PPA 415 – Research Methods in Public Administration Lecture 7 – Analysis of Variance.

Slides:



Advertisements
Similar presentations
Chapter 10 Hypothesis Testing Using Analysis of Variance (ANOVA)
Advertisements

Chapter 12 ANALYSIS OF VARIANCE.
Chapter 10 Analysis of Variance (ANOVA) Part III: Additional Hypothesis Tests Renee R. Ha, Ph.D. James C. Ha, Ph.D Integrative Statistics for the Social.
Design of Experiments and Analysis of Variance
Hypothesis Testing IV Chi Square.
ANALYSIS OF VARIANCE.
Independent Sample T-test Formula
Chapter 10 Hypothesis Testing III (ANOVA). Basic Logic  ANOVA can be used in situations where the researcher is interested in the differences in sample.
Analysis of Variance. Experimental Design u Investigator controls one or more independent variables –Called treatment variables or factors –Contain two.
Statistics Are Fun! Analysis of Variance
Chapter Topics The Completely Randomized Model: One-Factor Analysis of Variance F-Test for Difference in c Means The Tukey-Kramer Procedure ANOVA Assumptions.
PPA 415 – Research Methods in Public Administration Lecture 9 – Bivariate Association.
PPA 415 – Research Methods in Public Administration Lecture 6 – One-Sample and Two-Sample Tests.
PPA 415 – Research Methods in Public Administration Lecture 8 – Chi-square.
Chapter 9 Hypothesis Testing II. Chapter Outline  Introduction  Hypothesis Testing with Sample Means (Large Samples)  Hypothesis Testing with Sample.
PPA 501 – Analytical Methods in Administration Lecture 9 – Bivariate Association.
Hypothesis Testing Using The One-Sample t-Test
Chapter 9 Hypothesis Testing II. Chapter Outline  Introduction  Hypothesis Testing with Sample Means (Large Samples)  Hypothesis Testing with Sample.
COURSE: JUST 3900 Tegrity Presentation Developed By: Ethan Cooper Final Exam Review.
Hypothesis Testing and T-Tests. Hypothesis Tests Related to Differences Copyright © 2009 Pearson Education, Inc. Chapter Tests of Differences One.
Choosing Statistical Procedures
Chapter 11(1e), Ch. 10 (2/3e) Hypothesis Testing Using the Chi Square ( χ 2 ) Distribution.
Statistics: A Tool For Social Research
Copyright © 2012 by Nelson Education Limited. Chapter 9 Hypothesis Testing III: The Analysis of Variance 9-1.
AM Recitation 2/10/11.
© Buddy Freeman, 2015 H 0 : H 1 : α = Decision Rule: If then do not reject H 0, otherwise reject H 0. Test Statistic: Decision: Conclusion: We have found.
Week 10 Chapter 10 - Hypothesis Testing III : The Analysis of Variance
Copyright © 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins Chapter 17 Inferential Statistics.
Copyright © 2008 Wolters Kluwer Health | Lippincott Williams & Wilkins Chapter 22 Using Inferential Statistics to Test Hypotheses.
Chapter 9 Hypothesis Testing II: two samples Test of significance for sample means (large samples) The difference between “statistical significance” and.
Chapter 11 HYPOTHESIS TESTING USING THE ONE-WAY ANALYSIS OF VARIANCE.
Hypothesis Testing Using the Two-Sample t-Test
Chapter 9: Non-parametric Tests n Parametric vs Non-parametric n Chi-Square –1 way –2 way.
Chapter 10 Analysis of Variance.
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap th Lesson Analysis of Variance.
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.
1 Chapter 13 Analysis of Variance. 2 Chapter Outline  An introduction to experimental design and analysis of variance  Analysis of Variance and the.
Testing Hypotheses about Differences among Several Means.
Business Statistics, A First Course (4e) © 2006 Prentice-Hall, Inc. Chap 10-1 Chapter 10 Two-Sample Tests and One-Way ANOVA Business Statistics, A First.
Chapter 14 – 1 Chapter 14: Analysis of Variance Understanding Analysis of Variance The Structure of Hypothesis Testing with ANOVA Decomposition of SST.
PPA 501 – Analytical Methods in Administration Lecture 6a – Normal Curve, Z- Scores, and Estimation.
Chapter 14 – 1 Chapter 14: Analysis of Variance Understanding Analysis of Variance The Structure of Hypothesis Testing with ANOVA Decomposition of SST.
Chapter Seventeen. Figure 17.1 Relationship of Hypothesis Testing Related to Differences to the Previous Chapter and the Marketing Research Process Focus.
Inferential Statistics. The Logic of Inferential Statistics Makes inferences about a population from a sample Makes inferences about a population from.
Introduction to the t Test Part 1: One-sample t test
Copyright © Cengage Learning. All rights reserved. 12 Analysis of Variance.
Chapter 10 Hypothesis Testing III (ANOVA). Chapter Outline  Introduction  The Logic of the Analysis of Variance  The Computation of ANOVA  Computational.
CHAPTER 12 ANALYSIS OF VARIANCE Prem Mann, Introductory Statistics, 7/E Copyright © 2010 John Wiley & Sons. All right reserved.
The Analysis of Variance ANOVA
Statistics for Political Science Levin and Fox Chapter Seven
CHAPTER 10 ANOVA - One way ANOVa.
Statistical Analysis ANOVA Roderick Graham Fashion Institute of Technology.
1 Testing Statistical Hypothesis The One Sample t-Test Heibatollah Baghi, and Mastee Badii.
Significance Tests for Regression Analysis. A. Testing the Significance of Regression Models The first important significance test is for the regression.
CHAPTER 10: ANALYSIS OF VARIANCE(ANOVA) Leon-Guerrero and Frankfort-Nachmias, Essentials of Statistics for a Diverse Society.
 List the characteristics of the F distribution.  Conduct a test of hypothesis to determine whether the variances of two populations are equal.  Discuss.
McGraw-Hill/Irwin © 2003 The McGraw-Hill Companies, Inc.,All Rights Reserved. Part Four ANALYSIS AND PRESENTATION OF DATA.
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.
Inference concerning two population variances
Lecture Nine - Twelve Tests of Significance.
Part Four ANALYSIS AND PRESENTATION OF DATA
Practice Questions for ANOVA
Statistics: A Tool For Social Research
10 Chapter Chi-Square Tests and the F-Distribution Chapter 10
Hypothesis Testing Review
CHAPTER 12 ANALYSIS OF VARIANCE
Hypothesis Testing Using the Chi Square (χ2) Distribution
PPA 501 – Analytical Methods in Administration
Chapter 10 – Part II Analysis of Variance
Introduction to the t Test
Presentation transcript:

PPA 415 – Research Methods in Public Administration Lecture 7 – Analysis of Variance

Introduction Analysis of variance (ANOVA) can be considered an extension of the t-test. The t-test assumes that the independent variable has only two categories. ANOVA assumes that the nominal or ordinal independent variable has two or more categories.

Introduction The null hypothesis is that the populations from which the each of samples (categories) are drawn are equal on the characteristic measured (usually a mean or proportion).

Introduction If the null hypothesis is correct, the means for the dependent variable within each category of the independent variable should be roughly equal. ANOVA proceeds by making comparisons across the categories of the independent variable.

Computation of ANOVA The computation of ANOVA compares the amount of variation within each category (SSW) to the amount of variation between categories (SSB). Total sum of squares.

Computation of ANOVA Sum of squares within (variation within categories). Sum of squares between (variation between categories).

Computation of ANOVA Degrees of freedom.

Computation of ANOVA Mean square estimates.

Computation of ANOVA Computational steps for shortcut. Find SST using computation formula. Find SSB. Find SSW by subtraction. Calculate degrees of freedom. Construct the mean square estimates. Compute the F-ratio.

Five-Step Hypothesis Test for ANOVA. Step 1. Making assumptions. Independent random samples. Interval ratio measurement. Normally distributed populations. Equal population variances. Step 2. Stating the null hypothesis.

Five-Step Hypothesis Test for ANOVA. Step 3. Selecting the sampling distribution and establishing the critical region. Sampling distribution = F distribution. Alpha =.05 (or.01 or...). Degrees of freedom within = N – k. Degrees of freedom between = k – 1. F-critical=Use Appendix D, p Step 4. Computing the test statistic. Use the procedure outlined above.

Five-Step Hypothesis Test for ANOVA. Step 5. Making a decision. If F(obtained) is greater than F(critical), reject the null hypothesis of no difference. At least one population mean is different from the others.

ANOVA – Example 1 – JCHA 2000 What impact does marital status have on respondent’s rating Of JCHA services? Sum of Rating Squared is 615

ANOVA – Example 1 – JCHA 2000 Step 1. Making assumptions. Independent random samples. Interval ratio measurement. Normally distributed populations. Equal population variances. Step 2. Stating the null hypothesis.

ANOVA – Example 1 – JCHA 2000 Step 3. Selecting the sampling distribution and establishing the critical region. Sampling distribution = F distribution. Alpha =.05. Degrees of freedom within = N – k = 38 – 5 = 33. Degrees of freedom between = k – 1 = 5 – 1 = 4. F-critical=2.69.

ANOVA – Example 1 – JCHA 2000 Step 4. Computing the test statistic.

ANOVA – Example 1 – JCHA 2000

ANOVA – Example 1 – JCHA Step 5. Making a decision. F(obtained) is F(critical) is F(obtained) < F(critical). Therefore, we fail to reject the null hypothesis of no difference. Approval of JCHA services does not vary significantly by marital status.

ANOVA – Example 2 – Ford- Carter Disaster Data Set What impact does Presidential administration have on the president’s recommendation of disaster assistance?

ANOVA – Example 2 – Ford- Carter Disaster Data Set Step 1. Making assumptions. Independent random samples. Interval ratio measurement. Normally distributed populations. Equal population variances. Step 2. Stating the null hypothesis.

ANOVA – Example 2 – Ford- Carter Disaster Data Set Step 3. Selecting the sampling distribution and establishing the critical region. Sampling distribution = F distribution. Alpha =.05. Degrees of freedom within = N – k = 371 – 2 = 369. Degrees of freedom between = k – 1 = 2 – 1 = 1. F-critical=3.84.

ANOVA – Example 2 – Ford- Carter Disaster Data Set Step 4. Computing the test statistic.

ANOVA – Example 2 – Ford- Carter Disaster Data Set Step 5. Making a decision. F(obtained) is F(critical) is F(obtained) > F(critical). Therefore, we can reject the null hypothesis of no difference. Approval of federal disaster assistance does vary by presidential administration.