STAT0023 STATISTICS FOR PRACTICAL COMPUTING —
ASSESSMENT 2 (2018/19 SESSION)
Your solutions should be your own work and are to be submitted electronically to the
course Moodle page by 12 noon on TUESDAY, 23RD APRIL 2019.
Ensure that you electronically ‘sign’ the plagiarism declaration on the Moodle page
when submitting your work.
Late submission will incur a penalty unless there are extenuating circumstances (e.g.
medical) supported by appropriate documentation and notified within one week of the
deadline above. Penalties, and the procedure in case of extenuating circumstances, are
set out in the latest editions of the Statistical Science Department student handbooks
which are available from the departmental web pages.
Failure to submit this in-course assessment will mean that your overall examination
mark is recorded as “non-complete”, i.e. you will not obtain a pass for the course.
Submitted work that exceeds the specified word count will be penalized. The penalties
are described in the detailed instructions below.
Your solutions should be your own work. When uploading your scripts, you will be
required to electronically sign a statement confirming this, and that you have read the
Statistical Science department’s guidelines on plagiarism and collusion (see below).
Any plagiarism or collusion can lead to serious penalties for all students involved,
and may also mean that your overall examination mark is recorded as non-complete.
Guidelines as to what constitutes plagiarism may be found in the departmental student
handbooks: the relevant extract is provided on the ‘In-course assessment 2’ tab on the
STAT0023 Moodle page. The Turn-It-In plagiarism detection system may be used to
scan your submission for evidence of plagiarism and collusion.
You will receive feedback on your work via Moodle, and you will receive a provisional
grade. Grades are provisional until confirmed by the Statistics Examiners’ Meeting in
June 2019.
Background and overview
Anemia is a condition in which the oxygen-carrying capacity of blood is reduced relative
to the body’s requirements. These requirements depend on factors including age, altitude,
and pregnancy status. According to the World Health Organisation (WHO), “Anemia is
the world’s second leading cause of disability and thus one of the most serious global public
health problems. Anemia affects over half of pre-school children and pregnant women in
developing countries and at least 30-40% in industrialized countries” (see https://www.
who.int/medical_devices/initiatives/anaemia_control/en/ — click the blue text to
follow the link).
The oxygen-carrying capacity of blood itself is determined by the concentration of haemoglobin
(https://www.webmd.com/a-to-z-guides/understanding-anemia-basics): if haemoglobin
levels are too low then the body will not get enough oxygen. There are many forms of anemia,
although the most common is caused by iron deficiency. Women of childbearing age
are particularly susceptible to anemia, because of the blood loss from menstruation and
increased blood supply demands during pregnancy (see the URL just cited). In principle,
iron deficiency can be controlled to some extent by diet: red meat is a good source of iron,
for example. In developed countries, iron supplements are readily available; in less developed
countries however, many people may be unable to afford such supplements or may be
unaware of their existence.
In a study published in 20161
, an attempt was made to identify social, demographic and
economic factors associated with anemia among women between 15 and 49 years of age in
Afghanistan. The study authors used data from the 2010 UNICEF Multiple Indicator Cluster
Survey (MICS) for Afghanistan: see http://mics.unicef.org/ for general information
about the MICS surveys, and http://cso.gov.af/Content/files/AMICS.pdf for a report
describing the 2010 survey for Afghanistan. The latter report can also be downloaded from
the ‘In-course assessment 2’ tab of the STAT0023 Moodle page.
The main points to note about the 2010 MICS survey for Afghanistan are as follows:
The survey is designed to be nationally representative, with 13 314 households visited
over eight regions of the country. Responses were obtained from 98.5% of these households,
in which 22 053 women were identified between the ages of 15 and 49. Interviews
were carried out with 21 290 of these women.
Most of the interview questions are related to the social, economic and demographic
characteristics of households and individual women: the questions are given in Appendix
F of the Afghan survey report (see link above). Additionally, haemoglobin
tests were administered to women in half of the households. Haemoglobin levels (in
g/Dl) are available for 9 199 women in the survey.
STAT0023作业代做、Moodle留学生作业代做
The authors of the 2016 anemia study used the MICS survey data for the 9 199 women with
haemoglobin measurements. They discarded cases with haemoglobin levels above 24g/Dl as
being unrealistic and therefore probably erroneous: this left 9 174 women in the data set
used for further analysis. Each woman was classified as being anaemic or not, depending
on whether her altitude-adjusted haemoglobin level was below or above a critical threshold:
the thresholds were obtained from WHO guidelines (11g/Dl for pregnant women, 12g/Dl
for others). The altitude adjustment aimed to provide an ‘equivalent haemoglobin level’
in terms of blood oxygen capacity, taking into account the fact that oxygen saturation in
blood reduces at high altitudes (see https://en.wikipedia.org/wiki/Effects_of_high_
altitude_on_humans): the adjustment was based on the altitude of the province in which
each woman lived. The authors then used logistic regression to model the dependence of
anemia status on selected covariates from the survey data. They carried out some further
analyses as well, but we don’t need to consider these.
From the 2016 anemia study, three features of the authors’ analysis are worth noting:
1Citation: Flores-Martinez A, Zanello G, Shankar B, Poole N (2016). Reducing Anemia Prevalence in
Afghanistan: Socioeconomic Correlates and the Particular Role of Agricultural Assets. PLoS ONE 11(6):
e0156878. doi:10.1371/journal.pone.0156878. A copy of this paper can also be downloaded from the ‘Incourse
assessment 2’ tab of the STAT0023 Moodle page.
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The altitude-based adjustments to the haemoglobin measurements are imperfect, for
two reasons. The first is that it’s hard to find any clear justification anywhere for
these particular adjustments (the study authors used the adjustments recommended
in a 2008 paper,2 but this doesn’t explain how they were derived — nor does it give
any indication of how accurate they are). The second is that the precise altitudes at
which each woman lived are not known: they have been approximated by the altitude
of the provincial capital, which may be very inaccurate in mountainous areas.
The authors pre-selected a group of potential covariates and included them all in
their model, without attempting to further refine the analysis e.g. by dropping nonsignificant
covariates or investigating potential interactions.
There were many instances of multiple women living in the same household. There is
likely to be an association between the anemia status of people in the same household,
for example due to shared diets — and it is unlikely that this association can be
explained entirely using the covariate information available in the survey. This means
that the responses are probably not independent given the covariates. In turn, the
standard errors, p-values and so forth in the study may be incorrect.
The data used in the 2016 study have kindly been provided to me by one of its authors: Prof
Bhavani Shankar at the School of Oriental and African Studies. Moreover, we have permission
from UNICEF to use the data for this assessment. Therefore: on the ‘In-course assessment
2’ tab of the STAT0023 Moodle page, you will find a CSV file called AnemiaData.csv
which contains a modified version of the data used by the authors of the 2016 study. In this
dataset, one woman has been randomly sampled from each household so that there is no
remaining within-household dependence. The file contains 5 421 records (i.e. rows of data):
each record represents one woman. Haemoglobin measurements (in g/Dl) are provided for
the first 4 382 women, along with other information about the women and their households:
full details can be found in the Appendix to these instructions. For the remaining 1 039
women, however, the haemoglobin measurements are not provided: they are given as ‘1’.
Your task in this assessment is to carry out some data preprocessing and then to use the
data from the first 4 382 records, to build a statistical model that will help you to:
Understand the social, demographic and economic factors associated with variation in
haemoglobin levels between Afghan women in the 15-49 age range; and
Estimate the haemoglobin levels for each of the 1 039 records where you don’t have
this information.
Detailed instructions
You may use either R or SAS for this assessment.
2Citation: Sullivan, K. M., Mei, Z., Grummer-Strawn, L. and Parvanta, I. (2008). Haemoglobin adjustments
to define anemia. Tropical Medicine & International Health, 13: 1267-1271. doi:10.1111/j.1365-
3156.2008.02143.x
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1. Read the data into your chosen software package, and carry out any necessary recoding
(e.g. to deal with the fact that ‘?1’ represents a missing value).
2. Carry out an exploratory analysis that will help you to start building a sensible statistical
model to understand and predict each woman’s haemoglobin level. This analysis
should aim to identify an appropriate set of candidate variables to take into the subsequent
modelling exercise, as well as to identify any important features of the data that
may have some implications for the modelling. You will need to consider the context
of the problem to guide your choice of exploratory analysis. See the ‘Hints’ below for
some ideas.
3. Using your exploratory analysis as a starting point, develop a statistical model that
enables you to predict each woman’s haemoglobin level based on (a subset of) the other
variables in the dataset, and also to understand the variation of haemoglobin levels
between women. To be convincing, you will need to consider a range of models and
to use an appropriate suite of diagnostics to assess them. Ultimately however, you
are required to recommend a single model that is suitable for interpretation, and to
justify your recommendation. Your chosen model should be either a linear model, a
generalized linear model or a generalized additive model.
4. Use your chosen model to predict the haemoglobin levels for each of the women where
this information is missing, and also to estimate the standard deviation of your prediction
errors.
Submission for this assessment is electronic, via the STAT0023 Moodle page. You are required
to submit three files, as follows:
A report on your analysis, not exceeding 2 500 words of text plus two pages of graphs
and / or tables. The word count includes titles, footnotes, appendices, references etc.
— in fact, it includes everything except the two pages of graphs / tables. Your report
should be in three sections, as follows:
I Describe briefly what aspects of the problem context you considered at the outset,
how you used these to start your exploratory analysis, and what were the
important points to emerge from this exploratory analysis.
II Describe briefly (without too many technical details) what models you considered
in step (3) above, and why you chose the model that you did.
III State your final model clearly, summarise what your model tells you about the
factors associated with variation of haemoglobin levels in Afghan women in the
15–49 age range, and discuss any potential limitations of the model.
Your report should not include any computer code. It should include some graphs and
/ or tables, but only those that support your main points.
Your report should be in PDF (recommended) or Word, and should be named as
########_rpt.pdf or ########_rpt.docx as appropriate, where ######## is your
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student ID number. For example, if your ID number is 150123456 and you are using
PDF, your script should be named 150123456_rpt.pdf.
An R script or SAS program corresponding to your analysis and predictions. Your
script /program should run without user intervention on any computer with R or
SAS installed, providing the file AnemiaData.csv is present in the current working
directory / current folder. When run, it should produce any results that are mentioned
in your report, together with the predictions and the associated standard deviations.
The script / program should be named ########_ICA2.r or ########_ICA2.sas as
appropriate, where ######## is your student ID number. You may not create any
additional input files that can be referenced by your script: if you use R however, you
may use additional libraries if you wish (see ‘Hints’ below).
A text file containing your predictions for the 1 039 women with missing haemoglobin
measurements. This file should be named ########_pred.dat, where ######## is your
student ID number. The file should contain three columns, separated by spaces and
with no header. The first column should be the record identifier (corresponding to variable
ID in file AnemiaData.csv); the second should be the corresponding haemoglobin
prediction, and the third should be the standard deviation of your prediction error.
Marking criteria
There are 75 marks for this exercise. These are broken down as follows:
Report: 40 marks. The marks here are for: displaying awareness of the context for the
problem and using this to inform the statistical analysis; good judgement in the choice
of exploratory analysis and in the model-building process; a clear and well-justified
argument; clear conclusions that are supported by the analysis; and appropriate choice
and presentation of graphs and / or tables. The mark breakdown is as follows:
Awareness of context: 5 marks.
Exploratory analysis: 10 marks. These marks are for (a) tackling the problem in
a sensible way that is justified by the context (b) carrying out analyses that are
designed to inform the subsequent modelling.
Model-building: 10 marks. The marks are for (a) starting in a sensible place that
is justified from the exploratory analysis (b) appropriate use of model output and
diagnostics to identify potential areas for improvement (c) awareness of different
modelling options and their advantages and disadvantages (d) consideration of
the social, economic and demographic context during the model-building process.
Quality of argument: 5 marks. The marks are for assembling a coherent ‘narrative’,
for example by drawing together the results of the exploratory analysis so
as to provide a clear starting point for model development, presenting the modelbuilding
exercise in a structured and systematic way and, at each stage, linking
the development to what has gone before.
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Clarity and validity of conclusions: 5 marks. These marks are for stating clearly
what you have learned about how and why haemoglobin levels vary between
women, and for ensuring that this is supported by your analysis and modelling.
Graphs and / or tables: 5 marks. Graphs and / or tables need to be relevant,
clear and well presented (for example, with appropriate choices of symbols, line
types, captions, axis labels and so forth). There is a one-slide guide to ‘Using
graphics effectively’ in the slides / handouts for Lecture 1 of the course. Note
that you will only receive credit for any graphs in your report if your submitted
script / program generates and automatically saves these graphs, appropriately
labelled, when it is run.
Note that you will be penalised if your report exceeds EITHER the specified 2 500-word
limit or the number of pages of graphs and / or tables. Following the UCL guidelines at
https: // www. ucl. ac. uk/ academic-manual/ chapters/ chapter-4-assessment-framework-taught-programmes/
section-3-module-assessment# 3. 13 , the maximum penalty is 7 marks, and no
penalty will be imposed that takes the final mark below 30/75 if it was originally higher.
Subject to these conditions, penalties are as follows:
More than two pages of graphs and / or tables: zero marks for graphs and / or
tables, in the marking scheme given above.
Exceeding the word count by 10% or less: mark reduced by 4.
Exceeding the word count by more than 10%: mark reduced by 7.
In the event of disagreement between reported word counts on different software systems,
the count used will be that from the examiner’s system. If you submit your
report as a PDF file, the count will be obtained using an R function called PDFcount:
this is available from the Moodle page in file PDFcount.r.
Coding: 15 marks. There are 3 marks here for reading the data, preprocessing and handling
missing values correctly and efficiently; 7 marks for effective use of your chosen
software in the exploratory analysis and modelling (e.g. programming efficiently and
correctly); and 5 marks for clarity of your code — commenting, layout, choice of variable
/ object names and so forth.
Prediction quality: 20 marks. The remaining 20 marks are for the quality of your predictions.
Note, however, that you will only receive credit for your predictions if your
submitted ########_pred.dat file is identical to that produced by your script / program
when it is run: if this is not the case, your predictions will earn zero marks.
For these marks, you are competing against each other. Your predictions will be assessed
using the following score:
is the actual haemoglobin measurement (which I know) for the ith prediction;
μi = E (Yi) is your corresponding prediction;
σi
is your quoted standard deviation for the prediction error.
The score S is an approximate version of a proper scoring rule, which is designed to
reward predictions that are close to the actual observation and are also accompanied by
an accurate assessment of uncertainty (this was discussed during the Week 10 lecture,
along with the rationale for using this score for the assessment). Low values are better.
The scores of all of the students in the class (and the lecturer) will be compared:
students with the lowest scores will receive all 20 marks, whereas those with the highest
scores will receive fewer marks. The precise allocation of marks will depend on the
distribution of scores in the class.
If you don’t supply standard deviations for your prediction errors, the values of the
{σi} will be taken as zero: this means that your score will be ?∞ if you predict every
value perfectly (this is the smallest possible score, so you’ll get 20 marks in this case),
and +∞ otherwise (this will earn you zero marks).
STAT0023 Assessment 2 — Hints
1. There is not a single ‘right’ answer to this assignment. There is a huge range of options
available to you, and many of them will be sensible.
2. You are being assessed not only on your computing skills, but also on your ability to
carry out an informed statistical analysis: material from other statistics courses (in
particular STAT0006/2002, for students who have taken it) will be relevant here. To
earn high marks, you need to take a structured and critical approach to the analysis
and to demonstrate appropriate judgement in your choice of material to present.
3. At first sight, the task will appear challenging. However, there is a lot of information
that can guide you: look at some of the web links earlier in these instructions, and at
other commentaries on anemia as a disease, to gain some understanding of what kinds
of relationships you might look for in the data.
4. When building your model, you have two main decisions to make. The first is: should
it be a linear, generalized linear or generalized additive model? The second is: which
covariates should you include? You might consider the following points:
Linear, generalized linear or generalized additive? This is best broken down into
two further questions, as follows:
Conditional on the covariates, can the response variable be assumed to follow a
normal distribution with constant variance? In this assignment, the response
variable cannot be negative; nor can it exceed 24g/Dl (see above). Therefore,
it cannot have exactly a normal distribution. However, you may find that
the residuals from a linear regression model are approximately normal —
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and you may judge that the approximation is adequate for your purposes.
The ‘constant variance’ assumption may also be suspect: for positive-valued
quantities, it is common for the variability to increase with the mean. If this
is the case here, you need to decide whether it varies enough to matter: you
need to think about whether the effect is big enough that you can improve
your predictions (and hence your score!) by accounting for it e.g. using
a GLM. You might consider using your exploratory analysis to gain some
preliminary insights into this point.
Are the covariate effects best represented parametrically or nonparametrically?
Again, your exploratory analysis can be used to gain some preliminary insights
into this. You may want to look at the material from week 6, for
examples of situations where a nonparametric approach is needed.
Which covariates? The data file contains a lot of potential covariates, some of them
factors with several levels. You have many choices here, and you will need to take
a structured approach to the problem in order to avoid running into difficulties.
The following are some potentially useful ideas:
Look at other literature on anemia and on the structure of Afghan society.
What factors are considered to be the most important characteristics
controlling haemoglobin levels? Are there known health inequalities within
Afghanistan? Can these be linked to covariates for which you have information?
Obviously, if you do this then you will need to acknowledge your
sources in your report.
Define useful summary measures on contextual grounds, and work with these.
For example, many of the potential covariates are binary factors indicating
ownership of different types of animals: you might decide to combine these
by summing them. Another covariate is ‘age’: you might decide to divide
this into three or so groups.
Define new variables based on the correlations between the existing variables,
and work with these. If several continuous variables are highly correlated, then
it is difficult to disentangle their effects and it may be preferable to work with
a single ‘index’ that combines all of them. This is the basis of techniques such
as Principal Components Analysis, that were discussed during the Week 10
lecture (along with how to implement them in R and SAS).
You should not start to build any models until you have formed a fairly clear
strategy for how to proceed. Your decisions should be guided by your exploratory
analysis, as well as your understanding of the context.
5. Don’t forget to look for interactions! For example, one of the variables in the data
set is Sheep, which is a factor (i.e. categorical covariate) indicating whether or not
the woman’s household owns sheep: the authors of the 2016 study concluded that this
variable was significantly associated with a woman’s anemia status. Another variable is
WealthScore, which is an aggregate index of household wealth. It is conceivable that
sheep ownership is important for lower-income families where home-produced food
may contribute a substantial proportion to the diet, but that it is less important for
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wealthier families who can afford to buy food from elsewhere. Look at the analysis of
the iris data from Workshop 2, for a similar kind of situation.
6. You probably won’t find a perfect model in which all the assumptions are satisfied:
models are just models. Moreover, you should not necessarily expect that your model
will have much predictive power: it may be that the covariates in the data set just
don’t provide very much useful information about a woman’s haemoglobin levels. You
should focus on finding the best model that you can, therefore — and acknowledge any
deficiencies in your discussion.
7. If you use R for this assignment, you may load additional libraries if you wish. You
should only do this, however, if you really understand what they are doing: overall,
it is strongly recommended that you keep things fairly simple. See the feedback from
last year’s assignment (available from the Moodle page) for more on this.
8. If you use a linear model, it is straightforward to obtain the standard deviations of
your prediction errors using either R or SAS (look at the material in Workshops 2 and 9
respectively, to find out how to do it). However, for generalized linear and generalized
additive models you need some additional computations. Specifically:
(a) Suppose μi = E? (Yi) is your ith predicted haemoglobin level and that Yi
is the
corresponding actual value.
(b) Then your prediction error will be Yi μi
.
(c) Yi and μi are independent, because μi
is computed using only information from
the first 4 382 records and Yi relates to one of the ‘new’ records.
(d) The variance of your prediction error is thus equal to Var (Yi) + Var (?μi).
(e) You can calculate the standard error of ?μi
in both R and SAS, when making
predictions for new observations — see Workshops 6 and 9. Squaring this standard
error gives you Var (?μi).
(f) You can estimate Var (Yi) by plugging in the appropriate formula for your chosen
distribution — for example, if you’re using a gamma distribution (which is a
possibility when using GLMs for non-negative response variables) then Var ( ?, where φ is the estimated dispersion parameter for your model (see Section
2.1 of the notes for Workshop 6).
(g) Hence you can estimate the standard deviation of your prediction error as
Var ( Yi) + Var (μi).
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Appendix: the AnemiaData.csv data set
Data sources and processing
The data provided in AnemiaData.csv are ultimately derived from the full 2010 Afghanistan
MICS dataset, available from http://mics.unicef.org/surveys. The authors of the 2016
study selected a subset of the variables from this survey as described in their supporting
information (click the blue text to follow the link). These authors’ data have subsequently
been processed in the following way to create AnemiaData.csv:
1. The variable names were modified for ease of interpretation.
2. One women was randomly sampled from each household, so that the resulting data set
does not contain any within-household dependence.
3. The original dataset contained many dummy variables representing different levels of
the same factor: for example, there were binary variables representing each of the
eight regions of Afghanistan. Each group of dummy variables has been aggregated
into a single factor variable with multiple levels: for example, the eight binary regional
variables have been aggregated into a single factor Region with eight levels.
4. Some less relevant variables, variables with large quantities of missing data, and variables
that could be calculated from other information in the data set, were removed.
An example of a ‘less relevant’ variable is the survey weight given to a particular
woman: this would be useful if we wanted to estimate (say) the mean haemoglobin
level for all women in Afghanistan, but it is not needed here. A variable with large
quantities of missing data is the mean upper-arm circumference (MUAC), which was
not measured for any pregnant woman. Variables that could be calculated from other
information include ‘wealth quintiles’: these can be calculated from the WealthScore
variable.
5. The Province variable, originally provided as a numeric code, was relabelled with the
actual province names.
6. The rows of the dataset were randomly shuffled: this is just to make it harder to
identify the rows on the basis of information that may be available on the internet.
7. A sample of roughly 80% of the records was selected for use in the ‘model building’ part
of the assessment (this will be referred to as ‘Group 1’ below), with the remaining 20%
used for ‘prediction’ (‘Group 2’). This was done in such a way that the two samples
were non-overlapping but had very similar distributions of all potential covariates.
Specifically:
(a) For each combination of the Province and Pregnant variables (see Appendix),
80% of the women were sampled at random, without replacement, as candidates
to use in Group 1; and the remaining 20% were allocated to Group 2.
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(b) For each of the numeric covariates in the data set, a Kolmogorov-Smirnov test was
performed to test the null hypothesis that the underlying distributions in Groups
1 and 2 are the same.
(c) For each of the categorical covariates in the data set, a chi-squared test was
performed to test the null hypothesis that the category proportions in Groups 1
and 2 are the same.
(d) The samples were accepted only if the p-values for all of the Kolmogorov-Smirnov
and chi-squared tests were greater than 0.01. Otherwise, a new candidate sample
was drawn in step (a) and the procedure was repeated.
The Kolmogorov-Smirnov and chi-squared tests are used here as a convenient way
to measure whether two distributions are roughly similar. Note, however, that the
haemoglobin levels were not included in this balancing exercise: this is because the
performance of predictions would be artificially enhanced if they were included (for
example, we would know that the mean haemoglobin level for Group 2 is similar to
that for Group 1). Note also that no attempt has been made to balance the groups in
terms of combinations of the covariates.
8. The ‘Group 2’ records were placed at the end of the data table, with their haemoglobin
levels set to ?1; and a new ID variable was created so that each record has an ID number
between 1 and 5 421.
Description of variables
This section gives a brief description of each of the variables in AnemiaData.csv.
Variable name Description
ID Record ID, from 1 to 5 421
Haemoglobin Individual’s haemoglobin level (g/Dl)
Age Individual’s age (years)
RecentBirth Has the individual given birth in the last two years? This takes
one of two values: Yes and No.
HHSize Number of household members.
HHUnder5s Number of children under the age of 5 in the household.
CleanWater
Does the household have access either to water from a protected
source (including a borehole), or to treated drinking water? (Yes
/ No)
TreatedWater Is the household’s water treated for drinking? (Yes / No)
Electricity Does the household have electricity? (Yes / No)
Continued on next page . . .
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. . . continued from previous page
Variable name Description
Toilet
Does the household have toilet facilities (flushing toilet, pit latrine,
composting toilet, bucket, vault or sanitation)? (Yes /
No)
BikeScootCar
What proportion of the following does the household own: (a)
bike (b) scooter / motorcycle (c) car / truck (recorded as a value
of 0, 1/3, 2/3 or 1).
AnimCart Does any household member own an animal-drawn cart? (Yes /
No)
AgricLandOwn Does any household member own agricultural land? (Yes / No)
Cows Does the household own any cattle? (Yes / No)
Horses Does the household own any horses, donkeys or mules? (Yes /
No)
Goats Does the household own any goats? (Yes / No)
Sheep Does the household own any sheep? (Yes / No)
Chickens Does the household own any chickens? (Yes / No)
Rural Is the household in a rural area? (Yes / No)
Province
Which province is the household in? (this is a factor with 34
levels, corresponding to the provinces in Afghanistan — see
Wikipedia page for maps).
TotalChildren Total number of children ever born to the individual
WealthScore
Index of household wealth, provided as part of the MICS dataset
and created using a principal components analysis incorporating
information on water source, sanitation facility, house construction
characteristics, ratio of people to rooms, cooking fuel type,
and ownership of appliances such as a refrigerator, TV and radio.
Negative values indicate households that are less wealthy than
average, positive values are more wealthy.
AgricArea
How much agricultural land does the household own (hectares).
A value of 0 when AgricLandOwn is Yes means that the household
owns less than 1Ha of land. Values up to 94 are rounded down;
a value of 95 means ‘at least 95Ha’.
Pregnant Is the individual currently pregnant? (Yes / No)
Education
To what level is the individual educated? This is a factor with
three levels: None (no education), Primary (educated to primary
level) and Secondary+ (educated to secondary level or above).
Continued on next page . . .
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. . . continued from previous page
Variable name Description
HHEducation To what level is the head of the household educated? This is a
factor with three levels, coded as for Education.
Region
Name of the region in which the household is situated. There
are several provinces in a region. The values are central,
central_highlands, east, northeast, south, southeast, west
and north.
Ethnicity
Head of the household’s ethnic group or primary language.
Possible values are Dari, Pashto, Uzbek, Turkmen and
Other/missing. The MICS survey data do not distinguish between
‘Other’ groups and missing values.
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