Документ с описанием импорта данных - http://rpubs.com/Petr001/454541
Документ с описательной статистикой - http://rpubs.com/Petr001/455155
Документ с описанием различий - http://rpubs.com/Petr001/DifferencesDLT
Проверка достоверности различий - http://rpubs.com/Petr001/CriteriaDifferencesDLT
date()## [1] "Mon Jan 7 23:50:46 2019"sessionInfo()## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS 10.14.2
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] compiler_3.5.1 backports_1.1.2 magrittr_1.5 rprojroot_1.3-2
## [5] tools_3.5.1 htmltools_0.3.6 yaml_2.2.0 Rcpp_1.0.0
## [9] stringi_1.2.4 rmarkdown_1.10 knitr_1.20 stringr_1.3.1
## [13] digest_0.6.15 evaluate_0.11Загрузка данных и библиотек
library(ggplot2)
library(reshape2)
library(likert)## Loading required package: xtableload("Import.RData")
load("DecriptiveStatistics.RData")
load("Differences.RData")Minurova <- data.frame(Minurova, dfGroup)shapiro.test(Minurova$PriverzhSum)##
## Shapiro-Wilk normality test
##
## data: Minurova$PriverzhSum
## W = 0.94918, p-value = 0.00112by(Minurova$PriverzhSum, INDICES = dfGroup$Q3_DC1, FUN = shapiro.test)## dfGroup$Q3_DC1: Да, являюсь
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.93152, p-value = 0.01311
##
## --------------------------------------------------------
## dfGroup$Q3_DC1: Нет, не являюсь
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.95434, p-value = 0.04796by(Minurova$PriverzhSum, INDICES = dfGroup$Q5_DLT1, FUN = shapiro.test)## dfGroup$Q5_DLT1: Да, использую
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.93638, p-value = 0.002356
##
## --------------------------------------------------------
## dfGroup$Q5_DLT1: Нет, не использую
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.95997, p-value = 0.3281by(Minurova$PriverzhSum, INDICES = dfGroup$Group, FUN = shapiro.test)## dfGroup$Group: Не включен
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.92509, p-value = 0.1591
##
## --------------------------------------------------------
## dfGroup$Group: Пользователь
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.95428, p-value = 0.1772
##
## --------------------------------------------------------
## dfGroup$Group: Разработчик
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.92787, p-value = 0.3897
##
## --------------------------------------------------------
## dfGroup$Group: Разработчик и пользователь
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.89614, p-value = 0.004945Есть значимые отличия в некоторых подвыборках.
Проверка на равенство дисперсий
bartlett.test(PriverzhSum ~ Group, Minurova)##
## Bartlett test of homogeneity of variances
##
## data: PriverzhSum by Group
## Bartlett's K-squared = 5.6532, df = 3, p-value = 0.1298bartlett.test(PriverzhSum ~ Q3_DC1, Minurova)##
## Bartlett test of homogeneity of variances
##
## data: PriverzhSum by Q3_DC1
## Bartlett's K-squared = 0.025327, df = 1, p-value = 0.8736bartlett.test(PriverzhSum ~ Q5_DLT1, Minurova)##
## Bartlett test of homogeneity of variances
##
## data: PriverzhSum by Q5_DLT1
## Bartlett's K-squared = 0.48027, df = 1, p-value = 0.4883Сравнение по всем трём направлениям не показывает отличия в дисперсиях.
С некоторой натяжкой можно попробовать сделать Т-тест. не везде сохраняется нормальность распределения. Есть скосы. Но есть равенство дисперсий.
t.test(PriverzhSum ~ Q3_DC1, Minurova)##
## Welch Two Sample t-test
##
## data: PriverzhSum by Q3_DC1
## t = -0.41499, df = 90, p-value = 0.6791
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -8.734984 5.716289
## sample estimates:
## mean in group Да, являюсь mean in group Нет, не являюсь
## 44.62791 46.13725t.test(PriverzhSum ~ Q5_DLT1, Minurova)##
## Welch Two Sample t-test
##
## data: PriverzhSum by Q5_DLT1
## t = -0.8175, df = 48.887, p-value = 0.4176
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -11.562003 4.875531
## sample estimates:
## mean in group Да, использую mean in group Нет, не использую
## 44.41538 47.75862Нет значимых различий ни по использованию ДОТ ни по Авторству ДОТ
Посмотрим на отдельные шкалыъ
Авторство ДОТ:
t.test(Affection ~ Q3_DC1, Minurova)##
## Welch Two Sample t-test
##
## data: Affection by Q3_DC1
## t = -0.023188, df = 89.684, p-value = 0.9816
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.320178 3.243571
## sample estimates:
## mean in group Да, являюсь mean in group Нет, не являюсь
## 21.25581 21.29412t.test(Stability ~ Q3_DC1, Minurova)##
## Welch Two Sample t-test
##
## data: Stability by Q3_DC1
## t = -0.20148, df = 91.452, p-value = 0.8408
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.668803 2.177239
## sample estimates:
## mean in group Да, являюсь mean in group Нет, не являюсь
## 12.55814 12.80392t.test(Activity ~ Q3_DC1, Minurova)##
## Welch Two Sample t-test
##
## data: Activity by Q3_DC1
## t = -0.73179, df = 92.755, p-value = 0.4661
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.521734 1.625121
## sample estimates:
## mean in group Да, являюсь mean in group Нет, не являюсь
## 11.09091 12.03922Отличий нет.
Использование ДОТ:
t.test(Affection ~ Q5_DLT1, Minurova)##
## Welch Two Sample t-test
##
## data: Affection by Q5_DLT1
## t = -0.60617, df = 51.928, p-value = 0.547
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.724392 2.532350
## sample estimates:
## mean in group Да, использую mean in group Нет, не использую
## 20.93846 22.03448t.test(Stability ~ Q5_DLT1, Minurova)##
## Welch Two Sample t-test
##
## data: Stability by Q5_DLT1
## t = -0.39104, df = 47.924, p-value = 0.6975
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.352807 2.261030
## sample estimates:
## mean in group Да, использую mean in group Нет, не использую
## 12.52308 13.06897t.test(Activity ~ Q5_DLT1, Minurova)##
## Welch Two Sample t-test
##
## data: Activity by Q5_DLT1
## t = -1.0089, df = 47.031, p-value = 0.3182
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.547187 1.509569
## sample estimates:
## mean in group Да, использую mean in group Нет, не использую
## 11.13636 12.65517По второму напрвалению различий тоже нет.
Переводим в числа факторы анкеты. дипазон 1:4
df2 <- df
df2[ ,22:57] <- sapply(df[ ,22:57], as.numeric)t(aggregate(df2[22:57], by = list(df2$Q3_DC1), FUN = mean))## [,1] [,2]
## Group.1 "Да, являюсь" "Нет, не являюсь"
## Q22_Mot_pos1 "3.318182" "3.274510"
## Q23_Mot_neg2 "1.909091" "2.176471"
## Q24_Cog_pos3 "3.000000" "2.509804"
## Q25_Em_pos4 "2.954545" "2.705882"
## Q26_Beh_pos5 "3.159091" "2.803922"
## Q27_Mot_neg6 "2.090909" "2.294118"
## Q28_Cog_neg7 "2.090909" "2.686275"
## Q29_Em_neg8 "1.977273" "2.176471"
## Q30_Beh_neg9 "1.568182" "1.960784"
## Q31_Mot_pos10 "3.340909" "3.254902"
## Q32_Cog_pos11 "3.363636" "3.078431"
## Q33_Em_pos12 "3.272727" "3.019608"
## Q34_Beh_pos13 "2.954545" "2.803922"
## Q35_Mot_neg14 "2.727273" "2.666667"
## Q36_Cog_neg15 "2.568182" "2.921569"
## Q37_Em_neg16 "1.818182" "2.000000"
## Q38_Beh_neg17 "1.431818" "1.823529"
## Q39_Mot_pos18 "3.386364" "3.196078"
## Q40_Cog_pos19 "1.704545" "1.745098"
## Q41_Em_pos20 "3.250000" "3.078431"
## Q42_Beh_pos21 "3.181818" "2.784314"
## Q43_Mot_neg22 "1.409091" "1.705882"
## Q44_Cog_neg23 "3.363636" "3.509804"
## Q45_Em_neg24 "1.636364" "1.960784"
## Q46_Mot_pos25 "3.159091" "2.960784"
## Q47_Cog_pos26 "2.772727" "2.333333"
## Q48_Beh_pos27 "3.045455" "2.862745"
## Q49_Mot_neg28 "1.659091" "1.980392"
## Q50_Cog_neg29 "2.340909" "2.137255"
## Q51_Beh_neg30 "1.568182" "1.960784"
## Q52_Mot_pos31 "3.250000" "3.137255"
## Q53_Cog_pos32 "2.727273" "2.705882"
## Q54_Mot_neg33 "2.386364" "2.352941"
## Q55_Cog_neg34 "2.750000" "2.666667"
## Q56_Beh_neg35 "2.340909" "2.588235"
## Q57_Mot_pos36 "3.136364" "3.058824"for (i in 22:57) {
#m <- aggregate(df2[i], by = list(df2$Q3_DC1), FUN = mean)
w <- wilcox.test(df2[df2$Q3_DC1 == "Да, являюсь", i]
, df2[df2$Q3_DC1 == "Нет, не являюсь", i])
print(names(df[i]))
print(w)
}## [1] "Q22_Mot_pos1"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1219.5, p-value = 0.4284
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q23_Mot_neg2"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 873, p-value = 0.04751
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q24_Cog_pos3"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1486.5, p-value = 0.00322
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q25_Em_pos4"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1306.5, p-value = 0.1391
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q26_Beh_pos5"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1403.5, p-value = 0.02323
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q27_Mot_neg6"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 981.5, p-value = 0.2701
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q28_Cog_neg7"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 699, p-value = 0.0008403
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q29_Em_neg8"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 978.5, p-value = 0.252
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q30_Beh_neg9"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 826.5, p-value = 0.01641
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q31_Mot_pos10"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1173, p-value = 0.6801
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q32_Cog_pos11"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1349.5, p-value = 0.06651
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q33_Em_pos12"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1265, p-value = 0.2545
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q34_Beh_pos13"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1235.5, p-value = 0.3674
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q35_Mot_neg14"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1157, p-value = 0.781
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q36_Cog_neg15"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 873.5, p-value = 0.0516
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q37_Em_neg16"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1005, p-value = 0.3506
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q38_Beh_neg17"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 874.5, p-value = 0.04074
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q39_Mot_pos18"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1300, p-value = 0.1484
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q40_Cog_pos19"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1112, p-value = 0.9386
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q41_Em_pos20"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1272, p-value = 0.2309
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q42_Beh_pos21"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1421.5, p-value = 0.01735
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q43_Mot_neg22"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 922.5, p-value = 0.09392
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q44_Cog_neg23"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 984.5, p-value = 0.2436
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q45_Em_neg24"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 853, p-value = 0.02958
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q46_Mot_pos25"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1293.5, p-value = 0.1671
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q47_Cog_pos26"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1399.5, p-value = 0.02896
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q48_Beh_pos27"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1251, p-value = 0.3058
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q49_Mot_neg28"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 906.5, p-value = 0.08355
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q50_Cog_neg29"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1234, p-value = 0.3814
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q51_Beh_neg30"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 777.5, p-value = 0.005175
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q52_Mot_pos31"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1174.5, p-value = 0.6744
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q53_Cog_pos32"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1139.5, p-value = 0.8953
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q54_Mot_neg33"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1149.5, p-value = 0.8335
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q55_Cog_neg34"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1179, p-value = 0.6556
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q56_Beh_neg35"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 970.5, p-value = 0.2356
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q57_Mot_pos36"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q3_DC1 == "Да, являюсь", i] and df2[df2$Q3_DC1 == "Нет, не являюсь", i]
## W = 1232, p-value = 0.3788
## alternative hypothesis: true location shift is not equal to 0for (i in 22:57) {
w <- wilcox.test(df2[df2$Q3_DC1 == "Да, являюсь", i]
, df2[df2$Q3_DC1 == "Нет, не являюсь", i])
if (w$p.value <= 0.05) {
print(c(dfNames$Name[i]
, round(w$p.value, 4)
, t(aggregate(df2[i], by = list(df2$Q3_DC1), FUN = mean))
, dfNames$Descript[i]
, " "))
}
}## [1] "Q23_Mot_neg2"
## [2] "0.0475"
## [3] "Да, являюсь"
## [4] "1.909091"
## [5] "Нет, не являюсь"
## [6] "2.176471"
## [7] "[Я как преподаватель не обязан(а) использовать ДОТ]"
## [8] " "
## [1] "Q24_Cog_pos3"
## [2] "0.0032"
## [3] "Да, являюсь"
## [4] "3.000000"
## [5] "Нет, не являюсь"
## [6] "2.509804"
## [7] "[Я хорошо знаю алгоритм действий при работе с ДОТ]"
## [8] " "
## [1] "Q26_Beh_pos5"
## [2] "0.0232"
## [3] "Да, являюсь"
## [4] "3.159091"
## [5] "Нет, не являюсь"
## [6] "2.803922"
## [7] "[Я готов(а) тратить свое время на освоение ДОТ]"
## [8] " "
## [1] "Q28_Cog_neg7"
## [2] "8e-04"
## [3] "Да, являюсь"
## [4] "2.090909"
## [5] "Нет, не являюсь"
## [6] "2.686275"
## [7] "[Многое для меня является незнакомым и непонятным в дистанционных технологиях]"
## [8] " "
## [1] "Q30_Beh_neg9"
## [2] "0.0164"
## [3] "Да, являюсь"
## [4] "1.568182"
## [5] "Нет, не являюсь"
## [6] "1.960784"
## [7] "[Я не готов(а) включаться в работу по использованию ДОТ, т.к. все равно не буду чувствовать себя уверенно в этом деле]"
## [8] " "
## [1] "Q38_Beh_neg17"
## [2] "0.0407"
## [3] "Да, являюсь"
## [4] "1.431818"
## [5] "Нет, не являюсь"
## [6] "1.823529"
## [7] "[Я не готов(а) включаться в работу по использованию ДОТ, т.к. у меня все равно не получится хорошо разобраться с этими технологиями]"
## [8] " "
## [1] "Q42_Beh_pos21"
## [2] "0.0174"
## [3] "Да, являюсь"
## [4] "3.181818"
## [5] "Нет, не являюсь"
## [6] "2.784314"
## [7] "[В процессе работы с ДОТ я отмечаю у себя видимые успехи]"
## [8] " "
## [1] "Q45_Em_neg24"
## [2] "0.0296"
## [3] "Да, являюсь"
## [4] "1.636364"
## [5] "Нет, не являюсь"
## [6] "1.960784"
## [7] "[Как правило, я испытываю негативные эмоции (страх, тревогу), когда вынужден(а) разбираться с дистанционными технологиями]"
## [8] " "
## [1] "Q47_Cog_pos26"
## [2] "0.029"
## [3] "Да, являюсь"
## [4] "2.772727"
## [5] "Нет, не являюсь"
## [6] "2.333333"
## [7] "[Я знаю, как можно справиться с негативными последствиями при использовании ДОТ]"
## [8] " "
## [1] "Q51_Beh_neg30"
## [2] "0.0052"
## [3] "Да, являюсь"
## [4] "1.568182"
## [5] "Нет, не являюсь"
## [6] "1.960784"
## [7] "[Процесс работы с ДОТ сопровождается низкой результативность, у меня мало что получается]"
## [8] " "t(aggregate(df2[22:57], by = list(df2$Q5_DLT1), FUN = mean))## [,1] [,2]
## Group.1 "Да, использую" "Нет, не использую"
## Q22_Mot_pos1 "3.318182" "3.241379"
## Q23_Mot_neg2 "2.045455" "2.068966"
## Q24_Cog_pos3 "2.803030" "2.586207"
## Q25_Em_pos4 "2.893939" "2.655172"
## Q26_Beh_pos5 "3.060606" "2.758621"
## Q27_Mot_neg6 "2.075758" "2.482759"
## Q28_Cog_neg7 "2.393939" "2.448276"
## Q29_Em_neg8 "1.954545" "2.379310"
## Q30_Beh_neg9 "1.666667" "2.034483"
## Q31_Mot_pos10 "3.272727" "3.344828"
## Q32_Cog_pos11 "3.257576" "3.103448"
## Q33_Em_pos12 "3.151515" "3.103448"
## Q34_Beh_pos13 "2.939394" "2.724138"
## Q35_Mot_neg14 "2.742424" "2.586207"
## Q36_Cog_neg15 "2.787879" "2.689655"
## Q37_Em_neg16 "1.803030" "2.172414"
## Q38_Beh_neg17 "1.545455" "1.862069"
## Q39_Mot_pos18 "3.333333" "3.172414"
## Q40_Cog_pos19 "1.772727" "1.620690"
## Q41_Em_pos20 "3.227273" "3.000000"
## Q42_Beh_pos21 "3.060606" "2.758621"
## Q43_Mot_neg22 "1.469697" "1.793103"
## Q44_Cog_neg23 "3.484848" "3.344828"
## Q45_Em_neg24 "1.787879" "1.862069"
## Q46_Mot_pos25 "3.106061" "2.931034"
## Q47_Cog_pos26 "2.651515" "2.275862"
## Q48_Beh_pos27 "2.969697" "2.896552"
## Q49_Mot_neg28 "1.787879" "1.931034"
## Q50_Cog_neg29 "2.181818" "2.344828"
## Q51_Beh_neg30 "1.727273" "1.896552"
## Q52_Mot_pos31 "3.227273" "3.103448"
## Q53_Cog_pos32 "2.757576" "2.620690"
## Q54_Mot_neg33 "2.424242" "2.241379"
## Q55_Cog_neg34 "2.742424" "2.620690"
## Q56_Beh_neg35 "2.454545" "2.517241"
## Q57_Mot_pos36 "3.227273" "2.793103"for (i in 22:57) {
#m <- aggregate(df2[i], by = list(df2$Q5_DLT1), FUN = mean)
w <- wilcox.test(df2[df2$Q5_DLT1 == "Да, использую", i]
, df2[df2$Q5_DLT1 == "Нет, не использую", i])
print(names(df[i]))
print(w)
}## [1] "Q22_Mot_pos1"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1017, p-value = 0.5989
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q23_Mot_neg2"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 951, p-value = 0.9621
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q24_Cog_pos3"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1054, p-value = 0.3978
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q25_Em_pos4"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1100, p-value = 0.2149
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q26_Beh_pos5"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1112, p-value = 0.1766
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q27_Mot_neg6"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 731, p-value = 0.05443
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q28_Cog_neg7"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 945.5, p-value = 0.925
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q29_Em_neg8"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 714.5, p-value = 0.03582
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q30_Beh_neg9"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 777, p-value = 0.1139
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q31_Mot_pos10"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 916.5, p-value = 0.7236
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q32_Cog_pos11"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1054, p-value = 0.3983
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q33_Em_pos12"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 994, p-value = 0.752
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q34_Beh_pos13"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1044, p-value = 0.455
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q35_Mot_neg14"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1056, p-value = 0.39
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q36_Cog_neg15"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 994, p-value = 0.7564
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q37_Em_neg16"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 773.5, p-value = 0.1123
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q38_Beh_neg17"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 793.5, p-value = 0.1437
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q39_Mot_pos18"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1057, p-value = 0.3803
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q40_Cog_pos19"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1075.5, p-value = 0.3
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q41_Em_pos20"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1046.5, p-value = 0.4399
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q42_Beh_pos21"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1115, p-value = 0.1748
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q43_Mot_neg22"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 822, p-value = 0.2202
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q44_Cog_neg23"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1081, p-value = 0.2551
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q45_Em_neg24"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 932.5, p-value = 0.8332
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q46_Mot_pos25"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1028, p-value = 0.5374
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q47_Cog_pos26"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1155.5, p-value = 0.09095
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q48_Beh_pos27"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 945.5, p-value = 0.9244
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q49_Mot_neg28"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 922, p-value = 0.7637
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q50_Cog_neg29"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 903.5, p-value = 0.6523
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q51_Beh_neg30"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 870, p-value = 0.4465
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q52_Mot_pos31"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1001.5, p-value = 0.7003
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q53_Cog_pos32"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1020, p-value = 0.6004
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q54_Mot_neg33"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1059.5, p-value = 0.3898
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q55_Cog_neg34"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1033, p-value = 0.5188
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q56_Beh_neg35"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 936, p-value = 0.8616
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "Q57_Mot_pos36"
##
## Wilcoxon rank sum test with continuity correction
##
## data: df2[df2$Q5_DLT1 == "Да, использую", i] and df2[df2$Q5_DLT1 == "Нет, не использую", i]
## W = 1169.5, p-value = 0.06501
## alternative hypothesis: true location shift is not equal to 0for (i in 22:57) {
#m <- aggregate(df2[i], by = list(df2$Q5_DLT1), FUN = mean)
w <- wilcox.test(df2[df2$Q5_DLT1 == "Да, использую", i]
, df2[df2$Q5_DLT1 == "Нет, не использую", i])
if (w$p.value <= 0.05) {
print(c(dfNames$Name[i]
, round(w$p.value, 4)
, t(aggregate(df2[i], by = list(df2$Q5_DLT1), FUN = mean))
, dfNames$Descript[i]
, " "))
}
}## [1] "Q29_Em_neg8"
## [2] "0.0358"
## [3] "Да, использую"
## [4] "1.954545"
## [5] "Нет, не использую"
## [6] "2.379310"
## [7] "[В основном я чувствую неудовлетворенность в процессе работы с ДОТ]"
## [8] " "Используем критерий Хи-квадрат. так как у нас номинальная шкала.
Fobia_DC_t <- list()
for (i in 60:65) {
t <- table(df[[i]], df$Q3_DC1)
chi <- chisq.test(t)
print(names(df[i]))
print(t)
print(chi)
}## [1] "Q60_Att1"
##
## Да, являюсь Нет, не являюсь
## Да 5 11
## Нет 33 34
## Трудно сказать 6 6
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 1.7587, df = 2, p-value = 0.4151
##
## [1] "Q61_Att2"
##
## Да, являюсь Нет, не являюсь
## Да 19 17
## Нет 18 25
## Трудно сказать 7 9
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 0.99023, df = 2, p-value = 0.6095## Warning in chisq.test(t): Chi-squared approximation may be incorrect## [1] "Q62_Att3"
##
## Да, являюсь Нет, не являюсь
## Да 12 19
## Нет 30 25
## Трудно сказать 2 7
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 4.3206, df = 2, p-value = 0.1153
##
## [1] "Q63_Att4"
##
## Да, являюсь Нет, не являюсь
## Да 3 10
## Нет 30 34
## Трудно сказать 11 7
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 4.4163, df = 2, p-value = 0.1099
##
## [1] "Q64_Att5"
##
## Да, являюсь Нет, не являюсь
## Да 9 11
## Нет 28 26
## Трудно сказать 7 14
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 2.103, df = 2, p-value = 0.3494
##
## [1] "Q65_Att6"
##
## Да, являюсь Нет, не являюсь
## Да 20 26
## Нет 17 19
## Трудно сказать 7 6
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 0.45734, df = 2, p-value = 0.7956(так как в некоторых ячейках меньше пяти и хи-квадрат может давать ошибку)
Fobia_DC_t <- list()
for (i in 60:65) {
t <- table(df[[i]], df$Q3_DC1)
fi <- fisher.test(t)
print(names(df[i]))
print(t)
print(chi)
}## [1] "Q60_Att1"
##
## Да, являюсь Нет, не являюсь
## Да 5 11
## Нет 33 34
## Трудно сказать 6 6
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 0.45734, df = 2, p-value = 0.7956
##
## [1] "Q61_Att2"
##
## Да, являюсь Нет, не являюсь
## Да 19 17
## Нет 18 25
## Трудно сказать 7 9
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 0.45734, df = 2, p-value = 0.7956
##
## [1] "Q62_Att3"
##
## Да, являюсь Нет, не являюсь
## Да 12 19
## Нет 30 25
## Трудно сказать 2 7
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 0.45734, df = 2, p-value = 0.7956
##
## [1] "Q63_Att4"
##
## Да, являюсь Нет, не являюсь
## Да 3 10
## Нет 30 34
## Трудно сказать 11 7
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 0.45734, df = 2, p-value = 0.7956
##
## [1] "Q64_Att5"
##
## Да, являюсь Нет, не являюсь
## Да 9 11
## Нет 28 26
## Трудно сказать 7 14
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 0.45734, df = 2, p-value = 0.7956
##
## [1] "Q65_Att6"
##
## Да, являюсь Нет, не являюсь
## Да 20 26
## Нет 17 19
## Трудно сказать 7 6
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 0.45734, df = 2, p-value = 0.7956Fobia_DC_t <- list()
for (i in 60:65) {
t <- table(df[[i]], df$Q5_DLT1)
chi <- chisq.test(t)
print(names(df[i]))
print(t)
print(chi)
}## Warning in chisq.test(t): Chi-squared approximation may be incorrect## [1] "Q60_Att1"
##
## Да, использую Нет, не использую
## Да 12 4
## Нет 47 20
## Трудно сказать 7 5
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 0.94706, df = 2, p-value = 0.6228## Warning in chisq.test(t): Chi-squared approximation may be incorrect## [1] "Q61_Att2"
##
## Да, использую Нет, не использую
## Да 26 10
## Нет 27 16
## Трудно сказать 13 3
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 2.0801, df = 2, p-value = 0.3534## Warning in chisq.test(t): Chi-squared approximation may be incorrect## [1] "Q62_Att3"
##
## Да, использую Нет, не использую
## Да 24 7
## Нет 34 21
## Трудно сказать 8 1
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 4.0424, df = 2, p-value = 0.1325## Warning in chisq.test(t): Chi-squared approximation may be incorrect## [1] "Q63_Att4"
##
## Да, использую Нет, не использую
## Да 8 5
## Нет 45 19
## Трудно сказать 13 5
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 0.47133, df = 2, p-value = 0.79
##
## [1] "Q64_Att5"
##
## Да, использую Нет, не использую
## Да 14 6
## Нет 37 17
## Трудно сказать 15 6
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 0.063684, df = 2, p-value = 0.9687## Warning in chisq.test(t): Chi-squared approximation may be incorrect## [1] "Q65_Att6"
##
## Да, использую Нет, не использую
## Да 34 12
## Нет 21 15
## Трудно сказать 11 2
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 3.9396, df = 2, p-value = 0.1395(так как в некоторых ячейках меньше пяти и хи-квадрат может давать ошибку)
Fobia_DC_t <- list()
for (i in 60:65) {
t <- table(df[[i]], df$Q5_DLT1)
fi <- fisher.test(t)
print(names(df[i]))
print(t)
print(chi)
}## [1] "Q60_Att1"
##
## Да, использую Нет, не использую
## Да 12 4
## Нет 47 20
## Трудно сказать 7 5
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 3.9396, df = 2, p-value = 0.1395
##
## [1] "Q61_Att2"
##
## Да, использую Нет, не использую
## Да 26 10
## Нет 27 16
## Трудно сказать 13 3
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 3.9396, df = 2, p-value = 0.1395
##
## [1] "Q62_Att3"
##
## Да, использую Нет, не использую
## Да 24 7
## Нет 34 21
## Трудно сказать 8 1
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 3.9396, df = 2, p-value = 0.1395
##
## [1] "Q63_Att4"
##
## Да, использую Нет, не использую
## Да 8 5
## Нет 45 19
## Трудно сказать 13 5
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 3.9396, df = 2, p-value = 0.1395
##
## [1] "Q64_Att5"
##
## Да, использую Нет, не использую
## Да 14 6
## Нет 37 17
## Трудно сказать 15 6
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 3.9396, df = 2, p-value = 0.1395
##
## [1] "Q65_Att6"
##
## Да, использую Нет, не использую
## Да 34 12
## Нет 21 15
## Трудно сказать 11 2
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 3.9396, df = 2, p-value = 0.1395