mandatory reading

194 Researching Violence Against Women

Indonesian women who had ever experi-enced partner violence reported morerecent symptoms of ill health such as pain,dizziness, ulcers, and intestinal problems.Figure 12.9 shows that Indonesian menwho have been violent towards their wivesare also more likely to have used alcohol,and to have had extra-marital relationships.They are more likely to have beeninvolved in fights with other men, to prefersons over daughters, and to have little orno education.

ASSESSING THE VALIDITYOF SURVEY RESULTS

Once you have found what seem to be themost important results from your data, andyou have performed basic statistical testsbetween variables (for example, violenceand ill health), you need to assess theirvalidity. This means you need to deter-mine to what degree the study measuredwhat it was supposed to, and whether thefindings mean what they are supposed to.Internal validity refers to the extent to

which variations in a specific outcome (forexample, the risk of violence) may beattributed to variations in an independentvariable (for example women’s age or edu-cation). External validity refers to thedegree to which results from a given studymay be used to draw conclusions about alarger population. If a study is performedon a randomly selected population, itshould be possible to generalize the resultsof the study to the general population fromwhich the sample was drawn.

Another important question is Are thefindings consistent? That is, do they makesense, according to what is known aboutthe subject locally and internationally? Ifthey differ greatly from previous findings,are there any additional data to support thenew results? Are there any aspects of datacollection, sampling, design, or analysisthat might have altered the results by intro-ducing bias? Has the analysis taken intoaccount possible sources of confounding?The following pages present different waysto address these issues.

The effects of confounding in data analysisIn studying the association between riskfactors and a specific problem, confound-ing can occur when another characteristicexists in the study population and is asso-ciated with both the problem and the riskfactor under study.

Confounding can have a very impor-tant effect on study results, and can cre-ate the appearance of a cause-effectrelationship that in reality does not exist.Age and social class are often con-founders in epidemiological studies. Inthe study of risk factors for violence, con-founding variables can give misleadingimpressions about what risk factors influ-ence the occurrence of violence.Stratified analysis and multivariateanalysis are two ways to control for theeffects of confounding variables.

C H A P T E R T W E LV EYFIGURE 12.9 HUSBAND’S CHARACTERISTICS ACCORDING TO USE OF VIOLENCE

(AS REPORTED BY WIFE) IN CENTRAL JAVA, INDONESIA

Alcohol use

Son preference

Low education

Fights with men

Has other women

0 10 20 30 40 50 60

** p<.001 N=765 men

! Not violent ! Violent

3**13

20**30

42**49

2**6

2**13

Percentages are given for the proportion of men with each characteristic,according to whether or not they have ever used physical or sexual violenceagainst their wives.5

Husband characteristics

Percent of men

(From Hakimi et al, 2002.5)

A Practical Guide for Researchers and Activists 195

Stratification involves analyzing dataseparately using defined categories of theconfounding factor, such as age groups.For example, some studies using bivari-ate analysis (analysis using only twovariables) have found that pregnantwomen were more likely to be abusedthan non-pregnant women. However,after analyzing the same data stratified byage groups, it turned out that this associa-tion was confounded by age. It turnedout that being young was the real riskfactor for violence rather than pregnancy.It just happened that younger womenwere more likely to be pregnant thanolder women. This explained theincreased prevalence of violence amongpregnant women.

Violence can also be analyzed as a con-founding variable for other risk factors, asshown in the following example of a studyon mental distress. Preliminary resultsfound that women who had been marriedat least once in their lives had twice asmuch emotional distress as women whohad never been married (Table 12.3). Thiswould imply that marriage is an importantrisk factor for mental distress.

However, when the prevalence of mentaldistress among ever-married women wasanalyzed separately according to whetherwomen had experienced wife abuse, alarge difference was found between thetwo groups. Thirty-one percent of abusedwomen suffered mental distress, comparedto only seven percent of women who hadnever been abused, which is even less thanthe prevalence of distress among never-married women (Table 12.4).

Since wife abuse is associated with mar-riage (by definition only ever-partneredwomen can experience wife abuse) and itis also associated with mental distress, ithas a confounding effect on the associationbetween marriage and mental distress(Figure 12.10). Therefore, after stratifiedanalysis it becomes evident that it is wife

abuse and not marriage itself that accountsfor the increase in mental distress amongmarried women.

This analysis is further strengthened bycomparing women’s current mental dis-tress according to the severity of violencethey experienced and when it took place,as shown in Figure 12.11. Breaking downthe analysis this way demonstrates thatwomen who were severely abused in thelast 12 months were over ten times morelikely to be distressed than women whohad never been abused. Further, itrevealed that the severity of abusewas more important than when it tookplace, since women experiencing severe

A N A LY Z I N G Q U A N T I TAT I V E D ATA Y

Selection of women Percentage of emotional distressAll women 15–49 (n=488) 17%Ever-married women (n=360) 20%Never-married women (n=128) 10%

TABLE 12.3 PREVALENCE OF EMOTIONAL DISTRESS ACCORDING TOMARITAL STATUS AMONG NICARAGUAN WOMEN

Experience of wife abuse Prevalence of emotional distressNever abused (n=172) 7%Ever abused (n=188) 31%

TABLE 12.4 PREVALENCE OF EMOTIONAL DISTRESS AMONG EVER-MARRIEDNICARAGUAN WOMEN ACCORDING TO EXPERIENCES OF WIFE ABUSE

FIGURE 12.10 THE CONFOUNDING EFFECT OF VIOLENCE ON THEASSOCIATION BETWEEN MARRIAGE AND MENTAL DISTRESS

Marriage

Violence

Mental Distress

(From Ellsberg et al, 1999.6)



violence formerly were still more likely tobe currently distressed than women whohad suffered only minor abuse, eventhough it took place more recently.

The use of multivariate analysis toadjust for confounding factorsWhen it appears that there are severalvariables confounding an association, thenit is no longer practical to use stratifiedanalysis, as it would be excessively com-plex to perform. For example, in Figure12.7, we saw that in the León study therewere several variables, such as poverty,living in the urban area, and number ofchildren, which were associated with therisk of violence. It was further shown thatthese three variables are associated witheach other as well as with women’s levelof education. Could it be that poverty isthe true underlying factor influencingwomen’s risk of violence, and therefore

that urban women and women with manychildren are found to have greater levelsof violence, simply because they are morelikely to be poor? How can we unravelthe complex relationships between thesevariables?

Multivariate analysis techniques, suchas logistic regression modeling, areuseful for examining the relationshipsbetween several explanatory factors and aspecific outcome variable. Logisticregression helps to uncover the degreeto which several explanatory variables arerelated and to control for confoundingvariables. In Table 12.5 (next page), thesame relationships presented in Figure12.7 are examined using crude or unad-justed odds ratios as well as multivariateor adjusted odds ratios. The 95 percentconfidence intervals are used to assessthe statistical significance of the associa-tion by indicating that there is a 95

C H A P T E R T W E LV EYFIGURE 12.11 PREVALENCE OF EMOTIONAL DISTRESS ACCORDING TO

EXPERIENCES OF VIOLENCE AMONG NICARAGUAN WOMEN

Percentages are given for the proportion of ever-married women who experienced emotional distress inthe four weeks prior to the survey, according to whether they had experienced physical partner violence.Violence was classified by severity and by whether it took place within the 12 months previous to thestudy, or earlier. In the right hand columns, crude (unadjusted) odds ratios and their corresponding confi-dence intervals are given. Intervals where the lower and upper figures do not include 1.0 are consideredstatistically significant. (In this case, all types of violence except for former moderate violence are signifi-cantly associated with emotional distress.)

No violence

Former moderate

Current moderate

Former severe

Current severe

7%

15%

21%

27%

44%

Emotional Distress %

10 20 30 40 50 Crude Odds Ratios 95% Confidence Interval

1.0

2.3

3.6

4.8

10.3

.7–7.8

1.0 –12.0

2.2–10.8

4.9–21.6

Experiences ofViolence


percent probability that the true figurelies between this range. If the rangebetween the lower and upper figure inthe confidence interval does not includeone, then it can be said that there is a 95percent probability that the association isnot due to chance.

When comparing the crude and multi-variate odds ratios for each variable, onecan see that they do not vary much inmost of the cases. The associationbetween violence and poverty, havingmore than four children, and a history offamily violence in the husband’s familyare all maintained. Living in the urbanarea, which had a confidence intervalslightly below one in the crude analysis,becomes significant in the multivariatemodel, while a history of family violencein the wife’s family becomes insignificant.After performing the multivariate analy-sis, it is possible to say that althoughpoverty, urban/rural residence, and highparity are all related, their effect onwomen’s risk of violence is independentand should not be interpreted as theresult of confounding.

Using advanced statistical analysis creativelyIn earlier sections of this chapter, we pre-sented the most commonly used tech-niques for statistical analysis of survey data


Crude AdjustedVariable Categories OR (95% CI) OR (95% CI)Poverty Nonpoor 1.0 1.0

Poor 1.91 (1.12-–3.23) 1.82 (1.03–3.23)Zone Rural 1.0 1.0

Urban 1.62 (.94-–2.78) 2.07 (1.12–3.82)Number of 0–1 1.0 1.0children 2–3 1.40 (.82-–2.39) 1.34 (.74–-2.43)

4 or more 2.77 (1.59-–4.82) 2.23 (1.21–-4.15)Family No historyhistory in wife’s family 1.0 1.0of abuse Wife’s mother

abused 1.8 (1.24-–2.90) 1.28 (.79-–2.09)No history inhusband’s family 1.0 1.0Husband’s motherabused 3.13 (2.00-–4.96) 2.98 (1.86–4.73)

Crude and adjusted odds ratios are given (together with 95 percent confi-dence intervals) for having experienced violence at least once in their lives.

TABLE 12.5 ASSOCIATIONS BETWEEN BACKGROUND FACTORSAND PREVALENCE OF VIOLENCE AMONG 360 EVER-MARRIED

NICARAGUAN WOMEN AGES 15–49

FIGURE 12.12 TIME FROM THE START OF A RELATIONSHIP TO THE ONSET OF VIOLENCE

Number of years from start of marriage to onset of violence

100–

80–

60–

40–

20–

0–0 2 4 6 8 10 12 14 16 18 20 22

Percentageof women

The figure shows the cumulative incidence of domestic violence over time among 188 women who reportedhaving experienced marital violence at least once in their lives, using Kaplan Meier Life Table Analysis.8




on violence. However, additional insightmay be revealed by the creative use ofmore advanced statistical techniques.

For example, life table or survivalanalysis was used to gain a deeperunderstanding of the relationship betweenviolence and high parity in Nicaragua.Many international studies have found asimilar association.7 One interpretation forthis is that having many children placesadditional stress on a marriage andincreases a woman’s likelihood of beingbeaten by her husband.

However, using life table analysis, astatistical technique which measures theprobability of events occurring over time,it was possible to determine that violencebegan early on in relationships, in manycases well before women had startedbearing children. Figure 12.12 shows that50 percent of violence begins within twoyears of marriage, while 80 percent ofabuse starts within four years. This

implies that high parity, instead of beinga risk factor for abuse, is more likely tobe a result of violence, because batteredwomen are less likely to be able to con-trol the timing of sex or the use of birthcontrol.

The same techniques were applied tothe likelihood of a woman leaving anabusive relationship, and it was foundthat 70 percent of women eventually didleave their abusers, although somewomen stayed as long as 25 years ormore before separating. Stratifying thisanalysis according to age groups showsthat younger women are more likely tohave left an abusive relationship withinfour years, compared to womenbetween 35-49 years (Figure 12.13). Thisindicates that younger women are lesslikely to tolerate abuse than olderwomen. In order to use survival analysistechniques, it is necessary to collectdetailed data regarding each of a

C H A P T E R T W E LV EYFIGURE 12.13 THE PROBABILITY OF LEAVING AN ABUSIVE RELATIONSHIP OVER TIME,

BASED ON A WOMAN’S CURRENT AGE GROUP

Number of years from onset of violence to leaving

100–90–80–70–60–50–40–30–20–10–0–

0 2 4 6 8 10 12 14 16 18 20 22 24 26

Percentage ofwomen leaving

This figure shows that 50% of young women (ages 15–25) leave a relationship within four years ofviolence starting, whereas 20 years was the median time of older women.

15–24 25–34 35–49 p<.01

(From Ellsberg, 2000.10)


woman’s relationships: when did it start,how long did it last, was there violence,and if so, when did the first and lastincidents of violence take place. Thesetypes of analyses are somewhat compli-cated to perform and interpret, so it isimportant to consult with an experi-enced statistician.

INTERPRET INGTHE RESULTS

The process of data analysis will oftentake longer than you initially expect.However, you can plan data analysis instages, so that initial findings, such asprevalence and descriptive characteristics,can be made available as soon as possibleto the communities and local institutionsthat have been supporting the research,and that will be anxiously awaiting results.Further analysis can be performed over alonger period to explore some of the moreinteresting findings in greater depth. Box12.2 presents guidelines for writing upresearch results for publication in scientificjournals. Chapter 14 will discuss in detailhow research results may be tailored to fitthe needs of different groups.

When interpreting and writing up theresults of data analysis, it is important tobe cautious. Each research design yieldsdifferent kinds of data, with their respec-tive limitations. Be careful not to drawconclusions that are not supported by thedata, as overstating your results can seri-ously undermine the credibility of theresearch. People are more likely to listento your findings when you are openabout whatever limitations the study hadin terms of design, data collection, oranalysis. Some examples of common pit-falls are the following:

! Inferring causal relationships fromcross-sectional data. Cross-sectional sur-veys can highlight associations between

two variables, but unless you havegood information about when differentconditions or events occurred it is diffi-cult to know with certainty what camefirst. A good example of how causalrelationships can be misinterpreted isthe relationship between parity and vio-lence presented in the last section. It isa good idea when presenting resultsfrom cross-sectional surveys to talkabout “associations” rather than causes.The discussion section can assesswhich variables are most likely to becauses or outcomes, based on yourconceptual framework and other stud-ies on the subject.

! Inferring causal relationships from bi-variate analysis. As we showed in the


AbstractApproximately 100 words.

BackgroundLiterature, national context, objectives.

MethodsDescribe the study population, how the sample was selected, what instruments wereused, how the fieldwork was conducted, how data were analyzed, how ethicalclearance was obtained, and any special measures, such as safety procedures.

ResultsThis section should describe all the major results of data analysis, including relevanttables and figures, and measures of statistical significance.

DiscussionThe purpose of this section is to interpret the meaning of data, assessing the validityand generalizability, possible sources of bias, how the findings relate to interna-tional and national studies on the same subject, and possible explanations for themost important findings.

ConclusionsThese are sometimes included in the discussion section. How might these findingsbe used for improving interventions and policy? What are areas that might benefitfrom future research?

ReferencesMake sure to include citations from the most relevant literature in the field of study.

(From Persson and Wall, 2003.1)

BOX 12.2 SUGGESTED GUIDELINES FOR WRITING A SCIENTIFIC PAPER


example on marriage and emotionaldistress, other variables may confounda relationship between two variables. Ifyou have not performed stratified ormultivariate analysis, it is wise to becautious in interpreting your results.

! Generalizing conclusions for differentpopulations than the study population.Results that are representative for oneregion are not necessarily true forother regions in the country, or for thecountry as a whole. This does notmean that regional studies cannot pro-vide important insights that are rele-vant for a much broader context. Thereare many examples of regional studiesthat made critical contributions forguiding national policies and programs.However, it is still important to becareful in stating clearly what the limi-tations of the sample are, both in termsof its power to capture important asso-ciations and its generalizability.

1. Persson LÅ, Wall S. Epidemiology for PublicHealth. Umeå, Sweden: Umeå InternationalSchool of Public Health; 2003.

2. Yoshihama M, Sorenson SB. Physical, sexual, andemotional abuse by male intimates: Experiencesof women in Japan. Violence and Victims.1994;9(1):63-77.

3. Rosales J, Loaiza E, Primante D, et al. EncuestaNicaraguense de Demografia y Salud, 1998.Managua, Nicaragua: Instituto Nacional deEstadisticas y Censos, INEC; 1999.

4. Ellsberg MC, Peña R, Herrera A, Liljestrand J,Winkvist A. Wife abuse among women of child-bearing age in Nicaragua. American Journal ofPublic Health. 1999;89(2):241-244.

5. Hakimi M, Nur Hayati E, Ellsberg M, Winkvist A.Silence for the Sake of Harmony: DomesticViolence and Health in Central Java, Indonesia.Yogyakarta, Indonesia: Gadjah Mada University;2002.

6. Ellsberg M, Caldera T, Herrera A, Winkvist A,Kullgren G. Domestic violence and emotionaldistress among Nicaraguan women: Results froma population-based study. American Psychologist.1999;54(1):30-36.

7. Kishor S, Johnson K. Domestic Violence in NineDeveloping Countries: A Comparative Study.Calverton, MD: Macro International; 2004.

8. Ellsberg M, Peña R, Herrera A, Liljestrand J,Winkvist A. Candies in hell: Women's experiencesof violence in Nicaragua. Social Science andMedicine. 2000;51(11):1595-1610.

9. Ellsberg MC, Winkvist A, Peña R, Stenlund H.Women's strategic responses to violence inNicaragua. Journal of Epidemiology andCommunity Health. 2001;55(8):547-555.

10.Ellsberg M. Candies in Hell: Research andAction on Domestic Violence in Nicaragua[Doctoral Dissertation]. Umeå, Sweden: UmeåUniversity; 2000.

C H A P T E R T W E LV EY

mandatory reading

Sports