Analysis
Daniel Ebbert
2017-08-09 10:02:01
Introduction
In the following the analysis of the data from the study “Patterns in the appropriaton of a learning management system by instructors based on Q-methodology” is being done.
Dataset
At first the data set has to be read in. The dataset (Ebbert 2017) is added as a submodule in the data/ folder.
Pre-sorting questionnaire data
Import the questionnaire data, convert the scales to factors and add the participant names as the first column.
# import questionnaire
pre.files <- dir("data/questionnaire", pattern = "\\.csv", full.names = TRUE)
pre.tables <- lapply(pre.files, read_csv)
pre.question <- do.call(rbind, pre.tables)
# add levels
cols <- c(9:32)
pre.question %<>% mutate_at(cols, funs(factor(., levels = c(1:5), labels = c("Strongly Disagree",
"Disagree", "Undecided", "Agree", "Strongly Agree"))))
pset.question <- str_sub(dir("data/questionnaire", pattern = "\\.csv", full.names = FALSE),
1, str_length(dir("data/questionnaire", pattern = "\\.csv", full.names = FALSE)) -
4)
pre.question <- cbind(pset.question, pre.question)
# add colummn names
colnames.question <- read_csv("data/colnames/questionnaire.csv")
colnames(pre.question) <- colnames.question
# convert to tibble
pre.question <- as_tibble(pre.question)Course metadata
Import the course metadata and add the participant names as the first column.
# import course metadata
course.files <- dir("data/course-metadata", pattern = "\\.csv", full.names = TRUE)
course.tables <- lapply(course.files, read_csv)
course.meta <- do.call(rbind, course.tables)
pset.meta <- str_sub(dir("data/course-metadata", pattern = "\\.csv", full.names = FALSE),
1, str_length(dir("data/course-metadata", pattern = "\\.csv", full.names = FALSE)) -
4)
course.meta <- cbind(pset.meta, course.meta)
# add column names
colnames.meta <- read_csv("data/colnames/course-metadata.csv")
colnames(course.meta) <- colnames.meta
# convert to tibble
course.meta <- as_tibble(course.meta)Q-Sorts
The data about the Q-Sort consists of multiple parts, these are:
- The concourse
- The Q-set
- The distribution of the cards in the three sorting categories
- The Q-sorts
Load the concourse:
# import concourse
q.concourse <- import.q.concourse(q.concourse.dir = "data/sample/concourse/",
languages = c("english", "german"))## Gathered 46 full items, each in 2 languages.Sample and build the Q-set:
# remove items
concourse.remove <- c("external-tools", "programming-assignment", "stats", "scorm-package",
"ims-content-package")
q.concourse <- q.concourse[!rownames(q.concourse) %in% concourse.remove, ]
# create the distribution
q.distribution <- make.distribution(nrow(q.concourse))
# define the sample
q.sample <- row.names(q.concourse)
# build the q-set
q.set <- build.q.set(q.concourse, q.sample, q.distribution)## Build a q.set of 41 items.The first step in sorting the cards was to sort the cards into three categories. The following data represents how many cards were sorted into each category:
# import card distribution
cards.files <- dir("data/cards-distro", pattern = "\\.csv", full.names = TRUE)
cards.tables <- lapply(cards.files, read_csv)
cards.distro <- do.call(rbind, cards.tables)
pset.distro <- str_sub(dir("data/cards-distro", pattern = "\\.csv", full.names = FALSE),
1, str_length(dir("data/cards-distro", pattern = "\\.csv", full.names = FALSE)) -
4)
cards.distro <- cbind(pset.distro, cards.distro)
# add column names
colnames.distro <- read_csv("data/colnames/cards-distro.csv")
colnames(cards.distro) <- colnames.distro
# convert to tibble
cards.distro <- as_tibble(cards.distro)Next the actual Q-sorts are being imported:
# import q-sorts
q.sorts <- import.q.sorts(q.sorts.dir = "data/qsorts/", q.set, q.distribution)Post-sorting interview data
The post-sorting interview data is split up into two parts.
- The Feedback which is directly linked to a specific item in the Q-set
- The questions concerning missing or new items
The feedback data is imported using the provided function by the qmethod package (Zabala 2014):
# import feedback
q.feedback <- import.q.feedback(q.feedback.dir = "data/feedback/", q.sorts,
q.set)The interview data is imported from the provided .csv’s:
# import interview
interview.files <- dir("data/interview", pattern = "\\.csv", full.names = TRUE)
interview.tables <- lapply(interview.files, read_csv)
interview <- do.call(rbind, interview.tables)
pset.interview <- str_sub(dir("data/interview", pattern = "\\.csv", full.names = FALSE),
1, str_length(dir("data/interview", pattern = "\\.csv", full.names = FALSE)) -
4)
interview <- cbind(pset.interview, interview)
# add column names
colnames.interview <- read_csv("data/colnames/interview.csv")
colnames(interview) <- colnames.interview
# convert to tibble
interview <- as_tibble(interview)Participants
There were 21 participants which were on average 34.3333333 years old. 5 participants were female and 16 were male. On average the participants were teaching 4 hours per week.
Faculty
table(pre.question[4])##
## FB10 FB11 FB12 FB14 FB2 FB4 FB6 FB7 Other
## 1 2 1 1 1 7 2 3 3Position
table(pre.question[5])## < table of extent 0 >Experience with Moodle
The average experience with Moodle in years was 5.3928571 and was distributed as follows:
table(pre.question[7])##
## 0.75 1 2 3 3.5 4 5 6 7 8 9 12
## 1 2 1 2 1 1 3 3 3 1 1 2Experience with other platforms
Number of people who have experience with other platforms:
table(pre.question[8])##
## Ja Nein
## 12 9Details as to which platforms:
table(pre.question[9])##
## Als Student BSCW BSCW, Asana
## 1 1 1
## BSCW, OpenUss ein paar Jahre EWS, Blackboard
## 1 1 1
## Kenne 2-3 Plattformen StudIP StudIP, Adobe Connect
## 1 2 1
## StudIP, Ilias xLx, learnr
## 1 1Digital skills
Subsetting the scale from the main data set:
digitalskills <- as.data.frame(pre.question[10:16])Provide a summary of the scale:
likertdigital <- likert(digitalskills)
sumdigital <- summary(likertdigital)
kable(sumdigital)| Item | low | neutral | high | mean | sd | |
|---|---|---|---|---|---|---|
| 4 | I am confident in using learning management systems (e.g. Moodle, Ilias or Blackboard). | 0.00000 | 0.000000 | 100.00000 | 4.761905 | 0.4364358 |
| 1 | I am confident in using the office suite (e.g. word processing, spreadsheet or presentation software). | 0.00000 | 4.761905 | 95.23810 | 4.857143 | 0.4780914 |
| 5 | I am familiar with content management systems (e.g. WordPress, Drupal or Joomla). | 14.28571 | 4.761905 | 80.95238 | 4.095238 | 1.4108424 |
| 7 | I have the skills to use web 2.0 technologies (e.g. Wikis, Blogs or Podcasts). | 14.28571 | 9.523810 | 76.19048 | 4.238095 | 1.2611408 |
| 3 | I am familiar with audio / video editing software (e.g. Audacity or Premiere). | 19.04762 | 9.523810 | 71.42857 | 3.952381 | 1.4992061 |
| 6 | I am familiar with markup languages (e.g. HTML, XML or Markdown). | 19.04762 | 14.285714 | 66.66667 | 3.809524 | 1.3645163 |
| 2 | I am familiar with screen recording software (e.g. Camtasia). | 19.04762 | 19.047619 | 61.90476 | 3.761905 | 1.4800257 |
Plot the scale:
plot(likertdigital)
Personal innovativeness
Subset the data:
inno <- as.data.frame(pre.question[17:20])Calculate alpha:
numinno <- as.matrix(as.data.frame(lapply(inno, as.numeric)))
inno.alpha <- alpha(numinno, keys = c(1, 1, -1, 1))
inno.alpha$total$raw_alpha## [1] 0.8067744The following table shows the alpha if items were dropped:
kable(inno.alpha$alpha.drop)| raw_alpha | std.alpha | G6(smc) | average_r | S/N | alpha se | |
|---|---|---|---|---|---|---|
| If.I.heard.about.a.new.information.technology..I.would.look.for.ways.to.experiment.with.it. | 0.7199427 | 0.7290735 | 0.6426909 | 0.4728554 | 2.691038 | 0.1016790 |
| Among.my.peers..I.am.usually.the.first.to.try.out.a.new.information.technology.system. | 0.7466887 | 0.7538215 | 0.6742303 | 0.5051214 | 3.062093 | 0.0934455 |
| In.general..I.am.hesitant.to.try.out.new.information.technologies..reverse.scored..- | 0.7733200 | 0.7817501 | 0.7161510 | 0.5442049 | 3.581904 | 0.0814130 |
| I.like.to.experiment.with.new.information.technologies. | 0.7869674 | 0.7955530 | 0.7298798 | 0.5646650 | 3.891244 | 0.0801578 |
Provide a summary of the scale:
# reverse scale
inno[3] <- reverse.levels(inno[3])
# summarize scale
likertinno <- likert(inno)
suminno <- summary(likertinno)
kable(suminno)| Item | low | neutral | high | mean | sd | |
|---|---|---|---|---|---|---|
| 4 | I like to experiment with new information technologies. | 0.000000 | 23.80952 | 76.19048 | 4.047619 | 0.7400129 |
| 1 | If I heard about a new information technology, I would look for ways to experiment with it. | 4.761905 | 28.57143 | 66.66667 | 3.809524 | 0.8135753 |
| 2 | Among my peers, I am usually the first to try out a new information technology system. | 14.285714 | 23.80952 | 61.90476 | 3.761905 | 1.0442587 |
| 3 | In general, I am hesitant to try out new information technologies (reverse-scored). | 23.809524 | 19.04762 | 57.14286 | 3.428571 | 0.9783368 |
Plot the scale:
plot(likertinno)
User interface
Subset the data:
userinterface <- as.data.frame(pre.question[21:24])Calculate alpha:
numinter <- as.matrix(as.data.frame(lapply(userinterface, as.numeric)))
interface.alpha <- alpha(numinter)
interface.alpha$total$raw_alpha## [1] 0.5940928The following table shows the alpha if items were dropped:
kable(interface.alpha$alpha.drop)| raw_alpha | std.alpha | G6(smc) | average_r | S/N | alpha se | |
|---|---|---|---|---|---|---|
| The.layout.of.Moodle.is.user.friendly. | 0.4192308 | 0.4811027 | 0.4275959 | 0.2360899 | 0.9271635 | 0.2179636 |
| The.computerized.instruction.provided.by.Moodle.is.clear. | 0.7150838 | 0.7406077 | 0.6581035 | 0.4876318 | 2.8551641 | 0.0962927 |
| The.layout.of.Moodle.is.in.good.structure. | 0.5043328 | 0.5488562 | 0.4944821 | 0.2885243 | 1.2165879 | 0.1899862 |
| The.overall.user.interface.design.of.Moodle.is.satisfactory. | 0.4231518 | 0.4726807 | 0.4383429 | 0.2300554 | 0.8963841 | 0.2153336 |
Drop the second item to increase the reliability of the scale:
userinterface <- userinterface[, c(1, 3, 4)]Calculate alpha again:
numinter <- as.matrix(as.data.frame(lapply(userinterface, as.numeric)))
interface.alpha <- alpha(numinter)
interface.alpha$total$raw_alpha## [1] 0.7150838The following table shows the alpha if items were dropped:
kable(interface.alpha$alpha.drop)| raw_alpha | std.alpha | G6(smc) | average_r | S/N | alpha se | |
|---|---|---|---|---|---|---|
| The.layout.of.Moodle.is.user.friendly. | 0.6067416 | 0.6094562 | 0.4382863 | 0.4382863 | 1.560533 | 0.1702768 |
| The.layout.of.Moodle.is.in.good.structure. | 0.6467066 | 0.6831910 | 0.5188232 | 0.5188232 | 2.156476 | 0.1382140 |
| The.overall.user.interface.design.of.Moodle.is.satisfactory. | 0.6143791 | 0.6717899 | 0.5057859 | 0.5057859 | 2.046829 | 0.1419971 |
Provide a summary of the scale:
likertinterface <- likert(userinterface)
suminterface <- summary(likertinterface)
kable(suminterface)| Item | low | neutral | high | mean | sd | |
|---|---|---|---|---|---|---|
| 3 | The overall user interface design of Moodle is satisfactory. | 0.00000 | 9.52381 | 90.47619 | 4.142857 | 0.5732115 |
| 2 | The layout of Moodle is in good structure. | 0.00000 | 23.80952 | 76.19048 | 3.809524 | 0.5117663 |
| 1 | The layout of Moodle is user-friendly. | 9.52381 | 33.33333 | 57.14286 | 3.619048 | 0.8646497 |
Plot the scale:
plot(likertinterface)
Perceived usefulness
Subset the data:
useful <- as.data.frame(pre.question[25:28])Calculate alpha:
numuseful <- as.matrix(as.data.frame(lapply(useful, as.numeric)))
useful.alpha <- alpha(numuseful)
useful.alpha$total$raw_alpha## [1] 0.8440257The following table shows the alpha if items were dropped:
kable(useful.alpha$alpha.drop)| raw_alpha | std.alpha | G6(smc) | average_r | S/N | alpha se | |
|---|---|---|---|---|---|---|
| Using.Moodle.increases.the.quality.of.my.educational.work. | 0.8437500 | 0.8570708 | 0.8169941 | 0.6665359 | 5.996470 | 0.0506427 |
| Using.Moodle.helps.me.in.being.more.productive.in.my.educational.work. | 0.7615385 | 0.7847543 | 0.7215222 | 0.5485907 | 3.645853 | 0.0833430 |
| Using.Moodle.makes.me.a.more.productive.teacher. | 0.7852853 | 0.7860121 | 0.7113940 | 0.5504379 | 3.673161 | 0.0804806 |
| Overall..Moodle.is.useful.in.performing.my.educational.work. | 0.8075356 | 0.8206557 | 0.7854382 | 0.6040057 | 4.575866 | 0.0688310 |
Provide a summary of the scale:
likertuseful <- likert(useful)
sumuseful <- summary(likertuseful)
kable(sumuseful)| Item | low | neutral | high | mean | sd | |
|---|---|---|---|---|---|---|
| 4 | Overall, Moodle is useful in performing my educational work. | 4.761905 | 4.761905 | 90.47619 | 4.571429 | 0.8106435 |
| 1 | Using Moodle increases the quality of my educational work. | 4.761905 | 9.523810 | 85.71429 | 4.476191 | 0.8728716 |
| 2 | Using Moodle helps me in being more productive in my educational work. | 4.761905 | 14.285714 | 80.95238 | 4.380952 | 0.9206623 |
| 3 | Using Moodle makes me a more productive teacher. | 19.047619 | 14.285714 | 66.66667 | 3.857143 | 1.2761549 |
Plot the scale:
plot(likertuseful)
User satisfaction
Subset the data:
satisfaction <- as.data.frame(pre.question[29:33])Calculate alpha:
satnum <- as.matrix(as.data.frame(lapply(satisfaction, as.numeric)))
sat.alpha <- alpha(satnum)
sat.alpha$total$raw_alpha## [1] 0.7857811kable(sat.alpha$alpha.drop)| raw_alpha | std.alpha | G6(smc) | average_r | S/N | alpha se | |
|---|---|---|---|---|---|---|
| I.am.satisfied.with.the.experience.of.using.Moodle. | 0.6999022 | 0.7221170 | 0.7446454 | 0.3938142 | 2.598637 | 0.1106287 |
| I.think.that.I.did.the.right.thing.when.I.decided.to.use.Moodle. | 0.6959064 | 0.7157577 | 0.7041055 | 0.3863266 | 2.518125 | 0.1110976 |
| I.am.satisfied.with.my.decision.to.use.Moodle. | 0.7119741 | 0.7278457 | 0.7049565 | 0.4006939 | 2.674386 | 0.1051142 |
| I.am.very.satisfied.with.the.services.provided.by.Moodle. | 0.7810753 | 0.8070424 | 0.8148363 | 0.5111509 | 4.182485 | 0.0838548 |
| My.decision.to.use.Moodle.was.a.wise.one. | 0.8301887 | 0.8334287 | 0.8482796 | 0.5557251 | 5.003436 | 0.0634308 |
Provide a summary of the scale:
likertsatisfaction <- likert(satisfaction)
sumsatisfaction <- summary(likertsatisfaction)
kable(sumsatisfaction)| Item | low | neutral | high | mean | sd | |
|---|---|---|---|---|---|---|
| 2 | I think that I did the right thing when I decided to use Moodle. | 0 | 4.761905 | 95.23810 | 4.666667 | 0.5773503 |
| 3 | I am satisfied with my decision to use Moodle. | 0 | 4.761905 | 95.23810 | 4.761905 | 0.5389584 |
| 4 | I am very satisfied with the services provided by Moodle. | 0 | 9.523810 | 90.47619 | 4.190476 | 0.6015852 |
| 1 | I am satisfied with the experience of using Moodle. | 0 | 14.285714 | 85.71429 | 4.047619 | 0.5895923 |
| 5 | My decision to use Moodle was a wise one. | 0 | 14.285714 | 85.71429 | 4.428571 | 0.7464200 |
Plot the scale:
plot(likertsatisfaction)
Scales summary
The following table shows a summary of the scales:
| Scale | Mean | Alpha |
|---|---|---|
| Personal innovativeness | 3.7619048 | 0.8067744 |
| User interface | 3.8571429 | 0.7150838 |
| Perceived usefulness | 4.3214286 | 0.8440257 |
| User satisfaction | 4.4190476 | 0.7857811 |
Course metadata
The average course size was 106.3333333 and the median was at 25. The smallest course consisted of 12 students and the biggest of 700 students. The following boxplot shows the course size:
boxplot(course.meta[2])
The performance assessment was distributed as follows:
table(course.meta[3])##
## Assignment Assignment and exam Exam
## 12 2 4
## Exercises and exam
## 3The type of course was distributed as follows:
table(course.meta[4])##
## Blended Learning Workshop Lecture
## 1 5
## Lecture and seminar Online
## 3 1
## Seminar
## 11The sort of course management was distributed as follows:
table(course.meta[5])##
## Alone Team
## 15 6Distribution
On average 21.047619 were sorted as unimportant, 6.2380952 were sorted as neurtal and 13.7142857 were sorted as important.
The following plot shows the distrubution of the number of cards per category:
melted.cards <- melt(cards.distro)## Using Participant as id variablesggplot(melted.cards, aes(x = variable, y = value)) + geom_boxplot()
Correlation
The first step in analysing the Q-data data is to create a correlation matrix:
cor <- cor(q.sorts)
kable(cor)| Acineth_26bfa05e | Alicia_10c3d34f | Amber_e0ddb543 | An_9538d924 | Beatris_92453cbf | Calvin_a2d97424 | Daniel_126b50df | Derrick_72ff92ad | Devin_e3d1b473 | Donovan_f640ccfa | Edna_fb09399c | Gwendolyn_a896d60a | Kendra_287fb81 | Kevin_e2cfea5d | Kyle_15f6c5fe | Nigel_75f54d25 | Paolo_192c118d | Saami_8dfa5a4a | Shaaheen_2dbbe518 | Sharaf_65e09f0d | Tyler_eb15d206 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Acineth_26bfa05e | 1.0000000 | -0.1272727 | 0.1045455 | 0.0045455 | 0.3090909 | 0.0500000 | -0.0409091 | -0.0454545 | 0.1590909 | 0.0636364 | 0.3454545 | -0.1045455 | -0.1909091 | 0.2545455 | 0.3227273 | 0.2136364 | 0.2000000 | 0.3454545 | -0.3590909 | 0.3181818 | 0.2363636 |
| Alicia_10c3d34f | -0.1272727 | 1.0000000 | -0.0909091 | 0.3727273 | 0.2318182 | 0.2318182 | 0.3545455 | 0.3500000 | 0.4272727 | 0.3590909 | -0.0318182 | 0.4772727 | 0.0681818 | -0.0181818 | -0.0772727 | 0.2272727 | 0.2500000 | 0.0590909 | 0.2090909 | 0.0909091 | 0.2863636 |
| Amber_e0ddb543 | 0.1045455 | -0.0909091 | 1.0000000 | 0.3181818 | 0.2272727 | 0.3181818 | 0.0318182 | -0.2500000 | 0.4000000 | 0.1681818 | 0.2409091 | 0.1954545 | 0.3363636 | 0.6045455 | 0.5136364 | 0.4318182 | 0.5045455 | 0.5000000 | 0.2954545 | 0.4090909 | 0.2363636 |
| An_9538d924 | 0.0045455 | 0.3727273 | 0.3181818 | 1.0000000 | 0.3772727 | 0.5545455 | 0.4227273 | 0.3136364 | 0.5863636 | 0.6727273 | 0.1363636 | 0.2636364 | 0.3000000 | 0.6636364 | 0.5545455 | 0.6772727 | 0.6954545 | 0.5000000 | 0.1954545 | 0.3045455 | 0.6454545 |
| Beatris_92453cbf | 0.3090909 | 0.2318182 | 0.2272727 | 0.3772727 | 1.0000000 | 0.2045455 | 0.4727273 | 0.2409091 | 0.3318182 | 0.1545455 | 0.2863636 | -0.0363636 | -0.0636364 | 0.2000000 | 0.3227273 | 0.6000000 | 0.4545455 | 0.1909091 | -0.0500000 | 0.2227273 | 0.6045455 |
| Calvin_a2d97424 | 0.0500000 | 0.2318182 | 0.3181818 | 0.5545455 | 0.2045455 | 1.0000000 | 0.1681818 | 0.0909091 | 0.5272727 | 0.5272727 | 0.3227273 | 0.5136364 | 0.2045455 | 0.5545455 | 0.4136364 | 0.2954545 | 0.5454545 | 0.4454545 | 0.2590909 | 0.4636364 | 0.3863636 |
| Daniel_126b50df | -0.0409091 | 0.3545455 | 0.0318182 | 0.4227273 | 0.4727273 | 0.1681818 | 1.0000000 | 0.5818182 | 0.2454545 | 0.2227273 | -0.0727273 | 0.3181818 | 0.1636364 | 0.0909091 | 0.1863636 | 0.4227273 | 0.4136364 | 0.0318182 | 0.0818182 | 0.1227273 | 0.6272727 |
| Derrick_72ff92ad | -0.0454545 | 0.3500000 | -0.2500000 | 0.3136364 | 0.2409091 | 0.0909091 | 0.5818182 | 1.0000000 | 0.0454545 | 0.1636364 | -0.0318182 | 0.0000000 | -0.1545455 | -0.0772727 | -0.0318182 | 0.3909091 | 0.2181818 | -0.0545455 | -0.0954545 | -0.0409091 | 0.3181818 |
| Devin_e3d1b473 | 0.1590909 | 0.4272727 | 0.4000000 | 0.5863636 | 0.3318182 | 0.5272727 | 0.2454545 | 0.0454545 | 1.0000000 | 0.6363636 | 0.4681818 | 0.3909091 | 0.3272727 | 0.6181818 | 0.4318182 | 0.4409091 | 0.5818182 | 0.5363636 | 0.2545455 | 0.3363636 | 0.4909091 |
| Donovan_f640ccfa | 0.0636364 | 0.3590909 | 0.1681818 | 0.6727273 | 0.1545455 | 0.5272727 | 0.2227273 | 0.1636364 | 0.6363636 | 1.0000000 | 0.2636364 | 0.4318182 | 0.1818182 | 0.4500000 | 0.5045455 | 0.4181818 | 0.4136364 | 0.4590909 | 0.0590909 | 0.1909091 | 0.4272727 |
| Edna_fb09399c | 0.3454545 | -0.0318182 | 0.2409091 | 0.1363636 | 0.2863636 | 0.3227273 | -0.0727273 | -0.0318182 | 0.4681818 | 0.2636364 | 1.0000000 | 0.0772727 | 0.0636364 | 0.4363636 | 0.3454545 | 0.2636364 | 0.3500000 | 0.2863636 | -0.0318182 | 0.1863636 | 0.3000000 |
| Gwendolyn_a896d60a | -0.1045455 | 0.4772727 | 0.1954545 | 0.2636364 | -0.0363636 | 0.5136364 | 0.3181818 | 0.0000000 | 0.3909091 | 0.4318182 | 0.0772727 | 1.0000000 | 0.2454545 | 0.2500000 | 0.2318182 | 0.1818182 | 0.3000000 | 0.2000000 | 0.3181818 | 0.3681818 | 0.2500000 |
| Kendra_287fb81 | -0.1909091 | 0.0681818 | 0.3363636 | 0.3000000 | -0.0636364 | 0.2045455 | 0.1636364 | -0.1545455 | 0.3272727 | 0.1818182 | 0.0636364 | 0.2454545 | 1.0000000 | 0.4181818 | 0.2454545 | 0.0954545 | 0.4045455 | 0.1954545 | 0.6727273 | 0.1545455 | 0.0181818 |
| Kevin_e2cfea5d | 0.2545455 | -0.0181818 | 0.6045455 | 0.6636364 | 0.2000000 | 0.5545455 | 0.0909091 | -0.0772727 | 0.6181818 | 0.4500000 | 0.4363636 | 0.2500000 | 0.4181818 | 1.0000000 | 0.6045455 | 0.5818182 | 0.6136364 | 0.6727273 | 0.2500000 | 0.5181818 | 0.4909091 |
| Kyle_15f6c5fe | 0.3227273 | -0.0772727 | 0.5136364 | 0.5545455 | 0.3227273 | 0.4136364 | 0.1863636 | -0.0318182 | 0.4318182 | 0.5045455 | 0.3454545 | 0.2318182 | 0.2454545 | 0.6045455 | 1.0000000 | 0.5045455 | 0.5772727 | 0.6227273 | 0.1500000 | 0.4227273 | 0.5363636 |
| Nigel_75f54d25 | 0.2136364 | 0.2272727 | 0.4318182 | 0.6772727 | 0.6000000 | 0.2954545 | 0.4227273 | 0.3909091 | 0.4409091 | 0.4181818 | 0.2636364 | 0.1818182 | 0.0954545 | 0.5818182 | 0.5045455 | 1.0000000 | 0.6272727 | 0.4318182 | 0.0090909 | 0.3227273 | 0.5772727 |
| Paolo_192c118d | 0.2000000 | 0.2500000 | 0.5045455 | 0.6954545 | 0.4545455 | 0.5454545 | 0.4136364 | 0.2181818 | 0.5818182 | 0.4136364 | 0.3500000 | 0.3000000 | 0.4045455 | 0.6136364 | 0.5772727 | 0.6272727 | 1.0000000 | 0.5500000 | 0.3363636 | 0.2954545 | 0.5863636 |
| Saami_8dfa5a4a | 0.3454545 | 0.0590909 | 0.5000000 | 0.5000000 | 0.1909091 | 0.4454545 | 0.0318182 | -0.0545455 | 0.5363636 | 0.4590909 | 0.2863636 | 0.2000000 | 0.1954545 | 0.6727273 | 0.6227273 | 0.4318182 | 0.5500000 | 1.0000000 | 0.2090909 | 0.4772727 | 0.4545455 |
| Shaaheen_2dbbe518 | -0.3590909 | 0.2090909 | 0.2954545 | 0.1954545 | -0.0500000 | 0.2590909 | 0.0818182 | -0.0954545 | 0.2545455 | 0.0590909 | -0.0318182 | 0.3181818 | 0.6727273 | 0.2500000 | 0.1500000 | 0.0090909 | 0.3363636 | 0.2090909 | 1.0000000 | 0.1545455 | 0.0227273 |
| Sharaf_65e09f0d | 0.3181818 | 0.0909091 | 0.4090909 | 0.3045455 | 0.2227273 | 0.4636364 | 0.1227273 | -0.0409091 | 0.3363636 | 0.1909091 | 0.1863636 | 0.3681818 | 0.1545455 | 0.5181818 | 0.4227273 | 0.3227273 | 0.2954545 | 0.4772727 | 0.1545455 | 1.0000000 | 0.3590909 |
| Tyler_eb15d206 | 0.2363636 | 0.2863636 | 0.2363636 | 0.6454545 | 0.6045455 | 0.3863636 | 0.6272727 | 0.3181818 | 0.4909091 | 0.4272727 | 0.3000000 | 0.2500000 | 0.0181818 | 0.4909091 | 0.5363636 | 0.5772727 | 0.5863636 | 0.4545455 | 0.0227273 | 0.3590909 | 1.0000000 |
Factors to extract
In the following the number of factors to extraxt is going to be determined. This is going to be done using the Eigenvalues (or the Kaiser-Guttman criterion), parallel analysis and non graphical versions of the scree test.
Eigenvalues (or the Kaiser-Guttman criterion)
Eigenvalues according to the Kaiser-Guttman criterion (Guttman 1954) (Henry F Kaiser 1960) (Henry F. Kaiser 1970)
ev <- eigen(cor)
ev## eigen() decomposition
## $values
## [1] 7.56540814 2.59403267 2.32612437 1.38079178 1.03426030 0.95113608
## [7] 0.69639947 0.65643710 0.64229904 0.53262802 0.47702001 0.43751446
## [13] 0.39913173 0.32966149 0.25258533 0.19567741 0.17461833 0.14683728
## [19] 0.08627190 0.06597966 0.05518545
##
## $vectors
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.09051479 0.062108120 0.47385975 0.13898414 -0.20529468
## [2,] 0.12153999 -0.313173809 -0.28936086 0.28255000 -0.09210706
## [3,] 0.20694344 0.294134441 0.03093329 -0.26515979 -0.13747523
## [4,] 0.29855611 -0.127035331 -0.07682574 -0.04724484 0.25526763
## [5,] 0.18491896 -0.251195564 0.24433363 -0.23367183 -0.14217355
## [6,] 0.24848369 0.065795511 -0.11702649 0.28520710 -0.06623035
## [7,] 0.16050921 -0.413905467 -0.10512876 -0.23700126 -0.22027454
## [8,] 0.07006503 -0.481753746 -0.01198557 -0.07593735 0.08850643
## [9,] 0.28190453 0.033614165 -0.07209837 0.20974532 0.18000939
## [10,] 0.24215686 -0.059197384 -0.08560449 0.36935770 0.36162383
## [11,] 0.15815410 0.128091577 0.23539432 0.18871028 0.23638659
## [12,] 0.16761486 -0.009505841 -0.32848803 0.36103146 -0.37672031
## [13,] 0.13489752 0.227295559 -0.36735056 -0.32012911 0.09602476
## [14,] 0.29187105 0.231053069 0.05869030 -0.05030681 0.10139162
## [15,] 0.26559354 0.143959762 0.14996162 -0.07736877 0.03742507
## [16,] 0.26722375 -0.167838651 0.14843468 -0.20296011 0.06232467
## [17,] 0.30179948 -0.017952896 -0.02504780 -0.20173032 0.08398226
## [18,] 0.25740909 0.200601227 0.11249454 0.05869144 0.01447392
## [19,] 0.10765578 0.187129957 -0.45371566 -0.26809717 -0.07543001
## [20,] 0.20059797 0.147897425 0.06971505 0.11080011 -0.61369104
## [21,] 0.26970079 -0.228143668 0.13237951 -0.05972614 -0.09501812
## [,6] [,7] [,8] [,9] [,10]
## [1,] 0.134770279 0.01572267 0.550383066 -0.059459897 -0.17427506
## [2,] 0.246123429 -0.46752083 0.146142705 -0.190105134 -0.01366246
## [3,] -0.003079246 -0.29521415 -0.393038591 0.054355774 -0.27425488
## [4,] -0.281539679 -0.09413507 -0.025016714 -0.082287214 0.13290591
## [5,] 0.356716160 -0.23446850 -0.159737054 0.121106205 0.32700281
## [6,] -0.049040985 0.34428174 -0.339361222 -0.175994916 0.37646896
## [7,] -0.001968104 0.24524041 0.109719893 0.310540011 -0.12654844
## [8,] -0.124881506 0.34465716 0.050183676 -0.466560668 -0.20825491
## [9,] 0.287914415 -0.20852968 0.088633418 -0.006673788 -0.03607733
## [10,] -0.209513826 -0.06244619 0.080938555 0.250366270 -0.06902999
## [11,] 0.591982839 0.37225630 -0.180248968 -0.030304644 -0.12759430
## [12,] 0.008092949 0.08817946 -0.118697142 0.305634337 -0.33767792
## [13,] 0.161475833 0.17012100 0.355326753 0.051268197 -0.20707842
## [14,] -0.105351921 0.07367612 -0.050850295 -0.186029200 -0.13149102
## [15,] -0.229903488 0.11601784 0.108488239 0.390051540 0.05422877
## [16,] -0.096146604 -0.20367085 -0.230184595 -0.164484693 -0.39057759
## [17,] 0.113746755 0.07578187 0.041666321 -0.074893288 0.02015264
## [18,] -0.221834429 -0.17489004 0.291816603 -0.178023404 0.19953291
## [19,] 0.197606663 0.03444808 0.147635424 -0.152847597 0.27942852
## [20,] -0.137328934 0.07699148 0.008083827 -0.292156113 0.01714401
## [21,] -0.010286037 0.07898025 0.045156243 0.267087602 0.31286961
## [,11] [,12] [,13] [,14] [,15]
## [1,] -0.11682898 0.363576690 -0.10909410 0.0005625275 -0.0190778175
## [2,] -0.07533470 -0.004429913 0.06830610 0.1654161066 0.3646927075
## [3,] -0.21153783 -0.086635728 -0.18498390 -0.3712471495 0.2676840527
## [4,] 0.23181572 0.177778077 -0.02545786 0.2077473584 0.2324173788
## [5,] 0.09307088 0.253683542 0.25013724 -0.1411086696 -0.2881326556
## [6,] -0.08967148 0.403855777 -0.16022247 -0.1688791976 -0.0821643913
## [7,] 0.08864736 -0.109420560 -0.26411600 -0.2435241596 -0.1045669902
## [8,] -0.21837111 -0.164330550 0.13687165 -0.2653880357 0.0731160359
## [9,] 0.20390832 -0.174279195 -0.21353835 -0.3852361065 -0.0219141742
## [10,] 0.07619849 0.087743058 0.25749865 -0.2914694440 -0.1509009055
## [11,] -0.01788908 -0.277608084 0.17829858 0.1283211986 0.0898582485
## [12,] -0.21476894 0.026495030 -0.02197317 0.2707695479 -0.2389951667
## [13,] 0.30265432 0.223493788 0.06129471 -0.0711692640 0.0001702197
## [14,] 0.32400758 -0.071075385 -0.22384118 0.2833624779 -0.1127258892
## [15,] -0.25059851 -0.021803535 0.46497839 -0.0077277270 0.3510065188
## [16,] 0.00609502 0.077105561 0.24606761 0.2650556168 -0.3577342625
## [17,] -0.38019679 0.288662562 -0.27561370 0.1652568978 0.2266690813
## [18,] -0.32088145 -0.361708163 -0.13112149 -0.0667689512 -0.3785784074
## [19,] -0.21387521 -0.145574619 0.28334978 0.0692477567 -0.1907061070
## [20,] 0.38037901 -0.100571230 0.28452959 -0.1266229687 0.1910704477
## [21,] 0.13692440 -0.373526146 -0.20649749 0.2864842526 0.1224858786
## [,16] [,17] [,18] [,19] [,20]
## [1,] -0.33168105 -0.20805372 -0.06498474 0.048064637 0.02970224
## [2,] 0.09360546 -0.23233909 0.25859762 0.027861592 0.19493647
## [3,] -0.08973518 -0.36807036 -0.06371543 -0.021727538 -0.06830346
## [4,] -0.01720870 -0.02670553 -0.04498174 0.447222853 -0.26433420
## [5,] 0.09777647 -0.04142167 0.09896592 0.011981588 -0.39829742
## [6,] -0.05198258 -0.12453093 0.26100695 -0.128202305 0.28974223
## [7,] 0.05214051 -0.03496179 0.06709576 0.470277290 0.32030658
## [8,] -0.12510138 -0.06028995 0.07537678 -0.197185091 -0.34383662
## [9,] -0.27884427 0.56512689 0.08218048 -0.111139175 -0.01371900
## [10,] 0.03738403 -0.28202213 -0.45956464 -0.045696858 0.04472535
## [11,] 0.20375397 -0.14280869 -0.03462826 0.241979788 0.05144815
## [12,] -0.01465753 0.08564418 0.03064892 -0.019732051 -0.39858104
## [13,] 0.32774484 -0.16276197 0.20281095 -0.327577091 -0.04379378
## [14,] -0.27020508 -0.12435177 0.20691640 0.102328831 -0.17887263
## [15,] -0.12987168 0.22356402 0.39792403 0.004667759 0.05740116
## [16,] -0.02602844 0.13760932 0.00642559 -0.163647406 0.45636817
## [17,] 0.26930107 0.35369111 -0.41843687 -0.051290065 -0.01924902
## [18,] 0.43273868 -0.06061384 0.18302496 0.094488642 -0.04004369
## [19,] -0.45937429 -0.07892707 -0.25235487 0.165725991 0.09202390
## [20,] 0.20057611 0.16564069 -0.27034491 0.033608467 0.04134734
## [21,] -0.11113322 -0.21099382 -0.16413016 -0.511560140 0.04282342
## [,21]
## [1,] 0.166997384
## [2,] -0.197860844
## [3,] 0.109568626
## [4,] 0.502156774
## [5,] -0.143117940
## [6,] 0.083029874
## [7,] -0.102543447
## [8,] -0.005985963
## [9,] 0.139176463
## [10,] -0.240369039
## [11,] 0.122248164
## [12,] 0.129572739
## [13,] 0.148561925
## [14,] -0.589293147
## [15,] -0.120171748
## [16,] 0.174720319
## [17,] -0.274860738
## [18,] 0.083630340
## [19,] 0.035192681
## [20,] -0.041684684
## [21,] 0.148873739Parallel analysis
Parallel analysis according to Horn (1965)
ap <- parallel(subject = nrow(q.sorts), var = ncol(q.sorts), rep = 1000, cent = 0.05,
model = "components")
ap## $eigen
## mevpea sevpea qevpea sqevpea
## V1 2.58696375 0.19124987 2.29830553 0.012780245
## V2 2.23366809 0.14156096 2.00741688 0.009459791
## V3 1.98312282 0.11806836 1.80394209 0.007889901
## V4 1.77894740 0.10005065 1.62328471 0.006685870
## V5 1.60094214 0.08647911 1.46563261 0.005778954
## V6 1.44383417 0.07533998 1.33050102 0.005034583
## V7 1.30505960 0.06892657 1.19514990 0.004606008
## V8 1.17546176 0.06265028 1.06658393 0.004186596
## V9 1.06054611 0.05899830 0.97104295 0.003942553
## V10 0.95040513 0.05460243 0.86450514 0.003648800
## V11 0.84826924 0.05159584 0.76639455 0.003447885
## V12 0.75629971 0.05225587 0.66530295 0.003491991
## V13 0.66886377 0.05375117 0.58695189 0.003591914
## V14 0.58790237 0.05168466 0.50497310 0.003453820
## V15 0.50786862 0.04915181 0.42942837 0.003284563
## V16 0.43383465 0.04857612 0.35514118 0.003246092
## V17 0.36126863 0.04882987 0.27747668 0.003263049
## V18 0.29196050 0.05092657 0.20553104 0.003403161
## V19 0.22240541 0.05438242 0.12683708 0.003634098
## V20 0.14961165 0.05728398 0.05455441 0.003827994
## V21 0.05276448 0.07177239 -0.07641479 0.004796180
##
## $subject
## [1] 41
##
## $variables
## [1] 21
##
## $centile
## [1] 0.05
##
## attr(,"class")
## [1] "parallel"Scree test
The scree test by Cattell (1966) can be difficult to interpret, for this non-graphical versions of the scree test have been developed (Raîche et al. 2013).
nS <- nScree(x = ev$values, aparallel = ap$eigen$qevpea)
nS## noc naf nparallel nkaiser
## 1 3 1 3 5Thus, the number of factors to extrat according to the Kaiser-Gutman criterion is 5 and according to the parallel analysis is 3. For the scree test the non graphical variants of the acceleration factor and optimal cooridnates have been applied and according to the acceleration factor 1 factors should be extracted and according to the optimal coordinates 3 factors should be extracted. See the following graphical representation.
plotnScree(nS, main = "")
So the number of factors to extract is 3.
factors <- 3Analysis
The main analysis is being doing using the qmethod function (Zabala 2014). Principal components analysis is being used with varimax rotation and Pearson coefficient.
results <- qmethod(q.sorts, nfactors = factors)## Q-method analysis.
## Finished on: Wed Aug 9 10:02:11 2017
## Original data: 41 statements, 21 Q-sorts
## Forced distribution: TRUE
## Number of factors: 3
## Rotation: varimax
## Flagging: automatic
## Correlation coefficient: pearsonsummary(results)## Q-method analysis.
## Finished on: Wed Aug 9 10:02:11 2017
## Original data: 41 statements, 21 Q-sorts
## Forced distribution: TRUE
## Number of factors: 3
## Rotation: varimax
## Flagging: automatic
## Correlation coefficient: pearson
##
## Factor scores
## fsc_f1 fsc_f2 fsc_f3
## announcements 4 2 3
## appointment-choice -3 -3 1
## assignment-in-database 2 -4 2
## assignment 4 2 -1
## badges -4 4 -1
## book -3 0 -2
## calendar -1 -2 -2
## chat -5 0 -5
## choice 1 -1 0
## completion-tracking -4 2 0
## conditional-availability 0 2 0
## database 2 -2 4
## embed-page -2 0 -3
## fair-allocation 0 -2 -4
## feedback 1 0 1
## file 5 3 1
## folder 1 1 2
## forum 3 3 3
## glossary -2 1 1
## grades-in-database -3 -4 -2
## grades -1 1 -1
## group-choice 2 -1 -1
## groups 3 0 0
## label 1 1 3
## learning-diary -2 -5 1
## lecture-recordings 1 0 -2
## lesson -2 4 -1
## library-resources -1 -2 -3
## link-sciebo -1 0 -3
## literature-database -1 -1 2
## page 0 1 0
## portfolio -1 -1 4
## questionnaire 0 -1 2
## quiz 0 5 0
## rights-management-in-course 1 1 -4
## signup-database 2 -2 -1
## topic-choice 0 -3 -2
## url 2 2 1
## visibility 3 3 0
## wiki 0 -1 2
## workshop -2 -3 5
## f1 f2 f3
## Average reliability coefficient 0.80 0.80 0.80
## Number of loading Q-sorts 9.00 5.00 4.00
## Eigenvalues 6.04 3.77 2.67
## Percentage of explained variance 28.77 17.96 12.73
## Composite reliability 0.97 0.95 0.94
## Standard error of factor scores 0.16 0.22 0.24The three factors together take into account 18 of the 21 Q-sorts. Together the three factors accounted for 59.4550723 of the explained variance.
In the following plot the statements with the highest consensus are at the bottom and the ones with the highest disagreement on top.
par(lwd = 1.5, mar = c(4, 12, 0, 0) + 0.1)
plot(results, ylab = "")
abline(h = seq(from = 2, to = 41, by = 3), col = grey(0.2), lty = 2)
Statements by factor
The show the ordered statements by factor these first have to be ordered:
scores <- cbind(round(results$zsc, digits = 2), results$zsc_n)
nfactors <- ncol(results$zsc)
col.order <- as.vector(rbind(1:nfactors, (1:nfactors) + nfactors))
scores <- scores[col.order]
scores## zsc_f1 fsc_f1 zsc_f2 fsc_f2 zsc_f3 fsc_f3
## announcements 1.57 4 1.34 2 1.17 3
## appointment-choice -1.13 -3 -1.27 -3 0.61 1
## assignment-in-database 0.66 2 -1.41 -4 0.88 2
## assignment 1.72 4 1.06 2 -0.27 -1
## badges -1.73 -4 1.71 4 -0.18 -1
## book -1.34 -3 -0.01 0 -0.83 -2
## calendar -0.66 -1 -0.78 -2 -0.92 -2
## chat -1.84 -5 -0.02 0 -1.84 -5
## choice 0.29 1 -0.54 -1 -0.10 0
## completion-tracking -1.48 -4 1.04 2 0.06 0
## conditional-availability -0.22 0 1.03 2 -0.16 0
## database 0.69 2 -1.19 -2 1.55 4
## embed-page -0.79 -2 -0.16 0 -1.27 -3
## fair-allocation -0.02 0 -1.06 -2 -1.61 -4
## feedback 0.23 1 -0.13 0 0.39 1
## file 2.53 5 1.40 3 0.46 1
## folder 0.50 1 0.35 1 1.12 2
## forum 1.53 3 1.34 3 1.22 3
## glossary -0.76 -2 0.32 1 0.46 1
## grades-in-database -1.06 -3 -1.52 -4 -1.22 -2
## grades -0.34 -1 0.79 1 -0.58 -1
## group-choice 0.70 2 -0.42 -1 -0.62 -1
## groups 1.46 3 -0.20 0 -0.13 0
## label 0.60 1 0.55 1 1.18 3
## learning-diary -0.86 -2 -1.66 -5 0.28 1
## lecture-recordings 0.27 1 -0.05 0 -1.08 -2
## lesson -0.94 -2 1.40 4 -0.24 -1
## library-resources -0.47 -1 -0.94 -2 -1.50 -3
## link-sciebo -0.69 -1 -0.29 0 -1.28 -3
## literature-database -0.48 -1 -0.64 -1 1.15 2
## page 0.14 0 0.64 1 -0.08 0
## portfolio -0.65 -1 -0.66 -1 1.58 4
## questionnaire 0.15 0 -0.48 -1 0.66 2
## quiz -0.33 0 1.83 5 0.26 0
## rights-management-in-course 0.51 1 0.11 1 -1.71 -4
## signup-database 0.72 2 -0.71 -2 -0.22 -1
## topic-choice 0.10 0 -1.39 -3 -0.94 -2
## url 1.05 2 1.00 2 0.47 1
## visibility 1.17 3 1.34 3 0.28 0
## wiki -0.06 0 -0.48 -1 0.92 2
## workshop -0.75 -2 -1.24 -3 2.09 5Distinguishing and consensus statements
The distinguishing and consensus statements are:
results$qdc## dist.and.cons f1_f2 sig_f1_f2
## announcements Consensus 0.232336429
## appointment-choice Distinguishes f3 only 0.142127116
## assignment-in-database Distinguishes f2 only 2.069403664 ****
## assignment Distinguishes all 0.663988144 *
## badges Distinguishes all -3.436863801 ****
## book Distinguishes f2 only -1.326225814 ****
## calendar Consensus 0.116136542
## chat Distinguishes f2 only -1.826738416 ****
## choice 0.831438416 **
## completion-tracking Distinguishes all -2.528010532 ****
## conditional-availability Distinguishes f2 only -1.245082515 ***
## database Distinguishes all 1.876890058 ****
## embed-page Distinguishes f2 only -0.631861702 *
## fair-allocation Distinguishes f1 only 1.034357036 ***
## feedback Consensus 0.365842267
## file Distinguishes all 1.131841212 ***
## folder Distinguishes f3 only 0.153365153
## forum Consensus 0.185396059
## glossary Distinguishes f1 only -1.071404502 ***
## grades-in-database Consensus 0.467185555
## grades Distinguishes f2 only -1.127569014 ***
## group-choice Distinguishes f1 only 1.121193320 ***
## groups Distinguishes f1 only 1.657340395 ****
## label 0.048371890
## learning-diary Distinguishes all 0.805194914 **
## lecture-recordings Distinguishes f3 only 0.322694049
## lesson Distinguishes all -2.346180849 ****
## library-resources 0.472131240
## link-sciebo Distinguishes f3 only -0.400046707
## literature-database Distinguishes f3 only 0.165518152
## page -0.501512005
## portfolio Distinguishes f3 only 0.007855565
## questionnaire Distinguishes f2 only 0.628071275 *
## quiz Distinguishes all -2.157465868 ****
## rights-management-in-course Distinguishes f3 only 0.393711309
## signup-database Distinguishes f1 only 1.423991170 ****
## topic-choice Distinguishes f1 only 1.486965219 ****
## url 0.050347985
## visibility Distinguishes f3 only -0.175559327
## wiki Distinguishes f3 only 0.428793596
## workshop Distinguishes f3 only 0.492033322
## f1_f3 sig_f1_f3 f2_f3 sig_f2_f3
## announcements 0.400599958 0.16826353
## appointment-choice -1.735018575 **** -1.87714569 ****
## assignment-in-database -0.217809335 -2.28721300 ****
## assignment 1.991807377 **** 1.32781923 ***
## badges -1.545639817 **** 1.89122398 ****
## book -0.506332495 0.81989332 *
## calendar 0.257592578 0.14145604
## chat -0.009045982 1.81769243 ****
## choice 0.394276239 -0.43716218
## completion-tracking -1.545305573 **** 0.98270496 **
## conditional-availability -0.059984786 1.18509773 ***
## database -0.863148757 ** -2.74003882 ****
## embed-page 0.480163949 1.11202565 ***
## fair-allocation 1.584295023 **** 0.54993799
## feedback -0.160344031 -0.52618630
## file 2.071029090 **** 0.93918788 **
## folder -0.616187757 * -0.76955291 *
## forum 0.301575066 0.11617901
## glossary -1.215479897 *** -0.14407539
## grades-in-database 0.165505528 -0.30168003
## grades 0.244150765 1.37171978 ***
## group-choice 1.318742925 *** 0.19754961
## groups 1.585031274 **** -0.07230912
## label -0.577087666 * -0.62545956
## learning-diary -1.134671273 *** -1.93986619 ****
## lecture-recordings 1.358307619 *** 1.03561357 **
## lesson -0.702851231 * 1.64332962 ****
## library-resources 1.029554286 *** 0.55742305
## link-sciebo 0.596539411 * 0.99658612 **
## literature-database -1.625301011 **** -1.79081916 ****
## page 0.219777930 0.72128994 *
## portfolio -2.227915430 **** -2.23577100 ****
## questionnaire -0.515590995 -1.14366227 ***
## quiz -0.589120284 * 1.56834558 ****
## rights-management-in-course 2.215535403 **** 1.82182409 ****
## signup-database 0.932658428 ** -0.49133274
## topic-choice 1.036669983 *** -0.45029524
## url 0.582163437 * 0.53181545
## visibility 0.891201304 ** 1.06676063 **
## wiki -0.975476241 *** -1.40426984 ***
## workshop -2.834866439 **** -3.32689976 ****Consensus statements are:
results$qdc[which(results$qdc$dist.and.cons == "Consensus"), ]## dist.and.cons f1_f2 sig_f1_f2 f1_f3 sig_f1_f3
## announcements Consensus 0.2323364 0.4006000
## calendar Consensus 0.1161365 0.2575926
## feedback Consensus 0.3658423 -0.1603440
## forum Consensus 0.1853961 0.3015751
## grades-in-database Consensus 0.4671856 0.1655055
## f2_f3 sig_f2_f3
## announcements 0.1682635
## calendar 0.1414560
## feedback -0.5261863
## forum 0.1161790
## grades-in-database -0.3016800Statements distinguishing all factors are:
results$qdc[which(results$qdc$dist.and.cons == "Distinguishes all"), ]## dist.and.cons f1_f2 sig_f1_f2 f1_f3
## assignment Distinguishes all 0.6639881 * 1.9918074
## badges Distinguishes all -3.4368638 **** -1.5456398
## completion-tracking Distinguishes all -2.5280105 **** -1.5453056
## database Distinguishes all 1.8768901 **** -0.8631488
## file Distinguishes all 1.1318412 *** 2.0710291
## learning-diary Distinguishes all 0.8051949 ** -1.1346713
## lesson Distinguishes all -2.3461808 **** -0.7028512
## quiz Distinguishes all -2.1574659 **** -0.5891203
## sig_f1_f3 f2_f3 sig_f2_f3
## assignment **** 1.3278192 ***
## badges **** 1.8912240 ****
## completion-tracking **** 0.9827050 **
## database ** -2.7400388 ****
## file **** 0.9391879 **
## learning-diary *** -1.9398662 ****
## lesson * 1.6433296 ****
## quiz * 1.5683456 ****Flagged Q-sorts
The following Q-sorts were flagged for each factor:
results$flagged## flag_f1 flag_f2 flag_f3
## Acineth_26bfa05e FALSE FALSE TRUE
## Alicia_10c3d34f FALSE TRUE FALSE
## Amber_e0ddb543 TRUE FALSE FALSE
## An_9538d924 FALSE FALSE FALSE
## Beatris_92453cbf FALSE TRUE FALSE
## Calvin_a2d97424 TRUE FALSE FALSE
## Daniel_126b50df FALSE TRUE FALSE
## Derrick_72ff92ad FALSE TRUE FALSE
## Devin_e3d1b473 TRUE FALSE FALSE
## Donovan_f640ccfa FALSE FALSE FALSE
## Edna_fb09399c TRUE FALSE FALSE
## Gwendolyn_a896d60a FALSE FALSE TRUE
## Kendra_287fb81 FALSE FALSE TRUE
## Kevin_e2cfea5d TRUE FALSE FALSE
## Kyle_15f6c5fe TRUE FALSE FALSE
## Nigel_75f54d25 FALSE FALSE FALSE
## Paolo_192c118d TRUE FALSE FALSE
## Saami_8dfa5a4a TRUE FALSE FALSE
## Shaaheen_2dbbe518 FALSE FALSE TRUE
## Sharaf_65e09f0d TRUE FALSE FALSE
## Tyler_eb15d206 FALSE TRUE FALSEData by factors
Factor 1
Factor 1 contains 9 Q-sorts.
Subset pre-sorting questionnaire for factor 1:
pre_f1 <- pre.question[results$flagged[, 1], ]The participants that loaded on factor 1 were on average 31.8888889 years old and 3 of them were female and 6 male. They were teaching on average 3 hours a week.
Faculty:
table(pre_f1[4])##
## FB11 FB14 FB4 FB6 FB7 Other
## 1 1 3 1 2 1Position:
table(pre_f1[5])## < table of extent 0 >On average they had 5.8888889 years of experience with Moodle which was distributed as follows:
table(pre_f1[7])##
## 1 3 5 6 7 9 12
## 1 1 3 1 1 1 1Number of them who had experience with other platfoms:
table(pre_f1[8])##
## Ja Nein
## 7 2And in detail:
table(pre_f1[9])##
## Als Student BSCW BSCW, Asana
## 1 1 1
## ein paar Jahre Kenne 2-3 Plattformen StudIP
## 1 1 2Subset digital skills
digitalskills_f1 <- digitalskills[results$flagged[, 1], ]
likertdigital_f1 <- likert(digitalskills_f1)
sumdigital_f1 <- summary(likertdigital_f1)
kable(sumdigital_f1)| Item | low | neutral | high | mean | sd | |
|---|---|---|---|---|---|---|
| 1 | I am confident in using the office suite (e.g. word processing, spreadsheet or presentation software). | 0.00000 | 0.00000 | 100.00000 | 4.888889 | 0.3333333 |
| 4 | I am confident in using learning management systems (e.g. Moodle, Ilias or Blackboard). | 0.00000 | 0.00000 | 100.00000 | 4.666667 | 0.5000000 |
| 7 | I have the skills to use web 2.0 technologies (e.g. Wikis, Blogs or Podcasts). | 11.11111 | 0.00000 | 88.88889 | 4.555556 | 1.0137938 |
| 2 | I am familiar with screen recording software (e.g. Camtasia). | 11.11111 | 11.11111 | 77.77778 | 4.111111 | 1.3642255 |
| 5 | I am familiar with content management systems (e.g. WordPress, Drupal or Joomla). | 11.11111 | 11.11111 | 77.77778 | 4.000000 | 1.3228757 |
| 6 | I am familiar with markup languages (e.g. HTML, XML or Markdown). | 22.22222 | 0.00000 | 77.77778 | 3.777778 | 1.3944334 |
| 3 | I am familiar with audio / video editing software (e.g. Audacity or Premiere). | 22.22222 | 11.11111 | 66.66667 | 3.888889 | 1.5365907 |
Plot digital skills
plot(likertdigital_f1)
Subset personal innovativeness
inno_f1 <- inno[results$flagged[, 1], ]
likertinno_f1 <- likert(inno_f1)
suminno_f1 <- summary(likertinno_f1)
kable(suminno_f1)| Item | low | neutral | high | mean | sd | |
|---|---|---|---|---|---|---|
| 4 | I like to experiment with new information technologies. | 0.00000 | 11.11111 | 88.88889 | 4.111111 | 0.6009252 |
| 3 | In general, I am hesitant to try out new information technologies (reverse-scored). | 33.33333 | 0.00000 | 66.66667 | 3.444444 | 1.1303883 |
| 1 | If I heard about a new information technology, I would look for ways to experiment with it. | 0.00000 | 44.44444 | 55.55556 | 4.000000 | 1.0000000 |
| 2 | Among my peers, I am usually the first to try out a new information technology system. | 11.11111 | 33.33333 | 55.55556 | 3.666667 | 1.0000000 |
Plot personal innovativeness
plot(likertinno_f1)
Subset user interface
userinterface_f1 <- userinterface[results$flagged[, 1], ]
likertinterface_f1 <- likert(userinterface_f1)
suminterface_f1 <- summary(likertinterface_f1)
kable(suminterface_f1)| Item | low | neutral | high | mean | sd | |
|---|---|---|---|---|---|---|
| 3 | The overall user interface design of Moodle is satisfactory. | 0.00000 | 0.00000 | 100.00000 | 4.333333 | 0.5000000 |
| 2 | The layout of Moodle is in good structure. | 0.00000 | 11.11111 | 88.88889 | 3.888889 | 0.3333333 |
| 1 | The layout of Moodle is user-friendly. | 11.11111 | 11.11111 | 77.77778 | 3.888889 | 0.9279607 |
Plot user interface
plot(likertinterface_f1)
Subset perceived usefulness
useful_f1 <- useful[results$flagged[, 1], ]
likertuseful_f1 <- likert(useful_f1)
sumuseful_f1 <- summary(likertuseful_f1)
kable(sumuseful_f1)| Item | low | neutral | high | mean | sd | |
|---|---|---|---|---|---|---|
| 1 | Using Moodle increases the quality of my educational work. | 0.00000 | 11.11111 | 88.88889 | 4.555556 | 0.7264832 |
| 4 | Overall, Moodle is useful in performing my educational work. | 11.11111 | 11.11111 | 77.77778 | 4.444444 | 1.1303883 |
| 2 | Using Moodle helps me in being more productive in my educational work. | 0.00000 | 22.22222 | 77.77778 | 4.444444 | 0.8819171 |
| 3 | Using Moodle makes me a more productive teacher. | 22.22222 | 0.00000 | 77.77778 | 3.888889 | 1.4529663 |
Plot perceived usefulness
plot(likertuseful_f1)
Subset user satisfaction
satisfaction_f1 <- satisfaction[results$flagged[, 1], ]
likertsatisfaction_f1 <- likert(satisfaction_f1)
sumsatisfaction_f1 <- summary(likertsatisfaction_f1)
kable(sumsatisfaction_f1)| Item | low | neutral | high | mean | sd | |
|---|---|---|---|---|---|---|
| 4 | I am very satisfied with the services provided by Moodle. | 0 | 0.00000 | 100.00000 | 4.222222 | 0.4409586 |
| 5 | My decision to use Moodle was a wise one. | 0 | 0.00000 | 100.00000 | 4.555556 | 0.5270463 |
| 1 | I am satisfied with the experience of using Moodle. | 0 | 11.11111 | 88.88889 | 4.000000 | 0.5000000 |
| 2 | I think that I did the right thing when I decided to use Moodle. | 0 | 11.11111 | 88.88889 | 4.666667 | 0.7071068 |
| 3 | I am satisfied with my decision to use Moodle. | 0 | 11.11111 | 88.88889 | 4.666667 | 0.7071068 |
Plot user satisfaction
plot(likertsatisfaction_f1)
The following table shows a summary of the scales for factor 1:
| Scale | Mean |
|---|---|
| Personal innovativeness | 3.8055556 |
| User interface | 4.037037 |
| Perceived usefulness | 4.3333333 |
| User satisfaction | 4.4222222 |
Subset course metadata:
course_meta_f1 <- course.meta[results$flagged[, 1], ]The average course size was 39.7777778 and the median was at 22. The smallest course consisted of 12 students and the biggest of 150 students. The following boxplot shows the course size:
boxplot(course_meta_f1[2])
The performance assessment was distributed as follows:
table(course_meta_f1[3])##
## Assignment Assignment and exam Exam
## 5 2 1
## Exercises and exam
## 1The type of course was distributed as follows:
table(course_meta_f1[4])##
## Lecture Lecture and seminar Seminar
## 2 1 6The sort of course management was distributed as follows:
table(course_meta_f1[5])##
## Alone Team
## 6 3Cards distrbution:
cards.distro_f1 <- cards.distro[results$flagged[, 1], ]
melted.cards_f1 <- melt(cards.distro_f1)## Using Participant as id variablesggplot(melted.cards_f1, aes(x = variable, y = value)) + geom_boxplot()
On average 22 were sorted as unimportant, 6.1111111 were sorted as neurtal and 12.8888889 were sorted as important.
Scores sorted for factor 1:
scores[order(scores$zsc_f1, decreasing = T), ]## zsc_f1 fsc_f1 zsc_f2 fsc_f2 zsc_f3 fsc_f3
## file 2.53 5 1.40 3 0.46 1
## assignment 1.72 4 1.06 2 -0.27 -1
## announcements 1.57 4 1.34 2 1.17 3
## forum 1.53 3 1.34 3 1.22 3
## groups 1.46 3 -0.20 0 -0.13 0
## visibility 1.17 3 1.34 3 0.28 0
## url 1.05 2 1.00 2 0.47 1
## signup-database 0.72 2 -0.71 -2 -0.22 -1
## group-choice 0.70 2 -0.42 -1 -0.62 -1
## database 0.69 2 -1.19 -2 1.55 4
## assignment-in-database 0.66 2 -1.41 -4 0.88 2
## label 0.60 1 0.55 1 1.18 3
## rights-management-in-course 0.51 1 0.11 1 -1.71 -4
## folder 0.50 1 0.35 1 1.12 2
## choice 0.29 1 -0.54 -1 -0.10 0
## lecture-recordings 0.27 1 -0.05 0 -1.08 -2
## feedback 0.23 1 -0.13 0 0.39 1
## questionnaire 0.15 0 -0.48 -1 0.66 2
## page 0.14 0 0.64 1 -0.08 0
## topic-choice 0.10 0 -1.39 -3 -0.94 -2
## fair-allocation -0.02 0 -1.06 -2 -1.61 -4
## wiki -0.06 0 -0.48 -1 0.92 2
## conditional-availability -0.22 0 1.03 2 -0.16 0
## quiz -0.33 0 1.83 5 0.26 0
## grades -0.34 -1 0.79 1 -0.58 -1
## library-resources -0.47 -1 -0.94 -2 -1.50 -3
## literature-database -0.48 -1 -0.64 -1 1.15 2
## portfolio -0.65 -1 -0.66 -1 1.58 4
## calendar -0.66 -1 -0.78 -2 -0.92 -2
## link-sciebo -0.69 -1 -0.29 0 -1.28 -3
## workshop -0.75 -2 -1.24 -3 2.09 5
## glossary -0.76 -2 0.32 1 0.46 1
## embed-page -0.79 -2 -0.16 0 -1.27 -3
## learning-diary -0.86 -2 -1.66 -5 0.28 1
## lesson -0.94 -2 1.40 4 -0.24 -1
## grades-in-database -1.06 -3 -1.52 -4 -1.22 -2
## appointment-choice -1.13 -3 -1.27 -3 0.61 1
## book -1.34 -3 -0.01 0 -0.83 -2
## completion-tracking -1.48 -4 1.04 2 0.06 0
## badges -1.73 -4 1.71 4 -0.18 -1
## chat -1.84 -5 -0.02 0 -1.84 -5Statements distinguishing factor 1 are:
results$qdc[which(results$qdc$dist.and.cons == "Distinguishes f1 only"), ]## dist.and.cons f1_f2 sig_f1_f2 f1_f3
## fair-allocation Distinguishes f1 only 1.034357 *** 1.5842950
## glossary Distinguishes f1 only -1.071405 *** -1.2154799
## group-choice Distinguishes f1 only 1.121193 *** 1.3187429
## groups Distinguishes f1 only 1.657340 **** 1.5850313
## signup-database Distinguishes f1 only 1.423991 **** 0.9326584
## topic-choice Distinguishes f1 only 1.486965 **** 1.0366700
## sig_f1_f3 f2_f3 sig_f2_f3
## fair-allocation **** 0.54993799
## glossary *** -0.14407539
## group-choice *** 0.19754961
## groups **** -0.07230912
## signup-database ** -0.49133274
## topic-choice *** -0.45029524Subset feedback:
f1.feedback <- q.feedback[, results$flagged[, 1]]
f1.feedback <- f1.feedback[rowSums(is.na(f1.feedback)) != ncol(f1.feedback),
]
f1.feedback <- f1.feedback[, colSums(is.na(f1.feedback)) != nrow(f1.feedback)]
kable(f1.feedback)| Amber_e0ddb543 | Calvin_a2d97424 | Edna_fb09399c | Paolo_192c118d | Saami_8dfa5a4a | Sharaf_65e09f0d | |
|---|---|---|---|---|---|---|
| assignment | NA | Assignments are extremely important in courses because it makes recording the performance easier. | NA | The assignments are important because the participants have to do written assignment and I think it’s very handy if they submit these in the course. | NA | NA |
| badges | NA | NA | NA | NA | NA | Never used it and it is not what I would use in Moodle. |
| chat | I once tried it and it did not convince me. | NA | It is in my opinion a little too complicated. | The technical implementation in Moodle is not good. | NA | NA |
| file | NA | File upload is the base of all our courses. | That is just really important for me, that is what Moodle is for me. | I use the course to provide additional material. | NA | Because I use this the most and it is the most important to me. It is the easiest way to distribute material among the students. It is easier than email. |
| folder | NA | NA | NA | Folders are for me also materials or file. | NA | This could be useful to struture material but I don’t use it because it is not handy. |
| forum | NA | NA | NA | NA | NA | The forum is also important because I don’t have to send an email to inform the students. |
| glossary | I do not need something like a glossary because it is just for defining. | NA | Something like that I would create in a seminar. | I can’t imagine using that. | NA | NA |
| grades-in-database | NA | NA | NA | I do not need that seperatly in a database. | NA | NA |
| groups | NA | NA | NA | NA | The group allocation influences everything. | NA |
| label | NA | NA | NA | NA | NA | I use this to add headings to the files I upload to create structure. |
| lecture-recordings | Due to legal concerns it was not possible, but we would have loved doing it. | NA | NA | NA | NA | NA |
| library-resources | NA | NA | NA | I don’t use it because I don’t work much with literature. | NA | NA |
| portfolio | NA | NA | NA | The portfolio I think is a good method, but not well implemented in Moodle. | NA | This is important for my seminars because the students have to write a report in form of a portfolio. But the functions in Moodle are horrible for this. In place of this the students make their portfolio using WordPress. |
| rights-management-in-course | NA | NA | It is just important for the Moodle courses that I can assign different roles to different users because we sometimes have users who need more rights than the students. | NA | NA | NA |
| url | NA | NA | NA | Links are also material for me. | NA | This is the same for me as uploading files. |
| visibility | NA | I prepare the course in the beginning of the semester and choose the structure without confusing the participant with all the things that are already standing there. | NA | I work a lot with hiding activities which is why I sorted the visibility high. I reveal the weeks in the course after the seminar. | NA | NA |
Subset interview data:
interview_f1 <- interview[results$flagged[, 1], ]Missing functionalities:
missing_f1 <- interview_f1[1:3]
kable(missing_f1)| Participant | Are you missing any functionalities or appropriations? | If any are missing, which ones? |
|---|---|---|
| Amber_e0ddb543 | yes | Blogger or Wordpress +5, Dropbox +5, Socrative Quiz +5, Padlet +3, Tricider +3 |
| Calvin_a2d97424 | no | NA |
| Devin_e3d1b473 | yes | Marking new content |
| Edna_fb09399c | yes | Video Upload +5 |
| Kevin_e2cfea5d | no | NA |
| Kyle_15f6c5fe | yes | Live surveys with Moodle +2 |
| Paolo_192c118d | no | NA |
| Saami_8dfa5a4a | yes | Group based folders +3 |
| Sharaf_65e09f0d | no | NA |
New functionalities:
new_f1 <- interview_f1[c(1, 4:5)]
kable(new_f1)| Participant | Were there any for you new functionalities among the items you sorted that you might be interested in using now? | If any are new and interesing, which ones? |
|---|---|---|
| Amber_e0ddb543 | yes | Lecture recordings |
| Calvin_a2d97424 | yes | Wiki, Glossary, Literaturedatabase |
| Devin_e3d1b473 | yes | Feedback, Quiz, Questionnaire |
| Edna_fb09399c | yes | Signing up for consultation hour, Assigning groups, Fair allocation |
| Kevin_e2cfea5d | yes | Portfolio |
| Kyle_15f6c5fe | yes | Grades, Portfolio, Feedback |
| Paolo_192c118d | no | NA |
| Saami_8dfa5a4a | no | NA |
| Sharaf_65e09f0d | no | NA |
Factor 2
Factor 2 contains 5 Q-sorts.
Subset pre-sorting questionnaire for factor 2:
pre_f2 <- pre.question[results$flagged[, 2], ]The participants that loaded on factor 2 were on average 36.6 years old and 5 of them were male. They were teaching on average 2.6 hours a week.
Faculty:
table(pre_f2[4])##
## FB10 FB4 Other
## 1 3 1Position:
table(pre_f2[5])## < table of extent 0 >On average they had 4.35 years of experience with Moodle which was distributed as follows:
table(pre_f2[7])##
## 0.75 2 6 7
## 1 1 2 1Number of them who had experience with other platfoms:
table(pre_f2[8])##
## Ja Nein
## 2 3And in detail:
table(pre_f2[9])##
## StudIP, Adobe Connect xLx, learnr
## 1 1Subset digital skills
digitalskills_f2 <- digitalskills[results$flagged[, 2], ]
likertdigital_f2 <- likert(digitalskills_f2)
sumdigital_f2 <- summary(likertdigital_f2)
kable(sumdigital_f2)| Item | low | neutral | high | mean | sd | |
|---|---|---|---|---|---|---|
| 1 | I am confident in using the office suite (e.g. word processing, spreadsheet or presentation software). | 0 | 0 | 100 | 5.0 | 0.0000000 |
| 4 | I am confident in using learning management systems (e.g. Moodle, Ilias or Blackboard). | 0 | 0 | 100 | 4.8 | 0.4472136 |
| 5 | I am familiar with content management systems (e.g. WordPress, Drupal or Joomla). | 0 | 0 | 100 | 4.6 | 0.5477226 |
| 3 | I am familiar with audio / video editing software (e.g. Audacity or Premiere). | 20 | 0 | 80 | 4.0 | 1.7320508 |
| 6 | I am familiar with markup languages (e.g. HTML, XML or Markdown). | 0 | 20 | 80 | 4.6 | 0.8944272 |
| 7 | I have the skills to use web 2.0 technologies (e.g. Wikis, Blogs or Podcasts). | 0 | 20 | 80 | 4.6 | 0.8944272 |
| 2 | I am familiar with screen recording software (e.g. Camtasia). | 20 | 20 | 60 | 3.8 | 1.7888544 |
Plot digital skills
plot(likertdigital_f2)
Subset personal innovativeness
inno_f2 <- inno[results$flagged[, 2], ]
likertinno_f2 <- likert(inno_f2)
suminno_f2 <- summary(likertinno_f2)
kable(suminno_f2)| Item | low | neutral | high | mean | sd | |
|---|---|---|---|---|---|---|
| 4 | I like to experiment with new information technologies. | 0 | 20 | 80 | 4.2 | 0.8366600 |
| 1 | If I heard about a new information technology, I would look for ways to experiment with it. | 0 | 40 | 60 | 3.6 | 0.5477226 |
| 2 | Among my peers, I am usually the first to try out a new information technology system. | 20 | 40 | 40 | 3.4 | 1.1401754 |
| 3 | In general, I am hesitant to try out new information technologies (reverse-scored). | 20 | 60 | 20 | 3.0 | 0.7071068 |
Plot personal innovativeness
plot(likertinno_f2)
Subset user interface
userinterface_f2 <- userinterface[results$flagged[, 2], ]
likertinterface_f2 <- likert(userinterface_f2)
suminterface_f2 <- summary(likertinterface_f2)
kable(suminterface_f2)| Item | low | neutral | high | mean | sd | |
|---|---|---|---|---|---|---|
| 2 | The layout of Moodle is in good structure. | 0 | 40 | 60 | 3.6 | 0.5477226 |
| 3 | The overall user interface design of Moodle is satisfactory. | 0 | 40 | 60 | 3.8 | 0.8366600 |
| 1 | The layout of Moodle is user-friendly. | 20 | 40 | 40 | 3.2 | 0.8366600 |
Plot user interface
plot(likertinterface_f2)
Subset perceived usefulness
useful_f2 <- useful[results$flagged[, 2], ]
likertuseful_f2 <- likert(useful_f2)
sumuseful_f2 <- summary(likertuseful_f2)
kable(sumuseful_f2)| Item | low | neutral | high | mean | sd | |
|---|---|---|---|---|---|---|
| 4 | Overall, Moodle is useful in performing my educational work. | 0 | 0 | 100 | 4.6 | 0.5477226 |
| 2 | Using Moodle helps me in being more productive in my educational work. | 20 | 0 | 80 | 4.0 | 1.2247449 |
| 1 | Using Moodle increases the quality of my educational work. | 20 | 20 | 60 | 4.0 | 1.4142136 |
| 3 | Using Moodle makes me a more productive teacher. | 40 | 40 | 20 | 3.0 | 1.2247449 |
Plot perceived usefulness
plot(likertuseful_f2)
Subset user satisfaction
satisfaction_f2 <- satisfaction[results$flagged[, 2], ]
likertsatisfaction_f2 <- likert(satisfaction_f2)
sumsatisfaction_f2 <- summary(likertsatisfaction_f2)
kable(sumsatisfaction_f2)| Item | low | neutral | high | mean | sd | |
|---|---|---|---|---|---|---|
| 2 | I think that I did the right thing when I decided to use Moodle. | 0 | 0 | 100 | 4.6 | 0.5477226 |
| 3 | I am satisfied with my decision to use Moodle. | 0 | 0 | 100 | 4.6 | 0.5477226 |
| 4 | I am very satisfied with the services provided by Moodle. | 0 | 20 | 80 | 4.4 | 0.8944272 |
| 1 | I am satisfied with the experience of using Moodle. | 0 | 40 | 60 | 3.8 | 0.8366600 |
| 5 | My decision to use Moodle was a wise one. | 0 | 60 | 40 | 3.8 | 1.0954451 |
Plot user satisfaction
plot(likertsatisfaction_f2)
The following table shows a summary of the scales for factor 2:
| Scale | Mean |
|---|---|
| Personal innovativeness | 3.55 |
| User interface | 3.5333333 |
| Perceived usefulness | 3.9 |
| User satisfaction | 4.24 |
Subset course metadata:
course_meta_f2 <- course.meta[results$flagged[, 2], ]The average course size was 190.4 and the median was at 127. The smallest course consisted of 25 students and the biggest of 600 students. The following boxplot shows the course size:
boxplot(course_meta_f2[2])
The performance assessment was distributed as follows:
table(course_meta_f2[3])##
## Assignment Exam Exercises and exam
## 2 1 2The type of course was distributed as follows:
table(course_meta_f2[4])##
## Blended Learning Workshop Lecture
## 1 2
## Lecture and seminar Online
## 1 1The sort of course management was distributed as follows:
table(course_meta_f2[5])##
## Alone Team
## 3 2Cards distrbution:
cards.distro_f2 <- cards.distro[results$flagged[, 2], ]
melted.cards_f2 <- melt(cards.distro_f2)## Using Participant as id variablesggplot(melted.cards_f2, aes(x = variable, y = value)) + geom_boxplot()
On average 23.8 were sorted as unimportant, 4.2 were sorted as neurtal and 13 were sorted as important.
Scores sorted for factor 2:
scores[order(scores$zsc_f2, decreasing = T), ]## zsc_f1 fsc_f1 zsc_f2 fsc_f2 zsc_f3 fsc_f3
## quiz -0.33 0 1.83 5 0.26 0
## badges -1.73 -4 1.71 4 -0.18 -1
## file 2.53 5 1.40 3 0.46 1
## lesson -0.94 -2 1.40 4 -0.24 -1
## announcements 1.57 4 1.34 2 1.17 3
## forum 1.53 3 1.34 3 1.22 3
## visibility 1.17 3 1.34 3 0.28 0
## assignment 1.72 4 1.06 2 -0.27 -1
## completion-tracking -1.48 -4 1.04 2 0.06 0
## conditional-availability -0.22 0 1.03 2 -0.16 0
## url 1.05 2 1.00 2 0.47 1
## grades -0.34 -1 0.79 1 -0.58 -1
## page 0.14 0 0.64 1 -0.08 0
## label 0.60 1 0.55 1 1.18 3
## folder 0.50 1 0.35 1 1.12 2
## glossary -0.76 -2 0.32 1 0.46 1
## rights-management-in-course 0.51 1 0.11 1 -1.71 -4
## book -1.34 -3 -0.01 0 -0.83 -2
## chat -1.84 -5 -0.02 0 -1.84 -5
## lecture-recordings 0.27 1 -0.05 0 -1.08 -2
## feedback 0.23 1 -0.13 0 0.39 1
## embed-page -0.79 -2 -0.16 0 -1.27 -3
## groups 1.46 3 -0.20 0 -0.13 0
## link-sciebo -0.69 -1 -0.29 0 -1.28 -3
## group-choice 0.70 2 -0.42 -1 -0.62 -1
## questionnaire 0.15 0 -0.48 -1 0.66 2
## wiki -0.06 0 -0.48 -1 0.92 2
## choice 0.29 1 -0.54 -1 -0.10 0
## literature-database -0.48 -1 -0.64 -1 1.15 2
## portfolio -0.65 -1 -0.66 -1 1.58 4
## signup-database 0.72 2 -0.71 -2 -0.22 -1
## calendar -0.66 -1 -0.78 -2 -0.92 -2
## library-resources -0.47 -1 -0.94 -2 -1.50 -3
## fair-allocation -0.02 0 -1.06 -2 -1.61 -4
## database 0.69 2 -1.19 -2 1.55 4
## workshop -0.75 -2 -1.24 -3 2.09 5
## appointment-choice -1.13 -3 -1.27 -3 0.61 1
## topic-choice 0.10 0 -1.39 -3 -0.94 -2
## assignment-in-database 0.66 2 -1.41 -4 0.88 2
## grades-in-database -1.06 -3 -1.52 -4 -1.22 -2
## learning-diary -0.86 -2 -1.66 -5 0.28 1Statements distinguishing factor 2 are:
results$qdc[which(results$qdc$dist.and.cons == "Distinguishes f2 only"), ]## dist.and.cons f1_f2 sig_f1_f2
## assignment-in-database Distinguishes f2 only 2.0694037 ****
## book Distinguishes f2 only -1.3262258 ****
## chat Distinguishes f2 only -1.8267384 ****
## conditional-availability Distinguishes f2 only -1.2450825 ***
## embed-page Distinguishes f2 only -0.6318617 *
## grades Distinguishes f2 only -1.1275690 ***
## questionnaire Distinguishes f2 only 0.6280713 *
## f1_f3 sig_f1_f3 f2_f3 sig_f2_f3
## assignment-in-database -0.217809335 -2.2872130 ****
## book -0.506332495 0.8198933 *
## chat -0.009045982 1.8176924 ****
## conditional-availability -0.059984786 1.1850977 ***
## embed-page 0.480163949 1.1120257 ***
## grades 0.244150765 1.3717198 ***
## questionnaire -0.515590995 -1.1436623 ***Subset feedback for factor 2:
f2.feedback <- q.feedback[, results$flagged[, 2]]
f2.feedback <- f2.feedback[rowSums(is.na(f2.feedback)) != ncol(f2.feedback),
]
f2.feedback <- f2.feedback[, colSums(is.na(f2.feedback)) != nrow(f2.feedback)]
kable(f2.feedback)| Alicia_10c3d34f | Beatris_92453cbf | Daniel_126b50df | Tyler_eb15d206 | |
|---|---|---|---|---|
| announcements | I think announcements are handy to point out things like news, appointments, new material or similar things. | NA | NA | Because we could distribute changes with short notice easily among the students. |
| appointment-choice | NA | The sign up for a consolation-hour is basical something you could introduce but not doable with 600 students. | NA | NA |
| assignment | NA | The main thing I’m doing with this course is letting the students work on assignments during exercises and rate them. | The function is the most important for me because the content is very structured and the students benefit from it. The course would not be usable without this. | We have 2 exercises a week and the handing in via the Moodle course was handy for collecting the assignments. The deadline can be kept and the grades distributed. |
| badges | NA | NA | I perceive these as important and motivating but I also got the feedback from the students that they perceive the badges as positive and motivating. Once I introduced the badges there was an increase in quality. | NA |
| fair-allocation | NA | We had to distribute 600 students in 15 groups and did not want to use first-come-first serve. | NA | NA |
| file | NA | NA | NA | Because this is the central feature for distribution lecture content. |
| grades-in-database | NA | NA | I do not need to record student performance in this course. | I don’t see the use, we recorded the grades using the grades function in Moodle based on the activities. |
| group-choice | NA | NA | NA | It would have been handy to automate this in Moodle. |
| learning-diary | NA | NA | NA | I don’t see the use for our course. Because we document the learning process with the 2 weekly exercises. |
| lesson | NA | NA | This is very important for the students to be admitted to the workshop und to prepare a project planning in advance by way of doing assignments. This has proven to be a robust way to get all students to the same level. | NA |
| portfolio | NA | NA | NA | I don’t see the use for our course. Because we document the learning process with the 2 weekly exercises. |
| quiz | I wanted the students to practise with the quizzes. Depending on how good or bad the students did in the quiz I adjust my lecture. | NA | NA | This would have been nice for the students to check their learning progress. |
| rights-management-in-course | NA | Because I had many tutors. | NA | NA |
| topic-choice | NA | Theme choosing is not possible in this course context. | NA | NA |
| url | I am making the material avalible on a separate platform using links. Therefore that is the primary way of distributing content. | NA | NA | NA |
| visibility | NA | Based on time, because I prepared many assignments weeks before. They are automatically unlocked every monday at the same time. Otherwise I had to be at my computer every monday at this time. | NA | NA |
Subset interview data:
interview_f2 <- interview[results$flagged[, 2], ]Missing functionalities:
missing_f2 <- interview_f2[1:3]
kable(missing_f2)| Participant | Are you missing any functionalities or appropriations? | If any are missing, which ones? |
|---|---|---|
| Alicia_10c3d34f | yes | Quiz in the lecture hall +5 |
| Beatris_92453cbf | yes | XP-Block +2, Code evaluation +4 |
| Daniel_126b50df | yes | Linda.com Videos +2, H5P Module +4 |
| Derrick_72ff92ad | no | NA |
| Tyler_eb15d206 | no | NA |
New functionalities:
new_f2 <- interview_f2[c(1, 4:5)]
kable(new_f2)| Participant | Were there any for you new functionalities among the items you sorted that you might be interested in using now? | If any are new and interesing, which ones? |
|---|---|---|
| Alicia_10c3d34f | no | NA |
| Beatris_92453cbf | yes | Library resources |
| Daniel_126b50df | no | NA |
| Derrick_72ff92ad | no | NA |
| Tyler_eb15d206 | no | NA |
Factor 3
Factor 3 contains 4 Q-sorts.
Subset pre-sorting questionnaire for factor 3:
pre_f3 <- pre.question[results$flagged[, 3], ]The participants that loaded on factor 3 were on average 34.25 years old and 2 of them were female and 2 male. They were teaching on average 5 hours a week.
Faculty:
table(pre_f3[4])##
## FB4 FB6 FB7 Other
## 1 1 1 1Position:
table(pre_f3[5])## < table of extent 0 >On average they had 6.375 years of experience with Moodle which was distributed as follows:
table(pre_f3[7])##
## 3 3.5 7 12
## 1 1 1 1Number of them who had experience with other platfoms:
table(pre_f3[8])##
## Ja Nein
## 2 2And in detail:
table(pre_f3[9])##
## BSCW, OpenUss StudIP, Ilias
## 1 1Subset digital skills
digitalskills_f3 <- digitalskills[results$flagged[, 3], ]
likertdigital_f3 <- likert(digitalskills_f3)
sumdigital_f3 <- summary(likertdigital_f3)
kable(sumdigital_f3)| Item | low | neutral | high | mean | sd | |
|---|---|---|---|---|---|---|
| 4 | I am confident in using learning management systems (e.g. Moodle, Ilias or Blackboard). | 0 | 0 | 100 | 5.00 | 0.000000 |
| 1 | I am confident in using the office suite (e.g. word processing, spreadsheet or presentation software). | 0 | 25 | 75 | 4.50 | 1.000000 |
| 3 | I am familiar with audio / video editing software (e.g. Audacity or Premiere). | 25 | 0 | 75 | 3.75 | 1.892969 |
| 5 | I am familiar with content management systems (e.g. WordPress, Drupal or Joomla). | 25 | 0 | 75 | 4.00 | 2.000000 |
| 7 | I have the skills to use web 2.0 technologies (e.g. Wikis, Blogs or Podcasts). | 25 | 0 | 75 | 3.75 | 1.892969 |
| 2 | I am familiar with screen recording software (e.g. Camtasia). | 50 | 0 | 50 | 3.25 | 2.061553 |
| 6 | I am familiar with markup languages (e.g. HTML, XML or Markdown). | 25 | 50 | 25 | 3.25 | 1.258306 |
Plot digital skills
plot(likertdigital_f3)
Subset personal innovativeness
inno_f3 <- inno[results$flagged[, 3], ]
likertinno_f3 <- likert(inno_f3)
suminno_f3 <- summary(likertinno_f3)
kable(suminno_f3)| Item | low | neutral | high | mean | sd |
|---|---|---|---|---|---|
| If I heard about a new information technology, I would look for ways to experiment with it. | 25 | 0 | 75 | 3.50 | 1.0000000 |
| Among my peers, I am usually the first to try out a new information technology system. | 25 | 0 | 75 | 4.00 | 1.4142136 |
| In general, I am hesitant to try out new information technologies (reverse-scored). | 25 | 25 | 50 | 3.25 | 0.9574271 |
| I like to experiment with new information technologies. | 0 | 50 | 50 | 3.75 | 0.9574271 |
Plot personal innovativeness
plot(likertinno_f3)
Subset user interface
userinterface_f3 <- userinterface[results$flagged[, 3], ]
likertinterface_f3 <- likert(userinterface_f3)
suminterface_f3 <- summary(likertinterface_f3)
kable(suminterface_f3)| Item | low | neutral | high | mean | sd | |
|---|---|---|---|---|---|---|
| 3 | The overall user interface design of Moodle is satisfactory. | 0 | 0 | 100 | 4.00 | 0.0 |
| 2 | The layout of Moodle is in good structure. | 0 | 25 | 75 | 3.75 | 0.5 |
| 1 | The layout of Moodle is user-friendly. | 0 | 75 | 25 | 3.25 | 0.5 |
Plot user interface
plot(likertinterface_f3)
Subset perceived usefulness
useful_f3 <- useful[results$flagged[, 3], ]
likertuseful_f3 <- likert(useful_f3)
sumuseful_f3 <- summary(likertuseful_f3)
kable(sumuseful_f3)| Item | low | neutral | high | mean | sd |
|---|---|---|---|---|---|
| Using Moodle increases the quality of my educational work. | 0 | 0 | 100 | 4.75 | 0.5000000 |
| Using Moodle helps me in being more productive in my educational work. | 0 | 0 | 100 | 4.75 | 0.5000000 |
| Using Moodle makes me a more productive teacher. | 0 | 0 | 100 | 4.50 | 0.5773503 |
| Overall, Moodle is useful in performing my educational work. | 0 | 0 | 100 | 4.50 | 0.5773503 |
Plot perceived usefulness
plot(likertuseful_f3)
Subset user satisfaction
satisfaction_f3 <- satisfaction[results$flagged[, 3], ]
likertsatisfaction_f3 <- likert(satisfaction_f3)
sumsatisfaction_f3 <- summary(likertsatisfaction_f3)
kable(sumsatisfaction_f3)| Item | low | neutral | high | mean | sd |
|---|---|---|---|---|---|
| I am satisfied with the experience of using Moodle. | 0 | 0 | 100 | 4.0 | 0.0000000 |
| I think that I did the right thing when I decided to use Moodle. | 0 | 0 | 100 | 4.5 | 0.5773503 |
| I am satisfied with my decision to use Moodle. | 0 | 0 | 100 | 5.0 | 0.0000000 |
| I am very satisfied with the services provided by Moodle. | 0 | 0 | 100 | 4.0 | 0.0000000 |
| My decision to use Moodle was a wise one. | 0 | 0 | 100 | 4.5 | 0.5773503 |
Plot user satisfaction
plot(likertsatisfaction_f3)
The following table shows a summary of the scales for factor 3:
| Scale | Mean |
|---|---|
| Personal innovativeness | 3.625 |
| User interface | 3.6666667 |
| Perceived usefulness | 4.625 |
| User satisfaction | 4.4 |
Subset course metadata:
course_meta_f3 <- course.meta[results$flagged[, 3], ]The average course size was 186.75 and the median was at 16.5. The smallest course consisted of 14 students and the biggest of 700 students. The following boxplot shows the course size:
boxplot(course_meta_f3[2])
The performance assessment was distributed as follows:
table(course_meta_f3[3])##
## Assignment Exam
## 3 1The type of course was distributed as follows:
table(course_meta_f3[4])##
## Lecture Seminar
## 1 3The sort of course management was distributed as follows:
table(course_meta_f3[5])##
## Alone
## 4Cards distrbution:
cards.distro_f3 <- cards.distro[results$flagged[, 3], ]
melted.cards_f3 <- melt(cards.distro_f3)## Using Participant as id variablesggplot(melted.cards_f3, aes(x = variable, y = value)) + geom_boxplot()
On average 21.5 were sorted as unimportant, 4.5 were sorted as neurtal and 15 were sorted as important.
Scores sorted for factor 3:
scores[order(scores$zsc_f3, decreasing = T), ]## zsc_f1 fsc_f1 zsc_f2 fsc_f2 zsc_f3 fsc_f3
## workshop -0.75 -2 -1.24 -3 2.09 5
## portfolio -0.65 -1 -0.66 -1 1.58 4
## database 0.69 2 -1.19 -2 1.55 4
## forum 1.53 3 1.34 3 1.22 3
## label 0.60 1 0.55 1 1.18 3
## announcements 1.57 4 1.34 2 1.17 3
## literature-database -0.48 -1 -0.64 -1 1.15 2
## folder 0.50 1 0.35 1 1.12 2
## wiki -0.06 0 -0.48 -1 0.92 2
## assignment-in-database 0.66 2 -1.41 -4 0.88 2
## questionnaire 0.15 0 -0.48 -1 0.66 2
## appointment-choice -1.13 -3 -1.27 -3 0.61 1
## url 1.05 2 1.00 2 0.47 1
## file 2.53 5 1.40 3 0.46 1
## glossary -0.76 -2 0.32 1 0.46 1
## feedback 0.23 1 -0.13 0 0.39 1
## learning-diary -0.86 -2 -1.66 -5 0.28 1
## visibility 1.17 3 1.34 3 0.28 0
## quiz -0.33 0 1.83 5 0.26 0
## completion-tracking -1.48 -4 1.04 2 0.06 0
## page 0.14 0 0.64 1 -0.08 0
## choice 0.29 1 -0.54 -1 -0.10 0
## groups 1.46 3 -0.20 0 -0.13 0
## conditional-availability -0.22 0 1.03 2 -0.16 0
## badges -1.73 -4 1.71 4 -0.18 -1
## signup-database 0.72 2 -0.71 -2 -0.22 -1
## lesson -0.94 -2 1.40 4 -0.24 -1
## assignment 1.72 4 1.06 2 -0.27 -1
## grades -0.34 -1 0.79 1 -0.58 -1
## group-choice 0.70 2 -0.42 -1 -0.62 -1
## book -1.34 -3 -0.01 0 -0.83 -2
## calendar -0.66 -1 -0.78 -2 -0.92 -2
## topic-choice 0.10 0 -1.39 -3 -0.94 -2
## lecture-recordings 0.27 1 -0.05 0 -1.08 -2
## grades-in-database -1.06 -3 -1.52 -4 -1.22 -2
## embed-page -0.79 -2 -0.16 0 -1.27 -3
## link-sciebo -0.69 -1 -0.29 0 -1.28 -3
## library-resources -0.47 -1 -0.94 -2 -1.50 -3
## fair-allocation -0.02 0 -1.06 -2 -1.61 -4
## rights-management-in-course 0.51 1 0.11 1 -1.71 -4
## chat -1.84 -5 -0.02 0 -1.84 -5Statements distinguishing factor 3 are:
results$qdc[which(results$qdc$dist.and.cons == "Distinguishes f3 only"), ]## dist.and.cons f1_f2 sig_f1_f2
## appointment-choice Distinguishes f3 only 0.142127116
## folder Distinguishes f3 only 0.153365153
## lecture-recordings Distinguishes f3 only 0.322694049
## link-sciebo Distinguishes f3 only -0.400046707
## literature-database Distinguishes f3 only 0.165518152
## portfolio Distinguishes f3 only 0.007855565
## rights-management-in-course Distinguishes f3 only 0.393711309
## visibility Distinguishes f3 only -0.175559327
## wiki Distinguishes f3 only 0.428793596
## workshop Distinguishes f3 only 0.492033322
## f1_f3 sig_f1_f3 f2_f3 sig_f2_f3
## appointment-choice -1.7350186 **** -1.8771457 ****
## folder -0.6161878 * -0.7695529 *
## lecture-recordings 1.3583076 *** 1.0356136 **
## link-sciebo 0.5965394 * 0.9965861 **
## literature-database -1.6253010 **** -1.7908192 ****
## portfolio -2.2279154 **** -2.2357710 ****
## rights-management-in-course 2.2155354 **** 1.8218241 ****
## visibility 0.8912013 ** 1.0667606 **
## wiki -0.9754762 *** -1.4042698 ***
## workshop -2.8348664 **** -3.3268998 ****Subset feedback for factor 3:
f3.feedback <- q.feedback[, results$flagged[, 3]]
f3.feedback <- f3.feedback[rowSums(is.na(f3.feedback)) != ncol(f3.feedback),
]
f3.feedback <- f3.feedback[, colSums(is.na(f3.feedback)) != nrow(f3.feedback)]
kable(f3.feedback)| Acineth_26bfa05e | Gwendolyn_a896d60a | Kendra_287fb81 | Shaaheen_2dbbe518 | |
|---|---|---|---|---|
| calendar | NA | NA | I didn’t need this before. | NA |
| chat | NA | NA | Chat is a bit out. | I think communication is easier in direct contact. |
| database | NA | NA | I prefer using the database over the assignment, because they are not only doing the assignment for me but for the whole course and with database the students can access the other students assignments. | It is a learning reflexion that is predefined in a database. |
| embed-page | NA | NA | NA | This is not so important for me because if I have files I just manually upload those if I think they are important. |
| fair-allocation | I used it sometimes to distribute a course of 700 students into smaller courses. | That does not make sense for the seminar size. | NA | NA |
| feedback | NA | NA | For me as a instructor it is easier when I have it written. Because I often have the feeling that the students express more online then they would in a face to face talk. | NA |
| forum | NA | The forum is used as study diary where the students can give feedback to enteries. | I gave every student a seperate forum to communicate with me. | NA |
| groups | NA | NA | I gave every student a seperate forum to communicate with me. | NA |
| label | If I upload a text file it already has a name. | NA | NA | NA |
| learning-diary | NA | The forum is used as study diary where the students can give feedback to enteries. | NA | NA |
| lecture-recordings | The lecture recordings are what the students want to have the most, so it is the most important. | There are just a few students and no exam so it does not make sense. | NA | NA |
| library-resources | NA | NA | NA | I didn’t use that. |
| literature-database | NA | NA | We use this so that the students can see what the other students are doing. | NA |
| portfolio | I do not know what they are doing with a portfolio. | NA | NA | It is a learning reflexion that is predefined in a database. |
| rights-management-in-course | NA | NA | Role assignment I don’t need in this course because it is a small course that I manage myself. | NA |
| topic-choice | NA | The students can choose their themes themselves. | NA | NA |
| workshop | The workshop is not relevant for my course because the course is too big. | NA | The students provided feedback to each other based on a provided template in preparation for a meeting. | It is important to me that the students get a template which they use to write their concept. While they are writing a first concept they can also see what other students are writing. |
Subset interview data:
interview_f3 <- interview[results$flagged[, 3], ]Missing functionalities:
missing_f3 <- interview_f3[1:3]
kable(missing_f3)| Participant | Are you missing any functionalities or appropriations? | If any are missing, which ones? |
|---|---|---|
| Acineth_26bfa05e | yes | Anonymous survey +3 |
| Gwendolyn_a896d60a | yes | Moderne Portfolio +4, Individual projectmanagement +4 |
| Kendra_287fb81 | no | NA |
| Shaaheen_2dbbe518 | yes | Annotating video +1, Etherpad +3, Blog (Wordpress) +4, Adobe Connect +4, Automated Emails +5 |
New functionalities:
new_f3 <- interview_f3[c(1, 4:5)]
kable(new_f3)| Participant | Were there any for you new functionalities among the items you sorted that you might be interested in using now? | If any are new and interesing, which ones? |
|---|---|---|
| Acineth_26bfa05e | no | NA |
| Gwendolyn_a896d60a | yes | Workshop |
| Kendra_287fb81 | yes | Time based visibility |
| Shaaheen_2dbbe518 | yes | Calendar |
Comparison of the factors
| Scale | Overall Mean | Mean F1 | Mean F2 | Mean F3 |
|---|---|---|---|---|
| Personal innovativeness | 3.7619048 | 3.8055556 | 3.55 | 3.625 |
| User interface | 3.8571429 | 4.037037 | 3.5333333 | 3.6666667 |
| Perceived usefulness | 4.3214286 | 4.3333333 | 3.9 | 4.625 |
| User satisfaction | 4.4190476 | 4.4222222 | 4.24 | 4.4 |
Comparing the scales to see if there is a difference between the factors using the Kruskal-Wallis test (Kruskal and Wallis 1952):
Compare personal innovativeness:
numinnof1 <- as.matrix(as.data.frame(lapply(inno_f1, as.numeric)))
numinnof2 <- as.matrix(as.data.frame(lapply(inno_f2, as.numeric)))
numinnof3 <- as.matrix(as.data.frame(lapply(inno_f3, as.numeric)))
kruskal.test(list(numinnof1, numinnof2, numinnof3))##
## Kruskal-Wallis rank sum test
##
## data: list(numinnof1, numinnof2, numinnof3)
## Kruskal-Wallis chi-squared = 1.2363, df = 2, p-value = 0.5389Compare user interface:
numinterf1 <- as.matrix(as.data.frame(lapply(userinterface_f1, as.numeric)))
numinterf2 <- as.matrix(as.data.frame(lapply(userinterface_f2, as.numeric)))
numinterf3 <- as.matrix(as.data.frame(lapply(userinterface_f3, as.numeric)))
kruskal.test(list(numinterf1, numinterf2, numinterf3))##
## Kruskal-Wallis rank sum test
##
## data: list(numinterf1, numinterf2, numinterf3)
## Kruskal-Wallis chi-squared = 7.2403, df = 2, p-value = 0.02678Compare perceived usefulness:
numusef1 <- as.matrix(as.data.frame(lapply(useful_f1, as.numeric)))
numusef2 <- as.matrix(as.data.frame(lapply(useful_f2, as.numeric)))
numusef3 <- as.matrix(as.data.frame(lapply(useful_f3, as.numeric)))
kruskal.test(list(numusef1, numusef2, numusef3))##
## Kruskal-Wallis rank sum test
##
## data: list(numusef1, numusef2, numusef3)
## Kruskal-Wallis chi-squared = 3.3504, df = 2, p-value = 0.1873Compare user satisfaction:
numsatf1 <- as.matrix(as.data.frame(lapply(satisfaction_f1, as.numeric)))
numsatf2 <- as.matrix(as.data.frame(lapply(satisfaction_f2, as.numeric)))
numsatf3 <- as.matrix(as.data.frame(lapply(satisfaction_f3, as.numeric)))
kruskal.test(list(numsatf1, numsatf2, numsatf3))##
## Kruskal-Wallis rank sum test
##
## data: list(numsatf1, numsatf2, numsatf3)
## Kruskal-Wallis chi-squared = 0.59366, df = 2, p-value = 0.7432Only the difference in the user interface scale is significant at an alpha of .05.
Check which factor differs significantly on the user interface scale using the Dunn test (Dunn 1964):
posthoc.kruskal.dunn.test(list(numinterf1, numinterf2, numinterf3), p.adjust.method = "bonferroni")## Warning in posthoc.kruskal.dunn.test.default(list(numinterf1, numinterf2, :
## Ties are present. z-quantiles were corrected for ties.##
## Pairwise comparisons using Dunn's-test for multiple
## comparisons of independent samples
##
## data: list(numinterf1, numinterf2, numinterf3)
##
## 1 2
## 2 0.04 -
## 3 0.20 1.00
##
## P value adjustment method: bonferroniFactor 1 differs significantly.
Session Info
sessionInfo()## R version 3.4.1 (2017-06-30)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Arch Linux
##
## Matrix products: default
## BLAS: /usr/lib/libblas.so.3.7.1
## LAPACK: /usr/lib/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=de_DE.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=de_DE.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] bindrcpp_0.2 PMCMR_4.1 likert_1.3.5 xtable_1.8-2
## [5] reshape2_1.4.2 nFactors_2.3.3 lattice_0.20-35 boot_1.3-19
## [9] psych_1.7.5 MASS_7.3-47 qmethod_1.5.2 magrittr_1.5
## [13] stringr_1.2.0 dplyr_0.7.2 purrr_0.2.2.2 readr_1.1.1
## [17] tidyr_0.6.3 tibble_1.3.3 ggplot2_2.2.1 tidyverse_1.1.1
## [21] knitr_1.16
##
## loaded via a namespace (and not attached):
## [1] haven_1.1.0 colorspace_1.3-2 htmltools_0.3.6
## [4] yaml_2.1.14 rlang_0.1.1 foreign_0.8-69
## [7] glue_1.1.1 modelr_0.1.1 readxl_1.0.0
## [10] bindr_0.1 plyr_1.8.4 GPArotation_2014.11-1
## [13] munsell_0.4.3 gtable_0.2.0 cellranger_1.1.0
## [16] rvest_0.3.2 evaluate_0.10.1 labeling_0.3
## [19] forcats_0.2.0 parallel_3.4.1 highr_0.6
## [22] broom_0.4.2 Rcpp_0.12.12 formatR_1.5
## [25] scales_0.4.1 backports_1.1.0 jsonlite_1.5
## [28] gridExtra_2.2.1 mnormt_1.5-5 hms_0.3
## [31] digest_0.6.12 stringi_1.1.5 grid_3.4.1
## [34] rprojroot_1.2 tools_3.4.1 lazyeval_0.2.0
## [37] pkgconfig_2.0.1 xml2_1.1.1 lubridate_1.6.0
## [40] assertthat_0.2.0 rmarkdown_1.6 httr_1.2.1
## [43] R6_2.2.2 nlme_3.1-131 compiler_3.4.1References
Cattell, Raymond B. 1966. “The Scree Test for the Number of Factors.” Multivariate Behavioral Research 1 (2): 245–76. doi:10.1207/s15327906mbr0102_10.
Dunn, Olive Jean. 1964. “Multiple Comparisons Using Rank Sums.” Technometrics 6 (3): 241–52. doi:10.1080/00401706.1964.10490181.
Ebbert, Daniel. 2017. “Patterns in the appropriation of a learning management system by instructors based on Q-methodology: Dataset.” doi:10.5281/zenodo.835415.
Guttman, Louis. 1954. “Some necessary conditions for common-factor analysis.” Psychometrika 19 (2): 149–61. doi:10.1007/BF02289162.
Horn, John L. 1965. “A rationale and test for the number of factors in factor analysis.” Psychometrika 30 (2): 179–85. doi:10.1007/BF02289447.
Kaiser, Henry F. 1960. “The Application of Electronic Computers to Factor Analysis.” Educational and Psychological Measurement 20 (1): 141–51. doi:10.1177/001316446002000116.
Kaiser, Henry F. 1970. “A second generation little jiffy.” Psychometrika 35 (4): 401–15. doi:10.1007/BF02291817.
Kruskal, William H., and W. Allen Wallis. 1952. “Use of Ranks in One-Criterion Variance Analysis.” Journal of the American Statistical Association 47 (260): 583–621. doi:10.1080/01621459.1952.10483441.
Raîche, Gilles, Theodore A. Walls, David Magis, Martin Riopel, and Jean-Guy Blais. 2013. “Non-Graphical Solutions for Cattell’s Scree Test.” Methodology 9 (1): 23–29. doi:10.1027/1614-2241/a000051.
Zabala, Aiora. 2014. “qmethod: A Package to Explore Human Perspectives Using Q Methodology.” The R Journal 6 (2): 163–73.