LECTURE RHETORICAL ANALYSIS 2 THE DATA COMMENARY

LECTURE: RHETORICAL ANALYSIS #2 THE DATA COMMENARY

WHAT IS A DATA COMMENTARY? A data commentary is the textual explication, analysis and/or evaluation of material presented in a table, graph, figure or some other non-verbal illustration.

Five things you might do in a data commentary: • Highlight the results represented in the graphic • Assess standard theory, common beliefs, or general practice in light of the given data • Compare and evaluate different sets of data • Assess the reliability of the data in terms of the methodology that produced it • Discuss the implications of the data

Why a data commentary is called a commentary: Because it does not (a) simply repeat all the graphic’s details in words; or (b) attempt to cover all of the information in the graphic.

WHAT A DATA COMMENTARY IS NOT:

Figure 2

Figure 2 shows how age and gender correlate with the frequency of heart attacks in the US. Less than 6% of all heart attacks occur in the 29 -44 age group. The number of women who suffer heart attacks in this group is negligible —only 3000 per year, compared to 123, 000 men. However, the proportion of men and women with heart attacks rises between 45 and 64, with over half a million per year. The incidence among women also increases, so that women have one heart attack for every three men in this age group. Over the age of 65, the number of men suffering heart attacks only increases slightly, while the number of women suffering heart attacks increases 66%, from 136, 000 to 374, 000.

WHAT A DATA COMMENTARY IS:

TABLE 1 Children killed in UK road accidents in a 3 -month period in 2004 Where accidents occurred Age 2– 4 Age 5 -7 Ag 8 – 11 Age 12 – 14 Age 15 - 16 Total Going to school 2 12 8 2 1 25 Returning 2 from school 17 10 3 2 34 Playing in the street 98 81 28 5 2 214 Cycling in the street 1 10 25 8 1 45 Shopping for their parents 5 32 12 2 1 52 TOTAL 108 152 83 20 7 370

Table 1 shows the number of deaths suffered by children as the result of road accidents in the UK in 2004, as those deaths were distributed across five age groups. As the table indicates, by far the highest number of such fatalities, 260 of the 370 total deaths, involved the youngest children, those between 2 and 7 years of age. These accidents occurred most frequently when children were playing in the street (214 out of the 370 total, or 58%). Children in all age groups suffered such fatalities, but the highest incidence occurs in the two youngest groups, which accounted for 98 and 81 deaths, respectively. After age 7, the number declines for each subsequent age group, with a particularly steep drop after age 12, so that only 7 deaths (1. 9%) occurred for children between the ages of 12 and 16.

Children who were shopping for their parents accounted for the next highest total at 52 (14%), with the greatest number of deaths in the 5 -7 age group. Close behind, at 45 deaths, were children who were cycling in the street, the majority of whom (25) were aged 8 -11. Children were least vulnerable going to and coming from school (7% and 9%, respectively), although here again there was a direct correlation between age and fatalities: the younger the child, the more likely the death. Thus, while all these numbers are obviously tragic overall, they would appear to be absolutely alarming with regard to children between 2 and 7. The 260 deaths in that age range account for 70% of the total. In an automobile-dependent culture, some such accidents may be inevitable. But surely by enacting changes in traffic laws, increasing driver training and, most particularly, by improving parenting skills, this figure can be reduced significantly.

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

Location elements and/or summary statements (ACTIVE): Location Element Table 1 shows Summary the number of [road accident] deaths in the UK in 2004, as distributed across five age groups. Figure 2 shows how age and gender correlate with the frequency of heart attacks in the U. S.

Location elements and/or summary statements (PASSIVE): Location Element Summary The way that the number of [road accident] deaths in the UK in 2004 were distributed across five age groups is shown in Figure 1. The way that age correlates with gender in relation to the frequency of heart attacks in the U. S. is shown in Figure 2.

Indicative vs. Informative Summaries Indicative: “Table 1 shows the number of deaths suffered by children as the result of road accidents in the UK in 2004, as those deaths were distributed across five age groups. ” Informative: “Table 1 shows that by far the highest number of road accident fatalities in the UK, 260 of 370 total deaths, involved children between 2 and 7 years of age. ”

Indicative vs. Informative Summaries (cont. ) Indicative: “Figure 2 shows how age and gender correlate with the frequency of heart attacks in the U. S. ” Informative: “Figure 2 shows that while men in the U. S. suffer the greatest number of heart attacks overall, the number of women suffering such attacks rises at an alarming rate after the age of 50. ”

Top 8 active verbs following reference to visual* shows presents illustrates summarizes demonstrates contains provides depicts *Study by Ken Hyland cited in Academic Writing for Graduate Students, p. 120. 46 (of 90) 13 10 6 5 5 3 2

Top 8 passive verbs following reference to visual* shown in 44 (of 114) illustrated in 13 presented in 10 given in 6 listed in 5 seen in 5 provided in 3 summarized in 2 *Study by Ken Hyland cited in Academic Writing for Graduate Students, p. 120.

• 1. As Table 1 defines it, fraud is a form of intentional deception resulting in injury. • 2. As can be seen in Figure 4, earnings have decreased. • 3. As Figure 2 reveals, the lightweight materials outperformed traditional metals. • 4. As has been demonstrated previously, passives are common in process descriptions.

Using prepositions in reference to graphics: in, on, by, from in: “As shown in Table 1. . . ” by: “As predicted by the model. . . ” on: “As described on the previous page. . . ” from: “As can be seen from the pattern outlined in Figure 7. . . ”

1. As revealed by Table 2, the lightweight materials outperformed traditional metals 2. As can be seen from the trends mapped in Figure 4, earnings have decreased. 3. As shown in Table 7, there is no clear statistical relationship between fiscal costs and crisis length. 4. As illustrated by the graph on the previous page, international one-year migrants were simply not a factor in this development.

Highlighting statements: What are they? Highlighting statements are generalizations you can draw from the data display.

Sample highlighting statements: “As the table indicates, by far the highest number* of such fatalities, 260 of the 370 total deaths, involved the youngest children, those between 2 and 7 years of age. ” *Highlighted element of graphic

Sample highlighting statements: “However, the proportion of both men and women heart attacks rises dramatically* between 45 and 64, to over half a million per year. And while 420, 000 of the attacks in this range occur in men, perhaps the more striking shift* is among women: they now account for one attack out of every four, or 25%--a tenfold increase over the 29 -44 age group. ” *Highlighted element of graphic

What do highlighting statements allow you to do? • To point out trends or regularities in the data (and prove to your supervisor that you can!) • To separate more important findings from less important ones • To make claims of appropriate strength regarding the data

Making claims of appropriate strength “And while 420, 000 of the attacks in this range occur in men, perhaps the more striking shift is among women. . ” “Thus, while all these numbers are obviously tragic overall, they would appear to be absolutely alarming. . ”

According to our results, impulsive buying is on the rise. Further, our survey data suggests that buying goods to improve one’s self-image is probably a motivation that plays some role in most buying behavior, but it might be particularly important when people make unplanned or “spur of the moment” purchases. These unplanned purchases may well be regretted later and can lead to financial difficulty.

1. As Table 2 illustrates, the results indicate/ establish that there is a link between smoking and lung cancer. 2. As per Figure 1, the latest studies question/ challenge the conclusions of much previous research. 3. As shown in Figure 7, the results of this study create/suggest a basis for product modification. 4. As shown in Table 3, the new tax laws have encouraged/stimulated industrial development.

Implications: What are they? To explain the implications of a graph, table or chart is to say ought to be done next. Some possibilities:

Some typical implications: • Further research (building on current study) • Better research (based on flaws revealed) • Policy changes • Specific real-world actions

Sample implications: “In an automobile-dependent culture, some such accidents may be inevitable. But surely by enacting changes in traffic laws, increasing driver training and, most particularly, by improving parenting skills, this figure can be reduced significantly. *” *Policy changes and real-world actions

Sample implications (cont. ) “These data suggest that while our traditional emphasis on men’s heart health is clearly important, we may need to increase our emphasis on women’s heart health, and do so before women reach the danger zone beyond 50 years of age. *” *Policy changes and real-world actions

Sample implications: Future Research This survey has provided an important baseline from which future research can compare rates of change in the prevalence of gambling and problem gambling among youth in Oregon. Several shortcomings of this research should be taken into consideration for future research. First, because the minority population in Oregon is relatively small, future research should oversample minorities in order to more accurately gauge the level of gambling and problem gambling among non-Anglos.

Sample implications: Future Research Second, future research should utilize a larger sample size in order to provide more precise measures of problem gambling. Because problem gambling is a low probability event, accurately gauging the level of problem gambling will require a very large sample size. Additionally, a larger sample size will allow for more accurate analyses of various subgroups, such as age groups, as well as allow for more precise estimates of the affect of grade of onset and parental gambling on problem gambling.

Sample implications: Future Research Finally, a longitudinal, prospective research design is the best way to measure the change in gambling behavior over time. A recent study which reviewed all of the prevalence studies conducted over the last twenty years in the U. S. concluded that "researchers have conducted virtually no incidence studies in the field of disordered gambling"(Shaffer, Hall and Vander Bilt, 1997, p. 6). Only by following a very large sample of youth over time can certain important and difficult questions about the development of problem gambling, such as the influence of parental gambling and age of onset on problem gambling, be answered.

Sample implications (cont. ) “Despite the March uptick, Gallup's view of the U. S. jobs situation remains substantially less optimistic than the government's recent unemployment report might suggest. Added to this, late March Gallup Daily tracking results show a continuing decline in economic optimism, a pullback in consumer spending, and a drop in Gallup's Job Creation Index. This suggests that recent behavior on Main Street does not reflect the government's rosier assessment. It also implies that the recent marginal improvement Gallup finds may be more temporary than one might hope. ” *Policy changes and real-world actions