Statnote 6: post-hoc ANOVA tests

If data are analysed using ANOVA, and a significant F value obtained, a more detailed analysis of the differences between the treatment means will be required. The best option is to plan specific comparisons among the treatment means before the experiment is carried out and test them using ‘contrasts’. In some circumstances, post-hoc tests may be necessary and experimenters should think carefully which of the many tests available should be used. Different tests can lead to different conclusions and careful consideration as to the appropriate test should be given in each circumstance.

Induced vertical disparity effects on local and global stereopsis

Purpose: Although significant amounts of vertical misalignment could have a noticeable effect on visual performance, there is no conclusive evidence about the effect of very small amount of vertical disparity on stereopsis and binocular vision. Hence, the aim of this study was to investigate the effects of induced vertical disparity on local and global stereopsis at near. Materials and Methods: Ninety participants wearing best-corrected refraction had local and global stereopsis tested with 0.5 and 1.0 prism diopter (Δ) vertical prism in front of their dominant and non-dominant eye in turn. This was compared to local and global stereopsis in the same subjects without vertical prism. Data were analyzed in SPSS.17 software using the independent samples T and the repeated measures ANOVA tests. Results: Induced vertical disparity decreases local and global stereopsis. This reduction is greater when vertical disparity is induced in front of the non-dominant eye and affects global more than local stereopsis. Repeated measures ANOVA showed differences in the mean stereopsis between the different measured states for local and global values. Local stereopsis thresholds were reduced by 10s of arc or less on average with 1.0Δ of induced vertical prism in front of either eye. However, global stereopsis thresholds were reduced by over 100s of arc by the same 1.0Δ of induced vertical prism. Conclusion: Induced vertical disparity affects global stereopsis thresholds by an order of magnitude (or a factor of 10) more than local stereopsis. Hence, using a test that measures global stereopsis such as the TNO is more sensitive to vertical misalignment than a test such as the Stereofly that measures local stereopsis. © 2014 Informa Healthcare USA, Inc. All rights reserved: reproduction in whole or part not permitted.

An introduction to analysis of variance (ANOVA) with special reference to data from clinical experiments in optometry

This article is aimed primarily at eye care practitioners who are undertaking advanced clinical research, and who wish to apply analysis of variance (ANOVA) to their data. ANOVA is a data analysis method of great utility and flexibility. This article describes why and how ANOVA was developed, the basic logic which underlies the method and the assumptions that the method makes for it to be validly applied to data from clinical experiments in optometry. The application of the method to the analysis of a simple data set is then described. In addition, the methods available for making planned comparisons between treatment means and for making post hoc tests are evaluated. The problem of determining the number of replicates or patients required in a given experimental situation is also discussed. Copyright (C) 2000 The College of Optometrists.

Statnote 23: Non-parametric analysis of variance (ANOVA)

To carry out an analysis of variance, several assumptions are made about the nature of the experimental data which have to be at least approximately true for the tests to be valid. One of the most important of these assumptions is that a measured quantity must be a parametric variable, i.e., a member of a normally distributed population. If the data are not normally distributed, then one method of approach is to transform the data to a different scale so that the new variable is more likely to be normally distributed. An alternative method, however, is to use a non-parametric analysis of variance. There are a limited number of such tests available but two useful tests are described in this Statnote, viz., the Kruskal-Wallis test and Friedmann’s analysis of variance.

Data analysis methods in optometry Part 4: an introduction to analysis of variance (ANOVA)

In any investigation in optometry involving more that two treatment or patient groups, an investigator should be using ANOVA to analyse the results assuming that the data conform reasonably well to the assumptions of the analysis. Ideally, specific null hypotheses should be built into the experiment from the start so that the treatments variation can be partitioned to test these effects directly. If 'post-hoc' tests are used, then an experimenter should examine the degree of protection offered by the test against the possibilities of making either a type 1 or a type 2 error. All experimenters should be aware of the complexity of ANOVA. The present article describes only one common form of the analysis, viz., that which applies to a single classification of the treatments in a randomised design. There are many different forms of the analysis each of which is appropriate to the analysis of a specific experimental design. The uses of some of the most common forms of ANOVA in optometry have been described in a further article. If in any doubt, an investigator should consult a statistician with experience of the analysis of experiments in optometry since once embarked upon an experiment with an unsuitable design, there may be little that a statistician can do to help.