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One way anova examples applied behavior analysis code#

#Perform Levene's Test of Equality of Variances Labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw() Ggplot(data = dat, mapping = aes(sample = plant_weight, color = treatment, fill = treatment)) + `p-value` = shapiro.test(plant_weight)$p.value) Summarise(`W Stat` = shapiro.test(plant_weight)$statistic, #Perform the Shapiro-Wilk Test for Normality on each group Median = median(plant_weight, na.rm = TRUE), All assumption checks are provided along with the one-way ANOVA and post-hoc tests:ĭat% select(plant_weight, treatment) %>% group_by(treatment) %>%

One way anova examples applied behavior analysis code#

Here is the annotated code for the example. The center=”median” option is the default and can result in a slightly more robust solution to Levene’s Test. In the leveneTest statement, the center=”mean” option will correspond to the traditional test as reported by other commercially available software. Two versions of Levene’s Test for Equality of Variances are performed in order to demonstrate the traditional solution along with a more robust form of the test. The ‘lm’ (Linear Models) function is included in the base stats package. QQ plots are created with the qqplotr package. Boxplots are created using the ggplot2 package. Data manipulation and summary statistics are performed using the dplyr package. The R code below includes Shapiro-Wilk Normality Tests and QQ plots for each treatment group. Install.packages("emmeans", dependencies = TRUE) Install.packages("dplyr", dependencies = TRUE) Install.packages("qqplotr", dependencies = TRUE)

Install.packages("ggplot2", dependencies = TRUE) Install.packages("car", dependencies = TRUE) Install.packages("gmodels", dependencies = TRUE) The data for this example is available here: Treatment = One of three available treatments to enhance plant weight Plant_weight = The dried weight of each plant We want to answer the question, “Which treatment is optimal for enhancing the plant weight”? Each plant is treated with one out of three available treatments to enhance the weight of each plant. In this example, an experiment is performed to compare the dry weight of plants with one of three potential treatments. If variances are unequal, then a Welch’s one-way ANOVA is appropriate.

If variances are equal, then the assumptions of a standard one-way ANOVA are met.

A check for unequal variances will help determine which version of a one-way ANOVA is most appropriate:.

Experimental units only receive one treatment, and they do not overlap.

Treatment groups are independent of one another.

The response of interest is continuous and normally distributed for each treatment group.

In order to run a one-way ANOVA the following assumptions must be met:

The null hypothesis is that there is no difference between treatment group means, while the alternative hypothesis is that the mean of at least one treatment group differs. A one-way ANOVA is considered a “between-subjects” analysis. Thus, the treatment groups do not have overlapping membership and are considered independent. For example, you may want to see if first-year students scored differently than second or third-year students on an exam.Ī one-way ANOVA is appropriate when each experimental unit, (study subject) is only assigned one of the available treatment conditions. A one-way analysis of variance (ANOVA) is typically performed when an analyst would like to test for mean differences between three or more treatments or conditions.