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Workshop on Improving Gender Statistics in RwandaSession 6

Analysis and Presentationof Gender Statistics

Serena Lake Kivu Hotel, Rubavu DistrictMarch 25-27, 2014

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Learning Objectives

At the completion of this session, participants should understand and become familiar with:

• how analysis and presentation of gender statistics can enhance the usefulness of the statistics;

• the main types of analytic measures and analytic tools that can add value to basic data; and

• tools and techniques for presenting statistics in ways that ensure the visibility of meaningful differences and similarities between women and men.

Primary references:UNSD 2013, Integrating a Gender Perspective in Statistics, Chapter 4UNFPA 2013, Guide on Gender Analysis of Census Data UNSD and UNFPA presentations at the April 2013 UNSD Workshop in Japan

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Analysis of gender statistics

Analysis is an integral part of the statistical production process. In broad terms, analysis of gender statistics involves:

• Identifying the gender issues to be informed by the analysis.

• Obtaining statistics and other relevant data from available sources.– all variables of interest need to be disaggregated by sex as a primary

classification;– many variables may also need to be cross-tabulated, e.g. labour force

participation by sex by age group by geographic area.

• Analysing and interpreting the data, including derivation of indicators and other analytic measures.

• Reporting the findings, including presenting the statistics in easy-to-use formats that are appropriate to the statistical product in which they will be disseminated.

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Key steps in analysing gender statistics

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I. Gender analysis

• The purpose of the analysis determines the type and level used.

• The type and level of analysis usually vary by the type of statistical product to be used in reporting results.– Disseminating basic data collected in censuses and surveys typically involves tables with

minimum data processing and analysis.– More analytical reports or articles typically conduct additional processing and analysis.

• For most types of analysis, indicators and other analytic measures play an important role.– Use the basic data to select and construct relevant indicators and other analytic

measures.– Apply more complex analytic tools and techniques to the basic data to better

understand some issues.

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Analytic measures

• In analysing data from a gender perspective, use measures of composition and distribution of particular variables by sex.

• Such measures include:–proportions and percentages;–ratios and rates;–medians and quantiles, means and standard deviations.

• They provide the basis for constructing many of the gender indicators used to monitor progress towards gender equality.

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Proportions and percentages

In gender statistics, proportions and percentages can be calculated as relative measures of:

(a) Distributions of each sex across the categories of a characteristic--e.g.,• Proportion or percentage of women who are employed--compared to

women that are not employed or unemployed; • Labour force participation rate of women of the total female population; • Literacy rate of women—literate versus illiterate women.

(b) Sex distributions within the categories of a characteristic-e.g.,• Proportion or percentage of the employed who are women or men;• Proportion or percentage of parliament members who are women or men; • Share of women (or men) among older persons living alone.

The following 2 slides illustrate these two types of measures

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(a) Distribution of each sex across the categories of a characteristic

Source: Rwanda NISR, 2012, The Third Integrated Household Living Conditions Survey (Eicv3) Main Indicators report

Distribution of employed males and females by occupational group

EICV3 Male % Female % Urban Rural Total

Professionals 3.5% 2.0% 7.1% 1.9% 2.7% Senior officials and managers 0.1% 0.1% 0.5% 0.0% 0.1% Office clerks 1.0% 0.8% 3.7% 0.4% 0.9% Commercial and sales 7.7% 7.4% 15.7% 6.1% 7.5% Skilled service sector 7.0% 4.0% 18.3% 3.2% 5.4% Agricultural and fishery workers 61.3% 81.9% 34.5% 79.2% 72.6%

Semi-skilled operatives 12.9% 2.8% 12.3% 6.5% 7.4% Drivers and machine operators 5.2% 0.3% 6.4% 1.9% 2.5% Unskilled labourers 0.6% 0.1% 0.4% 0.3% 0.3% Missing information 0.5% 0.6% 1.2% 0.5% 0.6%

100.0% 100.0% 100.0% 100.0% 100.0% Persons aged 16 years and above who were working in the previous 12 months.

Rwanda. Occupational group by sex and urban/rural (percentages)

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(b) Sex distribution within the categories of a characteristic

Source: Rwanda NISR, 2012, Rwanda Demographic and Health Survey 2010, Final Report

Female and male distribution within marital status and education categories

Rwanda: Percent distribution of women and men age 15-49 by selected background characteristics, 2010

Women Men Background Characteristics

Weighted %

Weighted number

Weighted %

Weighted number

Total Number

% Women

% Men Total %

Marital Status Never Married 38.7 5,285 50.5 2,873 8,158 65 35 100%Married 35.1 4,799 34.1 1,938 6,737 71 29 100%Living together 15.3 2,098 13.4 761 2,859 73 27 100%Divorce/separated 5.5 746 1.6 92 838 89 11 100%Widowed 5.4 743 0.4 22 765 97 3 100%

Total 100 13,671 100 5,686 19,357 71 29 100%Education

No education 15.5 2,119 10.3 583 2,702 78 22 100%Primary 68.3 9,337 68.8 3,916 13,253 70 30 100%Secondary 14.7 2,008 18.7 1,064 3,072 65 35 100%More than secondary 1.5 207 2.2 125 332 62 38 100%

Total 100 13,671 100 5688 19,359 71 29 100%Note: Education categories refer to highest level of education attended, whether or not that level was completedna = Not applicable

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Rates

• Rates of incidence can be used to study the dynamics of change over time. – They are a special type of ratio obtained by dividing number of events during a period by

number of population exposed to the events during the period—for example:

• Fertility rates – an average based on number of births -- in Rwanda, the total fertility rate (in the last 3 years) was 4.6 (2010 DHS)

• Mortality rates, such as – Infant mortality rates (under 1 year) per 1000 live births – in Rwanda, 50

(2010 DHS)• Females: 55 • Males: 67 (2010 DHS),

– Under five mortality rate per 1000 births – in Rwanda, 76 (2010, DHS) • Females: 97• Males: 107 (2010 DHS)

• By convention, some percentage measures are also called rates–for example:– Literacy rate (percentage of population that is literate) – in Rwanda,

• Literacy rate among population 15 and older in 2010 was 70% (EICV3):• Among women 15 and older: 65% (EICV3)• Among men 15 and order: 76% (EICV3)

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Ratios

• Particular compositional aspects of a population can be made explicit by the use of ratios, where a single number expresses the relative size of two numbers– for example:

– Sex ratio (number of males per 100 females): 93 for Rwanda (2012 Census)– Sex ratio at birth (number of male live births per 100 female live births): 109.4 for

Rwanda (2010 DHS);– Maternal mortality ratio: 496 for Rwanda (2010 DHS)

• For some sex ratios, standardisation of the variables used may be necessary to adequately reflect gender differences– for example:

– The gender parity index for primary gross enrolment, calculated as the ratio of the gross enrolment rate for girls to the enrolment rate for boys,

– This rate controls for the sex composition of the school age population.– For Rwanda, the gender parity index for gross primary enrolment: 1.03 (2011, Rwanda

Education Statistics)

Girls’ gross primary enrolment rate = girls enrolled as a proportion of school-age girls 129Boys’ gross primary enrolment rate = boys enrolled as a proportion of school-age boys 126

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Measures of central tendency and dispersion

• Medians and quantiles– Often used in gender statistics to show the distribution of income or wealth across the

population; – They can be useful in studying gender issues associated with poverty or in analysing

the economic resources of different household types (such as single mother households).

• Means (averages) -- Examples of gender-relevant indicators: – Average (mean) time use on unpaid work – Average (mean) size of land owned– Mean age at first marriage– Mean age of mother at first child

• Standard deviations, coefficient of variation, etc.– These measures are important for measuring the degree of association between

variables and making population inferences based on sample data.– Although not often presented in gender statistics

• Projections– An example relevant to gender statistics is the projection of the male and female

populations to a specified date in the future.

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Understanding gender differences using analytic measuresIt often may be necessary to disaggregate simple summary measures or combine them with other data to adequately inform gender issues. This is illustrated in the following example which explores poverty among female or male headed households in Rwanda.

Data from the 2012 EICV3 revealed little difference in poverty incidence among female-headed and male-headed households: • 47% of female-headed households (which comprise 28% of all households), were poor compared to • 44% of male-headed households (which comprise 66% of all households).

However, the data revealed a higher poverty rate for de facto female-headed households (51% compared to 47% for de jure female-headed and 44% for male-headed households). In these households, which are only 6% of all households, male heads were absent for more than 3 months in the previous 12 months mainly because of detention or compulsory service (41%) or for work (28%).

For households in extreme poverty, the difference by household head is much larger: 34% of de facto female-headed households are extremely poor, compared to 26% of de jure female-headed and only 23% of male-headed households.

De facto female-headed households heads tend to be more similar to male than to other female heads in terms of marriage, age, education and literacy, but they are larger (6 members on average compared to 5 for male-headed and 4 for de jure female-headed households) and more likely to have younger children or grandchildren at home, and more likely to work as wage workers (32%) than de jure female-household heads (17%).

De jure female-headed households tend to be headed by older, widowed, less educated and literate women (almost 70% are 45 or older), who are much more likely to be small-scale farmers. These characteristics explains in part why they are different from male and de facto female-headed households.

Source: NISR. EICV3 Thematic Report on Gender, 2013.

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Use and value of standardisation

In some situations it can be useful to standardise a measure to better understand gender differences or to avoid it being misleading (or biased).

Examples where standardisation may be important for the analysis: • Risk of renewed divorce of men or women in second or third marriages.

Standardisation by order of marriage can take account of the fact that more men than women remarry after a first divorce or widowhood.

• Literacy rates of women and men. Age standardisation can take account of the fact that literacy rates are lower

at higher ages, were women predominate.

• Incidence of disability in women and men. Age standardisation can take account of the fact that there are more women

than men in the population and that the excess of women over men is concentrated in the oldest ages where disabilities are most common.

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A country example showing effect of age standardisation

Type of disability Percentage

Female Prevalence Standardized

Male Female Male Female Walking or moving 53.3 2.10 2.29 2.19 2.20 Seeing 52.2 1.14 1.19 1.18 1.15 Hearing 45.2 0.50 0.40 0.53 0.38 Speaking or communicating 43.0 0.42 0.30 0.42 0.30 Personal care 52.6 0.20 0.21 0.21 0.20 Paying attention or learning 45.9 0.21 0.17 0.21 0.17 Mental disabilities 43.8 0.47 0.35 0.47 0.34 Total 50.1 4.17 4.00 4.29 3.87

Un-standardised and Age Standardised Prevalence of Selected Types of Disabilities in Mexico, based on 2010 Population Census

Source: UNFPA Guide on Gender Analysis of Census Data

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Use and value of multivariate analysis

Multivariate analysis can • assist in disentangling variability and understanding interrelationships within a

population group • provide a more comprehensive view of different relationships, making it easier to

identify situations where, for example, the relationship between two variables can be accounted for by their common dependence on a third factor.

Examples of its use in the context of gender statistics are:• Understanding the relationship between women’s educational attainment and their

economic level in rural and urban areas and at varying ages;• Investigating whether the relationship between two characteristics that are highly

correlated, such as lower education and early marriage, is caused by another factor, such as belonging to a certain ethnic group;

• Understanding whether the marital status of a woman has a direct effect on her labour force participation after controlling for other intervening factors.

• Understanding the various factors that affect age of marriage;

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A country example: A study using multivariate analysis in Rwanda

Measuring the Success of Family Planning Initiatives in Rwanda:

A Multivariate Decomposition Analysis.

Contraceptive use in Rwanda has increased far more than the Ministry of Health projected for the year 2010. Moreover, other indicators of progress, such as women delivering in health facilities and reductions in infant and maternal mortality have also exceeded expectations.

This study described the family planning initiatives in Rwanda and analyzed the 2005 and 2010 RDHS data to identify factors that contribute to the increase in contraceptive use, by decomposing the contributions of women’s characteristics and their effects.

The study found a mean predicted increase of 0.342 in contraceptive prevalence rate between 2005 and 2010. • The largest increase (77 percent) results from changes in the effects of women’s characteristics compared

with changes in these characteristics (17 percent). • The variables showing significant contribution in effects are women’s education, experience of child

mortality, and place of residence. • As for compositional differences, woman’s education, exposure to family planning messages in the media or

at health facilities, husband’s desire for children compared with wife’s, and woman’s child mortality experience had relatively greater effects.

Additional research is needed to assess the contribution of supply side factors that would have been also important for the increased contraceptive use in Rwanda.

Source: Muhoza, Dieudonné Ndaruhuye, Pierre Claver Rutayisire, and Aline Umubyeyi. 2013. Measuring the Success of Family Planning Initiatives in Rwanda: A Multivariate Decomposition Analysis. DHS Working Papers, 2013, No. 94. Washington, DC: USAID.

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Some tips for analysing gender statistics

Assess data quality to avoid misinterpretation of results. Use appropriate analytic measures and techniques to construct

indicators that reflect the gender issues to be studied. Consider the value of using multivariate analysis to assist in

understanding gender inequality in its many dimensions. Interpret the results of analysis with careful consideration of the

different factors that may be involved (such as distinguishing the between the impact of socio-economic and biological factors on health outcomes).

Take care when combining data from different sources and use appropriate techniques.

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Concerns when integrating data from different sources

• When different sources need to be combined to calculate a particular analytic measure (e.g., a rate), check the sources for consistency and comparability. Comparability issues can arise because of:

P differences in concepts, definitions, coverage or time period; P errors or variations in classification or data processing procedures; or P variations in concepts or practices in different years within the same

source. • In most cases comparability checks can be made by reviewing each source’s

documentation. Consider consulting also the specialists who supply or use the data from that

source.

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Some further considerations ...

• Be aware of the different implications, for gender analysis, of data produced at different levels of statistical unit.

Statistics on poverty may be produced at household level and/or individual person level but the concepts used are not the same and thus not comparable

• Using sex of ‘head of household’ (or household headship) to analyse gender differences can be problematic.

‘Head of household’ can refer to many different concepts; it does not capture intra-household gender inequalities; and it can reinforce gender stereotypes.

There is no uniformity in country practices concerning the concept or its use.

• Comparing households with different characteristics can provide useful insights into gender issues.

It can be useful to disaggregate households by – size and composition (sex and age of each member), – type (one person, couples with/without children, single mother/father with

or without children, etc.) and – other characteristics

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Exercise 6.1: Data analysis

1. You are asked to prepare an article analysing a topical gender issue—on health, education, employment or violence--based on results of the 2012 Population Census. a) How would you go about the task?b) What analytic measures and tools would you expect to use?

2. Does the analysis of gender statistics need to be improved in any of the fields (topic areas) with which you are familiar? What are the specific field(s)? What needs to be done and why?

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II. Presentation of Gender Statistics

12.0%

16.9%

52.2%

18.8%

Women

Not paidIn-kind onlyCash and in-kindCash only

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• How the statistics are presented will influence understanding and use of the data for program or policy making

• Gender data presentation seeks to:– Highlight key gender issues– Facilitate comparisons between women and men– Convey the main messages resulting from data analysis– Reach a wide audience– Encourage further analysis– Stimulate demand for more information

• Tables, graphs and charts are the key forms of statistics’ presentation.

Presentation of gender statistics

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Graphs and Charts

• These are powerful ways to present data. They can:– Summarize trends, patterns and relationships between variables;– Illustrate and amplify the main messages of a publication, and inspire the

reader to continue reading;– Give a quick and easy understanding of the differences between women

and men.

• A graph or chart should:– Be simple, not too cluttered– Show data without changing the data’s message– Clearly show any trend or differences in the data– Be accurate in a visual sense—for example

– If one value is double another, it should appear to be double in the graph or chart.

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Types of Graphs and Charts

• There are many types of graphs and charts. It is important to select the right type for data being analysed.

• The selection may also be influenced by the message to be conveyed and the method of dissemination (e.g. printed or electronic).

• Some of the main types of graphs and charts used in presenting gender statistics are:– Line charts– Bar charts: vertical, stacked and horizontal– Age pyramids– Dot charts– Pie charts– Scatter plots– Maps

Line chartsLine charts can give a clear picture of trends over time—examples:• Trends in sex ratios; • Literacy rates over time; • Labour force participation rates by age group over time.

Source: NISR & Measure DHS. 2012. Rwanda Demographic and Health Survey (RDHS) 2010 26

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Line charts (continued)

Line charts also can give a clear picture of differences across age groups.

This chart shows that in Rwanda in 2010:

• Labor force participation rates were lower for women than for men at all ages.

• The largest gender gap is at 15-19 years of age.

Rwanda: Age-specific employment rate in the last 12 months, by sex, 2010 (RDHS)

Source: NISR & Measure DHS: Rwanda Demographic and Health Survey, 2010

15-19 20-24 25-29 30-34 35-39 40-44 45-490%

20%

40%

60%

80%

100%

120%

MenWomen

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Vertical bar charts

• Both vertical and horizontal bar charts are common for presenting gender statistics.

• A key feature is that the greater the value, the greater the length of the bar.

• Examples of use: • total fertility rate

by region; • antenatal care by

urban/rural area; • proportion of

women having third and higher order birth by education level.

Source: NISR. 2012. EICV3 Main Indicators Report

Figure 1.1.2 Sex Ratios: Number of males per 100 females by age group (EICV3)

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Vertical bar charts (continued)

Grouped (or clustered) bar charts can present a particular characteristic for women and men at the same time, facilitating comparisons between them.

Source: Ministry of Education, MINEDUC. 2013. 2012 Education Statistics Yearbook

Rwanda: Lower secondary education Gross and Net Enrolment Rate from 2008 to 2012

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Stacked bar charts

• Stacked bar charts illustrate data sets containing two or more categories

• Most effective for categories that add up to 100 per cent.

• Common problems: • Bars with more

than three segments are difficult to compare from one bar to another;

• one or more categories may be too short to be visible on the scale.

Source: NISR. 2013. EICV Thematic Report Gender

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Stacked bar charts (continued)

• Stacked bar charts are also used to present the distribution of a variable within the female and male population

• Examples• the distribution

of female and male deaths by cause of death;

• the distribution of female and male school attendance.

Rwanda: Poverty levels, by sex of household head, EICV3

Source: NISR. 2013. EICV Thematic Report Gender

Male headed Female headed De facto female headed

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

56.30% 53.00% 49.20%

21.10%21.00%

16.50%

22.50% 26.00%34.30%

Extremely poor Poor

Non poor

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Horizontal bar charts

Horizontal bar charts are often preferred when• many categories

need to be presented (e.g. regions of a country), or

• where categories have long labels.

May be preferred for showing some type of time use data, because the left-to-right motion on the x-axis generally implies the passage of time

Rwanda: Household composition (% household members) by sex of head

Source: EICV3. Note: composition estimates for de facto female-headed households include the absent head. Other relation or in-law includes parent, sister/brother, adopted child; Other includes servant and unrelated

Other

Other relation or inlaw

Grandchild

Spouse

Head

Child

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%

All house-holdsMale headed

Female headed

De facto fe-male headed

Age pyramids

Source: NISR. 2013. EICV3 Thematic Report Gender

• Age pyramids are useful for describing the age structure of a population and its changes over time.

• Pyramids can use percentages instead of absolute numbers to highlight the age groups where women or men are over-represented.

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Dot charts

• Preferred to bar charts when presenting many categories or data points, because bars can become too thin and difficult to interpret.

• Can convey a lot of information in a simple way without clutter.

• May be vertical or horizontal.

Primary school net attendance rate for girls and boys by wealth quintile and urban/rural areas Yemen, 2006

Source: UNSD presentation “From raw data to easily understood gender statistics,” at Workshop on Integrating a Gender Perspective into National Statistics, Kampala, Uganda 4 - 7 December 2012. Data from Yemen Ministry of Health and Population, and UNICEF, 2008.

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0

10

20

30

40

50

60

70

80

90

100

Poorest20%

Q2 Q3 Q4 Richest20%

Rural Urban

Boys

Girls

By wealth quintile By residencePer cent

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Pie charts• Used for simple comparisons of a small number of categories that make up a total.

• Can illustrate the percentage distributions of qualitative variables and as an alternative to bar charts. • Using more than five categories will generally make a pie chart difficult to read.

Source: NISR & Measure DHS. 2012. Rwanda Demographic and Health Survey 2010

12.0%

16.9%

52.2%

18.8%

Women

Not paidIn-kind onlyCash and in-kindCash only

14.7%

4.3%

44.8%

36.2%

Men

Not paidIn-kind onlyCash and in-kindCash only

Rwanda: % of type of earnings for currently married women and men, 15-49, 2010

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Scatter plots

• Often used to show the relationship between two variables.

• Useful when many data points need to be displayed, such as a large number of regions, sub-regions or countries.

• Also useful for identifying outliers in the data.

• Design note: the four states where girls have significantly lower school attendance rates than boys have been highlighted.

Source: UNSD presentation “From raw data to easily understood gender statistics,” at Workshop on Integrating a Gender Perspective into National Statistics, Kampala, Uganda 4 - 7 December 2012. Data from India Ministry of Health and Family Welfare, Government of India, 2007

School attendance rates for 6-17 years old by sex and state, India, 2005-06

Sikkim

Gujarat Rajasthan

Arunachal Pradesh

60

70

80

90

100

60 70 80 90 100

Per cent girls

Per cent boys

Higher school attendance rates for girls than for boys

Low er school attendance rates for girls than for boys

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Maps• Maps are used to show spatial patterns and geographic distributions for a particular variable.

• They can increase the visibility of regional clusters within a country and highlight regional pockets that deviate substantially from the norm.

Source: NISR. 2012. EICV3 Main Indicators Report

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Interactive graphs and charts

• A range of data visualization tools can be employed to enhance on-line dissemination of graphs and charts.

• These tools can animate presentations, provide other interactive features, and display three or four dimensions of data simultaneously. For example:

– A moving image can show transitions in a variable over time—changing the shape of an age pyramid;

– Actual values and other details underlying a particular point in a graph or chart can be displayed instantly on request--by hovering over the point;

– Bubble charts (a variation of the scatter plot) can be used – to visualize three or four dimensions of data and also– be animated to show changes over time.

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Tables

• Tables are a necessary form of presentation of data.

• Types of tables:1. Large comprehensive tables, often placed in a separate part of a publication

(e.g., in an annex).2. Text tables, which are smaller and part of the main text of a publication. They

often support a point made in the text.

• Always preferable to presenting many numbers in the text itself, as they allow more concise explanations.

• Selection of data to present should focus on most striking differences or similarities between women and men.

• Some data may be more easily conveyed in a table than in a graph--For example,

– when data do not vary much across categories of a characteristic, or– when data vary too much.

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Text tables with one column

• Can be used to present data with not much variation between categories,

• Often listed in ascending or descending order.

Source: NISR & Measure DHS. 2012. RDHS 2010.

Province

Total fertility rate* (average # of children per woman)

City of Kigali 3.5

North 4.1

South 4.6

East 4.9

West 5.0

Total (average) 4.6*Total fertility rate for the 3 years (1-36 months) preceding the survey

Rwanda: Fertility by province, 2010 DHS

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Text tables with two or more columns

Used to present data for females and males side by side so differences are clearly visible.

Source: NISR. 2013. EICV3 Thematic Report Gender.

Text tables with two or more columns (continued)

Source: NISR & Measure DHS. 2012. RDHS 2010 42

• Can be used as a form of presentation when the focus of analysis is a breakdown variable

• in this case, mother’s age--associated with a number of related indicators expressed in different units

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Some tips for user-friendly presentation of gender statistics1. Focus on a limited number of messages for each table, graph or chart.

The messages should generally relate to a specific gender issue.2. Adopt good design practices; for example:

Ensure charts have clear, simple headings; labels are clear and accurate; axes are clear and divided consistently; a key is provided; data sources are acknowledged.

3. Facilitate comparisons between women and men; for example: Present data for women and men side by side; Ensure consistency in the way data for women and men are presented (e.g.,

use the same color for women and men in all charts in a presentation).4. Consider the audience; for example:

Rounded numbers may communicate a message more easily to the general public.

5. Ensure simplicity of the visual layout; for example: Labels for values presented inside a graph or chart can be distracting and

often may be redundant; Including a third dimension on a two-dimensional graph/chart can be

misleading.

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Exercise 6.2

1. You are asked to prepare an article presenting the results for a topical gender issue based on the most recent Population Census—such as boys’ increasing dropout rates compared to girls’ permanency or women’s much lower access to and use of credit for agriculture(a) How would you go about the task?(b) What presentational tools would you expect to use?

2. Does the presentation of gender statistics need to be improved in any of the fields (topic areas) with which you are familiar? What are the specific field(s)? How would you improve the presentation and why?