<p>The data is regarding the perceived roundedness of given word stimuli (n=36). Each participant (n=44) rates each stimuli on two scales, roundedness &amp; familiarity (both from 1-7 not round (sharp)/familiar to round/familiar).</p>
<p>The stimuli contains 12 sharp, 12 neutral, 12 round words each and it has 18 long and 18 short words balanced across these categories as well (6 each per category). Additionally, the 12 round words have 6 &quot;more round&quot; and 6 &quot;less round&quot; words too.</p>
<p>The outcome measures maps the categories to ordinal sets roughly; sharp would be around 1-2 on the Likert and round words would be rated on an average around 6-7.  The hypothesis is that if participants rate all neutrals between these two extremes, they are proven neutrals.</p>
<p>Finally, we have collected response times for each of the rating responses as well.</p>
<p>What could be an appropriate way to do basic analysis of these stats and what could I do to prove the hypothesis that neutral words can be proven neutral given that the ratings of them lie between the ratings of sharp and round words?</p>
<p>(Note: roundedness of a word means its association with round objects (ref: kiki-bouba effect) and that this is not the actual research, the exact details are masked as this is still being drafted)</p>

CC BY-SA 4.0
2022-12-01T09:56:50.207Z
I don't follow your hypothesis '...that neutral words can be proven neutral given that the ratings of them lie between the ratings of sharp and round words.' I think the critical element missing is the outcome measure; what is it, and importantly, is it categorical ('round', 'sharp', 'neutral'), ordinal (integer scaled say 1, 2, 3) or continuous (e.g. Likert scaled)? Or do you wish to use the RTs to discern word types? That would be pretty far fetched, no?
AliceD
CC BY-SA 4.0
2022-12-01T10:35:14.337Z
the categories map to ordinal sets roughly, so sharp would be around 1/2 on the Likert and round words would be rated on an average around 6-7 and the hypothesis is that thus if participants rate all neutrals between these two extremes, they are proven neutrals. Is it clearer now? @AliceD
WiccanKarnak
CC BY-SA 4.0
2022-12-01T11:41:44.627Z
That helps, I've added this to the Q; the question now is what the RTs have to do with this?
AliceD
CC BY-SA 4.0
2022-12-01T15:30:23.170Z
Typically in science we do not *prove* hypotheses; in frequentist null-hypothesis significance testing, the approach is to report whether the data are more unusual/extreme than one would expect if there was no effect at all. So, start by making some predictions: what should the data look like if there is no effect? What should the data look like if the effect you propose exists? You need to think about these sorts of questions first.
Bryan Krause
CC BY-SA 4.0
2022-12-01T21:24:25.420Z
thanks @AliceD ! I have added in all the variables i have to play around with, if RTs aren't necessary, they can be not looked at. I collected them to understand if there was some participant who wasn't native enough to answer things at a standard time (thus delayed) or if someone was disinterested (this maybe super quick responses)
WiccanKarnak
CC BY-SA 4.0
2022-12-01T21:27:31.637Z
@BryanKrause oh yes yes disproving the null hypothesis would be that the stimuli i predict is neutral won't lie between round and sharp ratings by the participants. If there were no effect at all, then the ratings of the categories of words won't have consistency, so high standard deviations and such. does this get closer to what you meant?
WiccanKarnak
CC BY-SA 4.0
2022-12-02T13:57:10.003Z
So your hypothesis is that a certain set of neutral words falls within a certain range? And are these neutral words really neutral (reference set of established neutral words / a gold standard of neutrality), or do you expect them to be neutral (hypothesis)? In the latter case, you want to test whether a group of neutrally assumed words meets a certain characteristic. In the former, you are benchmarking your test set.
AliceD
CC BY-SA 4.0
2022-12-03T06:03:03.310Z
@AliceD the hypothesis is that all neutral words fall between sharp and round words and no since there is no measure of neutrality yet, this is an attempt to make one
WiccanKarnak
CC BY-SA 4.0
2022-12-03T19:43:19.127Z
If you wish to make one, then you can't really do any purposeful significance testing, because you don't know what the neutral words and hence you can't investigate whether a certain cluster of neutral words fall within a certain range of ratings, because you don't know what they are (bouba, kiki, or neither) in the first place. What you can do, however, is perform descriptive stats to see whether a certain cluster of words is consistently ranked between the kiki and bouba ratings (2 < cluster < 6)...
AliceD
CC BY-SA 4.0
2022-12-03T19:43:21.987Z
...Then you might investigate  whether the appointed neutral cluster significantly scores >2 and <6  using two separate one-sample t-tests, perhaps using Bonferroni correction for multiple comparisons (alpha = 0.025). You can consider an iterative definition of the cluster to perform multiple rounds of t testing, but then you have to consider what the correction for multiple corrections should be. Also, if you need a sturdy test, you may wish to define your neutral list stringently, e.g. by defining the neutral words with a significance of, say, P < 0.0001.
AliceD
CC BY-SA 4.0
2022-12-03T19:46:31.073Z
If we are coming to a solution here, let me know and I'll turn the comment string into a full blown answer.
AliceD
CC BY-SA 4.0
2022-12-03T20:54:00.200Z
hey @AliceD yes for sure  please go ahead and do so! and yeah that makes sense (till the point you started describing how bonferroni would help) and i get the sturdier test bit too. If you could expand a bit on the 'iterative definition', it would clear everything up. (and thanks for understanding that currently we don't know anything about neutral words to be able to do a quantitative evaluation)
WiccanKarnak
CC BY-SA 4.0
2022-12-05T07:59:33.167Z
Jusy a heads up -- I've been pretty sick and I'll try to get on this asap, OK? Thanks for your patience and the pleasant conversation
AliceD
CC BY-SA 4.0
2022-12-05T09:41:54.157Z
Hey @AliceD yes for sure, thanks a lot for the detailed answer and the heads up!
WiccanKarnak
<cognitive-psychology><experimental-psychology><statistics><linguistics>
What kind of statistical analysis is suitable for this simple but unresearched data?
WiccanKarnak
# What kind of statistical analysis is suitable for this simple but unresearched data?

(Asked by: username_0 on 2022-12-01)

The data is regarding the perceived roundedness of given word stimuli (n=36). Each participant (n=44) rates each stimuli on two scales, roundedness & familiarity (both from 1-7 not round (sharp)/familiar to round/familiar).

The stimuli contains 12 sharp, 12 neutral, 12 round words each and it has 18 long and 18 short words balanced across these categories as well (6 each per category). Additionally, the 12 round words have 6 "more round" and 6 "less round" words too.

The outcome measures maps the categories to ordinal sets roughly; sharp would be around 1-2 on the Likert and round words would be rated on an average around 6-7. The hypothesis is that if participants rate all neutrals between these two extremes, they are proven neutrals.

Finally, we have collected response times for each of the rating responses as well.

What could be an appropriate way to do basic analysis of these stats and what could I do to prove the hypothesis that neutral words can be proven neutral given that the ratings of them lie between the ratings of sharp and round words?

(Note: roundedness of a word means its association with round objects (ref: kiki-bouba effect) and that this is not the actual research, the exact details are masked as this is still being drafted)

**Tags:** cognitive-psychology, experimental-psychology, statistics, linguistics

> **Comments:**
> **username_1**: I don't follow your hypothesis '...that neutral words can be proven neutral given that the ratings of them lie between the ratings of sharp and round words.' I think the critical element missing is the outcome measure; what is it, and importantly, is it categorical ('round', 'sharp', 'neutral'), ordinal (integer scaled say 1, 2, 3) or continuous (e.g. Likert scaled)? Or do you wish to use the RTs to discern word types? That would be pretty far fetched, no?  
> **username_0**: the categories map to ordinal sets roughly, so sharp would be around 1/2 on the Likert and round words would be rated on an average around 6-7 and the hypothesis is that thus if participants rate all neutrals between these two extremes, they are proven neutrals. Is it clearer now? @username_1  
> **username_1**: That helps, I've added this to the Q; the question now is what the RTs have to do with this?  
> **username_2**: Typically in science we do not *prove* hypotheses; in frequentist null-hypothesis significance testing, the approach is to report whether the data are more unusual/extreme than one would expect if there was no effect at all. So, start by making some predictions: what should the data look like if there is no effect? What should the data look like if the effect you propose exists? You need to think about these sorts of questions first.  
> **username_0**: thanks @username_1 ! I have added in all the variables i have to play around with, if RTs aren't necessary, they can be not looked at. I collected them to understand if there was some participant who wasn't native enough to answer things at a standard time (thus delayed) or if someone was disinterested (this maybe super quick responses)  
> **username_0**: @BryanKrause oh yes yes disproving the null hypothesis would be that the stimuli i predict is neutral won't lie between round and sharp ratings by the participants. If there were no effect at all, then the ratings of the categories of words won't have consistency, so high standard deviations and such. does this get closer to what you meant?  
> **username_1**: So your hypothesis is that a certain set of neutral words falls within a certain range? And are these neutral words really neutral (reference set of established neutral words / a gold standard of neutrality), or do you expect them to be neutral (hypothesis)? In the latter case, you want to test whether a group of neutrally assumed words meets a certain characteristic. In the former, you are benchmarking your test set.  
> **username_0**: @username_1 the hypothesis is that all neutral words fall between sharp and round words and no since there is no measure of neutrality yet, this is an attempt to make one  
> **username_1**: If you wish to make one, then you can't really do any purposeful significance testing, because you don't know what the neutral words and hence you can't investigate whether a certain cluster of neutral words fall within a certain range of ratings, because you don't know what they are (bouba, kiki, or neither) in the first place. What you can do, however, is perform descriptive stats to see whether a certain cluster of words is consistently ranked between the kiki and bouba ratings (2 < cluster < 6)...  
> **username_1**: ...Then you might investigate  whether the appointed neutral cluster significantly scores >2 and <6  using two separate one-sample t-tests, perhaps using Bonferroni correction for multiple comparisons (alpha = 0.025). You can consider an iterative definition of the cluster to perform multiple rounds of t testing, but then you have to consider what the correction for multiple corrections should be. Also, if you need a sturdy test, you may wish to define your neutral list stringently, e.g. by defining the neutral words with a significance of, say, P < 0.0001.  
> **username_1**: If we are coming to a solution here, let me know and I'll turn the comment string into a full blown answer.  
> **username_0**: hey @username_1 yes for sure  please go ahead and do so! and yeah that makes sense (till the point you started describing how bonferroni would help) and i get the sturdier test bit too. If you could expand a bit on the 'iterative definition', it would clear everything up. (and thanks for understanding that currently we don't know anything about neutral words to be able to do a quantitative evaluation)  
> **username_1**: Jusy a heads up -- I've been pretty sick and I'll try to get on this asap, OK? Thanks for your patience and the pleasant conversation  
> **username_0**: Hey @username_1 yes for sure, thanks a lot for the detailed answer and the heads up!  

---

## 0 answers

