Hi all,

Hopefully, somebody can help me. I've been struggling for two weeks now on how to analyze the data for my thesis.

The design of the original study is as follows: 38 participants filled in multiple questionnaires and did a memory test in the evening. During the night, they were awoken nine times and asked to report everything that was going through their mind before they woke, and were asked a few other questions after this. After the final awakening in the morning, they did the memory test again.

Now, I did exploratory analyses on all data and decided that the aim of my study would be to explore/examine differences between anxious and non-anxious participants in information processing during the night (so the content of their dreams, etc.)

For most variables, like the questionnaires that were only taken once, this is relatively easy: I can perform a t-test on the dependent variables.

Since there are many dependent variables and just one categorical predictor I am interested in (anxious vs. non-anxious) I have given thought to performing a MANOVA, but I found online that this is discouraged when the dependent variables are not correlated. In my specific case, they aren’t, or at least not all of them. So I guess I would have to perform many t-tests. (tips on this are welcome as well, regarding type I error increasing with doing multiple tests and correcting for this).

However, my main problem is how to analyze the data from the awakenings. For some participants, there are a few awakenings missing because they slept through the phone call, or because an awakening was skipped, or in some cases because the experimenter accidentally fell asleep. This results in different numbers of awakenings per participant, which rules out a repeated measures ANOVA.

But comparing all awakenings from the two different anxiety groups is also not the way to go, since then the between-subject variance is lost.

I've tried using a Mixed Model analysis, but when I select both participants and awakenings as random effects, and anxiety and one of the dependent variables of interest as fixed effects, I have trouble interpreting the results and it seems as if the different awakenings are compared, which is not what I want.

Instead, I'm looking for a way to "nest" or "weigh" the awakenings hierarchically to each participant. I want to compare the different variables that were measured at each awakening between the groups, without discarding between-subject differences. What statistical model can I use for this?

I'm sorry for the long story. Hopefully there's somebody who can help me. I can also send the R-scripts of what I've been doing so far so we can discuss together how to interpret the results and what I can change or do. I've been using the lme & lmer functions, but the output confuses me.

Hopefully, somebody can help me. I've been struggling for two weeks now on how to analyze the data for my thesis.

The design of the original study is as follows: 38 participants filled in multiple questionnaires and did a memory test in the evening. During the night, they were awoken nine times and asked to report everything that was going through their mind before they woke, and were asked a few other questions after this. After the final awakening in the morning, they did the memory test again.

Now, I did exploratory analyses on all data and decided that the aim of my study would be to explore/examine differences between anxious and non-anxious participants in information processing during the night (so the content of their dreams, etc.)

For most variables, like the questionnaires that were only taken once, this is relatively easy: I can perform a t-test on the dependent variables.

Since there are many dependent variables and just one categorical predictor I am interested in (anxious vs. non-anxious) I have given thought to performing a MANOVA, but I found online that this is discouraged when the dependent variables are not correlated. In my specific case, they aren’t, or at least not all of them. So I guess I would have to perform many t-tests. (tips on this are welcome as well, regarding type I error increasing with doing multiple tests and correcting for this).

However, my main problem is how to analyze the data from the awakenings. For some participants, there are a few awakenings missing because they slept through the phone call, or because an awakening was skipped, or in some cases because the experimenter accidentally fell asleep. This results in different numbers of awakenings per participant, which rules out a repeated measures ANOVA.

But comparing all awakenings from the two different anxiety groups is also not the way to go, since then the between-subject variance is lost.

I've tried using a Mixed Model analysis, but when I select both participants and awakenings as random effects, and anxiety and one of the dependent variables of interest as fixed effects, I have trouble interpreting the results and it seems as if the different awakenings are compared, which is not what I want.

Instead, I'm looking for a way to "nest" or "weigh" the awakenings hierarchically to each participant. I want to compare the different variables that were measured at each awakening between the groups, without discarding between-subject differences. What statistical model can I use for this?

I'm sorry for the long story. Hopefully there's somebody who can help me. I can also send the R-scripts of what I've been doing so far so we can discuss together how to interpret the results and what I can change or do. I've been using the lme & lmer functions, but the output confuses me.

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