Running a One-Way or Two-Way/Factorial ANOVA
Sharina Hernandez-Villalobos
June 12 2024
1 Loading Libraries
library(psych) # for the describe() command
library(ggplot2) # to visualize our results##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
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## %+%, alphalibrary(expss) # for the cross_cases() command## Loading required package: maditr##
## To get total summary skip 'by' argument: take_all(mtcars, mean)##
## Attaching package: 'maditr'## The following object is masked from 'package:base':
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## sort_by##
## Use 'expss_output_viewer()' to display tables in the RStudio Viewer.
## To return to the console output, use 'expss_output_default()'.##
## Attaching package: 'expss'## The following object is masked from 'package:ggplot2':
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## varslibrary(car) # for the leveneTest() command## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:expss':
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## recode## The following object is masked from 'package:psych':
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## logitlibrary(afex) # to run the ANOVA and plot results## Loading required package: lme4## Loading required package: Matrix##
## Attaching package: 'lme4'## The following object is masked from 'package:expss':
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## dummy## ************
## Welcome to afex. For support visit: http://afex.singmann.science/## - Functions for ANOVAs: aov_car(), aov_ez(), and aov_4()
## - Methods for calculating p-values with mixed(): 'S', 'KR', 'LRT', and 'PB'
## - 'afex_aov' and 'mixed' objects can be passed to emmeans() for follow-up tests
## - Get and set global package options with: afex_options()
## - Set sum-to-zero contrasts globally: set_sum_contrasts()
## - For example analyses see: browseVignettes("afex")
## ************##
## Attaching package: 'afex'## The following object is masked from 'package:lme4':
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## lmerlibrary(emmeans) # for posthoc tests## Welcome to emmeans.
## Caution: You lose important information if you filter this package's results.
## See '? untidy'2 Importing Data
# import the dataset you cleaned previously
# this will be the dataset you'll use throughout the rest of the semester
# use ARC data
d <- read.csv(file="Data/eammi2_clean.csv", header=T)
# new code! this adds a column with a number for each row. it makes it easier when we drop outliers later
d$row_id <- 1:nrow(d)3 State Your Hypothesis
Note: You can chose to run either a one-way ANOVA (a single IV with more than 2 levels) or a two-way/factorial ANOVA (at least two IVs) for the homework. You will need to specify your hypothesis and customize your code based on the choice you make. I will run both versions of the test here for illustrative purposes.
One-Way: We predict that there will be a significant effect of need to belong on swb, as measured by the perceived swb scale.
4 Check Your Variables
# you only need to check the variables you're using in the current analysis
# although you checked them previously, it's always a good idea to look them over again and be sure that everything is correct
str(d)## 'data.frame': 3166 obs. of 7 variables:
## $ ResponseId: chr "R_BJN3bQqi1zUMid3" "R_2TGbiBXmAtxywsD" "R_12G7bIqN2wB2N65" "R_39pldNoon8CePfP" ...
## $ gender : chr "f" "m" "m" "f" ...
## $ race_rc : chr "white" "white" "white" "other" ...
## $ swb : num 4.33 4.17 1.83 5.17 3.67 ...
## $ belong : num 2.8 4.2 3.6 4 3.4 4.2 3.9 3.6 2.9 2.5 ...
## $ efficacy : num 3.4 3.4 2.2 2.8 3 2.4 2.3 3 3 3.7 ...
## $ row_id : int 1 2 3 4 5 6 7 8 9 10 ...# make our categorical variables factors
d$ResponseId <- as.factor(d$ResponseId) #we'll actually use our ID variable for this analysis, so make sure it's coded as a factor
d$belong <- as.factor(d$belong)
d$race_rc <- as.factor(d$race_rc)
d$row_id <- as.factor(d$row_id)
# we're going to recode our race/ethnicity variable into two groups: poc and white
table(d$race_rc)##
## asian black hispanic multiracial nativeamer other
## 210 247 286 293 12 97
## white
## 2021d$poc[d$race_rc == "asian"] <- "poc"
d$poc[d$race_rc == "black"] <- "poc"
d$poc[d$race_rc == "mideast"] <- "poc"
d$poc[d$race_rc == "multiracial"] <- "poc"
d$poc[d$race_rc == "other"] <- "poc"
d$poc[d$race_rc == "prefer_not"] <- NA
d$poc[d$race_rc == "white"] <- "white"
table(d$poc)##
## poc white
## 847 2021d$poc <- as.factor(d$poc)
# you can use the describe() command on an entire dataframe (d) or just on a single variable
describe(d$swb)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3166 4.47 1.32 4.67 4.53 1.48 1 7 6 -0.36 -0.45 0.02# we'll use the describeBy() command to view skew and kurtosis across our IVs
describeBy(d$swb, group = d$belong)##
## Descriptive statistics by group
## group: 1.3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2 5.25 0.59 5.25 5.25 0.62 4.83 5.67 0.83 0 -2.75 0.42
## ------------------------------------------------------------
## group: 1.4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 5 4.07 1.3 4.67 4.07 0.99 2.67 5.33 2.67 -0.22 -2.22 0.58
## ------------------------------------------------------------
## group: 1.5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 7 4.62 1.96 5.17 4.62 1.73 1.83 6.83 5 -0.36 -1.74 0.74
## ------------------------------------------------------------
## group: 1.6
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 4.93 1.69 5.33 4.93 0.99 1 7 6 -1.14 0.51 0.56
## ------------------------------------------------------------
## group: 1.7
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 15 4.29 1.34 4.33 4.26 1.98 2.17 6.83 4.67 0.15 -1.16 0.35
## ------------------------------------------------------------
## group: 1.8
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 18 4.95 1.36 5.17 5 1.48 2.33 6.83 4.5 -0.51 -1.01 0.32
## ------------------------------------------------------------
## group: 1.9
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 28 4.65 1.56 4.92 4.69 1.24 1.67 7 5.33 -0.38 -0.8 0.29
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 30 4.87 1.23 4.92 4.93 1.36 2.17 6.83 4.67 -0.34 -0.86 0.22
## ------------------------------------------------------------
## group: 2.1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 31 4.53 1.54 4.33 4.58 1.98 1.5 7 5.5 -0.22 -0.95 0.28
## ------------------------------------------------------------
## group: 2.2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 51 4.48 1.34 4.33 4.52 1.24 1 7 6 -0.22 -0.19 0.19
## ------------------------------------------------------------
## group: 2.3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 53 4.92 1.19 4.83 4.94 1.48 2.83 7 4.17 -0.2 -1.16 0.16
## ------------------------------------------------------------
## group: 2.4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 83 4.82 1.25 5 4.87 1.24 1.33 7 5.67 -0.45 -0.32 0.14
## ------------------------------------------------------------
## group: 2.5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 78 4.74 1.3 5 4.82 1.24 1.33 7 5.67 -0.61 -0.18 0.15
## ------------------------------------------------------------
## group: 2.6
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 136 4.59 1.2 4.67 4.63 1.24 2 7 5 -0.21 -0.56 0.1
## ------------------------------------------------------------
## group: 2.7
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 106 4.95 1.22 5.08 5.04 1.36 1.33 7 5.67 -0.62 -0.29 0.12
## ------------------------------------------------------------
## group: 2.8
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 148 4.5 1.31 4.67 4.54 1.24 1 7 6 -0.3 -0.35 0.11
## ------------------------------------------------------------
## group: 2.9
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 175 4.47 1.38 4.67 4.52 1.48 1.17 7 5.83 -0.27 -0.78 0.1
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 194 4.62 1.26 4.67 4.67 1.24 1 7 6 -0.32 -0.27 0.09
## ------------------------------------------------------------
## group: 3.1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 177 4.45 1.25 4.67 4.5 1.48 1 7 6 -0.34 -0.65 0.09
## ------------------------------------------------------------
## group: 3.2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 221 4.69 1.33 4.83 4.76 1.48 1 7 6 -0.46 -0.35 0.09
## ------------------------------------------------------------
## group: 3.3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 223 4.54 1.27 4.67 4.61 1.24 1.17 7 5.83 -0.46 -0.41 0.09
## ------------------------------------------------------------
## group: 3.4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 181 4.34 1.2 4.5 4.38 1.24 1 7 6 -0.36 -0.3 0.09
## ------------------------------------------------------------
## group: 3.5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 191 4.45 1.3 4.67 4.52 1.24 1 6.83 5.83 -0.46 -0.38 0.09
## ------------------------------------------------------------
## group: 3.6
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 190 4.49 1.24 4.58 4.54 1.36 1.17 7 5.83 -0.45 -0.25 0.09
## ------------------------------------------------------------
## group: 3.7
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 174 4.41 1.35 4.5 4.47 1.48 1.17 7 5.83 -0.29 -0.63 0.1
## ------------------------------------------------------------
## group: 3.8
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 157 4.29 1.3 4.5 4.32 1.48 1.33 7 5.67 -0.18 -0.78 0.1
## ------------------------------------------------------------
## group: 3.9
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 107 4.22 1.37 4.33 4.29 1.48 1 7 6 -0.34 -0.44 0.13
## ------------------------------------------------------------
## group: 4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 107 4.35 1.27 4.5 4.4 1.24 1.17 6.67 5.5 -0.37 -0.6 0.12
## ------------------------------------------------------------
## group: 4.1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 89 4 1.54 4.17 4.05 1.73 1 7 6 -0.37 -0.92 0.16
## ------------------------------------------------------------
## group: 4.2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 70 4.05 1.24 4 4 1.24 1 7 6 0.33 -0.14 0.15
## ------------------------------------------------------------
## group: 4.3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 42 3.89 1.47 3.83 3.85 1.73 1.33 7 5.67 0.06 -0.92 0.23
## ------------------------------------------------------------
## group: 4.4
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 3.61 1.45 4 3.6 1.98 1.17 6.33 5.17 0 -1.12 0.29
## ------------------------------------------------------------
## group: 4.5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 21 3.98 1.32 4 4.05 1.48 1 6 5 -0.34 -0.83 0.29
## ------------------------------------------------------------
## group: 4.6
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 16 3.58 1.48 3.67 3.58 2.35 1.67 5.5 3.83 0.02 -1.81 0.37
## ------------------------------------------------------------
## group: 4.7
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3 3.44 0.63 3.17 3.44 0.25 3 4.17 1.17 0.35 -2.33 0.36
## ------------------------------------------------------------
## group: 4.8
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1 5 NA 5 5 0 5 5 0 NA NA NA
## ------------------------------------------------------------
## group: 4.9
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1 1.67 NA 1.67 1.67 0 1.67 1.67 0 NA NA NA
## ------------------------------------------------------------
## group: 5
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 1 1 NA 1 1 0 1 1 0 NA NA NAdescribeBy(d$swb, group = d$poc)##
## Descriptive statistics by group
## group: poc
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 847 4.27 1.34 4.33 4.31 1.48 1 7 6 -0.22 -0.53 0.05
## ------------------------------------------------------------
## group: white
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2021 4.58 1.3 4.67 4.65 1.48 1 7 6 -0.44 -0.38 0.03# also use histograms to examine your continuous variable
hist(d$swb)
# and cross_cases() to examine your categorical variables
cross_cases(d, belong, poc)| poc | ||
|---|---|---|
| poc | white | |
| belong | ||
| 1.3 | 1 | |
| 1.4 | 4 | 1 |
| 1.5 | 2 | 4 |
| 1.6 | 5 | 2 |
| 1.7 | 9 | 4 |
| 1.8 | 7 | 9 |
| 1.9 | 16 | 8 |
| 2 | 12 | 14 |
| 2.1 | 13 | 15 |
| 2.2 | 17 | 24 |
| 2.3 | 21 | 26 |
| 2.4 | 32 | 41 |
| 2.5 | 23 | 39 |
| 2.6 | 49 | 60 |
| 2.7 | 32 | 60 |
| 2.8 | 47 | 79 |
| 2.9 | 51 | 103 |
| 3 | 52 | 120 |
| 3.1 | 52 | 103 |
| 3.2 | 59 | 140 |
| 3.3 | 52 | 153 |
| 3.4 | 45 | 119 |
| 3.5 | 37 | 145 |
| 3.6 | 42 | 140 |
| 3.7 | 35 | 128 |
| 3.8 | 34 | 116 |
| 3.9 | 24 | 78 |
| 4 | 23 | 80 |
| 4.1 | 19 | 66 |
| 4.2 | 12 | 56 |
| 4.3 | 6 | 35 |
| 4.4 | 6 | 18 |
| 4.5 | 4 | 17 |
| 4.6 | 3 | 13 |
| 4.7 | 3 | |
| 4.8 | 1 | |
| 4.9 | 1 | |
| 5 | 1 | |
| #Total cases | 847 | 2021 |
5 Check Your Assumptions
5.1 ANOVA Assumptions
- DV should be normally distributed across levels of the IV
- All levels of the IVs should have equal number of cases and there should be no empty cells. Cells with low numbers decrease the power of the test (increase change of Type II error)
- Homogeneity of variance should be assured
- Outliers should be identified and removed
- If you have confirmed everything about, the sampling distribution should be normal. (For a demonstration of what the sampling distribution is, go here.)
5.1.1 Check levels of IVs, no cross_cases for hw
table(d$belong)##
## 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2
## 2 5 7 9 15 18 28 30 31 51 53 83 78 136 106 148 175 194 177 221
## 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5
## 223 181 191 190 174 157 107 107 89 70 42 25 21 16 3 1 1 1cross_cases(d, belong, poc)| poc | ||
|---|---|---|
| poc | white | |
| belong | ||
| 1.3 | 1 | |
| 1.4 | 4 | 1 |
| 1.5 | 2 | 4 |
| 1.6 | 5 | 2 |
| 1.7 | 9 | 4 |
| 1.8 | 7 | 9 |
| 1.9 | 16 | 8 |
| 2 | 12 | 14 |
| 2.1 | 13 | 15 |
| 2.2 | 17 | 24 |
| 2.3 | 21 | 26 |
| 2.4 | 32 | 41 |
| 2.5 | 23 | 39 |
| 2.6 | 49 | 60 |
| 2.7 | 32 | 60 |
| 2.8 | 47 | 79 |
| 2.9 | 51 | 103 |
| 3 | 52 | 120 |
| 3.1 | 52 | 103 |
| 3.2 | 59 | 140 |
| 3.3 | 52 | 153 |
| 3.4 | 45 | 119 |
| 3.5 | 37 | 145 |
| 3.6 | 42 | 140 |
| 3.7 | 35 | 128 |
| 3.8 | 34 | 116 |
| 3.9 | 24 | 78 |
| 4 | 23 | 80 |
| 4.1 | 19 | 66 |
| 4.2 | 12 | 56 |
| 4.3 | 6 | 35 |
| 4.4 | 6 | 18 |
| 4.5 | 4 | 17 |
| 4.6 | 3 | 13 |
| 4.7 | 3 | |
| 4.8 | 1 | |
| 4.9 | 1 | |
| 5 | 1 | |
| #Total cases | 847 | 2021 |
# our number of small nb participants is going to hurt us for the two-way anova, but it should be okay for the one-way anova
# so we'll create a new dataframe for the two-way analysis and call it d2, no for hw
d2 <- subset(d, belong != "nb")
d2$belong <- droplevels(d2$belong)
# to double-check any changes we made
cross_cases(d2, belong, poc)| poc | ||
|---|---|---|
| poc | white | |
| belong | ||
| 1.3 | 1 | |
| 1.4 | 4 | 1 |
| 1.5 | 2 | 4 |
| 1.6 | 5 | 2 |
| 1.7 | 9 | 4 |
| 1.8 | 7 | 9 |
| 1.9 | 16 | 8 |
| 2 | 12 | 14 |
| 2.1 | 13 | 15 |
| 2.2 | 17 | 24 |
| 2.3 | 21 | 26 |
| 2.4 | 32 | 41 |
| 2.5 | 23 | 39 |
| 2.6 | 49 | 60 |
| 2.7 | 32 | 60 |
| 2.8 | 47 | 79 |
| 2.9 | 51 | 103 |
| 3 | 52 | 120 |
| 3.1 | 52 | 103 |
| 3.2 | 59 | 140 |
| 3.3 | 52 | 153 |
| 3.4 | 45 | 119 |
| 3.5 | 37 | 145 |
| 3.6 | 42 | 140 |
| 3.7 | 35 | 128 |
| 3.8 | 34 | 116 |
| 3.9 | 24 | 78 |
| 4 | 23 | 80 |
| 4.1 | 19 | 66 |
| 4.2 | 12 | 56 |
| 4.3 | 6 | 35 |
| 4.4 | 6 | 18 |
| 4.5 | 4 | 17 |
| 4.6 | 3 | 13 |
| 4.7 | 3 | |
| 4.8 | 1 | |
| 4.9 | 1 | |
| 5 | 1 | |
| #Total cases | 847 | 2021 |
5.1.2 Check homogeneity of variance
# use the leveneTest() command from the car package to test homogeneity of variance
# uses the 'formula' setup: formula is y~x1*x2, where y is our DV and x1 is our first IV and x2 is our second IV, only 1 for hw
leveneTest(swb~belong, data = d)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 37 1.0595 0.3725
## 3128leveneTest(swb~belong*poc, data = d2)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 70 1.1199 0.2334
## 27975.1.3 Check for outliers using Cook’s distance and Residuals vs Leverage plot
5.1.3.1 Run a Regression
# use this commented out section only if you need to remove outliers
# to drop a single outlier, remove the # at the beginning of the line and use this code:
d <- subset(d, row_id!=c(1898))
# to drop multiple outliers, remove the # at the beginning of the line and use this code:
# d <- subset(d, row_id!=c(1108) & row_id!=c(602))
# use the lm() command to run the regression
# formula is y~x1*x2 + c, where y is our DV, x1 is our first IV, x2 is our second IV, and c is our covariate
reg_model <- lm(swb ~ belong, data = d) #for one-way
reg_model2 <- lm(swb ~ belong*poc, data = d2) #for two-way5.1.3.2 Check for outliers (One-Way)
# Cook's distance
plot(reg_model, 4)
# Residuals vs Leverage
plot(reg_model, 5)## Warning: not plotting observations with leverage one:
## 55, 746, 1969
5.1.3.3 Check for outliers (Two-Way)
# Cook's distance
plot(reg_model2, 4)
# Residuals vs Leverage
plot(reg_model2, 5)## Warning: not plotting observations with leverage one:
## 55, 390, 391, 681, 1765
5.2 Issues with My Data
Our cell sizes are very unbalanced. A small sample size for one of the levels of our variable limits our power and increases our Type II error rate.
Levene’s test is significant for our three-level gender variable. We are ignoring this and continuing with the analysis anyway, but in the real world this is something we would have to correct for.
We identified and removed a single outlier.
6 Run an ANOVA
aov_model <- aov_ez(data = d,
id = "ResponseId",
between = c("belong"),
dv = "swb",
anova_table = list(es = "pes"))## Contrasts set to contr.sum for the following variables: belong7 View Output
Effect size cutoffs from Cohen (1988):
- η2 = 0.01 indicates a small effect
- η2 = 0.06 indicates a medium effect
- η2 = 0.14 indicates a large effect
nice(aov_model)## Anova Table (Type 3 tests)
##
## Response: swb
## Effect df MSE F pes p.value
## 1 belong 37, 3127 1.70 3.39 *** .039 <.001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 18 Visualize Results
afex_plot(aov_model, x = "belong")
9 Run Posthoc Tests (One-Way)
Only run posthocs if the test is significant! E.g., only run the posthoc tests on gender if there is a main effect for gender.
emmeans(aov_model, specs="belong", adjust="tukey")## Note: adjust = "tukey" was changed to "sidak"
## because "tukey" is only appropriate for one set of pairwise comparisons## belong emmean SE df lower.CL upper.CL
## 1.3 5.25 0.9211 3127 2.295 8.21
## 1.4 4.07 0.5826 3127 2.198 5.94
## 1.5 4.62 0.4924 3127 3.039 6.20
## 1.6 5.42 0.4606 3127 3.939 6.89
## 1.7 4.29 0.3363 3127 3.210 5.37
## 1.8 4.95 0.3070 3127 3.969 5.94
## 1.9 4.65 0.2462 3127 3.859 5.44
## 2 4.87 0.2378 3127 4.109 5.64
## 2.1 4.53 0.2340 3127 3.776 5.28
## 2.2 4.48 0.1824 3127 3.898 5.07
## 2.3 4.92 0.1789 3127 4.341 5.49
## 2.4 4.82 0.1430 3127 4.361 5.28
## 2.5 4.74 0.1475 3127 4.262 5.21
## 2.6 4.59 0.1117 3127 4.235 4.95
## 2.7 4.95 0.1265 3127 4.547 5.36
## 2.8 4.50 0.1071 3127 4.160 4.85
## 2.9 4.47 0.0985 3127 4.158 4.79
## 3 4.62 0.0935 3127 4.323 4.92
## 3.1 4.45 0.0979 3127 4.133 4.76
## 3.2 4.69 0.0876 3127 4.410 4.97
## 3.3 4.54 0.0872 3127 4.264 4.82
## 3.4 4.34 0.0968 3127 4.025 4.65
## 3.5 4.45 0.0943 3127 4.149 4.75
## 3.6 4.49 0.0945 3127 4.182 4.79
## 3.7 4.41 0.0988 3127 4.098 4.73
## 3.8 4.29 0.1040 3127 3.954 4.62
## 3.9 4.22 0.1259 3127 3.814 4.62
## 4 4.35 0.1259 3127 3.945 4.75
## 4.1 4.00 0.1381 3127 3.555 4.44
## 4.2 4.05 0.1557 3127 3.546 4.54
## 4.3 3.89 0.2010 3127 3.244 4.53
## 4.4 3.61 0.2605 3127 2.777 4.45
## 4.5 3.98 0.2843 3127 3.072 4.90
## 4.6 3.58 0.3257 3127 2.539 4.63
## 4.7 3.44 0.7521 3127 1.032 5.86
## 4.8 5.00 1.3026 3127 0.821 9.18
## 4.9 1.67 1.3026 3127 -2.513 5.85
## 5 1.00 1.3026 3127 -3.179 5.18
##
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 38 estimatespairs(emmeans(aov_model, specs="belong", adjust="tukey"))## contrast estimate SE df t.ratio p.value
## belong1.3 - belong1.4 1.183333 1.090 3127 1.086 1.0000
## belong1.3 - belong1.5 0.630952 1.044 3127 0.604 1.0000
## belong1.3 - belong1.6 -0.166667 1.030 3127 -0.162 1.0000
## belong1.3 - belong1.7 0.961111 0.981 3127 0.980 1.0000
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##
## P value adjustment: tukey method for comparing a family of 38 estimates10 Run Posthoc Tests (Two-Way)
Only run posthocs if the test is significant! E.g., only run the posthoc tests on gender if there is a main effect for gender.
# Write Up Results
## One-Way ANOVA
To test our hypothesis that there would be a significant effect of belong on swb, we used a one-way ANOVA. Our data was unbalanced, with many more white people participating in our survey (*n* = 2021) than men (*n* = 847). This significantly reduces the power of our test and increases the chances of a Type II error. We also identified and removed a single outlier following visual analysis of a Residuals vs Leverage plot. A significant Levene's test (*p* = .001) also indicates that our data violates the assumption of homogeneity of variance. This suggests that there is an increased chance of Type I error. We continued with our analysis for the purpose of this class.
We found a significant effect of gender, *F*(2,1246) = 27.54, *p* < .001, η~p~^2^ = .042 (large effect size; Cohen, 1988). Posthoc tests using Tukey's HSD revealed that women reported more stress than men but less stress than non-binary and other gender participants, while non-binary and other gender participants reported the highest amount of stress overall (see Figure 1 for a comparison).
## Two-Way ANOVA
To test our hypothesis that gender and race would impact stress and would interact significantly, we used a two-way/factorial ANOVA. Our data met most of the assumptions of the test, although our data was unbalanced, with many more women participating in our survey (*n* = 1004) than men (*n* = 195). We identified and removed a single outlier following visual analysis of a Residuals vs Leverage plot.
As predicted, we found a significant main effect for gender, *F*(1,1195) = 28.31, *p* < .001, η~p~^2^ .023 (small effect size; Cohen, 1988). As predicted, women reported significantly more stress than men. Contrary to our expectations, we did not find a significant main effect for race (*p* = .453).
Lastly, we found a significant interaction between gender and race (see Figure 2), *F*(1,1195) = 3.43, *p* = .064, η~p~^2^ = .003 (trivial effect size; Cohen, 1988). When comparing by race, women of color (M = 3.16, SE = .07) reported significantly more stress than men of color (M = 2.52, SE = .14; *p* < .001), as did white women (M = 2.93, SE = .03) compared to white men (M = 2.62, SE = .08; *p* < .001). When comparing by gender, women of color reported significantly more stress than white women (p = .002), while men of color and white men reported similar levels of stress (*p* = .546).
References
Cohen J. (1988). Statistical Power Analysis for the Behavioral Sciences. New York, NY: Routledge Academic.