RESEARCH MANAGEMENTDATA ANALYSIS

Assoc. Prof. Dr. Abdul Hamid b. Hj. Mar Iman,Centre for Real Estate Studies,

Faculty of Geoinformation Engineering & Sciences,Universiti Teknologi Malaysia.

E-mail: hamidiman@utm.myWeb: http://ac.utm.my/web/hamidiman

PTK Course for Local Governments, UTM, Skudai, 20-25/11/2008

(C) Copyrights of the Author. No part of materials in these slides should be extracted in any electronic or non-electronic method without permission from the Author. 1

DefinitionA systematic programme of planning ,

coordinating, implementing, and controlling knowledge process through information development, with a view to obtaining a strategic fit between an organisation’s goals and its internal capabilities.

It is basically a practice-related research management.

The nature of the research may be fundamental, developmental, or commercial.

2PTK Course for Local Governments, UTM, Skudai, 20-25/11/2008

Purpose of Research

Intelligence purposesAd-hoc or planned problem-solving.Strengthening overall research

programs within a particular organisation.

Enhancing organisational capabilities, e.g. → medium-term & long-term planning, strategy, decision-making ability, etc.

Else?3

PTK Course for Local Governments, UTM, Skudai, 20-25/11/2008

Basic Structure of Research Unit

Targets (e.g. groups)

4PTK Course for Local Governments, UTM, Skudai, 20-25/11/2008

The ‘state of affair’ of each component of this structure?

What, how, how much, when, and who to improve?

Possible outcomes & obstacles?

Organisational research philosophy.Strategic research areas.Proper administrative structure.Adequate & good facilities.Qualified staff.Research training: * Research programs; * Research methodology; * Intelligence gathering & ad-hoc research; * Information management.Funding.

Developing Research Skills

5PTK Course for Local Governments, UTM, Skudai, 20-25/11/2008

Go beyond administrative functions.Producing practice-related research outcomes.Fulfilling organisational mission.Directed research: * Problem-solving research. * Industry orientation (applied research), aligning with government’s policies & within the ambit of organisational policies.

Organisational Research Philosophy

6PTK Course for Local Governments, UTM, Skudai, 20-25/11/2008

♦ A research that is pivoted on the riority areas of an organisation in which it has the expertise, resources, and institutional set-up readily available.

♦ To help an organisation focus on some strategic research areas, reflecting its research niches and strength and thus giving it competitive advantages in those areas.

♦ These focus areas are the “shooting targets” at which Key Performance Indicators (KPI) are used to gauge institutional achievement.

♦ Can be implemented in collaboration with universities through Intensification of Research in Priority Areas (IRPA), E-Science, Technofund, and the National Property Research Coordinator (NAPREC), etc.

7PTK Course for Local Governments, UTM, Skudai, 20-25/11/2008

What is Directed Research

Strategic Research AreasNeed for strategic research planning.Purpose: to identify research niches, strengths,

and thus, comparative advantages.Should be established at departmental level.Example: * Set research mission, goal & objectives,

portfolio & functional strategies; * Establish two-tier research programs: (1)

priority research; (2) fundamental research; * Documentation of departmental strategy.

8PTK Course for Local Governments, UTM, Skudai, 20-25/11/2008

Strategic Research Planning

9PTK Course for Local Governments, UTM, Skudai, 20-25/11/2008

Assoc. Prof. Dr. Abdul Hamid b. Hj. Mar Iman

Centre for Real Estate StudiesFaculty of Engineering and Geoinformation

ScienceUniversiti Tekbnologi Malaysia

Skudai, Johor

Objectives

Overall: Reinforce your understanding from the main lecture

Specific: * Concepts of data analysis * Some data analysis techniques * Some tips for data analysis

What I will not do: * To teach every bit and pieces of statistical

analysis techniques

Data analysis – “The Concept”Approach to de-synthesizing data,

informational, and/or factual elements to answer research questions

Method of putting together facts and figures to solve research problem

Systematic process of utilizing data to address research questions

Breaking down research issues through utilizing controlled data and factual information

Categories of data analysisNarrative (e.g. laws, arts)Descriptive (e.g. social sciences)Statistical/mathematical (pure/applied

sciences)Audio-Optical (e.g. telecommunication)Others

Most research analyses, arguably, adopt the firstthree.

The second and third are, arguably, most popular

in pure, applied, and social sciences

Statistical MethodsSomething to do with “statistics”Statistics: “meaningful” quantities about a sample

of objects, things, persons, events, phenomena, etc.

Widely used in social sciences.Simple to complex issues. E.g. * correlation * anova * manova * regression * econometric modellingTwo main categories: * Descriptive statistics * Inferential statistics

Descriptive StatisticsUse sample information to explain/make

abstraction of population “phenomena”. Common “phenomena”:* Association (e.g. σ1,2.3 = 0.75) * Tendency (left-skew, right-skew)* Causal relationship (e.g. if X, then, Y)* Trend, pattern, dispersion, rangeUsed in non-parametric analysis (e.g. chi-

square, t-test, 2-way anova)Basically non-parametric

Examples of “abstraction” of phenomena

Trends in property loan, shop house demand & supply

0

50000

100000

150000

200000

Year (1990 - 1997)

Loan to property sector (RM

million)

32635.8 38100.6 42468.1 47684.7 48408.2 61433.6 77255.7 97810.1

Demand for shop shouses (units) 71719 73892 85843 95916 101107 117857 134864 86323

Supply of shop houses (units) 85534 85821 90366 101508 111952 125334 143530 154179

1 2 3 4 5 6 7 8

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

Batu P

ahat

Joho

r Bah

ru

Kluang

Kota T

ingg

i

Mer

sing

Mua

r

Pontia

n

Segam

at

District

No

. o

f h

ou

ses

1991

2000

0

2

4

6

8

10

12

14

0-4

10-1

4

20-2

4

30-3

4

40-4

4

50-5

4

60-6

4

70-7

4

Age Category (Years Old)

Pro

po

rtio

n (

%)

Demand (% sales success)

120100806040200

Pri

ce (

RM

/sq

. ft

of

bu

ilt a

rea

)

200

180

160

140

120

100

80

Examples of “abstraction” of phenomena

Demand (% sales success)

12010080604020

Pri

ce

(R

M/s

q.f

t. b

uilt

are

a)

200

180

160

140

120

100

80

10.00 20.00 30.00 40.00 50.00 60.00

10.00

20.00

30.00

40.00

50.00

-100.00

-80.00

-60.00

-40.00

-20.00

0.00

20.00

40.00

60.00

80.00

100.00

Distance from Rakaia (km)

Distance from Ashurton (km)

% prediction

error

Inferential statisticsUsing sample statistics to infer some

“phenomena” of population parametersCommon “phenomena”: cause-and-effect * One-way r/ship * Multi-directional r/ship * Recursive

Use parametric analysis

Y1 = f(Y2, X, e1)Y2 = f(Y1, Z, e2)

Y1 = f(X, e1)Y2 = f(Y1, Z, e2)

Y = f(X)

Examples of relationship

Coefficientsa

1993.108 239.632 8.317 .000

-4.472 1.199 -.190 -3.728 .000

6.938 .619 .705 11.209 .000

4.393 1.807 .139 2.431 .017

-27.893 6.108 -.241 -4.567 .000

34.895 89.440 .020 .390 .697

(Constant)

Tanah

Bangunan

Ansilari

Umur

Flo_go

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: Nilaisma.

Dep=9t – 215.8

Dep=7t – 192.6

Which one to use?Nature of research * Descriptive in nature? * Attempts to “infer”, “predict”, find “cause-and-

effect”, “influence”, “relationship”? * Is it both?Research design (incl. variables involved). E.g.Outputs/results expected * research issue * research questions * research hypotheses

At post-graduate level research, failure to choose the correct data analysis technique is an almost sure ingredient for thesis failure.

Common mistakes in data analysisWrong techniques. E.g.

Infeasible techniques. E.g. How to design ex-ante effects of KLIA?

Development occurs “before” and “after”! What is the control treatment?

Further explanation! Abuse of statistics. E.g.Simply exclude a technique

Note: No way can Likert scaling show “cause-and-effect” phenomena!

Issue Data analysis techniques

Wrong technique Correct technique

To study factors that “influence” visitors to come to a recreation site

“Effects” of KLIA on the development of Sepang

Likert scaling based on interviews

Likert scaling based on interviews

Data tabulation based on open-ended questionnaire survey

Descriptive analysis based on ex-ante post-ante experimental investigation

Common mistakes (contd.) – “Abuse of statistics”

Issue Data analysis techniques

Example of abuse Correct technique

Measure the “influence” of a variable on another

Using partial correlation

(e.g. Spearman coeff.)

Using a regression parameter

Finding the “relationship” between one variable with another

Multi-dimensional scaling, Likert scaling

Simple regression coefficient

To evaluate whether a model fits data better than the other

Using R2 Many – a.o.t. Box-Cox 2 test for model equivalence

To evaluate accuracy of “prediction” Using R2 and/or F-value of a model

Hold-out sample’s MAPE

“Compare” whether a group is different from another

Multi-dimensional scaling, Likert scaling

Many – a.o.t. two-way anova, 2, Z test

To determine whether a group of factors “significantly influence” the observed phenomenon

Multi-dimensional scaling, Likert scaling

Many – a.o.t. manova, regression

How to avoid mistakes - Useful tipsCrystalize the research problem → operability

of it! Read literature on data analysis techniques.Evaluate various techniques that can do similar

things w.r.t. to research problemKnow what a technique does and what it

doesn’tConsult people, esp. supervisorPilot-run the data and evaluate resultsDon’t do research??

Principles of analysisGoal of an analysis: * To explain cause-and-effect phenomena * To relate research with real-world event * To predict/forecast the real-world phenomena based on research * Finding answers to a particular problem * Making conclusions about real-world event based on the problem * Learning a lesson from the problem

Data can’t “talk” An analysis contains some aspects of scientific reasoning/argument: * Define * Interpret * Evaluate * Illustrate * Discuss * Explain * Clarify * Compare * Contrast

Principles of analysis (contd.)

Principles of analysis (contd.)An analysis must have four elements: * Data/information (what) * Scientific reasoning/argument (what? who? where? how? what happens?) * Finding (what results?) * Lesson/conclusion (so what? so how? therefore,…)Example

Principles of data analysisBasic guide to data analysis: * “Analyse” NOT “narrate” * Go back to research flowchart * Break down into research objectives and research questions * Identify phenomena to be investigated * Visualise the “expected” answers * Validate the answers with data * Don’t tell something not supported by data

Principles of data analysis (contd.)

Shoppers Number

Male

Old

Young

6

4

Female

Old

Young

10

15

More female shoppers than male shoppers

More young female shoppers than young male shoppers

Young male shoppers are not interested to shop at the shopping complex

Data analysis (contd.)When analysing: * Be objective * Accurate * TrueSeparate facts and opinionAvoid “wrong” reasoning/argument. E.g.

mistakes in interpretation.

Some Principles of Statistical Methods in Data Analysis

What is Statistics

“Meaningful” quantities about a sample of objects, things, persons, events, phenomena, etc.

Something to do with “data”Widely used in various discipline of sciences.Used to solve simple to complex issues. Three main categories: * Descriptive statistics * Inferential statistics * Probability theory

Forms of “Statistical” RelationshipRelationship can be non-parametric or

parametricE.g. of non-parametric r/ships: * Correlation * ContingencyE.g. of parametric → cause-and-effect * Causal * Feedback * Multi-directional * RecursiveThe “parametric” categories are normally

dealt with through regression

Non-Parametric Data Analysis Methods – A Summary

Scale of measurement

One-sample Two independent Sample

K independent Sample

Measures of Association

Independent Sample

Single treatment repeat Measures

Multiple treatment repeat Measures

Nominal Binomial test; one-way contingency Table

McNemar test

Cochrane Q Test

Two-way contingency Table

Contingency Table

Contingency

Coefficients

Ordinal Runs test Wilcoxon signed rank

test

Friedman test

Mann-Whitney Test

Kruskal-Wallis Test

Spearman rank Correlation

Interval/ratio Z- or t-test of variance

Paired t-test Repeat measures ANOVA

Unpairedt-test; tests of variance

ANOVA Regression, Pearson correlation, time series

34

Parametric Analysis - Regression

Coefficients(a)

Unstandardized CoefficientsStandardizedCoefficients

Model B Std. Error Beta t Sig.(Constant) 29680.695 2885.532 10.286 .000

AGE -705.817 38.491 -.212 -18.337 .000

NB211001 12374.064 2176.815 .061 5.684 .000

NB211002 -1094.891 1527.977 -.008 -.717 .474

NB211003 -938.838 1136.671 -.010 -.826 .409

NB211005 12639.946 2139.489 .066 5.908 .000

NB211006 852.109 2535.266 .004 .336 .737

SQFT1 31.388 7.815 .039 4.016 .000

SQFT2 44.166 1.365 .595 32.349 .000

SQFT3 52.939 1.265 .808 41.857 .000

SQFT4 60.447 3.561 .164 16.974 .000

SQFT5 94.723 2.943 .312 32.186 .000

LAND75 11.788 .433 .303 27.240 .000

BATHS 7714.093 1338.204 .076 5.765 .000

POOL 13359.275 1184.469 .105 11.279 .000

1

GARAGE 10.750 3.137 .038 3.427 .001

a Dependent Variable: SALEPRIC

Rule of Thumb: “t” scores

Should be 2.0 or greater.

Nilai “t” seharusnya lebih

Besar atau sama dengan

2,0

The significance of each variable to the model can be determined by looking at the “t” values.

NB211002

NB211003

NB211006

are insignificant