SOME NOTES ON STATISTICAL INTERPRETATION. Below I provide some basic notes on statistical interpretation for some selected procedures.
|
|
- Adam Small
- 7 years ago
- Views:
Transcription
1 1 SOME NOTES ON STATISTICAL INTERPRETATION Below I provide some basic notes on statistical interpretation for some selected procedures. The information provided here is not exhaustive. There is more to learn about assumptions, applications, and interpretation of these procedures. Further information can be obtained in statistics textbooks and statistics courses. Crosstabs: Crosstab is short for cross-tabulation or cross-classification table. In its basic form it is a bivariate table. Usually the independent variable is represented by the columns and the dependent variable is represented by the rows. One can use any variables with any level of measurement in a crosstab but usually they are constructed using nominal or ordinal variables. Because interval/ratio variables tend to have many potential variables, crosstabs are usually impractical for these levels of measurement. More complex multivariate crosstabs can also be constructed (e.g., where a third variable is controlled). The data in crosstabs is usually presented either as percentages, or frequencies. Percentages can pertain to the cell as a function of either: 1) the column, 2) the row, 3) the total. In constructing a crosstabulation for a report you should make clear which of these types of percentages are being calculated. (This can often be done easily by providing a total percentage at the end of the row or column.) In providing descriptive interpretation of results one can discuss the relative frequency or percentage of cases falling in particular cells. Usually this is done in reference to the column variable. E.g., 35% of women strongly agreed with statement X, while only 15% of men strongly agreed with statement X.
2 2 Chi-Square: Technically this is a test of statistical independence. That is, if two variable are unrelated then they are independent of one another. If not, they are dependent. Another way of thinking about this is that they are associated. Chi-square can be used with nominal and ordinal variables. If the significance value corresponding to the chi-square test is less than or equal to.05, then the test is deemed to be statistically significant and you can interpret the two variables in the test as being dependent or associated. There are several limitations to the chi-square test. Two of these are: 1) the test does not tell you about the direction of an association (e.g., positive or negative), 2) the test does not tell you about the strength of an association. From the chi-square statistic (and its related level of significance) all you can say is that the variables are statistically associated or not. You can, however, try to interpret the percentages in the related crosstabulation. In Table 1, the chi-square is significant. This means that employment status and gender are statistically associated. The results in the crosstabulation suggest that men are more likely to be employed full-time.
3 3 Pearson s Correlation: Pearson s correlation is a bi-variate measure of association for interval/ratio level variables. Pearson s correlation ranges from 0 to the absolute value of 1 (e.g. 1 or -1). A correlation of 0 means that there is no linear statistical association between two variables. A correlation of 1 means that there is a perfect positive correlation (or linear association) between two variables. A correlation of -1 means that there is a perfect negative correlation between two variables. A correlation of.50 means that there is a moderately strong positive correlation between two variables. There is also an associated test of significance. If the significance value (p.) is.05, then the correlation is deemed to be statistically significant. In Table 2 the correlation between years of education and personal income is.42, and p. is <.01. Thus there is a significant, moderately strong positive correlation between education and income. (Another way of saying this is that there is a significant moderately strongly positive linear association between education and income.) In other words, people with higher levels of education tend to earn higher levels of income, people with lower levels of education tend to earn lower levels of income.
4 4 Multiple Regression Analysis. Multiple regression analysis examines the strength of the linear relationship between a set of independent variables and a single dependent variable (measured at the interval/ratio level). 2 The R provides the proportion of variation in the dependent variable that is explained by the independent variables in the model. For example, the independent variables in Model 5 of Table 7 explain.20 of the variation in environmentally friendly behaviour, or, converted into a percentage, they explain 20% of the variation in environmentally friendly behaviour. There are two types of coefficients that are typically be displayed in a multiple regression table: unstandardized coefficients, and standardized coefficients. To interpret an unstandardized regression coefficient: for every metric unit change in the independent variable, the dependent variable changes by X units. For instance, if income is the dependent variable, and years of education is one of the independent variables, and the unstandardized regression coefficient for education is 3,000, then this would mean that for every additional year of education a respondent has, their income increases by $3, (controlling for the other independent variables in the equation). In multiple regression, the effects of the independent variables are always net effects controlling simultaneously for the effects of the other variables in the equation. One advantage of using unstandardized coefficients is that they have readily interpretable substantive meaning (such as in the example of education and income given above). One disadvantage is that the independent variables usually have different metrics (e.g. income in dollars, age in years, attitudes on a rating scale, etc.). This makes it difficult to compare the relative influence of different independent variables upon the dependent variable. Standardized regression coefficients are based on changes in standard deviation units. For example, in Model 5 of Table 7, for every standard deviation unit increase in activism, the respondent s score on the environmentally friendly behaviour index increases by.18 standard deviation units.
5 One advantage of using standardized regression coefficients is that you can compare the relative strength of the coefficients. Generally, the closer to the absolute value of 1 the coefficient is, the stronger the effect of that independent variable on the dependent variable (controlling for other variables in the equation). The closer the coefficient is to 0, the weaker the effect of that independent variable. For example, in Model 1 of Table 1, Age has the strongest effect on environmentally friendly behaviour (-.23), while income (log) has the smallest effect (-.08). (0 means no net effect; under unusual circumstances in multiple regression, standardized regression coefficients can be greater than the absolute value of 1; in bivariate regression the standardized regression coefficient also known as Pearson s Correlation Coefficient has a maximum value of the absolute value of 1.) 5 Usually independent variables are measured at the interval/ratio level. While it is technically not supposed to be done, sometimes ordinal variables (measured in likerttype scales) are treated as interval/ratio level variables and used as independent variables. It is also possible to include categorical variables as independent variables but they have to be binarized, and coded as 0 or 1. Also, at least one category has to be left out to serve as a reference category. Variables coded in this way are referred to as dummy variables. For example, in Table 7 gender is coded as male = 1, and female = 0. If one had income as a dependent variable in a multiple regression, and the unstandardized regression coefficient for gender was 10,000 then (assuming the previous coding scheme) men would make 10,000 more than women controlling for other variables in the equation. Another example in Table 7 is Gendpar where female parents are coded as 1, and everyone else is coded as 0. It is somewhat more difficult to interpret standardized regression coefficients for dummy variables because standard deviation unit changes are somewhat meaningless when there are only two categories. In Model 1 of Table 7, it can be said that there is a significant effect for gender, females have higher scores for environmentally friendly behaviour. In multiple regression analysis, significance levels are usually also reported that are associated with the individual regression coefficients, and also a separate significance level is reported for 2 the equation as a whole and associated with the R.
6 6 Usually.05 is the minimal criterial for indicating a result is significant (though in Table 7, the level of.10 is also reported.) For example, in Model 2 of Table 7, the following independent variables are significant at the.05 level: gender, age, and education (squared). The following variables are not significant at the.05 level: income (log), parent. 2 In Model 2 of Table 7 the equation as a whole is significant. (See the asterix next to the R.) There are a variety of different ways of displaying information in a multiple regression table. Sometimes a series of models is presented (such as in Table 7) where conceptually similar variables are grouped together and added in a block, and then different blocks are added in sequence usually associated with theoretical arguments. This is often referred to as hierarchal regression analysis. Sometimes only the results associated with a single model are presented. Sometimes only the unstandardized coefficients are provided. Sometimes only the standardized coefficients are provided (this is the case in Table 7). Sometimes the standard error associated with the coefficient is provided. 2 Sometimes R Changes are provides in association with different models. (This could have been done in Table 7). Also, the number of cases used to create the regression model are usually indicated (N). These are just some of the basics. There is a good deal of additional information to know associated with assumptions underlying the variables, regression diagnostics, and interpreting regression equations. There are also a variety of specialized types of regression equations (e.g. for non-linear effects, for interaction effects, etc.)
7 7 Difference in Means and t-test: When you wish to examine the relationship between a nominal (or ordinal) variable with two categories that is an independent variable, and a dependent variable that is measured at the interval/ratio level then an appropriate then an appropriate procedure and test is to examine the difference in means, and calculate a t-test. To see the direction of the difference in means just examine the respective means for the two groups. For the t-test there is an associated significance level. If the significance level is.05, then the difference in means is statistically significant. For example, examine the third row of Table 3. This displays the mean personal income for women and men. Men made an average of $46,968 while women made a an average of $24,268. This difference is statistically significant (p..01). Thus you can conclude that (for this sample) men make more than women.
8 8 Univariate Statistics: Frequencies and Percentages: Often it is useful to provide basic univariate statistics describing key variables. For nominal and ordinal variables this can be done by providing frequencies and percentages. (There are also a variety of other useful statistics that will not be discussed here.) Technically, you can also provide frequencies and percentages for interval/ratio variables but it is usually not practical to do so because there are so many potential values. (Instead, such data are sometimes portrayed in graphs.) When you provide tables of frequencies and percentages you should provide totals. Also, if there is missing data you should indicate this in the table. In Table 4, the response category with the largest number of cases is strongly agree. 7 out of 20 people or 35% of the sample selected this response.
9 9 Univariate Statistics: Means, Standard Deviations, and N For interval/ratio level variables, one way of summarizing data is to provide means, standard deviations, and N. The mean is the arithmetic average of the data. The standard deviation is a measure of how dispersed the data are. The N is the number of (valid) cases that were used to calculate these statistics. In row 2 of Table 5 we see that for this sample the mean years of education were 15.36, and the standard deviation was These statistics were calculated from 183 cases. The standard deviation means that about 68% of the cases fell between and 17.53, and about 95% of all the cases fell between and
10 10 Percentage Tables for Multiple Items: Sometimes it is useful to provide tables that summarize multiple variables at the same time. Table 2 does this for some correlations. Table 5 does this for means, standard deviations, and Ns. When you have likert-type scales it is sometimes useful to present data in the form of a matrix with the categories across the top (or columns) and the different questionnaire items down the side (or rows). Table 6 does this for the political efficacy items. For example, for item #4, 35% strongly disagreed, 15% disagreed, 0% had no opinion, 20% agreed, and 30% strongly agreed. When the data are displayed this way we can try to discern patterns by comparing across the items. In this particular instance the responses look pretty similar across items with lots of responses in the extreme categories and fewer responses in the middle of the scale (especially for no opinion).
Chapter Seven. Multiple regression An introduction to multiple regression Performing a multiple regression on SPSS
Chapter Seven Multiple regression An introduction to multiple regression Performing a multiple regression on SPSS Section : An introduction to multiple regression WHAT IS MULTIPLE REGRESSION? Multiple
More informationAssociation Between Variables
Contents 11 Association Between Variables 767 11.1 Introduction............................ 767 11.1.1 Measure of Association................. 768 11.1.2 Chapter Summary.................... 769 11.2 Chi
More informationIntroduction to Quantitative Methods
Introduction to Quantitative Methods October 15, 2009 Contents 1 Definition of Key Terms 2 2 Descriptive Statistics 3 2.1 Frequency Tables......................... 4 2.2 Measures of Central Tendencies.................
More informationData exploration with Microsoft Excel: analysing more than one variable
Data exploration with Microsoft Excel: analysing more than one variable Contents 1 Introduction... 1 2 Comparing different groups or different variables... 2 3 Exploring the association between categorical
More informationCHAPTER 14 ORDINAL MEASURES OF CORRELATION: SPEARMAN'S RHO AND GAMMA
CHAPTER 14 ORDINAL MEASURES OF CORRELATION: SPEARMAN'S RHO AND GAMMA Chapter 13 introduced the concept of correlation statistics and explained the use of Pearson's Correlation Coefficient when working
More informationII. DISTRIBUTIONS distribution normal distribution. standard scores
Appendix D Basic Measurement And Statistics The following information was developed by Steven Rothke, PhD, Department of Psychology, Rehabilitation Institute of Chicago (RIC) and expanded by Mary F. Schmidt,
More informationThe Dummy s Guide to Data Analysis Using SPSS
The Dummy s Guide to Data Analysis Using SPSS Mathematics 57 Scripps College Amy Gamble April, 2001 Amy Gamble 4/30/01 All Rights Rerserved TABLE OF CONTENTS PAGE Helpful Hints for All Tests...1 Tests
More informationStatistics. Measurement. Scales of Measurement 7/18/2012
Statistics Measurement Measurement is defined as a set of rules for assigning numbers to represent objects, traits, attributes, or behaviors A variableis something that varies (eye color), a constant does
More informationCHAPTER 15 NOMINAL MEASURES OF CORRELATION: PHI, THE CONTINGENCY COEFFICIENT, AND CRAMER'S V
CHAPTER 15 NOMINAL MEASURES OF CORRELATION: PHI, THE CONTINGENCY COEFFICIENT, AND CRAMER'S V Chapters 13 and 14 introduced and explained the use of a set of statistical tools that researchers use to measure
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 informationNonparametric Tests. Chi-Square Test for Independence
DDBA 8438: Nonparametric Statistics: The Chi-Square Test Video Podcast Transcript JENNIFER ANN MORROW: Welcome to "Nonparametric Statistics: The Chi-Square Test." My name is Dr. Jennifer Ann Morrow. In
More informationRow vs. Column Percents. tab PRAYER DEGREE, row col
Bivariate Analysis - Crosstabulation One of most basic research tools shows how x varies with respect to y Interpretation of table depends upon direction of percentaging example Row vs. Column Percents.
More informationChapter 10. Key Ideas Correlation, Correlation Coefficient (r),
Chapter 0 Key Ideas Correlation, Correlation Coefficient (r), Section 0-: Overview We have already explored the basics of describing single variable data sets. However, when two quantitative variables
More informationMULTIPLE REGRESSION WITH CATEGORICAL DATA
DEPARTMENT OF POLITICAL SCIENCE AND INTERNATIONAL RELATIONS Posc/Uapp 86 MULTIPLE REGRESSION WITH CATEGORICAL DATA I. AGENDA: A. Multiple regression with categorical variables. Coding schemes. Interpreting
More informationHow to Get More Value from Your Survey Data
Technical report How to Get More Value from Your Survey Data Discover four advanced analysis techniques that make survey research more effective Table of contents Introduction..............................................................2
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 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 informationClass 19: Two Way Tables, Conditional Distributions, Chi-Square (Text: Sections 2.5; 9.1)
Spring 204 Class 9: Two Way Tables, Conditional Distributions, Chi-Square (Text: Sections 2.5; 9.) Big Picture: More than Two Samples In Chapter 7: We looked at quantitative variables and compared the
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 THE TWO-WAY ANOVA
UNDERSTANDING THE e have seen how the one-way ANOVA can be used to compare two or more sample means in studies involving a single independent variable. This can be extended to two independent variables
More informationLean Six Sigma Analyze Phase Introduction. TECH 50800 QUALITY and PRODUCTIVITY in INDUSTRY and TECHNOLOGY
TECH 50800 QUALITY and PRODUCTIVITY in INDUSTRY and TECHNOLOGY Before we begin: Turn on the sound on your computer. There is audio to accompany this presentation. Audio will accompany most of the online
More informationSession 7 Bivariate Data and Analysis
Session 7 Bivariate Data and Analysis Key Terms for This Session Previously Introduced mean standard deviation New in This Session association bivariate analysis contingency table co-variation least squares
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 informationSPSS Explore procedure
SPSS Explore procedure One useful function in SPSS is the Explore procedure, which will produce histograms, boxplots, stem-and-leaf plots and extensive descriptive statistics. To run the Explore procedure,
More informationHYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION
HYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION HOD 2990 10 November 2010 Lecture Background This is a lightning speed summary of introductory statistical methods for senior undergraduate
More informationBinary Logistic Regression
Binary Logistic Regression Main Effects Model Logistic regression will accept quantitative, binary or categorical predictors and will code the latter two in various ways. Here s a simple model including
More informationMode and Patient-mix Adjustment of the CAHPS Hospital Survey (HCAHPS)
Mode and Patient-mix Adjustment of the CAHPS Hospital Survey (HCAHPS) April 30, 2008 Abstract A randomized Mode Experiment of 27,229 discharges from 45 hospitals was used to develop adjustments for the
More informationModule 5: Multiple Regression Analysis
Using Statistical Data Using to Make Statistical Decisions: Data Multiple to Make Regression Decisions Analysis Page 1 Module 5: Multiple Regression Analysis Tom Ilvento, University of Delaware, College
More informationModule 3: Correlation and Covariance
Using Statistical Data to Make Decisions Module 3: Correlation and Covariance Tom Ilvento Dr. Mugdim Pašiƒ University of Delaware Sarajevo Graduate School of Business O ften our interest in data analysis
More informationUsing Excel for Statistical Analysis
Using Excel for Statistical Analysis You don t have to have a fancy pants statistics package to do many statistical functions. Excel can perform several statistical tests and analyses. First, make sure
More informationDescriptive Analysis
Research Methods William G. Zikmund Basic Data Analysis: Descriptive Statistics Descriptive Analysis The transformation of raw data into a form that will make them easy to understand and interpret; rearranging,
More informationSimple Linear Regression, Scatterplots, and Bivariate Correlation
1 Simple Linear Regression, Scatterplots, and Bivariate Correlation This section covers procedures for testing the association between two continuous variables using the SPSS Regression and Correlate analyses.
More informationLinear Models in STATA and ANOVA
Session 4 Linear Models in STATA and ANOVA Page Strengths of Linear Relationships 4-2 A Note on Non-Linear Relationships 4-4 Multiple Linear Regression 4-5 Removal of Variables 4-8 Independent Samples
More informationAn introduction to IBM SPSS Statistics
An introduction to IBM SPSS Statistics Contents 1 Introduction... 1 2 Entering your data... 2 3 Preparing your data for analysis... 10 4 Exploring your data: univariate analysis... 14 5 Generating descriptive
More informationDirections for using SPSS
Directions for using SPSS Table of Contents Connecting and Working with Files 1. Accessing SPSS... 2 2. Transferring Files to N:\drive or your computer... 3 3. Importing Data from Another File Format...
More informationCanonical Correlation Analysis
Canonical Correlation Analysis LEARNING OBJECTIVES Upon completing this chapter, you should be able to do the following: State the similarities and differences between multiple regression, factor analysis,
More informationConstructing a TpB Questionnaire: Conceptual and Methodological Considerations
Constructing a TpB Questionnaire: Conceptual and Methodological Considerations September, 2002 (Revised January, 2006) Icek Ajzen Brief Description of the Theory of Planned Behavior According to the theory
More informationSCHOOL OF HEALTH AND HUMAN SCIENCES DON T FORGET TO RECODE YOUR MISSING VALUES
SCHOOL OF HEALTH AND HUMAN SCIENCES Using SPSS Topics addressed today: 1. Differences between groups 2. Graphing Use the s4data.sav file for the first part of this session. DON T FORGET TO RECODE YOUR
More informationCorrelation Coefficient The correlation coefficient is a summary statistic that describes the linear relationship between two numerical variables 2
Lesson 4 Part 1 Relationships between two numerical variables 1 Correlation Coefficient The correlation coefficient is a summary statistic that describes the linear relationship between two numerical variables
More informationAnswer: C. The strength of a correlation does not change if units change by a linear transformation such as: Fahrenheit = 32 + (5/9) * Centigrade
Statistics Quiz Correlation and Regression -- ANSWERS 1. Temperature and air pollution are known to be correlated. We collect data from two laboratories, in Boston and Montreal. Boston makes their measurements
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 informationBivariate Statistics Session 2: Measuring Associations Chi-Square Test
Bivariate Statistics Session 2: Measuring Associations Chi-Square Test Features Of The Chi-Square Statistic The chi-square test is non-parametric. That is, it makes no assumptions about the distribution
More informationStatistical tests for SPSS
Statistical tests for SPSS Paolo Coletti A.Y. 2010/11 Free University of Bolzano Bozen Premise This book is a very quick, rough and fast description of statistical tests and their usage. It is explicitly
More informationSection 3 Part 1. Relationships between two numerical variables
Section 3 Part 1 Relationships between two numerical variables 1 Relationship between two variables The summary statistics covered in the previous lessons are appropriate for describing a single variable.
More informationSimple Regression Theory II 2010 Samuel L. Baker
SIMPLE REGRESSION THEORY II 1 Simple Regression Theory II 2010 Samuel L. Baker Assessing how good the regression equation is likely to be Assignment 1A gets into drawing inferences about how close the
More informationSimple Predictive Analytics Curtis Seare
Using Excel to Solve Business Problems: Simple Predictive Analytics Curtis Seare Copyright: Vault Analytics July 2010 Contents Section I: Background Information Why use Predictive Analytics? How to use
More informationCourse Objective This course is designed to give you a basic understanding of how to run regressions in SPSS.
SPSS Regressions Social Science Research Lab American University, Washington, D.C. Web. www.american.edu/provost/ctrl/pclabs.cfm Tel. x3862 Email. SSRL@American.edu Course Objective This course is designed
More informationDATA ANALYSIS AND INTERPRETATION OF EMPLOYEES PERSPECTIVES ON HIGH ATTRITION
DATA ANALYSIS AND INTERPRETATION OF EMPLOYEES PERSPECTIVES ON HIGH ATTRITION Analysis is the key element of any research as it is the reliable way to test the hypotheses framed by the investigator. This
More informationWe are often interested in the relationship between two variables. Do people with more years of full-time education earn higher salaries?
Statistics: Correlation Richard Buxton. 2008. 1 Introduction We are often interested in the relationship between two variables. Do people with more years of full-time education earn higher salaries? Do
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 informationJanuary 26, 2009 The Faculty Center for Teaching and Learning
THE BASICS OF DATA MANAGEMENT AND ANALYSIS A USER GUIDE January 26, 2009 The Faculty Center for Teaching and Learning THE BASICS OF DATA MANAGEMENT AND ANALYSIS Table of Contents Table of Contents... i
More informationCorrelation key concepts:
CORRELATION Correlation key concepts: Types of correlation Methods of studying correlation a) Scatter diagram b) Karl pearson s coefficient of correlation c) Spearman s Rank correlation coefficient d)
More informationThis chapter will demonstrate how to perform multiple linear regression with IBM SPSS
CHAPTER 7B Multiple Regression: Statistical Methods Using IBM SPSS This chapter will demonstrate how to perform multiple linear regression with IBM SPSS first using the standard method and then using the
More informationIntroduction to Statistics Used in Nursing Research
Introduction to Statistics Used in Nursing Research Laura P. Kimble, PhD, RN, FNP-C, FAAN Professor and Piedmont Healthcare Endowed Chair in Nursing Georgia Baptist College of Nursing Of Mercer University
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 informationMEASURES OF VARIATION
NORMAL DISTRIBTIONS MEASURES OF VARIATION In statistics, it is important to measure the spread of data. A simple way to measure spread is to find the range. But statisticians want to know if the data are
More informationSPSS Tests for Versions 9 to 13
SPSS Tests for Versions 9 to 13 Chapter 2 Descriptive Statistic (including median) Choose Analyze Descriptive statistics Frequencies... Click on variable(s) then press to move to into Variable(s): list
More informationNursing Journal Toolkit: Critiquing a Quantitative Research Article
A Virtual World Consortium: Using Second Life to Facilitate Nursing Journal Clubs Nursing Journal Toolkit: Critiquing a Quantitative Research Article 1. Guidelines for Critiquing a Quantitative Research
More informationIntroduction to Statistics and Quantitative Research Methods
Introduction to Statistics and Quantitative Research Methods Purpose of Presentation To aid in the understanding of basic statistics, including terminology, common terms, and common statistical methods.
More information4. Multiple Regression in Practice
30 Multiple Regression in Practice 4. Multiple Regression in Practice The preceding chapters have helped define the broad principles on which regression analysis is based. What features one should look
More informationLecture 11: Chapter 5, Section 3 Relationships between Two Quantitative Variables; Correlation
Lecture 11: Chapter 5, Section 3 Relationships between Two Quantitative Variables; Correlation Display and Summarize Correlation for Direction and Strength Properties of Correlation Regression Line Cengage
More information/-- / \ CASE STUDY APPLICATIONS STATISTICS IN INSTITUTIONAL RESEARCH. By MARY ANN COUGHLIN and MARIAN PAGAN(
; /-- / \ \ CASE STUDY APPLICATIONS OF STATISTICS IN INSTITUTIONAL RESEARCH By MARY ANN COUGHLIN and MARIAN PAGAN( Case Study Applications of Statistics in Institutional Research by Mary Ann Coughlin and
More informationWHAT IS A JOURNAL CLUB?
WHAT IS A JOURNAL CLUB? With its September 2002 issue, the American Journal of Critical Care debuts a new feature, the AJCC Journal Club. Each issue of the journal will now feature an AJCC Journal Club
More informationSPSS Guide How-to, Tips, Tricks & Statistical Techniques
SPSS Guide How-to, Tips, Tricks & Statistical Techniques Support for the course Research Methodology for IB Also useful for your BSc or MSc thesis March 2014 Dr. Marijke Leliveld Jacob Wiebenga, MSc CONTENT
More information03 The full syllabus. 03 The full syllabus continued. For more information visit www.cimaglobal.com PAPER C03 FUNDAMENTALS OF BUSINESS MATHEMATICS
0 The full syllabus 0 The full syllabus continued PAPER C0 FUNDAMENTALS OF BUSINESS MATHEMATICS Syllabus overview This paper primarily deals with the tools and techniques to understand the mathematics
More informationAn introduction to using Microsoft Excel for quantitative data analysis
Contents An introduction to using Microsoft Excel for quantitative data analysis 1 Introduction... 1 2 Why use Excel?... 2 3 Quantitative data analysis tools in Excel... 3 4 Entering your data... 6 5 Preparing
More informationQUANTITATIVE METHODS BIOLOGY FINAL HONOUR SCHOOL NON-PARAMETRIC TESTS
QUANTITATIVE METHODS BIOLOGY FINAL HONOUR SCHOOL NON-PARAMETRIC TESTS This booklet contains lecture notes for the nonparametric work in the QM course. This booklet may be online at http://users.ox.ac.uk/~grafen/qmnotes/index.html.
More informationSimple linear regression
Simple linear regression Introduction Simple linear regression is a statistical method for obtaining a formula to predict values of one variable from another where there is a causal relationship between
More informationScientific Method. 2. Design Study. 1. Ask Question. Questionnaire. Descriptive Research Study. 6: Share Findings. 1: Ask Question.
Descriptive Research Study Investigation of Positive and Negative Affect of UniJos PhD Students toward their PhD Research Project : Ask Question : Design Study Scientific Method 6: Share Findings. Reach
More informationIBM SPSS Statistics 20 Part 1: Descriptive Statistics
CALIFORNIA STATE UNIVERSITY, LOS ANGELES INFORMATION TECHNOLOGY SERVICES IBM SPSS Statistics 20 Part 1: Descriptive Statistics Summer 2013, Version 2.0 Table of Contents Introduction...2 Downloading the
More informationWhen to Use a Particular Statistical Test
When to Use a Particular Statistical Test Central Tendency Univariate Descriptive Mode the most commonly occurring value 6 people with ages 21, 22, 21, 23, 19, 21 - mode = 21 Median the center value the
More informationDESCRIPTIVE STATISTICS & DATA PRESENTATION*
Level 1 Level 2 Level 3 Level 4 0 0 0 0 evel 1 evel 2 evel 3 Level 4 DESCRIPTIVE STATISTICS & DATA PRESENTATION* Created for Psychology 41, Research Methods by Barbara Sommer, PhD Psychology Department
More informationDescriptive Statistics and Measurement Scales
Descriptive Statistics 1 Descriptive Statistics and Measurement Scales Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample
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 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 informationCorrelational Research. Correlational Research. Stephen E. Brock, Ph.D., NCSP EDS 250. Descriptive Research 1. Correlational Research: Scatter Plots
Correlational Research Stephen E. Brock, Ph.D., NCSP California State University, Sacramento 1 Correlational Research A quantitative methodology used to determine whether, and to what degree, a relationship
More informationUnivariate Regression
Univariate Regression Correlation and Regression The regression line summarizes the linear relationship between 2 variables Correlation coefficient, r, measures strength of relationship: the closer r is
More informationData analysis process
Data analysis process Data collection and preparation Collect data Prepare codebook Set up structure of data Enter data Screen data for errors Exploration of data Descriptive Statistics Graphs Analysis
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 informationRelationships Between Two Variables: Scatterplots and Correlation
Relationships Between Two Variables: Scatterplots and Correlation Example: Consider the population of cars manufactured in the U.S. What is the relationship (1) between engine size and horsepower? (2)
More informationSPSS Tutorial, Feb. 7, 2003 Prof. Scott Allard
p. 1 SPSS Tutorial, Feb. 7, 2003 Prof. Scott Allard The following tutorial is a guide to some basic procedures in SPSS that will be useful as you complete your data assignments for PPA 722. The purpose
More informationProjects Involving Statistics (& SPSS)
Projects Involving Statistics (& SPSS) Academic Skills Advice Starting a project which involves using statistics can feel confusing as there seems to be many different things you can do (charts, graphs,
More informationThe Chi-Square Test. STAT E-50 Introduction to Statistics
STAT -50 Introduction to Statistics The Chi-Square Test The Chi-square test is a nonparametric test that is used to compare experimental results with theoretical models. That is, we will be comparing observed
More informationPredicting the probabilities of participation in formal adult education in Hungary
Péter Róbert Predicting the probabilities of participation in formal adult education in Hungary SP2 National Report Status: Version 24.08.2010. 1. Introduction and motivation Participation rate in formal
More informationCrosstabulation & Chi Square
Crosstabulation & Chi Square Robert S Michael Chi-square as an Index of Association After examining the distribution of each of the variables, the researcher s next task is to look for relationships among
More informationSolving Insurance Business Problems Using Statistical Methods Anup Cheriyan
Solving Insurance Business Problems Using Statistical Methods Anup Cheriyan Ibexi Solutions Page 1 Table of Contents Executive Summary...3 About the Author...3 Introduction...4 Common statistical methods...4
More informationMeans, standard deviations and. and standard errors
CHAPTER 4 Means, standard deviations and standard errors 4.1 Introduction Change of units 4.2 Mean, median and mode Coefficient of variation 4.3 Measures of variation 4.4 Calculating the mean and standard
More informationCORRELATIONAL ANALYSIS: PEARSON S r Purpose of correlational analysis The purpose of performing a correlational analysis: To discover whether there
CORRELATIONAL ANALYSIS: PEARSON S r Purpose of correlational analysis The purpose of performing a correlational analysis: To discover whether there is a relationship between variables, To find out the
More informationDESCRIPTIVE STATISTICS. The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses.
DESCRIPTIVE STATISTICS The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses. DESCRIPTIVE VS. INFERENTIAL STATISTICS Descriptive To organize,
More informationCorrelation. What Is Correlation? Perfect Correlation. Perfect Correlation. Greg C Elvers
Correlation Greg C Elvers What Is Correlation? Correlation is a descriptive statistic that tells you if two variables are related to each other E.g. Is your related to how much you study? When two variables
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 informationMultivariate Analysis of Variance. The general purpose of multivariate analysis of variance (MANOVA) is to determine
2 - Manova 4.3.05 25 Multivariate Analysis of Variance What Multivariate Analysis of Variance is The general purpose of multivariate analysis of variance (MANOVA) is to determine whether multiple levels
More informationRATIOS, PROPORTIONS, PERCENTAGES, AND RATES
RATIOS, PROPORTIOS, PERCETAGES, AD RATES 1. Ratios: ratios are one number expressed in relation to another by dividing the one number by the other. For example, the sex ratio of Delaware in 1990 was: 343,200
More informationIntroduction to Analysis of Variance (ANOVA) Limitations of the t-test
Introduction to Analysis of Variance (ANOVA) The Structural Model, The Summary Table, and the One- Way ANOVA Limitations of the t-test Although the t-test is commonly used, it has limitations Can only
More informationThe first three steps in a logistic regression analysis with examples in IBM SPSS. Steve Simon P.Mean Consulting www.pmean.com
The first three steps in a logistic regression analysis with examples in IBM SPSS. Steve Simon P.Mean Consulting www.pmean.com 2. Why do I offer this webinar for free? I offer free statistics webinars
More informationMidterm Review Problems
Midterm Review Problems October 19, 2013 1. Consider the following research title: Cooperation among nursery school children under two types of instruction. In this study, what is the independent variable?
More informationInferential Statistics. What are they? When would you use them?
Inferential Statistics What are they? When would you use them? What are inferential statistics? Why learn about inferential statistics? Why use inferential statistics? When are inferential statistics utilized?
More information2x + y = 3. Since the second equation is precisely the same as the first equation, it is enough to find x and y satisfying the system
1. Systems of linear equations We are interested in the solutions to systems of linear equations. A linear equation is of the form 3x 5y + 2z + w = 3. The key thing is that we don t multiply the variables
More informationA Study to Predict No Show Probability for a Scheduled Appointment at Free Health Clinic
A Study to Predict No Show Probability for a Scheduled Appointment at Free Health Clinic Report prepared for Brandon Slama Department of Health Management and Informatics University of Missouri, Columbia
More informationPart 3. Comparing Groups. Chapter 7 Comparing Paired Groups 189. Chapter 8 Comparing Two Independent Groups 217
Part 3 Comparing Groups Chapter 7 Comparing Paired Groups 189 Chapter 8 Comparing Two Independent Groups 217 Chapter 9 Comparing More Than Two Groups 257 188 Elementary Statistics Using SAS Chapter 7 Comparing
More information