Design and analysis of integrated urban household survey of nigeria
Table Of Contents
Chapter ONE
1.1 Introduction1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Research
1.9 Definition of Terms
Chapter TWO
2.1 Overview of Urban Household Surveys2.2 Importance of Integrated Surveys
2.3 Historical Context of Household Surveys in Nigeria
2.4 Methodologies in Urban Survey Design
2.5 Data Collection Techniques in Household Surveys
2.6 Sampling Methods in Household Surveys
2.7 Data Analysis in Household Surveys
2.8 Challenges in Urban Household Surveys
2.9 Innovations in Survey Design
2.10 Best Practices in Integrated Household Surveys
Chapter THREE
3.1 Research Methodology Overview3.2 Research Design and Approach
3.3 Sampling Strategy
3.4 Data Collection Methods
3.5 Data Analysis Techniques
3.6 Quality Assurance Measures
3.7 Ethical Considerations
3.8 Limitations of Methodology
Chapter FOUR
4.1 Analysis of Survey Data4.2 Household Characteristics and Demographics
4.3 Socioeconomic Indicators
4.4 Urban Infrastructure and Services
4.5 Housing Conditions
4.6 Employment and Income Patterns
4.7 Education and Health Access
4.8 Comparison with Previous Surveys
Chapter FIVE
5.1 Summary of Findings5.2 Conclusions
5.3 Recommendations for Policy and Practice
5.4 Implications for Future Research
5.5 Contribution to the Field
5.6 Reflections on the Research Process
5.7 Limitations of the Study
5.8 Areas for Further Investigation
Thesis Abstract
/ E+uiy d i f f e r e n t methods of laride e s t i m a t i o n i n
complex Surveys have been proposed by many r e s e a r a h e r s .
quenoville (1956) introduced t h e jackknife procedure as
a means of reducing bias of rgtio estimates. Hansen,
Hurvtitz, and Madow ( 1953) described t h e random groups
procedure b’jhich is c l o s e s t .LG the ori8inal concept of
saxpling, The b o o t s t r a p method, introduced
o r i g i n a l l y by jzfron (1979) for the independent and
identically distribute4 case, r e l i e s on recomputing t h e
es-Limzte 0 a laybe nuiiiber B of times by resampling t h e
ori,i.~al cample. simple r e p l i c a t i o n is both easy t o compute
and by r e p l i c a t i n g at both phases, t r u e l y independent
gamples ire foriiied,
ChapLer 1 UP t h i s p r o j e c t reviews the meaning of
~~rilpl-ii.;s urvey arid sample survey d e s i g n . I t a l s o contai.
11~ the dei’iriicion of i n t e g r a t e d survey wiih its
advailtzbes and .J;i;;advantates, I have included b r i e f
notes on Plas-ker scimple and i-Ls usefulnessi
Chapter 2 include the d e s c r i p t i o n of the 1900 – 1986
FISH de5ign and 1987 – 1992 NISI- design, It a l s o
exarni~~etsh e o r e t i c a l l y t h e vvsights used f o r t h e
estimzcion i n the two designs. I have applied the
, ” estirAi;icr;; .o; i~roceitureco f t h e two de s igns based 011 t h e
198?/88 survey ddta i n order t o comp&re t h e i r relatim
per f ori~iances~
iii
ch;igtY~-3; -= -*- -r r- . .- *..
f o r obtaining variallce i n lar$e scale sample surveys, .
They incluue t h e jackknife and b~otstrapm ethods, In
chapt e r 4, t h e method of u~ingt h e mean of t h e repli~aktre
r a t i o e.ticuates has been considered ix order to compare
convenizntly trith the ,bootstrap hod, Finally chapter
5 concludes and brings out s stions based on my
obsorv~L.i ons,
Thesis Overview
1 .I ssili Survey_:Meanis
::;mpling Survey i s a sy~tema-ticc o l l e c t i c n of d a t a
from a p a r t of a population iiirough ,lie use of personal
interviews or other dsta &athering devices in order t o
make inference ahout the whole population. The need to
callect d a t a arises i:il eve?y conceivabl-e sphere of human
ac Livi. ty . exe oula knobsled,e of beha viour and
c h a r a c t e r i s t i c s of a parJcicul.zr set oP ul1i.L~i n a popul
a t i o n i s obtained. by F; tudyj..ng a r e p r e s e n t a t i v e crosss
e c t i o n of some of the u n i t s . We do not ritudy each
i n d i v i d u a l rile~nber of the mits because wc b e l i e v e t h a t
a r e p r e s e n t a t i v e i s cufficient for making i n f e r e n c e about
the whol-e set,>
k ,sample survey has noc come t o be con~idered as
a n organised f a c t – finding ins-irurfient, ~tirsng ortance
to modern c i v i l i z a t i o n l i e s i n the f a c t t h a t i t can be
used t o surninarize, for the guidance of admi.nistrat.ion,
f a c t s vihick would otherwise be i n a c c e s s i b l e owing t o the
remoteness a:ld obscurnity of the persons or other u n i t s
concerned, or .their numerousnesc. AS a fact- finding
i n s t r ume n t , a sai~~~sjulrev e y i s concerned wi t h t h e
a c c u r a t e ascertainment of t h e i n d i v i d u a l facts recorded
and with t h e i r cornpilL2tion and summari.zation,
1.2 zmple su-r veyJLesign
~osotf the s t e p s i n v o l v e d i n p l a n n i n g a sample
survey a r e common t o those for a complete enumeration,
Some of the important a s p e c t s r e q u i r i n g a t t e n t i o n at
the plaming staLe a r e the following; Formulation of
d a t a reqiiiremen-ts, adhoc or r e p e t i t i v e survey, methods
of d a t a c o l l e c t i o n , survey r e f e r e n c e and r e p o r t i n g
periods, pr ciSlem of sanip:Ling frame, planning of p i l o t
survey, choice of sampling desj. gn e t c. The p r i n c i p a l
airii of sampling desigil is t o choose the design with the
siaallest e r r or because cli f i’erent sampling designs
r e s u l t in d i f f e r e n t sa~lpling errors, However, i n
cnoosini, a design one h~,st o be si-tisfied wiLh a
ratioml sam~lingd e s i g n chosen from among a few
a1 tcrnacives. s p e c i a l attejkion should be $aid .to the
wo r k a b i l i t y o f t h e p a r t i c u l a r sampl ing des~gnu nder
the p r e v a i l i n g o p e r a t i o n a l conc?:i t i o n s . Generally a
stratf f i e d mu1 tistage desip is adopted for l a r g e s c a l e
sample sul-veys. Many surveys taken by sampling seek
information of obvious importance t o n a t i o n a l planning,
~ccht o p i c s a s agrl-cultural p r o d u c t i o n and l a n d us e ,
~nbour force, i n d u s t r i a l production wholesale and r e t a i l
p r i c e s , h e a l t h s t a t u s of’ the people and family incomes
and expenditure are normally covered, These t o p i c s a r e
based on 1:ousehold survey.
– – 2
~ousel-iold surveys on these v a r i e t y of t o p i c s form a
sizeade and indispei~sablc p a r t of the o f f i c i a l s t a t i s t i c a l
operatioris of rriany c o u n t r i e s . Since. f i r s t 5mbarkFng on
household surveys tilose sta-kistlcal oi’fices have been
conductrin~ these surveys on an adhoc b a s i s . Recently
t h e r e have been need t o in.tegrate t h e s e surveys t o obtain
an i n t e r r e l a t e d s t a t i s t i c s on ~ontinuing b a s i s . For
example, according t o I(ordos (l983), Poland, a f t e r t r y i n g
other methodology of household ml’veys f’rom 1957 up t o
1981 decided i n 1962 t o ernbark on the i n t e g r a t e d system
of household surveys, The implementation wac doile i n
two sjlasas. The fj-rst ghase span 3 982 – 1985 whereby
the so-called minimum progrmme was implemented, The
sec2rid phase covered the period ? 986 – 1 9909 d.uring which
househulci surveys methodology w2s improved, u n i f i c a t i o n
of conce yts, d.e f i n i t i o n s slid classific&tions which
r e l a t e t o hnuse~icjlci surveys were f i n a l i s e d ,
A. number o f developing c o u n t r i e s are developing
integrz’ced on-gorixg houseiiold surveys under ‘il-ie ~~lspices
of t h e u n i t e d Nations National Household Survey Capability
programme (~?TH,scpa) s a ra-tionaliza+Son of existing
survey programmec;. ‘!’he Kenya Bureau of statistic^ s e t
up a Ns~tl~liailn teqrated sample s u r v e y progralllnie (NIssP)
i n 1974 (lciregyera and philip, 1985), The Federal
o f f i c e of sta’cistics (FO,s) Nigeria, has been involved
i n household survey since t h e ecrljr f i f t i e s , but r e s o r t e d
t o a:l integrated system of household surveys i n 1980,
The implernent~,tionw as t o be done i n two p h a s e s , t h e
j:i l, t ~hases san t h e p e r i o d 1380-1 386 and t h e second
phase from 1987 – 1992.
An integrated survey my be defined as a survey
i n which continuous s e r i e s of d a t a on a wide v a r i e t y of
t o p i c s are c o l l e c t e d f o r t.,,e same s e t of u l t i m a t e
sa!nplil?g u n i t s or wl. tnin the sampling a r e a uiiits. ~hus
accordi-ng; t o Fozeman (1903), the casual arrangemeiits
t h a t had earlier on s u f f i c e d for adhoc surveys could
hardly be effective nor econoinical i n handling frequent
household surveys. This l e d to the i d e a of an i n t e g r a t e d
rnuliis’ub ject survey which makes i t p o s s i b l e f c r data on
se~e~2s.uib Jcct!~t: o be collzcted f o r t h e same s e t o f
ul-timzte samplin; u n i t s ,
An integrsted survey is u s e f u l i n the maximum and
effective uti%iza.tion of the availade f i e l d s t a f f on a
2errnancnt bdsis; helps i n studying the interrelc.:i-l;ionships
amon; i t e m s belongLng t o difilerent s u b j e c t f i e l d s
or t o p i c s . ~t is c o s t e f f e c t i v e and convenient,
I n t e g r a t i o n rnzkes c r o s s – c l a s s i f i c a t i o n using r e l e v a n t
itenis of informat,ion belonging t o d i f f e r e n t s u b j e c t
f i e l d p o s c i b l e and aims at u n i f i c a t i o n of’ b a s i c concepts,
d e f i n i t i o n s and. c l a s s i f i c a t i c n used i n d i f f e r e n t surveys.
4,
-.__’,
It is becauce of t h e s e advan-es that the United Nations
la~mched the NHsCP i n 1979 to assist the developing
c o u n t r i e s t o undertake systematic progrmrnes of household
surveys and t o develoy t h e i r survey c a p a b i l i t i e s .
since an integrated survey c o n s i s t s of a number of
succe~sive surveys on the same or s e v e r a l t o p i c s , i t
becomeb advantageous: t o hevre a s e t of sample units from
which subs ample^ can be s e l e c t e d t o s u i t each successive
survej-s or t o . il~ve a nuibber of independent subsamples
such t h a t any given stlrvey is undertaken over one or
more subsamgles, such a sample is c a l l e d a rqasker Saraple,
A Master Sample is t h e r e f o r e defined as a general
purpose sample from which subsamples can be drawn to serve
the needs of any s p e c i f i c survey or survey rounds conducted
over a period of time,
I n a multis-Lage sample, the same u l t i m a t e smple
u n i t s could be used f o r more than one survey, Altern
a t i v e l y , u l t i m a t e sample u n i t s could be r o t a t e d
s y s t e m a t i c a l l y for a s e r i e s of surveys or a new u l t i m a t e
u n i t s s e l e c t e d f o r each survey. For the sane s e t of
primaries. Another method is t o have a number of
independent subsample of p r i m a r i e s , i n such a way t h a t
any given survey is c a r r i e d out over one or more
subsamples dependiug on its sample size requirments.
Each aliernhtivo has i t s own m e r i t s and demerits,
1LA.- 7~ mnr-i tr; of a ass- ->be ara that it a13nw$
consideraole f1exibilj.t~ i n the sample s e l e c t i o n , f o r t h e
most frequent r e c u r r i n g surveys. It is c o s t e f f e c t i v e i n
c o n s t r u c t i n g frarne for su,bsanpling ~incel i s t i n g i s done
only once t llr oughout the auration of the survey programme.
Other advantages as given by Torene et a1 (1987) a r e :
( i ) A master sam~le of p r i m a r i e s i n t e g r a t e s a l l
the surveys in the survey programme.
( i i ) l’TJlumerators iivuld become more familiar ivith
the iTlas’ier swail~le oi’ p r i m a r i e s than i f new
and d i f f e r e n t pri~lsriesw ere s e l e c t e d f o r
eacil of tlie v~riouss urveys .
(iii) Future surveys could be placed i n the fielci
with more convenience and speed s i n c e he
would already have a f i r s t s t a g e sample.
he i,lain limitation of a master sample design is
-chat its dasigii is usually t a i l o r e d towards the core
survey, The sampling requiremen-ts for the other surveys
i n t h e progarme a r e merely accornrnodated i n t h i s ol?e
master sainb~led esign. The ma s t e r sample de s ign i s n o t
an opti~lal design for a l l the surveys i n t h e programle.
Even at that, the master sample is still a valuable
elelL.ent for i n t e g r a t e d household survey programmes. The
liiaster. san,ple desihn should be determined by the availhble
ccst i,r:j I.ersor~ne;l which a l s o a f f e c t the d e c i s i o n on the
sampl~ &ze, The desidn adopted should be c a r e f u l l y ,
considerea sirice according t o Foreman (1 983) any inade-
%..
qu~ciesi n ihe master samde selecti.on would be r e f l e c t e d
i n all octirnateo QQ i t , Fin~llyt h e desikn should
be such L!~c. t the sarupling errors a r e easily colliputed f o r
any of the survey est~-mtes, e.g. by replication.
The ]?OE s.dcpced n r.otdtional ma s t e r sarfi~~loef p r ima r i e s
with a nei, selec tioil 01 :louseholds for each survey, ‘.?he
ma s t e r sample p r ima r i e s w2re selected 1~~9cli.e ~,<~A frzl;e
created ucring the 1973 popul-z tion cencus, Tao d i f f e r e n t
desi~ns:! ere u s e d i n u, ch of t h e 19 s t a t e s thzt rnade up
Nigeixia, one for the urPar: s e c t o r and the other f o r t h e
rural secior. of the Sttte. urban sectorL c o n s i s t e d of
thcse LownG, wiLh 20 EA’S or more, while the rural sector
consi~ted he tcxns with lesc than 20 KA~,s,
l,3 -&–i-.-r n~ and Ob-j e c t i v e s ..–7
~riie o b j e c t i v e s of this tvorli is t o study criticc;tlly
the two NI$H designs used by FO,S during the 1980-1 986
and 1987-1 992 cnvey periods i n order t o ascertaic t h e i r
relati ue per i or~na:[email protected],
~lsot h e cstl~nationp roc edur e used by the T~fso r
( i ) l?cmlolr~ group procedure
( iij Group Jackknife
(iFi) 50~li;s~-r ~npa i v e and improved procel i-.es
Blazingprojects Mobile App
📚 Over 50,000 Research Thesis
📱 100% Offline: No internet needed
📝 Over 98 Departments
🔍 Thesis-to-Journal Publication
🎓 Undergraduate/Postgraduate Thesis
📥 Instant Whatsapp/Email Delivery
Related Research
Applying Machine Learning Techniques to Detect Financial Fraud in Online Transaction...
The project titled "Applying Machine Learning Techniques to Detect Financial Fraud in Online Transactions" aims to address the critical issue of detec...
Anomaly Detection in IoT Networks Using Machine Learning Algorithms...
The project titled "Anomaly Detection in IoT Networks Using Machine Learning Algorithms" focuses on addressing the critical challenge of detecting ano...
Applying Machine Learning Algorithms for Predicting Stock Market Trends...
The project titled "Applying Machine Learning Algorithms for Predicting Stock Market Trends" aims to explore the application of machine learning algor...
Applying Machine Learning Algorithms for Sentiment Analysis in Social Media Data...
The project titled "Applying Machine Learning Algorithms for Sentiment Analysis in Social Media Data" focuses on utilizing machine learning algorithms...
Applying Machine Learning for Predictive Maintenance in Industrial IoT Systems...
The project titled "Applying Machine Learning for Predictive Maintenance in Industrial IoT Systems" focuses on leveraging machine learning techniques ...
Implementation of a Machine Learning Algorithm for Predicting Stock Prices...
The project, "Implementation of a Machine Learning Algorithm for Predicting Stock Prices," aims to leverage the power of machine learning techniques t...
Development of an Intelligent Traffic Management System using Machine Learning Algor...
The project titled "Development of an Intelligent Traffic Management System using Machine Learning Algorithms" aims to revolutionize the traditional t...
Anomaly Detection in Network Traffic Using Machine Learning Algorithms...
No response received....
Applying Machine Learning for Intrusion Detection in IoT Networks...
The project titled "Applying Machine Learning for Intrusion Detection in IoT Networks" aims to address the increasing cybersecurity threats targeting ...
