...Correlation as a Measure of Association Summary Alethea Cooper BSHS/435 June 8, 2015 Jason Cantone Correlation as a Measure of Association Summary Correlation research examines the extent to which differences in one characteristic or variable are related to differences in one or more other characteristics or variables. A correlation exist when one variable increases, another variable either increases or decreases in a predictable manner (Leedy, Ormrod, 2010). There are two different methods of correlation research; positive and negative. Positive is a positive relationship where both variables tend to move in the same direction. If one variable increases, the other tends to also increase (Jackson, 2011). If one variable decreases the other tends to as well. The example above; GPA and Math SAT are positively related. As GPA (or Math SAT) increases, the other variable also has a tendency to increase. Negative is a negative relationship the variables tend to move in the opposite directions. If one variable increases, the other tends to decrease, and vice-versa (Jackson, 2011). Correlational research allows researchers to collect more data than experiments. Because correlational research usually takes place outside of the lab, the results tend to be more applicable to everyday life. Another benefit of correlational research is that it opens up a great deal of further research to other scholars. When researchers begin investigating a phenomenon or relationship for the very...
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...USING EXCEL TO FIND MEASURES OF ASSOCIATION by Edward F. Stafford, Jr., PhD Professor of Management Science, UAH Purpose of Handout The purpose of this handout is to describe how to obtain measures of association between two variables using the Microsoft Excel software. These measures include covariance and correlation. For the most part, each measure may be obtained in two ways: (1) “by hand”; and (2) by Excel’s fX function operator. “By hand” does not mean to actually do the computations by hand; rather, it means to use Excel for statistical computations as though the fX function operator did not exist. Example Problem An example problem is used to demonstrate all of the features described in this handout. The problem is extracted from Case Problem 2, “National Health Care Association,” Chapter 3 of the Anderson, Sweeney, and Williams textbook assigned for this course. In particular, the “University Hospitals” data is used in this handout. The data are scales indicating respondent’s “degree of satisfaction” in their work, their pay, and their opportunities for promotion. Scale values range from 0 to 100. The actual data values used are shown in Figure 1. The user may acquire these data, user-ready, in an Excel file by going to Dr. Stafford’s home page on the web {http://cas.uah.edu/stafford/}, then clicking on the following, in order: (1) MSC 287; (2) scroll down then Special Handouts; (3) Excel Materials including Instructions for Statistical Calculations {click on...
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...Chapter 22 Correlation Coefficients 22 Correlation Coefficients The Meaning of Correlation Correlation and Data Types Pearson’s r Spearman rho Other Coefficients of Note Coefficient of Determination r2 The concept of correlation was introduced in Chapters 1 and 5. Our focus since Chapter 16 has been basic statistical procedures that measure differences between groups -- one-sample, two-sample, and k-sample tests. Now we turn our attention to basic statistical procedures that measure the degree of association between variables. Dr. Wesley Black studied the relationship between rankings of selected learning objectives in a youth discipleship taxonomy between full-time church staff youth ministers and seminary students enrolled in youth education courses at Southwestern Seminary.1 Questionnaires were returned by 318 students and 184 youth ministers.2 Ten objectives in each of five categories (Personal Ministry, Christian Theology and Baptist Doctrine, Christian Ethics, Baptist Heritage, and Church Polity and Organization) were ranked by these two groups. The basic question raised by Black in this study was whether students prioritized discipleship training objectives for youth in the same way as full-time ministers in the field. Using the Spearman rho correlation coefficient, Black found the correlations of rankings generated by students and ministers of the ten items for each of five categories were as follows: Personal Ministry, 0.915; Christian Theology...
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...Basics of Statistics Jarkko Isotalo 30 20 10 Std. Dev = 486.32 Mean = 3553.8 N = 120.00 0 2400.0 2800.0 2600.0 3200.0 3000.0 3600.0 3400.0 4000.0 3800.0 4400.0 4200.0 4800.0 4600.0 5000.0 Birthweights of children during years 1965-69 Time to Accelerate from 0 to 60 mph (sec) 30 20 10 0 0 Horsepower 100 200 300 1 Preface These lecture notes have been used at Basics of Statistics course held in University of Tampere, Finland. These notes are heavily based on the following books. Agresti, A. & Finlay, B., Statistical Methods for the Social Sciences, 3th Edition. Prentice Hall, 1997. Anderson, T. W. & Sclove, S. L., Introductory Statistical Analysis. Houghton Mifflin Company, 1974. Clarke, G.M. & Cooke, D., A Basic course in Statistics. Arnold, 1998. Electronic Statistics Textbook, http://www.statsoftinc.com/textbook/stathome.html. Freund, J.E.,Modern elementary statistics. Prentice-Hall, 2001. Johnson, R.A. & Bhattacharyya, G.K., Statistics: Principles and Methods, 2nd Edition. Wiley, 1992. Leppälä, R., Ohjeita tilastollisen tutkimuksen toteuttamiseksi SPSS for Windows -ohjelmiston avulla, Tampereen yliopisto, Matematiikan, tilastotieteen ja filosofian laitos, B53, 2000. Moore, D., The Basic Practice of Statistics. Freeman, 1997. Moore, D. & McCabe G., Introduction to the Practice of Statistics, 3th Edition. Freeman, 1998. Newbold, P., Statistics for Business and Econometrics...
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...organizations?” That’s probably stated a little too generally. More specific examples include: “Will implementation of a market orientation strategy improve my organization’s performance?” and “Will implementation of JIT and TQM improvement programs combine to improve my organization’s performance?” 2. State the hypotheses. Hypotheses are formulated from the research question. They must be stated in a form that is statistically testable. For example, “Market orientation is positively associated with operational performance.” Another example is: “JIT and TQM are positively associated with organizational performance.” These statements are statistically testable using correlation and regression analyses. Correlation analysis gives the type, strength, and significance level of the association, and regression analysis provides information necessary to predict OP based on given levels of market orientation, JIT,...
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...Incorporated From: , Statistical Consultants, LLC Subject: Statistical Analysis Results for Nevada Gaming, Incorporated Thank you for trusting Statistical Consultants, LLC to provide you with a summary for the data you provided in regards to your gaming empire. The purpose of this report is to explain the results of our research. I. Introduction Your factory in Jean, Nevada has 800 employees. Your personnel department selected 36 employees at random and provided their data to us. The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. There were three tasks that you requested we research for you. First is to examine the gender and age of your factory workers. Next to see if your workers productivity performance ratings are better with hand held computers. Then finally we will analyze your screening process to see if both the General Aptitude and Manual Dexterity test are necessary to screen potential employees. II. Employee Profile The use of descriptive statistics will provide simple summaries about the sample and about the observations that have been made from that sample. Such summaries may be either quantitative, i.e. summary statistics, or visual, i.e. simple-to-understand graphs. These summaries may either form the basis of the initial description of the data as part of a more extensive statistical analysis, or they may be sufficient in and of themselves for a particular investigation....
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...even lets you create some of those rules yourself, so instead of looking at it like a lot of memorization, it's best to see it as an extension of the research mentality, something researchers do anyway (i.e., play with or crunch numbers). Once you realize that YOU have complete and total power over how you want to arrange numbers, your fear of them will disappear. It helps, of course, if you know some basic algebra and arithmetic, at a level where you might be comfortable solving the following equation There are three (3) general areas that make up the field of statistics: descriptive statistics, relational statistics, and inferential statistics. 1. Descriptive statistics fall into one of two categories: measures of central tendency (mean, median, and mode) or measures of dispersion (standard deviation and variance). Their purpose is to explore hunches that may have come up during the course of the research process, but most people compute them to look at the normality of their numbers. Examples include descriptive analysis of sex, age, race, social class, and so forth. 2. Relationalstatistics fall into one of three categories: univariate, bivariate, and multivariate analysis. Univariate analysis is the study of one variable for a subpopulation, for example, age of murderers, and the analysis is often descriptive, but you'd be surprised how many advanced statistics can be computed using just one variable. Bivariate analysis is the study of a relationship between two variables...
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...P-value is 0.001. The chance of an association between Expend and Number of this magnitude occurring just by chance is around 1 in 1000. So we very strongly suspect that there really is an underlying association. b) The average or expected monthly expenditure on extras this year is $12.26 for customers who did not take any options last year. For every option the customer took last year you can add another $2.79 to this. c) The prediction equation is not very accurate. The model error measures the typical difference between the fitted values and the observed values. This is a rough estimator of the prediction error. The sample size was n=83 so there is a typical prediction error of [pic] which is the large in comparison with the typical size of expenditures in this data set (roughly $10-$30 judging by the given mean and standard deviation). The percentage of variation in expenditure explained by optional purchases last year is only 12%. d) We predict $12.26+5x$2.79=$26.21 e) This is a simple regression (single input variable) and chapter 15 gave an explicit formula for the slope estimate. The equation for slope is [pic] and we know this equals 2.787. We also know that the standard deviation of expenditure is 14.064 and of number is 1.828. Solving for the correlation gives the answer 0.362. For simple regression, you can calculate the slope from the correlation or visa-versa. For multiple regression, it is the partial correlations that are directly related to the slopes...
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...Charismatic leader Well known and respected members Received award Known as a brand to women entrepreneurs Outstanding performance of NAWEM member PERSONAL ENTREPRENEURIAL COMPETENCIES • an (2001) defines entrepreneurial competencies as individual characteristics, M knowledge and skill that lead to effective or higher entrepreneurial job performance; which can be assessed through the behaviors of entrepreneurs. INTERNAL MOTIVATION • efer to the driving force that comes from inner side of someone that lead to a R good performance because of the satisfied feeling when success to accomplish something (Fleming, 2010). FAMILY SUPPORT • efer to the driving force that comes from inner side of someone that lead to a R good performance because of the satisfied feeling when success to accomplish something (Fleming, 2010). INSTITUTIONAL SUPPORT • he external support received by women entrepreneurs obtain from government T organizations, financial institutions and other institutional support (Ranasinge, 2011) Variables Women Entrepreneurs’ Success Personal Entrepreneurial Competencies Internal Motivation Family Support Institutional Support Model/Author Syed, Fauzi and Asiah, (2010), Salfiya and Gunapalan, (2012), Ranasinghe, (2011) Ranasinghe, (2011) Syed, Fauzi and Asiah, (2010), Salfiya and Gunapalan, (2012) Syed, Fauzi and Asiah, (2010) Salfiya and Gunapalan, (2012) Adapted/New Adapted Adapted Adapted Adapted Adapted Personal Entrepreneurial...
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...Part 1: Summary statistics were computed using MS-excel: Summary Statistics: Full-Time Enrollment | | | | Mean | 165.16 | Standard Error | 28.1682256 | Median | 126 | Mode | 30 | Standard Deviation | 140.841128 | Sample Variance | 19836.22333 | Kurtosis | -0.751273971 | Skewness | 0.756612995 | Range | 451 | Minimum | 12 | Maximum | 463 | Sum | 4129 | Count | 25 | Insights: 1. Average full time enrollment is 165 students and median enrollment is 126 students. 2. It appears that the distribution of full-time enrollments is positively skewed where median appears to be a better measure of central tendency. 3. Maximum enrollment is 436 students and minimum enrollments are 12 enrollments. Standard deviation is 140 students. So the data appears to have large spread around the mean. Students per Faculty | | | | Mean | 8.48 | Standard Error | 1.011797081 | Median | 7 | Mode | 5 | Standard Deviation | 5.058985406 | Sample Variance | 25.59333333 | Kurtosis | -0.705506483 | Skewness | 0.762103551 | Range | 17 | Minimum | 2 | Maximum | 19 | Sum | 212 | Count | 25 | Insights: 1. There are on average 8.5 students per faculty member, where the maximum number of students per faculty is 19 and minimum number of students per faculty is 2. 2. Most schools have 5 students per faculty member since mode is 5. Local Tuition ($) | | | | Mean | 12374.92 | Standard Error | 1555.684696 | Median...
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...mine valid association rules, which are identical to the content Professor Chen introduced to you in class. Note that you do not need to pay more attention to the algorithm or codes of this method. Instead, ideas and related examples are more important for you to understand this method and it is enough to help you complete the assignment. Furthermore, to resolve the problem 2.(c) in EXERCISE 3, you need to read section 5.3.1 to know how to do. This part gives you the concept of multi-level association rule or generalized association rule. 基本阅读：英文资料 5.1,5.2.1 和 5.2.2，这部分内容与老师上课所介 绍的内容一致，不必过分专注于其中的算法和代码部分，更重要的是 理解方法意思,过程及其中的相关例子。扩展阅读：为了解决作业问 题 2 中的（c）小问，你还最好阅读 5.3.1 部分。 Mining Frequent Patterns, Associations, and Correlations Frequent patterns are patterns (such as itemsets, subsequences, or substructures) that appear in a data set frequently. For example, a set of items, such as milk and bread, that appear frequently together in a transaction data set is a frequent itemset. A subsequence, such as buying first a PC, then a digital camera, and then a memory card, if it occurs frequently in a shopping history database, is a (frequent) sequential pattern. A substructure can refer to different structural forms, such as subgraphs, subtrees, or sublattices, which may be combined with itemsets or subsequences. If a substructure occurs frequently, it is called a (frequent) structured pattern. Finding such frequent patterns plays an essential role in mining associations, correlations...
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...A Handbook on Thesis Writing 1 BASICS OF THESIS WRITING Thesis Writing is a requirement for graduating students particularly those who opted for Plan A (Thesis) instead of a comprehensive examination. This book focuses on the mechanics on how to develop a thesis and is made simple for easy understanding of the processes. Many students are confronted with tasks that demand good writing skills. They need guidance on how to go about the complex job of writing a thesis or dissertation. This book will therefore serve as a guide. Thesis Writing is subsumed under Technical Writing. All professional fields require technical documents that help readers perform tasks and understand specific tasks. As mentioned by some authors, thesis is classified under technical writing as it provides vital information to specific readers. Technical Writing refers to the provision of specific information to clearly identified readers who will use the information for a specific purpose. KEY FEATURES OF TECHNICAL WRITING Three Elements of Technical Writing. The elements are reader, purpose and writing situation. The reader seeks information for a specific purpose. The purpose is to design a document that will serve the reader's need and help the reader understand the information. The writing situation consist of both reader and purpose, as well as such factors such as the sponsoring organization's size, budget, ethics, deadlines, policies, competition and priorities. Three General Stages of Technical...
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...Diagnostic Testing 1. Beck Anxiety Inventory 3 Description: 4 Rationale: 4 Personnel, Training, Administration, and Scoring Requirements: 5 Population Used to Develop Measure 5 What Are The Symptoms Of An Anxiety Attack? 5 The Beck Anxiety Inventory of the 21 most common symptoms: 5 Scoring: 6 Interpretation of score 6 Psychometric Properties: 7 Reliability: 7 Validity 7 Advantages: 8 Disadvantages: 9 Suggested Uses: 9 Beck Anxiety Inventory used in Pakistani Settings 10 2. Hamilton Anxiety Scale (HAS) 11 Rating: 11 Purpose 11 Use in the field 12 Scale 12 Scoring 12 Criteria for interpretation 12 Versions 12 Psychometric Properties 13 Applied in different researches 14 3. Hamilton Depression Scale 15 Description/Purpose 15 Use in the field 15 Scale 16 Criteria for interpretation 16 Psychometric properties 16 Applied in different researches 18 Correlations among Depression Rating Scales and A Self-Rating Anxiety Scale In Depressive Outpatients 18 Limitations 19 4. Adaptive Behavior Assessment System Second Edition 19 Rationale: 20 What’s New in ABAS–II 20 ABAS–II Rating Forms 21 Scores Reported 22 Sample Items: 22 Psychometric Properties: 23 Standardization 23 Validity 25 Advantages of Using ABAS–II 25 Adaptive behaviour assessment system in Pakistan: 26 5. Symptom Assessment-45 26 Purpose: 26 Description: 26 SA-45 Scales: 27 Psychometric properties: 28 Reliability and Validity:...
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...MKT 424 EXAM 3 REVIEW GUIDE CHAPTER 18 Characterizing Relationships Between Variables • 1. Presence: whether any systematic relationship exists between two variables of interest • 2. Direction: whether the relationship is positive or negative • 3. Strength of Association: how strong/consistent the relationship is (strong, moderate, weak) o Relationships should be assessed in this order How to Analyze Relationships 1. Choose variables to analyze 2. Determine if the variables are interval/ratio or nominal/ordinal 3. Use the correct relationship analysis a. For two interval/ratio variables – use correlation b. For two nominal/ordinal variables – use cross-tabs 4. Does a relationship exist? 5. If relationship exists, determine the direction a. Monotonic will be increasing/decreasing b. Nonmonotonic will be looking for a pattern 6. Assess the strength of relationship a. With correlation – size of coefficient denotes the strength b. With cross-tabs – the pattern is assessed Cross-Tabulations and Chi Square • Cross-tabulations o Consists of rows and columns defined by the categories classifying each variable. Used for nonmonotonic relationships o Sometimes referred to as an “r x c” table (rows x columns) ▪ Crosstabulation cell – intersection of a row and a column o Interested in inner cells to determine relationship before statistically...
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...|[pic] |Course Design Guide | | |College of Social Sciences | | |PSY/315 Version 3 | | |Statistical Reasoning in Psychology | Copyright © 2012, 2010, 2006 by University of Phoenix. All rights reserved. Course Description This is an introductory course in applied statistics, with particular emphasis in psychology. Both descriptive and inferential statistics are included. In addition, this course provides the basic statistical background and understanding needed. Policies Faculty and students/learners will be held responsible for understanding and adhering to all policies contained within the following two documents: • University policies: You must be logged into the student website to view this document. • Instructor policies: This document is posted in the Course Materials forum. University policies are subject to change. Be sure to read the policies at the beginning of each class. Policies may be slightly different depending on the modality in which you attend class. If you have recently...
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