informationStandard Scores. Richard S. Balkin, Ph.D., LPC-S, NCC
Standard Scores Richard S. Balkin, Ph.D., LPC-S, NCC 1 Normal Distributions While Best and Kahn (2003) indicated that the normal curve does not actually exist, measures of populations tend to demonstrate
More informationBiostatistics. Donna Kritz-Silverstein, Ph.D. Professor Department of Family & Preventive Medicine University of California, San Diego
Biostatistics Donna Kritz-Silverstein, Ph.D. Professor Department of Family & Preventive Medicine University of California, San Diego (858) 534-1818 dsilverstein@ucsd.edu Introduction Overview of statistical
More informationAnnouncement. Homework #2 due next Friday at 5pm. Midterm is in 2 weeks. It will cover everything through the end of next week (week 5).
Announcement Homework #2 due next Friday at 5pm. Midterm is in 2 weeks. It will cover everything through the end of next week (week 5). Political Science 15 Lecture 8: Descriptive Statistics (Part 1) Data
More informationWelcome to OSA Training Statistics Part II
Welcome to OSA Training Statistics Part II Course Summary Using data about a population to draw graphs Frequency distribution and variability within populations Bell Curves: What are they and where do
More informationDescribe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo
Business Statistics The following was provided by Dr. Suzanne Delaney, and is a comprehensive review of Business Statistics. The workshop instructor will provide relevant examples during the Skills Assessment
More informationAnalysis and Interpretation of Data Part 1
Analysis and Interpretation of Data Part 1 DATA ANALYSIS: PRELIMINARY STEPS 1. Editing Field Edit Completeness Legibility Comprehensibility Consistency Uniformity Central Office Edit 2. Coding Specifying
More informationReadings: Textbook readings: OpenStax - Chapters 1 11 Online readings: Appendix D, E & F Plous Chapters 10, 11, 12 and 14
Readings: Textbook readings: OpenStax - Chapters 1 11 Online readings: Appendix D, E & F Plous Chapters 10, 11, 12 and 14 Still important ideas Contrast the measurement of observable actions (and/or characteristics)
More informationDescribe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo
Please note the page numbers listed for the Lind book may vary by a page or two depending on which version of the textbook you have. Readings: Lind 1 11 (with emphasis on chapters 5, 6, 7, 8, 9 10 & 11)
More informationLesson 9 Presentation and Display of Quantitative Data
Lesson 9 Presentation and Display of Quantitative Data Learning Objectives All students will identify and present data using appropriate graphs, charts and tables. All students should be able to justify
More informationData, frequencies, and distributions. Martin Bland. Types of data. Types of data. Clinical Biostatistics
Clinical Biostatistics Data, frequencies, and distributions Martin Bland Professor of Health Statistics University of York http://martinbland.co.uk/ Types of data Qualitative data arise when individuals
More informationStatistics is the science of collecting, organizing, presenting, analyzing, and interpreting data to assist in making effective decisions
Readings: OpenStax Textbook - Chapters 1 5 (online) Appendix D & E (online) Plous - Chapters 1, 5, 6, 13 (online) Introductory comments Describe how familiarity with statistical methods can - be associated
More informationChapter 12. The One- Sample
Chapter 12 The One- Sample z-test Objective We are going to learn to make decisions about a population parameter based on sample information. Lesson 12.1. Testing a Two- Tailed Hypothesis Example 1: Let's
More informationObservational studies; descriptive statistics
Observational studies; descriptive statistics Patrick Breheny August 30 Patrick Breheny University of Iowa Biostatistical Methods I (BIOS 5710) 1 / 38 Observational studies Association versus causation
More informationPRINCIPLES OF STATISTICS
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 informationLecture 1 An introduction to statistics in Ichthyology and Fisheries Science
Lecture 1 An introduction to statistics in Ichthyology and Fisheries Science What is statistics and why do we need it? Statistics attempts to make inferences about unknown values that are common to a population
More informationSTATISTICS AND RESEARCH DESIGN
Statistics 1 STATISTICS AND RESEARCH DESIGN These are subjects that are frequently confused. Both subjects often evoke student anxiety and avoidance. To further complicate matters, both areas appear have
More informationUndertaking statistical analysis of
Descriptive statistics: Simply telling a story Laura Delaney introduces the principles of descriptive statistical analysis and presents an overview of the various ways in which data can be presented by
More information9 research designs likely for PSYC 2100
9 research designs likely for PSYC 2100 1) 1 factor, 2 levels, 1 group (one group gets both treatment levels) related samples t-test (compare means of 2 levels only) 2) 1 factor, 2 levels, 2 groups (one
More informationMeasuring the User Experience
Measuring the User Experience Collecting, Analyzing, and Presenting Usability Metrics Chapter 2 Background Tom Tullis and Bill Albert Morgan Kaufmann, 2008 ISBN 978-0123735584 Introduction Purpose Provide
More informationStatistics is the science of collecting, organizing, presenting, analyzing, and interpreting data to assist in making effective decisions
Readings: OpenStax Textbook - Chapters 1 5 (online) Appendix D & E (online) Plous - Chapters 1, 5, 6, 13 (online) Introductory comments Describe how familiarity with statistical methods can - be associated
More informationPolitical Science 15, Winter 2014 Final Review
Political Science 15, Winter 2014 Final Review The major topics covered in class are listed below. You should also take a look at the readings listed on the class website. Studying Politics Scientifically
More informationOne-Way ANOVAs t-test two statistically significant Type I error alpha null hypothesis dependant variable Independent variable three levels;
1 One-Way ANOVAs We have already discussed the t-test. The t-test is used for comparing the means of two groups to determine if there is a statistically significant difference between them. The t-test
More informationUnit 7 Comparisons and Relationships
Unit 7 Comparisons and Relationships Objectives: To understand the distinction between making a comparison and describing a relationship To select appropriate graphical displays for making comparisons
More informationStatisticians deal with groups of numbers. They often find it helpful to use
Chapter 4 Finding Your Center In This Chapter Working within your means Meeting conditions The median is the message Getting into the mode Statisticians deal with groups of numbers. They often find it
More informationSummarizing Data. (Ch 1.1, 1.3, , 2.4.3, 2.5)
1 Summarizing Data (Ch 1.1, 1.3, 1.10-1.13, 2.4.3, 2.5) Populations and Samples An investigation of some characteristic of a population of interest. Example: You want to study the average GPA of juniors
More informationReadings: Textbook readings: OpenStax - Chapters 1 13 (emphasis on Chapter 12) Online readings: Appendix D, E & F
Readings: Textbook readings: OpenStax - Chapters 1 13 (emphasis on Chapter 12) Online readings: Appendix D, E & F Plous Chapters 17 & 18 Chapter 17: Social Influences Chapter 18: Group Judgments and Decisions
More informationIntroduction to statistics Dr Alvin Vista, ACER Bangkok, 14-18, Sept. 2015
Analysing and Understanding Learning Assessment for Evidence-based Policy Making Introduction to statistics Dr Alvin Vista, ACER Bangkok, 14-18, Sept. 2015 Australian Council for Educational Research Structure
More informationA Case Study: Two-sample categorical data
A Case Study: Two-sample categorical data Patrick Breheny January 31 Patrick Breheny BST 701: Bayesian Modeling in Biostatistics 1/43 Introduction Model specification Continuous vs. mixture priors Choice
More informationStudents will understand the definition of mean, median, mode and standard deviation and be able to calculate these functions with given set of
Students will understand the definition of mean, median, mode and standard deviation and be able to calculate these functions with given set of numbers. Also, students will understand why some measures
More informationVARIABLES AND MEASUREMENT
ARTHUR SYC 204 (EXERIMENTAL SYCHOLOGY) 16A LECTURE NOTES [01/29/16] VARIABLES AND MEASUREMENT AGE 1 Topic #3 VARIABLES AND MEASUREMENT VARIABLES Some definitions of variables include the following: 1.
More informationStandard Deviation and Standard Error Tutorial. This is significantly important. Get your AP Equations and Formulas sheet
Standard Deviation and Standard Error Tutorial This is significantly important. Get your AP Equations and Formulas sheet The Basics Let s start with a review of the basics of statistics. Mean: What most
More informationDesigning Psychology Experiments: Data Analysis and Presentation
Data Analysis and Presentation Review of Chapter 4: Designing Experiments Develop Hypothesis (or Hypotheses) from Theory Independent Variable(s) and Dependent Variable(s) Operational Definitions of each
More informationThis is Descriptive Statistics, chapter 12 from the book Psychology Research Methods: Core Skills and Concepts (index.html) (v. 1.0).
This is Descriptive Statistics, chapter 12 from the book Psychology Research Methods: Core Skills and Concepts (index.html) (v. 1.0). This book is licensed under a Creative Commons by-nc-sa 3.0 (http://creativecommons.org/licenses/by-nc-sa/
More informationANOVA. Thomas Elliott. January 29, 2013
ANOVA Thomas Elliott January 29, 2013 ANOVA stands for analysis of variance and is one of the basic statistical tests we can use to find relationships between two or more variables. ANOVA compares the
More informationExample The median earnings of the 28 male students is the average of the 14th and 15th, or 3+3
Lecture 3 Nancy Pfenning Stats 1000 We learned last time how to construct a stemplot to display a single quantitative variable. A back-to-back stemplot is a useful display tool when we are interested in
More informationStatistics. Nur Hidayanto PSP English Education Dept. SStatistics/Nur Hidayanto PSP/PBI
Statistics Nur Hidayanto PSP English Education Dept. RESEARCH STATISTICS WHAT S THE RELATIONSHIP? RESEARCH RESEARCH positivistic Prepositivistic Postpositivistic Data Initial Observation (research Question)
More informationCCM6+7+ Unit 12 Data Collection and Analysis
Page 1 CCM6+7+ Unit 12 Packet: Statistics and Data Analysis CCM6+7+ Unit 12 Data Collection and Analysis Big Ideas Page(s) What is data/statistics? 2-4 Measures of Reliability and Variability: Sampling,
More informationElementary Statistics:
1. How many full chapters of reading in the text were assigned for this lecture? 1. 1. 3. 3 4. 4 5. None of the above SOC497 @ CSUN w/ Ellis Godard 1 SOC497 @ CSUN w/ Ellis Godard 5 SOC497/L: SOCIOLOGY
More informationUsing Analytical and Psychometric Tools in Medium- and High-Stakes Environments
Using Analytical and Psychometric Tools in Medium- and High-Stakes Environments Greg Pope, Analytics and Psychometrics Manager 2008 Users Conference San Antonio Introduction and purpose of this session
More informationSTAT 200. Guided Exercise 4
STAT 200 Guided Exercise 4 1. Let s Revisit this Problem. Fill in the table again. Diagnostic tests are not infallible. We often express a fale positive and a false negative with any test. There are further
More informationMODULE 2 FOUNDATIONAL DEFINITIONS
MODULE 2 FOUNDATIONAL DEFINITIONS Contents 2.1 Definitions............................................ 6 2.2 Performing an IVPPSS..................................... 8 2.3 Variable Types..........................................
More informationMedical Statistics 1. Basic Concepts Farhad Pishgar. Defining the data. Alive after 6 months?
Medical Statistics 1 Basic Concepts Farhad Pishgar Defining the data Population and samples Except when a full census is taken, we collect data on a sample from a much larger group called the population.
More informationResearch Methods 1 Handouts, Graham Hole,COGS - version 1.0, September 2000: Page 1:
Research Methods 1 Handouts, Graham Hole,COGS - version 10, September 000: Page 1: T-TESTS: When to use a t-test: The simplest experimental design is to have two conditions: an "experimental" condition
More informationStudent name: SOCI 420 Advanced Methods of Social Research Fall 2017
SOCI 420 Advanced Methods of Social Research Fall 2017 EXAM 1 RUBRIC Instructor: Ernesto F. L. Amaral, Assistant Professor, Department of Sociology Date: October 12, 2017 (Thursday) Section 903: 9:35 10:50am
More informationUNEQUAL CELL SIZES DO MATTER
1 of 7 1/12/2010 11:26 AM UNEQUAL CELL SIZES DO MATTER David C. Howell Most textbooks dealing with factorial analysis of variance will tell you that unequal cell sizes alter the analysis in some way. I
More informationEasy Progression With Mini Sets
Easy Progression With Mini Sets Mark Sherwood For more information from the author visit: http://www.precisionpointtraining.com/ Copyright 2018 by Mark Sherwood Easy Progression With Mini Sets By Mark
More informationActivity: Smart Guessing
Activity: Smart Guessing GENERATE EQUIVALENT FORMS OF FRACTIONS & DECIMALS USE MULTIPLICATION & DIVISION TO SOLVE PROBLEMS INVOLVING FRACTIONS ESTIMATE TO APPROXIMATE REASONABLE RESULTS WHERE EXACT ANSWERS
More informationPopulation. Sample. AP Statistics Notes for Chapter 1 Section 1.0 Making Sense of Data. Statistics: Data Analysis:
Section 1.0 Making Sense of Data Statistics: Data Analysis: Individuals objects described by a set of data Variable any characteristic of an individual Categorical Variable places an individual into one
More informationPart III Taking Chances for Fun and Profit
Part III Taking Chances for Fun and Profit Chapter 8 Are Your Curves Normal? Probability and Why it Counts What You Will Learn in Chapter 8 How probability relates to statistics Characteristics of the
More informationISC- GRADE XI HUMANITIES ( ) PSYCHOLOGY. Chapter 2- Methods of Psychology
ISC- GRADE XI HUMANITIES (2018-19) PSYCHOLOGY Chapter 2- Methods of Psychology OUTLINE OF THE CHAPTER (i) Scientific Methods in Psychology -observation, case study, surveys, psychological tests, experimentation
More informationMethodological skills
Methodological skills rma linguistics, week 3 Tamás Biró ACLC University of Amsterdam t.s.biro@uva.nl Tamás Biró, UvA 1 Topics today Parameter of the population. Statistic of the sample. Re: descriptive
More informationProbability and Statistics. Chapter 1
Probability and Statistics Chapter 1 Individuals and Variables Individuals and Variables Individuals are objects described by data. Individuals and Variables Individuals are objects described by data.
More informationStats 95. Statistical analysis without compelling presentation is annoying at best and catastrophic at worst. From raw numbers to meaningful pictures
Stats 95 Statistical analysis without compelling presentation is annoying at best and catastrophic at worst. From raw numbers to meaningful pictures Stats 95 Why Stats? 200 countries over 200 years http://www.youtube.com/watch?v=jbksrlysojo
More informationEmpirical Knowledge: based on observations. Answer questions why, whom, how, and when.
INTRO TO RESEARCH METHODS: Empirical Knowledge: based on observations. Answer questions why, whom, how, and when. Experimental research: treatments are given for the purpose of research. Experimental group
More informationAppendix B Statistical Methods
Appendix B Statistical Methods Figure B. Graphing data. (a) The raw data are tallied into a frequency distribution. (b) The same data are portrayed in a bar graph called a histogram. (c) A frequency polygon
More informationUnit 1 Exploring and Understanding Data
Unit 1 Exploring and Understanding Data Area Principle Bar Chart Boxplot Conditional Distribution Dotplot Empirical Rule Five Number Summary Frequency Distribution Frequency Polygon Histogram Interquartile
More information25. Two-way ANOVA. 25. Two-way ANOVA 371
25. Two-way ANOVA The Analysis of Variance seeks to identify sources of variability in data with when the data is partitioned into differentiated groups. In the prior section, we considered two sources
More information3.2 Least- Squares Regression
3.2 Least- Squares Regression Linear (straight- line) relationships between two quantitative variables are pretty common and easy to understand. Correlation measures the direction and strength of these
More informationMATH 1040 Skittles Data Project
Laura Boren MATH 1040 Data Project For our project in MATH 1040 everyone in the class was asked to buy a 2.17 individual sized bag of skittles and count the number of each color of candy in the bag. The
More informationCHAPTER 2 Means to an End: Computing and Understanding Averages
CHAPTER 2 Means to an End: Computing and Understanding Averages Part I. Multiple-Choice Questions (20 items) 1. The mode measures central tendency in terms of which of these? a. the most common score b.
More informationHuman-Computer Interaction IS4300. I6 Swing Layout Managers due now
Human-Computer Interaction IS4300 1 I6 Swing Layout Managers due now You have two choices for requirements: 1) try to duplicate the functionality of an existing applet; or, 2) create your own (ideally
More informationQualitative and Quantitative Approaches Workshop. Comm 151i San Jose State U Dr. T.M. Coopman Okay for non-commercial use with attribution
Qualitative and Quantitative Approaches Workshop Comm 151i San Jose State U Dr. T.M. Coopman Okay for non-commercial use with attribution This Workshop This is a research skill workshop. This workshop
More informationWhy? "the post workout period that results in metabolic disturbance, elevating EPOC,
Q: What do you look for when designing a fat loss training program? Do you look at what people have done in the gyms before, or do you read the research and then try to recreate that? A: The primary goals
More information2.75: 84% 2.5: 80% 2.25: 78% 2: 74% 1.75: 70% 1.5: 66% 1.25: 64% 1.0: 60% 0.5: 50% 0.25: 25% 0: 0%
Capstone Test (will consist of FOUR quizzes and the FINAL test grade will be an average of the four quizzes). Capstone #1: Review of Chapters 1-3 Capstone #2: Review of Chapter 4 Capstone #3: Review of
More informationSTP226 Brief Class Notes Instructor: Ela Jackiewicz
CHAPTER 2 Organizing Data Statistics=science of analyzing data. Information collected (data) is gathered in terms of variables (characteristics of a subject that can be assigned a numerical value or nonnumerical
More informationStudents were asked to report how far (in miles) they each live from school. The following distances were recorded. 1 Zane Jackson 0.
Identifying Outliers Task Students were asked to report how far (in miles) they each live from school. The following distances were recorded. Student Distance 1 Zane 0.4 2 Jackson 0.5 3 Benjamin 1.0 4
More informationDO NOT OPEN THIS BOOKLET UNTIL YOU ARE TOLD TO DO SO
NATS 1500 Mid-term test A1 Page 1 of 8 Name (PRINT) Student Number Signature Instructions: York University DIVISION OF NATURAL SCIENCE NATS 1500 3.0 Statistics and Reasoning in Modern Society Mid-Term
More informationRunning Head: VISUAL SCHEDULES FOR STUDENTS WITH AUTISM SPECTRUM DISORDER
Running Head: VISUAL SCHEDULES FOR STUDENTS WITH AUTISM SPECTRUM DISORDER Visual Schedules for Students with Autism Spectrum Disorder Taylor Herback 200309600 University of Regina VISUAL SCHEDULES FOR
More informationBusiness Statistics (ECOE 1302) Spring Semester 2011 Chapter 3 - Numerical Descriptive Measures Solutions
The Islamic University of Gaza Faculty of Commerce Department of Economics and Political Sciences Business Statistics (ECOE 1302) Spring Semester 2011 Chapter 3 - Numerical Descriptive Measures Solutions
More information