![]() ![]() # 3 group therapy twice per day 1 12 18 27 107 22.12712Īssumptions of ANCOVA Correlations Between the Covariates # 1 group therapy and individual therapy each day 0 12 17 23 157 20.81250 # 3 group therapy twice per day 18 42 53.5 70 100 55.14407įavstats(total_sessions~treatment, data=neuroFIM) # 1 group therapy and individual therapy each day 18 57 68.5 79 123 68.00000 #loading the datasetįavstats(admfim~treatment, data=neuroFIM) We will need to factor the treatment variable, and then run some basic descriptive statistics to get a feel for what is going on with the data. We will start by loading and cleaning the dataset. What is the IV? What is the DV? What are the TWO covariates you should include in the model?.You believe that the number of therapy sessions might have an influence on the change in FIM score. You wonder if the other modes of delivery would get different results. One of your main research questions is the following: Do discharge FIM scores differ across the 3 treatment groups? The usual mode of delivery is individual therapy twice per day. Patients receive one of three different modes of treatment delivery: group and individual therapy per day, individual therapy twice a day, or group therapy twice a day. Your team has initiated a clinical trial to examine the effect of mode of treatment delivery on FIM outcomes. You are a manager of rehabilitation services at a large healthcare system with several in-patient rehabilitation facilities. We will return to the neurological FIM dataset we have used in the past. ![]() ANCOVA can be used when you want to compare groups (categorical variable), but also want to control for the effects of a covariate (hence the “covariance” component of Analysis of covariance). This week, we will examine how to run an ANCOVA model–an analysis of covariance. Through the first four weeks of this course, we have learned how to run correlations, simple regression models, simultaneous and hierarchical models with continuous variables, and have learned how to perform regression models with categorical predictors (analogous to ANOVA). Haven, mosaic, dplyr, ggplot2, forcats, ez, emmeans, Hmisc, stargazer, emmeans, and car. Packages you should have loaded to successfully run this week’s exercises: ![]()
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