EPS 625 INTERMEDIATE STATISTICS ONE-WAY ANOVA IN-CLASS EXAMPLE KEY
|
|
- Lizbeth Bradley
- 7 years ago
- Views:
Transcription
1 EPS 625 INTERMEDIATE STATISTICS ONE-WAY You are interested in whether male college students with black, blond, brunette, or red hair differ with respect to their social extrovertedness. For this study, 60 male college students from a local college (15 for each hair color) have been randomly selected to participate in the study using a stratified random sampling approach. The students were given a measure of social extroversion with a range of 0 (low level of social extroversion) to 10 (high level of social extroversion). Conduct an ANOVA to investigate the relationship between hair color and social extroversion. For this example, use an a priori alpha level of significance of =.05 for each statistical analysis (except, use an α =.001 for the Shapiro-Wilks test) to answer the following questions. 1. What would the null hypothesis be for this study? Show/write the appropriate symbols or expression in words. H 0 : µ black = µ blond = µ brunette = µ redhead or H 0 : µ 1 = µ 2 = µ 3 = µ The group means (average social extrovertedness scores) for the four (hair color) groups are equal. 2. What would the research/alternative hypothesis be for this study? Show/write the appropriate symbols or expression in words. H a : µ i µ k for some i, k or H a : µ i µ j for some i, j At least one of the (hair color) group means significantly differs from the other (hair color) group means. -or- At least two of the (hair color) group means are significantly different from each other. 3. Prior to examining whether group means differ, you need to test the assumptions underlying the one-way ANOVA. a. Was the assumption of independence met for these data? Indicate how you made this determination. YES, the assumption of independence was met. This is indicated by the four groups of male college students being independent of each other. A stratified random sampling technique was used, which focused on obtaining a random selection of male college students for each of the four hair colors. b. Was the assumption of normality met for these data? Indicate how you made this determination. YES, the assumption of normality was met for this set of data. This is indicated by the fact that none of the standardized skewness values exceeded +3.29, nor were any of the probability values less than (or equal to) the.001 alpha level set for the Shapiro-Wilks test.
2 Descriptives Social Extroversion Hair Color Black 95% Confidence Interval for Lower Bound Upper Bound Statistic Std. Error Blond 5% Trimmed Median Variance Std. Deviation Minimum Maximum Range Interquartile Range Skewness Kurtosis 95% Confidence Interval for Lower Bound Upper Bound Brunette 5% Trimmed Median Variance Std. Deviation Minimum Maximum Range Interquartile Range Skewness Kurtosis 95% Confidence Interval for Lower Bound Upper Bound Redhead 5% Trimmed Median Variance Std. Deviation Minimum Maximum Range Interquartile Range Skewness Kurtosis 95% Confidence Interval for Lower Bound Upper Bound % Trimmed Median Variance Std. Deviation Minimum Maximum Range Interquartile Range Skewness Kurtosis PAGE 2
3 .122 Black: = Blond: = Brunette: = Redhead: = Social Extroversion Hair Color Black Blond Brunette Redhead *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Tests of Normality Kolmogorov-Smirnov a Shapiro-Wilk Statistic df Sig. Statistic df Sig * * * * Black: p (.160) > (.001) Blond: p (.315) > (.001) Brunette: p (.097) > (.001) Redhead: p (.117) > (.001) c. Was the assumption of homogeneity of variance met for these data? Indicate how you made this determination. YES, the assumption of homogeneity is met, because p (.08) > (.05). This is indicated by the Levene s Test of Homogeneity of Variances, F(3, 56) = 2.3, p =.08. With an alpha level of.05, p (.08) > α (.05), which indicates nonsignificance, the null hypothesis (no variance difference) is retained as such, indicating that the assumption of homogeneity of variance is met. Test of Homogeneity of Variances Extroversion Social Extroversion Levene Statistic df1 df2 Sig PAGE 3
4 . The next question that needs to be answered is whether all of the groups are the same on their social extroversion means. This is answered by conducting a One-way Analysis of Variance using hair color as the independent variable and students scores on a measure of social extroversion as the dependent variable. If applicable, use the Welch statistic. What is your conclusion (at this point) from this analysis? Indicate how you came to your conclusion. Since the obtained F ratio (8.89) was significant at the.05 alpha level (p shown as.000, that is p <.001) reported as F(3, 56) = 8.89, p <.001 (or, p <.05), we can conclude that at least two of the four hair color groups differ significantly on their average social extrovertedness scores. That is, p (.000) < α (.05). The Welch statistic was not required since the equal variance assumption was met. However, beyond that, post hoc follow-up procedures will need to be conducted to test the difference between all unique pairwise comparisons. ANOVA Extroversion Social Extroversion Sum of Squares df Square F Sig. Between Groups Within Groups Total Calculate the measure of association and interpret its meaning if applicable, or indicate why this measure would not be needed. 2 ω = SSB ( K 1) MS SS + MS T W W ( 1).167 = (3) = = = = ω =.2828 Therefore, we conclude that approximately 28% (ω 2 =.28) of the total variance in the student s average social extrovertedness scores is accounted for by the four levels of hair color. -or- This indicates that the independent variable (four levels of hair color) accounts for approximately 28% (ω 2 =.28) of the total variance in the dependent variable (average social extrovertedness scores) for this sample. PAGE
5 6. Write the statistical strand for this one-way ANOVA analysis. F(3, 56) = 8.89, p <.001, ω 2 =.28 -or- F(3, 56) = 8.89, p <.05, ω 2 = Assuming that you found a significant F, how do the pairs of groups differ? Indicate which post hoc procedure you used and why. Indicate your findings from the post hoc analysis. That is, how did you determine the pair to be significant or not (this must go beyond the * as an indication)? Be sure to discuss all unique pairwise comparisons. Tukey s post hoc procedure is used since the homogeneity of variance assumption was met Using an a priori alpha level of.05 Black vs. Blond ( difference = 1.067) is not significant, p (.86) > α (.05) Black vs. Brunette ( difference = 3.667) is significant, p (.000) < α (.05) Black vs. Redhead ( difference = 2.200) is significant, p (.023) < α (.05) Blond vs. Brunette ( difference = 2.600) is significant, p (.005) < α (.05) Blond vs. Redhead ( difference = 1.133) is not significant, p (.32) > α (.05) Brunette vs. Redhead ( difference = 1.67) is not significant, p (.212) > α (.05) Dependent Variable: Extroversion Social Extroversion Tukey HSD Multiple Comparisons (I) Hair Hair Color 1 Black 2 Blond 3 Brunette Redhead (J) Hair Hair Color 2 Blond 3 Brunette Redhead 1 Black 3 Brunette Redhead 1 Black 2 Blond Redhead 1 Black 2 Blond 3 Brunette *. The mean difference is significant at the.05 level. Difference 95% Confidence Interval (I-J) Std. Error Sig. Lower Bound Upper Bound * * * * * * PAGE 5
6 8. For all significant pairwise comparisons, calculate and report the effect size. Using ES X i X k = where MS W =.167 = MS W ES for Black vs. Brunette = ES = = = ES for Black vs. Redhead = ES = = = ES for Blond vs. Brunette = ES = = = Complete the three tables below that present your statistical results from these analyses. Use the values reported in the SPSS output you do not need to round (Note that in reporting your data, APA suggests being consistent in your reporting of values, e.g., all rounded to two or three decimals, with the exception of p values, which are typically reported with three decimal places.). Results A One-way Analysis of Variance (ANOVA) was used to examine the question of whether male college students with black, blond, brunette, or red hair differ with respect to their social extrovertedness. The independent variable represented the different hair colors with four groups being represented: 1) black; 2) blond; 3) brunette; and ) red. The dependent variable was the average score that students made on a measure of social extroversion with a range of 0 (low level of social extroversion) to 10 (high level of social extroversion). See Table 1 for the means and standard deviations for each of the four groups. PAGE 6
7 Table 1 s and Standard Deviations of Social Extroversion Scores by Hair Color Hair Color n SD Black Blond Brunette Red Total The test for normality, examining standardized skewness and the Shapiro-Wilks test, indicated the data were statistically normal. Additionally, homogeneity of variance was not significant, Levene s F(3, 56) = 2.3, p =.08, indicating that this assumption underlying the application of ANOVA was met. An alpha level of.05 was used for all subsequent analyses. The one-way ANOVA of student s average score on the measure of social extroversion (see Table 2) revealed a statistically significant main effect, F(3, 56) = 8.89, p <.001, indicating that not all hair colors had the same average score on the measure of social extroversion. Omega squared (ω 2 =.28) indicated that approximately 28% of the total variation in average score on students measure of social extroversion is attributable to differences between the four colors of hair. Table 2 Analysis of Variance for Social Extroversion Scores by Hair Color Source SS df MS F p Between Within Total PAGE 7
8 Post hoc comparisons, using the Tukey post hoc procedure, were conducted to determine which pairs of the four hair color means differed significantly. These results are given in Table 3 and indicate that students with black hair (M = 7.33, SD = 2.06) had a significantly higher average score on the measure of social extroversion than students with brunette hair (M = 3.67, SD = 1.0) as well as students with red hair (M = 5.13, SD = 2.20). The effect sizes for these two significant effects were 1.80 and 1.08, respectively. Additionally, students with blond hair (M = 6.27, SD = 2.37) had a significantly higher average score on the measure of social extroversion than students with brunette hair, with an effect size of Table 3 Post Hoc Results for Social Extroversion Scores by Hair Color Hair Color Differences ( X i X j ) (Effect Sizes are indicated in parentheses) Black Blond Brunette *** (1.80) 2.60 ** (1.27). Red * (1.08) *p <.05, ** p <.01, *** p <.001 PAGE 8
INTERPRETING THE ONE-WAY ANALYSIS OF VARIANCE (ANOVA)
INTERPRETING THE ONE-WAY ANALYSIS OF VARIANCE (ANOVA) As with other parametric statistics, we begin the one-way ANOVA with a test of the underlying assumptions. Our first assumption is the assumption of
More informationChapter 5 Analysis of variance SPSS Analysis of variance
Chapter 5 Analysis of variance SPSS Analysis of variance Data file used: gss.sav How to get there: Analyze Compare Means One-way ANOVA To test the null hypothesis that several population means are equal,
More informationIndependent t- Test (Comparing Two Means)
Independent t- Test (Comparing Two Means) The objectives of this lesson are to learn: the definition/purpose of independent t-test when to use the independent t-test the use of SPSS to complete an independent
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 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 informationChapter 2 Probability Topics SPSS T tests
Chapter 2 Probability Topics SPSS T tests Data file used: gss.sav In the lecture about chapter 2, only the One-Sample T test has been explained. In this handout, we also give the SPSS methods to perform
More informationPSYCHOLOGY 320L Problem Set #3: One-Way ANOVA and Analytical Comparisons
PSYCHOLOGY 30L Problem Set #3: One-Way ANOVA and Analytical Comparisons Name: Score:. You and Dr. Exercise have decided to conduct a study on exercise and its effects on mood ratings. Many studies (Babyak
More informationBiostatistics: DESCRIPTIVE STATISTICS: 2, VARIABILITY
Biostatistics: DESCRIPTIVE STATISTICS: 2, VARIABILITY 1. Introduction Besides arriving at an appropriate expression of an average or consensus value for observations of a population, it is important to
More informationChapter 9. Two-Sample Tests. Effect Sizes and Power Paired t Test Calculation
Chapter 9 Two-Sample Tests Paired t Test (Correlated Groups t Test) Effect Sizes and Power Paired t Test Calculation Summary Independent t Test Chapter 9 Homework Power and Two-Sample Tests: Paired Versus
More informationTHE KRUSKAL WALLLIS TEST
THE KRUSKAL WALLLIS TEST TEODORA H. MEHOTCHEVA Wednesday, 23 rd April 08 THE KRUSKAL-WALLIS TEST: The non-parametric alternative to ANOVA: testing for difference between several independent groups 2 NON
More informationOne-Way Analysis of Variance
One-Way Analysis of Variance Note: Much of the math here is tedious but straightforward. We ll skim over it in class but you should be sure to ask questions if you don t understand it. I. Overview A. We
More informationEPS 625 INTERMEDIATE STATISTICS FRIEDMAN TEST
EPS 625 INTERMEDIATE STATISTICS The Friedman test is an extension of the Wilcoxon test. The Wilcoxon test can be applied to repeated-measures data if participants are assessed on two occasions or conditions
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 informationANOVA ANOVA. Two-Way ANOVA. One-Way ANOVA. When to use ANOVA ANOVA. Analysis of Variance. Chapter 16. A procedure for comparing more than two groups
ANOVA ANOVA Analysis of Variance Chapter 6 A procedure for comparing more than two groups independent variable: smoking status non-smoking one pack a day > two packs a day dependent variable: number of
More informationExamining Differences (Comparing Groups) using SPSS Inferential statistics (Part I) Dwayne Devonish
Examining Differences (Comparing Groups) using SPSS Inferential statistics (Part I) Dwayne Devonish Statistics Statistics are quantitative methods of describing, analysing, and drawing inferences (conclusions)
More informationSection 13, Part 1 ANOVA. Analysis Of Variance
Section 13, Part 1 ANOVA Analysis Of Variance Course Overview So far in this course we ve covered: Descriptive statistics Summary statistics Tables and Graphs Probability Probability Rules Probability
More informationUNDERSTANDING THE INDEPENDENT-SAMPLES t TEST
UNDERSTANDING The independent-samples t test evaluates the difference between the means of two independent or unrelated groups. That is, we evaluate whether the means for two independent groups are significantly
More informationPsychTests.com advancing psychology and technology
PsychTests.com advancing psychology and technology tel 514.745.8272 fax 514.745.6242 CP Normandie PO Box 26067 l Montreal, Quebec l H3M 3E8 contact@psychtests.com Psychometric Report Resilience Test Description:
More informationUNDERSTANDING THE DEPENDENT-SAMPLES t TEST
UNDERSTANDING THE DEPENDENT-SAMPLES t TEST A dependent-samples t test (a.k.a. matched or paired-samples, matched-pairs, samples, or subjects, simple repeated-measures or within-groups, or correlated groups)
More informationChapter 7. One-way ANOVA
Chapter 7 One-way ANOVA One-way ANOVA examines equality of population means for a quantitative outcome and a single categorical explanatory variable with any number of levels. The t-test of Chapter 6 looks
More information13: Additional ANOVA Topics. Post hoc Comparisons
13: Additional ANOVA Topics Post hoc Comparisons ANOVA Assumptions Assessing Group Variances When Distributional Assumptions are Severely Violated Kruskal-Wallis Test Post hoc Comparisons In the prior
More informationOne-Way ANOVA using SPSS 11.0. SPSS ANOVA procedures found in the Compare Means analyses. Specifically, we demonstrate
1 One-Way ANOVA using SPSS 11.0 This section covers steps for testing the difference between three or more group means using the SPSS ANOVA procedures found in the Compare Means analyses. Specifically,
More informationDDBA 8438: The t Test for Independent Samples Video Podcast Transcript
DDBA 8438: The t Test for Independent Samples Video Podcast Transcript JENNIFER ANN MORROW: Welcome to The t Test for Independent Samples. My name is Dr. Jennifer Ann Morrow. In today's demonstration,
More informationUNDERSTANDING ANALYSIS OF COVARIANCE (ANCOVA)
UNDERSTANDING ANALYSIS OF COVARIANCE () In general, research is conducted for the purpose of explaining the effects of the independent variable on the dependent variable, and the purpose of research design
More informationAnalysis of Data. Organizing Data Files in SPSS. Descriptive Statistics
Analysis of Data Claudia J. Stanny PSY 67 Research Design Organizing Data Files in SPSS All data for one subject entered on the same line Identification data Between-subjects manipulations: variable to
More informationABSORBENCY OF PAPER TOWELS
ABSORBENCY OF PAPER TOWELS 15. Brief Version of the Case Study 15.1 Problem Formulation 15.2 Selection of Factors 15.3 Obtaining Random Samples of Paper Towels 15.4 How will the Absorbency be measured?
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 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 information1.5 Oneway Analysis of Variance
Statistics: Rosie Cornish. 200. 1.5 Oneway Analysis of Variance 1 Introduction Oneway analysis of variance (ANOVA) is used to compare several means. This method is often used in scientific or medical experiments
More informationindividualdifferences
1 Simple ANalysis Of Variance (ANOVA) Oftentimes we have more than two groups that we want to compare. The purpose of ANOVA is to allow us to compare group means from several independent samples. In general,
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 information1. What is the critical value for this 95% confidence interval? CV = z.025 = invnorm(0.025) = 1.96
1 Final Review 2 Review 2.1 CI 1-propZint Scenario 1 A TV manufacturer claims in its warranty brochure that in the past not more than 10 percent of its TV sets needed any repair during the first two years
More 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 informationAn analysis method for a quantitative outcome and two categorical explanatory variables.
Chapter 11 Two-Way ANOVA An analysis method for a quantitative outcome and two categorical explanatory variables. If an experiment has a quantitative outcome and two categorical explanatory variables that
More informationChapter 7 Section 7.1: Inference for the Mean of a Population
Chapter 7 Section 7.1: Inference for the Mean of a Population Now let s look at a similar situation Take an SRS of size n Normal Population : N(, ). Both and are unknown parameters. Unlike what we used
More informationA Basic Guide to Analyzing Individual Scores Data with SPSS
A Basic Guide to Analyzing Individual Scores Data with SPSS Step 1. Clean the data file Open the Excel file with your data. You may get the following message: If you get this message, click yes. Delete
More informationReporting Statistics in Psychology
This document contains general guidelines for the reporting of statistics in psychology research. The details of statistical reporting vary slightly among different areas of science and also among different
More informationTwo Related Samples t Test
Two Related Samples t Test In this example 1 students saw five pictures of attractive people and five pictures of unattractive people. For each picture, the students rated the friendliness of the person
More informationStudy Guide for the Final Exam
Study Guide for the Final Exam When studying, remember that the computational portion of the exam will only involve new material (covered after the second midterm), that material from Exam 1 will make
More informationAnalysis of Variance ANOVA
Analysis of Variance ANOVA Overview We ve used the t -test to compare the means from two independent groups. Now we ve come to the final topic of the course: how to compare means from more than two populations.
More informationGeneral Method: Difference of Means. 3. Calculate df: either Welch-Satterthwaite formula or simpler df = min(n 1, n 2 ) 1.
General Method: Difference of Means 1. Calculate x 1, x 2, SE 1, SE 2. 2. Combined SE = SE1 2 + SE2 2. ASSUMES INDEPENDENT SAMPLES. 3. Calculate df: either Welch-Satterthwaite formula or simpler df = min(n
More informationCOMPARISONS OF CUSTOMER LOYALTY: PUBLIC & PRIVATE INSURANCE COMPANIES.
277 CHAPTER VI COMPARISONS OF CUSTOMER LOYALTY: PUBLIC & PRIVATE INSURANCE COMPANIES. This chapter contains a full discussion of customer loyalty comparisons between private and public insurance companies
More informationIntroduction to Statistics with SPSS (15.0) Version 2.3 (public)
Babraham Bioinformatics Introduction to Statistics with SPSS (15.0) Version 2.3 (public) Introduction to Statistics with SPSS 2 Table of contents Introduction... 3 Chapter 1: Opening SPSS for the first
More informationOne-Way Analysis of Variance (ANOVA) Example Problem
One-Way Analysis of Variance (ANOVA) Example Problem Introduction Analysis of Variance (ANOVA) is a hypothesis-testing technique used to test the equality of two or more population (or treatment) means
More information12: Analysis of Variance. Introduction
1: Analysis of Variance Introduction EDA Hypothesis Test Introduction In Chapter 8 and again in Chapter 11 we compared means from two independent groups. In this chapter we extend the procedure to consider
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 informationPremaster Statistics Tutorial 4 Full solutions
Premaster Statistics Tutorial 4 Full solutions Regression analysis Q1 (based on Doane & Seward, 4/E, 12.7) a. Interpret the slope of the fitted regression = 125,000 + 150. b. What is the prediction for
More informationOutline. Definitions Descriptive vs. Inferential Statistics The t-test - One-sample t-test
The t-test Outline Definitions Descriptive vs. Inferential Statistics The t-test - One-sample t-test - Dependent (related) groups t-test - Independent (unrelated) groups t-test Comparing means Correlation
More informationStatistics Review PSY379
Statistics Review PSY379 Basic concepts Measurement scales Populations vs. samples Continuous vs. discrete variable Independent vs. dependent variable Descriptive vs. inferential stats Common analyses
More informationFinal Exam Practice Problem Answers
Final Exam Practice Problem Answers The following data set consists of data gathered from 77 popular breakfast cereals. The variables in the data set are as follows: Brand: The brand name of the cereal
More informationRegression step-by-step using Microsoft Excel
Step 1: Regression step-by-step using Microsoft Excel Notes prepared by Pamela Peterson Drake, James Madison University Type the data into the spreadsheet The example used throughout this How to is a regression
More informationResearch Methods & Experimental Design
Research Methods & Experimental Design 16.422 Human Supervisory Control April 2004 Research Methods Qualitative vs. quantitative Understanding the relationship between objectives (research question) and
More informationLesson 1: Comparison of Population Means Part c: Comparison of Two- Means
Lesson : Comparison of Population Means Part c: Comparison of Two- Means Welcome to lesson c. This third lesson of lesson will discuss hypothesis testing for two independent means. Steps in Hypothesis
More informationSimple Tricks for Using SPSS for Windows
Simple Tricks for Using SPSS for Windows Chapter 14. Follow-up Tests for the Two-Way Factorial ANOVA The Interaction is Not Significant If you have performed a two-way ANOVA using the General Linear Model,
More informationTesting for differences I exercises with SPSS
Testing for differences I exercises with SPSS Introduction The exercises presented here are all about the t-test and its non-parametric equivalents in their various forms. In SPSS, all these tests can
More informationOne-Way Independent ANOVA: Exam Practice Sheet
One-Way Independent AOVA: Exam Practice Sheet Questions Question 1 Students were given different drug treatments before revising for their exams. Some were given a memory drug, some a placebo drug and
More informationFriedman's Two-way Analysis of Variance by Ranks -- Analysis of k-within-group Data with a Quantitative Response Variable
Friedman's Two-way Analysis of Variance by Ranks -- Analysis of k-within-group Data with a Quantitative Response Variable Application: This statistic has two applications that can appear very different,
More information7. Comparing Means Using t-tests.
7. Comparing Means Using t-tests. Objectives Calculate one sample t-tests Calculate paired samples t-tests Calculate independent samples t-tests Graphically represent mean differences In this chapter,
More informationHow To Check For Differences In The One Way Anova
MINITAB ASSISTANT WHITE PAPER This paper explains the research conducted by Minitab statisticians to develop the methods and data checks used in the Assistant in Minitab 17 Statistical Software. One-Way
More 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 informationChapter 7. Comparing Means in SPSS (t-tests) Compare Means analyses. Specifically, we demonstrate procedures for running Dependent-Sample (or
1 Chapter 7 Comparing Means in SPSS (t-tests) This section covers procedures for testing the differences between two means using the SPSS Compare Means analyses. Specifically, we demonstrate procedures
More informationAdverse Impact Ratio for Females (0/ 1) = 0 (5/ 17) = 0.2941 Adverse impact as defined by the 4/5ths rule was not found in the above data.
1 of 9 12/8/2014 12:57 PM (an On-Line Internet based application) Instructions: Please fill out the information into the form below. Once you have entered your data below, you may select the types of analysis
More informationBusiness Statistics. Successful completion of Introductory and/or Intermediate Algebra courses is recommended before taking Business Statistics.
Business Course Text Bowerman, Bruce L., Richard T. O'Connell, J. B. Orris, and Dawn C. Porter. Essentials of Business, 2nd edition, McGraw-Hill/Irwin, 2008, ISBN: 978-0-07-331988-9. Required Computing
More informationStatistiek II. John Nerbonne. October 1, 2010. Dept of Information Science j.nerbonne@rug.nl
Dept of Information Science j.nerbonne@rug.nl October 1, 2010 Course outline 1 One-way ANOVA. 2 Factorial ANOVA. 3 Repeated measures ANOVA. 4 Correlation and regression. 5 Multiple regression. 6 Logistic
More informationComparing Means in Two Populations
Comparing Means in Two Populations Overview The previous section discussed hypothesis testing when sampling from a single population (either a single mean or two means from the same population). Now we
More informationSPSS TUTORIAL & EXERCISE BOOK
UNIVERSITY OF MISKOLC Faculty of Economics Institute of Business Information and Methods Department of Business Statistics and Economic Forecasting PETRA PETROVICS SPSS TUTORIAL & EXERCISE BOOK FOR BUSINESS
More informationNCSS Statistical Software
Chapter 06 Introduction This procedure provides several reports for the comparison of two distributions, including confidence intervals for the difference in means, two-sample t-tests, the z-test, the
More informationChapter 13 Introduction to Linear Regression and Correlation Analysis
Chapter 3 Student Lecture Notes 3- Chapter 3 Introduction to Linear Regression and Correlation Analsis Fall 2006 Fundamentals of Business Statistics Chapter Goals To understand the methods for displaing
More informationWeek 4: Standard Error and Confidence Intervals
Health Sciences M.Sc. Programme Applied Biostatistics Week 4: Standard Error and Confidence Intervals Sampling Most research data come from subjects we think of as samples drawn from a larger population.
More informationTwo-sample t-tests. - Independent samples - Pooled standard devation - The equal variance assumption
Two-sample t-tests. - Independent samples - Pooled standard devation - The equal variance assumption Last time, we used the mean of one sample to test against the hypothesis that the true mean was a particular
More informationProfile analysis is the multivariate equivalent of repeated measures or mixed ANOVA. Profile analysis is most commonly used in two cases:
Profile Analysis Introduction Profile analysis is the multivariate equivalent of repeated measures or mixed ANOVA. Profile analysis is most commonly used in two cases: ) Comparing the same dependent variables
More informationMain Effects and Interactions
Main Effects & Interactions page 1 Main Effects and Interactions So far, we ve talked about studies in which there is just one independent variable, such as violence of television program. You might randomly
More informationA study on impact of RBI decision to deregulate savings account interest rates on the banking industry.
Sharada P. Chauhan Entire Research Academy s SPC ERA International Journal of E-Commerce and E-Banking Exact Papers URL :www.spcera.in/home/ Issue IJECEB A study on impact of RBI decision to deregulate
More informationDoing Multiple Regression with SPSS. In this case, we are interested in the Analyze options so we choose that menu. If gives us a number of choices:
Doing Multiple Regression with SPSS Multiple Regression for Data Already in Data Editor Next we want to specify a multiple regression analysis for these data. The menu bar for SPSS offers several options:
More information2 Sample t-test (unequal sample sizes and unequal variances)
Variations of the t-test: Sample tail Sample t-test (unequal sample sizes and unequal variances) Like the last example, below we have ceramic sherd thickness measurements (in cm) of two samples representing
More informationSTA-201-TE. 5. Measures of relationship: correlation (5%) Correlation coefficient; Pearson r; correlation and causation; proportion of common variance
Principles of Statistics STA-201-TE This TECEP is an introduction to descriptive and inferential statistics. Topics include: measures of central tendency, variability, correlation, regression, hypothesis
More informationCourse Text. Required Computing Software. Course Description. Course Objectives. StraighterLine. Business Statistics
Course Text Business Statistics Lind, Douglas A., Marchal, William A. and Samuel A. Wathen. Basic Statistics for Business and Economics, 7th edition, McGraw-Hill/Irwin, 2010, ISBN: 9780077384470 [This
More informationOne-Way Analysis of Variance: A Guide to Testing Differences Between Multiple Groups
One-Way Analysis of Variance: A Guide to Testing Differences Between Multiple Groups In analysis of variance, the main research question is whether the sample means are from different populations. The
More informationT-test & factor analysis
Parametric tests T-test & factor analysis Better than non parametric tests Stringent assumptions More strings attached Assumes population distribution of sample is normal Major problem Alternatives Continue
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 informationSPSS 3: COMPARING MEANS
SPSS 3: COMPARING MEANS UNIVERSITY OF GUELPH LUCIA COSTANZO lcostanz@uoguelph.ca REVISED SEPTEMBER 2012 CONTENTS SPSS availability... 2 Goals of the workshop... 2 Data for SPSS Sessions... 3 Statistical
More informationConfidence Intervals for the Difference Between Two Means
Chapter 47 Confidence Intervals for the Difference Between Two Means Introduction This procedure calculates the sample size necessary to achieve a specified distance from the difference in sample means
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 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 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 informationTesting Group Differences using T-tests, ANOVA, and Nonparametric Measures
Testing Group Differences using T-tests, ANOVA, and Nonparametric Measures Jamie DeCoster Department of Psychology University of Alabama 348 Gordon Palmer Hall Box 870348 Tuscaloosa, AL 35487-0348 Phone:
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 informationChapter 14: Repeated Measures Analysis of Variance (ANOVA)
Chapter 14: Repeated Measures Analysis of Variance (ANOVA) First of all, you need to recognize the difference between a repeated measures (or dependent groups) design and the between groups (or independent
More informationIntroduction to Statistics with GraphPad Prism (5.01) Version 1.1
Babraham Bioinformatics Introduction to Statistics with GraphPad Prism (5.01) Version 1.1 Introduction to Statistics with GraphPad Prism 2 Licence This manual is 2010-11, Anne Segonds-Pichon. This manual
More information1 Nonparametric Statistics
1 Nonparametric Statistics When finding confidence intervals or conducting tests so far, we always described the population with a model, which includes a set of parameters. Then we could make decisions
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 informationGood luck! BUSINESS STATISTICS FINAL EXAM INSTRUCTIONS. Name:
Glo bal Leadership M BA BUSINESS STATISTICS FINAL EXAM Name: INSTRUCTIONS 1. Do not open this exam until instructed to do so. 2. Be sure to fill in your name before starting the exam. 3. You have two hours
More informationNCSS Statistical Software
Chapter 06 Introduction This procedure provides several reports for the comparison of two distributions, including confidence intervals for the difference in means, two-sample t-tests, the z-test, the
More informationChapter 8 Hypothesis Testing Chapter 8 Hypothesis Testing 8-1 Overview 8-2 Basics of Hypothesis Testing
Chapter 8 Hypothesis Testing 1 Chapter 8 Hypothesis Testing 8-1 Overview 8-2 Basics of Hypothesis Testing 8-3 Testing a Claim About a Proportion 8-5 Testing a Claim About a Mean: s Not Known 8-6 Testing
More informationNonparametric Statistics
Nonparametric Statistics J. Lozano University of Goettingen Department of Genetic Epidemiology Interdisciplinary PhD Program in Applied Statistics & Empirical Methods Graduate Seminar in Applied Statistics
More informationLearning Equity between Online and On-Site Mathematics Courses
Learning Equity between Online and On-Site Mathematics Courses Sherry J. Jones Professor Department of Business Glenville State College Glenville, WV 26351 USA sherry.jones@glenville.edu Vena M. Long Professor
More informationNon-Parametric Tests (I)
Lecture 5: Non-Parametric Tests (I) KimHuat LIM lim@stats.ox.ac.uk http://www.stats.ox.ac.uk/~lim/teaching.html Slide 1 5.1 Outline (i) Overview of Distribution-Free Tests (ii) Median Test for Two Independent
More informationOverview of Non-Parametric Statistics PRESENTER: ELAINE EISENBEISZ OWNER AND PRINCIPAL, OMEGA STATISTICS
Overview of Non-Parametric Statistics PRESENTER: ELAINE EISENBEISZ OWNER AND PRINCIPAL, OMEGA STATISTICS About Omega Statistics Private practice consultancy based in Southern California, Medical and Clinical
More informationMean = (sum of the values / the number of the value) if probabilities are equal
Population Mean Mean = (sum of the values / the number of the value) if probabilities are equal Compute the population mean Population/Sample mean: 1. Collect the data 2. sum all the values in the population/sample.
More information