PSYC 6130 One-Way Independent ANOVA. PSYC 6130, PROF. J. ELDER 2 Generalizing t-Tests t-Tests allow us to test hypotheses about differences between two.

Slides:



Advertisements
Similar presentations
Hypothesis Testing Steps in Hypothesis Testing:
Advertisements

2  How to compare the difference on >2 groups on one or more variables  If it is only one variable, we could compare three groups with multiple ttests:
Design of Experiments and Analysis of Variance
One-Way Between Subjects ANOVA. Overview Purpose How is the Variance Analyzed? Assumptions Effect Size.
Statistics II: An Overview of Statistics. Outline for Statistics II Lecture: SPSS Syntax – Some examples. Normal Distribution Curve. Sampling Distribution.
Part I – MULTIVARIATE ANALYSIS
Independent t-Test CJ 526 Statistical Analysis in Criminal Justice.
Lecture 9: One Way ANOVA Between Subjects
Chapter 11 Multiple Regression.
8. ANALYSIS OF VARIANCE 8.1 Elements of a Designed Experiment
One-way Between Groups Analysis of Variance
Inferences About Process Quality
Chi-Square and F Distributions Chapter 11 Understandable Statistics Ninth Edition By Brase and Brase Prepared by Yixun Shi Bloomsburg University of Pennsylvania.
PSY 307 – Statistics for the Behavioral Sciences Chapter 19 – Chi-Square Test for Qualitative Data Chapter 21 – Deciding Which Test to Use.
Summary of Quantitative Analysis Neuman and Robson Ch. 11
Chapter 9: Introduction to the t statistic
Chapter 14 Inferential Data Analysis
Inferential Statistics
Chapter 12 Inferential Statistics Gay, Mills, and Airasian
Leedy and Ormrod Ch. 11 Gray Ch. 14
Analysis of Variance. ANOVA Probably the most popular analysis in psychology Why? Ease of implementation Allows for analysis of several groups at once.
Statistics for the Social Sciences Psychology 340 Fall 2013 Thursday, November 21 Review for Exam #4.
AM Recitation 2/10/11.
F-Test ( ANOVA ) & Two-Way ANOVA
Estimation and Hypothesis Testing Faculty of Information Technology King Mongkut’s University of Technology North Bangkok 1.
Inferential Statistics: SPSS
Hypothesis Testing:.
Hypothesis testing – mean differences between populations
1 Tests with two+ groups We have examined tests of means for a single group, and for a difference if we have a matched sample (as in husbands and wives)
One-Way Analysis of Variance Comparing means of more than 2 independent samples 1.
Introduction To Biological Research. Step-by-step analysis of biological data The statistical analysis of a biological experiment may be broken down into.
Analysis of Variance ST 511 Introduction n Analysis of variance compares two or more populations of quantitative data. n Specifically, we are interested.
Chapter 11 HYPOTHESIS TESTING USING THE ONE-WAY ANALYSIS OF VARIANCE.
Statistical Decision Making. Almost all problems in statistics can be formulated as a problem of making a decision. That is given some data observed from.
QMS 6351 Statistics and Research Methods Regression Analysis: Testing for Significance Chapter 14 ( ) Chapter 15 (15.5) Prof. Vera Adamchik.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Chapter 15 Multiple Regression n Multiple Regression Model n Least Squares Method n Multiple.
Statistics 11 Confidence Interval Suppose you have a sample from a population You know the sample mean is an unbiased estimate of population mean Question:
ANOVA (Analysis of Variance) by Aziza Munir
Between-Groups ANOVA Chapter 12. >When to use an F distribution Working with more than two samples >ANOVA Used with two or more nominal independent variables.
Chapter 14 – 1 Chapter 14: Analysis of Variance Understanding Analysis of Variance The Structure of Hypothesis Testing with ANOVA Decomposition of SST.
Inferential Statistics
Multiple Regression and Model Building Chapter 15 Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin.
Education 793 Class Notes Presentation 10 Chi-Square Tests and One-Way ANOVA.
Independent t-Test CJ 526 Statistical Analysis in Criminal Justice.
Educational Research Chapter 13 Inferential Statistics Gay, Mills, and Airasian 10 th Edition.
General Linear Model 2 Intro to ANOVA.
ANOVA: Analysis of Variance.
Chapter 14 – 1 Chapter 14: Analysis of Variance Understanding Analysis of Variance The Structure of Hypothesis Testing with ANOVA Decomposition of SST.
Chapter 13 - ANOVA. ANOVA Be able to explain in general terms and using an example what a one-way ANOVA is (370). Know the purpose of the one-way ANOVA.
Analysis of Variance (One Factor). ANOVA Analysis of Variance Tests whether differences exist among population means categorized by only one factor or.
I271B The t distribution and the independent sample t-test.
1 ANALYSIS OF VARIANCE (ANOVA) Heibatollah Baghi, and Mastee Badii.
Repeated Measures Analysis of Variance Analysis of Variance (ANOVA) is used to compare more than 2 treatment means. Repeated measures is analogous to.
Copyright (C) 2002 Houghton Mifflin Company. All rights reserved. 1 Understandable Statistics S eventh Edition By Brase and Brase Prepared by: Lynn Smith.
Chapter 12 Introduction to Analysis of Variance PowerPoint Lecture Slides Essentials of Statistics for the Behavioral Sciences Eighth Edition by Frederick.
Two-Way (Independent) ANOVA. PSYC 6130A, PROF. J. ELDER 2 Two-Way ANOVA “Two-Way” means groups are defined by 2 independent variables. These IVs are typically.
Inferences Concerning Variances
Hypothesis test flow chart frequency data Measurement scale number of variables 1 basic χ 2 test (19.5) Table I χ 2 test for independence (19.9) Table.
IE241: Introduction to Design of Experiments. Last term we talked about testing the difference between two independent means. For means from a normal.
CHAPTER 10 ANOVA - One way ANOVa.
Simple ANOVA Comparing the Means of Three or More Groups Chapter 9.
SUMMARY EQT 271 MADAM SITI AISYAH ZAKARIA SEMESTER /2015.
Lecture 7: Bivariate Statistics. 2 Properties of Standard Deviation Variance is just the square of the S.D. If a constant is added to all scores, it has.
Educational Research Inferential Statistics Chapter th Chapter 12- 8th Gay and Airasian.
Chapter 10: The t Test For Two Independent Samples.
Statistical Decision Making. Almost all problems in statistics can be formulated as a problem of making a decision. That is given some data observed from.
Chapter 12 Introduction to Analysis of Variance
The 2 nd to last topic this year!!.  ANOVA Testing is similar to a “two sample t- test except” that it compares more than two samples to one another.
ANOVA Econ201 HSTS212.
Math 4030 – 10b Inferences Concerning Variances: Hypothesis Testing
Presentation transcript:

PSYC 6130 One-Way Independent ANOVA

PSYC 6130, PROF. J. ELDER 2 Generalizing t-Tests t-Tests allow us to test hypotheses about differences between two groups or conditions (e.g., treatment and control). What do we do if we wish to compare multiple groups or conditions simultaneously? Examples: –Effects of 3 different therapies for autism –Effects of 4 different SSRIs on seratonin re-uptake –Effects of 5 different body orientations on judgement of induced self-motion.

PSYC 6130, PROF. J. ELDER 3 Reinterpreting the 2-Sample t-Statistic

PSYC 6130, PROF. J. ELDER 4 Reinterpreting the 2-Sample t-Statistic

PSYC 6130, PROF. J. ELDER 5 Example

PSYC 6130, PROF. J. ELDER 6 The F Distribution F distribution for 2 groups of size n=

PSYC 6130, PROF. J. ELDER 7 Within and Between Variances Recall that the variance is, by definition, the mean squared deviation of scores from their mean. Since the numerator of the t 2 statistic estimates the variance from the deviations of group means, it is called the mean-square-between MS bet. Since the denominator of the t 2 statistic estimates the variance from the deviations within groups, it is called the mean-square-within MS W. These definitions allow us to generalize to an arbitrary number of groups.

PSYC 6130, PROF. J. ELDER 8 Generalizing to > 2 Groups

PSYC 6130, PROF. J. ELDER 9 Degrees of Freedom Recall that the sample variance follows a scaled chi- square distribution, parameterized by the degrees of freedom. Thus the F distribution is a ratio of two chi-square distributions, each with different degrees of freedom.

PSYC 6130, PROF. J. ELDER 10 Properties of the F Distribution n=2 n=5 n=10 n= n=2 n=5 n=10 n=100

PSYC 6130, PROF. J. ELDER 11 The F Statistic

PSYC 6130, PROF. J. ELDER 12 Testing Hypotheses p(F) F distribution for 3 groups of size n=13

PSYC 6130, PROF. J. ELDER 13

PSYC 6130, PROF. J. ELDER 14 When k=2 ANOVA will give exactly the same result as two-tailed t- test. One-tailed tests must be done using t-tests.

PSYC 6130, PROF. J. ELDER 15 Example From the Canadian Generalized Social Survey, Cycle 6 (1992)

PSYC 6130, PROF. J. ELDER 16 Example

PSYC 6130, PROF. J. ELDER 17 Reporting Results A one-way ANOVA demonstrates that frequency of contact with clinical psychologists depends on marital status. Widowed individuals had the least contact (M=0.082). Married individuals (M=0.185) had somewhat more contact. Single (M=0.620) and separated or divorced (M=0.900) had substantially more contact. F(3,11807)=33.3, MSE = 7.8, p<.001.

PSYC 6130, PROF. J. ELDER 18 Summary Table (SPSS)

PSYC 6130, PROF. J. ELDER 19 Interpreting the F Ratio

PSYC 6130, PROF. J. ELDER 20 Effect Size and Proportion of Variance Accounted For

PSYC 6130, PROF. J. ELDER 21 (Approxiately) Unbiased Effect Size

PSYC 6130, PROF. J. ELDER 22 Reporting Results A one-way ANOVA demonstrates that frequency of contact with clinical psychologists depends on marital status. Widowed individuals had the least contact (M=0.082). Married individuals (M=0.185) had somewhat more contact. Single (M=0.620) and separated or divorced (M=0.900) had substantially more contact. F(3,11807)=33.3, p<.001. However, the size of the effect was relatively small:

PSYC 6130, PROF. J. ELDER 23 Planning a Study: ANOVA and Power

PSYC 6130, PROF. J. ELDER 24 Example You are interested in whether there is a link between PSYC 6130 final grades and the professor teaching the section. Grades typically have a standard deviation of about 15% There are typically 3 sections, each with around 12 students. What is the probability you would pick up an effect if the standard deviation of the mean grade is around 5%?

PSYC 6130, PROF. J. ELDER 25 Advantages of ANOVA Avoid inflation in error rate due to multiple comparisons Can detect an effect of the treatment even when no 2 groups are significantly different.

PSYC 6130, PROF. J. ELDER 26 6-Step Process for ANOVA 1.State the hypotheses 2.Select the statistical test and significance level 3.Select the samples and collect the data 4.Find the region of rejection 5.Calculate the test statistic 6.Make the statistical decision

PSYC 6130, PROF. J. ELDER 27 Sums of Squares Approach

PSYC 6130, PROF. J. ELDER 28 ANOVA Assumptions Independent random sampling Normal distributions Homogeneity of variance

PSYC 6130, PROF. J. ELDER 29 More on Homogeneity of Variance

PSYC 6130, PROF. J. ELDER 30 Levene’s Test: Basic Idea SPSS reports an F-statistic for Levene’s test Allows the homogeneity of variance for two or more variables to be tested.

PSYC 6130, PROF. J. ELDER 31 What to do if Homogeneity of Variance Assumption is Rejected Some adjustment procedures are available in SPSS (e.g., Welch 1951). We will not cover the theory behind these adjustments.

PSYC 6130, PROF. J. ELDER 32 Fixed vs Random Effects Fixed Effects: interested only in the specified levels of the independent variable (e.g., single/married/divorced/widowed) Random Effects: interested in a large number of possible levels of the independent variable – randomly sampling only a few of these. e.g., –Does the order of questions on a questionnaire effect the results? –Does the order of stimuli in a psychophysical experiment effect the results?

PSYC 6130, PROF. J. ELDER 33 Fixed vs Random Effects One-Way Independent ANOVA calculation is the same for fixed and random effect designs. Power and effect size calculations differ. More complex ANOVA designs differ. We restrict our attention in this course to fixed effect designs.

PSYC 6130, PROF. J. ELDER 34 Qualitative vs Quantitative Independent Variables In principle, ANOVA can be applied to either qualitative or quantitative variables. If IV is quantitative and effect is roughly linear, usually have more power using regression (only using up 2 degrees of freedom, instead of k). If effect is complex (e.g., non-monotonic): –Use a higher-order regression model (e.g., quadratic) –Use ANOVA (makes no smoothness assumptions)