5 Listening to the data

Transcription

1 D:\My Documents\dango\text\ch05v15.doc Page 5-1 of 27 5 Listening to the data Truth will ultimately prevail where there is pains taken to bring it to light. George Washington, Maxims 5.1 What are the kinds of data? We work with three basic kinds of data, strings, numbers, and dates. The ethnicity of a victim is a string (Indigenous, Ladino, Unknown). The type of violation is a string (Murder, Lack of Access to Health Care, Torture). The language of an interview is a string (Spanish, English, Ixil, Tagolog). Note that what is often called a category is represented by a string. However, there are other kinds of strings, such as identification numbers (S/N001). Numbers are counts or measurements. The count of victims of a group killing is a number (124, 5760, 2). The Gross National Product (GNP) of a country is a number (965.2 billion, trillion). The percentage of literate persons is a number (98%, 76.4%). Dates are calendar dates that indicate when an event occurred or is due to occur. Thus, we use dates for the time of such events such as births, deaths, violations, reports, updates, elections, postings of army personnel, ends of regimes, entrances into programs, treaty signings, etc. Dates are often entered into databases as strings such as 7 December 1941, 12/7/41, 07 XII 1941, or even This is acceptable if you only want to know the actual date or to put records in order. 1 But if you want to know the elapsed time between two dates you must convert the date to a form in which it can be processed in the database or the data analysis program. All the standard statistical programs and EXCEL have functions for converting dates. You do calculations with numbers, and in general, you do not do calculations with strings. However, you often record dates as string data and in some cases you may be able to compute elapsed times with dates, as for ages of victims, or times between events. Most of your data analysis for human rights will reduce the strings to counts (numbers). We want to know how many Ladinos (non-indigenous people) are in our dataset, or how many people were tortured, or how many tortured Ladinos were denied access to health care. 5.2 What do we want to do with the data? Our first concern in human rights data analysis is to determine the facts. After we have determined the facts, we may go on to try to explain the causes that led to the facts. We emphasize may because in human rights data analysis as opposed to research the facts alone are often all that you need. A truth commission, tribunal, or NGO concerned with advocacy may only need to know what violations were committed where, to whom, in what locations, and by which perpetrators. These are all facts, which can be determined by data. The social and political causes may be of no concern in the first

2 D:\My Documents\dango\text\ch05v15.doc Page 5-2 of 27 phases of analysis. Of course, if you do not know what the facts are, you will never be able to rationally deal with causes and explanations. To show you how we analyze human rights data, we use the Guatemala CIIDH dataset. 5.3 Caution! We are going to work with a dataset of all the named victims recorded in the dataset of violations collected by the CIIDH for the period 1960 to The full dataset includes anonymous victims whose names were not known, but we do not have their personal data, such as sex and age. Our discussion in this chapter concerns only named victims. While these data do not come from a random sample, we believe that they are a good representation of the facts. Most importantly, the findings in these data are confirmed by findings of the data gathered by two other projects gathering survey data independently. 5.4 Where do we start? Anyone concerned with the violations in Guatemala from 1960 to 1995 will want to know how many times each type of violation (Killing, Disappearance, Illegal Detention, Injury, and Torture) was committed. Violations are our unit of analysis, and knowing the distribution of violations is the basic set of facts. By distribution, we mean how many violations of each kind were recorded in the data set. We represent the distribution of violations (or any other string variable) in a table that is obtained by counting the number of times a given category (string) occurs. We will demonstrate this first by showing the results of such a count, and later in this chapter, show you in detail how we do this. The violations are variables whose values are the strings (Mu, Ds, Se, Hr, and To) representing Killings, Disappearance, Illegal Detention, Injury, and Torture, as shown in Chapter 1 along with a segment of the database. By counting the number of the occurrence of each of the string values we get their frequency of occurrence, usually abbreviated to frequency or freq. After counting to determine these frequencies, we get the table shown in Figure 5-1. Figure 5-1. Types of violations ranked alphabetically Type of violation Freq. Percent Ds 1,611 12% Hr 328 2% Mu 8,610 64% Se 2,600 19% To 378 3% Total 13, % As discussed in Chapter 1, this table gives an understanding of the relative

3 D:\My Documents\dango\text\ch05v15.doc Page 5-3 of 27 numbers of the violations. The table of Figure 5-1 shows the distribution of the types of violations. However, by showing the percentages (as we do in Figure 5-1) and by using a ranked (or ordered) table, as in Figure 5-2, below, we gain greater understanding. Figure 5-2 Types of violations ranked by frequency of occurrence Type of violation Freq. Percent Mu 8,610 64% Se 2,600 19% Ds 1,611 12% To 378 3% Hr 328 2% Total 13, % With this summary of the major facts, you can see that killing (Mu) dominated (64% of the violations) in the repression and that injury (Hr) and torture (To) together accounted for only 5% of the violations. Making and ranking a table helps you to think meaningfully about the data that you have collected. For example, in Figure 5-2 the relatively low proportions of torture (To), and injury (Hr) stand out clearly. Is it possible that these violations, particularly torture were only a few percent of the violations in Guatemala? Could it be that in the presence of so much killing, sources (both victims and witnesses) did not think that torture without murder was worthy of attention? Or is it possible that the data are essentially correct and that the sources simply reported the most serious violation of killing? Perhaps researchers will one day conduct studies to determine how such violations are repeated in complex events. 5.5 How do you decide whether to rank a table? If your interest is in focusing attention on the magnitude of frequencies or percentages in a particular dataset, then rank your table by descending magnitude, as we have done in Figure 5-2. If, however, you simply want to find out what frequency or percentage corresponds to a given value, such as the type of violation, then you can rank alphabetically, as in Figure 5-1. This makes it easier to compare this dataset with other datasets, violation by violation. You will almost always want to rank your tables, to separate the vital few from the trivial many. In general, to achieve this goal, rank your tables in descending order, from the largest value of the ranking variable to the lowest. 5.6 What kinds of questions can we answer with tables? We will assume that you are concerned with the treatment of women during the period of repression in Guatemala. Who were the victims? As you are concerned with the treatment of women, your next concern would be

4 D:\My Documents\dango\text\ch05v15.doc Page 5-4 of 27 to produce a table showing the distribution of violations by sex. Figure 5-3 Sex of victims of violations ranked by frequency of occurrence Victim Sex Freq. Percent Male 11,445 85% Female 2,001 15% Unknown 81 1% Total 13, % From Figure 5-3 you can see that the violations were inflicted primarily on male victims (85%), and that the percentage of violations against female victims is low (15%). What was the distribution of violations inflicted on the women? Figure 5-4 gives the answer to this question for the 2,001 female victims women in the dataset. Figure 5-4 Violations against women ranked by frequency of occurrence Type of Violation Freq. Percent Mu 1,331 67% Se % Ds % To 51 3% Hr 82 4% Total 2, % You can see from Figure 5-4 that killing is the most common reported violation against women (1,331/2,001=67%). If you assume that the disappeared were also killed, then the recorded proportion of killings inflicted on female victims is 79% (67%+12%). 5.7 How do we determine if sex makes a difference? You want to compare the women to the men, to see if sex makes a difference to the distribution of violations. If sex does not make a difference, we say that Type of Violation is independent of Sex. If the Sex does make a difference to the types of violations, we say that Type of Violation is dependent on Sex. If you find dependence, then you would say that these two variables are associated, or have an association. In Chapter 7 we discuss association in detail. To see if there is association, you need a table like Figure 5-5. Such a table is called a crosstabulation (or two-way table, or pivot tables). When you make a crosstabulation, you say that you will crosstabulate Type of Violation by Sex.

5 D:\My Documents\dango\text\ch05v15.doc Page 5-5 of 27 Are women subject to a different distribution of violations than men? Using the crosstabulation of type of violation by sex (Figure 5-5) you can answer this question. Figure 5-5 Type of violation by sex Victim Sex Female Male Total Type of Violation Freq. Percent Freq. Percent Freq. % Mu 1,331 67% 7,233 63% 8,564 64% Se % 2,291 20% 2,578 19% Ds % 1,350 12% 1,600 12% To 51 3% 327 3% 378 3% Hr 82 4% 244 2% 326 2% Total 2, % 11, % 13, % What can you learn from this crosstabulation? The percentage distributions of the types of violations for women and men are similar. However, of the 11,445 violations against males, 20% are illegal detentions (Se). Of the 2,001 violations against, females only 14% are illegal detentions. The percentage of killings of females is slightly higher than males (67% vs. 63%). Thus, from this table, you can conclude that for the whole dataset, there is relatively little association between Type of violation and Sex; the distribution of types of violation does not strongly depend on sex. Another way to say the same thing is that essentially the same distribution of violations holds for women and men (subject to the slight differences noted above). This is true in the aggregate (all regions included), but is this true in each region? The rural and urban areas of Guatemala differ in many ways; but rural areas tend to be similar to each other, as do urban areas. The process of breaking the dataset apart to look within each region is called disaggregation. In this case you disaggregate to look for association within each region. You can disaggregate by any variable for which you have the data. You could disaggregate by sex, by ethnicity, by perpetrator type, and so forth. Disaggregation can be one of your most important methods in data analysis. By disaggregating, you can make sure that you do not apply to all situations general findings that disguise, rather than reveal, facts. When discussing the facts you have summarized in your crosstabulations, be sure to state them in the way we do. In particular, give the values (frequencies, percentages) in parentheses in your statements of findings. Back up your general conclusions with the values you use to draw those conclusions ( 67% vs. 64% ). It is essential to quantify the number of cases involved. If there are 11,445 violations against males, and 20% are illegal detentions, the reader will probably consider this a serious matter (2,289 males

6 D:\My Documents\dango\text\ch05v15.doc Page 5-6 of 27 were detained). But if, for example, the last example you had only 50 violations against males of which 20% are illegal detentions, then because the total number of cases is so small, the reader would rightly consider the 20% as a less significant number. 5.8 How do we find out if sex affects violations differently in different locations? Is it possible that type of violation is associated with sex if we look at locations separately (rural and urban)? To answer this question, you disaggregate the data by region. Get the distribution of type of violations by sex for both rural (Figure 5-6) and urban (Figure 5-7) locations of violation. Note that we have shaded certain cells of the crosstabulations so that you can more easily make the comparisons. Is there an association between type of violation and sex in rural locations? You can answer this question with Figure 5-6 in which you look only at rural locations. Figure 5-6 Type of violation by sex for rural locations Victim Sex Female Male Total Type of Violation Freq. % Freq. % Freq. % Mu 1,192 82% 5,759 70% 6,951 72% Se 131 9% 1,523 19% % Ds 51 4% 539 7% 590 6% To 48 3% 292 4% 340 4% Hr 28 2% 108 1% 136 1% Total 1, % 8, % 9, % What can you learn from this crosstabulation? There were 9,671 violations reported for rural locations for which we have sex information (we dropped missing values). Of the 1,450 violations against women, 82% were killings as opposed to only 70% of the 8,221 violations against men. Of the 8,221 violations against men, 19% were illegal detention, as opposed to only 9% of the 1,450 violations against women. Thus, from this table you can conclude that the type of violations in rural locations depends on sex. Women are treated differently from men; there are proportionately more killings of women and fewer illegal detentions. Why don t we discuss the other three violations (disappearance, torture, and injury)? Because the numbers of women and the percentages are so small we do not feel comfortable drawing conclusions.

7 D:\My Documents\dango\text\ch05v15.doc Page 5-7 of 27 Is there an association between type of violation and sex in urban locations? You can answer this question with Figure 5-7 in which you look only at urban locations. Figure 5-7 Type of violation by sex for urban locations 0505 Victim Sex Female Male Total Type of Violation Freq. Percent Freq. Percent Freq. Percent Mu % 1,457 45% 1,594 42% Ds % % 1,009 27% Se % % % Hr 54 10% 136 4% 190 5% To 3 1% 35 1% 38 1% Total % 3, % 3, % What can we learn from this crosstabulation? There were 3,754 violations reported for urban locations for which we have sex information (we dropped missing values). Of the 549 violations against women, only 25% were killings as opposed to 45% of the 3,205 violations against men. Of the 549 violations against women, 36% were disappearances as opposed to 25% of the men. Thus, from this table we conclude that the type of violations in urban locations depend on sex. Women are treated differently from men; there are proportionately more killings of men (45% compared to 25%) and more disappearances of women (36% compared to 25%). We do not discuss the other three violations (disappearance, torture, and injury) because the numbers of women and the percentages are so small that we do not feel comfortable drawing conclusions. 5.9 Is there another way to look at the same crosstabulation? Yes! You have to be careful how you look at crosstabulations, how you draw conclusions, and how you describe your conclusions. For example, you have found that in the aggregate, 67% of the violations against females are killings (see Figure 5-5). This does not mean that 67% of the killed victims were female! What was the percentage of all killings that were female? To answer such questions, you need a crosstabulation showing row percentages. In this case, a row percentage tells you the percentage of killed victims that were female or male. From a table of row percentages you can also find the percentage of detentions, or disappearances, or other violations against females. Figure 5-8 below, shows the row percentages for these data.

8 D:\My Documents\dango\text\ch05v15.doc Page 5-8 of 27 Figure 5-8 Type of violation by sex showing row percentages Victim Sex Female Male Total Type of Violation Freq. % Freq. % Freq. % Mu 1,331 16% 7,233 84% 8, % Se % 2,291 89% 2, % Ds % 1,350 84% 1, % To 51 13% % % Hr 82 25% % % Total 2,001 15% 11,445 85% 13, % From this crosstabulation showing row percentage, you can now see what percentage of all killings were females. If you look at the shaded columns above, you will see that of the 8,564 killings, 16% were of females, and 84% of men. We repeat. Of the 2,001 violations against women, 82% were killings (you can see this in Figure 5-6). Of the 8,564 killings, 16% were women (Figure 5-8). Make sure that you can see this yourself by looking at the tables. The failure to understand this difference is common. In the courtroom, attorneys and even judges make this error so frequently that it is called The Prosecutor s Fallacy How do you answer questions about percentages of all violations? What percentage of all violations were killings of females? To answer this, and similar questions about the percentage of violations represented by cells in the table, you need a crosstabulation of cell percentages. This is a table showing the percent of the total represented by each cell in the table. To create a crosstabulation of cell percentages, start with the crosstabulation of all violations as shown in Figure 5-9 Figure 5-9 Crosstabulation of all violations by sex Sex Female Male Total Type of Violation Freq. Freq. Freq. Mu 1,331 7,233 8,564 Se 287 2,291 2,578 Ds 250 1,350 1,600 To Hr Total 2,001 11,445 13,446 Then you find the percentage each cell represents of the total of all violations (13,446) as shown in Figure 5-10.

9 D:\My Documents\dango\text\ch05v15.doc Page 5-9 of 27 Figure 5-10 First step in computation of cell percentages Sex Female Male Total Type of Violation Freq. Freq. Freq. Mu 1,331/13,446 7,233/13,446 8,564/13,446 Se 287/13,446 2,291/13,446 2,578/13,446 Ds 250/13,446 1,350/13,446 1,600/13,446 To 51/13, /13, /13,446 Hr 82/13, /13, /13,446 Total 2,001/13,446 11,445/13,446 13,446/13,446 Next, you convert these proportions to percentages and get the cell percentage crosstabulation of Figure 5-11, below. Figure 5-11 Cell percentages for type of violation by sex Victim Sex Female Male Total Type of Violation Percent Percent Percent Mu 10% 54% 64% Se 2% 17% 19% Ds 2% 10% 12% To 0% 2% 3% Hr 1% 2% 2% Total 15% 85% 100% From this crosstabulation, you can see in the upper left hand cell of Figure 5-11 that the percentage of all violations that were killings of females is 10% (1,331/13,446). Similarly, from the upper right cell of Figure 5-11, you can see that the percentage of males who were killed is 54% (7,233/13,446) Is there more we can learn from the crosstabulations that we have shown? Yes, our analysis above shows the necessary first steps, but there are many more questions to be asked and answered. Some of these questions become obvious only upon doing these first steps in the analysis. This is a measure of the importance of data analysis and thinking hard about the relationships among the facts in numerical terms. We will give you a chance to deal with the other questions in the exercises, using a small dataset that you can analyze without the use of a computer How do you make a crosstabulation? We made our crosstabulations using a statistical program in a computer. While ours is a special program only used for statistical purposes (Stata), you can also use another such program (SPSS), and you can do a number of these functions in EXCEL, the Microsoft spreadsheet program, using the Pivot Table function. In Appendix TK, we

10 D:\My Documents\dango\text\ch05v15.doc Page 5-10 of 27 give you instructions for using EXCEL. If you do not have a computer you will have to make your crosstabulations manually. This is easy when the number of entries in your dataset is limited. Accordingly, we will show you how to manually make a crosstabulation. For this example, you will use the random selection of 29 records from the Guatemala database shown in Figure 5-12.

11 D:\My Documents\dango\text\ch05v15.doc Page 5-11 of 27 Figure 5-12 Random selection of records from the Guatemala database, edited for manually making a crosstabulation, female rows shaded v_num v_age v_ind v_sex n_type 1 sv Indigenous M Mu 2 sv Indigenous M Mu 3 sv Indigenous M Mu 4 sv Indigenous F Mu 5 sv Indigenous F Mu 6 sv Indigenous M Mu 7 sv Indigenous F Mu 8 sv Indigenous M Mu 9 sv Indigenous M Se 10 sv Indigenous F Mu 11 sv Indigenous M Mu 12 sv Indigenous M Mu 13 sv Indigenous M Se 14 sv Indigenous M Mu 15 sv Indigenous F Mu 16 sv Indigenous M Se 17 sv Indigenous M Mu 18 sv Indigenous F Mu 19 sv Indigenous F Mu 20 sv Indigenous M Se 21 sv Indigenous M Mu 22 sv Indigenous M Mu 23 sv Indigenous M Ds 24 sv Ladino M Mu 25 sv Ladino M Mu 26 sv Ladino M Mu 27 sv Ladino M Se 28 sv Ladino M Mu 29 sv Ladino M Se Below is a table similar to Figure 5-9, but for Ethnicity by Sex, as shown in Figure 5-13 for the 19 victims. There are two rows (Ladino and Indigenous) and two columns, (M and F) plus a row and a column for total, and four cells as shown below: Figure 5-13 Layout and definition of cell counts for crosstabulation of killing victims ethnicity by sex Sex Female Male Total Ethnicity Freq. Freq. Freq. Indigenous Indigenous and Female Indigenous and Male Indigenous Ladino Ladino and Female Ladino and Female Ladino Total Female Male All To show how you get the count for victims who are Indigenous and Female, we have shaded their records in Figure Figure 5-14 Dataset showing Indigenous and Female victims

12 D:\My Documents\dango\text\ch05v15.doc Page 5-12 of 27 vnum vage v ind v sex n type 1 sv Indigenous M Mu 2 sv Indigenous M Mu 3 sv Indigenous M Mu 4 sv Indigenous F Mu 5 sv Indigenous F Mu 6 sv Indigenous M Mu 7 sv Indigenous F Mu 8 sv Indigenous M Mu 9 sv Indigenous M Se 10 sv Indigenous F Mu 11 sv Indigenous M Mu 12 sv Indigenous M Mu 13 sv Indigenous M Se 14 sv Indigenous M Mu 15 sv Indigenous F Mu 16 sv Indigenous M Se 17 sv Indigenous M Mu 18 sv Indigenous F Mu 19 sv Indigenous F Mu 20 sv Indigenous M Se 21 sv Indigenous M Mu 22 sv Indigenous M Mu 23 sv Indigenous M Ds 24 sv Ladino M Mu 25 sv Ladino M Mu 26 sv Ladino M Mu 27 sv Ladino M Se 28 sv Ladino M Mu 29 sv Ladino M Se Your count should be seven Indigenous and Female victims. How many killing victims that were Ladino and Female do you find on this list? You will find none. At this point, you can enter these two counts in your table, and add them to get the total number of Female victims.

13 D:\My Documents\dango\text\ch05v15.doc Page 5-13 of 27 Figure 5-15 Partially completed crosstabulation of victims by ethnicity and sex; first step Sex Female Male Total Ethnicity Freq. Freq. Freq. Indigenous 7 Indigenous and Male Indigenous Ladino 0 Ladino and Female Ladino Total 7 Male All Now you can count the Indigenous and Male victims (16), the Ladino and Male victims (6), and add them to get the total number of Male victims (22). This will give you the crosstabulation of Figure 5-16 below. Figure 5-16 Partially completed crosstabulation of victims by ethnicity and sex; second step Sex Female Male Total Ethnicity Freq. Freq. Freq. Indigenous 7 16 Indigenous Ladino 0 6 Ladino Total 7 22 All You can now add the column and row totals to get the total number of Indigenous and Ladino victims in the dataset. The end result is Figure 5-17 to make sure you didn t make a mistake, getting this result: sex Figure 5-17 Completed crosstabulation of counts of victims by ethnicity and Sex Female Male Total Ethnicity Freq. Freq. Freq. Indigenous Ladino Total To get row, column, and cell percentages perform the calculations shown below in Figure 5-18, Figure 5-19, and Figure Figure 5-18 Completed crosstabulation of counts and percentages of killing victims by ethnicity and sex with column percentages Sex Female Male Total Ethnicity Freq. percent Freq. percent Freq. percent Indigenous 7 7/7=100% 16 16/22=73% 23 23/29=79% Ladino 0 0/7=0% 6 6/22=27% 6 6/29=21% Total 7 7/7=100% 22 22/22=100% 29 29/29=100%

14 D:\My Documents\dango\text\ch05v15.doc Page 5-14 of 27 Figure 5-19 Completed crosstabulation of counts and percentages of killing victims by ethnicity and sex with row percentages Sex Female Male Total Ethnicity Freq. percent Freq. percent Freq. percent Indigenous 7 7/23=30% 16 16/23=70% 23 23/23=100% Ladino 0 0/6=0% 6 6/6=100% 6 6/6=100% Total 7 7/29=24% 22 22/29=76% 29 29/29=100% Figure 5-20 Completed crosstabulation of counts and percentages of killing victims by ethnicity and sex with cell percentages Sex Female Male Total Ethnicity Freq. percent Freq. percent Freq. percent Indigenous 7 7/29=24% 16 16/29=55% 23 23/29=79% Ladino 0 0/29=0% 6 6/29=21% 6 6/29=21% Total 7 7/29=24% 22 22/29=76% 29 29/29=100% If this were the result of a single event in which there were 29 victims, what are some of the things that you can say about it based on Figure 5-18, Figure 5-19, and Figure 5-20? (You will get a chance to draw other conclusions in the exercises.) Males dominated among the victims, accounting for 76% (22/29) and females only 24% (7/29) of the victims. Indigenous people dominated among the victims, accounting for 79% (23/29) of the victims and Ladinos only 21% (6/29). Of the 23 Indigenous victims, 70% (16/23) were males, and 30% females. All of the six Ladino victims were males. Of the seven female victims, all were Indigenous, as were 73% (16/22) of the males. You can summarize by noting that the victims were primarily male and indigenous and that no Ladino females were victims. This is demanding material for people who are new to data analysis. NOW is the time to do the exercises at the end, marked with this symbol:. You will be a lot happier if you do so!

15 D:\My Documents\dango\text\ch05v15.doc Page 5-15 of How do you know if a crosstabulation shows row, column, or cell percentages? If a crosstabulation shows column percentages, then all the percentages will add down the columns to 100%. If a crosstabulation shows row percentages, then all the percentages will add across the rows to 100%. It is that easy. If a crosstabulation shows cell percentages, then you will find that not all the percentages will add across the rows or down the columns to 100%. It is that easy How do you present tables and crosstabulations graphically? Graphical presentation makes it easy to comprehend complex relationships. Good graphical representation communicates by using images that are not readily forgotten. Less is often more in graphs. Elaborate and complex chart formats (for example, threedimensional bars, the use of drawings, and too many gridlines) confuse rather than reveal. You do not need to be an artist to make graphs (also called charts), nor do you necessarily need a computer. You can draw graphs by hand using graph paper (also called quadrille paper). Or you can sketch them out and have an artist make them look good for you. Today, however, most graphs are drawn using a computer. You can use the EXCEL or any of many statistical computer programs. In Appendix TK, we show how to make simple tables, crosstabulations and charts in EXCEL. 2 Figure 5-2 at the beginning of this chapter shows the ranked tabulation of violations. The corresponding bar chart is shown in Figure Figure 5-21 Bar chart for table of Figure 5-2, number of victims by type of violation Freq. 10,000 9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 Mu Se Ds To Hr Type of violation Figure 5-21 shows the frequencies of the violations. You can also show the percentages in a bar chart, as in Figure 5-22 below.

16 D:\My Documents\dango\text\ch05v15.doc Page 5-16 of 27 Figure 5-22 Percentage of victims by violation Percent Mu Se Ds To Hr Type of violation Figure 5-5 compares the violations suffered by women against those suffered by men. Thus, a bar chart to show this comparison will have two sets of bars, one for female percentages and the other for male percentages, as in Figure 5-23.

17 D:\My Documents\dango\text\ch05v15.doc Page 5-17 of 27 Figure 5-23 Percent of type of violation by sex 75% Percentage of violations 60% 45% 30% 15% Female % Male % 0% Mu Se Ds To Hr Type of violation Figure 5-23 shows how similar the distribution of violations is for men and women. To interpret this graph, note that the height of the Mu bar is a bit more than 65%. This means that about 65% of the violations inflicted on women were killings. But the male bar is slightly below 65%. (If you look at Figure 5-5, you will see that the actual numbers in the table are 67% and 63%.) Thus, the percentage of male killings was slightly less than the percentage of female killings. Look at the pairs of bars and you can compare the percentages of different types of violations against men and women.

18 D:\My Documents\dango\text\ch05v15.doc Page 5-18 of 27 However, Figure 5-23 does not show the percentages of killing victims that were male and female. For this, you need Figure 5-24, derived from the table of Figure 5-8. Figure 5-24 Percent of males and females by type of violation, bar chart Percentage of violation victims Mu Se Ds To Hr Type of Violation Female% Male % Some people prefer to use a stacked bar chart, since the pairs of percentages represented by the pairs of bars must add up to 100%. Such a chart is shown in Figure chart Figure 5-25 Percent of males and females by type of violation, stacked bar 100% Percentage of violation victims 80% 60% 40% 20% 0% Mu Se Ds To Hr Male % Female% Type of Violation

19 D:\My Documents\dango\text\ch05v15.doc Page 5-19 of 27 In this chart it is clear that the violations against women are a small proportion of the total violations for each type. However, those proportions are largest for injury and smallest for illegal detention.

20 D:\My Documents\dango\text\ch05v15.doc Page 5-20 of What are the lessons of this chapter? 1. Data come in three types, strings, numbers, and dates. 2. You turn strings into numbers by counting how frequently particular strings occur. 3. To perform calculations on dates, you will have to convert them to an appropriate form for your data analysis program. 4. You analyze data to understand and present the facts about a human rights act, status, or event. 5. A simple tabulation of the major categories (violations, sex, etc.) is the first step in any analysis. 6. Showing percentages and ranking the entries in the table in descending order of frequency (number) to get a ranked table enhances your understanding and the effectiveness of presentation. 7. If one variable makes a difference to another variable, the two variables are said to be dependent; if not, they are said to be independent. 8. The relationship of dependence is also called an association. 9. You can look for dependence of one variable on another by making a crosstabulation, a table in which one variable is for the rows, the other for the columns. 10. Dependence or independence is determined by comparing percentages. 11. To investigate whether there are other variables that affect our findings, we will break down our dataset into finer distinctions, a process called disaggregation. 12. There are three percentages to look at in crosstabulations: by rows, columns, and percentages of the whole. Avoid the prosecutor s fallacy by looking at both row and column percentages. 13. You can represent a simple tabulation in a bar chart. 14. Comparisons of distributions in crosstabulations can be made with a bar chart with adjacent columns for the columns you are comparing. 15. If you are comparing the effect of two factors that add up to 100% (such as males and females), you can use a stacked bar chart to emphasize the relationship Exercises The exercises that follow use the CIIDH dataset. They can all be done manually using Figure 5-27, a short segment of the first 94 entries in that database. You will find this figure at the end of the exercises. You can find the names of the abbreviated variables in Figure If you have a computer available you can use whatever statistical programs you have. As we have mentioned, EXCEL can be used for all of the analyses that we have

21 D:\My Documents\dango\text\ch05v15.doc Page 5-21 of 27 shown to you. Do you want to use EXCEL or another computer program to analyze the shortened list of data in Figure 5-27? If so, and you have this chapter available as a word document, you can select the table, copy it, and paste it into EXCEL or most other programs. If you have this chapter in printed form, you can obtain the full CIIDH dataset at the web site and TK. It is called cif1 Full CIIDH dataset and available as an EXCEL worksheet. When we refer to the CIIDH dataset in these exercises, use whichever one is convenient for or of interest to you, Figure 5-27 or the full dataset from the website above. Before going on to Section 5.13 above, you should have done the exercises marked. Exercise 5.1 List the names of all the string data in the data set shown in Figure Exercise 5.2 List the names of all the number data in the data set shown in Figure Exercise 5.3 List the names of all the string data in the data set shown in Figure Exercise 5.4 List the names of all the number data in the dataset shown in Figure 5-27, the dataset for this chapter. Exercise 5.5 What is the distribution of types of violations in the dataset? To answer this question, make a table of the frequencies and percentages of the types of violations in the CIIDH dataset. Refer to Figure 5-1 for guidance. Your results will not be identical to those in Figure 5-1. Exercise 5.6 What is the ranked distribution of types of violations in the dataset? Your table will be similar to Figure 5-2. Your results will not be identical to those in Figure 5-2. Exercise 5.7 What is the distribution of violations by occupation? Make a ranked tabulation similar to Figure 5-3, with frequencies and percentages, but for victim occupation instead of sex. What conclusions do you draw from this table?

22 D:\My Documents\dango\text\ch05v15.doc Page 5-22 of 27 Exercise 5.8 What is the distribution of violations against s? Make a tabulation similar to Figure 5-4. What do you conclude? Exercise 5.9 What is the distribution of violations by location of violation? Make a tabulation similar to Figure 5-5 for type of violation by location (urban, rural). (Hint: Replace the sex columns F and M by u and r.) Exercise 5.10 What can you learn from the table of Exercise 5.9? Just below Figure 5-5 is a discussion of the implications of the table of type of violation by sex. Prepare a similar discussion for the table you made in Exercise 5.9. Exercise 5.11 Is there an association between type of violation and occupation in rural locations? To answer this question, make a crosstabulation similar to Figure 5-6. Note that you will have a pair of columns (freq. and %) for each occupation listed in the database. Exercise 5.12 Is there an association between type of violation and occupation in urban locations? To answer this question, make a crosstabulation similar to Figure 5-7. Note that you will have a pair of columns (freq. and %) for each occupation listed in the database. Exercise 5.13 We want to know what percent of the total number of violations were committed on victims having different occupations. That is, you would like to answer questions such as, What percentage of the total killings were committed against s? and What percentage of the total torture violations were committed against housewives? To answer these types of questions you will have to crosstabulate type of violation by victim occupation. We did this for sex in Figure Your tabulation will have a column for each of the victim occupations, but otherwise you will calculate the percentages exactly the same way. What conclusions do you draw? Exercise 5.14 What percentage of all violations to s were killings? From the table you created in Exercise 5.7 you can tell what percentage of the violations against s were killings, but you cannot answer the question without calculating the row percentages (as in Figure 5-8). Find and report both percentages, the percentage of violations against s that were killings and the percentage of killing victims that were s.

23 D:\My Documents\dango\text\ch05v15.doc Page 5-23 of 27 Exercise 5.15 What percentage of all violations were killings of victims of unknown occupation? From the table you created in Exercise 5.7 you can tell what percentage of the violations against victims with unknown occupations were killings, but you cannot answer the question without calculating the row percentages (as in Figure 5-8). Find and report both percentages, the percentage of violations against victims with unknown occupations that were killings and the percentage of killing victims that were of unknown occupation. Exercise 5.16 Refer to Figure 5-8. By examining this crosstabulation, you can see that the percentage of females subject to all violations except injury is in the range from 11% to 16%. Injury is exceptional, in that of the 326 injury violations, 25% were against females. It is more likely that a female will be an injury victim than any other violation. Do you feel that the high percentage of the injury violations relative to the other violations against females is an indication of the behavior of perpetrators towards women? Why or why not? Exercise 5.17 In Section 5.10 we gave some observations about the dataset based on examination of the crosstabulations of column, row, and cell percentages. Can you add any more observations that have meaning in terms of human rights issues? Exercise 5.18 What is the distribution of types of violations in the dataset? Answer this question using a bar chart based on your answer to Exercise 5.5. Exercise 5.19 What is the ranked distribution of types of violations in the dataset? Answer this question using a bar chart based on your answer to Exercise 5.6. Exercise 5.20 What is the distribution of violations against s? Answer this question using a bar chart based on your answer to Exercise 5.8. Exercise 5.21 Is there an association between type of violation and occupation in rural locations? Answer this question using a bar chart based on your answer to Exercise Your bars can represent either frequency or percent. Exercise 5.22 Is there an association between type of violation and occupation in urban locations? Answer this question using a bar chart based on your answer to Exercise Your bars can represent either frequency or percent.

24 D:\My Documents\dango\text\ch05v15.doc Page 5-24 of 27 Exercise 5.23 What percentages of the totals of each kind of violation were committed against victims having each of the occupations? You can answer this with a series of bar charts, one for each victim occupation. Base your work on your answer to Exercise Figure 5-26 Variable names and abbreviations for Figure 5-27 Variable name Abbreviation victim age v_age victim ethnic category v_ind victim sex v_sex victim id v_num victim occupation v_occ violation location n_ur violation month n_mon violation type n_type violation year n_year

25 D:\My Documents\dango\text\ch05v15.doc Page 5-25 of 27 Figure 5-27 Shortened CIIDH Dataset for exercises in Chapter 5 v_num v_age v_occ v_ind v_sex n_grp n_mon n_year n_type n_ur 1 sv other Unknown M Mu u 2 sv unknown Unknown F Se r 3 sv unknown Unknown M Se u 4 sv unknown Unknown M Ds u 5 sv unknown Unknown M Se r 6 sv unknown Unknown M Mu r 7 sv unknown Unknown M Ds u 8 sv unknown Unknown F Ds u 9 sv unknown Unknown M Se r 10 sv unknown Unknown M Se u 11 sv unknown Unknown F Se r 12 sv unknown Unknown M Mu u 13 sv small Unknown M Mu r 14 sv other Unknown M Se u 15 sv unknown Indigenous F Mu r 16 sv unknown Unknown M Mu r 17 sv small Ladino M Mu r 18 sv small Indigenous M Ds r 19 sv small Unknown M Mu r 20 sv unknown Unknown M Mu u 21 sv unknown Unknown M Mu r 22 sv unknown Unknown M Mu r 23 sv unknown Unknown M Mu r 24 sv unknown Unknown M Mu r 25 sv unknown Unknown M Se u 26 sv unknown Unknown F Se r 27 sv unknown Unknown M Ds r 28 sv small Indigenous M Mu r 29 sv unknown Unknown M Mu r 30 sv small Indigenous M Mu r 31 sv unknown Unknown M Mu r 32 sv small Indigenous F Mu r 33 sv small Ladino M Se r 34 sv small Unknown M Mu r 35 sv small Ladino M To r 36 sv unknown Unknown M Se r 37 sv unknown Unknown M Mu u 38 sv unknown Indigenous M Mu r

26 D:\My Documents\dango\text\ch05v15.doc Page 5-26 of 27 v_num v_age v_occ v_ind v_sex n_grp n_mon n_year n_type n_ur 39 sv unknown Unknown F Mu r 40 sv unknown Unknown M Mu r 41 sv small Unknown M Mu r 42 sv unknown Unknown F Ds u 43 sv unknown Unknown M Mu r 44 sv other Unknown F Se u 45 sv small Ladino M Se r 46 sv unknown Unknown F Mu r 47 sv small Unknown M Se r 48 sv unknown Indigenous M Mu r 49 sv housewife Ladino F Mu r 50 sv other Unknown M Ds r 51 sv unknown Unknown M Se r 52 sv small Unknown M Mu r 53 sv unknown Unknown M Mu u 54 sv unknown Unknown M Ds u 55 svv small Indigenous M Mu r 56 sv unknown Unknown M Mu r 57 sv small Unknown M Mu r 58 sv small Indigenous M Mu r 59 sv unknown Unknown M Se r 60 sv unknown Unknown F Mu u 61 sv other Unknown M Ds u 62 sv small Indigenous M Se r 63 sv unknown Unknown F Mu r 64 sv small Indigenous M Mu r 65 sv rural worker Ladino M Mu r 66 svv small Indigenous M Mu r 67 sv unknown Unknown F Mu r 68 sv rural Unknown M Mu u worker 69 sv unknown Unknown M Mu r 70 sv unknown Unknown M Ds u 71 sv unknown Unknown M Mu u 72 sv unknown Unknown F Se u 73 sv other Unknown M Mu u 74 sv unknown Indigenous M To r 75 sv unknown Unknown M Mu r 76 sv unknown Unknown M Mu r

27 D:\My Documents\dango\text\ch05v15.doc Page 5-27 of 27 v_num v_age v_occ v_ind v_sex n_grp n_mon n_year n_type n_ur 77 sv unknown Unknown M Se u 78 sv small Unknown M Mu r 79 sv rural worker Indigenous M Se r 80 sv unknown Unknown M Se u 81 sv unknown Unknown M Mu r 82 sv small Indigenous M Se r 83 sv unknown Unknown M Mu u 84 sv unknown Unknown M Mu r 85 sv other Unknown M Mu u 86 sv small Indigenous M Mu r 87 sv other Ladino M Mu r 88 sv unknown Unknown M Se r 89 sv unknown Unknown M Ds u 90 sv small Indigenous M To r 91 sv unknown Unknown M Ds u 1 The last form shown, allows sorting on the string value and putting the dates in order. Some people call this form of date the Russian date. 2 It is also possible to draw most of the graphs we show here using WORD. To find out how to do it, go to HELP in WORD and look for Create a chart from a Word table.