SlideShare a Scribd company logo
1 of 41
ELT- 702 Academic Writing


 Data commentary

 by, Betül GÜLERYÜZ
     Osman AYDOĞAR
                   16.03.2012
 While data commentaries may be "stand-alone"
  pieces of writing, they generally occur in the
  Results or Discussion sections of a report or
  thesis.
 The main purposes of a data commentary are to
  present the results of research, interpret these
  results, and to discuss the significance and
  implications of the results.
 Data can often be best expressed by a chart,
   graph, table, or other illustration.
 The type of writing that accompanies a visual
  display is called data commentary.
 The data should be presented and analysed in logical manner;
  in other words you are expected to analyse and evaluate the
  data, not just describe it.
Why use a data commentary?

 Highlight results
 Assess standard theories, common beliefs, or
generals practices in light of the results
 Compare and evaluate different data sets
 Assess the reliability of data in terms of the methods
  that produced it
 Discuss implications of the data
 Calls attention to something not directly apparent
 from the table, chart, or graph.
 Analyzes data for a reason: to support a claim which
 in turn helps achieve the main goal of the paper.
Structure of Data Commentary

 Data commentaries usually have the following
   elements in the following order.

1. Location elements and/or summary statements
2. Highlighting statements
3. Discussions of implications, problems, exceptions,
    recommendations, etc.
Paragraph structure of a data commentary:

 Topic sentence (claim)
 Location elements and summaries (support)
 Highlights (examples)
 Implications (restatement of claim)
Highlighting Statements
 The central sections of data commentaries consist of
  highlighting statements.
 Highlighting statements are generalizations that you
  can draw from the details of the data display.
 Highlighting statements need good judgment.
 They are an opportunity to show your intelligence..
 In particular, they are an opportunity for you to
 demonstrate
   that you can spot trends or regularities in the data,
   that you can separate more important findings from
    less important ones, and
   that you can make claims of appropriate strength
So, do not

 simply repeat all the details in words,
 attempt to cover all the information, or
 claim more than is reasonable or defensible.
Verbs for introducing highlights:
 Table 2 shows the most common factors are…
 Figure 2.3 illustrates the results of a study
 that…
 Table 9 demonstrates how the use of…
 …the most common are displayed in Table 3.
 …details of the operation are given in Figure
 4.4.
 …these qualities are suggested by Figure 9.3.
 Other verbs: provide, present, summarize, reveal,
  indicate
Language Focus: Linking as-Clauses
 These linking clauses (where as does not equal since or
  because) are exceptional in English grammar. In the
  passive, these linking clauses have no subjects.
  Compare the following sentences.
a. As it has been proved, the theory may have practical
  importance.
b. As has been proved, the theory may have practical
  importance.
 In sentence a there is a causal relationship between the
  as-clause and the main clause. Because the theory has
  been proved, it may have practical importance.
Some Specific ways for qualifying or moderating a
claim
 1-Probability
2-Distance
3-Generalization
4-Weaker verbs
Indicative Summary
 - Indicates what has been done in the work.
 •Table 5 shows the most common modes of computer
  infection for U.S. businesses.
 •Figure 4.2 gives the results of the second experiment.
Indicating the strengths of data results
 A reduced speed limit will result in fewer
  accidents.
 A reduced speed limit may result in fewer accidents.
 A reduced speed limit could result in fewer
   accidents.
 It is certain that…
 It is almost certain that…
 It is highly probable that…
 It is possible that…
 It is unlikely that…
 There is a strong possibility that…
 There is a slight possibility that…
 There is a remote possibility that…
Informative Summary
 - Provides a summary of the data.
 •Table 5 shows that home disks are the major source
  of computer viruses.
 •Table 4.2 suggests that the experimental results
  confirm the hypothesis.
Table 5. Means of PC Infection in U.S. Businesses

Source Percentage
 Disks from home                        43%
 Electronic bulletin board              7%
 Sales demonstration disk               6%
 Repair or service disk                 6%
 Company, client, or consultant disk    4%
 Other                                  9%
 Undetermined                           29%
 1) A computer virus is a program that is specifically and
  maliciously designed to attack a computer system, destroying
  data. 2) As businesses have become increasingly dependent
  on computer systems, concern over the potential
  destructiveness of such viruses has also grown. 3) Table 5
  shows the most common modes of infection for U.S.
  businesses.-location and indicative summary

 4) As can be seen, in the 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%). 5) However, it is alarming to note that the source of
  nearly 30% of viruses cannot be determined.-highlightment
 6) While it may be possible to eliminate home-to-
  workplace infection by requiring computer users to run
  antiviral software on diskettes brought from home,
  businesses are still vulnerable to major data loss,
  especially from unidentifiable sources of infection.
    -implications
Combined qualifications
 A strong claim
We add some qualifications
We have a new claim:
Passive voice:

 a. The most common modes of infection are shown
  in Table 5.
 b. Details of the fertilizers used are provided in
  Table 2.
 c. The results of the second experiment are given in
  Figure 4.2.
Passive Verbs in Reference to a Visual


  Shown in
  Illustrated in
  Presented in
  Given in
  Listed in
  Seen in
  Provided in
  Summarized in
  Seen from
Active voice:

 a. Table 5 shows the most common modes of
  computer infections.
 b. Table 2 provides details of the fertilizer used.
 c. Figure 4.2 gives the results of the second
  experiment
Active Verbs Following Reference to a Visual


  Shows              *Presents
  Illustrates         *Summarizes
  Demonstrates         *Contains
  Provides            *Depicts
  Lists
  Reports
In order to investigate the hypothesis that 8-year old boys
are more aggressive than 8-year old girls, 8-year old
children were observed playing in schoolyards and incidents
of certain aggressive behaviors were recorded.


     Aggressive        Girls          Boys
     behavior
     Pushing           21%            35%
     Kicking/Hitting   15%            61%
     Cursing           9%             30%
     Chasing           78%            1%
Commentary 1
 In order to investigate the hypothesis that 8-year old
 boys are more aggressive than 8-year old girls, 8-year old
 children were observed playing in schoolyards and
 incidents of certain aggressive behaviors were recorded.
 2)Table 1 shows that boys are more aggressive than girls.
 3)The percentage of pushing is 21% of girl; on the other
 hand that of boys is 35%. 4)Except for chasing, the
 percentage of aggressive behavior is higher in boys.
 5)From this data you can agree that boys are more
 aggressive than girls. (Rating: 73)
Commentary 2
 In order to investigate the hypothesis that 8-year old
 boys are more aggressive than 8-year old girls, 8-year old
 children were observed playing in schoolyards and
 incidents of certain aggressive behaviors were recorded.
 2)As you can see in Table 1, we only considered four
 human aggressive behaviors in our study. 3)The most
 common children aggressive conduct are pushing,
 kicking/hitting, cursing, and chasing. 4)After several
 weeks of observation in different schools playground we
 found the percentage that appeared on table 1. 5) (See
 attachment 1) 6) Sixty percent (61%) of the boys like to
 kick and hit compared to fifteen percent (15%) of the
 girls. 7)This is more aggressive than chasing. 8)The
 chasing behavior was the only one girls were more
 aggressive than boys. (Rating: 77)
Commentary 3
 In order to investigate the hypothesis that 8-year old
 boys are more aggressive than 8-year old girls, 8-year old
 children were observed playing in schoolyards and
 incidents of certain aggressive behaviors were recorded.
 2)It was assumed that aggressive behavior consisted of
 the following: i) pushing, ii) kicking and hitting, iii)
 cursing, and iv) chasing. 3)As can be seen from the table
 above, the average 8-year old boy was more aggressive
 than the 8-year old girls. 4)Chasing was the one behavior
 that was more pronounced for the girls. 5)This result,
 however, does not disprove the theory since chasing
 seems to be a less aggressive behavior than the other
 behaviors that were tested. 6The 8-year old boys got
 more involved with the more aggressive behavior, which
 is kicking/hitting, much more than the 8-year old girls.
 (Rating: 93)
Commentary 4
 In order to investigate the hypothesis that 8-year old boys are more
  aggressive than 8-year old girls, 8-year old children were observed
  playing in schoolyards and incidents of certain aggressive behaviors
  were recorded. 2)At first glance it appears that 8-year old boys exhibit
  more aggressive behavior than 8-year old girls if all four recorded
  behaviors are equally weighed. 3)But, this last assertion is false.
  4)Since the ability to record will vary with playground size and the
  number of observers (not to mention the skills of the observers or
  accounting for children entering or leaving the playground), and that it
  takes a certain amount of an observer's time to note the behavior,
  short-lived behaviors such as cursing or pushing could be under-
  represented. 5)Simply because more can occur during the time an
  observer notes another behavior. 6)Conversely, long-lived behaviors
  such as chasing could be over-represented because they occur over a
  longer period of time and thus allow more latitude for the observer
  marking the behavior. (Rating: 93)

More Related Content

What's hot

Lesson hypertext and intertext
Lesson hypertext and intertextLesson hypertext and intertext
Lesson hypertext and intertextCristinaGrumal
 
Constructing Reasonable Academic Arguments
Constructing Reasonable Academic ArgumentsConstructing Reasonable Academic Arguments
Constructing Reasonable Academic Argumentsvlequire
 
Intertextuality
IntertextualityIntertextuality
IntertextualitySteve Hoy
 
Understanding the Types of speech
Understanding the Types of speechUnderstanding the Types of speech
Understanding the Types of speechCher Jessa
 
Readers Theater and Chamber Theater
Readers Theater and Chamber TheaterReaders Theater and Chamber Theater
Readers Theater and Chamber TheaterKia Sales Soneja
 
Hypertext & intertext - Reading and writing Skills - grade 11 - 2nd sem
Hypertext & intertext - Reading and writing Skills - grade 11 - 2nd semHypertext & intertext - Reading and writing Skills - grade 11 - 2nd sem
Hypertext & intertext - Reading and writing Skills - grade 11 - 2nd semAshley Minerva
 
Using Appropriate Grammatical Signals.pptx
Using Appropriate Grammatical Signals.pptxUsing Appropriate Grammatical Signals.pptx
Using Appropriate Grammatical Signals.pptxcarlo842542
 
Techniques in organizing information
Techniques in organizing informationTechniques in organizing information
Techniques in organizing informationmary katrine belino
 
Tekstong Persweysiv o Nanghihikayat
Tekstong Persweysiv  o NanghihikayatTekstong Persweysiv  o Nanghihikayat
Tekstong Persweysiv o Nanghihikayatmaricel panganiban
 
Claims of Fact, Value and Policy.pptx
Claims of  Fact, Value and Policy.pptxClaims of  Fact, Value and Policy.pptx
Claims of Fact, Value and Policy.pptxKristineDeLeon16
 
Compose an Independent Critique of a Chosen Selection.pptx
Compose an Independent Critique of a Chosen Selection.pptxCompose an Independent Critique of a Chosen Selection.pptx
Compose an Independent Critique of a Chosen Selection.pptxgretchencarino1
 

What's hot (20)

Lesson hypertext and intertext
Lesson hypertext and intertextLesson hypertext and intertext
Lesson hypertext and intertext
 
Tekstong deskriptibo - Grade 11
Tekstong deskriptibo - Grade 11Tekstong deskriptibo - Grade 11
Tekstong deskriptibo - Grade 11
 
Constructing Reasonable Academic Arguments
Constructing Reasonable Academic ArgumentsConstructing Reasonable Academic Arguments
Constructing Reasonable Academic Arguments
 
tekstong-impormatibo.pptx
tekstong-impormatibo.pptxtekstong-impormatibo.pptx
tekstong-impormatibo.pptx
 
Intertextuality
IntertextualityIntertextuality
Intertextuality
 
Understanding the Types of speech
Understanding the Types of speechUnderstanding the Types of speech
Understanding the Types of speech
 
Readers Theater and Chamber Theater
Readers Theater and Chamber TheaterReaders Theater and Chamber Theater
Readers Theater and Chamber Theater
 
HYPERTEXT.pptx
HYPERTEXT.pptxHYPERTEXT.pptx
HYPERTEXT.pptx
 
Hypertext & intertext - Reading and writing Skills - grade 11 - 2nd sem
Hypertext & intertext - Reading and writing Skills - grade 11 - 2nd semHypertext & intertext - Reading and writing Skills - grade 11 - 2nd sem
Hypertext & intertext - Reading and writing Skills - grade 11 - 2nd sem
 
Using Appropriate Grammatical Signals.pptx
Using Appropriate Grammatical Signals.pptxUsing Appropriate Grammatical Signals.pptx
Using Appropriate Grammatical Signals.pptx
 
Parody and pastiche
Parody and pasticheParody and pastiche
Parody and pastiche
 
Techniques in organizing information
Techniques in organizing informationTechniques in organizing information
Techniques in organizing information
 
WEEK-6-RWS-INTERTEXTUALITY.pptx
WEEK-6-RWS-INTERTEXTUALITY.pptxWEEK-6-RWS-INTERTEXTUALITY.pptx
WEEK-6-RWS-INTERTEXTUALITY.pptx
 
Daily lesson plan
Daily lesson planDaily lesson plan
Daily lesson plan
 
Intertext
IntertextIntertext
Intertext
 
Tekstong Persweysiv o Nanghihikayat
Tekstong Persweysiv  o NanghihikayatTekstong Persweysiv  o Nanghihikayat
Tekstong Persweysiv o Nanghihikayat
 
Claims of Fact, Value and Policy.pptx
Claims of  Fact, Value and Policy.pptxClaims of  Fact, Value and Policy.pptx
Claims of Fact, Value and Policy.pptx
 
Tekstong Argumentatibo
Tekstong ArgumentatiboTekstong Argumentatibo
Tekstong Argumentatibo
 
Explicit v implicit
Explicit v  implicitExplicit v  implicit
Explicit v implicit
 
Compose an Independent Critique of a Chosen Selection.pptx
Compose an Independent Critique of a Chosen Selection.pptxCompose an Independent Critique of a Chosen Selection.pptx
Compose an Independent Critique of a Chosen Selection.pptx
 

Similar to 3+data+commentary

Visual Information
Visual InformationVisual Information
Visual Informationfeueacmrq
 
Answer questions Minimum 100 words each and reference (questions.docx
Answer questions Minimum 100 words each and reference (questions.docxAnswer questions Minimum 100 words each and reference (questions.docx
Answer questions Minimum 100 words each and reference (questions.docxamrit47
 
Data Analysis for Graduate Studies Summary
Data Analysis for Graduate Studies SummaryData Analysis for Graduate Studies Summary
Data Analysis for Graduate Studies SummaryKelvinNMhina
 
Statistics for management
Statistics for managementStatistics for management
Statistics for managementJohn Prarthan
 
2. This exercise uses the dataset WholeFoods.” (a) Use Excel to.docx
2. This exercise uses the dataset WholeFoods.” (a) Use Excel to.docx2. This exercise uses the dataset WholeFoods.” (a) Use Excel to.docx
2. This exercise uses the dataset WholeFoods.” (a) Use Excel to.docxeugeniadean34240
 
Critical thinking fall 2014 2015 (chapters 6,7,8,11 and 12 analyzing and eval...
Critical thinking fall 2014 2015 (chapters 6,7,8,11 and 12 analyzing and eval...Critical thinking fall 2014 2015 (chapters 6,7,8,11 and 12 analyzing and eval...
Critical thinking fall 2014 2015 (chapters 6,7,8,11 and 12 analyzing and eval...XixiViolet
 
Chapter 0: the what and why of statistics
Chapter 0: the what and why of statisticsChapter 0: the what and why of statistics
Chapter 0: the what and why of statisticsChristian Robert
 
Bus 308 Effective Communication - snaptutorial.com
Bus 308  Effective Communication - snaptutorial.comBus 308  Effective Communication - snaptutorial.com
Bus 308 Effective Communication - snaptutorial.comHarrisGeorg10
 
Statistical Calculations 5Statistical Calculations.docx
Statistical Calculations 5Statistical Calculations.docxStatistical Calculations 5Statistical Calculations.docx
Statistical Calculations 5Statistical Calculations.docxdessiechisomjj4
 
Stat11t Chapter1
Stat11t Chapter1Stat11t Chapter1
Stat11t Chapter1gueste87a4f
 
There’s No Escape from External Validity – Reporting Habits of Randomized Con...
There’s No Escape from External Validity – Reporting Habits of Randomized Con...There’s No Escape from External Validity – Reporting Habits of Randomized Con...
There’s No Escape from External Validity – Reporting Habits of Randomized Con...Stockholm Institute of Transition Economics
 
Pg. 05Question FiveAssignment #Deadline Day 22.docx
Pg. 05Question FiveAssignment #Deadline Day 22.docxPg. 05Question FiveAssignment #Deadline Day 22.docx
Pg. 05Question FiveAssignment #Deadline Day 22.docxmattjtoni51554
 
Chapter 3 part2- Sampling Design
Chapter 3 part2- Sampling DesignChapter 3 part2- Sampling Design
Chapter 3 part2- Sampling Designnszakir
 
The Outsiders Essay Questions And Answers
The Outsiders Essay Questions And AnswersThe Outsiders Essay Questions And Answers
The Outsiders Essay Questions And AnswersAshley Mason
 

Similar to 3+data+commentary (20)

Visual Information
Visual InformationVisual Information
Visual Information
 
Answer questions Minimum 100 words each and reference (questions.docx
Answer questions Minimum 100 words each and reference (questions.docxAnswer questions Minimum 100 words each and reference (questions.docx
Answer questions Minimum 100 words each and reference (questions.docx
 
1.1 statistical and critical thinking
1.1 statistical and critical thinking1.1 statistical and critical thinking
1.1 statistical and critical thinking
 
Results
ResultsResults
Results
 
Data Analysis for Graduate Studies Summary
Data Analysis for Graduate Studies SummaryData Analysis for Graduate Studies Summary
Data Analysis for Graduate Studies Summary
 
Statistics for management
Statistics for managementStatistics for management
Statistics for management
 
2. This exercise uses the dataset WholeFoods.” (a) Use Excel to.docx
2. This exercise uses the dataset WholeFoods.” (a) Use Excel to.docx2. This exercise uses the dataset WholeFoods.” (a) Use Excel to.docx
2. This exercise uses the dataset WholeFoods.” (a) Use Excel to.docx
 
Critical thinking fall 2014 2015 (chapters 6,7,8,11 and 12 analyzing and eval...
Critical thinking fall 2014 2015 (chapters 6,7,8,11 and 12 analyzing and eval...Critical thinking fall 2014 2015 (chapters 6,7,8,11 and 12 analyzing and eval...
Critical thinking fall 2014 2015 (chapters 6,7,8,11 and 12 analyzing and eval...
 
Chapter 0: the what and why of statistics
Chapter 0: the what and why of statisticsChapter 0: the what and why of statistics
Chapter 0: the what and why of statistics
 
Analytic Essay Examples
Analytic Essay ExamplesAnalytic Essay Examples
Analytic Essay Examples
 
metrology data
metrology datametrology data
metrology data
 
Bus 308 Effective Communication - snaptutorial.com
Bus 308  Effective Communication - snaptutorial.comBus 308  Effective Communication - snaptutorial.com
Bus 308 Effective Communication - snaptutorial.com
 
Statistical Calculations 5Statistical Calculations.docx
Statistical Calculations 5Statistical Calculations.docxStatistical Calculations 5Statistical Calculations.docx
Statistical Calculations 5Statistical Calculations.docx
 
Stat11t chapter1
Stat11t chapter1Stat11t chapter1
Stat11t chapter1
 
Stat11t Chapter1
Stat11t Chapter1Stat11t Chapter1
Stat11t Chapter1
 
There’s No Escape from External Validity – Reporting Habits of Randomized Con...
There’s No Escape from External Validity – Reporting Habits of Randomized Con...There’s No Escape from External Validity – Reporting Habits of Randomized Con...
There’s No Escape from External Validity – Reporting Habits of Randomized Con...
 
Statistics
StatisticsStatistics
Statistics
 
Pg. 05Question FiveAssignment #Deadline Day 22.docx
Pg. 05Question FiveAssignment #Deadline Day 22.docxPg. 05Question FiveAssignment #Deadline Day 22.docx
Pg. 05Question FiveAssignment #Deadline Day 22.docx
 
Chapter 3 part2- Sampling Design
Chapter 3 part2- Sampling DesignChapter 3 part2- Sampling Design
Chapter 3 part2- Sampling Design
 
The Outsiders Essay Questions And Answers
The Outsiders Essay Questions And AnswersThe Outsiders Essay Questions And Answers
The Outsiders Essay Questions And Answers
 

Recently uploaded

Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024The Digital Insurer
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure servicePooja Nehwal
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...apidays
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationRadu Cotescu
 
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live StreamsTop 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live StreamsRoshan Dwivedi
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘RTylerCroy
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonAnna Loughnan Colquhoun
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountPuma Security, LLC
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Servicegiselly40
 
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...gurkirankumar98700
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking MenDelhi Call girls
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsMaria Levchenko
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityPrincipled Technologies
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slidespraypatel2
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slidevu2urc
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfEnterprise Knowledge
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 

Recently uploaded (20)

Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live StreamsTop 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path Mount
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slides
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 

3+data+commentary

  • 1. ELT- 702 Academic Writing  Data commentary by, Betül GÜLERYÜZ Osman AYDOĞAR 16.03.2012
  • 2.  While data commentaries may be "stand-alone" pieces of writing, they generally occur in the Results or Discussion sections of a report or thesis.  The main purposes of a data commentary are to present the results of research, interpret these results, and to discuss the significance and implications of the results.
  • 3.  Data can often be best expressed by a chart, graph, table, or other illustration.  The type of writing that accompanies a visual display is called data commentary.
  • 4.  The data should be presented and analysed in logical manner; in other words you are expected to analyse and evaluate the data, not just describe it.
  • 5. Why use a data commentary?  Highlight results  Assess standard theories, common beliefs, or generals practices in light of the results  Compare and evaluate different data sets  Assess the reliability of data in terms of the methods that produced it
  • 6.  Discuss implications of the data  Calls attention to something not directly apparent  from the table, chart, or graph.  Analyzes data for a reason: to support a claim which  in turn helps achieve the main goal of the paper.
  • 7. Structure of Data Commentary  Data commentaries usually have the following elements in the following order. 1. Location elements and/or summary statements 2. Highlighting statements 3. Discussions of implications, problems, exceptions, recommendations, etc.
  • 8. Paragraph structure of a data commentary:  Topic sentence (claim)  Location elements and summaries (support)  Highlights (examples)  Implications (restatement of claim)
  • 9.
  • 10. Highlighting Statements  The central sections of data commentaries consist of highlighting statements.  Highlighting statements are generalizations that you can draw from the details of the data display.  Highlighting statements need good judgment.  They are an opportunity to show your intelligence..
  • 11.  In particular, they are an opportunity for you to demonstrate  that you can spot trends or regularities in the data,  that you can separate more important findings from less important ones, and  that you can make claims of appropriate strength
  • 12. So, do not  simply repeat all the details in words,  attempt to cover all the information, or  claim more than is reasonable or defensible.
  • 13. Verbs for introducing highlights:  Table 2 shows the most common factors are…  Figure 2.3 illustrates the results of a study  that…  Table 9 demonstrates how the use of…  …the most common are displayed in Table 3.  …details of the operation are given in Figure  4.4.  …these qualities are suggested by Figure 9.3.  Other verbs: provide, present, summarize, reveal, indicate
  • 14. Language Focus: Linking as-Clauses  These linking clauses (where as does not equal since or because) are exceptional in English grammar. In the passive, these linking clauses have no subjects. Compare the following sentences. a. As it has been proved, the theory may have practical importance. b. As has been proved, the theory may have practical importance.  In sentence a there is a causal relationship between the as-clause and the main clause. Because the theory has been proved, it may have practical importance.
  • 15.
  • 16. Some Specific ways for qualifying or moderating a claim  1-Probability
  • 18.
  • 20.
  • 21.
  • 23. Indicative Summary  - Indicates what has been done in the work.  •Table 5 shows the most common modes of computer infection for U.S. businesses.  •Figure 4.2 gives the results of the second experiment.
  • 24. Indicating the strengths of data results  A reduced speed limit will result in fewer accidents.  A reduced speed limit may result in fewer accidents.  A reduced speed limit could result in fewer accidents.  It is certain that…  It is almost certain that…  It is highly probable that…
  • 25.  It is possible that…  It is unlikely that…  There is a strong possibility that…  There is a slight possibility that…  There is a remote possibility that…
  • 26. Informative Summary  - Provides a summary of the data.  •Table 5 shows that home disks are the major source of computer viruses.  •Table 4.2 suggests that the experimental results confirm the hypothesis.
  • 27. Table 5. Means of PC Infection in U.S. Businesses Source Percentage  Disks from home  43%  Electronic bulletin board  7%  Sales demonstration disk  6%  Repair or service disk  6%  Company, client, or consultant disk  4%  Other  9%  Undetermined  29%
  • 28.  1) A computer virus is a program that is specifically and maliciously designed to attack a computer system, destroying data. 2) As businesses have become increasingly dependent on computer systems, concern over the potential destructiveness of such viruses has also grown. 3) Table 5 shows the most common modes of infection for U.S. businesses.-location and indicative summary  4) As can be seen, in the 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%). 5) However, it is alarming to note that the source of nearly 30% of viruses cannot be determined.-highlightment
  • 29.  6) While it may be possible to eliminate home-to- workplace infection by requiring computer users to run antiviral software on diskettes brought from home, businesses are still vulnerable to major data loss, especially from unidentifiable sources of infection. -implications
  • 31. We add some qualifications
  • 32. We have a new claim:
  • 33. Passive voice:  a. The most common modes of infection are shown in Table 5.  b. Details of the fertilizers used are provided in Table 2.  c. The results of the second experiment are given in Figure 4.2.
  • 34. Passive Verbs in Reference to a Visual  Shown in  Illustrated in  Presented in  Given in  Listed in  Seen in  Provided in  Summarized in  Seen from
  • 35. Active voice:  a. Table 5 shows the most common modes of computer infections.  b. Table 2 provides details of the fertilizer used.  c. Figure 4.2 gives the results of the second experiment
  • 36. Active Verbs Following Reference to a Visual  Shows *Presents  Illustrates *Summarizes  Demonstrates *Contains  Provides *Depicts  Lists  Reports
  • 37. In order to investigate the hypothesis that 8-year old boys are more aggressive than 8-year old girls, 8-year old children were observed playing in schoolyards and incidents of certain aggressive behaviors were recorded. Aggressive Girls Boys behavior Pushing 21% 35% Kicking/Hitting 15% 61% Cursing 9% 30% Chasing 78% 1%
  • 38. Commentary 1  In order to investigate the hypothesis that 8-year old boys are more aggressive than 8-year old girls, 8-year old children were observed playing in schoolyards and incidents of certain aggressive behaviors were recorded. 2)Table 1 shows that boys are more aggressive than girls. 3)The percentage of pushing is 21% of girl; on the other hand that of boys is 35%. 4)Except for chasing, the percentage of aggressive behavior is higher in boys. 5)From this data you can agree that boys are more aggressive than girls. (Rating: 73)
  • 39. Commentary 2  In order to investigate the hypothesis that 8-year old boys are more aggressive than 8-year old girls, 8-year old children were observed playing in schoolyards and incidents of certain aggressive behaviors were recorded. 2)As you can see in Table 1, we only considered four human aggressive behaviors in our study. 3)The most common children aggressive conduct are pushing, kicking/hitting, cursing, and chasing. 4)After several weeks of observation in different schools playground we found the percentage that appeared on table 1. 5) (See attachment 1) 6) Sixty percent (61%) of the boys like to kick and hit compared to fifteen percent (15%) of the girls. 7)This is more aggressive than chasing. 8)The chasing behavior was the only one girls were more aggressive than boys. (Rating: 77)
  • 40. Commentary 3  In order to investigate the hypothesis that 8-year old boys are more aggressive than 8-year old girls, 8-year old children were observed playing in schoolyards and incidents of certain aggressive behaviors were recorded. 2)It was assumed that aggressive behavior consisted of the following: i) pushing, ii) kicking and hitting, iii) cursing, and iv) chasing. 3)As can be seen from the table above, the average 8-year old boy was more aggressive than the 8-year old girls. 4)Chasing was the one behavior that was more pronounced for the girls. 5)This result, however, does not disprove the theory since chasing seems to be a less aggressive behavior than the other behaviors that were tested. 6The 8-year old boys got more involved with the more aggressive behavior, which is kicking/hitting, much more than the 8-year old girls. (Rating: 93)
  • 41. Commentary 4  In order to investigate the hypothesis that 8-year old boys are more aggressive than 8-year old girls, 8-year old children were observed playing in schoolyards and incidents of certain aggressive behaviors were recorded. 2)At first glance it appears that 8-year old boys exhibit more aggressive behavior than 8-year old girls if all four recorded behaviors are equally weighed. 3)But, this last assertion is false. 4)Since the ability to record will vary with playground size and the number of observers (not to mention the skills of the observers or accounting for children entering or leaving the playground), and that it takes a certain amount of an observer's time to note the behavior, short-lived behaviors such as cursing or pushing could be under- represented. 5)Simply because more can occur during the time an observer notes another behavior. 6)Conversely, long-lived behaviors such as chasing could be over-represented because they occur over a longer period of time and thus allow more latitude for the observer marking the behavior. (Rating: 93)