Presentation on theme: "MULTIPLE COMPARISON TESTS"— Presentation transcript:
1MULTIPLE COMPARISON TESTS A PRIORI OR PLANNED CONTRASTSMULTIPLE COMPARISON TESTS
2ANOVA ANOVA is used to compare means. However, if a difference is detected, and more than two means are being compared, ANOVA cannot tell you where the difference lies.In order to figure out which means differ, you can do a series of tests:Planned or unplanned comparisons of means.
3PLANNED or A PRIORI CONTRASTS A comparison between means identified as being of utmost interest during the design of a study, prior to data collection.You can only do one or a very small number of planned comparisons, otherwise you risk inflating the Type 1 error rate.You do not need to perform an ANOVA first.
4UNPLANNED or A POSTERIORI CONTRASTS A form of “data dredging” or “data snooping”, where you may perform comparisons between all potential pairs of means in order to figure out where the difference(s) lie.No prior justification for comparisons.Increased risk of committing a Type 1 error.The probability of making at least one type 1 error is not greater than α= 0.05.
5PLANNED ORTHOGONAL AND NON-ORTHOGONAL CONTRASTS Planned comparisons may be orthogonal or non-orthogonal.Orthogonal: mutually non-redundant and uncorrelated contrasts (i.e.: independent).Non-Orthogonal: Not independent.For example:4 means: Y1 ,Y2, Y3, and Y4Orthogonal: Y1- Y2 and Y3- Y4Non-Orthogonal: Y1-Y2 and Y2-Y3
6ORTHOGONAL CONTRASTSLimited number of contrasts can be made, simultaneously.Any set of contrasts may have k-1 number of contrasts.
7ORTHOGONAL CONTRASTS For Example: k=4 means. Therefore, you can make 3 (i.e.: 4-1) orthogonal contrasts at once.
8HOW DO YOU KNOW IF A SET OF CONTRASTS IS ORTHOGONAL? ∑cijci’j=0where the c’s are the particular coefficients associated with each of the means and the i indicates the particular comparison to which you are referring.Multiply all of the coefficients for each particular mean together across all comparisons.Then add them up!If that sum is equal to zero, then the comparisons that you have in your set may be considered orthogonal.
9HOW DO YOU KNOW IF A SET OF CONTRASTS IS ORTHOGONAL? For Example:SetCoefficientsContrastsc1c2c3c41-1-Y1-Y2Y3-Y4-½(Y1+Y2)/2 –(Y3+Y4)/2cijci’j∑cijci’j(After Kirk 1982)(c1)Y1 + (c2)Y2 = Y1 – Y2Therefore c1 = 1 and c2 = -1 because(1)Y1 + (-1)Y2 = Y1-Y2
10ORTHOGONAL CONTRASTSThere are always k-1 non-redundant questions that can be answered.An experimenter may not be interested in asking all of said questions, however.
11PLANNED COMPARISONS USING A t STATISTIC A planned comparison addresses the null hypothesis that all of your comparisons between means will be equal to zero.Ho=Y1-Y2=0Ho= Y3-Y4=0Ho= (Y1+Y2)/2 –(Y3+Y4)/2These types of hypotheses can be tested using a t statistic.
12PLANNED COMPARISONS USING A t STATISTIC Very similar to a two sample t-test, but the standard error is calculated differently.Specifically, planned comparisons use the pooled sample variance (MSerror )based on all k groups (and the corresponding error degrees of freedom) rather than that based only on the two groups being compared.This step increases precision and power.
13PLANNED COMPARISONS USING A t STATISTIC Evaluate just like any other t-test.Look up the critical value for t in the same table.If the absolute value of your calculated t statistic exceeds the critical value, the null hypothesis is rejected.
14PLANNED COMPARISONS USING A t STATISTIC: NOTE All of the t statistic calculations for all of the comparisons in a particular set will use the same MSerror.Thus, the tests themselves are not statistically independent, even though the comparisons that you are making are.However, it has been shown that, if you have a sufficiently large number of degrees of freedom (40+), this shouldn’t matter.(Norton and Bulgren, as cited by Kirk, 1982)
15PLANNED COMPARISONS USING AN F STATISTIC You can also use an F statistic for these tests, because t2 = F.Different books prefer different methods.The book I liked most used the t statistic, so that’s what I’m going to use throughout.SAS uses F, however.
16CONFIDENCE INTERVALS FOR ORTHOGONAL CONTRASTS A confidence interval is a way of expressing the precision of an estimate of a parameter.Here, the parameter that we are estimating is the value of the particular contrast that we are making.So, the actual value of the comparison (ψ) should be somewhere between the two extremes of the confidence interval.
17CONFIDENCE INTERVALS FOR ORTHOGONAL CONTRASTS The values at the extremes are the 95% confidence limits.With them, you can say that you are 95% confident that the true value of the comparison lies between those two values.If the confidence interval does not include zero, then you can conclude that the null hypothesis can be rejected.
18ADVANTAGES OF USING CONFIDENCE INTERVALS When the data are presented this way, it is possible for the experimenter to consider all possible null hypotheses – not just the one that states that the comparison in question will equal 0.If any hypothesized value lies outside of the 95% confidence interval, it can be rejected.
19CHOOSING A METHOD Orthogonal tests can be done in either way. Both methods make the same assumptions and are equally significant.
20ASSUMPTIONS Assumptions: The populations are approximately normally distributed.Their variances are homogenous.The t statistic is relatively robust to violations of these assumptions when the number of observations for each sample are equal.However, when the sample sizes are not equal, the t statistic is not robust to the heterogeneity of variances.
21HOW TO DEAL WITH VIOLATIONS OF ASSUMPTIONS When population variances are unequal, you can replace the pooled estimator of variance, MSerror, with individual variance estimators for the means that you are comparing.There are a number of possible procedures that can be used when the variance between populations is heterogeneous:Cochran and CoxWelchDixon, Massey, Satterthwaite and Smith
22TYPE I ERRORS AND ORTHOGONAL CONTRASTS For C independent contrasts at some level of significance (α), the probability of making one or more Type 1 errors is equal to: 1-(1-α)CAs the number of independent tests increases, so does the probability of committing a Type 1 error.This problem can be reduced (but not eliminated) by restricting the use of multiple t-tests to a priori orthogonal contrasts.
23A PRIORI NON-ORTHOGONAL CONTRASTS Contrasts of interest that ARE NOT independent.In order to reduce the probability of making a Type 1 error, the significance level (α) is set for the whole family of comparisons that is being made, as opposed to for each individual comparison.For Example:Entire value of α for all comparisons combined is 0.05.The value for each individual comparison would thus be less than that.
24WHEN DO YOU DO THESE? When contrasts are planned in advance. They are relatively few in number.BUT the comparisons are non-orthogonal (they are not independent).i.e.: When one wants to contrast a control group mean with experimental group means.
25DUNN’S MULTIPLE COMPARISON PROCEDURE A.K.A.: Bonferoni t procedure.Involves splitting up the value of α among a set of planned contrasts in an additive way.For example:Total α = 0.05, for all contrasts.One is doing 2 contrasts.α for each contrast could be 0.025, if we wanted to divide up the α equally.
26DUNN’S MULTIPLE COMPARISON PROCEDURE If the consequences of making a Type 1 error are not equally serious for all contrasts, then you may choose to divide α unequally across all of the possible comparisons in order to reflect that concern.
27DUNN’S MULTIPLE COMPARISON PROCEDURE This procedure also involves the calculation of a t statistic (tD).The calculation involved in finding tD is identical to that for determining t for orthogonal tests:
28DUNN’S MULTIPLE COMPARISON PROCEDURE However, you use a different table in order to look up the critical value (tDα;C,v ).Your total α value (not the value per comparison).Number of comparisons (C).And v, the number of degrees of freedom.
29DUNN’S MULTIPLE COMPARISON PROCEDURE: ONE-TAILED TESTS The table also only shows the critical values for two-tailed tests.However, you can determine the approximate value of tDα;C,v for a one-tailed test by using the following equation: tDα;C,v ≈ zα/C + (z3α/C + zα/C)/4(v-2)Where the value of zα/C can be looked up in yet another table (“Areas under the Standard Normal Distribution”).
30DUNN’S MULTIPLE COMPARISON PROCEDURE Instead of calculating tD for all contrasts of interest, you can simply calculate the critical difference (ψD) that a particular comparison must exceed in order to be significant:ψD = tDα/2;C,v √(2MSerror/n).Then compare this critical difference value to the absolute values of the differences between the means that you compared.If they exceed ψD, they are significant.
31DUNN’S MULTIPLE COMPARISON PROCEDURE For Example:You have 5 means (Y1 through Y5).ΨD = 8.45Differences between means are:Those differences that exceed the calculated value of ΨD (8.45, in this case) are significant.MEANSY1Y2Y3Y4Y5-12.06.710.53.65.31.188.8.131.52.9(After Kirk 1982)
32DUNN-SIDAK PROCEDURE A modification of the Dunn procedure. t statistic (tDS) and critical difference (ψDS).There isn’t much difference between the two procedures at α < 0.01.However, at increased values of α, this procedure is considered to be more powerful and more precise.Calculations are the same for t and ψD.Table is different.
33DUNN-SIDAK PROCEDUREHowever, it is not easy to allocate the total value of α unevenly across a particular set of comparisons.This is because the values of α for each individual comparison are related multiplicatively, as opposed to additively.Thus, you can’t simply add the α’s for each comparison together to get the total value of α for all contrasts combined.
34DUNNETT’S TEST For contrasts involving a control mean. Also uses a t statistic (tD’) and critical difference (ψD’).Calculations are the same for t and ψ.Different table.Instead of C, you use k, the number of means (including the control mean).Note: unlike Dunn’s and Dunn-Sidak’s, Dunnet’s procedure is limited to k-1 non-orthogonal comparisons.
35CHOOSING A PROCEDURE : A PRIORI NON-ORTHOGONAL TESTS Often, the use of more than one procedure will appear to be appropriate.In such cases, compute the critical difference (ψ) necessary to reject the null hypothesis for all of the possible procedures.Use the one that gives the smallest critical difference (ψ) value .
36A PRIORI ORTHOGONAL and NON-ORTHOGONAL CONTRASTS The advantage of being able to make all planned contrasts, not just those that are orthogonal, is gained at the expense of an increase in the probability of making Type 2 errors.
37A PRIORI and A POSTERIORI NON-ORTHOGONAL CONTRASTS When you have a large number of means, but only comparatively very few contrasts, a priori non-orthogonal contrasts are better suited.However, if you have relatively few means and a larger number of contrasts, you may want to consider doing an a posteriori test instead.
38A POSTERIORI CONTRASTS There are many kinds, all offering different degrees of protection from Type 1 and Type 2 errors:Least Significant Difference (LSD) TestTukey’s Honestly Significant Difference (HSD) TestSpjtotvoll and Stoline HSD TestTukey-Kramer HSD TestScheffé’s S TestBrown-Forsythe BF ProcedureNewman-Keuls TestDuncan’s New Multiple Range Test
39A POSTERIORI CONTRASTS Most are good for doing all possible pair-wise comparisons between means.One (Scheffé’s method) allows you to evaluate all possible contrasts between means, whether they are pair-wise or not.
40CHOOSING AN APPROPRIATE TEST PROCEDURE Trade-off between power and the probability of making Type 1 errors.When a test is conservative, it is less likely that you will make a Type 1 error.But it also would lack power, inflating the Type 2 error rate.You will want to control the Type 1 error rate without loosing too much power.Otherwise, you might reject differences between means that are actually significant.
41DOING PLANNED CONTRASTS USING SAS You have a data set with one dependent variable (y) and one independent variable (a).(a) has 4 different treatment levels (1, 2, 3, and 4).You want to do the following comparisons between treatment levels:1)Y1-Y22) Y3-Y43) (Y1-Y2)/2 - (Y3-Y4)/2
42...BUT THEY DON’T HAVE TO BE ARE THEY ORTHOGONAL?SETCOEFFICIENTSCONTRASTS1c1c2c3c4-1-Y1-Y2Y3-Y4-1/2(Y2+Y2)/2 – (Y3+Y4)/2cijci’j1/2∑cijci’jYES!...BUT THEY DON’T HAVE TO BE
43DOING PLANNED ORTHOGONAL CONTRASTS USING SAS SAS INPUT:data dataset;input y a;cards;3 14 27 37 4proc glm;class a;model y = a;contrast 'Compare 1 and 2' a ;contrast 'Compare 3 and 4' a ;contrast 'Compare 1 and 2 with 3 and 4' a ;run;Give your dataset an informative name.Tell SAS what you’ve inputted: column 1 is your y variable (dependent) and column 2 is your a variable (independent).This is followed by your actual data.“Model” tells SAS that you want to look at the effects that a has on y.Use SAS procedure proc glm.“Class” tells SAS that a is categorical.Indicate “weights” (kind of like the coefficients).Then enter your “contrast” statements.
44DOING PLANNED ORTHOGONAL CONTRASTS USING SAS ANOVA
45RECOMMENDED READINGKirk RE Experimental design: procedures for the behavioural sciences. Second ed. CA: Wadsworth, Inc.Field A, Miles J Discovering Statistics Using SAS. London: SAGE Publications Ltd.Institute for Digital Research and Education at UCLA:Stata, SAS, SPSS and R.
48PLANNED COMPARISONS USING A t STATISTIC t = ∑cjYj/ √MSerror∑cj/njWhere c is the coefficient, Y is the corresponding mean, n is the sample size, and MSerror is the Mean Square Error.
49PLANNED COMPARISONS USING A t STATISTIC For Example, you want to compare 2 means:Y1 = 48.7 and Y2=43.4c1 = 1 and c2 = -1n=10MSerror=28.8t = (1) (-1)43.4[√28.8(12/10) + (-12/10)]= 2.21
50CONFIDENCE INTERVALS FOR ORTHOGONAL CONTRASTS (Y1-Y2) – tα/2,v(SE) ≤ ψ ≤ (Y1-Y2) – tα/2,v(SE)For Example:If, Y1 = 48.7, Y2 = 43.4, and SE(tα(2),df) = 4.8(Y1-Y2) – SE(tα(2),df) = 0.5(Y1-Y2) + SE(tα(2),df) = 10.1Thus, you can be 95% confident that the true value of ψ is between 0.5 and 10.1.Because the confidence interval does not include 0, you can also reject the null hypothesis that Y1-Y2 = 0.(Example after Kirk 1982)
51TYPE I ERRORS AND ORTHOGONAL CONTRASTS As the number of independent tests increases, so does the probability of committing a Type 1 error.For Example, when α = 0.05:1-(1-0.05)3=0.14 (C=3)1-(1-0.05)5=0.23 (C=5)1-(1-0.05)10=0.40 (C=10)(Example after Kirk 1982)