TPAPKB - W 4 - Writing Data Commentary

▪ Why data
commentary?

▪ Elements of data
commentary

▪ Qualifications and
Strength of Claim

▪ Quality data
commentary

▪ Rubrics for
assessment

Writing Data Commentary

Session - 4

WHY DATA COMMENTARY?

To support argument in academic writing by:

▪ Highlighting the results
▪ Assessing standard theory, common beliefs, or

general practice in the light of the given data.
▪ Comparing and evaluating different data sets.
▪ Assessing the reliability of the data in terms of the

methodology that produced it.
▪ Discussing the implications of the data.

Table 5. Means of PC Virus Infection in
US Business

Source Percentage
Flash disk from home 43%
Electronic Bulletin Board 7%
Sales Demonstration Disk 6%
6%
Repair or Service Disk 4%
3%
Company, Client or Consultant Dist 2%
Shrink – wrapped application 1%
Other Download 1%
Disk from School 1%
Local Area Network Supervisor Disk 1%
Purposely Planted 29%
Came with PC
Undetermined

Example of Data Commentary:

• Table 5 shows the most common modes of computer
virus infection for US Business. As can be seen, in
majority of cases, the source of the virus infection can
be detected, with disks being brought to the
workplace from home being by far the most
significant (43%). However, it is alarming to note that
the source of nearly 30% of viruses cannot be
determined. While it may be possible to eliminate
home-to-workplace infection by requiring computer
users to run antiviral software on diskettes brought
from home, business are still vulnerable to major data
loss, especially from unidentifiable source of infection.

ELEMENTS OF DATA COMMENTARY

▪ Location elements and/or summary statements
▪ Highlighting statements.
▪ Discussion of implications, problems,

exceptions, etc.

▪ Exercise: Identify the 3 elements above in the
Data Commentary in slide 4

QUALIFICATIONS AND
STRENGTH OF CLAIM

▪ Highlighting statements need good judgement
▪ “It is important for students to learn to be
confidently uncertain.” (Skeleton, 1988)
▪ Ways of qualifying or moderating claim

▪ Using probability, distance, generalization,
weaker verbs, combined qualifications

▪ Organize highlighting statements from general to
specific

Table 11. Years to Doctorate for Doctoral Programs at
University of Michigan, for Students Entering in 2011 - 2013

Division US Citizens / PR International Students
N %PhD
Years to N %PhD Years to
PhD PhD

Biology and 335 54 5.7 88 61 5.3
Health
44
Physical 469 5.3 430 55 5.0
Science 35
33
Social Sciences 409 30 6.0 80 59 5.3
38 5.3 91 53 5.0
Humanities 373 41 5.7 12 50 4.0
6.5 4 50 3.7
Education 141 5.3 705 56 5.0

Arts 16

Overall 1743

Exercise: Evaluate this Data
Commentary

Table 11 shows the number of years to complete a doctoral
program for both US and international students at a major
research university. As can be seen, international students on
average complete doctoral programs in less time than US
students in all divisions. The difference in years to completion
ranges from a relatively low 0.3 years in physical sciences /
engineering and humanities / arts to a high of 2.8 years in Art
program. The consistent difference in time to degree is not
fully understood at present. However, one key factor may be
motivation. Many international students have considerable
external pressures, including sponsorship / scholarship
restrictions, family obligations and employer demands which
could influence the length of time it takes to earn a doctorate

QUALITY DATA COMMENTARY

▪ Complete the elements
▪ Give comment, not reread the numbers.
▪ Order highlighting statements from general to specific
▪ Highlighting statements lead to relevant and

important discussion.
▪ Not speculating about the explanations.

Dealing with Graph

Exercise: Identify the elements

• The observed and predicted CO2 levels for 24 hours in
a commercial building is shown in figure 1. The actual
CO2 concentrations were observed directly from sites
in the building using CO2 Trapping Method. As can be
seen, the predicted CO2 concentrations increase
sharply after 8 AM and decrease steeply after 6 PM.
This is because the CO2 levels were predicted to be
dependent on the number of people in the building.
However, the model overestimates the CO2 levels
during occupancy periods (8 AM to 5 PM). The lower
CO2 levels found in the occupancy period may be
caused by several factors, such as the presence of
plants which generate oxygen, while using CO2 .

ASSIGNMENT

• Write data commentary for the table and
graph in the next slides (Exercise 1 and 2)

• Submit it in EMAS
• Deadline: Sunday, 18.00
• Thank you ☺

Exercise 1

Exercise 2

RUBRICS FOR ASSESSMENT

TRAIT Unsatisfactory Satisfactory Exemplary Score
(1) (2) (3) (Max 3)

1. Element completeness  Elements of table/figure and data  Almost all elements of table/figure  All elements of table/figure and
(15%) commentary are mostly
incomplete. and data commentary are data commentary are complete.

complete.

2. Organization of data  Data commentary is not well  Data commentary is well  Data commentary is well
commentary (15%)
organized. There is misplace-ment organized but highlighting organized with highlighting
3. Quality of highlighting
statement [HS] (25%) of some elements. statements are not correctly statements are correctly ordered

ordered from general to specific. from general to specific.

 Most HS are false or just restating  All the HS are correct, but the last  All the HS are correct and the last

the numbers. HS does not significantly lead to HS leads to important/ interesting

important/ interesting discussion discussion.

4. Sentence structure  Most sentences are not structured  In general, sentences are well  Almost all sentences are well
(10%) well. structured (effective).  structured (effective).

5. Language, spelling, and  Essay contains numerous errors in  Essay contains some errors but Essay is almost clear from
grammar (10%) language, spelling, and grammar. not distracting. language, spelling, and grammar
error.
6. Quality of discussion  There is no discussion or the  The discussion/argument is 
(15%) The discussion/argument is
resulting argument is not related to related to the last HS, but too related to the last HS and also
correct.
the last HS. speculative. Data commentary is academically
written, albeit without referencing.
7. Compliance with the  Data commentary is not  Data commentary is mostly 
academic writing rules
(10%) academically written. academically written, albeit without

referencing.

REFERENCE

Swales, J. M., & Feak, C. B. (2004). Academic Writing
for Graduate Students: Essential Tasks and
Skills (2nd ed.). Michigan: University of
Michigan Press