Chapters 8 & 9 Advanced Experimental Design. Experimental Designs Between-subject designs  Simple randomized design  Multilevel randomized design Factorial.

Slides:



Advertisements
Similar presentations
Copyright © Allyn & Bacon (2007) Single-Variable, Independent-Groups Designs Graziano and Raulin Research Methods: Chapter 10 This multimedia product and.
Advertisements

FACTORIAL ANOVA Overview of Factorial ANOVA Factorial Designs Types of Effects Assumptions Analyzing the Variance Regression Equation Fixed and Random.
Between- vs. Within-Subjects Designs
Chapter Fourteen The Two-Way Analysis of Variance.
Copyright © Allyn & Bacon (2010) Single-Variable, Independent-Groups Designs Graziano and Raulin Research Methods: Chapter 10 This multimedia product and.
Factorial and Mixed Factor ANOVA and ANCOVA
Design of Experiments and Analysis of Variance
Statistics for the Behavioral Sciences Two-Way Between-Groups ANOVA
PSY 307 – Statistics for the Behavioral Sciences
FACTORIAL DESIGNS F Terms for Factorials F Types of Factorial Designs F Notation for Factorials F Types of Effects F Looking at Tables of Means F Looking.
Using Statistics in Research Psych 231: Research Methods in Psychology.
Using Statistics in Research Psych 231: Research Methods in Psychology.
Lecture 13 Psyc 300A. Review Confounding, extraneous variables Operational definitions Random sampling vs random assignment Internal validity Null hypothesis.
PSY 307 – Statistics for the Behavioral Sciences
Inferences About Means of Two Independent Samples Chapter 11 Homework: 1, 2, 4, 6, 7.
What Is Multivariate Analysis of Variance (MANOVA)?
S519: Evaluation of Information Systems
Factorial Experiments Factorial Design = experiment in which more than one IV (factor) at a time is manipulated Uses all possible combinations of the levels.
Complex Design. Two group Designs One independent variable with 2 levels: – IV: color of walls Two levels: white walls vs. baby blue – DV: anxiety White.
BHS Methods in Behavioral Sciences I May 14, 2003 Chapter 8 (Ray) Between-Subjects Designs (Cont.)
Running Fisher’s LSD Multiple Comparison Test in Excel
Repeated Measures ANOVA Used when the research design contains one factor on which participants are measured more than twice (dependent, or within- groups.
Understanding the Two-Way Analysis of Variance
ANOVA Chapter 12.
Extension to ANOVA From t to F. Review Comparisons of samples involving t-tests are restricted to the two-sample domain Comparisons of samples involving.
Calculations of Reliability We are interested in calculating the ICC –First step: Conduct a single-factor, within-subjects (repeated measures) ANOVA –This.
Matched Pairs, Within-Subjects, and Mixed Designs
Repeated Measures Chapter 13.
Which Test Do I Use? Statistics for Two Group Experiments The Chi Square Test The t Test Analyzing Multiple Groups and Factorial Experiments Analysis of.
@ 2012 Wadsworth, Cengage Learning Chapter 9 Applying the Logic of Experimentation: Between-Subjects 2012 Wadsworth, Cengage Learning.
Chapter 11 HYPOTHESIS TESTING USING THE ONE-WAY ANALYSIS OF VARIANCE.
S519: Evaluation of Information Systems Social Statistics Inferential Statistics Chapter 12: Factor analysis.
Factorial ANOVA Chapter 12. Research Designs Between – Between (2 between subjects factors) Between – Between (2 between subjects factors) Mixed Design.
 When to analysis of variance with more than one factor  Main and interaction effects  ToolPak 2.
PSY 307 – Statistics for the Behavioral Sciences Chapter 16 – One-Factor Analysis of Variance (ANOVA)
Experimental Design: One-Way Correlated Samples Design
Psychology 301 Chapters & Differences Between Two Means Introduction to Analysis of Variance Multiple Comparisons.
Statistics Psych 231: Research Methods in Psychology.
Running Scheffe’s Multiple Comparison Test in Excel
Chapter 10: Analyzing Experimental Data Inferential statistics are used to determine whether the independent variable had an effect on the dependent variance.
Repeated Measures ANOVA factorial within-subjects designs.
Psych 5500/6500 Other ANOVA’s Fall, Factorial Designs Factorial Designs have one dependent variable and more than one independent variable (i.e.
ITEC6310 Research Methods in Information Technology Instructor: Prof. Z. Yang Course Website: c6310.htm Office:
Experimental Design Experimental Designs An Overview.
Chapter 14 Repeated Measures and Two Factor Analysis of Variance
1 Psych 5510/6510 Chapter 14 Repeated Measures ANOVA: Models with Nonindependent ERRORs Part 3: Factorial Designs Spring, 2009.
Stats/Methods II JEOPARDY. Jeopardy Compare & Contrast Repeated- Measures ANOVA Factorial Design Factorial ANOVA Surprise $100 $200$200 $300 $500 $400.
Mixed ANOVA Models combining between and within. Mixed ANOVA models We have examined One-way and Factorial designs that use: We have examined One-way.
IS 4800 Empirical Research Methods for Information Science Class Notes March 16, 2012 Instructor: Prof. Carole Hafner, 446 WVH Tel:
Chapter 12 Introduction to Analysis of Variance PowerPoint Lecture Slides Essentials of Statistics for the Behavioral Sciences Eighth Edition by Frederick.
Hypothesis Testing Chapter 6. Steps for Conducting Scientific Research Step 1: Formulate a hypothesis Step 2: Design a study Step 3: Collect & analyze.
ANOVAs.  Analysis of Variance (ANOVA)  Difference in two or more average scores in different groups  Simplest is one-way ANOVA (one variable as predictor);
Stats/Methods II JEOPARDY. Jeopardy Chi-Square Single-Factor Designs Factorial Designs Ordinal Data Surprise $100 $200$200 $300 $500 $400 $300 $400 $300.
Smith/Davis (c) 2005 Prentice Hall Chapter Fifteen Inferential Tests of Significance III: Analyzing and Interpreting Experiments with Multiple Independent.
Copyright c 2001 The McGraw-Hill Companies, Inc.1 Chapter 11 Testing for Differences Differences betweens groups or categories of the independent variable.
© 2006 by The McGraw-Hill Companies, Inc. All rights reserved. 1 Chapter 11 Testing for Differences Differences betweens groups or categories of the independent.
Factorial Design of Experiments. An Economy of Design Two or more levels of each factor (variable) are administered in combination with the two or more.
Outline of Today’s Discussion 1.Independent Samples ANOVA: A Conceptual Introduction 2.Introduction To Basic Ratios 3.Basic Ratios In Excel 4.Cumulative.
BHS Methods in Behavioral Sciences I May 9, 2003 Chapter 6 and 7 (Ray) Control: The Keystone of the Experimental Method.
F-Tables & Basic Ratios. Outline of Today’s Discussion 1.Some F-Table Exercises 2.Introduction to Basic Ratios [Between-Subject ANOVA] 3.Independent Samples.
Inferential Statistics Psych 231: Research Methods in Psychology.
Test the Main effect of A Not sign Sign. Conduct Planned comparisons One-way between-subjects designs Report that the main effect of A was not significant.
Chapter 12 Introduction to Analysis of Variance
Chapter 14 Repeated Measures and Two Factor Analysis of Variance PowerPoint Lecture Slides Essentials of Statistics for the Behavioral Sciences Seventh.
CHAPTER 15: THE NUTS AND BOLTS OF USING STATISTICS.
Factorial Experiments
2 independent Groups Graziano & Raulin (1997).
Interactions & Simple Effects finding the differences
Main Effects and Interaction Effects
Psych 231: Research Methods in Psychology
Presentation transcript:

Chapters 8 & 9 Advanced Experimental Design

Experimental Designs Between-subject designs  Simple randomized design  Multilevel randomized design Factorial designs Within-subject designs Group 1T (24oz Pepsi)M Group 2T (No Pepsi)M R R Group 1M Group 2M R R Group 3M Group 4M R R T (No Pepsi) T (24oz Pepsi) T (12oz Pepsi) T (16oz Pepsi)

Multilevel Between-Subject Designs and Hypothesis Testing Group 1 = Group 2 = Group 3 = Group 4 What is the null hypothesis for the multilevel Pepsi study? How do we test the null hypothesis? By calculating an F-ratio - Analysis of Variance (ANOVA) If the F-ratio is significant, how do we know which groups are different from one another? There are two approaches for finding out Post hoc tests A priori tests Group 1M Group 2M R R Group 3M Group 4M R R T (No Pepsi) T (24oz Pepsi) T (12oz Pepsi) T (16oz Pepsi)

Post Hoc Tests Group 1 vs. Group 2 Group 1 vs. Group 3 Group 1 vs. Group 4 Group 2 vs. Group 3 Group 2 vs. Group 4 Group 3 vs. Group 4 * * * * p <.05 Group 1 = 70 Group 2 = 72 Group 3 = 83 Group 4 = 75

a priori Tests Group 1 vs. Group 3 Group 2 vs. Group 3 Group 1 vs. Group 4 Group 2 vs. Group 4 * * * p <.05 Group 1 = 70 Group 2 = 72 Group 3 = 83 Group 4 = 75

Factorial Designs Pepsi No Pepsi 24 oz Pepsi Morning Afternoon Time Group C Group D Group AGroup B

Effects in Factorial Designs Main effects  Treatment differences between levels of 1 IV Interaction effects  Result of combination of two IVs There are many null hypotheses in factorial designs  There are no differences in test scores due to the amount of Pepsi consumed.  There are no differences in test scores due to the time of day the test was taken.  There are no interaction effects between amount of Pepsi consumed and time of day the test was taken.

Graphing Main and Interaction Effects Mean Score No Pepsi 24oz Pepsi Row Mean Morning70 Afternoon70 Column Mean 70 No main or interaction effects

Significant Pepsi Effects Mean Score No Pepsi 24oz Pepsi Row Mean Morning Afternoon Column Mean 7090 No time or interaction effects

Significant Time Effects Mean Score No Pepsi 24oz Pepsi Row Mean Morning90 Afternoon70 Column Mean 80 No Pepsi or interaction effects

Significant Interaction Effect Mean Score No Pepsi 24oz Pepsi Row Mean Morning Afternoon Column Mean 80 No main effects

Significant Pepsi & Interaction Effect Mean Score No Pepsi 24oz Pepsi Row Mean Morning Afternoon70 Column Mean 6080

Significant Time & Interaction Effect Mean Score No Pepsi 24oz Pepsi Row Mean Morning Afternoon Column Mean 70

Significant Pepsi & Time Effects Mean Score No Pepsi 24oz Pepsi Row Mean Morning Afternoon Column Mean 6080 No interaction effect

Significant Pepsi, Time, and Interaction Effects Mean Score No Pepsi 24oz Pepsi Row Mean Morning70 Afternoon Column Mean 7080

Within-Subject Designs Important Factors in Experimental Design  Groups must be equal before treatment Use random assignment  Try to reduce within-group (error) variance Expose same subjects to each level of treatment. Example: Effect of beer on dart throwing Group 1T (4 beers)M Group 2T (No beer)M R R