Quantitative Research - CPRsouth · Qualitative Quantitative Non-numerical data Numerical data...

Quantitative ResearchDr. Christoph Stork

Thursday 10 December 2009

Qualitative Quantitative

Non-numerical data Numerical data

In-depth understanding of human behaviour (why and how)

Representative figures(what where and when)

Focus group discussions, key informant interviews, ethnographic story-telling,

content analysis...

Primary Data collection: Surveys, automated recording (stock exchange)

demand side data

Secondary data analysis: cross-section, ime series, panel data

supply side or demand side data

Selection of respondents purposefully Random Sampling

Evidence through reasonEvidence through rejecting hypothesis

with a specific confidence.

The whole picture: Quant and Qual


How Rural is rural?


Example EA Classification


Electricity, Mobile Reception, Fixed-lines

in EA


Partly paved, partly dirt...semi-formal

urban


Quick way to get a feel for the topic and the EA


Primary Data Collection (Surveys)

Sampling, sample frame, sample size, weights, questionnaire design and question design

Representative of something...businesses, Indians, women, handicapped...


RIA 2007/8 household survey Survey Charateristics

Target PopulationAll households excluding institutional households such as army barracks, prisons and hospitals. All individuals 16 years or older.

Domains 1 = national levelTabulation groups Major Urban, Other Urban , Rural

OversamplingMajor Urban 40%Other Urban 30%Rural 30%

Clustering Enumerator Areas (EA) from national Census

None Response Random substitutionSample Frame Census sample from from NSOConfidence Level 95%

Design Factor 2Absolute precision (margin of relative error) 5%

P 0.5, for maximum sample sizeMinimum Sample Size 768


Representativeness

Known selection probability

Sample frame:

Household surveys: censusBusiness Survey: business registry, Ministry of finance, Census (informal businesses)

Secondary sample-frames: listings

Tertiary Sample-frames: Example in Household to select a household member randomly: List of all elegible household members, Kish Table, random number generators.


Sample Size

Desired level of accuracy typically confidence level of 95%

Absolute precision (relative margin of error) typically 5%.

Population proportion P conservatively would be 0.5 (yields the largest sample size)

n =Za p(1− p)

Cp

⎛

⎝⎜⎞

⎠⎟

2

n = 1.96 0.5(1− 0.5)0.05

⎛

⎝⎜⎞

⎠⎟

2

= 384


HHw = household weight

INDw = Individual weight 16+

PHH = Household Selection Probability

PEA = EA Selection Probability

PI = Individual Selection Probability

�

HHw = DW * 1PHH * PEA

�

INDw = DW * 1PHH *PEA *PI

�

PHH = NHHEA

�

PEA = m * HHEA

HHSTRATA

�

PI = 1HHm16+


National representative results for the BOP?

PPS Sample EAs

List all households in EA and Classify into BOP or not

Simple Randomly select from BOP households from List

Minimum sample size per country:

384 national only

768 national and rural / urban Times number of provinces, if representative for each province


Secondary Data analysis example: individuals 16+ that own a mobile phone or active Sim Card

!"#$%&

'$#(%&

)(#!%&

)!#*%&

*'#)%&

)+#*%&

,-./012345&

6-478&9:;2</&

=/05;-->&

?50/@5&

,/@5&


Average disposable income in US$16+

!"#$"!!!%%#$%!!

!&'#()!!

!*"#(&!!

!(*#"+!!

!%'%#+)!!

,-./0112! 314-.5678/! !918:;!<=06>-!

?/.-@/! 3-@/!


Average Age 16+

!"#$%&!!

!'($&&!! !')$&*!!!'*$'#!! !'%$+"!! !'*$(*!!

,-./0112! 314-.5678/! !918:;!<=06>-!

?/.-@/! 3-@/!


Highest Education:Tertiary 16+

!"#$%

&"'$%

()"($%

!"&$%

&"#$%

(&"!$%

*+,-.//0% 1/2+,3456-% 7/689%:;.4<+%

1+=-% >-,+=-%


Two-sample Wilcoxon rank-sum (Mann-Whitney) test - Prob > |z|

How confident are you: How confident are you: How confident are you: How confident are you: How confident are you: How confident are you: How confident are you: How confident are you:

Two-sample Wilcoxon rank-sum (Mann-Whitney) test - Prob > |z|

using a search engine to find information on the Internet

using e-mail to communicate with others

downloading and installing software onto a computer

identifying the cause for computer problems

understanding text written in English

typing a letter or CV on the computer

participate in an online discussion forum on a topic of your interest

making a call over the Internet

Benin 0.0679 0.0227 0.0015 0.0201 0.1886 0.0204 0.1268 0.0243

Botswana 0.0517 0.0647 0.001 0.1512 0.0338 0.0298 0.1475 0.3198

Burkina Faso 0.2235 0.7676 0.9622 0.2148 0.6915 0.3663 0.5399 0.205

Cameroon 0.0002 0.106 0.0002 0.0014 0.0491 0.0067 0.0116 0.0238

Cote d Ivoire 0.0352 0.7975 0.3917 0.8607 0.7829 0.9675 0.3364 0.9651

Ethiopia 0.0152 0.0429 0.0021 0.0053 0.0643 0.0183 0.0001 0.0043

Ghana 0.0039 0.0028 0.002 0.011 0.0033 0.0084 0.0632 0.1154

Kenya 0.1058 0.0665 0.0145 0.0624 0.2576 0.0908 0.0442 0.1073

Mozambique 0.0759 0.0634 0.0301 0.0387 0.092 0.1422 0.231 0.0238

Namibia 0.0034 0.0045 0.0005 0.0003 0.0012 0.0229 0.0004 0.0035

Nigeria 0 0 0 0.0003 0.0016 0.01 0.0088 0.0475

South Africa 0.0072 0.0057 0 0.0001 0.0067 0.0043 0.0063 0.0985

Uganda 0.087 0.1257 0.7256 0.0481 0.0578 0.1364 0.6342 0.5014


�

Mobilei = α0 +α1Inc+α2Inc2 + α3 Pr icem +α4 Pr ice f +α5Age+α6Age

2

+ α7Gender+α8Status+ α9Elec+ α10eEduee=1

3

∑ + α11Network +ω

Logit or Probit Model for ICT Adoption


Probit regression Number of obs = 1230 Wald chi2(6) = 194.38 Prob > chi2 = 0.0000Log pseudolikelihood = -648.59545 Pseudo R2 = 0.1980

------------------------------------------------------------------------------ | Robust

m_1re | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- iincomeus | .0030115 .0004944 6.09 0.000 .0020424 .0039806 Teritiary | 2.401994 .2427257 9.90 0.000 1.926261 2.877728 Secondary | 1.519771 .1809192 8.40 0.000 1.165176 1.874366 Primary | .7379503 .1757915 4.20 0.000 .3934054 1.082495 female | .3044066 .1001704 3.04 0.002 .1080763 .5007369 c_7 | .0073583 .003994 1.84 0.065 -.0004697 .0151863 _cons | -2.073208 .2499079 -8.30 0.000 -2.563019 -1.583398------------------------------------------------------------------------------

Note: 0 failures and 1 success completely determined.

Cameroon



------------------------------------------------------------------------------ | Robust

m_1re | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- iincomeus | .008232 .0031504 2.61 0.009 .0020574 .0144067 Teritiary | 3.194845 .4692621 6.81 0.000 2.275108 4.114582 Secondary | 1.205537 .2380523 5.06 0.000 .7389632 1.672111 Primary | .7314167 .1577807 4.64 0.000 .4221722 1.040661 female | .6654595 .143776 4.63 0.000 .3836638 .9472552 c_7 | -.0015514 .0049448 -0.31 0.754 -.0112429 .0081402 _cons | -1.411324 .2343641 -6.02 0.000 -1.87067 -.9519793------------------------------------------------------------------------------

Note: 0 failures and 8 successes completely determined.

Mozambique



------------------------------------------------------------------------------ | Robust

m_1re | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- iincomeus | .0023268 .0003987 5.84 0.000 .0015453 .0031082 Teritiary | 1.206233 .2353331 5.13 0.000 .7449881 1.667477 Secondary | .6712455 .1780839 3.77 0.000 .3222074 1.020283 Primary | .1876848 .1759168 1.07 0.286 -.1571057 .5324753 female | .4281095 .0838309 5.11 0.000 .2638039 .592415 c_7 | -.0126379 .0024616 -5.13 0.000 -.0174625 -.0078133 _cons | -.2840044 .2165149 -1.31 0.190 -.7083657 .140357------------------------------------------------------------------------------

Note: 0 failures and 37 successes completely determined.

South Africa


Topic Dependent Variable Methodology Application

AdoptionFixed Line in HH

Mobile Phone PossessionInternet User (Yes/No)

Logit and ProbitIncome barriersSkills barriers

Demand Elasticity

Expenditure for mobile or fixed LineWTP for mobile of fixed line

OLSIncome elasticityUsage barriers

Digital Poverty

Digital Poverty Index (Barrantes) Ordinal Logit &PorbitIdentify obstacles to

digital wealth

Gender

Fixed Line in HHMobile Phone Possession

Internet User (Yes/No)Expenditure for mobile or fixed Line

WTP for mobile of fixed line

Mean Rank Comparison (Mann-U,

Kruskal-Wallis)Logit & Probit,

OLSOrdinal Logit &Porbit

Nominal Logit & Probit

Obstacles to gender equality in access

and usage

e-Skills

e-Skills index: Using a search engine to find information,

using e-mail to communicate, downloading and installing software, identifying the cause for computer

problems, typing a letter or CV on the computer, participate in an online

discussion forum, making a call over the Internet.

Ordinal Logit &Porbit

Link between e-skills and

employabilityHow to measure e-skills for indicator

frameworks


Revealed preferences: Observed actual behaviour

Stated preference: State behaviour following a change or for a particular situation (contingent valuation)

Willingness and Ability to Pay (WTP)

!Thursday 10 December 2009

Questions please...


Quantitative Research - CPRsouth · Qualitative Quantitative Non-numerical data Numerical data...

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