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library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(readxl)
library(car)

## Loading required package: carData

## 
## Attaching package: 'car'

## The following object is masked from 'package:dplyr':
## 
##     recode

library(effectsize)

anova_data<-read_excel("anova_dataset.xlsx")

head(anova_data)

## # A tibble: 6 × 3
##   participant_id group       anxiety_score
##   <chr>          <chr>               <dbl>
## 1 MIN_001        Mindfulness          52.0
## 2 MIN_002        Mindfulness          46.9
## 3 MIN_003        Mindfulness          53.2
## 4 MIN_004        Mindfulness          60.2
## 5 MIN_005        Mindfulness          46.1
## 6 MIN_006        Mindfulness          46.1

anova_data$group<-as.factor(anova_data$group)

glimpse(anova_data)

## Rows: 277
## Columns: 3
## $ participant_id <chr> "MIN_001", "MIN_002", "MIN_003", "MIN_004", "MIN_005", …
## $ group          <fct> Mindfulness, Mindfulness, Mindfulness, Mindfulness, Min…
## $ anxiety_score  <dbl> 51.97, 46.89, 53.18, 60.18, 46.13, 46.13, 60.63, 54.14,…

table(anova_data$group)

## 
##     Control    Exercise Mindfulness 
##          90          95          92

by(anova_data$anxiety_score,
   anova_data$group,
   shapiro.test)

## anova_data$group: Control
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.99129, p-value = 0.8211
## 
## ------------------------------------------------------------ 
## anova_data$group: Exercise
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.98017, p-value = 0.1597
## 
## ------------------------------------------------------------ 
## anova_data$group: Mindfulness
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.98912, p-value = 0.652

leveneTest(anxiety_score~group, data = anova_data)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   2  0.8155 0.4435
##       274

anova_model<-aov(anxiety_score~group, data = anova_data)

summary(anova_model)

##              Df Sum Sq Mean Sq F value Pr(>F)    
## group         2   5119  2559.7   41.61 <2e-16 ***
## Residuals   274  16856    61.5                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

3 GRUBUN SINAV KAYGISI PUAN ORTALAMALARI ANLAMLI DÜZEYDE FARKLIDIR.FAKAT HANGİ GRUPLAR ARASINDA FARK OLDUĞUNU ANOVA TESTİNDE GÖREMEYİZ.

TukeyHSD(anova_model)

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = anxiety_score ~ group, data = anova_data)
## 
## $group
##                            diff        lwr       upr     p adj
## Exercise-Control      -4.551064  -7.269890 -1.832238 0.0002986
## Mindfulness-Control  -10.567418 -13.307724 -7.827111 0.0000000
## Mindfulness-Exercise  -6.016354  -8.719961 -3.312746 0.0000009

eta_squared(anova_model, partial = FALSE)

## # Effect Size for ANOVA (Type I)
## 
## Parameter | Eta2 |       95% CI
## -------------------------------
## group     | 0.23 | [0.16, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].

ÜÇ FARKLI MİNDFULNESS,EGZERSİZ,KONTROL GRUBUNUN ÜNİVERSİTE ÖĞRENCİLERİNİN SINAV KAYGISI ÜZERİNDEKİ ETKİSİ İNCELENMİŞTİR. TEK YÖNLÜ VARYANS ANALİZİ VE TUKEYHSD TESTİ SONUÇLARI MİNDFULNESS VE EGZERSİZ GRUPLARININ KONTROL GRUBUNA GÖRE SINAV KAYGISI ORTALAMALARININ DAHA DÜŞÜK OLDUĞUNU GÖSTERMİŞTİR.