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After 5 weeks, we get a dataset which includes the number of pauses per user per week, we also have the difficulty rating per user per week. The data set is organized as follows:

For the likert-scale questionnaire, the data is like

  group, person, numOfPauses, week, totalLengthOfVideoInTheWeek
  apricot,   A,    15,         1,             125
  apricot,   B,    23,         1,             125
  apricot,   C,    24,         1,             125
  apple,     A,    13,         1,             125
  apple,     B,    12,         1,             125
  apple,     C,     8,         1,             125
  orange,    A,    11,         4,             156
  orange,    B,     4,         4,             156
  orange,    C,     9,         4,             156

This of course generated a result, but I am not confident this is correct.I have two concerns:

1. My measures are actually longitudinal and repeated, each user in each group is measured 5 times.
2. The "task" for each week is actually different. Although they watched videos for 5 weeks, but each week they watch a different series of videos (with different content and lengths)

After 5 weeks, we get a dataset which includes the number of pauses per user per week and the difficulty rating per user per week. The data set is organised as follows:

For the Likert-scale questionnaire, the data is like

  group, person, numOfPauses, week, totalLengthOfVideoInTheWeekInMinute
  apricot,   A,    15,         1,             125
  apricot,   B,    23,         1,             125
  apricot,   C,    24,         1,             125
  apple,     A,    13,         1,             125
  apple,     B,    12,         1,             125
  apple,     C,     8,         1,             125
  orange,    A,    11,         4,             156
  orange,    B,     4,         4,             156
  orange,    C,     9,         4,             156

This of course has generated a result, but I am not confident this is correct.I have two concerns:

1. My measures are actually longitudinal and repeated, each user in each group is measured 5 times.
2. The "task" for each week is actually different. Although they watched videos of the same course for 5 weeks, but each week they watch a different series of videos (with different content and lengths)

  cor.test(pauseFrequency,video.difficulty,method="kendall")

This of course generated a result, but I am not confident this is correct.I have two concerns,

(1)my measures are actually longitudinal and repeated, each user in each group is measured 5 times.

(2) The "task" for each week is actually different. Although they watched videos for 5 weeks, but each week they watch a different series of videos (with different content and lengths)

cor.test(pauseFrequency,video.difficulty,method="kendall")

This of course generated a result, but I am not confident this is correct.I have two concerns:

1. My measures are actually longitudinal and repeated, each user in each group is measured 5 times.
2. The "task" for each week is actually different. Although they watched videos for 5 weeks, but each week they watch a different series of videos (with different content and lengths)

We are doing research on video lecture watching. We offer a course which last four 5 weeks, and there are 9 or 10 videos in each week. We organize group-watching activities. A group usually is composed of 4-5 people, and we have 3 groups. In each group, each participant watched all videos of that week on his/her own device. We collected data on how they navigated the videos. Right after each week's video watching session, we asked each person to rate the difficulty level of the whole weeks' lecture with 5 point Likert scale.

After 5 weeks, we get the following dataset, we have the number of pauses per user per week, we also have the difficulty rating per user per week. What I want to do is to answer the following question.

Does the number of pauses correlate with the perceived difficulty?

My problem is that, in this case, how can I measure the correlation since the video length of each week are different, and even the content of videos of each week is different from another. I am using R for analysis, how can I compensate for the within-subject factor (same users) and number of pauses for different weeks?

What I do now is that I tried to normalize the number of pauses, by dividing it with the total length of the video of that week (same for all users in that week), which yields pause frequency. I compose pairs of pause frequency and difficulty rating. I actually treat every pair in the same way, no matter in the same group or in the same week or not. And then I do a Kendall's Rank Correlation test like the following:

Of course this can generate a result, but I am not confident this is correct. What should I do in this case?

We are doing research on video lecture watching. We offer a course which lasts four 5 weeks, and there are 9 or 10 videos in each week. We organize group-watching activities. A group usually is composed of 4-5 people, and we have 3 groups. In each group, each participant watched all videos of that week on his/her own device with our software (so we could collect data). We collected data on how they navigated the videos. Right after each week's video watching session, we asked each person to rate the difficulty level of the whole weeks' lecture with 5 point Likert scale.

After 5 weeks, we get a dataset which includes the number of pauses per user per week, we also have the difficulty rating per user per week. The data set is organized as follows:

For the likert-scale questionnaire, the data is like

  group, person, video.difficultyOfTheWeek, week
  apricot,   A,     5,                       1
  apricot,   B,     3,                       1
  apricot,   C,     4,                       1
  apple,     A,     3,                       1
  apple,     B,     2,                       1
  apple,     C,     2,                       1
  orange,    A,     4,                       4
  orange,    B,     3,                       4
  orange,    C,     4,                       4

We also have pause data similar as follows:

  group, person, numOfPauses, week, totalLengthOfVideoInTheWeek
  apricot,   A,    15,         1,             125
  apricot,   B,    23,         1,             125
  apricot,   C,    24,         1,             125
  apple,     A,    13,         1,             125
  apple,     B,    12,         1,             125
  apple,     C,     8,         1,             125
  orange,    A,    11,         4,             156
  orange,    B,     4,         4,             156
  orange,    C,     9,         4,             156

What I want to do for the next step is to answer the following question.

Does the number of pauses correlate with the perceived difficulty?

I tried to normalize the number of pauses. I composed a new data set by introducing a new variable "pauseFrequency", which is computed by dividing the number of pauses with the total length of the video of that week (same for all users in that week).

  group, person,  difficulty, week,      pauseFrequency
  apricot,   A,     5,         1,             15/125
  apricot,   B,     3,         1,             23/125
  apricot,   C,     4,         1,             24/125
  apple,     A,     3,         1,             13/125
  apple,     B,     2,         1,             12/125
  apple,     C,     2,         1,             8/125
  orange,    A,     4,         4,             11/156
  orange,    B,     3,         4,             4/156
  orange,    C,     4,         4,             9/156

Then the problem seems to be easy. It seems that I just need to make correlation test with the difficulty column and the pauseFrequency column. I did it. I actually treat all difficulty/frequency pairs in the same way, no matter they are from the same group or from in the same week or whatever. I treat them as individual observation.

I am using R for analysis, then I did a Kendall's Rank Correlation test like the following:

This of course generated a result, but I am not confident this is correct.I have two concerns,

(1)my measures are actually longitudinal and repeated, each user in each group is measured 5 times.

(2) The "task" for each week is actually different. Although they watched videos for 5 weeks, but each week they watch a different series of videos (with different content and lengths)

The observations are actually not independent. How can I compensate for this non-independence in my data analysis?