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Thursday

Vietnam Stripping of its soul

by YAP MUN CHING on THE SUN DAILY

http://www.thesundaily.my/news/764803

THE air is choking and the noise incessant. Out on the streets of Saigon, one may survive the chaotic traffic but it may also be only a matter of time before the noxious fumes take their toll.

On my latest visit to the city, a little more than four years since my last and eight years since my first, Vietnam seems to have gone over something of an economic crest to end up somewhere in a trough. Although it was once assessed glowingly by fund managers and economists along with the other regional darling, Indonesia, the country seems to be going through a harsh reality check.

Double-digit inflation, rising inequality and a deeply troubled banking sector has brought the Vietnam economic miracle to a standstill, a fact admitted even by the state-controlled media. According to English language newspaper Viet Nam News, Vietnam is expected to miss this year's GDP growth target of 5.2-5.7%. With the spectre of rampant inflation looming and a domestic credit crunch, the government's hands are tied in attempting any expansionary measures. Instead, Prime Minister Nguyen Tan Dung has had to call for the State Bank of Viet Nam to restructure debts and reduce bad debts.

The effect of the heady years of the last decade can be felt most strongly here in Saigon, the de facto commercial capital of the country. On the leafy streets of District 1, once bustling shops now seem tired and jaded. Where stores on the main shopping street of Dong Khoi once did brisk business, storefronts are now either closed or taken over by large property developers to be converted into faceless shopping malls or bland office towers.

"It is very tough now. Locals are not spending and tourists are buying much less. Our export markets in Europe have also dried up so we are now only staying afloat with our stock inventory," said Duong, an entrepreneur who runs one of Vietnam's top interior furnishing stores.

To add to her difficulties, Duong has had to move her Saigon store from its prime location just off Dong Khoi, after 10 years building her business and reputation.

"The shop was sold to an owner who wants to break down 10 shops to build a hotel. We are being crowded out by big businesses with deeper pockets," she said.

The visual impact of this is that from the vantage point of the Saigon Opera House, a stone's throw from Duong's former store location, the charming skyline is now marred by glass towers and neon shopping centre lights which clash sharply with the unique architecture that so defined Vietnamese cities like Hanoi and Saigon.

In 2004, on my first visit, it appeared that Ho Chi Minh City thrived on local enterprise. Now, that spirit seems to have been replaced by a sense of desperation to get rich quick at a cost that was stripping the city of its very soul.

Some members of the Vietnamese art community believe that Vietnam's inability to get itself out of crisis mode has very much to do with this.

"The establishment is very conservative. It does not encourage creativity or independence. Even in art, the establishment believes there is only one type of Vietnamese art and everyone is supposed to abide by certain guidelines," said Dinh Le, one of Vietnam's top contemporary artist. "When you take this approach to deal with the crisis, you are in trouble. The government simply does not know what to do."

The one silver lining that the difficult times has created is the condition for more debates on how Vietnam sees its future. Long time Saigon resident, Australian Zoe Butt believes that the opening up of new media channels has enabled many more young Vietnamese to exchange views on how they want the country to go forward.

"The government's instinct is to clamp down on debate but it is so much harder to stop people from discussing these issues now," she said. "Facebook has made a huge difference."

It may be a while before Vietnam can regain its stride. As with many other developing countries, its fate is tied in with that of developed countries that are now going through their own crises. How soon and how strong the Vietnamese recovery will be depends on how the world economy picks itself up. It will also depend on whether enough time will have passed for the Vietnamese to figure out how they want to move forward.

Friday

When should we use ‘non-parametric’ techniques?

We only use parametric techniques (like t test, z test) when we are certain about the distribution of the variable of interest.

When we don’t know its distribution, it is safer to use non-parametric tests. These tests have no assumptions about distribution of the dependent variable.

In fact, it has been argued, quite sharply, that in all social sciences, we should use non-parametric, rather than parametric, tests.

Literature on adoption of innovation, sociology and anthropology of innovation

This can be a source for someone who is investigating social aspects of innovation, innovation adoption, etc.

http://www.ssrn.com/link/Sociology-Innovation.html

There is a vast literature on diffusion of innovations or adoption of innovation, here are some of them:

 

Boahene, K. (1995). Innovation adoption as a socio-economic process : the case of the Ghanaian cocoa industry. Amsterdam: Thesis Publishers.

Boahene, K., Snijders, T. A., & Folmer, H. (1999). An integrated socioeconomic analysis of innovation adoption: the case of hybrid cocoa in Ghana. Journal of Policy Modeling, 21(2), 167-184.

Boahene, K. S. T. A. B. F. H. (1999). An integrated socioeconomic analysis of innovation adoption: the case of hybrid cocoa in Ghana. Journal of Policy Modeling (New York), 21(02), 167-1874.

Dumett, R. (1971). The Rubber Trade of the Gold Coast and Asante in the Nineteenth Century: African Innovation and Market Responsiveness. The Journal of African History, 12(1), 79-101. doi: 10.2307/180568

Feder, G. (1985). Adoption of agricultural innovations in developing countries a survey. Economic Development and Cultural Change, Chicago, p. 255-298, Jan. 1985.

Kaplinsky, R. (2004). Competitions policy and the global coffee and cocoa value chains Retrieved March 13, 2013, from http://www.ids.ac.uk/FA0B8240-5056-8171-7B8943D52FF0DA62

Personal variables affecting adoption of agricultural innovations by Nigerian farmers. from http://ajol.info/index.php/sajae/article/view/3664

Pomp, M. B. K. (1995). Innovation and imitation : adoption of cocoa by Indonesian smallholders. World development., 23(3), 413-431.

Rogers, E. M. (1983). Diffusion of innovations. New York; London: Free Press ; Collier Macmillan.

Scott, J. C. (1976). The moral economy of the peasant : rebellion and subsistence in Southeast Asia. New Haven: Yale University Press.

Scott, J. C. (1998). Seeing like a state : how certain schemes to improve the human condition have failed. New Haven: Yale University Press.

Wejnert, B. (2002). Integrating models of diffusion of innovations: a conceptual framework. Annual review of sociology, 28, 297.

Reliability in qualitative analysis

Interrater reliability is the common term here. Two coders will code the same text separately and then discuss how much they agree with each other.

If such practice is impossible, the writer/researcher can make a brief report and ask interviewees to comment on it. Alternatively, the researcher can organize a workshop to present initial findings and ask invited interviewees to tell how much the initial findings reflect their stories.

This practice helps to reduce the possibility that researcher imposes his own ‘theoretical frame’ on the data and miss important ‘emic’ perspectives.

What is rural, rural frontier? What to do with rural health?

 

This site answers those questions. Though it is run by the government, it’s still a good source on rural health that I have happened to know.

http://www.nal.usda.gov/ric/ricpubs/what_is_rural.shtml

A serious discussion of ‘rural’ has never been seen in the universities where I worked and studied. Yet, the term ‘rural’ has been used widely and wildly. It is time to think about the concept ‘rural’ seriously.

Sunday

Vietocr

This website is useful for recognizing Vietnamese fonts from documents that could not be read by normal applications

http://vietocr.sourceforge.net/

Sa đà vào quyền lực và danh vọng

Có một sự khác biệt rất nhỏ giữa làm việc vì quyền lực và danh vọng, và làm việc để cống hiến. Khi chúng ta bắt đầu bằng số không, chúng ta ít nghĩ tới quyền lực và danh vọng mà chỉ muốn duy trì một công việc nào đó. Chúng ta cống hiến cho nơi mà chúng ta làm. Khi chúng ta đã có nhiều thứ để bảo vệ, chúng ta nghĩ nhiều hơn tới quyền lực và danh vọng. Đơn giản là vì chúng đem đến cho chúng ta của cải, những mối quan hệ tốt đẹp, và nhiều của cải hơn. Những thứ này ngày càng khiến chúng ta đắm đuối. Chúng ta trở nên yêu mình hơn và khó chịu với những người đang cạnh tranh hoặc có khả năng cạnh tranh quyền lực và danh vọng với mình. Động cơ cống hiến cho xã hội của chúng ta cứ phai nhạt dần vì chúng ta đã thực sự chỉ nghĩ đến việc bảo vệ lợi ích của bản thân, và có thể là của gia đình mình. Đôi khi, chúng ta sẽ tự trấn an mình bẳng cách nói rằng ‘mình thỉnh thoảng vẫn làm từ thiện đấy chứ’. Nhưng kỳ thực, chúng ta đang sa ngã vào những đam mê quyền lực và danh vọng.

Người lãnh đạo tốt là người loại trừ được những đam mê ấy khỏi bàn làm việc của mình. Cần phải sẵn sàng hy sinh vị trí của mình khi thấy mình không còn cần thiết nữa. 

Friday

Scale or Index?

 

1 - Scale: a class of quantitative data measures often used in survey research that captures the intensity, direction, level or potency of a variable construct along a continuum; most are at the ordinal level of measurement

2 - Index: The summing or combining of many separate measures of a construct or variable to create a single score

Principles of good measurement

 

1 – Mutually exclusive attributes: every response is clearly different from others

2 – Exhaustive attribute: every response has a place to go

3 – Uni-dimensionality : only one construct is measured

Wednesday

How to choose a statistical test in a software?

What statistical analysis should I use?

The following table shows general guidelines for choosing a statistical analysis. We emphasize that these are general guidelines and should not be construed as hard and fast rules. Usually your data could be analyzed in multiple ways, each of which could yield legitimate answers. The table below covers a number of common analyses and helps you choose among them based on the number of dependent variables (sometimes referred to as outcome variables), the nature of your independent variables (sometimes referred to as predictors). You also want to consider the nature of your dependent variable, namely whether it is an interval variable, ordinal or categorical variable, and whether it is normally distributed (see What is the difference between categorical, ordinal and interval variables? for more information on this). The table then shows one or more statistical tests commonly used given these types of variables (but not necessarily the only type of test that could be used) and links showing how to do such tests using SAS, Stata and SPSS.
Number of Dependent Variables Nature of Independent Variables Nature of Dependent Variable(s) Test(s) How to SAS How to Stata How to SPSS
1 0 IVs (1 population) interval & normal one-sample t-test SAS Stata SPSS
ordinal or interval one-sample median SAS Stata SPSS
categorical (2 categories) binomial test SAS Stata SPSS
categorical Chi-square goodness-of-fit SAS Stata SPSS
1 IV with 2 levels (independent groups) interval & normal 2 independent sample t-test SAS Stata SPSS
ordinal or interval Wilcoxon-Mann Whitney test SAS Stata SPSS
categorical Chi-square test SAS Stata SPSS
Fisher's exact test SAS Stata SPSS
1 IV with 2 or more levels (independent groups) interval & normal one-way ANOVA SAS Stata SPSS
ordinal or interval Kruskal Wallis SAS Stata SPSS
categorical Chi-square test SAS Stata SPSS
1 IV with 2 levels (dependent/matched groups) interval & normal paired t-test SAS Stata SPSS
ordinal or interval Wilcoxon signed ranks test SAS Stata SPSS
categorical McNemar SAS Stata SPSS
1 IV with 2 or more levels (dependent/matched groups) interval & normal one-way repeated measures ANOVA SAS Stata SPSS
ordinal or interval Friedman test SAS Stata SPSS
categorical repeated measures logistic regression SAS Stata SPSS
2 or more IVs (independent groups) interval & normal factorial ANOVA SAS Stata SPSS
ordinal or interval ordered logistic regression SAS Stata SPSS
categorical factorial logistic regression SAS Stata SPSS
1 interval IV interval & normal correlation SAS Stata SPSS
interval & normal simple linear regression SAS Stata SPSS
ordinal or interval non-parametric correlation SAS Stata SPSS
categorical simple logistic regression SAS Stata SPSS
1 or more interval IVs and/or 1 or more categorical IVs interval & normal multiple regression SAS Stata SPSS
analysis of covariance SAS Stata SPSS
categorical multiple logistic regression SAS Stata SPSS
discriminant analysis SAS Stata SPSS
2+ 1 IV with 2 or more levels (independent groups) interval & normal one-way MANOVA SAS Stata SPSS
2+ interval & normal multivariate multiple linear regression SAS Stata SPSS
0 interval & normal factor analysis SAS Stata SPSS
2 sets of 2+ 0 interval & normal canonical correlation SAS Stata SPSS
Number of Dependent Variables Nature of Independent Variables Nature of Dependent Variable(s) Test(s) How to SAS How to Stata How to SPSS
This page was adapted from Choosing the Correct Statistic developed by James D. Leeper, Ph.D.  We thank Professor Leeper for permission to adapt and distribute this page from our site.

Sunday

SEM Measurement Model with AMOS 18 (example 2)

1) Unconstrained Model
image
2) Constrained factor loading
image
3) Constrained structural covariance
image
4) Constrained measurement residuals
image
5) Model comparison

Assuming model Unconstrained to be correct:
Model DF CMIN P NFI
Delta-1
IFI
Delta-2
RFI
rho-1
TLI
rho2
Measurement weights 4 2.632 .621 .011 .012 -.010 -.012
Structural covariances 7 3.306 .855 .014 .015 -.023 -.026
Measurement residuals 13 11.212 .593 .048 .051 -.011 -.012
Assuming model Measurement weights to be correct:
Model DF CMIN P NFI
Delta-1
IFI
Delta-2
RFI
rho-1
TLI
rho2
Structural covariances 3 .674 .879 .003 .003 -.012 -.014
Measurement residuals 9 8.581 .477 .037 .040 -.001 -.001
Assuming model Structural covariances to be correct:
Model DF CMIN P NFI
Delta-1
IFI
Delta-2
RFI
rho-1
TLI
rho2
Measurement residuals 6 7.906 .245 .034 .037 .012 .013

Friday

A measurement model, cross-group validation, SEM, AMOS 18

fourmodels

First, what is 'constraint': A model of this type relates to in-variance. We want to test if factor loadings, variances of factors, covariances between factors, and variances of the 'error' terms remain the same ACROSS groups - or, if they change, the change is INSIGNIFICANT. We do that by artificially 'keep' constant one of the elements listed above (in the least restricted model, keep constant factor loadings only) or all of them (in the fully restricted model). The computer will do it for you. In AMOS, you should choose 'unstandardized estimates' if you want to see which element is kept constant.

Then we compare estimates of the less restricted model with estimates of the more restricted model, and hope that the change would be insignificant. Again, the computer will do it.

A good cross-group measurement model should have four components like above: Top left: No constraints (two models are run at the same time, chi-square difference is computed at the end). Top right: Constrained factor loadings (the least restricted model - this is minimum requirement). Bottom Left: Constrained structural covariances (the more restricted model). Bottom Right: Constrained measurement residuals (the fully restricted model).

Perfect: All models have good fit
Very good: Except the last model, other three have good fit
Good: The model constraining for factor loadings has good fit.
Poor: The model constraining for factor loadings has poor fit. In this case, we conclude that the groups that are compared do not share the same factors. Two separate measurements (one for men, another for women, for instance) should be used.

The above model has perfect fit. How can I say that?
  • All 4 models have RMSEA smaller than 0.06, GFI close to 1, p value is smaller 5%
  • Compared with the less constrained model, the more constrained model has a good fit and the 'gap' between 'less constrained' and 'more constrained' is insignificant. In AMOS, look for this at 'Model Comparison' tab.
Assuming model Unconstrained to be correct:
Model DF CMIN P NFI
Delta-1
IFI
Delta-2
RFI
rho-1
TLI
rho2
Measurement weights 4 2.632 .621 .011 .012 -.010 -.012
Structural covariances 7 3.306 .855 .014 .015 -.023 -.026
Measurement residuals 13 11.212 .593 .048 .051 -.011 -.012

  • The model constraining for factor loadings is  insignificantly different from the unconstrained model.

Thursday

A normal distribution: an univariate approach

 

If a variable is normally distributed, most of its values centre around the mean, within two standard deviations (up to the blue areas, at both sides). If value is more than two SD away from the mean, it is potentially an outlier. Remove it. If there are two many values like that, we should not consider the variable normally distributed.

In order to judge if a variable is ‘normally distributed’

  • calculate the mean and SD : SD = square root[sum((xi – mean)2)/mean]
  • convert each value into z score: (xi – mean)/SD
  • compare each z score with 1.96*SD
  • if the result is greater than 1.96*SD, that value is an outlier.

image

Discovering the hidden lives of servants

Figure Caption in SEM

This is the text to use:

chi squared = \cmin
df = \df
p = \p
RMR = \rmr
GFI = \gfi
RMSEA = \rmsea
image
image
This is the complete list:
\cmin is a "text macro", a code that Amos fills in with the minimum value of the discrepancy function, C (see Appendix B), once the minimum value is known. Similarly, \df is a text macro that Amos fills in with the number of degrees of freedom for testing the model and \p is a text macro that Amos fills in with the "p value" for testing the null hypothesis that the model is correct. Here is a list of text macros.
\agfi Adjusted goodness of fit index (AGFI)
\aic Akaike information criterion (AIC)
\bcc Browne-Cudeck criterion (BCC)
\bic Bayes information criterion (BIC)
\caic Consistent AIC (CAIC)
\cfi Comparative fit index (CFI)
\cmin Minimum value of the discrepancy function C in Appendix B
\cmindf Minimum value of the discrepancy function divided by degrees of freedom
\datafilename The name of the data file. \longdatafilename displays the fully qualified path name of the data file.
\datatablename The name of the data table (for those file formats that allow a single file to contain multiple data tables, such as Excel workbooks.)
\date Today's date in short format. \longdate displays today's date in long format. The displayed date is made current whenever the path diagram is read from a file, saved or printed.
\df Degrees of freedom
\ecvi Expected cross-validation index (ECVI)
\ecvihi Upper bound of 90% confidence interval on ECVI
\ecvilo Lower bound of 90% confidence interval on ECVI
\f0 Estimated population discrepancy (F0)
\f0hi Upper bound of 90% confidence interval on F0
\f0lo Lower bound of 90% confidence interval on F0
\filename Name of the current AMW file. Use \longfilename to display the complete path to the current AMW file.
\fmin Minimum value of discrepancy function F in Appendix B
\format Format name (See Formats tab.)
\gfi Goodness of fit index (GFI)
\group Group name (See Manage groups.)
\hfive Hoelter's critical N for =.05
\hone Hoelter's critical N for =.01
\ifi Incremental fit index (IFI)
\longdatafilenameThe fully qualified path name of the data file. \datafilename displays the data file name without the path.
\longdate Today's date in long format. \date display's today's date in short format. The displayed date is made current whenever the path diagram is read from a file, saved or printed.
\longfilename Fully qualified path name of the current AMW file. Use \filename to display the file name without the path.
\longtime The time in long format. \time displays the time in short format. The displayed time is made current whenever the path diagram is read from a file, saved or printed.
\mecvi Modified ECVI (MECVI)
\model Model name (See Manage models.)
\ncp Estimate of non-centrality parameter (NCP)
\ncphi Upper bound of 90% confidence interval on NCP
\ncplo Lower bound of 90% confidence interval on NCP
\nfi Normed fit index (NFI)
\npar Number of distinct parameters
\p "p value" associated with discrepancy function (test of perfect fit)
\pcfi Parsimonious comparative fit index (PCFI)
\pclose "p value" for testing the null hypothesis of close fit (RMSEA < .05)
\pgfi Parsimonious goodness of fit index (PGFI)
\pnfi Parsimonious normed fit index (PNFI)
\pratio Parsimony ratio
\rfi Relative fit index
\rmr Root mean square residual
\rmsea Root mean square error of approximation (RMSEA)
\rmseahi Upper bound of 90% confidence interval on RMSEA
\rmsealo Lower bound of 90% confidence interval on RMSEA
\time The time in short format. \longtime displays the time in long format. The displayed time is made current whenever the path diagram is read from a file, saved or printed.
\tli Tucker-Lewis index (TLI)

















































Understanding, checking collinearity

 

When you build a model to predict something, you would like to choose the ‘right’ predictor. There should be no redundancies among the set of predictors. Or, each predictor should explain some part of the variance of the dependence value that others don’t. In other words, predictions  should not overlap.

If you happen to use two predictors that correlate with each other significantly, they together can only explain a small amount of variance. Collinearity is a problem here. In stata a collinearity diagnosis can be obtained by typing vif  after regression


    Variable |       VIF       1/VIF 
-------------+----------------------
          d3 |      1.27    0.784681
          d2 |      1.25    0.800108
          d4 |      1.09    0.916960
-------------+----------------------
    Mean VIF |     1.20

If any individual score is larger than 10, that is the indication of collinearity. (None in this example)

If the mean of VIF is substantially larger than 1 –collinearity may exist. (Not very far from 1 in this example)

What to do?

Delete the variable with greater ViF. 

Using principle component analysis in Stata (PCA) to combine two or more variable to make an composite variable.

Monday

SEM in Stata 12 and SEM in AMOS 16: which one is better?

In Stata 12, it is much more difficult to draw a SEM diagram. You should use syntax instead.

The advantage: it allows to take into account effects of research design (survey, for example), estimation is usually better.

In AMOS 16, it is much easier to draw a SEM diagram. It is also much easier to interpret outputs because you can get everything (goodness of fit statistics) in one go. However, if your data need to be adjusted for research design, AMOS does not have any function for it.

Conclusion: if you know that research design is not a several problem, use AMOS, otherwise, use STATA 12.

Export graphs in Stata

Instead of doing it manually (copy and paste), we can do it much quicker: if you want to save a histogram graph:

1) hist d1, freq
2) graph export d1.eps, replace
3) graphout d1.eps using example.rtf
4) Open file example.rtf, you will see this:


The only problem is that it is not clear as we use Excel. 
Always use Excel if the graph is simple. If it is more complicated, use Stata's graphic functions.