PPT-Two-Way ANOVA Cohen – chapter

14 textbook example 3 x 4 twoway ANOVA complex motor tasks DV dependent variable score on a video game that simulates driving a large truck at night IV categorical independent variables. AMS 572 Group 5. Outline. Jia. Chen: Introduction of repeated measures ANOVA. Chewei. Lu: One-way repeated measures . Wei Xi: Two-factor repeated measures. Tomoaki. Sakamoto : Three-factor repeated measures. The . Univariate. Approach. An ANOVA Factor Can Be. Independent Samples. Between Subjects. Correlated Samples. Within Subjects, Repeated Measures. Randomized Blocks, Split Plot. Matched Pairs if . k. Andrea . Banino. & Punit . Shah . Samples . vs. Populations . Descriptive . vs. Inferential. William Sealy . Gosset. (‘Student’). Distributions, probabilities and P-values. Assumptions of t-tests. Hao. Chai. Dereck. . Shen. Skull dataset. 138 skulls from 10 regions. Thickness was measured at 219 locations on each skull. Other variables in dataset:. Age of person (at time of death). Sex of person. (a.k.a. Analysis of Variance). 1. Outline:. Testing for a difference. in means. Notation. Sums of squares. Mean. squares. The. F distribution. The. ANOVA table. Part II: multiple. comparisons. Worked example. Research Methods in Physical Activity. Research Methods in Physical Activity. Purpose and Protocol of the Statistical Test. The purpose of the statistical test is to evaluate the null hypothesis at a specific level of probability (e.g., p < .05). In other words, do the two levels of treatment differ significantly (p < .05) so that these differences are not attributable to a chance occurrence more than 5 times in 100?. Marshall University Genomics Core Facility. Two-Way ANOVA. In one-way ANOVA, we measured a continuous variable in three or more different categorical groups. We think of this as one dependent variable (the continuous “outcome” variable) and one independent variable (the group). Data . Set. Popcorn Oil amt. Batch Yield. plain . little. large 8.2. gourmet little large 8.6. plain . . lots large 10.4. gourmet lots large 9.2. Understand the basic principles of ANOVA. Why it is done?. What it tells us?. Theory of one-way independent ANOVA. Following up an ANOVA. :. Planned contrasts/comparisons. Choosing contrasts. Coding contrasts. Try Analysis of Variance (ANOVA). Part IV. Significantly Different:. Using Inferential Statistics. What you will learn in Chapter 13. What Analysis of Variance (ANOVA) is and when it is appropriate to use. At its lowest level it is essentially an extension of the logic of . t. -tests to those situations where we wish to . compare the means of three or more samples concurrently.. ANOVA. One-way ANOVA. One IV and one DV. Two-way ANOVA Factor Level Means Study Cells defined b y combinations of t w o or more discrete f actors All o ws ef f ects to be decomposed into main ef f ects and inte r actions Model assumptions remain unchanged ANOVA is comparison of means. Each possible value of a factor or combination of factor is a treatment.. The ANOVA is a powerful and common statistical procedure in the social sciences. It can handle a variety of situations.. ANOVA makes assumptions about error for significance tests. What are the assumptions?. What might happen (why would it be a problem) if the assumption of {normality, equality of error, independence of error} turned out to be false?.