[R-sig-ME] Linking a three level variable with a binary score to predict total score
Johnathan Jones
john@th@n@jone@ @end|ng |rom gm@||@com
Tue Jan 26 03:34:06 CET 2021
Hi all,
I am trying to analyse data from a listening perception experiment using
mixed models (suggested to me due to its ability to handle nested data).
New to mixed models, I have viewed several tutorials, but nothing quite
fits. I have a couple of things to work through, but the first is below.
I've tried numerous means of addressing the problem and have been going in
circles for longer than I'd like to admit. I'll avoid pasting the actual
data and results at this point as I expect less is more for clarity. Happy
to post more if it would help, however.
Explanation of the Problem
I have (what I'd like to be) a continuous dependent variable (DV = percent
correct of dichotomously scored items on a listening test, where 1 is
correct, 0 is incorrect) and several predictor variables. The key interest
is seeing how well listening accuracy with individual words (isolated
speech) predicts listening accuracy with sentences (connected speech).
Listening perception can be confounded by association (“Assn_status” in
Sample Data). If a word isn’t known or isn’t readily associated with the
context, it may be perceived as another word. Accurate perception is
further influenced by the listeners first language (L1). The equation would
be:
connected speech ~ isolated speech + association + L1 + 1|participant +
error
The snag is that association (categorical, three levels) is different for
each person, and I need to index the participants’ individual associations
for each item with their score on that item. It is unclear to me how to do
this, though I've tried what seems an infinite number of equally futile
options. It may be that I have to toss the idea of the continuous DV and go
with a logistic regression.
Sample Data (data = perception)
Participants from three language groups listened to sentences and
transcribed what they heard. Each sentence heard could reasonably include
one of two “target” words, as explained in the table notes under 1, 2 , and
3. The table below shows participant number (Participant), language (Lang),
item score (Score), the word that the participant associates with the
sentence (Assn), the actual word used in the audio (Key), whether the Assn
matches Key (Assn_status), overall performance on the isolated word task
(Isolated_prcnt) and overall performance on sentence task (Cnncted_prcnt).
Assn and Key are included below for illustrative purposes to help explain
Assn_status.
*Partcpnt*
*Lang*
*Score*
*Assn*
*Key*
*Assn_status*
*Isolated_prcnt*
*Cnncted_prcnt*
1
Mandarin
1
beat
beat
same1
75
62.5
1
Mandarin
0
beat
bit
opposite2
75
62.5
1
Mandarin
1
beaten
beaten
same
75
62.5
1
Mandarin
0
beaten
bitten
opposite
75
62.5
1
Mandarin
1
meals
meals
same
75
62.5
1
Mandarin
0
meals
mills
opposite
75
62.5
1
Mandarin
1
none
risen
neither3
75
62.5
1
Mandarin
1
none
reason
neither
75
62.5
2
Mandarin
1
none
beat
neither
70
50
2
Mandarin
1
none
bit
neither
70
50
2
Mandarin
0
bitten
beaten
opposite
70
50
2
Mandarin
0
bitten
bitten
same
70
50
2
Mandarin
1
meals
meals
same
70
50
2
Mandarin
0
meals
mills
opposite
70
50
2
Mandarin
0
reason
risen
opposite
70
50
2
Mandarin
1
reason
reason
same
70
50
3
Mandarin
1
bit
beat
neither
85
75
3
Mandarin
1
bit
bit
neither
85
75
3
Mandarin
1
none
beaten
neither
85
75
3
Mandarin
1
none
bitten
neither
85
75
3
Mandarin
1
mills
meals
opposite
85
75
3
Mandarin
0
mills
mills
same
85
75
3
Mandarin
0
reason
risen
opposite
85
75
3
Mandarin
1
reason
reason
same
85
75
1The target word (key) is the same as the participant’s indicated
association with the sentence. For example, in “The man was ____”, a
participant may associate the word *bitten* more than *beaten* to complete
the sentence. If the audio was “The man was bitten”, the Assn_Status column
would indicate “same”.
2The target word in the item was opposite of the word the participant
associated with the sentence. If a participant associates *bitten* with the
sentence context, “The man was ___”, but the audio was, “The man was
beaten”, Assn_Status would indicate “opposite”.
3To the participant, both words have equal association to the given
context. There is no preference for one word over the other.
Attempts
Using the variables described in the Sample Data table and the nlme
package, I tried and failed with: model = lme(Cnnctd_prcnt ~ Language *
Isolated_prcnt, data = perception, random = ~ 1|Participant)
summary(model). While it ran and provided output, the resulting data
yielded different parameter estimates than the null model (LM), which
suggests failure somewhere, but I don't know where. My hope is that it will
seem obvious to someone here as is, but as noted previously, I'll happily
post the data if needed.
Related: Surprisingly (to me), the LM estimates were the same when using
either Score (a binary variable) or Cnnctd_prcnt (continuous) as the DV. It
seems R automatically aggregates the binary scores; am I accurate in
assuming this? Would replacing Cnnctd_prcnt with Score be a suitable
workaround?
Thanks tremendously,
John Jones
[[alternative HTML version deleted]]
More information about the R-sig-mixed-models
mailing list