A Nvivo Model

I have discussed at some length on how to make a Nvivo model (see here). What exactly does it look like? Here is just an example:

This Nvivo model tells us that there are a number of factors relating to/causing drunkenness among rural men in Vietnam. These factors are contextual factors, embedded in the living conditions of people under the study. Each man mentioned a few factors. All together, they made a web of factors that link to drunkenness. This is a typical use of Nvivo. Note that in this model was created by Nvivo 2.0. The newest versions of this software (Nvivo 9 or 10) allows us to obtain this model just by a few clicks. It also provides a statistic, which is a correlation between one factor an another (see example at the end).
 

Now, the task is to:

1) Explain the model: How does a factor link to others? Which factor seems to be at the 'central' of all factors? What is the direction of the relationship (one-way or two-way arrow)? What you are doing here is to show 'the findings' of your study.

2) Discuss the model:

a) (Re) Considering available theories that explain drunkenness: does the model 'follow' any theory or 'way' of analysis? What does it add to the current scholarly understanding of drunkenness?
b) Considering available efforts to reduce alcohol consumption (that is the 'applied' aspects of your findings/or the 'so what' of your study): Does the model suggest anything that help the heavy drinkers reduce alcohol consumption?
c) Considering limitations of the study: Are there anything that should be in the model but they are not? Are there any 'cautions' for people reading this study? Can you do it better?
d) Considering the 'secrets' of your study (and don't tell anybody about them): After all, what made you do this research? Deep down inside, are you really satisfied with it? Are there anything that, without it, you would not be able to produce part of the findings? These questions are important for your intellectual development. The questions from a) to c) are for the readers your study.

Finally this is an Nvivo model created by Nvivo 9. It gives 'correlations' between factors:

Building links in a Nvivo 2.0 Model

As I have said in another post, Nvivo 2.0 allows us to make links and put the links in to a theoretical/explanatory model. A model is a fascinating feature that Nvivo offers us. But it has been underused for a number of reasons. One reason may relate to ability to build a link.

People are often quite reluctant to explain why a link is established in their study. This 'why' question is, however, difficult to answer. In this post, I will give answer these two questions:

 

1) if I have many people say a same thing and its relation to another same thing, then should I make a link?

2) if I have just few people say a same thing and its relation to another same thing, then should I make a link?

 

People who ask these questions are, actually, confused with the philosophical base of their research. In a qualitative study, researchers tend to use social constructionist perspective rather than an objectivist one.

Let' us review briefly two traditions in philosophy:

- objectivism: the idea that the world is independent of human mind. It is 'objectively' there, whatever you think of it. Therefore, the world could only be measured by scientific methods. (Maybe I should write, in a separate post, how people agree and disagree with each other regarding the world 'scientific').

- subjectivism (with social constructionist perspective as a branch):  the idea that how the world exists depend on human thoughts, perceptions, meanings they give to it. Therefore, the world could only be understood if we ask people to tell us what they think of it.

For instance: We have a public health problem, that is drunkenness. People in the first tradition would look at association between level of alcohol intake and drunkenness (relation between one behavior with one phenomenon). People in the second tradition would be more interested in knowing the association between meanings people give to drunkenness and drunkenness (relation between a certain type of meanings and one phenomenon). People in the former tradition is more interested in knowing frequencies, p-value, confident interval in establishing a link between things. People in the second one, instead, are likely to look at a 'map' of meanings, or the relatedness between things, or the thick descriptions of links between things.

Writers of Nvivo 2.0 are those who on the subjectivism side. Tools within Nvivo, including the modeling tool, are designed to support links between subjective meanings.

But the division between 'subjective' and 'objective' is not rigid, but changeable. That is the key message in this post.

 

In a quantitative data analysis, people must start with finding descriptive and/or inference parameters (mean, media, standard deviation, p-value, r square, so on). But then they have to tell us what these parameters do means. Remember, a lot of people are struggling with understanding and properly interpreting the statistics their fancy softwares (SAS, SPSS)  have just produced to them. Again, that should be the topic of another post. 

Vice versa, in a qualitative data analysis, one must start with finding subjective meanings, constantly comparing the meanings between informants, and putting meanings into 'codes' and 'themes'. When you are sure that a code has same meanings across cases/informants, you can count how many people say a same link. That is to say, after having properly dealt with subjective meanings, you are now interested in frequencies of links between them.

If a link is mentioned by a half of informants or more, it should be put in your model. REMEMBER: Qualitative researchers do not use a sample representing a population like quantitative researchers do. You put it in the model because it represents the meanings (AGAIN, not the INFORMANTS) of that phenomenon.

 

Let us consider the topic 'drunkenness'.

People in quantitative tradition will ask "if drunkenness in my sample represent drunkenness in a reference population?". In this way they have to give a precise definition of 'what is drunkenness' and develop tools to measure drunkenness before they can say anything about prevalence in a sample, and later in the reference population. Representativeness of RESPONDENTS are important for them.

People in qualitative tradition will ask "if meanings of drunkenness in my sample represent meanings of drunkenness in a reference population?". In this way they have to ask people "how do you define drunkenness", "what does drunkenness mean to you?". They will keep asking until the meanings given by informants are 'saturated'. At this point they can stop asking and say the meanings of drunkenness in the sample represent meanings of drunkenness in a reference population. Representativeness of MEANINGS are important for them. Sometimes only few (ten or fifteen) informants can tell you all meanings of drunkenness in a region.

 

Consider this table 1.

 

Attribute Themes	Belonging to a minority group	Landless	Jobless	Being male
Drunkenness	Informants A, B, C, D, E, F	Informants C, D, E, F	....so on...	....so on....
Excessive drinking	Informants A, B, C, D	....	...so on...	Informants C, D, A, F, E
Making fictive kinship	....	...	Informants A, E, F, C
Swearing brotherhood	Informants B, A, D, F

 

You can see that "Excessive drinking" and "Being male" are 'strongly' related to each other. Then, how we can say that the two themes are 'strongly' related? Because many people mention it.

But again, don't confuse with the number used in a quantitative study and a qualitative study: The number in a quantitative study is frequency of drunkenness as defined by researchers. The number in a qualitative study is frequency of drunkenness as defined by informants. If few experts can define what a scientific definition of drunkenness is, then few lay people can also say what drunkenness means to them.

 

NOW comes the art of a qualitative research: WHAT IF just few people mention a link, what can I do?

If a link is mentioned by less than half of informants, or only few, DON'T throw it away! Now you have to consider a 'refinement' method. The other name for this refinement is triangulation.

Triangulation is an important tool in qualitative data analysis. Its rule is simple: One piece of information is considered valid/true/believable if it is mentioned by different sources of information.

Let's say you have in-depth interviews, observation, and focus group discussions in your study. These methods provide you three different sources of information. If, when in 'the field', you observe one phenomenon, and that phenomenon is mentioned in both group discussions and individual interviews, then you should consider that phenomenon 'believable'.

If only few informants in your individual interviews mention the phenomenon, but you frequently observe it, and it is often mentioned in focus group discussed, you should put it in your model.

 

Here, let's go back to the two original questions:

 

1) if I have many people say a same thing and its relation to another same thing, then should I make a link?

Answer: Yes, but you have to make sure that all people in your study share the same meanings/links. Normally, all informants may share some 'core elements' of the link. It is this 'core' that should be added in the model. When writing your paper, you need to describe all 'variations' around the 'core'. These variations may depend on social demographic characteristics of each informants: being old/young, being female/male, being poor/rich, belonging to a minority ethnic group/majority ethnic group, so on so for.

Tips: In Nvivo 2.0, we can count how many percent of informant say a thing by using "Assay" tool. If you make some variables ('attributes' in Nvivo language) to describe social demographic characteristics of informants, it will be very useful to say about 'variations' in a later stage.

 

2) if I have just few people say a same thing and its relation to another same thing, then should I make a link?

Answer:

- Yes, if the link is repeatedly observed by you, mentioned in most focus group discussions.

- Yes, if the link is not directly observed you (especially some historical events), but mentioned in most focus group discussions.

- Yes, if the link is not directly observed by you, neither is it repeatedly mentioned in focus group discussion, BUT it has the power to link with many other elements in the model, or, it is an important element that help complete the picture you are drawing. Remember: Relatedness is the most obvious feature of a qualitative study.

- REMEMBER: Every piece of information has some meanings. Take your time to discover its meanings before throwing it away :)

Consider table 2. Suppose the link between Being Male and Drunkenness is mentioned by only few informants in your individual interviews.

Being male/ Drunkenness	Directly Observed?	Often mentioned in FGDs?	Does this link help in connecting the other seemingly unrelated links?	Shout it be a link?
Scenario 1	Yes	Yes	Yes	Yes, certainly there is a link. You witness it.
Scenario 2	No (it happened long ago!)	Yes	Yes	Yes, it is highly likely to be a link. More than one sources of information mention it and it has power to link.
Scenario 3	No (It occurred in the past or you just could not see it)	No	Yes	YES, if you decide only few informants have made the link because informants ARE constrained by something in answering your questions (sensitivity of the issue, the interview settings, and the 'taken for granted' beliefs. See table 3) NO, if you decide that the informants are NOT constrained by anything (as mentioned above) in answering your questions. REREAD the informants' transcripts to make sure the link are made by INFORMANTS, not YOU.
Scenario 4	No (It occurred in the past or you just could not see it)	Yes	No	YES, it is likely to be a link. The link are mentioned by more than one sources of information. But ALSO consider factors in Table 3.
Scenario 5	Yes	No	Yes	YES, it is likely to be a link. You witness it.
Scenario 6	No	No	No	NO. But consider factors in Table 3 if it could be "YES".
Scenario 7	Yes	No	No	YES, you witness it.
Scenario 8	Yes	Yes	No	YES, you witness it, and the link is mentioned by more than one source of information.

 

Table 3. Informants could be constrained in answering your questions in following ways.

Constraining factors	Ask yourself
Sensitivity of the topic	Example: Is the link less mentioned because I am asking them about their sexual practice?
Interview setting	Example: Is the link less mentioned because I am a 'stranger'? Or it is because of the presence of somebody else ?
Taken for granted belief	Example: Is the link less mentioned because the informants hold a certain important belief but they think it is unnecessary to speak out? For instance, many men say " I am drunk because I 've just visited a Muong village". Here they make a link between "being drunk" and "visiting a Muong village". The link that is hidden is: "being drunk" and "drinking norms in Muong village". The men take it for granted that going to a Muong village is to be drunk. Then, the research should consider putting 'drinking norms in Muong village' into his/her model, even though this is mentioned by only few informants.

I enjoy writing this post.

Resources for Qualitative Research

Seidel 1998   here

E-Mail Discussion Lists

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QUALRS-L is both an e-mail discussion list and a member of the Usenet newsgroup community. Discussions involve all facets of qualitative research. You can join the e-mail list by sending the message "sub QUALRS-L your name" to listserv@uga.cc.uga.edu. Or visit the newsgroup at bit.listserv.qualrs-l.

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Qualitative Data Analysis Software Sites

CAQDAS Networking Project

(Computer Assisted Qualitative Data Analysis Software)

This site aims to disseminate an understanding of the practical skills needed to use software which has been designed to assist qualitative data analysis (e.g. field research, ethnography, text analysis).

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Social Science Research Sites

Research Resources for the Social Sciences

This is the on-line site that goes with the book Using the Web for Social Research by Craig McKie. The site contains a wealth of links under such topics as Women's Studies, Sociology/Anthropology, Political Science, Economics, News/Journalism, Psychology, and more. The book can be ordered on-line as well.

QualPage (Resources for Qualitative Researchers)

This site offers many links to other resources on the web, including discussion forums, electronic journals, papers, publishers, and much more.

Social Science Information Gateway

SOSIG is an online catalogue of hundreds of high quality Internet resources relevant to social science education and research. You may search their database, or browse their topics.

A Sociological Tour Through Cyberspace

This site offers links organized under such topics as General Sociological Links; Sociological Theory; Data Resources; Methods and Statistics; Guide to Writing a Research Paper; and Exercising the Imagination: Subject-Based Inquiries.

The Argus Clearinghouse: Social Sciences & Social Issues

Links to sites relating to anthropology, archaeology, communities and urban planning, families, linguistics, political science, psychology, social activism, social issues, and sociology.

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On-Line Journals

The Qualitative Report

Nova Southeastern University, School of Social and Systemic Studies, produces this "online journal dedicated to qualitative research and critical inquiry."

FQS Forum - Qualitative and Quantitative Research: Conjunctions and Divergences

FQS is a peer-reviewed multilingual online journal for qualitative research. Established in 1999, we are currently in the process of re-organizing FQS by setting up broader information and communication resources for qualitative researchers, supported by the Deutsche Forschungsgemeinschaft as of July 2001.

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Specific Articles

Qualitative Data Analysis (PDF format)

John V. Seidel, Qualis Research

Theory Building in Qualitative Research and Computer Programs for the Management of Textual Data

Kelle, U. (1997), Sociological Research Online, vol. 2, no. 2,

Focus Group Data and Qualitative Analysis Programs: Coding the Moving Picture as Well as the Snapshots

Catterall, M. and Maclaran, P. (1997) , Sociological Research Online, vol. 2, no. 1,

Grounded Theory as Scientific Method

Brian D. Haig, University of Canterbury

Qualitative Measures: Comparing Qualitative and Quantitative Methods

From The Knowledge Base, An Online Research Methods Textbook.

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Data Archives

Data on the Net

(compiled by the University of California, San Diego)

An excellent site offering links to hundreds of Internet sites offering Social Science data.

ICPSR

Inter-university Consortium for Political and Social Research

National Data Archive on Child Abuse and Neglect

New Zealand Social Research Data Archives

Massey University

Swedish Social Science Data Service

Göteborgs Universitet

Why does my SAS data keep disappearing?

In SAS 9.*, after you have opened your data file, you need to make a 'temporary' data file to work on it. This will help avoid any harm to the original data you may unintentionally make.

Most SAS beginners can create the temporary file. Let's say you've already opened a file called 'worker'. Now you want to make a file called work2 to work on.

SAS syntax:

 

data work2;

set worker;

run;

 

When looking at variables, you see the need to re-code one or more variables, to make them suitable for some type of analysis. But it has happened to many that after recoding, data disappears, or you just see only few observations in the data 'work2'.

If the mistake lies on your thinking that you need to open file 'work2', you may run this:

data work2;

run;

Then, you continue with command 'if' to re-code variables.

If you do this, the data disappears after recoding. The newly created variables have just overridden all data in work2.

The correct way to do is to keep everything in one. That is:

data work2;

set worker;

if......then;

if.......then;

run;

Here you have new data set work2, with new variables.

Meta analysis of time-series and panel studies of Particular Matter and Ozone

This is a report for WHO, conducted by experts in London. Its main task is to judge the impact of air quality on human health in European region.

Important concepts are addressed in this report:

- Time-series studies

- Meta analysis

- Publication bias

- Many interesting (a bit complicated) tables and statistics

It's worth reading this 80 -page length report.

e82792.pdf (application/pdf Object)

Raised Blood Pressure by percents, Hanoi population 25+ years

 

What Is Raised Blood Pressure ?

Blood pressure is a measure of the force exerted by circulating blood on the walls of the main arteries. The pressure wave transmitted by the movement of blood along the arteries with each heartbeat is felt as the pulse. The highest pressure (systolic blood pressure) is created by the heart contracting (pumping blood outwards) and the lowest pressure (diastolic blood pressure) is measured as the heart fills with blood.

There are a number of disease outcomes associated with raised blood pressure. These include:

• stroke
• ischaemic heart disease
• renal disease
• hypertensive disease

In this study the level of measurement is SBP =>140 mmHg

Confidence Interval for each group is NOT given >> Careful with interpretation.

Title: Epidemiological survey of hypertension and its risk factors at 12 Hanoi urban communes – 2001: Final Report Year: 2001

Coverage: subnational

Final Response Rate: 95%

InfoBase Ref. #: 100342a1 Urban/Rural: urban

Notes:Aggregate data is available only by combined male and female or combined ag ... more

Click here to view bibliography,methodology and extended details on the information

[+] click to enlarge graph

Viet Nam

Data Group Information (Definitions, Quantities, etc.)

Definition Code: SBP ≥ 140 mmHg

WHO Global InfoBase: : All Data

Mean of Blood Pressure from Hanoi men and women age 25+

 

The information below is associated with the following survey:

Title: Epidemiological survey of hypertension and its risk factors at 12 Hanoi urban communes – 2001: Final Report Year: 2001

Coverage: subnational

Final Response Rate: 95%

InfoBase Ref. #: 100342a1 Urban/Rural: urban

Notes:Aggregate data is available only by combined male and female or combined ag ... more

Click here to view bibliography,methodology and extended details on the information

[+] click to enlarge graph

Viet Nam

Data Group Information (Definitions, Quantities, etc.)

Definition Code: mmHg

BP Type: SBP

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WHO Global InfoBase: : All Data

Southeast Asia accounted for 34% of new TB cases in 2005

 

Fact sheet N°104
Revised March 2007

Tuberculosis

Infection and transmission

Tuberculosis (TB) is a contagious disease. Like the common cold, it spreads through the air. Only people who are sick with TB in their lungs are infectious. When infectious people cough, sneeze, talk or spit, they propel TB germs, known as bacilli, into the air. A person needs only to inhale a small number of these to be infected.

Left untreated, each person with active TB disease will infect on average between 10 and 15 people every year. But people infected with TB bacilli will not necessarily become sick with the disease. The immune system "walls off" the TB bacilli which, protected by a thick waxy coat, can lie dormant for years. When someone's immune system is weakened, the chances of becoming sick are greater.

• Someone in the world is newly infected with TB bacilli every second.
• Overall, one-third of the world's population is currently infected with the TB bacillus.
• 5-10% of people who are infected with TB bacilli (but who are not infected with HIV) become sick or infectious at some time during their life. People with HIV and TB infection are much more likely to develop TB.

Global and regional incidence

The World Health Organization (WHO) estimates that the largest number of new TB cases in 2005 occurred in the South-East Asia Region, which accounted for 34% of incident cases globally. However, the estimated incidence rate in sub-Saharan Africa is nearly twice that of the South-East Asia Region, at nearly 350 cases per 100 000 population.

It is estimated that 1.6 million deaths resulted from TB in 2005. Both the highest number of deaths and the highest mortality per capita are in the Africa Region. The TB epidemic in Africa grew rapidly during the 1990s, but this growth has been slowing each year, and incidence rates now appear to have stabilized or begun to fall.

In 2005, estimated per capita TB incidence was stable or falling in all six WHO regions. However, the slow decline in incidence rates per capita is offset by population growth. Consequently, the number of new cases arising each year is still increasing globally and in the WHO regions of Africa, the Eastern Mediterranean and South-East Asia.

 

HIV and TB

HIV and TB form a lethal combination, each speeding the other's progress. HIV weakens the immune system. Someone who is HIV-positive and infected with TB bacilli is many times more likely to become sick with TB than someone infected with TB bacilli who is HIV-negative. TB is a leading cause of death among people who are HIV-positive. In Africa, HIV is the single most important factor contributing to the increase in incidence of TB since 1990.

WHO and its international partners have formed the TB/HIV Working Group, which develops global policy on the control of HIV-related TB and advises on how those fighting against TB and HIV can work together to tackle this lethal combination. The interim policy on collaborative TB/HIV activities describes steps to create mechanisms of collaboration between TB and HIV/AIDS programmes, to reduce the burden of TB among people and reducing the burden of HIV among TB patients.

Drug-resistant TB

Until 50 years ago, there were no medicines to cure TB. Now, strains that are resistant to a single drug have been documented in every country surveyed; what is more, strains of TB resistant to all major anti-TB drugs have emerged. Drug-resistant TB is caused by inconsistent or partial treatment, when patients do not take all their medicines regularly for the required period because they start to feel better, because doctors and health workers prescribe the wrong treatment regimens, or because the drug supply is unreliable. A particularly dangerous form of drug-resistant TB is multidrug-resistant TB (MDR-TB), which is defined as the disease caused by TB bacilli resistant to at least isoniazid and rifampicin, the two most powerful anti-TB drugs. Rates of MDR-TB are high in some countries, especially in the former Soviet Union, and threaten TB control efforts.

While drug-resistant TB is generally treatable, it requires extensive chemotherapy (up to two years of treatment) with second-line anti-TB drugs which are more costly than first-line drugs, and which produce adverse drug reactions that are more severe, though manageable. Quality-assured second-line anti-TB drugs are available at reduced prices for projects approved by the Green Light Committee.

The emergence of extensively drug-resistant (XDR) TB, particularly in settings where many TB patients are also infected with HIV, poses a serious threat to TB control, and confirms the urgent need to strengthen basic TB control and to apply the new WHO guidelines for the programmatic management of drug-resistant TB.

The Stop TB Strategy, the Global Plan to Stop TB, 2006–2015 and targets for TB control

In 2006, WHO launched the new Stop TB Strategy. The core of this strategy is DOTS, the TB control approach launched by WHO in 1995. Since its launch, more than 22 million patients have been treated under DOTS-based services. The new six-point strategy builds on this success, while recognizing the key challenges of TB/HIV and MDR-TB. It also responds to access, equity and quality constraints, and adopts evidence-based innovations in engaging with private health-care providers, empowering affected people and communities and helping to strengthen health systems and promote research.

The six components of the Stop TB Strategy are:

• Pursuing high-quality DOTS expansion and enhancement. Making high-quality services widely available and accessible to all those who need them, including the poorest and most vulnerable, requires DOTS expansion to even the remotest areas. In 2004, 183 countries (including all 22 of the high-burden countries which account for 80% of the world's TB cases) were implementing DOTS in at least part of the country.
• Addressing TB/HIV, MDR-TB and other challenges. Addressing TB/HIV, MDR-TB and other challenges requires much greater action and input than DOTS implementation and is essential to achieving the targets set for 2015, including the United Nations Millennium Development Goal relating to TB (Goal 6; Target 8).
• Contributing to health system strengthening. National TB control programmes must contribute to overall strategies to advance financing, planning, management, information and supply systems and innovative service delivery scale-up.
• Engaging all care providers. TB patients seek care from a wide array of public, private, corporate and voluntary health-care providers. To be able to reach all patients and ensure that they receive high-quality care, all types of health-care providers are to be engaged.
• Empowering people with TB, and communities. Community TB care projects have shown how people and communities can undertake some essential TB control tasks. These networks can mobilize civil societies and also ensure political support and long-term sustainability for TB control programmes.
• Enabling and promoting research. While current tools can control TB, improved practices and elimination will depend on new diagnostics, drugs and vaccines.

The strategy is to be implemented over the next 10 years as described in The Global Plan to Stop TB, 2006–2015. The Global Plan is a comprehensive assessment of the action and resources needed to implement the Stop TB Strategy and to achieve the following targets:

• Millennium Development Goal (MDG) 6, Target 8: Halt and begin to reverse the incidence of TB by 2015
• Targets linked to the MDGs and endorsed by the Stop TB Partnership:
  • by 2005: detect at least 70% of new sputum smear-positive TB cases and cure at least 85% of these cases
  • by 2015: reduce TB prevalence and death rates by 50% relative to 1990
  • by 2050: eliminate TB as a public health problem (1 case per million population)

Progress towards targets

In 2005, an estimated 60% of new smear-positive cases were treated under DOTS – just short of the 70% target.

Treatment success in the 2004 DOTS cohort of 2.1 million patients was 84% on average, close to the 85% target. However, cure rates in the African and European regions were only 74%.

The 2007 WHO report Global TB Control concluded that both the 2005 targets were met by the Western Pacific Region, and by 26 individual countries (including 3 of the 22 high-burden countries: China, the Philippines and Viet Nam.

The global TB incidence rate had probably peaked in 2005, and if the Stop TB Strategy is implemented as set out in the Global Plan, the resulting improvements in TB control should halve prevalence and death rates in all regions except Africa and Eastern Europe by 2015.

WHO

Understanding linear regression

Scenario: A sample of 30, two key variables: Health expenditure and Household Income

Question: Do health care expenditure rise with household income?

Model : E = a + bY

E	Health care expenditure
a	Intercept, or the money spent when income is zero
b	the slope of the (imagined) linear line between two variable
Y	income

The task now is to find a line that best describes the relationship between two variable.

Method to do the task: Ordinary least squares, or OLS. It finds the best line by minimizing the sum of the squared deviations.

What is 'squared deviations': Deviation is the distance between imagined 'best' line with an actual observation. This deviation is squared.

Why 'minimizing': The best line is the line that goes through a set (a cloud!) of observation, and the distance from each observation to the best line is minimum.

In other words: The fit/best line contains a series of dots. Each dot associates with one observation. The distance between each dot and its associated observation is smallest.

Let's say, after running it in SPSS, now we have this model:

E = 2,000 + 0.2Y

How to interpret it:

"2000" tells us the level of health care expenditure when income is zero. "0.2" says if income increases by one dollar, health care expenditure will increase by 20 cents (0.2*1 dollar).

But: We do not know yet, if the line is really a good one, or it just happens by chance. How can we determine it is a good one? Here we have to use the coefficient of determination, R square, and the t-statistic, t.

Understanding R square and t:

R square ranges between 0 and 1. The closer to 1, the better.

t-statistic of 2 or more: the value of the estimated parameter is at least twice as large as its average deviation. We can place 95 percent confidence in the estimated average value for the parameter.

t-statistic of 3 or more: We can place 99 percent confidence in the estimated parameter.

Now, let's say, SPPSS or SAS or STATA gave us this table:

E =	2,000 + .02 Y	R square = .47
	(2.52)    (3.40)	N = 30

How to interpret it:

For estimated parameter a (here is 2000), t = 2.52, that means the CI is about 95%

For estimated parameter b (here is 0.2), t = 3.40, that means the CI is about 99%

R square = .47 is what? It means income explains about 47 percent of the variation in health care expenditure.

If you now wonder what other factors that explain variation in health care expenditure, go and read about multiple regression. The rule is same. But you will have in the model more independent variables.

PEAS: Practical Exemplars and Survey Analysis

P|E|A|S (Practical Exemplars and Survey Analysis) is based in Scotland and it is a project aiming to teach people how to use statistical software.

 

Exemplars	The exemplars are an online learning tool to teach you how to use statistical packages and analyses complex data.
Theory	The theory section explains various aspects of survey design and analysis.
Software	Information on the following statistical packages: R, SPSS, SAS, STATA
Surveys	The surveys P|E|A|S is analyzing are: SHS, SHlthS and FRS.

 

          

       

Six PhD positions in History in Lund

The National Graduate School of History is a collaboration between Lund University, Malmö University, Södertörn University College and Växjö University. The 6 offered positions are located in the Department of History, Lund University, Sweden.
The legal announcement is in Swedish. Information about the PhD positions is herewith given in English. The regulations for terms of employment as postgraduate student can be found in Higher Education Ordinance chapter 5 (SFS 2006:1053). The positions focus on research studies leading to a PhD degree in History at the Faculty of Humanities, Lund University. The positions are restricted to at most 4 years and may (at most 20 %) include work in education, research or administration. In that case, after request, the employment may be extended. Deadline 12 March 08.

Link

Making links, building a model using Nvivo

In qualitative analysis, it is essential that a researcher knows how to link different categories to see:

1) who (or which documents) say relatively same things

2) what factors tend to be 'determinants', and what are 'dependants', according to the informants' accounts

 

So, linking categories help us to :

1) map out the net of meanings related to the issue under study

2) test and build theoretical model to explain the issue

 

Nvivo allows you to link a text, or a few lines of text to other sources of information in four ways:

1) link the selected text with a annotation - that is, you just make an internal note, to record something interesting about this document. In this case, the link does not actually 'go out'. The text is linked to your thinking only.

2) link the selected text with a file in a set of files you are working on. Let us say you want to link it with a another document you think says same thing about one issue.

3) link the selected text with a website. This is in case you want to resort to that website later to take some more evidences for your arguments

4) link the selected text with another selected text in another documents. Interesting! In this case, you must have already made a node somewhere else. Then link the text with it.

 

But what to do with all the links now? Later when you build your model, you will need to import nodes into it. Because the nodes are accompanied with links, all the links can go with them.

Open model explorer in Nvivo, create a New, and then import the nodes for your new model.

Here we see a network of things, thanks to the 'links'. Amazing!

But, that is quite descriptive. Then you should decide the direction for each link. Remember, you have to resort to informants' accounts to make direction of links. Otherwise, the model is contaminated by your pre-defined theoretical ideas. The same rule should apply when you make the links in the earlier step. Just let the informants determine factors they think important for their behaviors.

When you 've done with making link directions, what will you see: A model describing factors related to each other; some factors tend to be the 'core' ones.

Still, what you have done is purely descriptive.

if you are just reviewing a topic, then relax now.

If, however, you are writing a paper/thesis, the above is just the first, but extremely important, step.

What you have to do next is to interpret the 'results'.

Now, go back to your theoretical thinking, sit conveniently in a nice sofa, and ask yourself why the model appears in that way.

If you are thinking in a feminist way: Is this because of gender norms?

If you are thinking in a functionalist way: Is this because each 'factor' in the model has some 'function' in the life of people you are studying?

If you are thinking in a materialist way: Is this because of unequal access to resources?

So on and so for.

Now, go back to your theoretical chapter (if you have written one). You should rewrite it basing on your reflections with the model. Mandatory! The following chapters are just to describe the model components and 'variations' (using Assay function to see differences in ideas between different groups in regard to age, sex, occupation, so on so for. It is like to  make a cross-tab table in SPSS. But before that, you should have already grouped different age groups into different 'sets'. Set is synonym for 'group'). Nvivo will, of course, not produce numbers, but texts.

The Assay will help us to decide if there is any 'pattern' according to informants' attributes (being men/women, old/young, unemployed/employed). It even helps you to decide which kind of 'ideas' are more often repeated by showing you the percent of each idea.

Then come to the discussion chapter, write what you have reflected in above task. Check grammar, print it out, and submit it!

Keep discovering Nvivo!

Getting attributes and values from SPSS/Excel into Nvivo

You can import information to a casebook from any program that handles tables (a

spreadsheet, data base, statistics package, or just a table you make in MSWord.)

An attribute table can be created “outside” NVivo and imported. You can create it by

exporting attributes from a statistics package or spreadsheet. Or you can type up the

table in spreadsheet or word processor software. If you have a lot of attributes and

documents, this is more efficient than creating them in NVivo.

Please check the Help files for more detailed advice on the formatting of tables and

the format selected for import

To create a table for import to a casebook

1. Type a small table in any program that will create a table (e.g. Word or Excel),

with the names of your cases down the side. The top left corner cell can be blank,

or have any word in it (such as Schools or Interviewees).

Make sure that the case names are the same as the names of case nodes in the

NVivo project that you want to give these attributes to. If a case name is not

recognized, NVivo will optionally create a new case node for it. If you don’t yet have

those case nodes in your project, this may be what you want to do (it’s a quick way of

creating case nodes). But if you have them slightly different, you’ll get a lot of nodes!

2. Type names of attributes as the headings of the columns. (Keep them brief.)

3. Type into this table the names of the values for each case, under each attribute. If

the attribute isn’t applicable, leave the cell blank.

You will have a table that looks something like this (start small for a first try).

 

	Gender	Age	Income
Interview 1	Female	Under 20	None
Interview 2	Male	20-30	Middle

 

4. Save that table in your table-making software, selecting the options for cells to be

tab-separated, and the “encoding” to be Unicode Text (*.txt).

If you prepared your table in Word, check that there are no blank lines above or below

the table, then on the top menu go to Table>Select>Table, then

Table>Convert>Table to Text and choose Tabs for separation.

Now select File>Save As and from Save As Type and Plain Text (.*txt). When you

close the window, Word asks you to specify File Conversion. Unclick the Windows

(Default) and instead choose Other Encoding and scroll down to select Unicode.

This matches the default setting for importing Casebooks into NVivo

Or: if you leave the File Conversion at Windows (Default), the table will import so

long as you change the File encoding setting in NVivo to US-ASCII.

5. Save it somewhere you will find it again! see here

Cultural change, health, and help seeking behavior of Vietnamese Australians

An interesting thesis on acculturation and its impacts on immigrants - here Vietnamese. Measuring impacts of acculturation on health is always a difficult task. However, the writer did it quite well. See the thesis here. If this link does not work, probably the library has closed it up. Then click here instead.

Happy reading.

Water and Human Security

A short, but interesting text. One of the question it asks is how water relates to international conflict?

 

As human populations and economies grow exponentially, the amount of freshwater in the world remains roughly the same as it has been throughout history. While the total quantity of water in the world is immense, the vast majority is either saltwater (97.5 percent) or locked up in ice caps (1.75 percent). The amount economically available for human use is only 0.007 percent of the total, or about 13,500 km3. This comes out to only about 2300 m3 per person – a 37 percent drop since 1970 (United Nations 1997). Adding complexity to this increasing scarcity is the fact that almost half the globe’s land surface lies within international watersheds (i.e., that land which contributes to the world's 261 transboundary waterways)...go next

Dam building and the threat to indigenous people in Laos

Indigenous communities living along the Sesan, Srepok, and Sekong Rivers rely on the

rivers’ natural resources, along with traditional shifting cultivation for both cultural and

economic purposes. These remote communities have limited access to social services,

lack opportunities to participate in the processes of development and often suffer from

their rights either being violated or not fully respected.

Hydropower dam development along the Sesan, Srepok, and Sekong Rivers in Vietnam

and Lao PDR has been viewed as serious threat to several different indigenous

communities living downstream of these dams in Cambodia. Since the construction and

operation of some hydropower dams, such as the 720 MW Yali Falls dam on the Sesan

River, villagers have experienced large-scale social, economic and environmental

impacts during the last decade. These dam-affected communities are now living with

economic insecurity due to a sharp decline in fish catches and agricultural production, as

well as a fear of the river due to its erratic water-level changes (McKenney 2001)

The basic rights of villagers living along the Sesan River have been violated, such as their

right to life, right to food and water, right to access information, right to participate in

decision making, right to remedy for the loss of life and livelihoods they have suffered,

and right to be protected (NGO Forum on Cambodia 2005). In some cases, villagers are

even abandoning their villages along the river to move into the highlands, because they

can no longer rely on the rivers and its natural resources and are tired of living with fear

that the dam may break or their lives will be swept away in a water surge or flood. One

woman in Pawdal village along the Sesan River summarized her feeling by stating,

“Everyday people are scared of the water, the same feeling as if they have just seen a

cobra or a tiger.”

Dam-affected communities have yet to receive mitigation or compensation for the

impacts that they have suffered from Yali Falls dam. Despite communities’ efforts to

bring this issue to various responsible authorities, i.e. government of both countries, etc.,

dam builders, stakeholders, and governments involved have denied responsibility for the

negative impacts that have occurred along the Sesan River.

In addition to the Yali Falls dam, a cascade of dams is now being planned for the Sesan,

Srepok, and Sekong Rivers in both Vietnam and Lao PDR. Some of these dams are

currently under construction and were planned without people’s participation nor

adequate or complete Environmental and Social Impact Assessments. Full report.

Countries most hit by natural disaster, 2005

 

COUNTRY	DISASTERS
China	31
India	30
United States	16
Afghanistan	13
Bangladesh	12
Pakistan	11
Vietnam, Indonesia, Romania	10
Iran Russia	9
Haiti	8
Mexico, Turkey	7

More here

Water insecurity and the poor

Household water insecurity is a pressing problem in developing countries. Unsustainable

water withdrawal is increasing due to population growth, industrialization, urbanization, and

increasing agricultural production which leads to various problems. The number of countries

facing problems of water scarcity and insufficient water supply is rising. Already there are 1.2

billion people without access to clean water, many of whom live in 20 developing countries

classified as ‘water scarce’. Typically it is found in these countries, that the poor pay particularly

high prices for water and are most water insecure.

Progress towards water security can be made only if there is a more comprehensive

understanding of the interactions among waters’ various characteristics and functions. Water is

not only a natural resource, but also an economic commodity, and a human consumption good or

entitlement. The problems of water insecurity can be grouped under three main headings:

availability, access and usage. In the framework of a multidisciplinary approach to the analysis

of water problems, the paper elaborates on these three elements, defining sectoral and cross-

sectoral knowledge gaps. The paper concludes with a research agenda in support of improved

policy design and action.

Some interesting data about Vietnam is available.

Pregnant adolescents in Vietnam

Rationale: The number of childbearing adolescents in Vietnam is relatively low but they are more prone to experience adverse outcome than adult women. Reports of increasing rates of abortion and prevalence of STIs including HIV among young indicate a need to improve services and counseling for these groups.

Aim: To describe the prevalence and outcome of adolescent pregnancies in Vietnam; To explore social context and health care seeking behavior of pregnant adolescents; To explore perspectives of health care providers on this issue.

An interesting thesis.

Read here.

Young unemployment: mismatch between education and labor market demand

(Original title: Youth employment in Viet Nam, Characteristics, determinants and policy responses)

This working paper is a contribution to the Employment Policy Unit’s research programme, being

undertaken in the 2004-05 biennium, on youth employment in developing countries.

The transition to a market economy in Viet Nam involved a drastic modification of young men

and women’s transition from school-to-work. Today, many youth enter the labour market out of economic

necessity. Even though the potential to benefit from the country’s socio-economic successes of the past

15-20 years is great, youth in Viet Nam face a series of new challenges. For example, inequality,

polarisation and unemployment have appeared. The down-sizing of the public sector with disappointing

levels of foreign direct investment mean that job opportunities are confined to the predominant

agricultural sector where underemployment and poverty, though declining, are widespread. In that

context, the Vietnamese government has made the creation of decent jobs, the upgrading of skills and the

fight against unemployment its priority, with a special focus on youth.

The authors show that youth unemployment (at 5.7 per cent in 2002) is mainly linked to educated

unemployment among middle income households. They argue in favour of a mismatch between education

and labour market demand. Government policies have so far failed to redirect resources from general

higher education to vocational training and greater technical skills. The linkages between the education

and training system and the labour market have to be strengthened to close the gap between the skills in

demand and the skills offered on the labour market.

Premarital sex or reproductive health needs for young Vietnamese?

To the extent that research on Vietnamese adolescents has been conducted, it

has been concerned with unprotected and unsanctioned sexual activity and its health

consequences, namely abortion and sexually transmitted diseases, especially HIV.

The question we pose is whether this concern is warranted. Is the population com-

munity justified in focusing its attention on early sexual activity and HIV risk? Even

if the sexual behavior of young people can be considered problematic, are there

perhaps other aspects of young people’s lives that should give us greater pause? The

paper reviews the literature on adolescent sexual behavior in Vietnam and analyzes

data on premarital sex and reproductive behavior from a 1999 survey conducted in

six provinces among nearly 1,500 adolescent boys and girls aged 15–22. Data on

other aspects of young people’s lives are summarized, in particular schooling and

work, in order to put the sexual activity data in perspective. We conclude that the

lack of adequate employment opportunities may be more of a threat to adolescent

reproductive health than risky sexual behaviors per se—a situation that effective

economic policies can remedy.

go there

Patterns of data analysis

How do you carry out data analysis? There are few texts and
little theory. One approach could be to use a pattern language,
an idea which has been successful in fields as diverse as town
planning and software engineering. Patterns for data analysis
are defined and discussed, illustrated with examples.

How to use statistical data available on the Internet

Nowadays, we see tons of press articles every minute loaded on the Internet, most of them are full of statistics. How to use this type of information scientifically? Guidelines on this matter are either rare or unreliable.

But look at this document, it tells how one should evaluate the data, by using certain (simple) criteria. This guideline is developed by United Nations Statistical Commission and Economic Commission for Europe. Believable, is not it?

Statistics in One hour!

You may have learnt a bit statistics, and now what to learn more?

You may have learnt it, by not in a systematic way?

You don't have time?

You just want to know how to interpret key statistics?

You have not interested in knowing about logistical regression and think linear regression is just enough now?

Here you are!

Data Analysis with Excel

Essential for those who know basic Excel and want to explore the full potential of the program
* Teaches how to manipulate data to suit specific needs and achieve more by doing less work
* Self-contained two-page lessons, featuring high-resolution screen shots and minimal text, show how to create custom functions, retrieve data from databases, use value chains, and slice and pivot information from the Web with Excel’s PivotTable utility
* Covers data analyzing techniques for statistical functions, financial functions, data sharing, PivotTables and PivotCharts, Solver, and BackSolver

Global Warming

Global Warming is one of the most controversial scientific issues of the twenty-first century. This is a problem that has serious economic, sociological, geopolitical, political, and personal implications. This Very Short Introduction is an informative, up-to-date, and readable book about the predicted impacts of global warming and the surprises that could be in store for us in the near future. It unpacks the controversies that surround global warming, drawing on material from the recent report of the Intergovernmental Panel on Climate Change (IPCC), a huge collaborative study drawing together current thinking on the subject from experts in a range of disciplines, and for the first time presents the findings of the Panel for a general readership. The book also discusses the politics of global warming, and looks at what we can do now to adapt to climate change and mitigate its worst effects.

Political Philosophy

This book introduces readers to the concepts of political philosophy. It starts by explaining why the subject is important and how it tackles basic ethical questions such as 'how should we live together in society?' It looks at political authority, the reasons why we need politics at all, the limitations of politics, and whether there are areas of life that shouldn't be governed by politics. It explores the connections between political authority and justice, a constant theme in political philosophy, and the ways in which social justice can be used to regulate rather than destroy a market economy. David Miller discusses why nations are the natural units of government and whether the rise of multiculturalism and transnational co-operation will change this: will we ever see the formation of a world government?

Feminism

How much have women's lives really changed? In the West women still come up against the 'glass ceiling' at work, most earning considerably less than their male counterparts. What are we to make of the now commonplace insistence that feminism deprives men of their rights and dignities? And how does one tackle the issue of female emancipation in different cultural and economic environments - in, for example, the Middle East, the Indian sub-continent, and Africa? This book provides an historical account of feminism, exploring its earliest roots as well as key issues including voting rights, the liberation of the sixties, and its relevance today. Margaret Walters touches on the difficulties and inequities that women still face more than forty years after the 'new wave' of 1960s feminism, such as how successful women are at combining domesticity, motherhood, and work outside the house. She brings the subject completely up to date by providing an analysis of the current situation of women across the globe, from Europe and the United States to Third World countries.

Learning economics

Economics has the capacity to offer us deep insights into some of the most formidable problems of life, and offer solutions to them too. Combining a global approach with examples from everyday life, Partha Dasgupta describes the lives of two children who live very different lives in different parts of the world: in the Mid-West USA and in Ethiopia. He compares the obstacles facing them, and the processes that shape their lives, their families, and their futures. He shows how economics uncovers these processes, finds explanations for them, and how it forms policies and solutions. Along the way, Dasgupta provides an intelligent and accessible introduction to key economic factors and concepts such as individual choices, national policies, efficiency, equity, development, sustainability, dynamic equilibrium, property rights, markets, and public goods.

Foucault

Foucault is one of those rare philosophers who has become a cult figure. Born in 1926 in France, over the course of his life he dabbled in drugs, politics, and the Paris SM scene, all whilst striving to understand the deep concepts of identity, knowledge, and power. From aesthetics to the penal system; from madness and civilisation to avant-garde literature, Foucault was happy to reject old models of thinking and replace them with versions that are still widely debated today.
A major influence on Queer Theory and gender studies (he was openly gay and died of an AIDS-related illness in 1984), he also wrote on architecture, history, law, medicine, literature, politics and of course philosophy, and even managed a best-seller in France on a book dedicated to the history of systems of thought. Because of the complexity of his arguments, people trying to come to terms with his work have desperately sought introductory material that makes his theories clear and accessible for the beginner.
Ideally suited for the Very Short Introductions series, Gary Gutting presents a comprehensive but non-systematic treatment of some highlights of Foucault's life and thought. Beginning with a brief biography to set the social and political stage, he then tackles Foucault's thoughts on literature, in particular the avant-garde scene; his philosophical and historical work; his treatment of knowledge and power in modern society; and his thoughts on sexuality. Read it easy.

Learning Epidemiology quick?

Author: Who needs another introductory epidemiology text? Certainly, there are many introductory epidemiology books currently in print, and many of them are excellent. Nevertheless, there are four reasons why I believe that this new text is justified.
Firstly, it is much shorter than most introductory texts, many of which contain more material than is required for a short introductory course. This is a short introduction to epidemiology, and is not intended to be comprehensive. Secondly, I have endeavoured to show clearly how the different basic epidemiologic methods fit together in a logical and systematic manner.
For example, I attempt to show how the different possible study designs relate to each other, and how they are different approaches to a common task. Similarly, I attempt to show how the different study design issues (confounding and other types of bias) relate to each other, and how the principles and methods of data analysis are consistent across different study designs and data types. Thirdly, in this context, rather than attempt a comprehensive review of available methods (e.g. multiple methods for estimating confidence intervals for the summary risk ratio), I have attempted to select only one standard method for each application, which is reasonably robust and accurate, and which is consistent and coherent with the other methods presented in the text

Consider buying the book!

Short Introduction to Psychiatry

Psychiatry is now a highly visible activity - care in the community, compulsion, suicide, drug and alcohol abuse mean that few people are not touched by it. Indeed one in four of us will consult a psychiatrist in our life time. This book explains what psychiatry is, and what it is not. It starts with the identification of the major mental illnesses and why they are no longer considered just variations of 'normality'. It charts the rise of the Asylum and its demise with the developments of Care in the Community, and the flourishing of psychoanalysis and its later transformation into more accessible psychotherapies. More than any other branch of medicine psychiatry has been attacked and criticized. There is a long catalogue of abuses - from mundane neglect and bizarre treatments through to political abuse by totalitarian regimes. Modern psychiatry too brings with it new controversies such as the radicalization of normal life, the power of the drug companies and the use of psychiatry as an agent of social control. The book does not shy away from outlining these issues but provides the reader with a clear understanding of what psychiatry is capable of, and what it is not capable of, so that they can draw their own conclusions.

Housing estate   is free

Medical Ethics: A very short introduction

Issues in medical ethics are rarely out of the media and it is an area of ethics that has particular interest for the general public as well as the medical practitioner. This short and accessible introduction provides an invaluable tool with which to think about the ethical values that lie at the heart of medicine. Tony Hope deals with the thorny moral questions such as euthanasia and the morality of killing, and also explores political questions such as: how should health care resources be distributed fairly? Each chapter in this book considers a different issue: genetics, modern reproductive technologies, resource allocation, mental health, medical research, and discusses controversial questions such as: DT Who should have access to reproductive technology? Who should pay? DT Is it right to fund expensive drug treatment for individuals? DT Should active euthanasia be legalized? DT Should treatment for mental illness be imposed on patients without their consent? DT Who should have access to information from genetic testing? DT Should we require consent for use of dead bodies or organs in medical research?

estate housing

Global warming and the return of plague

Return of the Plague

Tuesday, Feb. 12, 2008 By LAURA BLUE


Like no other disease, plague evokes terror. One of the most lethal  illnesses in human history, it killed 

probably a third of Europe's population in the  14th century. It may also have been one of the first 

agents of biological  warfare: It's said that in the 1340s, invading Mongols catapulted their plague
dead over the city wall into Kaffa in the Crimea. 

Yet the plague is not just a disease of the distant past. While cases  tapered off in the mid-20th century, 

the World Health Organization (WHO) now  classifies plague as "re-emerging." No one is predicting 

another pandemic like the  Black Death that devastated Europe. The WHO now records at most only a 

few thousand cases worldwide per year; and, if detected early, the disease can be  treated effectively with 

antibiotics. But since the early 1990s, plague has returned to places — including India, Zambia, 

Mozambique, Algeria and parts of  China — that had not seen it in many years or even decades. 

Its global footprint has also shifted, according to a paper published last month in the journal PLoS  

Medicine.

In the 1970s, most plague cases were in Asia; today, more than 90% are  in Africa. The conundrum 

for epidemiologists: Why is human plague  reappearing now, even though nearby animal populations 

have likely harbored the culprit  Yersinia pestis bacteria all along?

Plague lives in many rodent species, and is most often transferred to  humans by the animals' fleas. 

Scientists know which regions of the world harbor  infected animals, but they are only just beginning 

to understand the dynamics of  plague infection. Its spread depends not just on Yersinia pestis but also 

on interactions among rodents and, crucially, on contact between humans  and wildlife. Madagascar is 

a good example. For decades, plague was  restricted to the highlands, according to a 2004 paper by 

researchers in Madagascar,  Senegal and France. But it showed up on the coast in 1991, when the Asian 

shrew  somehow picked up infected fleas. The plague's earlier comeback in the inland  capital, 

Antananarivo, arose as city sprawl and shoddy housing put residents in  closer contact with black rats. 

In 1998, inland villages reported cases, too,  perhaps caused by rats displaced through deforestation.

Even in the antibiotic age, then, containing plague requires monitoring  more than human cases, says 

Nils Christian Stenseth, head of the Center for Ecological and Evolutionary Synthesis in Oslo, and 

lead author of the  PLoS Medicine paper. Working with nearly 50 years of animal, human and 
bacteriological statistics from the former Soviet Union, his team found  that human plague in Kazakhstan 

occurs only when the local gerbil population  reaches a certain threshold in winter. Warmer winters 

mean more gerbils. That,  says Stenseth, suggests plague's "re-emergence might have a climate
 component."

If so, global warming may exacerbate the threat — an unsettling  thought, given the viciousness of 

the disease. "The plague bacillus is probably the  most pathogenic infectious agent on the planet right 

now, and we still don't  know why it's so virulent," says Elisabeth Carniel, a plague expert at the
 Institut Pasteur in Paris. It may no longer make history, but plague hasn't lost  its terrifying power.

see link here

Fake Chinese malaria medicine sold in Vietnam ?

Scientists trace fake anti-malaria pills to dealer in southern China
--------------------------------------------------------------------------------
Scientists and police have struck back against the global menace of
counterfeit drugs in a unique collaboration that has led to the
seizure of hundreds of thousands of fake anti-malaria tablets and the
arrest of a key dealer in southern China.

In a stunning piece of forensic detective work, scientists who
analysed pollen grains and minerals in the fake drugs were able to
trace their origin to the Yunnan province of southern China, where
almost a half of all blister packs of the antimalarial drug
artesunate are thought to be fake.

The arrested dealer is alleged to have traded 240 000 blister packs
of fake artesunate, enough to "treat" almost 250 000 adults with a
medicine that has no effect on the potentially fatal disease. Chinese
authorities seized 24 000 of the blister packs but the remainder are
thought to have been sold on the border between Yunnan province and
Burma [Myanmar].

Details of the collaboration, called Operation Jupiter, are published
for the 1st time today [13 Feb 2008] in the online journal of the
Public Library of Science, PLoS Medicine [The research article on
PLoS Medicine available at:
<http://medicine.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pmed.0050032>]. 

The investigation was co-ordinated by Interpol with the World Health
Organisation and scientists from the Wellcome Trust SE Asian Tropical
Research Programme at the University of Oxford.

The WHO estimates that fake drugs account for more than 10 percent of
the global medicines market but pharmaceutical companies are often
reluctant to admit they have fallen victim to the counterfeiters for
fear of damaging sales of the genuine product.

Paul Newton, who led the research, said fake anti-malarial drugs were
an increasing problem, especially in South-east Asia and Africa.
Malaria is widespread in Burma [Myanmar], Laos, Cambodia and Viet Nam
and up to half of all artesunate tablets in these regions are thought
to be counterfeit.

Dr Newton said: "Artesunate... is vital for malaria treatment and is
one of the most effective weapons we have against this terrible
scourge. Those who make fake anti-malarials have killed with
impunity, directly through the criminal production of a medicine
lacking active ingredients and by encouraging drug resistance to
spread. If malaria becomes resistant to artesunate the effect on
public health in the tropics will be catastrophic."

Fear about growing drug resistance have been raised because some fake
drugs seized in Operation Jupiter turned out to include dangerously
small quantities of artesunate, probably in an attempt to foil
screening tests implemented as quality checks. The doses included
were too low to tackle the disease but high enough to contribute to
the malaria parasites acquiring resistance to the drugs.

Most of the seized drugs contained no artesunate or a wide range of
potentially toxic ingredients. The scientists who analysed the drugs
used a sophisticated technique called forensic palynology to study
pollen contamination in the samples, from which they were able to
track the likely location of manufacture.

The pollen evidence suggested at least some of the counterfeit
artesunate came from southern China and this was backed up by
examination of the mineral, calcite, also found in some samples.
Scientists from 5 laboratories were involved in analysing the fake
drugs and their packaging.

Dr. Newton said the success of Operation Jupiter proved it was
possible to help countries facing a major threat from counterfeit
drugs. "Criminal investigations and legal action are important in
disrupting and inhibiting the trade in fake medicines, but to be
effective these will require financial support and resources," he
said. "Forensic tools may make it easier to identify the fake drugs
and allow over-stretched police forces to focus on objective leads,
greatly increasing the risks to counterfeiters of being caught."

"But there are very few laboratories with the resources to perform
detailed forensic chemistry or pollen analysis of fakes, particularly
in the countries where they are most needed."

Full story

A simple Start with EpiInfo - free CD software

Epi Info (hereafter referred to as Epi Info) is a public domain database and statistics program

for use by public health officials (doctors, nurses, epidemiologists, etc.) managing databases

for public health activities, conducting outbreak investigations, and performing statistical

applications. Epi Info allows the user to develop a questionnaire, customize the data entry

process, enter data, and analyze the data. Statistics, graphs, tables, and maps can be

produced with simple commands. Epi Info is free, downloadable software provided by the

CDC (www.cdc.gov/epiinfo/). It can also be obtained on CD-ROM. This guide is designed for

use by persons who may only have basic computer skills and will only use this software

periodically, giving step-by-step instructions for basic tasks. This manual also attempts to

correlate Epi Info 2002 commands with those of the Epi 6 DOS version.

C. Why use Epi Info?

Epi Info allows for a database to be created, from which data can be analyzed in an easy

manner that spreadsheet programs (e.g. Microsoft Excel) cannot perform. For example, if you

want to calculate sums, means, and ranges, Excel will be sufficient. However, if you want to

calculate something more complicated, such as the number of males between the ages of 15

and 24 who answered no to question #17, then using Epi Info would be very practical. It is

easy to use in a wide range of questionnaire-based inquiries, such as satisfaction surveys,

community needs assessments, and program evaluations.

This manual will help new users easily maneuver through the most basic components of Epi

Info, such as:

• setting up data entry screens, called “Views”

• inserting codes to assist in checking the data entry process

• entering data and editing records

• performing simple data analyses.

 

simple start

A Simple Start with SAS

A good introduction to SAS - a powerful software that any serious students, researchers would like to master.

What is SAS?

• Developed in the early 1970s at North Carolina State University

• Originally intended for management and analysis of agricultural field experiments

• Now the most widely used statistical software

• Used to stand for “Statistical Analysis System”, now it is not an acronym for anything

• Pronounced “sass”, not spelled out as three letters.

SAS

Copy SAS to Excel