Is this is the correct way to sort a dataframe to get sensible results in ANOVA (aov)?

Question

I have (theoretically) understood how to interpret results from ANOVA. I am also aware that the format presented herein is okay while performing LMER tests but since the sample size is small, I am restricted to Anova.

Basically, I want to see if the duration values of variables C2.dn show any durational differences when the column 'Consonant' value is 'Singleton' or 'Geminate'? Similarly, if V1.dn and V2.dn show any durational differences? I have confirmed that these do, by comparing the means of the phonemes. environment.

The words here, for example, chape, chappe (Row 1,2, Col 'Filename') are minimal pairs where V1_xsampa denotes vowel [a/@], C2_xsampa denotes consonant [p] or [p:] and V2_xsampa denotes word-ending [e:]. All these phonemes have their numerical (durational) values in their respective columns (V1.dn, C2.dn, V2.dn).

I hope I was clear in making understand. I am new to R. Any help would be great.

Here is some part of my data:

Filename    Speaker Consonant   Place   Manner  Voicing Beforevowel Gender  V1.dn   V1_xsampa   C2.dn   C2_xsampa   V2.dn   V2_xsampa
AK_chape.TextGrid   1   Singleton   Bilabial    Stop    Voiceless   Short   F   8.1905057   @   8.0042611   p   12.4374436  e:
AK_chappe.TextGrid  1   Geminate    Bilabial    Stop    Voiceless   Short   F   7.4699013   @   16.4554347  p:  11.58376    e:
AK_fati.TextGrid    1   Singleton   Retroflex   Stop    Voiceless   Short   F   5.7985668   @   8.422198    t`  12.3438846  i:
AK_fatti.TextGrid   1   Geminate    Retroflex   Stop    Voiceless   Short   F   5.8838506   @   15.216855   t`: 10.2798309  i:
AK_katha.TextGrid   1   Singleton   Dental/alveolar Stop    Voiceless   Short   F   7.4477162   @   8.7118953   t_d_h   11.3864323  A:
AK_kute.TextGrid    1   Singleton   Dental/alveolar Stop    Voiceless   Short   F   7.3607761   U   7.3607761   t_d 13.4668744  e:
AK_kutte.TextGrid   1   Geminate    Dental/alveolar Stop    Voiceless   Short   F   3.9207081   U   19.7175146  t_d:    13.7452732  e:
AK_saka.TextGrid    1   Singleton   Velar   Stop    Voiceless   Short   F   5.4760697   @   8.2999095   k   11.4918 A:
AK_sakka.TextGrid   1   Geminate    Velar   Stop    Voiceless   Short   F   3.9745773   @   20.1309756  k:  11.7704 A:
DS_chape.TextGrid   2   Singleton   Bilabial    Stop    Voiceless   Short   M   5.9323219   @   8.4378223   p   9.0162588   e:
DS_chappe.TextGrid  2   Geminate    Bilabial    Stop    Voiceless   Short   M   5.314411    @   19.3487061  p:  10.7748222  e:
DS_fatti.TextGrid   2   Geminate    Retroflex   Stop    Voiceless   Short   M   4.9362393   @   15.856267   t`: 10.0846991  i:
DS_katha.TextGrid   2   Singleton   Dental/alveolar Stop    Voiceless   Short   M   3.3544421   @   12.6160778  t_d_h   9.1561501   A:
DS_kattha.TextGrid  2   Geminate    Dental/alveolar Stop    Voiceless   Short   M   4.7437072   @   18.4249058  t_d_h:  9.9078116   A:
DS_kute.TextGrid    2   Singleton   Dental/alveolar Stop    Voiceless   Short   M   6.207953    U   8.1345232   t_d 10.9098312  e:
DS_kutte.TextGrid   2   Geminate    Dental/alveolar Stop    Voiceless   Short   M   4.8676863   U   18.8453944  t_d:    11.3500882  e:
DS_saka.TextGrid    2   Singleton   Velar   Stop    Voiceless   Short   M   5.5258074   @   7.9832433   k   10.4823813  A:
DS_sakka.TextGrid   2   Geminate    Velar   Stop    Voiceless   Short   M   5.3065367   @   17.4189197  k:  10.9633842  A:
MS_chape.TextGrid   3   Singleton   Bilabial    Stop    Voiceless   Short   F   7.0595707   @   8.3394356   p   7.7437615   e:
MS_chappe.TextGrid  3   Geminate    Bilabial    Stop    Voiceless   Short   F   5.7527086   @   14.4179403  p:  9.6957258   e:
MS_fati.TextGrid    3   Singleton   Retroflex   Stop    Voiceless   Short   F   5.5312929   @   10.1303456  t`  11.9326769  i:
MS_fatti.TextGrid   3   Geminate    Retroflex   Stop    Voiceless   Short   F   4.7868677   @   15.4153364  t`: 8.416162    i:
MS_katha.TextGrid   3   Singleton   Dental/alveolar Stop    Voiceless   Short   F   6.168022    @   10.7629914  t_d_h   9.2727311   A:
MS_kattha.TextGrid  3   Geminate    Dental/alveolar Stop    Voiceless   Short   F   5.2644637   @   15.2593152  t_d_h:  9.6438871   A:
MS_kute.TextGrid    3   Singleton   Dental/alveolar Stop    Voiceless   Short   F   5.855083    U   6.6463105   t_d 9.0199928   e:
MS_kutte.TextGrid   3   Geminate    Dental/alveolar Stop    Voiceless   Short   F   3.8559587   U   16.2366101  t_d:    9.9234229   e:
MS_saka.TextGrid    3   Singleton   Velar   Stop    Voiceless   Short   F   5.205498    @   7.4699608   k   7.7971282   A:
MS_sakka.TextGrid   3   Geminate    Velar   Stop    Voiceless   Short   F   5.894072    @   13.6923826  k:  9.1358116   A:
NS_chape.TextGrid   4   Singleton   Bilabial    Stop    Voiceless   Short   F   7.4994399   @   8.8423147   p   7.5200995   e: 
NS_chappe.TextGrid  4   Geminate    Bilabial    Stop    Voiceless   Short   F   4.5631368   @   15.3298145  p:  9.4684148   e:

EDIT:

head (df)

Filename Speaker Consonant Place Manner Voicing Beforevowel Gender V1.dn V1_xsampa C2.dn C2_xsampa 1 AK_chape.TextGrid 1 Singleton Bilabial Stop Voiceless Short F 8.190506 @ 8.004261 p 2 AK_chappe.TextGrid 1 Geminate Bilabial Stop Voiceless Short F 7.469901 @ 16.455435 p: 3 AK_fati.TextGrid 1 Singleton Retroflex Stop Voiceless Short F 5.798567 @ 8.422198 t 4 AK_fatti.TextGrid 1 Geminate Retroflex Stop Voiceless Short F 5.883851 @ 15.216855 t: 5 AK_katha.TextGrid 1 Singleton Dental/alveolar Stop Voiceless Short F 7.447716 @ 8.711895 t_d_h 6 AK_kute.TextGrid 1 Singleton Dental/alveolar Stop Voiceless Short F 7.360776 U 7.360776 t_d V2.dn V2_xsampa V1_dn C2_dn V2_dn 1 12.43744 e: 0.08190506 0.08004261 0.1243744 2 11.58376 e: 0.07469901 0.16455435 0.1158376 3 12.34388 i: 0.05798567 0.08422198 0.1234388 4 10.27983 i: 0.05883851 0.15216855 0.1027983 5 11.38643 A: 0.07447716 0.08711895 0.1138643 6 13.46687 e: 0.07360776 0.07360776 0.1346687

EDIT2 : dput(head(df))

Filename Speaker Consonant Place Manner Voicing Beforevowel Gender V1.dn V1_xsampa C2.dn C2_xsampa 1 AK_chape.TextGrid 1 Singleton Bilabial Stop Voiceless Short F 8.190506 @ 8.004261 p 2 AK_chappe.TextGrid 1 Geminate Bilabial Stop Voiceless Short F 7.469901 @ 16.455435 p: 3 AK_fati.TextGrid 1 Singleton Retroflex Stop Voiceless Short F 5.798567 @ 8.422198 t 4 AK_fatti.TextGrid 1 Geminate Retroflex Stop Voiceless Short F 5.883851 @ 15.216855 t: 5 AK_katha.TextGrid 1 Singleton Dental/alveolar Stop Voiceless Short F 7.447716 @ 8.711895 t_d_h 6 AK_kute.TextGrid 1 Singleton Dental/alveolar Stop Voiceless Short F 7.360776 U 7.360776 t_d V2.dn V2_xsampa V1_dn C2_dn V2_dn 1 12.43744 e: 0.08190506 0.08004261 0.1243744 2 11.58376 e: 0.07469901 0.16455435 0.1158376 3 12.34388 i: 0.05798567 0.08422198 0.1234388 4 10.27983 i: 0.05883851 0.15216855 0.1027983 5 11.38643 A: 0.07447716 0.08711895 0.1138643 6 13.46687 e: 0.07360776 0.07360776 0.1346687

what exactly do you mean by sort? Also please provide the output of `dput(data)` instead of pasting your data. Also include the expected results — Onyambu, Nov 12 '20 at 15:15
@Onyambu By 'sort', I mean the way the dataset is organised. Will anova test recognise it? See the EDIT, please. I have attached head (df) — Pranav_b, Nov 12 '20 at 15:40
You were supposed to paste the output of `dput(head(data))` . And NO, anova does not care whether your data is sorted or not. It compares means of different groups. A mean is not sorted. The results will be the same in a different order — Onyambu, Nov 12 '20 at 15:46
@Onyambu Since the original dataset has a lot of rows, I preferred to post the head. I am editing the thread with dput () — Pranav_b, Nov 12 '20 at 16:03
Probably you did not understand when I said `dput`. Run `dput(part_of_your_data shown_above)` then copy the output from the console and paste it here. So far the pasted output is not from the console — Onyambu, Nov 12 '20 at 16:08

Is this is the correct way to sort a dataframe to get sensible results in ANOVA (aov)?

0 Answers0