Post Hoc Tests.

Slides:



Advertisements
Similar presentations
Intro to ANOVA.
Advertisements

One-Way BG ANOVA Andrew Ainsworth Psy 420. Topics Analysis with more than 2 levels Deviation, Computation, Regression, Unequal Samples Specific Comparisons.
Chapter 12 A Priori and Post Hoc Comparisons Multiple t-tests Linear Contrasts Orthogonal Contrasts Trend Analysis Bonferroni t Fisher Least Significance.
One-Way ANOVA Multiple Comparisons.
More on ANOVA. Overview ANOVA as Regression Comparison Methods.
Comparing Means.
PSY 1950 Post-hoc and Planned Comparisons October 6, 2008.
Finals Schedule n Section 1: 9:00 AM Monday, May 15.
Analyses of K-Group Designs : Omnibus F & Pairwise Comparisons ANOVA for multiple condition designs Pairwise comparisons and RH Testing Alpha inflation.
One-way Between Groups Analysis of Variance
K-group ANOVA & Pairwise Comparisons ANOVA for multiple condition designs Pairwise comparisons and RH Testing Alpha inflation & Correction LSD & HSD procedures.
Effect Sizes, Power Analysis and Statistical Decisions Effect sizes -- what and why?? review of statistical decisions and statistical decision errors statistical.
If = 10 and = 0.05 per experiment = 0.5 Type I Error Rates I.Per Comparison II.Per Experiment (frequency) = error rate of any comparison = # of comparisons.
Analysis of Variance. ANOVA Probably the most popular analysis in psychology Why? Ease of implementation Allows for analysis of several groups at once.
Intermediate Applied Statistics STAT 460
1 Multiple Comparison Procedures Once we reject H 0 :   =   =...  c in favor of H 1 : NOT all  ’s are equal, we don’t yet know the way in which.
Stats Lunch: Day 7 One-Way ANOVA. Basic Steps of Calculating an ANOVA M = 3 M = 6 M = 10 Remember, there are 2 ways to estimate pop. variance in ANOVA:
Everyday is a new beginning in life. Every moment is a time for self vigilance.
Post Hoc Tests. What is a Post Hoc Test? Review: – Adjusting Alpha Level – Multiple A Priori Comparisons What makes a test Post Hoc? – Many tests could.
Chapter 13 For Explaining Psychological Statistics, 4th ed. by B. Cohen 1 Chapter 13: Multiple Comparisons Experimentwise Alpha (α EW ) –The probability.
One-Way Analysis of Variance Recapitulation Recapitulation 1. Comparing differences among three or more subsamples requires a different statistical test.
Simple ANOVA Comparing the Means of Three or More Groups Chapter 9.
Independent Samples ANOVA. Outline of Today’s Discussion 1.Independent Samples ANOVA: A Conceptual Introduction 2.The Equal Variance Assumption 3.Cumulative.
Six Easy Steps for an ANOVA 1) State the hypothesis 2) Find the F-critical value 3) Calculate the F-value 4) Decision 5) Create the summary table 6) Put.
Psych 200 Methods & Analysis
ANOVA: Analysis of Variation
Comparing Multiple Groups:
ANOVA: Analysis of Variation
Everyday is a new beginning in life.
Statistics for Managers Using Microsoft Excel 3rd Edition
Comparing several means: ANOVA (GLM 1)
Multiple comparisons
Comparing Three or More Means
Group Comparisons What is the probability that group mean differences occurred by chance? With Excel (or any statistics program) we computed p values to.
Comparing ≥ 3 Groups Analysis of Biological Data/Biometrics
Multiple Comparisons Q560: Experimental Methods in Cognitive Science Lecture 10.
Post Hoc Tests on One-Way ANOVA
Post Hoc Tests on One-Way ANOVA
Planned Comparisons & Post Hoc Tests
Differences Among Group Means: One-Way Analysis of Variance
Comparing Multiple Groups: Analysis of Variance ANOVA (1-way)
After ANOVA If your F < F critical: Null not rejected, stop right now!! If your F > F critical: Null rejected, now figure out which of the multiple means.
What if. . . You were asked to determine if psychology and sociology majors have significantly different class attendance (i.e., the number of days a person.
Analysis of Variance (ANOVA)
Linear Contrasts and Multiple Comparisons (§ 8.6)
Studentized Range Statistic
Multiple comparisons - multiple pairwise tests - orthogonal contrasts
Comparing Several Means: ANOVA
Analysis of Variance (ANOVA)
Multiple Comparisons: Example
Question-Bank 1g – Tukey/Dunnett
Reasoning in Psychology Using Statistics
1-Way Analysis of Variance - Completely Randomized Design
I. Statistical Tests: Why do we use them? What do they involve?
Analysis of Variance (ANOVA)
Chapter 12 A Priori and Post Hoc Comparisons Multiple t-tests
Chapter 12 A Priori and Post Hoc Comparisons Multiple t-tests
Chapter 14 Homework: problem 1
Multiple comparisons - multiple pairwise tests - orthogonal contrasts
Chapter 11- Lecture 2 More t tests for independent groups: Dunnett’s t, andTukey’s Honestly Significant Difference 2/19/2019.
Comparing Means.
Chapter 12 Power Analysis.

Factorial ANOVA 2 or More IVs.
Conceptual Understanding
SPSS SPSS Problem (Part 1). SPSS SPSS Problem (Part 1)
One-way Analysis of Variance
ANOVA Between-Subject Design: A conceptual approach
1-Way Analysis of Variance - Completely Randomized Design
Multiple comparisons - multiple pairwise tests - orthogonal contrasts
Presentation transcript:

Post Hoc Tests

What is a Post Hoc Test? Review: What makes a test Post Hoc? Adjusting Alpha Level Multiple A Priori Comparisons What makes a test Post Hoc? Many tests could be Post Hoc… But, there are set Post Hoc tests

Studentized Range Statistic q

Studentized Range Statistic q Independent Groups Largest mean Smallest mean Example 8.2 11.8 Fail to Reject Note: arrange means in ascending order! = 3.77 critical value

Studentized Range Statistic q q’s can tell us where differences are (more specific than F) Solving q’s is just like solving t’s But we solve a lot of q’s… can we speed things up?

Solving for the Smallest Significant Difference Example 8.2 11.8 = 3.77 critical value

Solving for the Smallest Significant Difference Solving for the smallest significant difference will help us make quicker comparisons But we still need a way to organize things nicely…

Newman-Kewls smallest difference required was 6.61 smallest significant difference If 2 steps Example T1 T2 T3 r 3.6 3 6.61 2 5.41 8.2 11.8

A Better Newman-Kewls Example 2 3 9 10

A Better Newman-Kewls Example * T1 T2 T3 T4 T5 r 1 7 8 5 4.04 6 4 3.79 3 3.44 2 2.86 Read Right to Left UNTIL 1. The row is completed 2. A nonsignificant difference is found 3. Reaching a column which was nonsignificant on the previous row

Newman-Kewls Summarized Newman-Kewls tables help organize your q’s When doing a set of post hoc comparisons it’s best to use a Newman-Kewls table

Unequal N’s Tukey-Kramer Replace with

Unequal N’s * * Behrens-Fisher Each particular pairing of means must be examined with a different critical value and their own Thus, the smallest significant difference will vary even for a given

Using q’s will give use more Type I Errors A Problem with q So Far… Why are we doing q’s anyway? Why not do t’s instead? But is q really controlling our alpha level? NO! Using q’s will give use more Type I Errors

What Happens to Alpha Level? Power? Trying to fix q Tukey's HSD Tukey's WSD N-K except If there are 4 means, all differences are treated as 4 steps. r = # of steps between the two means to be compared. What Happens to Alpha Level? Power?

Use Tukey’s WSD, not normal method for q Tukey’s HSD and WSD T1 T2 T3 T4 T5 r 1 7 8 5 4.04 6 4 3.79 3 3.44 2 2.86 Use Tukey’s WSD, not normal method for q

Back to Post Hoc in General What is a post hoc test again? What are the real issues with Post Hoc tests? Alpha and Power… q is just one type of post hoc (one way to balance alpha and power), what are others?

Dunnett’s Control vs. Treatment run standard and use or, solve for critical difference (CV) Example Go to Table for * Pros…? Cons…?

Sheffé’s Test Linear contrast MS(contrast) F = To evaluate 1) consult F table and find critical value F.05 (k-1, dferror) (CV) F = MS(error) 2) multiply CV by (k-1). (new CV) It sets the family-wise Type-I Error rate ( in our case) for ALL possible linear contrasts, not merely the pair-wise comparisons. Don’t use when only doing pair-wise, because it will be overly conservative.

Post Hoc Summary When to use what… q in most situations… but use Tukey’s WSD for critical value Put things in a Newman-Kewls table when N’s are unequal, use Tukey’s correction Dunnett’s when you have one control and multiple treatments Sheffé’s ONLY when you are doing complex comparisons (i.e., contrasts)

Post Hoc Summary Be aware of the alpha level and power issues… Why can’t we have a perfect test (i.e., low alpha level and high power)? How does Tukey’s WSD and HSD relate to this? How does Dunnett’s relate to this? How does Sheffé’s relate to this?