etc1000 live lecture 1

7/28/2019 ETC1000 Live Lecture 1

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ETC1000 / ETX9000 Business and Economic Statistics

Demonstration Lecture 1: Tables and Charts

This lecture provides examples of the material taught in this weeks lectures, to help you seeits potential for real world application, and to reinforce the ideas being communicated.

Case Study: The 2004 Tsunami

BackgroundOn December 26, 2004, and earthquake under the ocean near Sumatra created

a Tsunami that caused widespread devastation and loss of life in a number of

countries, especially Indonesia, Thailand, Sri Lanka, the Maldives and India. More

than 230,000 people died.

The recovery effort from a disaster like this was massive, involving emergency

provision of food, water, housing, etc, plus a long process of rebuilding whole

communities.

So what does this have to do with statistics?

Several months after the Tsunami, a group of Monash staff teamed up with a large

non-Government Organisation (NGO) to undertake a research project into the effect

of the Tsunami on children. We focused on the south and south-east coastal areas of

Sri Lanka. This research involved extensive data collection and analysis, and

illustrates some of the ideas we have looked at in lectures this week.


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Why do research in times like these?

As indicated, the relief and recovery effort after this huge natural disaster was

enormous, with billions of dollars being spent, and many tough decisions needing to

be made about spending prioritiesis it more important to rebuild housing first?

What about helping people re-establish their businesses / livelihoods? What about

schools and hospitals or other infrastructure (roads, etc)?

Research helps us learn about what works well and what doesnt, what are the critical

factors to improving outcomes, what are the cost effective strategies, etc.


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Why were we interested in the effects of the tsunami on children?

With any major disaster, children often suffer disproportionately. The Asian tsunami

of 26th December 2004 was no exception, with children comprising around one-third

of those who died. Hundreds of schools were destroyed and thousands of teachers also

died, with potentially significant implications for the education of child survivors.

Child survivors are also often more vulnerable than adults to disease, post-traumaticstress and exploitation. Displaced children are at particular risk, often living in

temporary camps away from their home area, sometimes separated from family or

relatives, and facing the loss of community, place, family members and sometimes

identity. The most extreme example of such risk was the potential for the unlawful

removal of injured children from hospitals after the tsunami for trafficking into

exploitationthough thankfully such cases seem to have been rare. The integration of

child protection practices into disaster relief and reconstruction activity is critical to

reduce the vulnerability of children. Such measures include child friendly spaces,

registration and reintegration procedures, child safety kits for assessment teams and

effective, well-implemented government and NGO (Non-Government Organisation)

programs designed specifically to meet the needs of children in the days and monthsafter a disaster.


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So how do we go about this research?

We need datawe need to talk with people. But what kind of data? Qualitative or

Quantitative data?

Qualitative: Participatory Rural Appraisal (PRA)

Participatory group research activities are justly regarded as a rich source of learning

for both researchers and participants, and potentially a source of community

empowerment. We certainly found a variety of PRA activities to be a useful

complement to interviews in our research in Sri Lanka. The research team included

local project staff and research assistants from a local university in Colombo, and

used PRA activities to build rapport and to open up communication with

communities, as well as to gather useful information. Some of the activities used

included:

drawing exercises in groups: children drew what their lives were like beforethe tsunami and what they were like now; children also drew faces reflecting

their emotions before and after the tsunami

ranking exercises in groups: children made lists of the activities they took part

in and the games they played, then ranked which they enjoyed most; children

also ranked the types of assistance they received from NGOs and government

sources from most appreciated to least.

Listing exercisessuch as organisations that were helpful to them after thetsunami

The PRA activities engaged the children, and gave us a considerable amount of usefulinformation. There were limitations however. Because the activities were all

conducted in groups, there was a non-negligible degree of cross-pollination in the

childrens answers, drawings and opinions.

Quantitative: Individual Interviews

PRA activities are overwhelmingly geared towards group activities and group

outcomes. In compiling the data, we end up with mainly aggregate statistics. For

example, we discovered as a result of our surveys and PRA activities that around 33%

of children performed significantly worse in their class ranking in the year following

the tsunami than they had in the year before. In addition, around 44% were no longerable to live in their original home because of serious tsunami damage or other

reasons.

This information is useful as far as it goes,but the problem is that it doesnt go very

far. It does not tell us anything about how damage to ones home and / or the need to

relocate might be related to school performance, or more generally, how programmes

can be designed to minimise the detrimental effects that disruption in a childs home

life can have on school performance. To make more useful inferences we need more

information. We need to be able to link data to particular individuals, to get a more

rounded picture of their story. Survey-based interviews are one of the best ways of

obtaining such information.


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Continuing the example above, information gleaned from the individual-level data

enables us to link each childs responses in both dimensions, and provide a more

complete analysis of the relationship between housing and school performance. Table

1 shows the results for the 148 children surveyed.

Table 1

Relationship between housing and school performance

with individual level data

Performed the same orbetter after Tsunami

Performed worse afterTsunami

Not living inoriginal house 24.7% 18.8% 43.5%

Living inOriginal house 42.0% 14.5% 56.5%

66.7% 33.3% 100%

Using the information in Table 1, take a child whose class ranking declined

significantly after the tsunami. There is a 56.5% chance that this child is not living in

their original home (18.8/33.3 = 0.565). In contrast, there is only a 37% chance that a

child whose performance was about the same or better had been relocated away from

their original home (24.7/66.7 = 0.370). Put the other way around, only 25.7% of

those able to continue living in their original home achieved a school result worse

than the previous year (14.5/56.5 = 0.257). On the other hand, almost half (43.2%) of

those who had to leave their original home had a significantly worse school result

(18.8/43.5 = 0.432). This result suggests that children who had to move from theiroriginal home because of the tsunami suffered in their school performance in the year

following the tsunami. In other words, we are able to establish a potential causal link

between the two factors. A statistical test for the significance of this result confirms

that there is a significant difference in school results attributable to changes in living

situation (p-value on the 2 test of 0.049).

It is essential to track individuals in order to discover correlations between different

factors and to get a sense of what seems to be causing what. We simply cannot do that

if all we have are aggregates in each category. Knowing that 33% of children are

performing worse in school in the year after the Tsunami and that 44% have had to

move from their original home doesn't tell us much at all about what may be affectingschool performance. On the other hand, discovering that significantly more children

who had to move out of their original home experienced a decline in their school

performance compared those who were able to stay in their family home, tells us

something about the link between home situation and school outcomes. It suggests

some priorities for educational and community programmes designed to support

children after disasters.

This example uses a simple 2 x 2 table but the principle is easily extended to

multivariable cases, where we could include such variables as family loss, impact on

parents income, measures of childrens health status, effect on the disaster on theschool, etc.


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In other words, we get far richer and more specific information from being able to

track the stories of individuals than if they are all lumped together in an aggregate.

The inability to track individuals is not merely a slight reduction in useful

information. It actually dramatically reduces the information available and our ability

to infer specific implications from it for development priorities and practice.

Ethical Issues

The UN Convention on the Rights of the Child specifically emphasises the need for

children to be given the opportunity to express their views about what is being done to

and for them. For example Articles 12 and 13 state:

Ar ticle 12

States Parties shall assure to the child who is capable of forming his or her own

views the right to express those views freely in all matters affecting the child, the

views of the child being given due weight in accordance with the age andmaturity of the child.

Ar ticle 13

1. The child shall have the right to freedom of expression; this right shall includefreedom to seek, receive and impart information and ideas of all kinds,regardless of frontiers, either orally, in writing or in print, in the form of art, orthrough any other media of the child's choice.

PLAN Internationals major report Children in Disasters: After the Cameras Have

Gone (Jabry, 2002), includes a case study of interviews with 315 children aged 8-17

years who survived the war in Sierra Leone. Questions were highly specific about thetrauma they had experienced, in order to be able to gauge the childrens need for

support and the extent to which the four week intervention assisted them. For

example, questions included:

1. Did you see or witness any violence during the fighting?2. Did you see the most violence after the May 1997 coup?

3. Did anyone in your family die from the fighting in the past two years?4. Who died from the fighting during the January 6, 1999 invasion?5. What kind(s) of violence did you see during the recent fighting?

Family members being killed?

Someone killed/injured by guns?

Someone killed/injured with machetes?

Someone burned to death?

Houses being destroyed?

Someone being raped or sexually assaulted?

Dead bodies or body parts?

Someone being tortured?6. Did you hear someone being killed/injured?

Hear people screaming for help?

Heard a family member being threatened?

Hear gunfire, bombing or shelling?


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Many of these questions would be expected to be quite traumatic for children (or

indeed anyone!). Yet the children were also asked the following question, with the

results listed below for the different options:

What were you feeling while you were drawing pictures, talking or writing about your bad

memories/painful feelings from the recent fighting?

Worry Fear Sadness Anger Relief Mixed Feelings No Feelings(%) 0.3 5.2 36.4 - 51.5 5.9 0.7

As expected, a large proportion felt sadness, but more surprisingly given the extremity

of some of the questions, more than half the children felt relief at being able to talk

about their experiences or otherwise express what they had been through.

Selecting a Sample

In this weeks lectures we talked about the importance of having a sample that is

representative of the population of interest. Our interview research with children in

Sri Lanka posed some real challenges here.

Ideally we would select our sample using random methods. e.g. Draw a random

number that determines which house in a street to visit, and then interview one

randomly selected child from that household.

BUT: In practice, this is very difficult, especially when there is little order to

communities that have been devastated by a disaster.

The solution? Some randomness (e.g. randomly select which villages to visit), and

some monitoring of the sample to ensure it is representativea good mix of obvious

characteristics (Male/Female, different age groups, etc).

Almost all children want to be interviewedno concerns with NON-RESPONSE

BIAS here. They were typically queuing up for their turn - but some are more pushy

than others. Dont just choose those at the front of the queue, or we will potentially

suffer from SELECTION BIAS (only interviewing the children with power and

influence among their peers).

The Data File.

We will now look at the data file of results and analyse some other possible

interesting findings.

A couple of key results.

Children and Play:

Almost 50% reported changes in where they play, and of those who were asked if

they still play at the beach, 65% said they did not. For many this was because of

concerns expressed by their parents about their safety.


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Emotional Wellbeing:

The first column of Table 2 below gives the distribution of responses when

respondents were asked to select the word that best describes how they feel now. The

second column gives the distribution of responses associated with the second word

chosen, and the last column is the distribution of choices when asked to describe howthey felt prior to the Tsunami.

Table 2

Comparing feelings before the Tsunami with after the Tsunami

1st word chosen

now

2n word chosen

now

1st word chosen a

year ago

Happy 46% 14.5% 78.9%

Sad 9.5% 14.5% 2.7%

Hopeful 16.9% 24.1% 4.8%

Anxious 1.3% 2.1% 0%

Relaxed 1.3% 3.5% 4.8%

Worried 2.7% 4.8% 0.7%

Optimistic 11.5% 24.1% 7.5%

Fearful 6.1% 7.6% 0%

Pessimistic 4.7% 4.8% 0.7%

A comparison of the first and last columns reveals that while there has been a sizeable

drop in the percent who choose happy, some of this drop has been absorbed in

increases in hopeful and optimistic. There is, however, still a notable increase inthe number who chose one of the more negative feelings (sad, anxious, worried,

fearful, pessimistic) from 4% to 24%.

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