Effect Sizes (continued)

Slides:



Advertisements
Similar presentations
Mixed Designs: Between and Within Psy 420 Ainsworth.
Advertisements

Effect Size Tutorial: Cohen’s d and Omega Squared
1 Chapter 4 Experiments with Blocking Factors The Randomized Complete Block Design Nuisance factor: a design factor that probably has an effect.
Chapter 4 Randomized Blocks, Latin Squares, and Related Designs
Design and Analysis of Experiments
1 G Lect 14b G Lecture 14b Within subjects contrasts Types of Within-subject contrasts Individual vs pooled contrasts Example Between subject.
Cal State Northridge  320 Andrew Ainsworth PhD Regression.
1 1 Slide © 2009, Econ-2030 Applied Statistics-Dr Tadesse Chapter 10: Comparisons Involving Means n Introduction to Analysis of Variance n Analysis of.
PSY 340 Statistics for the Social Sciences Chi-Squared Test of Independence Statistics for the Social Sciences Psychology 340 Spring 2010.
Analysis – Regression The ANOVA through regression approach is still the same, but expanded to include all IVs and the interaction The number of orthogonal.
PSY 307 – Statistics for the Behavioral Sciences
Crosstabs and Chi Squares Computer Applications in Psychology.
Hypothesis Testing Using The One-Sample t-Test
Factorial Within Subjects Psy 420 Ainsworth. Factorial WS Designs Analysis Factorial – deviation and computational Power, relative efficiency and sample.
Statistics for the Social Sciences Psychology 340 Fall 2013 Tuesday, November 19 Chi-Squared Test of Independence.
Statistics for the Social Sciences Psychology 340 Fall 2013 Thursday, November 21 Review for Exam #4.
T Test for One Sample. Why use a t test? The sampling distribution of t represents the distribution that would be obtained if a value of t were calculated.
ANOVA Chapter 12.
ANCOVA Lecture 9 Andrew Ainsworth. What is ANCOVA?
Repeated Measures ANOVA
Calculations of Reliability We are interested in calculating the ICC –First step: Conduct a single-factor, within-subjects (repeated measures) ANOVA –This.
1 1 Slide © 2006 Thomson/South-Western Slides Prepared by JOHN S. LOUCKS St. Edward’s University Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
1 1 Slide © 2005 Thomson/South-Western Chapter 13, Part A Analysis of Variance and Experimental Design n Introduction to Analysis of Variance n Analysis.
Chapter 11 HYPOTHESIS TESTING USING THE ONE-WAY ANALYSIS OF VARIANCE.
Measures of Variability. Variability Measure of the spread or dispersion of a set of data 4 main measures of variability –Range –Interquartile range –Variance.
Chapter 9: Non-parametric Tests n Parametric vs Non-parametric n Chi-Square –1 way –2 way.
Educational Research: Competencies for Analysis and Application, 9 th edition. Gay, Mills, & Airasian © 2009 Pearson Education, Inc. All rights reserved.
PSY 307 – Statistics for the Behavioral Sciences Chapter 16 – One-Factor Analysis of Variance (ANOVA)
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:
Psychology 301 Chapters & Differences Between Two Means Introduction to Analysis of Variance Multiple Comparisons.
Learning Objectives Copyright © 2002 South-Western/Thomson Learning Statistical Testing of Differences CHAPTER fifteen.
ANOVA: Analysis of Variance.
Chapter 14 Repeated Measures and Two Factor Analysis of Variance
Reasoning in Psychology Using Statistics Psychology
The Single-Sample t Test Chapter 9. t distributions >Sometimes, we do not have the population standard deviation. (that’s actually really common). >So.
Inferential Statistics 4 Maarten Buis 18/01/2006.
Chapter 13 Repeated-Measures and Two-Factor Analysis of Variance
Econ 3790: Business and Economic Statistics Instructor: Yogesh Uppal
Statistics for Political Science Levin and Fox Chapter Seven
CHAPTER 10 ANOVA - One way ANOVa.
95% CI and Width for Mean # of hrs watching TV for Three Different Sample Sizes Sample SizeConfidence Interval Interval WidthStand DevStand Error N=
Factorial BG ANOVA Psy 420 Ainsworth. Topics in Factorial Designs Factorial? Crossing and Nesting Assumptions Analysis Traditional and Regression Approaches.
Advanced Math Topics One-Way Anova. An ANOVA is an ANalysis Of VAriance. It is a table used to see if the means from a number of samples are.
©2013, The McGraw-Hill Companies, Inc. All Rights Reserved Chapter 4 Investigating the Difference in Scores.
1 G Lect 10M Contrasting coefficients: a review ANOVA and Regression software Interactions of categorical predictors Type I, II, and III sums of.
Chi Square Chi square is employed to test the difference between an actual sample and another hypothetical or previously established distribution such.
Effect Sizes for Continuous Variables William R. Shadish University of California, Merced.
Chapter 13 Analysis of Variance (ANOVA). ANOVA can be used to test for differences between three or more means. The hypotheses for an ANOVA are always:
I. ANOVA revisited & reviewed
Effect Sizes.
Dependent-Samples t-Test
Lecture8 Test forcomparison of proportion
Statistics for the Social Sciences
Post Hoc Tests on One-Way ANOVA
Hypothesis Testing Using the Chi Square (χ2) Distribution
Inferential Statistics
Ch. 14: Comparisons on More Than Two Conditions
Kin 304 Inferential Statistics
Statistics for the Social Sciences
Other Analysis of Variance Designs
M A R I O F. T R I O L A Estimating Population Proportions Section 6-5
Ch. 15: The Analysis of Frequency Tables
Reasoning in Psychology Using Statistics
Two Factor ANOVA with 1 Unit per Treatment
I. Statistical Tests: Why do we use them? What do they involve?
Two Factor ANOVA with 1 Unit per Treatment
Chapter 13 Group Differences
Review Questions III Compare and contrast the components of an individual score for a between-subject design (Completely Randomized Design) and a Randomized-Block.
Reasoning in Psychology Using Statistics
Reasoning in Psychology Using Statistics
Presentation transcript:

Effect Sizes (continued)

Hedges’ Correction for Small Sample Bias d overestimates effect size in small samples (< 10-15 total) Correction is I always use this correction as it never harms estimation. In SPSS COMPUTE D = ES*(1-(3/((4*(NT+NC))-9))).

Algebraic Equivalent: Between Groups t-test on raw posttest scores ,

Algebraic Equivalent: t-test for two matched groups, sample sizes, correlation between groups

Algebraic Equivalent: Two-group between-groups F-statistic on raw posttest scores (Data Set I)

Algebraic Equivalent: Multifactor Between Subjects ANOVA with Two Treatment Conditions Sums of Squares and Degrees of Freedom for all sources, and Marginal Means for Treatment Conditions Mean Squares and Degrees of Freedom for all sources, and Marginal Means for Treatment Conditions Sums of Squares and Degrees of Freedom for all sources, with Cell Means and Cell Sample Sizes Mean Squares and Degrees of Freedom for all sources, with Cell Means and Cell Sample Sizes Cell means, cell sample sizes, the F-statistic for the treatment factor, and the degrees of freedom for the error term F-statistics and degrees of freedom for all sources, sample size for treatment and comparison groups, where treatment factor has only two levels

Example: Sums of Squares and Degrees of Freedom for all sources, and Marginal Means for Treatment Conditions: Data Set II Row B1 B2 B3 Marginal A1 4.0 8.0 6.0 6.0 (3) (3) (3) (9) A2 10.0 2.0 12.0 8.0   Column 7.5 8.0 5.5 7.0 Marginal (6) (6) (6) (18) B1 B2 B3   A1 8 10 8 4 8 6 0 6 4 A2 14 4 15 10 2 12 6 0 9 Sum of Squares df Mean Square F Probability A 18.000 1 18.000 2.038 .179 B 48.000 2 24.000 2.717 .106 AB 144.000 2 72.000 8.151 .006 Residual 106.000 12 8.833 Total 316.000 17 18.588

Example: Sums of Squares and Degrees of Freedom for all sources, and Marginal Means for Treatment Conditions For a two group one factor ANOVA: For a two factor ANOVA: Which is the same as would have been obtained had Factor B not existed (with equal n per cell) ,

Algebraic Equivalent: Exact Probability and Sample Sizes If exact p value from t-test or two group F-test Use sample size to get df, which in turn allows you to get exact t statistic Then apply t-test method previously shown From Data Set I exact probability for t-test was p = .818. for df = 20-2 = 18, t = .2336 so d = -.104, same as before

Algebraic Equivalent: r to d To convert r to d uncorrected for small sample bias, using Data Set I: Which is the same as originally obtained using the standard formula for d

Converting d to r If already corrected for small sample bias: If not: where df = (n1 + n2 - 2).

Algebraic Equivalent: Raw Data Sometimes raw data is tabled as, say, Treatment group N = 10: A = 20%, B =20%, C = 30%, D = 20%, and F = 10% Comparison group N = 10: A = 10%, B = 20%, C = 20%, D = 30%, and F = 20% Create raw data as, say, A = 4, B = 3, C = 2, D = 1, and F = 0 treatment group is 4, 4, 3, 3, 2, 2, 2, 1, 1, 0 comparison group is 4, 3, 3, 2, 2, 1, 1, 1, 0, 0 Then d = .377