topic 5 quality datafile_management
TRANSCRIPT
Data File Management, Quality Checking a Dataset & Missing Values
Srinivasulu RajendranCentre for the Study of Regional Development (CSRD)
Jawaharlal Nehru University (JNU)New Delhi
Objective of the session
To understand Data File Management, Quality checking a dataset & missing
values through software packages
1. What are the procedure one should follow before proceeding for statistical analysis through a software?2. How do we check quality of data?3. How do we organize the dataset through a software?
Data sources
International Food Policy Research Institute (IFPRI) – 2006-07
Bangladesh Bureau of Statistics – Household Income and Expenditure Surveys (HIES) – 2004/2005
Bangladesh Demographic and Health Survey (BDHS) - 2007
IFPRI DatasetChronic Poverty Study (resurvey 3 studies)
1.Micronutrients Gender/Agricultural Technology (1996-97) – 5 Thanas
2. Food for Education/Cash for Education - (2000 (10 Thanas) & 2003 (8 Thanas))
3. Microfinance (1994 – 5 Thanas)Institute involved: IFPRI, Chronic Poverty Research Center, Data
Analysis and Technical Assistance
In the 2006-07 resurvey, all thanas from the 1994, 1996-97 & 2003 rounds were resurveyed
Micronutrients Gender/Agricultural Technology
Hereafter we refer MCG study also known as Agricultural Technology or Ag Tech
“A census of households was conducted in villages where the NGO had introduced the agricultural technology and comparable villages where NGO was operating, but where the new technologies had not yet been introduced”.
There are two major type of households selected from census
1. NGO – members adopting agricultural tech households
2. NGO members likely adopter households in villages where the technology was not yet introduced
330 Households 1304 HHs in the resurvey for AgrTech
AgriTech introduced –“A” type villages
AgriTech not introduced –
“B” type villages
110 NGO Members adopter HHs
“A” - HHs
55 Non adopter non-NGO Members & NGO
members UNLIKELY to adopt
“C1” HHs
110 NGO Members LIKELY adopter –“B”
HHs
55 Non LIKELY adopter non NGO members & NGO
members unlikely to adopt “C2” HHs
What are the procedure one should follow before proceeding for statistical analysis through a software?
SPSS
1. Identify the data file format and convert them into
relevant software (SPSS) data file format (*.sav)
2. Make sure that COMPLETE variables and observations
has been converted into SPSS Format
3. Identify the characteristics of the variables for the
analysis
4. Save name of the file smaller size
5. It is better to have no space in the file name
6. Organize the data file at one place and folder
7. When ever we work on data, please append the files
with the previous programme file.
How do we check quality of data?
There are few things that needs to be checked before we
proceed for any statistical analysis
1. Missing values
2. Wrong coding system
3. Outliers
4. Digits in the variables (specially for value term variables)
5. Unique numbers of id for the observation
6. Relevant variable characteristics i.e string, numberic etc
SPSS has some good routines for detecting outliers
There is always the FREQUENCIES routine, of course.
The PLOTS command can do scatterplots of 2 variables.
The EXAMINE procedure includes an option for printing out
the cases with the 5 lowest and 5 highest values.
The REGRESSION command can print out scatterplots
(particularly good is *ZRESID by *ZPRED, which is a plot of
the standardized residuals by the standardized predicted
values). In addition, the regression procedure will produce
output on CASEWISE DIAGNOSTICS, which indicate which
cases are extreme outliers.
Detecting the problem
Scatterplots, frequencies can reveal atypical cases
Can also look for cases with very large residuals.
Suspicious correlations sometimes indicate the presence of outliers.
The difference between STATA & SPSS
Probably the most critical difference between
SPSS and STATA is that STATA includes
additional routines (e.g. rreg, qreg) for
addressing the problem of outliers, which we
will discuss in future classes.